{ "cells": [ { "cell_type": "code", "execution_count": 46, "id": "ffda4f03-1e9b-4ed9-b697-54c97b707390", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The autoreload extension is already loaded. To reload it, use:\n", " %reload_ext autoreload\n" ] } ], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", "\n", "import sys\n", "import os\n", "sys.path.append(os.path.abspath(\"../../datasets/rsna-2023-abdominal-trauma-detection\"))" ] }, { "cell_type": "code", "execution_count": 49, "id": "efa360f8-68e2-41f0-b590-113187ff3d8c", "metadata": {}, "outputs": [], "source": [ "import transforms as custom_transforms\n", "\n", "vols = custom_transforms.volume_transforms(streaming=True)({\n", " 'img': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/4791/4622.nii.gz', \n", " 'metadata': {\n", " 'series_id': 4622, \n", " 'patient_id': 4791, \n", " 'incomplete_organ': False, \n", " 'aortic_hu': 223.0, \n", " 'pixel_representation': 1, \n", " 'bits_allocated': 16, \n", " 'bits_stored': 16\n", " }\n", "})" ] }, { "cell_type": "code", "execution_count": 55, "id": "3e0e18a9-296b-4c37-bef8-03b4aad73fd3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "isinstance(vols, (list, tuple))" ] }, { "cell_type": "code", "execution_count": 62, "id": "a3250c5e-f9f6-4e35-91f2-1161ab8b5552", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([4622, 4622, 4622, 6172])\n", "1 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([1, 1, 1, 1]) tensor([26343, 26343, 26343, 40430])\n", "2 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([2, 2, 2, 2]) tensor([55965, 55965, 55965, 62573])\n", "3 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([3, 3, 3, 3]) tensor([12114, 12114, 12114, 28432])\n", "4 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([ 6172, 6172, 12102, 12102])\n", "5 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([1, 1, 1, 1]) tensor([40430, 40430, 25359, 25359])\n", "6 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([2, 2, 2, 2]) tensor([62573, 62573, 8236, 8236])\n", "7 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([3, 3, 3, 3]) tensor([28432, 28432, 43416, 43416])\n", "8 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([12102, 18207, 18207, 18207])\n", "9 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([1, 1, 1, 1]) tensor([25359, 55694, 55694, 55694])\n", "10 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([2, 2, 2, 2]) tensor([ 8236, 20664, 20664, 20664])\n", "11 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([3, 3, 3, 3]) tensor([43416, 32670, 32670, 32670])\n", "12 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([26980, 26980, 26980, 50875])\n", "13 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([1, 1, 1, 1]) tensor([39628, 39628, 39628, 22397])\n", "14 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([2, 2, 2, 2]) tensor([55515, 55515, 55515, 44758])\n", "15 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([3, 3, 3, 3]) tensor([40186, 40186, 40186, 5260])\n", "16 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([50875, 50875, 29832, 29832])\n", "17 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([1, 1, 1, 1]) tensor([22397, 22397, 58548, 58548])\n", "18 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([2, 2, 2, 2]) tensor([44758, 44758, 4890, 4890])\n", "19 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([3, 3, 3, 3]) tensor([ 5260, 5260, 60961, 60961])\n", "20 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([29832, 38633, 38633, 38633])\n", "21 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([1, 1, 1, 1]) tensor([58548, 12900, 12900, 12900])\n", "22 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([2, 2, 2, 2]) tensor([4890, 7384, 7384, 7384])\n", "23 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([3, 3, 3, 3]) tensor([60961, 41663, 41663, 41663])\n", "24 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([55583, 55583, 55583, 31200])\n", "25 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([1, 1, 1, 1]) tensor([58697, 58697, 58697, 43088])\n", "26 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([2, 2, 2, 2]) tensor([31085, 31085, 31085, 54917])\n", "27 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([3, 3, 3, 3]) tensor([28079, 28079, 28079, 57769])\n", "28 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([31200, 31200, 22479, 22479])\n", "29 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([1, 1, 1, 1]) tensor([43088, 43088, 7818, 7818])\n", "30 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([2, 2, 2, 2]) tensor([54917, 54917, 31146, 31146])\n", "31 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([3, 3, 3, 3]) tensor([57769, 57769, 14286, 14286])\n", "32 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([22479, 60755, 60755, 60755])\n", "33 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([1, 1, 1, 1]) tensor([ 7818, 39013, 39013, 39013])\n", "34 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([2, 2, 2, 2]) tensor([31146, 6305, 6305, 6305])\n", "35 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([3, 3, 3, 3]) tensor([14286, 63418, 63418, 63418])\n", "36 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([55449, 55449, 55449, 15271])\n", "37 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([1, 1, 1, 1]) tensor([ 5425, 5425, 5425, 63205])\n", "38 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([2, 2, 2, 2]) tensor([63701, 63701, 63701, 32243])\n", "39 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([3, 3, 3, 3]) tensor([ 137, 137, 137, 36753])\n", "40 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([15271, 15271, 52279, 52279])\n", "41 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([1, 1, 1, 1]) tensor([63205, 63205, 52961, 52961])\n", "42 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([2, 2, 2, 2]) tensor([32243, 32243, 5118, 5118])\n", "43 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([3, 3, 3, 3]) tensor([36753, 36753, 17225, 17225])\n", "44 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([52279, 6631, 6631, 6631])\n", "45 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([1, 1, 1, 1]) tensor([52961, 6344, 6344, 6344])\n", "46 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([2, 2, 2, 2]) tensor([ 5118, 31852, 31852, 31852])\n", "47 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([3, 3, 3, 3]) tensor([17225, 8320, 8320, 8320])\n", "48 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([16066, 16066, 16066, 13666])\n", "49 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([1, 1, 1, 1]) tensor([24134, 24134, 24134, 43233])\n", "50 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([2, 2, 2, 2]) tensor([61403, 61403, 61403, 17605])\n", "51 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([3, 3, 3, 3]) tensor([30952, 30952, 30952, 30064])\n", "52 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([13666, 13666, 44612, 44612])\n", "53 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([1, 1, 1, 1]) tensor([43233, 43233, 25349, 25349])\n", "54 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([2, 2, 2, 2]) tensor([17605, 17605, 5104, 5104])\n", "55 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([3, 3, 3, 3]) tensor([30064, 30064, 8413, 8413])\n", "56 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([44612, 21282, 21282, 21282])\n", "57 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([1, 1, 1, 1]) tensor([25349, 41288, 41288, 41288])\n", "58 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([2, 2, 2, 2]) tensor([ 5104, 63843, 63843, 63843])\n", "59 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([3, 3, 3, 3]) tensor([ 8413, 29167, 29167, 29167])\n", "60 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([50212, 50212, 50212, 11748])\n", "61 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([1, 1, 1, 1]) tensor([29053, 29053, 29053, 12402])\n", "62 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([2, 2, 2, 2]) tensor([30843, 30843, 30843, 58027])\n", "63 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([3, 3, 3, 3]) tensor([39205, 39205, 39205, 40471])\n", "64 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([11748, 11748, 42680, 42680])\n", "65 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([1, 1, 1, 1]) tensor([12402, 12402, 20684, 20684])\n", "66 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([2, 2, 2, 2]) tensor([58027, 58027, 56245, 56245])\n", "67 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([3, 3, 3, 3]) tensor([40471, 40471, 8340, 8340])\n", "68 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([42680, 40781, 40781, 40781])\n", "69 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([1, 1, 1, 1]) tensor([20684, 13041, 13041, 13041])\n", "70 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([2, 2, 2, 2]) tensor([56245, 40496, 40496, 40496])\n", "71 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([3, 3, 3, 3]) tensor([ 8340, 19468, 19468, 19468])\n", "72 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([ 7397, 7397, 7397, 48324])\n", "73 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([1, 1, 1, 1]) tensor([47155, 47155, 47155, 58391])\n", "74 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([2, 2, 2, 2]) tensor([21057, 21057, 21057, 31021])\n", "75 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([3, 3, 3, 3]) tensor([10180, 10180, 10180, 47775])\n", "76 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([48324, 48324, 42973, 42973])\n", "77 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([1, 1, 1, 1]) tensor([58391, 58391, 35661, 35661])\n", "78 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([2, 2, 2, 2]) tensor([31021, 31021, 62556, 62556])\n", "79 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([3, 3, 3, 3]) tensor([47775, 47775, 33355, 33355])\n", "80 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([42973, 5103, 5103, 5103])\n", "81 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([1, 1, 1, 1]) tensor([35661, 24442, 24442, 24442])\n", "82 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([2, 2, 2, 2]) tensor([62556, 525, 525, 525])\n", "83 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([3, 3, 3, 3]) tensor([33355, 4759, 4759, 4759])\n", "84 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([26840, 26840, 26840, 52940])\n", "85 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([1, 1, 1, 1]) tensor([ 4123, 4123, 4123, 54830])\n", "86 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([2, 2, 2, 2]) tensor([34774, 34774, 34774, 12840])\n", "87 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([3, 3, 3, 3]) tensor([19657, 19657, 19657, 39222])\n", "88 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([52940, 52940, 30522, 30522])\n", "89 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([1, 1, 1, 1]) tensor([54830, 54830, 61569, 61569])\n", "90 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([2, 2, 2, 2]) tensor([12840, 12840, 15539, 15539])\n", "91 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([3, 3, 3, 3]) tensor([39222, 39222, 53345, 53345])\n", "92 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([30522, 34224, 34224, 34224])\n", "93 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([1, 1, 1, 1]) tensor([61569, 60307, 60307, 60307])\n", "94 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([2, 2, 2, 2]) tensor([15539, 26214, 26214, 26214])\n", "95 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([3, 3, 3, 3]) tensor([53345, 19927, 19927, 19927])\n", "96 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([13774, 13774, 13774, 49753])\n", "97 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([1, 1, 1, 1]) tensor([37032, 37032, 37032, 16097])\n", "98 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([2, 2, 2, 2]) tensor([47610, 47610, 47610, 47856])\n", "99 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([3, 3, 3, 3]) tensor([ 527, 527, 527, 51136])\n", "100 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([49753, 49753, 10252, 10252])\n", "101 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([1, 1, 1, 1]) tensor([16097, 16097, 12039, 12039])\n", "102 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([2, 2, 2, 2]) tensor([47856, 47856, 19360, 19360])\n", "103 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([3, 3, 3, 3]) tensor([51136, 51136, 47438, 47438])\n", "104 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([10252, 26942, 26942, 26942])\n", "105 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([1, 1, 1, 1]) tensor([12039, 45406, 45406, 45406])\n", "106 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([2, 2, 2, 2]) tensor([19360, 24774, 24774, 24774])\n", "107 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([3, 3, 3, 3]) tensor([47438, 64117, 64117, 64117])\n", "108 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([ 397, 397, 397, 61670])\n", "109 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([1, 1, 1, 1]) tensor([23837, 23837, 23837, 6575])\n", "110 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([2, 2, 2, 2]) tensor([13925, 13925, 13925, 31571])\n", "111 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([3, 3, 3, 3]) tensor([60302, 60302, 60302, 7334])\n", "112 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([61670, 61670, 778, 778])\n", "113 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([1, 1, 1, 1]) tensor([ 6575, 6575, 36257, 36257])\n", "114 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([2, 2, 2, 2]) tensor([31571, 31571, 26906, 26906])\n", "115 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([3, 3, 3, 3]) tensor([ 7334, 7334, 48901, 48901])\n", "116 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([ 778, 47305, 47305, 47305])\n", "117 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([1, 1, 1, 1]) tensor([36257, 16503, 16503, 16503])\n", "118 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([2, 2, 2, 2]) tensor([26906, 39864, 39864, 39864])\n", "119 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([3, 3, 3, 3]) tensor([48901, 52970, 52970, 52970])\n", "120 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([13848, 13848, 13848, 34232])\n", "121 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([1, 1, 1, 1]) tensor([30902, 30902, 30902, 51033])\n", "122 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([2, 2, 2, 2]) tensor([ 6130, 6130, 6130, 60881])\n", "123 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([3, 3, 3, 3]) tensor([22730, 22730, 22730, 41976])\n", "124 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([34232, 34232, 61747, 61747])\n", "125 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([1, 1, 1, 1]) tensor([51033, 51033, 1201, 1201])\n", "126 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([2, 2, 2, 2]) tensor([60881, 60881, 59325, 59325])\n", "127 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([3, 3, 3, 3]) tensor([41976, 41976, 18624, 18624])\n", "128 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([61747, 22232, 22232, 22232])\n", "129 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([1, 1, 1, 1]) tensor([ 1201, 17577, 17577, 17577])\n", "130 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([2, 2, 2, 2]) tensor([59325, 24373, 24373, 24373])\n", "131 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([3, 3, 3, 3]) tensor([18624, 5176, 5176, 5176])\n", "132 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([24149, 24149, 24149, 62680])\n", "133 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([1, 1, 1, 1]) tensor([53843, 53843, 53843, 24645])\n", "134 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([2, 2, 2, 2]) tensor([62307, 62307, 62307, 13307])\n", "135 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([3, 3, 3, 3]) tensor([12674, 12674, 12674, 49547])\n", "136 torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([62680, 62680, 50434, 50434])\n", "137 torch.Size([2, 1, 96, 96, 96]) torch.Size([2, 1, 96, 96, 96]) tensor([1, 1]) tensor([24645, 24645])\n", "138 torch.Size([2, 1, 96, 96, 96]) torch.Size([2, 1, 96, 96, 96]) tensor([2, 2]) tensor([13307, 13307])\n", "139 torch.Size([2, 1, 96, 96, 96]) torch.Size([2, 1, 96, 96, 96]) tensor([3, 3]) tensor([49547, 49547])\n", "140 torch.Size([1, 1, 96, 96, 96]) torch.Size([1, 1, 96, 96, 96]) tensor([0]) tensor([50434])\n" ] } ], "source": [ "from torch.utils.data import DataLoader\n", "from dataloaders import Segmentation3DDataset\n", "\n", "ds = Segmentation3DDataset(split=\"train\", streaming=True)\n", "dl = DataLoader(ds, batch_size=4, num_workers=4)\n", "for i, x in enumerate(dl):\n", " print(i, x[\"img\"].shape, x[\"seg\"].shape, x[\"worker_id\"], x[\"series_id\"])" ] }, { "cell_type": "code", "execution_count": 64, "id": "1471fede-9e72-4c84-933e-9107fb80ecf8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([False, False, False, False]) tensor([0, 0, 0, 0]) tensor([21872, 21872, 21872, 55910])\n", "1 torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([False, False, False, False]) tensor([1, 1, 1, 1]) tensor([13664, 13664, 13664, 44622])\n", "2 torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 1]) tensor([0, 0, 0, 0]) tensor([False, False, False, True]) tensor([2, 2, 2, 2]) tensor([23622, 23622, 23622, 14060])\n", "3 torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([1, 1, 1, 0]) tensor([0, 0, 0, 0]) tensor([2, 2, 2, 0]) tensor([ True, True, True, False]) tensor([3, 3, 3, 3]) tensor([62741, 62741, 62741, 1320])\n", "4 torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([False, False, False, False]) tensor([0, 0, 0, 0]) tensor([55910, 55910, 27601, 27601])\n", "5 torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([False, False, False, False]) tensor([1, 1, 1, 1]) tensor([44622, 44622, 30990, 30990])\n", "6 torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([1, 1, 0, 0]) tensor([0, 0, 0, 0]) tensor([ True, True, False, False]) tensor([2, 2, 2, 2]) tensor([14060, 14060, 29835, 29835])\n", "7 torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([False, False, False, False]) tensor([3, 3, 3, 3]) tensor([ 1320, 1320, 10745, 10745])\n", "8 torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 1, 1, 1]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([False, True, True, True]) tensor([0, 0, 0, 0]) tensor([27601, 63666, 63666, 63666])\n", "9 torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 1, 1, 1]) tensor([False, True, True, True]) tensor([1, 1, 1, 1]) tensor([30990, 15777, 15777, 15777])\n", "10 torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 1, 1, 1]) tensor([0, 0, 0, 0]) tensor([False, True, True, True]) tensor([2, 2, 2, 2]) tensor([29835, 15406, 15406, 15406])\n", "11 torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 1, 1, 1]) tensor([0, 0, 0, 0]) tensor([False, True, True, True]) tensor([3, 3, 3, 3]) tensor([10745, 32240, 32240, 32240])\n", "12 torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([False, False, False, False]) tensor([0, 0, 0, 0]) tensor([15782, 15782, 15782, 23062])\n", "13 torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 1]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([False, False, False, True]) tensor([1, 1, 1, 1]) tensor([30334, 30334, 30334, 7987])\n", "14 torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([False, False, False, False]) tensor([2, 2, 2, 2]) tensor([62641, 62641, 62641, 2260])\n", "15 torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([1, 1, 1, 0]) tensor([ True, True, True, False]) tensor([3, 3, 3, 3]) tensor([2703, 2703, 2703, 687])\n", "16 torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([False, False, False, False]) tensor([0, 0, 0, 0]) tensor([23062, 23062, 10084, 10084])\n", "17 torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([1, 1, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([ True, True, False, False]) tensor([1, 1, 1, 1]) tensor([ 7987, 7987, 31995, 31995])\n", "18 torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([False, False, False, False]) tensor([2, 2, 2, 2]) tensor([ 2260, 2260, 19576, 19576])\n", "19 torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([False, False, False, False]) tensor([3, 3, 3, 3]) tensor([ 687, 687, 2839, 2839])\n", "20 torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([False, False, False, False]) tensor([0, 0, 0, 0]) tensor([10084, 43451, 43451, 43451])\n", "21 torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([False, False, False, False]) tensor([1, 1, 1, 1]) tensor([31995, 23185, 23185, 23185])\n", "22 torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([False, False, False, False]) tensor([2, 2, 2, 2]) tensor([19576, 27222, 27222, 27222])\n", "23 torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([False, False, False, False]) tensor([3, 3, 3, 3]) tensor([ 2839, 10185, 10185, 10185])\n", "24 torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([False, False, False, False]) tensor([0, 0, 0, 0]) tensor([39290, 39290, 39290, 29209])\n", "25 torch.Size([4, 1, 96, 96, 96]) tensor([1, 1, 1, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([1, 1, 1, 0]) tensor([1, 1, 1, 0]) tensor([ True, True, True, False]) tensor([1, 1, 1, 1]) tensor([64168, 64168, 64168, 37039])\n", "26 torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([1, 1, 1, 0]) tensor([1, 1, 1, 0]) tensor([2, 2, 2, 0]) tensor([0, 0, 0, 0]) tensor([ True, True, True, False]) tensor([2, 2, 2, 2]) tensor([52940, 52940, 52940, 14091])\n", "27 torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([False, False, False, False]) tensor([3, 3, 3, 3]) tensor([12806, 12806, 12806, 11641])\n", "28 torch.Size([4, 1, 96, 96, 96]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([0, 0, 0, 0]) tensor([False, False, False, False]) tensor([0, 0, 0, 0]) tensor([29209, 29209, 7443, 7443])\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[64], line 5\u001b[0m\n\u001b[0;32m 3\u001b[0m ds \u001b[38;5;241m=\u001b[39m Classification3DDataset(split\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m\"\u001b[39m, streaming\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m 4\u001b[0m dl \u001b[38;5;241m=\u001b[39m DataLoader(ds, batch_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m4\u001b[39m, num_workers\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m4\u001b[39m)\n\u001b[1;32m----> 5\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, x \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(dl):\n\u001b[0;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(i, x[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mimg\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mshape, x[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbowel\u001b[39m\u001b[38;5;124m\"\u001b[39m], x[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mextravasation\u001b[39m\u001b[38;5;124m\"\u001b[39m], x[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkidney\u001b[39m\u001b[38;5;124m\"\u001b[39m], x[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mliver\u001b[39m\u001b[38;5;124m\"\u001b[39m], x[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mspleen\u001b[39m\u001b[38;5;124m\"\u001b[39m], x[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124many_injury\u001b[39m\u001b[38;5;124m\"\u001b[39m], x[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mworker_id\u001b[39m\u001b[38;5;124m\"\u001b[39m], x[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mseries_id\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:633\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 630\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 631\u001b[0m \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[0;32m 632\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset() \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[1;32m--> 633\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_next_data()\n\u001b[0;32m 634\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m 635\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[0;32m 636\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[0;32m 637\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called:\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:1328\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._next_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1325\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_process_data(data)\n\u001b[0;32m 1327\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_shutdown \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tasks_outstanding \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m-> 1328\u001b[0m idx, data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_data()\n\u001b[0;32m 1329\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tasks_outstanding \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m 1330\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable:\n\u001b[0;32m 1331\u001b[0m \u001b[38;5;66;03m# Check for _IterableDatasetStopIteration\u001b[39;00m\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:1294\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._get_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1290\u001b[0m \u001b[38;5;66;03m# In this case, `self._data_queue` is a `queue.Queue`,. But we don't\u001b[39;00m\n\u001b[0;32m 1291\u001b[0m \u001b[38;5;66;03m# need to call `.task_done()` because we don't use `.join()`.