Spaces:
Sleeping
Sleeping
File size: 8,210 Bytes
19fe404 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 |
import argparse
import re
import os
import pandas as pd
from accelerate import PartialState
from accelerate.utils import gather_object
from natsort import natsorted
from tqdm import tqdm
from torch.utils.data import DataLoader
import utils.image_evaluator as image_evaluator
from utils.logger import logger
from utils.video_dataset import VideoDataset, collate_fn
from utils.video_utils import get_video_path_list
def camel2snake(s: str) -> str:
"""Convert camel case to snake case."""
if not re.match("^[A-Z]+$", s):
pattern = re.compile(r"(?<!^)(?=[A-Z])")
return pattern.sub("_", s).lower()
return s
def parse_args():
parser = argparse.ArgumentParser(description="Compute scores of uniform sampled frames from videos.")
parser.add_argument("--video_folder", type=str, default="", help="The video folder.")
parser.add_argument(
"--video_metadata_path", type=str, default=None, help="The path to the video dataset metadata (csv/jsonl)."
)
parser.add_argument(
"--video_path_column",
type=str,
default="video_path",
help="The column contains the video path (an absolute path or a relative path w.r.t the video_folder).",
)
parser.add_argument(
"--caption_column",
type=str,
default=None,
help="The column contains the caption.",
)
parser.add_argument(
"--num_sampled_frames",
type=int,
default=4,
help="num_sampled_frames",
)
parser.add_argument("--metrics", nargs="+", type=str, required=True, help