\u001b[39;00m\n\u001b[0;32m 1292\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 1293\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m-> 1294\u001b[0m success, data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_try_get_data()\n\u001b[0;32m 1295\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m success:\n\u001b[0;32m 1296\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m data\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:1132\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._try_get_data\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m 1119\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_try_get_data\u001b[39m(\u001b[38;5;28mself\u001b[39m, timeout\u001b[38;5;241m=\u001b[39m_utils\u001b[38;5;241m.\u001b[39mMP_STATUS_CHECK_INTERVAL):\n\u001b[0;32m 1120\u001b[0m \u001b[38;5;66;03m# Tries to fetch data from `self._data_queue` once for a given timeout.\u001b[39;00m\n\u001b[0;32m 1121\u001b[0m \u001b[38;5;66;03m# This can also be used as inner loop of fetching without timeout, with\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1129\u001b[0m \u001b[38;5;66;03m# Returns a 2-tuple:\u001b[39;00m\n\u001b[0;32m 1130\u001b[0m \u001b[38;5;66;03m# (bool: whether successfully get data, any: data if successful else None)\u001b[39;00m\n\u001b[0;32m 1131\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1132\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data_queue\u001b[38;5;241m.\u001b[39mget(timeout\u001b[38;5;241m=\u001b[39mtimeout)\n\u001b[0;32m 1133\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m (\u001b[38;5;28;01mTrue\u001b[39;00m, data)\n\u001b[0;32m 1134\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 1135\u001b[0m \u001b[38;5;66;03m# At timeout and error, we manually check whether any worker has\u001b[39;00m\n\u001b[0;32m 1136\u001b[0m \u001b[38;5;66;03m# failed. Note that this is the only mechanism for Windows to detect\u001b[39;00m\n\u001b[0;32m 1137\u001b[0m \u001b[38;5;66;03m# worker failures.\u001b[39;00m\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\multiprocessing\\queues.py:113\u001b[0m, in \u001b[0;36mQueue.get\u001b[1;34m(self, block, timeout)\u001b[0m\n\u001b[0;32m 111\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m block:\n\u001b[0;32m 112\u001b[0m timeout \u001b[38;5;241m=\u001b[39m deadline \u001b[38;5;241m-\u001b[39m time\u001b[38;5;241m.\u001b[39mmonotonic()\n\u001b[1;32m--> 113\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_poll(timeout):\n\u001b[0;32m 114\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m Empty\n\u001b[0;32m 115\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_poll():\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\multiprocessing\\connection.py:256\u001b[0m, in \u001b[0;36m_ConnectionBase.poll\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m 254\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_closed()\n\u001b[0;32m 255\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_readable()\n\u001b[1;32m--> 256\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_poll(timeout)\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\multiprocessing\\connection.py:329\u001b[0m, in \u001b[0;36mPipeConnection._poll\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m 326\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_got_empty_message \u001b[38;5;129;01mor\u001b[39;00m\n\u001b[0;32m 327\u001b[0m _winapi\u001b[38;5;241m.\u001b[39mPeekNamedPipe(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_handle)[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m):\n\u001b[0;32m 328\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m--> 329\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mbool\u001b[39m(wait([\u001b[38;5;28mself\u001b[39m], timeout))\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\multiprocessing\\connection.py:878\u001b[0m, in \u001b[0;36mwait\u001b[1;34m(object_list, timeout)\u001b[0m\n\u001b[0;32m 875\u001b[0m ready_objects\u001b[38;5;241m.\u001b[39madd(o)\n\u001b[0;32m 876\u001b[0m timeout \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m--> 878\u001b[0m ready_handles \u001b[38;5;241m=\u001b[39m _exhaustive_wait(waithandle_to_obj\u001b[38;5;241m.\u001b[39mkeys(), timeout)\n\u001b[0;32m 879\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m 880\u001b[0m \u001b[38;5;66;03m# request that overlapped reads stop\u001b[39;00m\n\u001b[0;32m 881\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ov \u001b[38;5;129;01min\u001b[39;00m ov_list:\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\multiprocessing\\connection.py:810\u001b[0m, in \u001b[0;36m_exhaustive_wait\u001b[1;34m(handles, timeout)\u001b[0m\n\u001b[0;32m 808\u001b[0m ready \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m 809\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m L:\n\u001b[1;32m--> 810\u001b[0m res \u001b[38;5;241m=\u001b[39m _winapi\u001b[38;5;241m.\u001b[39mWaitForMultipleObjects(L, \u001b[38;5;28;01mFalse\u001b[39;00m, timeout)\n\u001b[0;32m 811\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m res \u001b[38;5;241m==\u001b[39m WAIT_TIMEOUT:\n\u001b[0;32m 812\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n", "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "from dataloaders import Classification3DDataset\n", "\n", "ds = Classification3DDataset(split=\"train\", streaming=True)\n", "dl = DataLoader(ds, batch_size=4, num_workers=4)\n", "for i, x in enumerate(dl):\n", " print(i, x[\"img\"].shape, x[\"bowel\"], x[\"extravasation\"], x[\"kidney\"], x[\"liver\"], x[\"spleen\"], x[\"any_injury\"], x[\"worker_id\"], x[\"series_id\"])" ] }, { "cell_type": "code", "execution_count": 68, "id": "bf5a7b87-7130-49d1-9ef5-ee1bdf2cdfb3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) tensor([42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173])\n", "1 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) tensor([53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000])\n", "2 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]) tensor([55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928])\n", "3 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]) tensor([5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218])\n", "4 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) tensor([42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173])\n", "5 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) tensor([53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000])\n", "6 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]) tensor([55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928])\n", "7 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]) tensor([5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218])\n", "8 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) tensor([42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173])\n", "9 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) tensor([53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000])\n", "10 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]) tensor([55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928])\n", "11 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]) tensor([5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218])\n", "12 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) tensor([42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173])\n", "13 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) tensor([53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000])\n", "14 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]) tensor([55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928])\n", "15 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]) tensor([5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218])\n", "16 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) tensor([42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173])\n", "17 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) tensor([53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000, 53000,\n", " 53000, 53000, 53000, 53000, 53000, 53000, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520])\n", "18 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]) tensor([55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928])\n", "19 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]) tensor([5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218])\n", "20 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) tensor([42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173, 42173,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929])\n", "21 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) tensor([64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520])\n", "22 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]) tensor([55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928])\n", "23 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]) tensor([5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218])\n", "24 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) tensor([4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929])\n", "25 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) tensor([64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520])\n", "26 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]) tensor([55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928])\n", "27 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]) tensor([5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218])\n", "28 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) tensor([4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929])\n", "29 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) tensor([64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520])\n", "30 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]) tensor([55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928,\n", " 55928, 55928, 55928, 55928, 55928, 55928, 55928, 55928, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494])\n", "31 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]) tensor([5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218])\n", "32 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) tensor([4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929])\n", "33 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) tensor([64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520, 64520,\n", " 64520, 64520, 35190, 35190, 35190, 35190])\n", "34 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]) tensor([10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494])\n", "35 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]) tensor([5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218])\n", "36 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) tensor([4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929])\n", "37 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) tensor([35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190])\n", "38 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]) tensor([10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494])\n", "39 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]) tensor([ 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218, 5218,\n", " 5218, 5218, 5218, 5218, 5218, 5218, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526])\n", "40 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) tensor([4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929])\n", "41 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) tensor([35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190])\n", "42 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]) tensor([10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494])\n", "43 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]) tensor([33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526])\n", "44 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) tensor([ 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929, 4929,\n", " 4929, 4929, 4929, 4929, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109])\n", "45 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) tensor([35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190])\n", "46 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]) tensor([10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494])\n", "47 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]) tensor([33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526])\n", "48 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) tensor([10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109])\n", "49 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) tensor([35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190])\n", "50 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]) tensor([10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494])\n", "51 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]) tensor([33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526])\n", "52 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) tensor([10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109])\n", "53 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) tensor([35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190])\n", "54 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]) tensor([10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494])\n", "55 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]) tensor([33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526])\n", "56 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) tensor([10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109])\n", "57 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) tensor([35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190])\n", "58 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]) tensor([10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494])\n", "59 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]) tensor([33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526])\n", "60 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) tensor([10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109])\n", "61 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) tensor([35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190, 35190,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415])\n", "62 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]) tensor([10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494, 10494,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748])\n", "63 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]) tensor([33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526])\n", "64 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) tensor([10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109])\n", "65 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) tensor([15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415])\n", "66 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]) tensor([15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748])\n", "67 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]) tensor([33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526])\n", "68 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) tensor([10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109])\n", "69 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) tensor([15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415])\n", "70 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]) tensor([15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748])\n", "71 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]) tensor([33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526])\n", "72 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) tensor([10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109, 10109,\n", " 10109, 10109, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991])\n", "73 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) tensor([15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415])\n", "74 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]) tensor([15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748])\n", "75 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]) tensor([33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526, 33526,\n", " 33526, 33526, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000])\n", "76 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) tensor([32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991])\n", "77 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) tensor([15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415])\n", "78 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]) tensor([15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748])\n", "79 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]) tensor([10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000])\n", "80 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) tensor([32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991])\n", "81 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) tensor([15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415])\n", "82 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]) tensor([15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748])\n", "83 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]) tensor([10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000])\n", "84 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) tensor([32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991])\n", "85 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) tensor([15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415])\n", "86 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]) tensor([15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748])\n", "87 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]) tensor([10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000])\n", "88 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) tensor([32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991])\n", "89 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) tensor([15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415,\n", " 15415, 15415, 15415, 15415, 15415, 15415, 15415, 15415, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122])\n", "90 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]) tensor([15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748,\n", " 15748, 15748, 15748, 15748, 15748, 15748, 15748, 15748, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385])\n", "91 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]) tensor([10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000])\n", "92 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) tensor([32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991])\n", "93 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) tensor([28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122])\n", "94 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]) tensor([10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385])\n", "95 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]) tensor([10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000])\n", "96 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) tensor([32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991, 32991,\n", " 32991, 32991, 32991, 32991, 32991, 32991])\n", "97 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) tensor([28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122, 28122,\n", " 28122, 28122, 28122, 28122, 28122, 28122])\n", "98 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]) tensor([10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385, 10385,\n", " 10385, 10385, 10385, 10385, 10385, 10385])\n", "99 torch.Size([256, 1, 224, 224]) torch.Size([256, 1, 224, 224]) tensor([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]) tensor([10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000,\n", " 10000, 10000, 10000, 10000, 10000, 10000])\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[68], line 6\u001b[0m\n\u001b[0;32m 4\u001b[0m ds \u001b[38;5;241m=\u001b[39m Segmentation2DDataset(split\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtest\u001b[39m\u001b[38;5;124m\"\u001b[39m, streaming\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, slice_transform_configs\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcrop_strategy\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mten\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshorter_edge_length\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m256\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mslice_size\u001b[39m\u001b[38;5;124m\"\u001b[39m: (\u001b[38;5;241m224\u001b[39m, \u001b[38;5;241m224\u001b[39m)})\n\u001b[0;32m 5\u001b[0m dl \u001b[38;5;241m=\u001b[39m DataLoader(ds, batch_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m256\u001b[39m, num_workers\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m4\u001b[39m)\n\u001b[1;32m----> 6\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, x \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(dl):\n\u001b[0;32m 7\u001b[0m \u001b[38;5;28mprint\u001b[39m(i, x[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mimg\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mshape, x[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mseg\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mshape, x[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mworker_id\u001b[39m\u001b[38;5;124m\"\u001b[39m], x[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mseries_id\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:633\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 630\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 631\u001b[0m \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[0;32m 632\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset() \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[1;32m--> 633\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_next_data()\n\u001b[0;32m 634\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m 635\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[0;32m 636\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[0;32m 637\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called:\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:1328\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._next_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1325\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_process_data(data)\n\u001b[0;32m 1327\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_shutdown \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tasks_outstanding \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m-> 1328\u001b[0m idx, data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_data()\n\u001b[0;32m 1329\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tasks_outstanding \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m 1330\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable:\n\u001b[0;32m 1331\u001b[0m \u001b[38;5;66;03m# Check for _IterableDatasetStopIteration\u001b[39;00m\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:1294\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._get_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1290\u001b[0m \u001b[38;5;66;03m# In this case, `self._data_queue` is a `queue.Queue`,. But we don't\u001b[39;00m\n\u001b[0;32m 1291\u001b[0m \u001b[38;5;66;03m# need to call `.task_done()` because we don't use `.join()`.\u001b[39;00m\n\u001b[0;32m 1292\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 1293\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m-> 1294\u001b[0m success, data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_try_get_data()\n\u001b[0;32m 1295\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m success:\n\u001b[0;32m 1296\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m data\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:1132\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._try_get_data\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m 1119\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_try_get_data\u001b[39m(\u001b[38;5;28mself\u001b[39m, timeout\u001b[38;5;241m=\u001b[39m_utils\u001b[38;5;241m.\u001b[39mMP_STATUS_CHECK_INTERVAL):\n\u001b[0;32m 1120\u001b[0m \u001b[38;5;66;03m# Tries to fetch data from `self._data_queue` once for a given timeout.\u001b[39;00m\n\u001b[0;32m 1121\u001b[0m \u001b[38;5;66;03m# This can also be used as inner loop of fetching without timeout, with\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1129\u001b[0m \u001b[38;5;66;03m# Returns a 2-tuple:\u001b[39;00m\n\u001b[0;32m 1130\u001b[0m \u001b[38;5;66;03m# (bool: whether successfully get data, any: data if successful else None)\u001b[39;00m\n\u001b[0;32m 1131\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1132\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data_queue\u001b[38;5;241m.\u001b[39mget(timeout\u001b[38;5;241m=\u001b[39mtimeout)\n\u001b[0;32m 1133\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m (\u001b[38;5;28;01mTrue\u001b[39;00m, data)\n\u001b[0;32m 1134\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 1135\u001b[0m \u001b[38;5;66;03m# At timeout and error, we manually check whether any worker has\u001b[39;00m\n\u001b[0;32m 1136\u001b[0m \u001b[38;5;66;03m# failed. Note that this is the only mechanism for Windows to detect\u001b[39;00m\n\u001b[0;32m 1137\u001b[0m \u001b[38;5;66;03m# worker failures.\u001b[39;00m\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\multiprocessing\\queues.py:113\u001b[0m, in \u001b[0;36mQueue.get\u001b[1;34m(self, block, timeout)\u001b[0m\n\u001b[0;32m 111\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m block:\n\u001b[0;32m 112\u001b[0m timeout \u001b[38;5;241m=\u001b[39m deadline \u001b[38;5;241m-\u001b[39m time\u001b[38;5;241m.\u001b[39mmonotonic()\n\u001b[1;32m--> 113\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_poll(timeout):\n\u001b[0;32m 114\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m Empty\n\u001b[0;32m 115\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_poll():\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\multiprocessing\\connection.py:256\u001b[0m, in \u001b[0;36m_ConnectionBase.poll\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m 254\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_closed()\n\u001b[0;32m 255\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_readable()\n\u001b[1;32m--> 256\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_poll(timeout)\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\multiprocessing\\connection.py:329\u001b[0m, in \u001b[0;36mPipeConnection._poll\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m 326\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_got_empty_message \u001b[38;5;129;01mor\u001b[39;00m\n\u001b[0;32m 327\u001b[0m _winapi\u001b[38;5;241m.\u001b[39mPeekNamedPipe(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_handle)[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m):\n\u001b[0;32m 328\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m--> 329\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mbool\u001b[39m(wait([\u001b[38;5;28mself\u001b[39m], timeout))\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\multiprocessing\\connection.py:878\u001b[0m, in \u001b[0;36mwait\u001b[1;34m(object_list, timeout)\u001b[0m\n\u001b[0;32m 875\u001b[0m ready_objects\u001b[38;5;241m.\u001b[39madd(o)\n\u001b[0;32m 876\u001b[0m timeout \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m--> 878\u001b[0m ready_handles \u001b[38;5;241m=\u001b[39m _exhaustive_wait(waithandle_to_obj\u001b[38;5;241m.\u001b[39mkeys(), timeout)\n\u001b[0;32m 879\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m 880\u001b[0m \u001b[38;5;66;03m# request that overlapped reads stop\u001b[39;00m\n\u001b[0;32m 881\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ov \u001b[38;5;129;01min\u001b[39;00m ov_list:\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\multiprocessing\\connection.py:810\u001b[0m, in \u001b[0;36m_exhaustive_wait\u001b[1;34m(handles, timeout)\u001b[0m\n\u001b[0;32m 808\u001b[0m ready \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m 809\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m L:\n\u001b[1;32m--> 810\u001b[0m res \u001b[38;5;241m=\u001b[39m _winapi\u001b[38;5;241m.\u001b[39mWaitForMultipleObjects(L, \u001b[38;5;28;01mFalse\u001b[39;00m, timeout)\n\u001b[0;32m 811\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m res \u001b[38;5;241m==\u001b[39m WAIT_TIMEOUT:\n\u001b[0;32m 812\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n", "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "import matplotlib.pyplot as plt\n", "from dataloaders import Segmentation2DDataset\n", "\n", "ds = Segmentation2DDataset(split=\"test\", streaming=True, slice_transform_configs={\"crop_strategy\": \"ten\", \"shorter_edge_length\": 256, \"slice_size\": (224, 224)})\n", "dl = DataLoader(ds, batch_size=256, num_workers=4)\n", "for i, x in enumerate(dl):\n", " print(i, x[\"img\"].shape, x[\"seg\"].shape, x[\"worker_id\"], x[\"series_id\"])" ] }, { "cell_type": "code", "execution_count": 40, "id": "4f2ccb53-4ae0-4a4a-b174-fa7a28ba2777", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n", "torch.Size([1, 224, 224])\n" ] }, { "ename": "TypeError", "evalue": "permute(): argument 'input' (position 1) must be Tensor, not tuple", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[40], line 82\u001b[0m\n\u001b[0;32m 80\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mslice\u001b[39m))\n\u001b[0;32m 81\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;28mslice\u001b[39m[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39msize())\n\u001b[1;32m---> 82\u001b[0m plt\u001b[38;5;241m.\u001b[39mimshow(torch\u001b[38;5;241m.\u001b[39mpermute(\u001b[38;5;28mslice\u001b[39m, (\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m0\u001b[39m))\u001b[38;5;241m.\u001b[39mnumpy(), cmap\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgray\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 83\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n", "\u001b[1;31mTypeError\u001b[0m: permute(): argument 'input' (position 1) must be Tensor, not tuple" ] } ], "source": [ "import torch\n", "import datasets\n", "import matplotlib.pyplot as plt\n", "import monai\n", "import torchvision\n", "import transforms as custom_transforms\n", "\n", "voxel_spacing = (3.0, 3.0, 3.0)\n", "volume_size = (96, 96, 96)\n", "slice_shorter_edge_size = 256\n", "slice_size = (224, 224)\n", "streaming = False\n", "volume_transforms = {\n", " \"oversample\": monai.transforms.Compose([\n", " custom_transforms.LoadNIfTIFromHFHub() if streaming else custom_transforms.LoadNIfTIFromLocalCache(),\n", " monai.transforms.EnsureType(data_type=\"tensor\", dtype=torch.float32),\n", " monai.transforms.EnsureChannelFirst(),\n", " monai.transforms.Orientation(axcodes=\"RAS\"),\n", " monai.transforms.Spacing(pixdim=voxel_spacing, mode=\"bilinear\"),\n", " monai.transforms.NormalizeIntensity(nonzero=False),\n", " monai.transforms.ScaleIntensity(minv=-1.0, maxv=1.0),\n", " monai.transforms.SpatialPad(spatial_size=volume_size),\n", " monai.transforms.RandSpatialCropSamples(roi_size=volume_size, num_samples=3, random_center=True, random_size=False)\n", " ]),\n", " \"center\": monai.transforms.Compose([\n", " custom_transforms.LoadNIfTIFromHFHub() if streaming else custom_transforms.LoadNIfTIFromLocalCache(),\n", " monai.transforms.EnsureType(data_type=\"tensor\", dtype=torch.float32),\n", " monai.transforms.EnsureChannelFirst(),\n", " monai.transforms.Orientation(axcodes=\"RAS\"),\n", " monai.transforms.Spacing(pixdim=voxel_spacing, mode=\"bilinear\"),\n", " monai.transforms.NormalizeIntensity(nonzero=False),\n", " monai.transforms.ScaleIntensity(minv=-1.0, maxv=1.0),\n", " monai.transforms.SpatialPad(spatial_size=volume_size),\n", " monai.transforms.CenterSpatialCrop(roi_size=volume_size)\n", " ]),\n", " \"none\": monai.transforms.Compose([\n", " custom_transforms.LoadNIfTIFromHFHub() if streaming else custom_transforms.LoadNIfTIFromLocalCache(),\n", " monai.transforms.EnsureType(data_type=\"tensor\", dtype=torch.float32),\n", " monai.transforms.EnsureChannelFirst(),\n", " monai.transforms.Orientation(axcodes=\"RAS\"),\n", " monai.transforms.Spacing(pixdim=voxel_spacing, mode=\"bilinear\"),\n", " monai.transforms.NormalizeIntensity(nonzero=False),\n", " monai.transforms.ScaleIntensity(minv=-1.0, maxv=1.0),\n", " ]),\n", "}\n", "\n", "seg_transforms = {\n", " \"oversample\": monai.transforms.Compose([\n", " custom_transforms.LoadNIfTIFromHFHub() if streaming else custom_transforms.LoadNIfTIFromLocalCache(),\n", " monai.transforms.EnsureType(data_type=\"tensor\", dtype=torch.float32),\n", " monai.transforms.EnsureChannelFirst(),\n", " monai.transforms.Orientation(axcodes=\"RAS\"),\n", " monai.transforms.Spacing(pixdim=voxel_spacing, mode=\"nearest\"),\n", " monai.transforms.SpatialPad(spatial_size=volume_size),\n", " monai.transforms.RandSpatialCropSamples(roi_size=volume_size, num_samples=3, random_center=True, random_size=False)\n", " ]),\n", " \"center\": monai.transforms.Compose([\n", " custom_transforms.LoadNIfTIFromHFHub() if streaming else custom_transforms.LoadNIfTIFromLocalCache(),\n", " monai.transforms.EnsureType(data_type=\"tensor\", dtype=torch.float32),\n", " monai.transforms.EnsureChannelFirst(),\n", " monai.transforms.Orientation(axcodes=\"RAS\"),\n", " monai.transforms.Spacing(pixdim=voxel_spacing, mode=\"nearest\"),\n", " monai.transforms.SpatialPad(spatial_size=volume_size),\n", " monai.transforms.CenterSpatialCrop(roi_size=volume_size)\n", " ]),\n", " \"none\": monai.transforms.Compose([\n", " custom_transforms.LoadNIfTIFromHFHub() if streaming else custom_transforms.LoadNIfTIFromLocalCache(),\n", " monai.transforms.EnsureType(data_type=\"tensor\", dtype=torch.float32),\n", " monai.transforms.EnsureChannelFirst(),\n", " monai.transforms.Orientation(axcodes=\"RAS\"),\n", " monai.transforms.Spacing(pixdim=voxel_spacing, mode=\"nearest\"),\n", " ]),\n", "}\n", "\n", "slice_transforms = {\n", " \"ten\": torchvision.transforms.Compose([\n", " torchvision.transforms.Resize(size=slice_shorter_edge_size, antialias=True),\n", " torchvision.transforms.TenCrop(size=slice_size),\n", " ]),\n", " \"five\": torchvision.transforms.Compose([\n", " torchvision.transforms.Resize(size=slice_shorter_edge_size, antialias=True),\n", " torchvision.transforms.FiveCrop(size=slice_size),\n", " ]),\n", " \"random\": torchvision.transforms.Compose([\n", " torchvision.transforms.Resize(size=slice_shorter_edge_size, antialias=True),\n", " torchvision.transforms.RandomCrop(size=slice_size),\n", " ]),\n", " \"center\": torchvision.transforms.Compose([\n", " torchvision.transforms.Resize(size=slice_shorter_edge_size, antialias=True),\n", " torchvision.transforms.CenterCrop(size=slice_size),\n", " ]),\n", "}\n", "\n", "temp_ds = datasets.load_dataset(\"jherng/rsna-2023-abdominal-trauma-detection\", \"segmentation\", split=\"train\", streaming=streaming)\n", "\n", "for i, x in enumerate(temp_ds):\n", " if i == 1:\n", " break\n", " \n", " img_path = x[\"img_path\"]\n", " seg_path = x[\"seg_path\"]\n", " \n", " vol = volume_transforms[\"none\"](img_path)\n", " \n", " for slice_idx in range(vol.size()[-1]):\n", " slice = vol[:, :, :, slice_idx]\n", " slice = slice_transforms[\"ten\"](slice)\n", " print(len(slice))\n", " print(slice[0].size())\n", " plt.imshow(torch.permute(slice, (1, 2, 0)).numpy(), cmap=\"gray\")\n", " plt.show()\n", " \n", " # print(x)\n", " # plt.subplot(3, 3, 1)\n", " # plt.title(tuple(img_oversample[0].size()))\n", " # plt.imshow(img_oversample[0][0, :, :, img_oversample[0].size()[3] // 2], cmap=\"gray\")\n", " # plt.subplot(3, 3, 4)\n", " # plt.title(tuple(img_oversample[1].size()))\n", " # plt.imshow(img_oversample[1][0, :, :, img_oversample[1].size()[3] // 2], cmap=\"gray\")\n", " # plt.subplot(3, 3, 7)\n", " # plt.title(tuple(img_oversample[2].size()))\n", " # plt.imshow(img_oversample[2][0, :, :, img_oversample[2].size()[3] // 2], cmap=\"gray\")\n", "\n", " # plt.subplot(3, 3, 2)\n", " # plt.title(tuple(img_center.size()))\n", " # plt.imshow(img_center[0, :, :, img_center.size()[3] // 2], cmap=\"gray\")\n", " # plt.subplot(3, 3, 3)\n", " # plt.title(tuple(img.size()))\n", " # plt.imshow(img[0, :, :, img.size()[3] // 2], cmap=\"gray\")\n", " # plt.tight_layout()\n", " # plt.show()\n", " \n", " # print()\n", " " ] }, { "cell_type": "code", "execution_count": null, "id": "bda3bac2-ec38-4967-b97d-2df2ee1d0438", "metadata": {}, "outputs": [], "source": [ "import time\n", "import datasets\n", "import torch\n", "from torch.utils.data import DataLoader\n", "\n", "if __name__ == \"__main__\":\n", " temp_ds = datasets.load_dataset(\"jherng/rsna-2023-abdominal-trauma-detection\", \"segmentation\", split=\"train\", streaming=False)\n", " temp_dl = DataLoader(temp_ds, batch_size=4, num_workers=4)\n", " \n", " for i, batch in enumerate(temp_dl):\n", " worker = torch.utils.data.get_worker_info()\n", " worker_id = worker.id if worker is not None else -1\n", " print(i, batch[\"metadata\"][\"series_id\"], worker_id, time.ctime())" ] }, { "cell_type": "code", "execution_count": 25, "id": "763023e4-9320-4c87-9b30-167b55587c52", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'train': ,\n", " 'test': }" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from dataloaders import get_segmentation_dataloaders\n", "\n", "dls = get_segmentation_dataloaders(\n", " split_types=[\"train\", \"test\"],\n", " streaming=True,\n", " train_batch_size=4,\n", " test_batch_size=1, \n", " num_workers=0,\n", " prefetch_factor=None\n", ")\n", "dls" ] }, { "cell_type": "code", "execution_count": 26, "id": "05e7998f-1baf-46d8-b29c-2133952585ff", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 [-1, -1, -1, -1] [4622, 4622, 26343, 26343] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "1 [-1, -1, -1, -1] [55965, 55965, 12114, 12114] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "2 [-1, -1, -1, -1] [6172, 6172, 40430, 40430] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "3 [-1, -1, -1, -1] [62573, 28432, 28432, 12102] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "4 [-1, -1, -1, -1] [12102, 12102, 25359, 25359] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "5 [-1, -1, -1, -1] [8236, 8236, 43416, 43416] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "6 [-1, -1, -1, -1] [18207, 18207, 55694, 55694] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "7 [-1, -1, -1, -1] [20664, 32670, 32670, 26980] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "8 [-1, -1, -1, -1] [26980, 39628, 39628, 55515] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "9 [-1, -1, -1, -1] [55515, 40186, 40186, 50875] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Exception ignored in: \n", "Traceback (most recent call last):\n", " File \"C:\\Users\\Jia Herng\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py\", line 1478, in __del__\n", " File \"C:\\Users\\Jia Herng\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py\", line 1442, in _shutdown_workers\n", " File \"C:\\Users\\Jia Herng\\miniconda3\\envs\\ml-env\\Lib\\multiprocessing\\process.py\", line 149, in join\n", " File \"C:\\Users\\Jia Herng\\miniconda3\\envs\\ml-env\\Lib\\multiprocessing\\popen_spawn_win32.py\", line 109, in wait\n", "KeyboardInterrupt: \n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[26], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m get_ipython()\u001b[38;5;241m.\u001b[39mrun_cell_magic(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtimeit\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m-n 1 -r 1\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfor i, batch in enumerate(dls[\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m]):\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m worker_ids, series_ids, x, y = batch\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m print(i, worker_ids.tolist(), series_ids.tolist(), x.shape, y.shape)\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\IPython\\core\\interactiveshell.py:2478\u001b[0m, in \u001b[0;36mInteractiveShell.run_cell_magic\u001b[1;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[0;32m 2476\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m 2477\u001b[0m args \u001b[38;5;241m=\u001b[39m (magic_arg_s, cell)\n\u001b[1;32m-> 2478\u001b[0m result \u001b[38;5;241m=\u001b[39m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 2480\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[0;32m 2481\u001b[0m \u001b[38;5;66;03m# when using magics with decodator @output_can_be_silenced\u001b[39;00m\n\u001b[0;32m 2482\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[0;32m 2483\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\IPython\\core\\magics\\execution.py:1174\u001b[0m, in \u001b[0;36mExecutionMagics.timeit\u001b[1;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[0;32m 1171\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m time_number \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.2\u001b[39m:\n\u001b[0;32m 1172\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m-> 1174\u001b[0m all_runs \u001b[38;5;241m=\u001b[39m timer\u001b[38;5;241m.\u001b[39mrepeat(repeat, number)\n\u001b[0;32m 1175\u001b[0m best \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmin\u001b[39m(all_runs) \u001b[38;5;241m/\u001b[39m number\n\u001b[0;32m 1176\u001b[0m worst \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmax\u001b[39m(all_runs) \u001b[38;5;241m/\u001b[39m number\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\timeit.py:206\u001b[0m, in \u001b[0;36mTimer.repeat\u001b[1;34m(self, repeat, number)\u001b[0m\n\u001b[0;32m 204\u001b[0m r \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m 205\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(repeat):\n\u001b[1;32m--> 206\u001b[0m t \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeit(number)\n\u001b[0;32m 207\u001b[0m r\u001b[38;5;241m.\u001b[39mappend(t)\n\u001b[0;32m 208\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m r\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\IPython\\core\\magics\\execution.py:158\u001b[0m, in \u001b[0;36mTimer.timeit\u001b[1;34m(self, number)\u001b[0m\n\u001b[0;32m 156\u001b[0m gc\u001b[38;5;241m.\u001b[39mdisable()\n\u001b[0;32m 157\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 158\u001b[0m timing \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minner(it, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimer)\n\u001b[0;32m 159\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m 160\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m gcold:\n", "File \u001b[1;32m:1\u001b[0m, in \u001b[0;36minner\u001b[1;34m(_it, _timer)\u001b[0m\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:633\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 630\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 631\u001b[0m \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[0;32m 632\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset() \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[1;32m--> 633\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_next_data()\n\u001b[0;32m 634\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m 635\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[0;32m 636\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[0;32m 637\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called:\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:677\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 675\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_next_data\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m 676\u001b[0m index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_next_index() \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m--> 677\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_fetcher\u001b[38;5;241m.\u001b[39mfetch(index) \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m 678\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory:\n\u001b[0;32m 679\u001b[0m data \u001b[38;5;241m=\u001b[39m _utils\u001b[38;5;241m.\u001b[39mpin_memory\u001b[38;5;241m.\u001b[39mpin_memory(data, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory_device)\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py:32\u001b[0m, in \u001b[0;36m_IterableDatasetFetcher.fetch\u001b[1;34m(self, possibly_batched_index)\u001b[0m\n\u001b[0;32m 30\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m possibly_batched_index:\n\u001b[0;32m 31\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 32\u001b[0m data\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28mnext\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset_iter))\n\u001b[0;32m 33\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[0;32m 34\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mended \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n", "File \u001b[1;32m~\\Documents\\Jia Herng's Docs\\Programming Projects\\rsna-2023-abdominal-trauma-detection\\datasets\\src\\dataloaders.py:83\u001b[0m, in \u001b[0;36mSegmentationDataset.__iter__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 81\u001b[0m worker_id \u001b[38;5;241m=\u001b[39m worker_info\u001b[38;5;241m.\u001b[39mid \u001b[38;5;28;01mif\u001b[39;00m worker_info \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[0;32m 82\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, data \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhf_dataset):\n\u001b[1;32m---> 83\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_process_one_sample(data, worker_id\u001b[38;5;241m=\u001b[39mworker_id)\n", "File \u001b[1;32m~\\Documents\\Jia Herng's Docs\\Programming Projects\\rsna-2023-abdominal-trauma-detection\\datasets\\src\\dataloaders.py:89\u001b[0m, in \u001b[0;36mSegmentationDataset._process_one_sample\u001b[1;34m(self, data, worker_id)\u001b[0m\n\u001b[0;32m 87\u001b[0m data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mimg\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mimg_path\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 88\u001b[0m data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mseg\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mseg_path\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m---> 89\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransform(data)\n\u001b[0;32m 91\u001b[0m \u001b[38;5;66;03m# Apply if oversample is true (supposedly on train only)\u001b[39;00m\n\u001b[0;32m 92\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcrop_strategy \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moversample\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m 93\u001b[0m \u001b[38;5;66;03m# Oversampling dynamically based on the shape of image\u001b[39;00m\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\monai\\transforms\\compose.py:322\u001b[0m, in \u001b[0;36mCompose.__call__\u001b[1;34m(self, input_, start, end, threading, lazy)\u001b[0m\n\u001b[0;32m 321\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, input_, start\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, end\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, threading\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, lazy: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m--> 322\u001b[0m result \u001b[38;5;241m=\u001b[39m execute_compose(\n\u001b[0;32m 323\u001b[0m input_,\n\u001b[0;32m 324\u001b[0m transforms\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransforms,\n\u001b[0;32m 325\u001b[0m start\u001b[38;5;241m=\u001b[39mstart,\n\u001b[0;32m 326\u001b[0m end\u001b[38;5;241m=\u001b[39mend,\n\u001b[0;32m 327\u001b[0m map_items\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmap_items,\n\u001b[0;32m 328\u001b[0m unpack_items\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39munpack_items,\n\u001b[0;32m 329\u001b[0m lazy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlazy,\n\u001b[0;32m 330\u001b[0m overrides\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moverrides,\n\u001b[0;32m 331\u001b[0m threading\u001b[38;5;241m=\u001b[39mthreading,\n\u001b[0;32m 332\u001b[0m log_stats\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlog_stats,\n\u001b[0;32m 333\u001b[0m )\n\u001b[0;32m 335\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\monai\\transforms\\compose.py:111\u001b[0m, in \u001b[0;36mexecute_compose\u001b[1;34m(data, transforms, map_items, unpack_items, start, end, lazy, overrides, threading, log_stats)\u001b[0m\n\u001b[0;32m 109\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m threading:\n\u001b[0;32m 110\u001b[0m _transform \u001b[38;5;241m=\u001b[39m deepcopy(_transform) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(_transform, ThreadUnsafe) \u001b[38;5;28;01melse\u001b[39;00m _transform\n\u001b[1;32m--> 111\u001b[0m data \u001b[38;5;241m=\u001b[39m apply_transform(\n\u001b[0;32m 112\u001b[0m _transform, data, map_items, unpack_items, lazy\u001b[38;5;241m=\u001b[39mlazy, overrides\u001b[38;5;241m=\u001b[39moverrides, log_stats\u001b[38;5;241m=\u001b[39mlog_stats\n\u001b[0;32m 113\u001b[0m )\n\u001b[0;32m 114\u001b[0m data \u001b[38;5;241m=\u001b[39m apply_pending_transforms(data, \u001b[38;5;28;01mNone\u001b[39;00m, overrides, logger_name\u001b[38;5;241m=\u001b[39mlog_stats)\n\u001b[0;32m 115\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m data\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\monai\\transforms\\transform.py:141\u001b[0m, in \u001b[0;36mapply_transform\u001b[1;34m(transform, data, map_items, unpack_items, log_stats, lazy, overrides)\u001b[0m\n\u001b[0;32m 139\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, (\u001b[38;5;28mlist\u001b[39m, \u001b[38;5;28mtuple\u001b[39m)) \u001b[38;5;129;01mand\u001b[39;00m map_items:\n\u001b[0;32m 140\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m [_apply_transform(transform, item, unpack_items, lazy, overrides, log_stats) \u001b[38;5;28;01mfor\u001b[39;00m item \u001b[38;5;129;01min\u001b[39;00m data]\n\u001b[1;32m--> 141\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _apply_transform(transform, data, unpack_items, lazy, overrides, log_stats)\n\u001b[0;32m 142\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 143\u001b[0m \u001b[38;5;66;03m# if in debug mode, don't swallow exception so that the breakpoint\u001b[39;00m\n\u001b[0;32m 144\u001b[0m \u001b[38;5;66;03m# appears where the exception was raised.\u001b[39;00m\n\u001b[0;32m 145\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m MONAIEnvVars\u001b[38;5;241m.\u001b[39mdebug():\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\monai\\transforms\\transform.py:98\u001b[0m, in \u001b[0;36m_apply_transform\u001b[1;34m(transform, data, unpack_parameters, lazy, overrides, logger_name)\u001b[0m\n\u001b[0;32m 95\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, \u001b[38;5;28mtuple\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m unpack_parameters:\n\u001b[0;32m 96\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m transform(\u001b[38;5;241m*\u001b[39mdata, lazy\u001b[38;5;241m=\u001b[39mlazy) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(transform, LazyTrait) \u001b[38;5;28;01melse\u001b[39;00m transform(\u001b[38;5;241m*\u001b[39mdata)\n\u001b[1;32m---> 98\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m transform(data, lazy\u001b[38;5;241m=\u001b[39mlazy) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(transform, LazyTrait) \u001b[38;5;28;01melse\u001b[39;00m transform(data)\n", "File \u001b[1;32m~\\Documents\\Jia Herng's Docs\\Programming Projects\\rsna-2023-abdominal-trauma-detection\\datasets\\src\\transforms.py:221\u001b[0m, in \u001b[0;36mLoadNIfTIFromHFHubd.__call__\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m 217\u001b[0m d \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(data)\n\u001b[0;32m 218\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, meta_key, meta_key_postfix \u001b[38;5;129;01min\u001b[39;00m 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length)\u001b[0m\n\u001b[0;32m 1787\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m length \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m 1788\u001b[0m \u001b[38;5;66;03m# don't even bother calling fetch\u001b[39;00m\n\u001b[0;32m 1789\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m-> 1790\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcache\u001b[38;5;241m.\u001b[39m_fetch(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloc, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloc \u001b[38;5;241m+\u001b[39m length)\n\u001b[0;32m 1791\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloc \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(out)\n\u001b[0;32m 1792\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m out\n", "File 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response\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m retry_on_status_codes:\n\u001b[0;32m 260\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m response\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\requests\\sessions.py:589\u001b[0m, in \u001b[0;36mSession.request\u001b[1;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[0;32m 584\u001b[0m send_kwargs \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m 585\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimeout\u001b[39m\u001b[38;5;124m\"\u001b[39m: timeout,\n\u001b[0;32m 586\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow_redirects\u001b[39m\u001b[38;5;124m\"\u001b[39m: allow_redirects,\n\u001b[0;32m 587\u001b[0m }\n\u001b[0;32m 588\u001b[0m send_kwargs\u001b[38;5;241m.\u001b[39mupdate(settings)\n\u001b[1;32m--> 589\u001b[0m resp 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\u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\huggingface_hub\\utils\\_http.py:63\u001b[0m, in \u001b[0;36mUniqueRequestIdAdapter.send\u001b[1;34m(self, request, *args, **kwargs)\u001b[0m\n\u001b[0;32m 61\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Catch any RequestException to append request id to the error message for debugging.\"\"\"\u001b[39;00m\n\u001b[0;32m 62\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 63\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39msend(request, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 64\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m requests\u001b[38;5;241m.\u001b[39mRequestException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 65\u001b[0m request_id \u001b[38;5;241m=\u001b[39m request\u001b[38;5;241m.\u001b[39mheaders\u001b[38;5;241m.\u001b[39mget(X_AMZN_TRACE_ID)\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\requests\\adapters.py:486\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m 483\u001b[0m timeout \u001b[38;5;241m=\u001b[39m TimeoutSauce(connect\u001b[38;5;241m=\u001b[39mtimeout, read\u001b[38;5;241m=\u001b[39mtimeout)\n\u001b[0;32m 485\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 486\u001b[0m resp \u001b[38;5;241m=\u001b[39m conn\u001b[38;5;241m.\u001b[39murlopen(\n\u001b[0;32m 487\u001b[0m method\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mmethod,\n\u001b[0;32m 488\u001b[0m url\u001b[38;5;241m=\u001b[39murl,\n\u001b[0;32m 489\u001b[0m body\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mbody,\n\u001b[0;32m 490\u001b[0m headers\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mheaders,\n\u001b[0;32m 491\u001b[0m redirect\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m 492\u001b[0m assert_same_host\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m 493\u001b[0m preload_content\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m 494\u001b[0m decode_content\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m 495\u001b[0m retries\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_retries,\n\u001b[0;32m 496\u001b[0m timeout\u001b[38;5;241m=\u001b[39mtimeout,\n\u001b[0;32m 497\u001b[0m chunked\u001b[38;5;241m=\u001b[39mchunked,\n\u001b[0;32m 498\u001b[0m )\n\u001b[0;32m 500\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (ProtocolError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m 501\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m(err, request\u001b[38;5;241m=\u001b[39mrequest)\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\urllib3\\connectionpool.py:714\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m 711\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prepare_proxy(conn)\n\u001b[0;32m 713\u001b[0m \u001b[38;5;66;03m# Make the request on the httplib connection object.\u001b[39;00m\n\u001b[1;32m--> 714\u001b[0m httplib_response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_request(\n\u001b[0;32m 715\u001b[0m conn,\n\u001b[0;32m 716\u001b[0m method,\n\u001b[0;32m 717\u001b[0m url,\n\u001b[0;32m 718\u001b[0m timeout\u001b[38;5;241m=\u001b[39mtimeout_obj,\n\u001b[0;32m 719\u001b[0m body\u001b[38;5;241m=\u001b[39mbody,\n\u001b[0;32m 720\u001b[0m headers\u001b[38;5;241m=\u001b[39mheaders,\n\u001b[0;32m 721\u001b[0m chunked\u001b[38;5;241m=\u001b[39mchunked,\n\u001b[0;32m 722\u001b[0m )\n\u001b[0;32m 724\u001b[0m \u001b[38;5;66;03m# If we're going to release the connection in ``finally:``, then\u001b[39;00m\n\u001b[0;32m 725\u001b[0m \u001b[38;5;66;03m# the response doesn't need to know about the connection. Otherwise\u001b[39;00m\n\u001b[0;32m 726\u001b[0m \u001b[38;5;66;03m# it will also try to release it and we'll have a double-release\u001b[39;00m\n\u001b[0;32m 727\u001b[0m \u001b[38;5;66;03m# mess.\u001b[39;00m\n\u001b[0;32m 728\u001b[0m response_conn \u001b[38;5;241m=\u001b[39m conn \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m release_conn \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\urllib3\\connectionpool.py:466\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[1;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[0;32m 461\u001b[0m httplib_response \u001b[38;5;241m=\u001b[39m conn\u001b[38;5;241m.\u001b[39mgetresponse()\n\u001b[0;32m 462\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 463\u001b[0m \u001b[38;5;66;03m# Remove the TypeError from the exception chain in\u001b[39;00m\n\u001b[0;32m 464\u001b[0m \u001b[38;5;66;03m# Python 3 (including for exceptions like SystemExit).\u001b[39;00m\n\u001b[0;32m 465\u001b[0m \u001b[38;5;66;03m# Otherwise it looks like a bug in the code.\u001b[39;00m\n\u001b[1;32m--> 466\u001b[0m six\u001b[38;5;241m.\u001b[39mraise_from(e, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m 467\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (SocketTimeout, BaseSSLError, SocketError) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 468\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_raise_timeout(err\u001b[38;5;241m=\u001b[39me, url\u001b[38;5;241m=\u001b[39murl, timeout_value\u001b[38;5;241m=\u001b[39mread_timeout)\n", "File \u001b[1;32m:3\u001b[0m, in \u001b[0;36mraise_from\u001b[1;34m(value, from_value)\u001b[0m\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\urllib3\\connectionpool.py:461\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[1;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[0;32m 458\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m 459\u001b[0m \u001b[38;5;66;03m# Python 3\u001b[39;00m\n\u001b[0;32m 460\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 461\u001b[0m httplib_response \u001b[38;5;241m=\u001b[39m conn\u001b[38;5;241m.\u001b[39mgetresponse()\n\u001b[0;32m 462\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 463\u001b[0m \u001b[38;5;66;03m# Remove the TypeError from the exception chain in\u001b[39;00m\n\u001b[0;32m 464\u001b[0m \u001b[38;5;66;03m# Python 3 (including for exceptions like SystemExit).\u001b[39;00m\n\u001b[0;32m 465\u001b[0m \u001b[38;5;66;03m# Otherwise it looks like a bug in the code.\u001b[39;00m\n\u001b[0;32m 466\u001b[0m six\u001b[38;5;241m.\u001b[39mraise_from(e, \u001b[38;5;28;01mNone\u001b[39;00m)\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\http\\client.py:1378\u001b[0m, in \u001b[0;36mHTTPConnection.getresponse\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1376\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1377\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1378\u001b[0m response\u001b[38;5;241m.\u001b[39mbegin()\n\u001b[0;32m 1379\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m:\n\u001b[0;32m 1380\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclose()\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\http\\client.py:318\u001b[0m, in \u001b[0;36mHTTPResponse.begin\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 316\u001b[0m \u001b[38;5;66;03m# read until we get a non-100 response\u001b[39;00m\n\u001b[0;32m 317\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m--> 318\u001b[0m version, status, reason \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_read_status()\n\u001b[0;32m 319\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m status \u001b[38;5;241m!=\u001b[39m CONTINUE:\n\u001b[0;32m 320\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\http\\client.py:279\u001b[0m, in \u001b[0;36mHTTPResponse._read_status\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 278\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_read_status\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m--> 279\u001b[0m line \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfp\u001b[38;5;241m.\u001b[39mreadline(_MAXLINE \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124miso-8859-1\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 280\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(line) \u001b[38;5;241m>\u001b[39m _MAXLINE:\n\u001b[0;32m 281\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m LineTooLong(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstatus line\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\socket.py:706\u001b[0m, in \u001b[0;36mSocketIO.readinto\u001b[1;34m(self, b)\u001b[0m\n\u001b[0;32m 704\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m 705\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 706\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sock\u001b[38;5;241m.\u001b[39mrecv_into(b)\n\u001b[0;32m 707\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m timeout:\n\u001b[0;32m 708\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_timeout_occurred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\ssl.py:1278\u001b[0m, in \u001b[0;36mSSLSocket.recv_into\u001b[1;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[0;32m 1274\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m flags \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m 1275\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 1276\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnon-zero flags not allowed in calls to recv_into() on \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m\n\u001b[0;32m 1277\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m)\n\u001b[1;32m-> 1278\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mread(nbytes, buffer)\n\u001b[0;32m 1279\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 1280\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mrecv_into(buffer, nbytes, flags)\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\ssl.py:1134\u001b[0m, in \u001b[0;36mSSLSocket.read\u001b[1;34m(self, len, buffer)\u001b[0m\n\u001b[0;32m 1132\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1133\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m buffer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1134\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sslobj\u001b[38;5;241m.\u001b[39mread(\u001b[38;5;28mlen\u001b[39m, buffer)\n\u001b[0;32m 1135\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 1136\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sslobj\u001b[38;5;241m.\u001b[39mread(\u001b[38;5;28mlen\u001b[39m)\n", "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "%%timeit -n 1 -r 1\n", "for i, batch in enumerate(dls[\"train\"]):\n", " worker_ids, series_ids, x, y = batch\n", " print(i, worker_ids.tolist(), series_ids.tolist(), x.shape, y.shape)" ] }, { "cell_type": "code", "execution_count": 35, "id": "e10d327a-5d14-453c-91fa-fc67afe7ba22", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'train': ,\n", " 'test': }" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dls = get_segmentation_dataloaders(\n", " split_types=[\"train\", \"test\"],\n", " streaming=False,\n", " train_batch_size=4,\n", " test_batch_size=1, \n", " num_workers=4,\n", " prefetch_factor=2\n", ")\n", "dls" ] }, { "cell_type": "code", "execution_count": 36, "id": "d7839b11-bbe0-4e6e-b2ce-ca32c9cffaa0", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 [0, 0, 0, 0] [4622, 4622, 6172, 6172] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "1 [1, 1, 1, 1] [26343, 26343, 40430, 40430] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "2 [2, 2, 2, 2] [55965, 55965, 62573, 8236] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "3 [3, 3, 3, 3] [12114, 12114, 28432, 28432] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "4 [0, 0, 0, 0] [12102, 12102, 12102, 18207] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "5 [1, 1, 1, 1] [25359, 25359, 55694, 55694] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "6 [2, 2, 2, 2] [8236, 20664, 55515, 55515] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "7 [3, 3, 3, 3] [43416, 43416, 32670, 32670] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "8 [0, 0, 0, 0] [18207, 26980, 26980, 50875] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "9 [1, 1, 1, 1] [39628, 39628, 22397, 22397] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "10 [2, 2, 2, 2] [44758, 44758, 4890, 4890] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "11 [3, 3, 3, 3] [40186, 40186, 5260, 5260] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "12 [0, 0, 0, 0] [50875, 29832, 29832, 29832] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "13 [1, 1, 1, 1] [58548, 58548, 12900, 12900] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "14 [2, 2, 2, 2] [4890, 7384, 7384, 7384] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "15 [3, 3, 3, 3] [5260, 60961, 41663, 41663] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "16 [0, 0, 0, 0] [38633, 38633, 55583, 55583] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "17 [1, 1, 1, 1] [58697, 58697, 43088, 43088] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "18 [2, 2, 2, 2] [31085, 31085, 54917, 54917] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "19 [3, 3, 3, 3] [28079, 28079, 57769, 57769] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "20 [0, 0, 0, 0] [31200, 31200, 22479, 22479] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "21 [1, 1, 1, 1] [7818, 7818, 7818, 39013] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "22 [2, 2, 2, 2] [31146, 31146, 6305, 6305] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "23 [3, 3, 3, 3] [14286, 14286, 63418, 63418] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "24 [0, 0, 0, 0] [60755, 60755, 60755, 55449] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "25 [1, 1, 1, 1] [39013, 39013, 5425, 5425] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "26 [2, 2, 2, 2] [63701, 63701, 32243, 32243] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "27 [3, 3, 3, 3] [137, 137, 36753, 36753] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "28 [0, 0, 0, 0] [55449, 55449, 15271, 15271] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "29 [1, 1, 1, 1] [63205, 63205, 52961, 52961] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "30 [2, 2, 2, 2] [5118, 5118, 31852, 31852] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "31 [3, 3, 3, 3] [36753, 17225, 17225, 8320] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "32 [0, 0, 0, 0] [52279, 52279, 6631, 6631] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "33 [1, 1, 1, 1] [6344, 6344, 6344, 24134] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "34 [2, 2, 2, 2] [61403, 61403, 17605, 17605] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "35 [3, 3, 3, 3] [8320, 30952, 30952, 30064] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "36 [0, 0, 0, 0] [16066, 16066, 13666, 13666] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "37 [1, 1, 1, 1] [24134, 43233, 43233, 25349] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "38 [2, 2, 2, 2] [5104, 5104, 63843, 63843] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "39 [3, 3, 3, 3] [30064, 8413, 8413, 29167] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "40 [0, 0, 0, 0] [13666, 44612, 44612, 21282] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "41 [1, 1, 1, 1] [25349, 41288, 41288, 29053] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "42 [2, 2, 2, 2] [30843, 30843, 30843, 58027] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "43 [3, 3, 3, 3] [29167, 39205, 39205, 40471] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "44 [0, 0, 0, 0] [21282, 21282, 50212, 11748] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "45 [1, 1, 1, 1] [29053, 12402, 12402, 20684] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "46 [2, 2, 2, 2] [58027, 56245, 56245, 40496] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "47 [3, 3, 3, 3] [40471, 8340, 8340, 19468] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "48 [0, 0, 0, 0] [42680, 42680, 40781, 40781] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "49 [1, 1, 1, 1] [20684, 13041, 13041, 13041] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "50 [2, 2, 2, 2] [40496, 21057, 21057, 31021] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "51 [3, 3, 3, 3] [19468, 10180, 10180, 47775] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "52 [0, 0, 0, 0] [7397, 7397, 48324, 48324] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "53 [1, 1, 1, 1] [47155, 47155, 58391, 58391] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "54 [2, 2, 2, 2] [31021, 62556, 62556, 62556] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "55 [3, 3, 3, 3] [47775, 33355, 33355, 4759] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "56 [0, 0, 0, 0] [42973, 42973, 5103, 5103] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "57 [1, 1, 1, 1] [35661, 35661, 24442, 24442] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "58 [2, 2, 2, 2] [525, 525, 525, 34774] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "59 [3, 3, 3, 3] [4759, 4759, 19657, 19657] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "60 [0, 0, 0, 0] [5103, 26840, 26840, 26840] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "61 [1, 1, 1, 1] [4123, 4123, 54830, 54830] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "62 [2, 2, 2, 2] [34774, 12840, 12840, 15539] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "63 [3, 3, 3, 3] [39222, 39222, 39222, 53345] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "64 [0, 0, 0, 0] [52940, 52940, 30522, 30522] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "65 [1, 1, 1, 1] [61569, 61569, 61569, 60307] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "66 [2, 2, 2, 2] [15539, 26214, 26214, 47610] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "67 [3, 3, 3, 3] [53345, 19927, 19927, 527] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "68 [0, 0, 0, 0] [34224, 34224, 13774, 13774] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "69 [1, 1, 1, 1] [60307, 37032, 37032, 16097] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "70 [2, 2, 2, 2] [47610, 47610, 47856, 47856] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "71 [3, 3, 3, 3] [527, 51136, 47438, 47438] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "72 [0, 0, 0, 0] [49753, 49753, 10252, 10252] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "73 [1, 1, 1, 1] [12039, 12039, 12039, 45406] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "74 [2, 2, 2, 2] [19360, 19360, 24774, 24774] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "75 [3, 3, 3, 3] [64117, 64117, 64117, 60302] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "76 [0, 0, 0, 0] [26942, 26942, 397, 397] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "77 [1, 1, 1, 1] [45406, 23837, 23837, 6575] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "78 [2, 2, 2, 2] [13925, 13925, 31571, 31571] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "79 [3, 3, 3, 3] [60302, 7334, 7334, 48901] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "80 [0, 0, 0, 0] [61670, 61670, 778, 778] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "81 [1, 1, 1, 1] [6575, 36257, 36257, 16503] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "82 [2, 2, 2, 2] [26906, 26906, 39864, 39864] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "83 [3, 3, 3, 3] [48901, 52970, 52970, 52970] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "84 [0, 0, 0, 0] [47305, 47305, 13848, 13848] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "85 [1, 1, 1, 1] [16503, 30902, 30902, 51033] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "86 [2, 2, 2, 2] [6130, 6130, 60881, 60881] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "87 [3, 3, 3, 3] [22730, 22730, 41976, 41976] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "88 [0, 0, 0, 0] [34232, 61747, 61747, 22232] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "89 [1, 1, 1, 1] [51033, 1201, 1201, 1201] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "90 [2, 2, 2, 2] [59325, 59325, 59325, 24373] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "91 [3, 3, 3, 3] [18624, 18624, 5176, 5176] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "92 [0, 0, 0, 0] [22232, 24149, 24149, 24149] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "93 [1, 1, 1, 1] [17577, 17577, 53843, 53843] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "94 [2, 2, 2, 2] [24373, 62307, 62307, 13307] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "95 [3, 3, 3, 3] [12674, 12674, 49547, 49547] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "96 [0, 0, 0, 0] [62680, 62680, 50434, 50434] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "97 [1, 1] [24645, 24645] torch.Size([2, 1, 96, 96, 96]) torch.Size([2, 1, 96, 96, 96])\n", "98 [2, 2] [13307, 13307] torch.Size([2, 1, 96, 96, 96]) torch.Size([2, 1, 96, 96, 96])\n", "99 [0] [50434] torch.Size([1, 1, 96, 96, 96]) torch.Size([1, 1, 96, 96, 96])\n", "1min 17s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n" ] } ], "source": [ "%%timeit -n 1 -r 1\n", "for i, batch in enumerate(dls[\"train\"]):\n", " worker_ids, series_ids, x, y = batch[\"worker_id\"], batch[\"series_id\"], batch[\"img\"], batch[\"seg\"]\n", " print(i, worker_ids.tolist(), series_ids.tolist(), x.shape, y.shape)" ] }, { "cell_type": "code", "execution_count": 29, "id": "1e53c631-96c5-460a-9d80-08b541061467", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'train': ,\n", " 'test': }" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from dataloaders import get_segmentation_dataloaders\n", "\n", "dls = get_segmentation_dataloaders(\n", " split_types=[\"train\", \"test\"],\n", " streaming=True,\n", " train_batch_size=4,\n", " test_batch_size=1, \n", " num_workers=1,\n", " prefetch_factor=None\n", ")\n", "dls" ] }, { "cell_type": "code", "execution_count": 30, "id": "1a2fbf1f-5f4d-436c-931d-e527cadd38ec", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 [0, 0, 0, 0] [4622, 4622, 26343, 26343] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "1 [0, 0, 0, 0] [55965, 55965, 12114, 12114] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "2 [0, 0, 0, 0] [6172, 6172, 40430, 40430] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "3 [0, 0, 0, 0] [62573, 28432, 28432, 12102] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "4 [0, 0, 0, 0] [12102, 12102, 25359, 25359] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "5 [0, 0, 0, 0] [8236, 8236, 43416, 43416] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[30], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m get_ipython()\u001b[38;5;241m.\u001b[39mrun_cell_magic(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtimeit\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m-n 1 -r 1\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfor i, batch in enumerate(dls[\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m]):\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m worker_ids, series_ids, x, y = batch\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m print(i, worker_ids.tolist(), series_ids.tolist(), x.shape, y.shape)\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\IPython\\core\\interactiveshell.py:2478\u001b[0m, in \u001b[0;36mInteractiveShell.run_cell_magic\u001b[1;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[0;32m 2476\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m 2477\u001b[0m args \u001b[38;5;241m=\u001b[39m (magic_arg_s, cell)\n\u001b[1;32m-> 2478\u001b[0m result \u001b[38;5;241m=\u001b[39m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 2480\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[0;32m 2481\u001b[0m \u001b[38;5;66;03m# when using magics with decodator @output_can_be_silenced\u001b[39;00m\n\u001b[0;32m 2482\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[0;32m 2483\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\IPython\\core\\magics\\execution.py:1174\u001b[0m, in \u001b[0;36mExecutionMagics.timeit\u001b[1;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[0;32m 1171\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m time_number \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.2\u001b[39m:\n\u001b[0;32m 1172\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m-> 1174\u001b[0m all_runs \u001b[38;5;241m=\u001b[39m timer\u001b[38;5;241m.\u001b[39mrepeat(repeat, number)\n\u001b[0;32m 1175\u001b[0m best \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmin\u001b[39m(all_runs) \u001b[38;5;241m/\u001b[39m number\n\u001b[0;32m 1176\u001b[0m worst \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmax\u001b[39m(all_runs) \u001b[38;5;241m/\u001b[39m number\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\timeit.py:206\u001b[0m, in \u001b[0;36mTimer.repeat\u001b[1;34m(self, repeat, number)\u001b[0m\n\u001b[0;32m 204\u001b[0m r \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m 205\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(repeat):\n\u001b[1;32m--> 206\u001b[0m t \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeit(number)\n\u001b[0;32m 207\u001b[0m r\u001b[38;5;241m.\u001b[39mappend(t)\n\u001b[0;32m 208\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m r\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\IPython\\core\\magics\\execution.py:158\u001b[0m, in \u001b[0;36mTimer.timeit\u001b[1;34m(self, number)\u001b[0m\n\u001b[0;32m 156\u001b[0m gc\u001b[38;5;241m.\u001b[39mdisable()\n\u001b[0;32m 157\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 158\u001b[0m timing \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minner(it, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimer)\n\u001b[0;32m 159\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m 160\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m gcold:\n", "File \u001b[1;32m:1\u001b[0m, in \u001b[0;36minner\u001b[1;34m(_it, _timer)\u001b[0m\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:633\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 630\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 631\u001b[0m \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[0;32m 632\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset() \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[1;32m--> 633\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_next_data()\n\u001b[0;32m 634\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m 635\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[0;32m 636\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[0;32m 637\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called:\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:1328\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._next_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1325\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_process_data(data)\n\u001b[0;32m 1327\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_shutdown \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tasks_outstanding \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m-> 1328\u001b[0m idx, data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_data()\n\u001b[0;32m 1329\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tasks_outstanding \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m 1330\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable:\n\u001b[0;32m 1331\u001b[0m \u001b[38;5;66;03m# Check for _IterableDatasetStopIteration\u001b[39;00m\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:1284\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._get_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1282\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory:\n\u001b[0;32m 1283\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory_thread\u001b[38;5;241m.\u001b[39mis_alive():\n\u001b[1;32m-> 1284\u001b[0m success, data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_try_get_data()\n\u001b[0;32m 1285\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m success:\n\u001b[0;32m 1286\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m data\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:1132\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._try_get_data\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m 1119\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_try_get_data\u001b[39m(\u001b[38;5;28mself\u001b[39m, timeout\u001b[38;5;241m=\u001b[39m_utils\u001b[38;5;241m.\u001b[39mMP_STATUS_CHECK_INTERVAL):\n\u001b[0;32m 1120\u001b[0m \u001b[38;5;66;03m# Tries to fetch data from `self._data_queue` once for a given timeout.\u001b[39;00m\n\u001b[0;32m 1121\u001b[0m \u001b[38;5;66;03m# This can also be used as inner loop of fetching without timeout, with\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1129\u001b[0m \u001b[38;5;66;03m# Returns a 2-tuple:\u001b[39;00m\n\u001b[0;32m 1130\u001b[0m \u001b[38;5;66;03m# (bool: whether successfully get data, any: data if successful else None)\u001b[39;00m\n\u001b[0;32m 1131\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1132\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data_queue\u001b[38;5;241m.\u001b[39mget(timeout\u001b[38;5;241m=\u001b[39mtimeout)\n\u001b[0;32m 1133\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m (\u001b[38;5;28;01mTrue\u001b[39;00m, data)\n\u001b[0;32m 1134\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 1135\u001b[0m \u001b[38;5;66;03m# At timeout and error, we manually check whether any worker has\u001b[39;00m\n\u001b[0;32m 1136\u001b[0m \u001b[38;5;66;03m# failed. Note that this is the only mechanism for Windows to detect\u001b[39;00m\n\u001b[0;32m 1137\u001b[0m \u001b[38;5;66;03m# worker failures.\u001b[39;00m\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\queue.py:180\u001b[0m, in \u001b[0;36mQueue.get\u001b[1;34m(self, block, timeout)\u001b[0m\n\u001b[0;32m 178\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m remaining \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.0\u001b[39m:\n\u001b[0;32m 179\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m Empty\n\u001b[1;32m--> 180\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnot_empty\u001b[38;5;241m.\u001b[39mwait(remaining)\n\u001b[0;32m 181\u001b[0m item \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get()\n\u001b[0;32m 182\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnot_full\u001b[38;5;241m.\u001b[39mnotify()\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\threading.py:324\u001b[0m, in \u001b[0;36mCondition.wait\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m 322\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 323\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m--> 324\u001b[0m gotit \u001b[38;5;241m=\u001b[39m waiter\u001b[38;5;241m.\u001b[39macquire(\u001b[38;5;28;01mTrue\u001b[39;00m, timeout)\n\u001b[0;32m 325\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 326\u001b[0m gotit \u001b[38;5;241m=\u001b[39m waiter\u001b[38;5;241m.\u001b[39macquire(\u001b[38;5;28;01mFalse\u001b[39;00m)\n", "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "%%timeit -n 1 -r 1\n", "for i, batch in enumerate(dls[\"train\"]):\n", " worker_ids, series_ids, x, y = batch\n", " print(i, worker_ids.tolist(), series_ids.tolist(), x.shape, y.shape)" ] }, { "cell_type": "code", "execution_count": 97, "id": "fd891d2b-3919-4445-a06e-396c46caaf0e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 [0] [42173] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "1 [1] [42173] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "2 [2] [42173] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "3 [3] [42173] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "4 [4] [42173] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "5 [5] [42173] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "6 [6] [42173] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "7 [7] [42173] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "8 [0] [53000] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "9 [1] [53000] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "10 [2] [53000] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "11 [3] [53000] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "12 [4] [53000] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "13 [5] [53000] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "14 [6] [53000] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "15 [7] [53000] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "16 [0] [55928] torch.Size([1, 1, 96, 96, 192]) torch.Size([1, 1, 96, 96, 192])\n", "17 [1] [55928] torch.Size([1, 1, 96, 96, 192]) torch.Size([1, 1, 96, 96, 192])\n", "18 [2] [55928] torch.Size([1, 1, 96, 96, 192]) torch.Size([1, 1, 96, 96, 192])\n", "19 [3] [55928] torch.Size([1, 1, 96, 96, 192]) torch.Size([1, 1, 96, 96, 192])\n", "20 [4] [55928] torch.Size([1, 1, 96, 96, 192]) torch.Size([1, 1, 96, 96, 192])\n", "21 [5] [55928] torch.Size([1, 1, 96, 96, 192]) torch.Size([1, 1, 96, 96, 192])\n", "22 [6] [55928] torch.Size([1, 1, 96, 96, 192]) torch.Size([1, 1, 96, 96, 192])\n", "23 [7] [55928] torch.Size([1, 1, 96, 96, 192]) torch.Size([1, 1, 96, 96, 192])\n", "24 [0] [5218] torch.Size([1, 1, 192, 192, 288]) torch.Size([1, 1, 192, 192, 288])\n", "25 [1] [5218] torch.Size([1, 1, 192, 192, 288]) torch.Size([1, 1, 192, 192, 288])\n", "26 [2] [5218] torch.Size([1, 1, 192, 192, 288]) torch.Size([1, 1, 192, 192, 288])\n", "27 [3] [5218] torch.Size([1, 1, 192, 192, 288]) torch.Size([1, 1, 192, 192, 288])\n", "28 [4] [5218] torch.Size([1, 1, 192, 192, 288]) torch.Size([1, 1, 192, 192, 288])\n", "29 [5] [5218] torch.Size([1, 1, 192, 192, 288]) torch.Size([1, 1, 192, 192, 288])\n", "30 [6] [5218] torch.Size([1, 1, 192, 192, 288]) torch.Size([1, 1, 192, 192, 288])\n", "31 [7] [5218] torch.Size([1, 1, 192, 192, 288]) torch.Size([1, 1, 192, 192, 288])\n", "32 [0] [4929] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "33 [1] [4929] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "34 [2] [4929] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "35 [3] [4929] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "36 [4] [4929] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "37 [5] [4929] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "38 [6] [4929] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "39 [7] [4929] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "40 [0] [64520] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n", "41 [1] [64520] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n", "42 [2] [64520] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n", "43 [3] [64520] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n", "44 [4] [64520] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n", "45 [5] [64520] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n", "46 [6] [64520] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n", "47 [7] [64520] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n", "48 [0] [10494] torch.Size([1, 1, 192, 192, 288]) torch.Size([1, 1, 192, 192, 288])\n", "49 [1] [10494] torch.Size([1, 1, 192, 192, 288]) torch.Size([1, 1, 192, 192, 288])\n", "50 [2] [10494] torch.Size([1, 1, 192, 192, 288]) torch.Size([1, 1, 192, 192, 288])\n", "51 [3] [10494] torch.Size([1, 1, 192, 192, 288]) torch.Size([1, 1, 192, 192, 288])\n", "52 [4] [10494] torch.Size([1, 1, 192, 192, 288]) torch.Size([1, 1, 192, 192, 288])\n", "53 [5] [10494] torch.Size([1, 1, 192, 192, 288]) torch.Size([1, 1, 192, 192, 288])\n", "54 [6] [10494] torch.Size([1, 1, 192, 192, 288]) torch.Size([1, 1, 192, 192, 288])\n", "55 [7] [10494] torch.Size([1, 1, 192, 192, 288]) torch.Size([1, 1, 192, 192, 288])\n", "56 [0] [33526] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "57 [1] [33526] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "58 [2] [33526] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "59 [3] [33526] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "60 [4] [33526] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "61 [5] [33526] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "62 [6] [33526] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "63 [7] [33526] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "64 [0] [10109] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "65 [1] [10109] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "66 [2] [10109] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "67 [3] [10109] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "68 [4] [10109] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "69 [5] [10109] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "70 [6] [10109] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "71 [7] [10109] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "72 [0] [35190] torch.Size([1, 1, 96, 96, 96]) torch.Size([1, 1, 96, 96, 96])\n", "73 [1] [35190] torch.Size([1, 1, 96, 96, 96]) torch.Size([1, 1, 96, 96, 96])\n", "74 [2] [35190] torch.Size([1, 1, 96, 96, 96]) torch.Size([1, 1, 96, 96, 96])\n", "75 [3] [35190] torch.Size([1, 1, 96, 96, 96]) torch.Size([1, 1, 96, 96, 96])\n", "76 [4] [35190] torch.Size([1, 1, 96, 96, 96]) torch.Size([1, 1, 96, 96, 96])\n", "77 [5] [35190] torch.Size([1, 1, 96, 96, 96]) torch.Size([1, 1, 96, 96, 96])\n", "78 [6] [35190] torch.Size([1, 1, 96, 96, 96]) torch.Size([1, 1, 96, 96, 96])\n", "79 [7] [35190] torch.Size([1, 1, 96, 96, 96]) torch.Size([1, 1, 96, 96, 96])\n", "80 [0] [15748] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "81 [1] [15748] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "82 [2] [15748] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "83 [3] [15748] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "84 [4] [15748] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "85 [5] [15748] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "86 [6] [15748] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "87 [7] [15748] torch.Size([1, 1, 192, 96, 192]) torch.Size([1, 1, 192, 96, 192])\n", "88 [0] [10000] torch.Size([1, 1, 192, 192, 288]) torch.Size([1, 1, 192, 192, 288])\n", "89 [1] [10000] torch.Size([1, 1, 192, 192, 288]) torch.Size([1, 1, 192, 192, 288])\n", "90 [2] [10000] torch.Size([1, 1, 192, 192, 288]) torch.Size([1, 1, 192, 192, 288])\n", "91 [3] [10000] torch.Size([1, 1, 192, 192, 288]) torch.Size([1, 1, 192, 192, 288])\n", "92 [4] [10000] torch.Size([1, 1, 192, 192, 288]) torch.Size([1, 1, 192, 192, 288])\n", "93 [5] [10000] torch.Size([1, 1, 192, 192, 288]) torch.Size([1, 1, 192, 192, 288])\n", "94 [6] [10000] torch.Size([1, 1, 192, 192, 288]) torch.Size([1, 1, 192, 192, 288])\n", "95 [7] [10000] torch.Size([1, 1, 192, 192, 288]) torch.Size([1, 1, 192, 192, 288])\n", "96 [0] [32991] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n", "97 [1] [32991] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n", "98 [2] [32991] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n", "99 [3] [32991] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n", "100 [4] [32991] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n", "101 [5] [32991] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n", "102 [6] [32991] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n", "103 [7] [32991] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n", "104 [0] [15415] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n", "105 [1] [15415] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n", "106 [2] [15415] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n", "107 [3] [15415] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n", "108 [4] [15415] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n", "109 [5] [15415] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n", "110 [6] [15415] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n", "111 [7] [15415] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n", "112 [0] [10385] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n", "113 [1] [10385] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n", "114 [2] [10385] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n", "115 [3] [10385] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n", "116 [4] [10385] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n", "117 [5] [10385] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n", "118 [6] [10385] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n", "119 [7] [10385] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n", "120 [0] [23644] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n", "121 [1] [23644] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n", "122 [2] [23644] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n", "123 [3] [23644] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n", "124 [4] [23644] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n", "125 [5] [23644] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n", "126 [6] [23644] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n", "127 [7] [23644] torch.Size([1, 1, 192, 192, 192]) torch.Size([1, 1, 192, 192, 192])\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[97], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m get_ipython()\u001b[38;5;241m.\u001b[39mrun_cell_magic(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtimeit\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m-n 1 -r 1\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfor i, batch in enumerate(dls[\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtest\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m]):\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m worker_ids, series_ids, x, y = batch\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m print(i, worker_ids.tolist(), series_ids.tolist(), x.shape, y.shape)\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\IPython\\core\\interactiveshell.py:2478\u001b[0m, in \u001b[0;36mInteractiveShell.run_cell_magic\u001b[1;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[0;32m 2476\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m 2477\u001b[0m args \u001b[38;5;241m=\u001b[39m (magic_arg_s, cell)\n\u001b[1;32m-> 2478\u001b[0m result \u001b[38;5;241m=\u001b[39m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 2480\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[0;32m 2481\u001b[0m \u001b[38;5;66;03m# when using magics with decodator @output_can_be_silenced\u001b[39;00m\n\u001b[0;32m 2482\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[0;32m 2483\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\IPython\\core\\magics\\execution.py:1174\u001b[0m, in \u001b[0;36mExecutionMagics.timeit\u001b[1;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[0;32m 1171\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m time_number \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.2\u001b[39m:\n\u001b[0;32m 1172\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m-> 1174\u001b[0m all_runs \u001b[38;5;241m=\u001b[39m timer\u001b[38;5;241m.\u001b[39mrepeat(repeat, number)\n\u001b[0;32m 1175\u001b[0m best \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmin\u001b[39m(all_runs) \u001b[38;5;241m/\u001b[39m number\n\u001b[0;32m 1176\u001b[0m worst \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmax\u001b[39m(all_runs) \u001b[38;5;241m/\u001b[39m number\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\timeit.py:206\u001b[0m, in \u001b[0;36mTimer.repeat\u001b[1;34m(self, repeat, number)\u001b[0m\n\u001b[0;32m 204\u001b[0m r \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m 205\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(repeat):\n\u001b[1;32m--> 206\u001b[0m t \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeit(number)\n\u001b[0;32m 207\u001b[0m r\u001b[38;5;241m.\u001b[39mappend(t)\n\u001b[0;32m 208\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m r\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\IPython\\core\\magics\\execution.py:158\u001b[0m, in \u001b[0;36mTimer.timeit\u001b[1;34m(self, number)\u001b[0m\n\u001b[0;32m 156\u001b[0m gc\u001b[38;5;241m.\u001b[39mdisable()\n\u001b[0;32m 157\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 158\u001b[0m timing \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minner(it, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimer)\n\u001b[0;32m 159\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m 160\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m gcold:\n", "File \u001b[1;32m:1\u001b[0m, in \u001b[0;36minner\u001b[1;34m(_it, _timer)\u001b[0m\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:633\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 630\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 631\u001b[0m \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[0;32m 632\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset() \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[1;32m--> 633\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_next_data()\n\u001b[0;32m 634\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m 635\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[0;32m 636\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[0;32m 637\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called:\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:1328\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._next_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1325\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_process_data(data)\n\u001b[0;32m 1327\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_shutdown \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tasks_outstanding \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m-> 1328\u001b[0m idx, data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_data()\n\u001b[0;32m 1329\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tasks_outstanding \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m 1330\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable:\n\u001b[0;32m 1331\u001b[0m \u001b[38;5;66;03m# Check for _IterableDatasetStopIteration\u001b[39;00m\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:1284\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._get_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1282\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory:\n\u001b[0;32m 1283\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory_thread\u001b[38;5;241m.\u001b[39mis_alive():\n\u001b[1;32m-> 1284\u001b[0m success, data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_try_get_data()\n\u001b[0;32m 1285\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m success:\n\u001b[0;32m 1286\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m data\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:1132\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._try_get_data\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m 1119\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_try_get_data\u001b[39m(\u001b[38;5;28mself\u001b[39m, timeout\u001b[38;5;241m=\u001b[39m_utils\u001b[38;5;241m.\u001b[39mMP_STATUS_CHECK_INTERVAL):\n\u001b[0;32m 1120\u001b[0m \u001b[38;5;66;03m# Tries to fetch data from `self._data_queue` once for a given timeout.\u001b[39;00m\n\u001b[0;32m 1121\u001b[0m \u001b[38;5;66;03m# This can also be used as inner loop of fetching without timeout, with\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1129\u001b[0m \u001b[38;5;66;03m# Returns a 2-tuple:\u001b[39;00m\n\u001b[0;32m 1130\u001b[0m \u001b[38;5;66;03m# (bool: whether successfully get data, any: data if successful else None)\u001b[39;00m\n\u001b[0;32m 1131\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1132\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data_queue\u001b[38;5;241m.\u001b[39mget(timeout\u001b[38;5;241m=\u001b[39mtimeout)\n\u001b[0;32m 1133\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m (\u001b[38;5;28;01mTrue\u001b[39;00m, data)\n\u001b[0;32m 1134\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 1135\u001b[0m \u001b[38;5;66;03m# At timeout and error, we manually check whether any worker has\u001b[39;00m\n\u001b[0;32m 1136\u001b[0m \u001b[38;5;66;03m# failed. Note that this is the only mechanism for Windows to detect\u001b[39;00m\n\u001b[0;32m 1137\u001b[0m \u001b[38;5;66;03m# worker failures.\u001b[39;00m\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\queue.py:180\u001b[0m, in \u001b[0;36mQueue.get\u001b[1;34m(self, block, timeout)\u001b[0m\n\u001b[0;32m 178\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m remaining \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.0\u001b[39m:\n\u001b[0;32m 179\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m Empty\n\u001b[1;32m--> 180\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnot_empty\u001b[38;5;241m.\u001b[39mwait(remaining)\n\u001b[0;32m 181\u001b[0m item \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get()\n\u001b[0;32m 182\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnot_full\u001b[38;5;241m.\u001b[39mnotify()\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\threading.py:324\u001b[0m, in \u001b[0;36mCondition.wait\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m 322\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 323\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m--> 324\u001b[0m gotit \u001b[38;5;241m=\u001b[39m waiter\u001b[38;5;241m.\u001b[39macquire(\u001b[38;5;28;01mTrue\u001b[39;00m, timeout)\n\u001b[0;32m 325\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 326\u001b[0m gotit \u001b[38;5;241m=\u001b[39m waiter\u001b[38;5;241m.\u001b[39macquire(\u001b[38;5;28;01mFalse\u001b[39;00m)\n", "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "%%timeit -n 1 -r 1\n", "for i, batch in enumerate(dls[\"test\"]):\n", " worker_ids, series_ids, x, y = batch\n", " print(i, worker_ids.tolist(), series_ids.tolist(), x.shape, y.shape)" ] }, { "cell_type": "code", "execution_count": 83, "id": "570706dd-7232-4ebb-b65a-f7c87d664a42", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'train': ,\n", " 'test': }" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dls = get_segmentation_dataloaders(\n", " split_types=[\"train\", \"test\"],\n", " streaming=True,\n", " train_batch_size=4,\n", " test_batch_size=1, \n", " num_workers=0,\n", " prefetch_factor=None\n", ")\n", "dls" ] }, { "cell_type": "code", "execution_count": 84, "id": "7ce959d7-f1f2-499e-8c53-acbf5017d707", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 [-1, -1, -1, -1] [4622, 4622, 26343, 26343] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "1 [-1, -1, -1, -1] [55965, 55965, 12114, 12114] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "2 [-1, -1, -1, -1] [6172, 6172, 40430, 40430] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "3 [-1, -1, -1, -1] [62573, 28432, 28432, 12102] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "4 [-1, -1, -1, -1] [12102, 12102, 25359, 25359] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "5 [-1, -1, -1, -1] [8236, 8236, 43416, 43416] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "6 [-1, -1, -1, -1] [18207, 18207, 55694, 55694] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "7 [-1, -1, -1, -1] [20664, 32670, 32670, 26980] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "8 [-1, -1, -1, -1] [26980, 39628, 39628, 55515] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "9 [-1, -1, -1, -1] [55515, 40186, 40186, 50875] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "10 [-1, -1, -1, -1] [50875, 22397, 22397, 44758] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "11 [-1, -1, -1, -1] [44758, 5260, 5260, 5260] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "12 [-1, -1, -1, -1] [29832, 29832, 29832, 58548] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "13 [-1, -1, -1, -1] [58548, 4890, 4890, 4890] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "14 [-1, -1, -1, -1] [60961, 38633, 38633, 12900] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "15 [-1, -1, -1, -1] [12900, 7384, 7384, 7384] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "16 [-1, -1, -1, -1] [41663, 41663, 55583, 55583] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "17 [-1, -1, -1, -1] [58697, 58697, 31085, 31085] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "18 [-1, -1, -1, -1] [28079, 28079, 31200, 31200] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n", "19 [-1, -1, -1, -1] [43088, 43088, 54917, 54917] torch.Size([4, 1, 96, 96, 96]) torch.Size([4, 1, 96, 96, 96])\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[84], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m get_ipython()\u001b[38;5;241m.\u001b[39mrun_cell_magic(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtimeit\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m-n 1 -r 1\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfor i, batch in enumerate(dls[\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m]):\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m worker_ids, series_ids, x, y = batch\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m print(i, worker_ids.tolist(), series_ids.tolist(), x.shape, y.shape)\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\IPython\\core\\interactiveshell.py:2478\u001b[0m, in \u001b[0;36mInteractiveShell.run_cell_magic\u001b[1;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[0;32m 2476\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m 2477\u001b[0m args \u001b[38;5;241m=\u001b[39m (magic_arg_s, cell)\n\u001b[1;32m-> 2478\u001b[0m result \u001b[38;5;241m=\u001b[39m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 2480\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[0;32m 2481\u001b[0m \u001b[38;5;66;03m# when using magics with decodator @output_can_be_silenced\u001b[39;00m\n\u001b[0;32m 2482\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[0;32m 2483\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\IPython\\core\\magics\\execution.py:1174\u001b[0m, in \u001b[0;36mExecutionMagics.timeit\u001b[1;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[0;32m 1171\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m time_number \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.2\u001b[39m:\n\u001b[0;32m 1172\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m-> 1174\u001b[0m all_runs \u001b[38;5;241m=\u001b[39m timer\u001b[38;5;241m.\u001b[39mrepeat(repeat, number)\n\u001b[0;32m 1175\u001b[0m best \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmin\u001b[39m(all_runs) \u001b[38;5;241m/\u001b[39m number\n\u001b[0;32m 1176\u001b[0m worst \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmax\u001b[39m(all_runs) \u001b[38;5;241m/\u001b[39m number\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\timeit.py:206\u001b[0m, in \u001b[0;36mTimer.repeat\u001b[1;34m(self, repeat, number)\u001b[0m\n\u001b[0;32m 204\u001b[0m r \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m 205\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(repeat):\n\u001b[1;32m--> 206\u001b[0m t \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeit(number)\n\u001b[0;32m 207\u001b[0m r\u001b[38;5;241m.\u001b[39mappend(t)\n\u001b[0;32m 208\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m r\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\IPython\\core\\magics\\execution.py:158\u001b[0m, in \u001b[0;36mTimer.timeit\u001b[1;34m(self, number)\u001b[0m\n\u001b[0;32m 156\u001b[0m gc\u001b[38;5;241m.\u001b[39mdisable()\n\u001b[0;32m 157\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 158\u001b[0m timing \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minner(it, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimer)\n\u001b[0;32m 159\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m 160\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m gcold:\n", "File \u001b[1;32m:1\u001b[0m, in \u001b[0;36minner\u001b[1;34m(_it, _timer)\u001b[0m\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:633\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 630\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 631\u001b[0m \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[0;32m 632\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset() \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[1;32m--> 633\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_next_data()\n\u001b[0;32m 634\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m 635\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[0;32m 636\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[0;32m 637\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called:\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:677\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 675\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_next_data\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m 676\u001b[0m index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_next_index() \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m--> 677\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_fetcher\u001b[38;5;241m.\u001b[39mfetch(index) \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m 678\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory:\n\u001b[0;32m 679\u001b[0m data \u001b[38;5;241m=\u001b[39m _utils\u001b[38;5;241m.\u001b[39mpin_memory\u001b[38;5;241m.\u001b[39mpin_memory(data, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory_device)\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py:32\u001b[0m, in \u001b[0;36m_IterableDatasetFetcher.fetch\u001b[1;34m(self, possibly_batched_index)\u001b[0m\n\u001b[0;32m 30\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m possibly_batched_index:\n\u001b[0;32m 31\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 32\u001b[0m data\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28mnext\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset_iter))\n\u001b[0;32m 33\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[0;32m 34\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mended \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n", "File \u001b[1;32m~\\Documents\\Jia Herng's Docs\\Programming Projects\\rsna-2023-abdominal-trauma-detection\\datasets\\src\\dataloaders.py:72\u001b[0m, in \u001b[0;36mSegmentationDataset.__iter__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 69\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransform(data)\n\u001b[0;32m 71\u001b[0m \u001b[38;5;66;03m# Apply if oversample is true (supposedly on train only)\u001b[39;00m\n\u001b[1;32m---> 72\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcrop_strategy \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moversample\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m 73\u001b[0m \u001b[38;5;66;03m# Oversampling dynamically based on the shape of image\u001b[39;00m\n\u001b[0;32m 74\u001b[0m n_crops \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmax\u001b[39m(data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mimg\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39msize()) \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m \u001b[38;5;28mmax\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mroi_size)\n\u001b[0;32m 76\u001b[0m \u001b[38;5;66;03m# Initialize the crop function with num_samples=n_crops if haven't already\u001b[39;00m\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\monai\\transforms\\compose.py:322\u001b[0m, in \u001b[0;36mCompose.__call__\u001b[1;34m(self, input_, start, end, threading, lazy)\u001b[0m\n\u001b[0;32m 321\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, input_, start\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, end\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, threading\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, lazy: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m--> 322\u001b[0m result \u001b[38;5;241m=\u001b[39m execute_compose(\n\u001b[0;32m 323\u001b[0m input_,\n\u001b[0;32m 324\u001b[0m transforms\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransforms,\n\u001b[0;32m 325\u001b[0m start\u001b[38;5;241m=\u001b[39mstart,\n\u001b[0;32m 326\u001b[0m end\u001b[38;5;241m=\u001b[39mend,\n\u001b[0;32m 327\u001b[0m map_items\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmap_items,\n\u001b[0;32m 328\u001b[0m unpack_items\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39munpack_items,\n\u001b[0;32m 329\u001b[0m lazy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlazy,\n\u001b[0;32m 330\u001b[0m overrides\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moverrides,\n\u001b[0;32m 331\u001b[0m threading\u001b[38;5;241m=\u001b[39mthreading,\n\u001b[0;32m 332\u001b[0m log_stats\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlog_stats,\n\u001b[0;32m 333\u001b[0m )\n\u001b[0;32m 335\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\monai\\transforms\\compose.py:111\u001b[0m, in \u001b[0;36mexecute_compose\u001b[1;34m(data, transforms, map_items, unpack_items, start, end, lazy, overrides, threading, log_stats)\u001b[0m\n\u001b[0;32m 109\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m threading:\n\u001b[0;32m 110\u001b[0m _transform \u001b[38;5;241m=\u001b[39m deepcopy(_transform) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(_transform, ThreadUnsafe) \u001b[38;5;28;01melse\u001b[39;00m _transform\n\u001b[1;32m--> 111\u001b[0m data \u001b[38;5;241m=\u001b[39m apply_transform(\n\u001b[0;32m 112\u001b[0m _transform, data, map_items, unpack_items, lazy\u001b[38;5;241m=\u001b[39mlazy, overrides\u001b[38;5;241m=\u001b[39moverrides, log_stats\u001b[38;5;241m=\u001b[39mlog_stats\n\u001b[0;32m 113\u001b[0m )\n\u001b[0;32m 114\u001b[0m data \u001b[38;5;241m=\u001b[39m apply_pending_transforms(data, \u001b[38;5;28;01mNone\u001b[39;00m, overrides, logger_name\u001b[38;5;241m=\u001b[39mlog_stats)\n\u001b[0;32m 115\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m data\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\monai\\transforms\\transform.py:141\u001b[0m, in \u001b[0;36mapply_transform\u001b[1;34m(transform, data, map_items, unpack_items, log_stats, lazy, overrides)\u001b[0m\n\u001b[0;32m 139\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, (\u001b[38;5;28mlist\u001b[39m, \u001b[38;5;28mtuple\u001b[39m)) \u001b[38;5;129;01mand\u001b[39;00m map_items:\n\u001b[0;32m 140\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m [_apply_transform(transform, item, unpack_items, lazy, overrides, log_stats) \u001b[38;5;28;01mfor\u001b[39;00m item \u001b[38;5;129;01min\u001b[39;00m data]\n\u001b[1;32m--> 141\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _apply_transform(transform, data, unpack_items, lazy, overrides, log_stats)\n\u001b[0;32m 142\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 143\u001b[0m \u001b[38;5;66;03m# if in debug mode, don't swallow exception so that the breakpoint\u001b[39;00m\n\u001b[0;32m 144\u001b[0m \u001b[38;5;66;03m# appears where the exception was raised.\u001b[39;00m\n\u001b[0;32m 145\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m MONAIEnvVars\u001b[38;5;241m.\u001b[39mdebug():\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\monai\\transforms\\transform.py:98\u001b[0m, in \u001b[0;36m_apply_transform\u001b[1;34m(transform, data, unpack_parameters, lazy, overrides, logger_name)\u001b[0m\n\u001b[0;32m 95\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, \u001b[38;5;28mtuple\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m unpack_parameters:\n\u001b[0;32m 96\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m transform(\u001b[38;5;241m*\u001b[39mdata, lazy\u001b[38;5;241m=\u001b[39mlazy) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(transform, LazyTrait) \u001b[38;5;28;01melse\u001b[39;00m transform(\u001b[38;5;241m*\u001b[39mdata)\n\u001b[1;32m---> 98\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m transform(data, lazy\u001b[38;5;241m=\u001b[39mlazy) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(transform, LazyTrait) \u001b[38;5;28;01melse\u001b[39;00m transform(data)\n", "File \u001b[1;32m~\\Documents\\Jia Herng's Docs\\Programming Projects\\rsna-2023-abdominal-trauma-detection\\datasets\\src\\transforms.py:130\u001b[0m, in \u001b[0;36m__call__\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m 120\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mUnifyUnusualDICOM\u001b[39;00m(MapTransform):\n\u001b[0;32m 121\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 122\u001b[0m \u001b[38;5;124;03m Correct DICOM pixel_array if PixelRepresentation == 1 and BitsAllocated != BitsStored\u001b[39;00m\n\u001b[0;32m 123\u001b[0m \n\u001b[0;32m 124\u001b[0m \u001b[38;5;124;03m Steps:\u001b[39;00m\n\u001b[0;32m 125\u001b[0m \u001b[38;5;124;03m 1. Convert data back to the original signed int16.\u001b[39;00m\n\u001b[0;32m 126\u001b[0m \u001b[38;5;124;03m 2. Compute the number of bits to shift over (BitsShift = BitsAllocated - BitsStored)\u001b[39;00m\n\u001b[0;32m 127\u001b[0m \u001b[38;5;124;03m 3. Left shift by BitsShift then right shift by BitsShift\u001b[39;00m\n\u001b[0;32m 128\u001b[0m \u001b[38;5;124;03m 4. Convert data back to the default dtype for metatensor (float32)\u001b[39;00m\n\u001b[0;32m 129\u001b[0m \n\u001b[1;32m--> 130\u001b[0m \u001b[38;5;124;03m By default all dicom files in this dataset `rsna-2023-abdominal-trauma-detection` is in\u001b[39;00m\n\u001b[0;32m 131\u001b[0m \u001b[38;5;124;03m - uint16 if Pixel Representation = 0\u001b[39;00m\n\u001b[0;32m 132\u001b[0m \u001b[38;5;124;03m - int16 if Pixel Representation = 1\u001b[39;00m\n\u001b[0;32m 133\u001b[0m \u001b[38;5;124;03m Refer: https://dicom.innolitics.com/ciods/rt-dose/image-pixel/00280103\u001b[39;00m\n\u001b[0;32m 134\u001b[0m \n\u001b[0;32m 135\u001b[0m \u001b[38;5;124;03m Warning:\u001b[39;00m\n\u001b[0;32m 136\u001b[0m \u001b[38;5;124;03m - Use this transform on the test set as we expect to take DICOM series as input instead of NIfTI.\u001b[39;00m\n\u001b[0;32m 137\u001b[0m \u001b[38;5;124;03m - The passed in metatensor must have the following DICOM metadata:\u001b[39;00m\n\u001b[0;32m 138\u001b[0m \u001b[38;5;124;03m - Pixel Representation\u001b[39;00m\n\u001b[0;32m 139\u001b[0m \u001b[38;5;124;03m - Bits Allocated\u001b[39;00m\n\u001b[0;32m 140\u001b[0m \u001b[38;5;124;03m - Bits Stored\u001b[39;00m\n\u001b[0;32m 141\u001b[0m \u001b[38;5;124;03m - To have a metatensor that has those metadata:\u001b[39;00m\n\u001b[0;32m 142\u001b[0m \u001b[38;5;124;03m - Set reader to be PydicomReader with prune_metadata=False, i.e., monai.transforms.LoadImaged(..., reader=PydicomReader(prune_metadata=False))\u001b[39;00m\n\u001b[0;32m 143\u001b[0m \n\u001b[0;32m 144\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m 146\u001b[0m DCM_ATTR2TAG \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m 147\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBits Allocated\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m00280100\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;66;03m# http://dicomlookup.com/lookup.asp?sw=Tnumber&q=(0028,0100)\u001b[39;00m\n\u001b[0;32m 148\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBits Stored\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m00280101\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;66;03m# http://dicomlookup.com/lookup.asp?sw=Tnumber&q=(0028,0101)\u001b[39;00m\n\u001b[0;32m 149\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPixel Representation\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m00280103\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;66;03m# http://dicomlookup.com/lookup.asp?sw=Tnumber&q=(0028,0103)\u001b[39;00m\n\u001b[0;32m 150\u001b[0m }\n\u001b[0;32m 152\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, keys: KeysCollection):\n", "File \u001b[1;32m~\\Documents\\Jia Herng's Docs\\Programming Projects\\rsna-2023-abdominal-trauma-detection\\datasets\\src\\transforms.py:57\u001b[0m, in \u001b[0;36mLoadNIfTIFromHFHub.__call__\u001b[1;34m(self, url)\u001b[0m\n\u001b[0;32m 54\u001b[0m url \u001b[38;5;241m=\u001b[39m LoadNIfTIFromHFHub\u001b[38;5;241m.\u001b[39m_convert_to_hffs_path(url)\n\u001b[0;32m 55\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfs\u001b[38;5;241m.\u001b[39mopen(url, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrb\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[0;32m 56\u001b[0m img \u001b[38;5;241m=\u001b[39m nib\u001b[38;5;241m.\u001b[39mNifti1Image\u001b[38;5;241m.\u001b[39mfrom_stream(\n\u001b[1;32m---> 57\u001b[0m IndexedGzipFile(fileobj\u001b[38;5;241m=\u001b[39mBytesIO(f\u001b[38;5;241m.\u001b[39mread()))\n\u001b[0;32m 58\u001b[0m )\n\u001b[0;32m 59\u001b[0m img \u001b[38;5;241m=\u001b[39m correct_nifti_header_if_necessary(img)\n\u001b[0;32m 60\u001b[0m img_array, meta_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreader\u001b[38;5;241m.\u001b[39mget_data(img)\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\fsspec\\spec.py:1790\u001b[0m, in \u001b[0;36mAbstractBufferedFile.read\u001b[1;34m(self, length)\u001b[0m\n\u001b[0;32m 1787\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m length \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m 1788\u001b[0m \u001b[38;5;66;03m# don't even bother calling fetch\u001b[39;00m\n\u001b[0;32m 1789\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m-> 1790\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcache\u001b[38;5;241m.\u001b[39m_fetch(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloc, 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"File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\huggingface_hub\\utils\\_http.py:258\u001b[0m, in \u001b[0;36mhttp_backoff\u001b[1;34m(method, url, max_retries, base_wait_time, max_wait_time, retry_on_exceptions, retry_on_status_codes, **kwargs)\u001b[0m\n\u001b[0;32m 255\u001b[0m kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mseek(io_obj_initial_pos)\n\u001b[0;32m 257\u001b[0m \u001b[38;5;66;03m# Perform request and return if status_code is not in the retry list.\u001b[39;00m\n\u001b[1;32m--> 258\u001b[0m response \u001b[38;5;241m=\u001b[39m session\u001b[38;5;241m.\u001b[39mrequest(method\u001b[38;5;241m=\u001b[39mmethod, url\u001b[38;5;241m=\u001b[39murl, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 259\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m response\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m retry_on_status_codes:\n\u001b[0;32m 260\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m response\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\requests\\sessions.py:589\u001b[0m, in \u001b[0;36mSession.request\u001b[1;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[0;32m 584\u001b[0m send_kwargs \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m 585\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimeout\u001b[39m\u001b[38;5;124m\"\u001b[39m: timeout,\n\u001b[0;32m 586\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow_redirects\u001b[39m\u001b[38;5;124m\"\u001b[39m: allow_redirects,\n\u001b[0;32m 587\u001b[0m }\n\u001b[0;32m 588\u001b[0m send_kwargs\u001b[38;5;241m.\u001b[39mupdate(settings)\n\u001b[1;32m--> 589\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msend(prep, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39msend_kwargs)\n\u001b[0;32m 591\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m resp\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\requests\\sessions.py:725\u001b[0m, in \u001b[0;36mSession.send\u001b[1;34m(self, request, **kwargs)\u001b[0m\n\u001b[0;32m 722\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m allow_redirects:\n\u001b[0;32m 723\u001b[0m \u001b[38;5;66;03m# Redirect resolving generator.\u001b[39;00m\n\u001b[0;32m 724\u001b[0m gen \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mresolve_redirects(r, request, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m--> 725\u001b[0m history \u001b[38;5;241m=\u001b[39m [resp \u001b[38;5;28;01mfor\u001b[39;00m resp \u001b[38;5;129;01min\u001b[39;00m gen]\n\u001b[0;32m 726\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 727\u001b[0m history \u001b[38;5;241m=\u001b[39m []\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\requests\\sessions.py:725\u001b[0m, in \u001b[0;36m\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 722\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m allow_redirects:\n\u001b[0;32m 723\u001b[0m \u001b[38;5;66;03m# Redirect resolving generator.\u001b[39;00m\n\u001b[0;32m 724\u001b[0m gen \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mresolve_redirects(r, request, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m--> 725\u001b[0m history \u001b[38;5;241m=\u001b[39m [resp \u001b[38;5;28;01mfor\u001b[39;00m resp \u001b[38;5;129;01min\u001b[39;00m gen]\n\u001b[0;32m 726\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 727\u001b[0m history \u001b[38;5;241m=\u001b[39m []\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\requests\\sessions.py:266\u001b[0m, in \u001b[0;36mSessionRedirectMixin.resolve_redirects\u001b[1;34m(self, resp, req, stream, timeout, verify, cert, proxies, yield_requests, **adapter_kwargs)\u001b[0m\n\u001b[0;32m 263\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m req\n\u001b[0;32m 264\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 266\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msend(\n\u001b[0;32m 267\u001b[0m req,\n\u001b[0;32m 268\u001b[0m stream\u001b[38;5;241m=\u001b[39mstream,\n\u001b[0;32m 269\u001b[0m timeout\u001b[38;5;241m=\u001b[39mtimeout,\n\u001b[0;32m 270\u001b[0m verify\u001b[38;5;241m=\u001b[39mverify,\n\u001b[0;32m 271\u001b[0m cert\u001b[38;5;241m=\u001b[39mcert,\n\u001b[0;32m 272\u001b[0m proxies\u001b[38;5;241m=\u001b[39mproxies,\n\u001b[0;32m 273\u001b[0m allow_redirects\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m 274\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39madapter_kwargs,\n\u001b[0;32m 275\u001b[0m )\n\u001b[0;32m 277\u001b[0m extract_cookies_to_jar(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcookies, prepared_request, resp\u001b[38;5;241m.\u001b[39mraw)\n\u001b[0;32m 279\u001b[0m \u001b[38;5;66;03m# extract redirect url, if any, for the next loop\u001b[39;00m\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\requests\\sessions.py:747\u001b[0m, in \u001b[0;36mSession.send\u001b[1;34m(self, request, **kwargs)\u001b[0m\n\u001b[0;32m 744\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[0;32m 746\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m stream:\n\u001b[1;32m--> 747\u001b[0m r\u001b[38;5;241m.\u001b[39mcontent\n\u001b[0;32m 749\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m r\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\requests\\models.py:899\u001b[0m, in \u001b[0;36mResponse.content\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 897\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_content \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 898\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 899\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_content \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39miter_content(CONTENT_CHUNK_SIZE)) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 901\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_content_consumed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m 902\u001b[0m \u001b[38;5;66;03m# don't need to release the connection; that's been handled by urllib3\u001b[39;00m\n\u001b[0;32m 903\u001b[0m \u001b[38;5;66;03m# since we exhausted the data.\u001b[39;00m\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\requests\\models.py:816\u001b[0m, in \u001b[0;36mResponse.iter_content..generate\u001b[1;34m()\u001b[0m\n\u001b[0;32m 814\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraw, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstream\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m 815\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 816\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraw\u001b[38;5;241m.\u001b[39mstream(chunk_size, decode_content\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m 817\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ProtocolError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 818\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m ChunkedEncodingError(e)\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\urllib3\\response.py:628\u001b[0m, in \u001b[0;36mHTTPResponse.stream\u001b[1;34m(self, amt, decode_content)\u001b[0m\n\u001b[0;32m 626\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 627\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_fp_closed(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fp):\n\u001b[1;32m--> 628\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mread(amt\u001b[38;5;241m=\u001b[39mamt, decode_content\u001b[38;5;241m=\u001b[39mdecode_content)\n\u001b[0;32m 630\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data:\n\u001b[0;32m 631\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m data\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\urllib3\\response.py:567\u001b[0m, in \u001b[0;36mHTTPResponse.read\u001b[1;34m(self, amt, decode_content, cache_content)\u001b[0m\n\u001b[0;32m 564\u001b[0m fp_closed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fp, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mclosed\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m 566\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_error_catcher():\n\u001b[1;32m--> 567\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fp_read(amt) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m fp_closed \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 568\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m amt \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 569\u001b[0m flush_decoder \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\urllib3\\response.py:533\u001b[0m, in \u001b[0;36mHTTPResponse._fp_read\u001b[1;34m(self, amt)\u001b[0m\n\u001b[0;32m 530\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m buffer\u001b[38;5;241m.\u001b[39mgetvalue()\n\u001b[0;32m 531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 532\u001b[0m \u001b[38;5;66;03m# StringIO doesn't like amt=None\u001b[39;00m\n\u001b[1;32m--> 533\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fp\u001b[38;5;241m.\u001b[39mread(amt) \u001b[38;5;28;01mif\u001b[39;00m amt \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fp\u001b[38;5;241m.\u001b[39mread()\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\http\\client.py:466\u001b[0m, in \u001b[0;36mHTTPResponse.read\u001b[1;34m(self, amt)\u001b[0m\n\u001b[0;32m 463\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlength \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m amt \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlength:\n\u001b[0;32m 464\u001b[0m \u001b[38;5;66;03m# clip the read to the \"end of response\"\u001b[39;00m\n\u001b[0;32m 465\u001b[0m amt \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlength\n\u001b[1;32m--> 466\u001b[0m s \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfp\u001b[38;5;241m.\u001b[39mread(amt)\n\u001b[0;32m 467\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m s \u001b[38;5;129;01mand\u001b[39;00m amt:\n\u001b[0;32m 468\u001b[0m \u001b[38;5;66;03m# Ideally, we would raise IncompleteRead if the content-length\u001b[39;00m\n\u001b[0;32m 469\u001b[0m \u001b[38;5;66;03m# wasn't satisfied, but it might break compatibility.\u001b[39;00m\n\u001b[0;32m 470\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_close_conn()\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\socket.py:706\u001b[0m, in \u001b[0;36mSocketIO.readinto\u001b[1;34m(self, b)\u001b[0m\n\u001b[0;32m 704\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m 705\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 706\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sock\u001b[38;5;241m.\u001b[39mrecv_into(b)\n\u001b[0;32m 707\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m timeout:\n\u001b[0;32m 708\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_timeout_occurred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\ssl.py:1278\u001b[0m, in \u001b[0;36mSSLSocket.recv_into\u001b[1;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[0;32m 1274\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m flags \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m 1275\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 1276\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnon-zero flags not allowed in calls to recv_into() on \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m\n\u001b[0;32m 1277\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m)\n\u001b[1;32m-> 1278\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mread(nbytes, buffer)\n\u001b[0;32m 1279\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 1280\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mrecv_into(buffer, nbytes, flags)\n", "File \u001b[1;32m~\\miniconda3\\envs\\ml-env\\Lib\\ssl.py:1134\u001b[0m, in \u001b[0;36mSSLSocket.read\u001b[1;34m(self, len, buffer)\u001b[0m\n\u001b[0;32m 1132\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1133\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m buffer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1134\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sslobj\u001b[38;5;241m.\u001b[39mread(\u001b[38;5;28mlen\u001b[39m, buffer)\n\u001b[0;32m 1135\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 1136\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sslobj\u001b[38;5;241m.\u001b[39mread(\u001b[38;5;28mlen\u001b[39m)\n", "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "%%timeit -n 1 -r 1\n", "for i, batch in enumerate(dls[\"train\"]):\n", " worker_ids, series_ids, x, y = batch\n", " print(i, worker_ids.tolist(), series_ids.tolist(), x.shape, y.shape)" ] }, { "cell_type": "code", "execution_count": null, "id": "d3c90536-e1a9-4cb2-a1a1-4533bb1fcd19", "metadata": {}, "outputs": [], "source": [ "%%timeit -n 1 -r 1\n", "for i, batch in enumerate(dls[\"test\"]):\n", " worker_ids, series_ids, x, y = batch\n", " print(i, worker_ids.tolist(), series_ids.tolist(), x.shape, y.shape)" ] }, { "cell_type": "code", "execution_count": 89, "id": "275b8a42-9eba-4557-9042-74eacd20c514", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[autoreload of transforms failed: Traceback (most recent call last):\n", " File \"C:\\Users\\Jia Herng\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\IPython\\extensions\\autoreload.py\", line 273, in check\n", " superreload(m, reload, self.old_objects)\n", " File \"C:\\Users\\Jia Herng\\miniconda3\\envs\\ml-env\\Lib\\site-packages\\IPython\\extensions\\autoreload.py\", line 471, in superreload\n", " module = reload(module)\n", " ^^^^^^^^^^^^^^\n", " File \"C:\\Users\\Jia Herng\\miniconda3\\envs\\ml-env\\Lib\\importlib\\__init__.py\", line 169, in reload\n", " _bootstrap._exec(spec, module)\n", " File \"\", line 621, in _exec\n", " File \"\", line 936, in exec_module\n", " File \"\", line 1074, in get_code\n", " File \"\", line 1004, in source_to_code\n", " File \"\", line 241, in _call_with_frames_removed\n", " File \"C:\\Users\\Jia Herng\\Documents\\Jia Herng's Docs\\Programming Projects\\rsna-2023-abdominal-trauma-detection\\datasets\\src\\transforms.py\", line 58\n", " img = nib.Nifti1Image.from\n", " ^^^^\n", "SyntaxError: invalid syntax\n", "]\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "372808287893432a8f0ad93a86e6df16", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading data files: 0%| | 0/206 [00:00" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_rsna_ds = datasets.load_dataset(\"jherng/rsna-2023-abdominal-trauma-detection\", \"segmentation\", split=\"train\", streaming=True, test_size=0.1, random_state=42)\n", "train_rsna_ds" ] }, { "cell_type": "code", "execution_count": 18, "id": "8f49697d-e6c8-458f-a81d-e6ae68f7fadf", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/4791/4622.nii.gz', 'seg_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/4622.nii.gz', 'metadata': {'series_id': 4622, 'patient_id': 4791, 'incomplete_organ': False, 'aortic_hu': 223.0, 'pixel_representation': 1, 'bits_allocated': 16, 'bits_stored': 16}}\n", "\n", "{'img_path': 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'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/50518/1201.nii.gz', 'seg_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/1201.nii.gz', 'metadata': {'series_id': 1201, 'patient_id': 50518, 'incomplete_organ': False, 'aortic_hu': 341.0, 'pixel_representation': 1, 'bits_allocated': 16, 'bits_stored': 16}}\n", "\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/37436/59325.nii.gz', 'seg_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/59325.nii.gz', 'metadata': {'series_id': 59325, 'patient_id': 37436, 'incomplete_organ': False, 'aortic_hu': 270.0, 'pixel_representation': 1, 'bits_allocated': 16, 'bits_stored': 16}}\n", "\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/37169/18624.nii.gz', 'seg_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/18624.nii.gz', 'metadata': {'series_id': 18624, 'patient_id': 37169, 'incomplete_organ': False, 'aortic_hu': 165.0, 'pixel_representation': 0, 'bits_allocated': 16, 'bits_stored': 16}}\n", "\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/38982/22232.nii.gz', 'seg_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/22232.nii.gz', 'metadata': {'series_id': 22232, 'patient_id': 38982, 'incomplete_organ': False, 'aortic_hu': 234.0, 'pixel_representation': 0, 'bits_allocated': 16, 'bits_stored': 12}}\n", "\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/33848/17577.nii.gz', 'seg_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/17577.nii.gz', 'metadata': {'series_id': 17577, 'patient_id': 33848, 'incomplete_organ': False, 'aortic_hu': 452.5, 'pixel_representation': 0, 'bits_allocated': 16, 'bits_stored': 12}}\n", "\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/28976/24373.nii.gz', 'seg_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/24373.nii.gz', 'metadata': {'series_id': 24373, 'patient_id': 28976, 'incomplete_organ': False, 'aortic_hu': 176.5, 'pixel_representation': 0, 'bits_allocated': 16, 'bits_stored': 12}}\n", "\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/4093/5176.nii.gz', 'seg_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/5176.nii.gz', 'metadata': {'series_id': 5176, 'patient_id': 4093, 'incomplete_organ': False, 'aortic_hu': 194.0, 'pixel_representation': 0, 'bits_allocated': 16, 'bits_stored': 12}}\n", "\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/9813/24149.nii.gz', 'seg_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/24149.nii.gz', 'metadata': {'series_id': 24149, 'patient_id': 9813, 'incomplete_organ': False, 'aortic_hu': 233.0, 'pixel_representation': 1, 'bits_allocated': 16, 'bits_stored': 16}}\n", "\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/14309/53843.nii.gz', 'seg_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/53843.nii.gz', 'metadata': {'series_id': 53843, 'patient_id': 14309, 'incomplete_organ': False, 'aortic_hu': 135.0, 'pixel_representation': 0, 'bits_allocated': 16, 'bits_stored': 12}}\n", "\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/62397/62307.nii.gz', 'seg_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/62307.nii.gz', 'metadata': {'series_id': 62307, 'patient_id': 62397, 'incomplete_organ': False, 'aortic_hu': 736.5, 'pixel_representation': 0, 'bits_allocated': 16, 'bits_stored': 12}}\n", "\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/28569/12674.nii.gz', 'seg_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/12674.nii.gz', 'metadata': {'series_id': 12674, 'patient_id': 28569, 'incomplete_organ': False, 'aortic_hu': 172.0, 'pixel_representation': 1, 'bits_allocated': 16, 'bits_stored': 16}}\n", "\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/37551/62680.nii.gz', 'seg_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/62680.nii.gz', 'metadata': {'series_id': 62680, 'patient_id': 37551, 'incomplete_organ': False, 'aortic_hu': 158.0, 'pixel_representation': 0, 'bits_allocated': 16, 'bits_stored': 12}}\n", "\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/13623/24645.nii.gz', 'seg_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/24645.nii.gz', 'metadata': {'series_id': 24645, 'patient_id': 13623, 'incomplete_organ': False, 'aortic_hu': 151.0, 'pixel_representation': 1, 'bits_allocated': 16, 'bits_stored': 16}}\n", "\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/3506/13307.nii.gz', 'seg_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/13307.nii.gz', 'metadata': {'series_id': 13307, 'patient_id': 3506, 'incomplete_organ': False, 'aortic_hu': 168.0, 'pixel_representation': 0, 'bits_allocated': 16, 'bits_stored': 12}}\n", "\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/60058/49547.nii.gz', 'seg_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/49547.nii.gz', 'metadata': {'series_id': 49547, 'patient_id': 60058, 'incomplete_organ': False, 'aortic_hu': 168.0, 'pixel_representation': 1, 'bits_allocated': 16, 'bits_stored': 16}}\n", "\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/37436/50434.nii.gz', 'seg_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/50434.nii.gz', 'metadata': {'series_id': 50434, 'patient_id': 37436, 'incomplete_organ': False, 'aortic_hu': 162.0, 'pixel_representation': 1, 'bits_allocated': 16, 'bits_stored': 16}}\n", "\n" ] } ], "source": [ "for i, x in enumerate(train_rsna_ds):\n", " print(x)\n", " print()" ] }, { "cell_type": "code", "execution_count": 17, "id": "9c0dfea5-c089-4921-a0e9-95d063ec5799", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "185" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "i + 1" ] }, { "cell_type": "code", "execution_count": 23, "id": "d191f908-3110-425c-a308-ebee2bc4d1b9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "185" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_rsna_ds.n_shards" ] }, { "cell_type": "code", "execution_count": 20, "id": "075c7372-9170-4e69-b3e0-60ab7b978ee7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_rsna_ds = datasets.load_dataset(\"jherng/rsna-2023-abdominal-trauma-detection\", \"segmentation\", split=\"test\", streaming=True, test_size=0.1, random_state=42)\n", "test_rsna_ds" ] }, { "cell_type": "code", "execution_count": 21, "id": "576ad580-5c6a-4813-8738-fa62b887da1b", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/13741/42173.nii.gz', 'seg_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/42173.nii.gz', 'metadata': {'series_id': 42173, 'patient_id': 13741, 'incomplete_organ': False, 'aortic_hu': 195.0, 'pixel_representation': 0, 'bits_allocated': 16, 'bits_stored': 12}}\n", "\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/10937/53000.nii.gz', 'seg_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/53000.nii.gz', 'metadata': {'series_id': 53000, 'patient_id': 10937, 'incomplete_organ': False, 'aortic_hu': 189.0, 'pixel_representation': 0, 'bits_allocated': 16, 'bits_stored': 12}}\n", "\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/65504/55928.nii.gz', 'seg_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/55928.nii.gz', 'metadata': {'series_id': 55928, 'patient_id': 65504, 'incomplete_organ': False, 'aortic_hu': 144.0, 'pixel_representation': 1, 'bits_allocated': 16, 'bits_stored': 16}}\n", "\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/32425/5218.nii.gz', 'seg_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/5218.nii.gz', 'metadata': {'series_id': 5218, 'patient_id': 32425, 'incomplete_organ': False, 'aortic_hu': 213.75, 'pixel_representation': 1, 'bits_allocated': 16, 'bits_stored': 16}}\n", "\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/25347/4929.nii.gz', 'seg_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/4929.nii.gz', 'metadata': {'series_id': 4929, 'patient_id': 25347, 'incomplete_organ': False, 'aortic_hu': 166.0, 'pixel_representation': 1, 'bits_allocated': 16, 'bits_stored': 16}}\n", "\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/36105/64520.nii.gz', 'seg_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/64520.nii.gz', 'metadata': {'series_id': 64520, 'patient_id': 36105, 'incomplete_organ': False, 'aortic_hu': 201.0, 'pixel_representation': 0, 'bits_allocated': 16, 'bits_stored': 12}}\n", "\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/60744/10494.nii.gz', 'seg_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/10494.nii.gz', 'metadata': {'series_id': 10494, 'patient_id': 60744, 'incomplete_organ': False, 'aortic_hu': 109.0, 'pixel_representation': 1, 'bits_allocated': 16, 'bits_stored': 16}}\n", "\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/54183/33526.nii.gz', 'seg_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/33526.nii.gz', 'metadata': {'series_id': 33526, 'patient_id': 54183, 'incomplete_organ': False, 'aortic_hu': 290.0, 'pixel_representation': 1, 'bits_allocated': 16, 'bits_stored': 16}}\n", "\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/48710/10109.nii.gz', 'seg_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/10109.nii.gz', 'metadata': {'series_id': 10109, 'patient_id': 48710, 'incomplete_organ': False, 'aortic_hu': 109.0, 'pixel_representation': 0, 'bits_allocated': 16, 'bits_stored': 12}}\n", "\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/61547/35190.nii.gz', 'seg_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/35190.nii.gz', 'metadata': {'series_id': 35190, 'patient_id': 61547, 'incomplete_organ': False, 'aortic_hu': 388.0, 'pixel_representation': 1, 'bits_allocated': 16, 'bits_stored': 16}}\n", "\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/37429/15748.nii.gz', 'seg_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/15748.nii.gz', 'metadata': {'series_id': 15748, 'patient_id': 37429, 'incomplete_organ': False, 'aortic_hu': 466.0, 'pixel_representation': 1, 'bits_allocated': 16, 'bits_stored': 16}}\n", "\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/54722/10000.nii.gz', 'seg_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/10000.nii.gz', 'metadata': {'series_id': 10000, 'patient_id': 54722, 'incomplete_organ': False, 'aortic_hu': 343.0, 'pixel_representation': 1, 'bits_allocated': 16, 'bits_stored': 16}}\n", "\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/25102/32991.nii.gz', 'seg_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/32991.nii.gz', 'metadata': {'series_id': 32991, 'patient_id': 25102, 'incomplete_organ': False, 'aortic_hu': 228.0, 'pixel_representation': 1, 'bits_allocated': 16, 'bits_stored': 16}}\n", "\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/57444/15415.nii.gz', 'seg_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/15415.nii.gz', 'metadata': {'series_id': 15415, 'patient_id': 57444, 'incomplete_organ': False, 'aortic_hu': 322.0, 'pixel_representation': 1, 'bits_allocated': 16, 'bits_stored': 16}}\n", "\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/28925/10385.nii.gz', 'seg_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/10385.nii.gz', 'metadata': {'series_id': 10385, 'patient_id': 28925, 'incomplete_organ': False, 'aortic_hu': 500.0, 'pixel_representation': 1, 'bits_allocated': 16, 'bits_stored': 16}}\n", "\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/35653/23644.nii.gz', 'seg_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/23644.nii.gz', 'metadata': {'series_id': 23644, 'patient_id': 35653, 'incomplete_organ': False, 'aortic_hu': 203.0, 'pixel_representation': 1, 'bits_allocated': 16, 'bits_stored': 16}}\n", "\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/13794/51141.nii.gz', 'seg_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/51141.nii.gz', 'metadata': {'series_id': 51141, 'patient_id': 13794, 'incomplete_organ': False, 'aortic_hu': 190.0, 'pixel_representation': 0, 'bits_allocated': 16, 'bits_stored': 12}}\n", "\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/50820/28122.nii.gz', 'seg_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/28122.nii.gz', 'metadata': {'series_id': 28122, 'patient_id': 50820, 'incomplete_organ': False, 'aortic_hu': 149.0, 'pixel_representation': 1, 'bits_allocated': 16, 'bits_stored': 16}}\n", "\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/44693/46912.nii.gz', 'seg_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/46912.nii.gz', 'metadata': {'series_id': 46912, 'patient_id': 44693, 'incomplete_organ': False, 'aortic_hu': 153.0, 'pixel_representation': 1, 'bits_allocated': 16, 'bits_stored': 16}}\n", "\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/54722/63146.nii.gz', 'seg_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/63146.nii.gz', 'metadata': {'series_id': 63146, 'patient_id': 54722, 'incomplete_organ': False, 'aortic_hu': 171.0, 'pixel_representation': 1, 'bits_allocated': 16, 'bits_stored': 16}}\n", "\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/22755/44712.nii.gz', 'seg_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/44712.nii.gz', 'metadata': {'series_id': 44712, 'patient_id': 22755, 'incomplete_organ': False, 'aortic_hu': 210.0, 'pixel_representation': 1, 'bits_allocated': 16, 'bits_stored': 16}}\n", "\n" ] } ], "source": [ "for i, x in enumerate(test_rsna_ds):\n", " print(x)\n", " print()" ] }, { "cell_type": "code", "execution_count": 22, "id": "a9011afb-e557-4274-8511-ff3f8e536ed7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "21" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "i + 1" ] }, { "cell_type": "markdown", "id": "66c70c70-07aa-439b-913c-09cad6b71383", "metadata": {}, "source": [ "## Segmentation" ] }, { "cell_type": "code", "execution_count": 26, "id": "898258b0-1e8d-468f-a71f-b891f23edcbb", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "43992bad9ed54975ad4a45ca1c77d127", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading readme: 0%| | 0.00/9.34k [00:00,\n", " 'test': }" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rsna_ds = datasets.load_dataset(\"jherng/rsna-2023-abdominal-trauma-detection\", \"segmentation\", streaming=True, test_size=0.1, random_state=42)\n", "rsna_ds" ] }, { "cell_type": "code", "execution_count": 24, "id": "39f6597e-614d-4018-893a-8ec97747a934", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8e20a8d395a1481a9509db74c1e3cb4e", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading data files: 0%| | 0/206 [00:00,\n", " 'test': }" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rsna_ds = datasets.load_dataset(\"jherng/rsna-2023-abdominal-trauma-detection\", \"classification\", streaming=True, test_size=0.1, random_state=42)\n", "rsna_ds" ] }, { "cell_type": "code", "execution_count": 5, "id": "1f7524ef-fa9a-4d35-a7b9-3668ea3c0fa3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/25899/21872.nii.gz', 'bowel': 0, 'extravasation': 0, 'kidney': 0, 'liver': 0, 'spleen': 0, 'any_injury': False, 'metadata': {'series_id': 21872, 'patient_id': 25899, 'incomplete_organ': False, 'aortic_hu': 113.0, 'pixel_representation': 0, 'bits_allocated': 16, 'bits_stored': 12}}\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/57581/13664.nii.gz', 'bowel': 0, 'extravasation': 0, 'kidney': 0, 'liver': 0, 'spleen': 0, 'any_injury': False, 'metadata': {'series_id': 13664, 'patient_id': 57581, 'incomplete_organ': False, 'aortic_hu': 176.5, 'pixel_representation': 0, 'bits_allocated': 16, 'bits_stored': 12}}\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/26659/23622.nii.gz', 'bowel': 0, 'extravasation': 0, 'kidney': 0, 'liver': 0, 'spleen': 0, 'any_injury': False, 'metadata': {'series_id': 23622, 'patient_id': 26659, 'incomplete_organ': False, 'aortic_hu': 228.0, 'pixel_representation': 1, 'bits_allocated': 16, 'bits_stored': 16}}\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/40251/62741.nii.gz', 'bowel': 0, 'extravasation': 0, 'kidney': 1, 'liver': 0, 'spleen': 2, 'any_injury': True, 'metadata': {'series_id': 62741, 'patient_id': 40251, 'incomplete_organ': False, 'aortic_hu': 187.0, 'pixel_representation': 1, 'bits_allocated': 16, 'bits_stored': 16}}\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/59299/55910.nii.gz', 'bowel': 0, 'extravasation': 0, 'kidney': 0, 'liver': 0, 'spleen': 0, 'any_injury': False, 'metadata': {'series_id': 55910, 'patient_id': 59299, 'incomplete_organ': False, 'aortic_hu': 158.0, 'pixel_representation': 0, 'bits_allocated': 16, 'bits_stored': 12}}\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/59214/44622.nii.gz', 'bowel': 0, 'extravasation': 0, 'kidney': 0, 'liver': 0, 'spleen': 0, 'any_injury': False, 'metadata': {'series_id': 44622, 'patient_id': 59214, 'incomplete_organ': False, 'aortic_hu': 154.0, 'pixel_representation': 1, 'bits_allocated': 16, 'bits_stored': 16}}\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/54953/14060.nii.gz', 'bowel': 0, 'extravasation': 0, 'kidney': 0, 'liver': 1, 'spleen': 0, 'any_injury': True, 'metadata': {'series_id': 14060, 'patient_id': 54953, 'incomplete_organ': False, 'aortic_hu': 168.25, 'pixel_representation': 1, 'bits_allocated': 16, 'bits_stored': 16}}\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/39839/1320.nii.gz', 'bowel': 0, 'extravasation': 0, 'kidney': 0, 'liver': 0, 'spleen': 0, 'any_injury': False, 'metadata': {'series_id': 1320, 'patient_id': 39839, 'incomplete_organ': False, 'aortic_hu': 131.0, 'pixel_representation': 0, 'bits_allocated': 16, 'bits_stored': 12}}\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/28672/27601.nii.gz', 'bowel': 0, 'extravasation': 0, 'kidney': 0, 'liver': 0, 'spleen': 0, 'any_injury': False, 'metadata': {'series_id': 27601, 'patient_id': 28672, 'incomplete_organ': False, 'aortic_hu': 117.0, 'pixel_representation': 0, 'bits_allocated': 16, 'bits_stored': 12}}\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/28371/30990.nii.gz', 'bowel': 0, 'extravasation': 0, 'kidney': 0, 'liver': 0, 'spleen': 0, 'any_injury': False, 'metadata': {'series_id': 30990, 'patient_id': 28371, 'incomplete_organ': False, 'aortic_hu': 343.0, 'pixel_representation': 0, 'bits_allocated': 16, 'bits_stored': 12}}\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/13953/29835.nii.gz', 'bowel': 0, 'extravasation': 0, 'kidney': 0, 'liver': 0, 'spleen': 0, 'any_injury': False, 'metadata': {'series_id': 29835, 'patient_id': 13953, 'incomplete_organ': False, 'aortic_hu': 156.0, 'pixel_representation': 1, 'bits_allocated': 16, 'bits_stored': 13}}\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/19600/10745.nii.gz', 'bowel': 0, 'extravasation': 0, 'kidney': 0, 'liver': 0, 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'pixel_representation': 1, 'bits_allocated': 16, 'bits_stored': 16}}\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/30510/34831.nii.gz', 'bowel': 0, 'extravasation': 0, 'kidney': 0, 'liver': 0, 'spleen': 0, 'any_injury': False, 'metadata': {'series_id': 34831, 'patient_id': 30510, 'incomplete_organ': False, 'aortic_hu': 211.0, 'pixel_representation': 0, 'bits_allocated': 16, 'bits_stored': 12}}\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/11803/57014.nii.gz', 'bowel': 0, 'extravasation': 0, 'kidney': 0, 'liver': 0, 'spleen': 0, 'any_injury': False, 'metadata': {'series_id': 57014, 'patient_id': 11803, 'incomplete_organ': False, 'aortic_hu': 133.0, 'pixel_representation': 0, 'bits_allocated': 16, 'bits_stored': 12}}\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/46505/55994.nii.gz', 'bowel': 0, 'extravasation': 1, 'kidney': 0, 'liver': 0, 'spleen': 0, 'any_injury': True, 'metadata': {'series_id': 55994, 'patient_id': 46505, 'incomplete_organ': False, 'aortic_hu': 382.0, 'pixel_representation': 1, 'bits_allocated': 16, 'bits_stored': 16}}\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/49350/56770.nii.gz', 'bowel': 0, 'extravasation': 0, 'kidney': 0, 'liver': 1, 'spleen': 0, 'any_injury': True, 'metadata': {'series_id': 56770, 'patient_id': 49350, 'incomplete_organ': False, 'aortic_hu': 153.0, 'pixel_representation': 0, 'bits_allocated': 16, 'bits_stored': 12}}\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/52714/37016.nii.gz', 'bowel': 0, 'extravasation': 0, 'kidney': 0, 'liver': 0, 'spleen': 0, 'any_injury': False, 'metadata': {'series_id': 37016, 'patient_id': 52714, 'incomplete_organ': False, 'aortic_hu': 171.0, 'pixel_representation': 0, 'bits_allocated': 16, 'bits_stored': 12}}\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/65118/29409.nii.gz', 'bowel': 0, 'extravasation': 0, 'kidney': 0, 'liver': 0, 'spleen': 0, 'any_injury': False, 'metadata': {'series_id': 29409, 'patient_id': 65118, 'incomplete_organ': False, 'aortic_hu': 257.0, 'pixel_representation': 0, 'bits_allocated': 16, 'bits_stored': 12}}\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/15758/46001.nii.gz', 'bowel': 0, 'extravasation': 0, 'kidney': 0, 'liver': 0, 'spleen': 0, 'any_injury': False, 'metadata': {'series_id': 46001, 'patient_id': 15758, 'incomplete_organ': False, 'aortic_hu': 132.0, 'pixel_representation': 1, 'bits_allocated': 16, 'bits_stored': 16}}\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/48527/29630.nii.gz', 'bowel': 0, 'extravasation': 0, 'kidney': 1, 'liver': 0, 'spleen': 0, 'any_injury': True, 'metadata': {'series_id': 29630, 'patient_id': 48527, 'incomplete_organ': False, 'aortic_hu': 203.0, 'pixel_representation': 1, 'bits_allocated': 16, 'bits_stored': 16}}\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/56689/24173.nii.gz', 'bowel': 0, 'extravasation': 0, 'kidney': 1, 'liver': 2, 'spleen': 0, 'any_injury': True, 'metadata': {'series_id': 24173, 'patient_id': 56689, 'incomplete_organ': False, 'aortic_hu': 186.0, 'pixel_representation': 0, 'bits_allocated': 16, 'bits_stored': 12}}\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/20399/428.nii.gz', 'bowel': 0, 'extravasation': 0, 'kidney': 0, 'liver': 0, 'spleen': 0, 'any_injury': False, 'metadata': {'series_id': 428, 'patient_id': 20399, 'incomplete_organ': False, 'aortic_hu': 196.0, 'pixel_representation': 1, 'bits_allocated': 16, 'bits_stored': 16}}\n" ] } ], "source": [ "for x in rsna_ds[\"train\"]:\n", " print(x)" ] }, { "cell_type": "markdown", "id": "b8568349-8e4b-4b4c-a432-4ba84e910a0d", "metadata": {}, "source": [ "## Classification with segmentation masks" ] }, { "cell_type": "code", "execution_count": 16, "id": "8d3539f4-e152-4299-b388-c6f5668c4c36", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'train': ,\n", " 'test': }" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rsna_ds = datasets.load_dataset(\"jherng/rsna-2023-abdominal-trauma-detection\", streaming=True, test_size=0.2, random_state=42)\n", "rsna_ds" ] }, { "cell_type": "code", "execution_count": 20, "id": "c470b9b5-33eb-4b80-8948-5e0f28ba032d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3768" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rsna_ds[\"train\"].n_shards" ] }, { "cell_type": "code", "execution_count": 21, "id": "1553daa7-a4dc-478f-bb32-fb2491d5998f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "943" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rsna_ds[\"test\"].n_shards" ] }, { "cell_type": "code", "execution_count": 22, "id": "3680d207-d1b9-4594-90de-20e979206291", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/36507/51963.nii.gz', 'bowel': 0, 'extravasation': 0, 'kidney': 0, 'liver': 0, 'spleen': 0, 'any_injury': False, 'metadata': {'series_id': 51963, 'patient_id': 36507, 'incomplete_organ': True, 'aortic_hu': 314.0, 'pixel_representation': 1, 'bits_allocated': 16, 'bits_stored': 16}}\n", "\n", "1\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/29637/36172.nii.gz', 'bowel': 0, 'extravasation': 0, 'kidney': 0, 'liver': 0, 'spleen': 0, 'any_injury': False, 'metadata': {'series_id': 36172, 'patient_id': 29637, 'incomplete_organ': False, 'aortic_hu': 191.0, 'pixel_representation': 0, 'bits_allocated': 16, 'bits_stored': 12}}\n", "\n", "2\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/63877/62433.nii.gz', 'bowel': 0, 'extravasation': 0, 'kidney': 0, 'liver': 0, 'spleen': 0, 'any_injury': False, 'metadata': {'series_id': 62433, 'patient_id': 63877, 'incomplete_organ': False, 'aortic_hu': 124.0, 'pixel_representation': 0, 'bits_allocated': 16, 'bits_stored': 12}}\n", "\n", "3\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/17304/16099.nii.gz', 'bowel': 0, 'extravasation': 0, 'kidney': 0, 'liver': 0, 'spleen': 0, 'any_injury': False, 'metadata': {'series_id': 16099, 'patient_id': 17304, 'incomplete_organ': False, 'aortic_hu': 154.0, 'pixel_representation': 0, 'bits_allocated': 16, 'bits_stored': 12}}\n", "\n", "4\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/51896/31972.nii.gz', 'bowel': 0, 'extravasation': 0, 'kidney': 0, 'liver': 0, 'spleen': 0, 'any_injury': False, 'metadata': {'series_id': 31972, 'patient_id': 51896, 'incomplete_organ': False, 'aortic_hu': 275.5, 'pixel_representation': 0, 'bits_allocated': 16, 'bits_stored': 12}}\n", "\n", "5\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/52160/24158.nii.gz', 'bowel': 0, 'extravasation': 0, 'kidney': 0, 'liver': 1, 'spleen': 0, 'any_injury': True, 'metadata': {'series_id': 24158, 'patient_id': 52160, 'incomplete_organ': False, 'aortic_hu': 322.0, 'pixel_representation': 1, 'bits_allocated': 16, 'bits_stored': 16}}\n", "\n", "6\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/34785/24288.nii.gz', 'bowel': 0, 'extravasation': 0, 'kidney': 0, 'liver': 0, 'spleen': 0, 'any_injury': False, 'metadata': {'series_id': 24288, 'patient_id': 34785, 'incomplete_organ': False, 'aortic_hu': 246.0, 'pixel_representation': 0, 'bits_allocated': 16, 'bits_stored': 12}}\n", "\n", "7\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/38129/39152.nii.gz', 'bowel': 0, 'extravasation': 1, 'kidney': 0, 'liver': 0, 'spleen': 0, 'any_injury': True, 'metadata': {'series_id': 39152, 'patient_id': 38129, 'incomplete_organ': False, 'aortic_hu': 150.0, 'pixel_representation': 1, 'bits_allocated': 16, 'bits_stored': 16}}\n", "\n", "8\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/15188/29661.nii.gz', 'bowel': 0, 'extravasation': 1, 'kidney': 0, 'liver': 0, 'spleen': 0, 'any_injury': True, 'metadata': {'series_id': 29661, 'patient_id': 15188, 'incomplete_organ': False, 'aortic_hu': 353.25, 'pixel_representation': 1, 'bits_allocated': 16, 'bits_stored': 16}}\n", "\n", "9\n", "{'img_path': 'https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/28144/63182.nii.gz', 'bowel': 0, 'extravasation': 0, 'kidney': 0, 'liver': 0, 'spleen': 0, 'any_injury': False, 'metadata': {'series_id': 63182, 'patient_id': 28144, 'incomplete_organ': False, 'aortic_hu': 332.0, 'pixel_representation': 0, 'bits_allocated': 16, 'bits_stored': 12}}\n", "\n" ] } ], "source": [ "for i, x in enumerate(rsna_ds[\"train\"]):\n", " if i == 10:\n", " break\n", " \n", " print(i)\n", " print(x)\n", " print()\n", " " ] } ], "metadata": { "kernelspec": { "display_name": "ml-env (conda)", "language": "python", "name": "ml-env" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.4" } }, "nbformat": 4, "nbformat_minor": 5 }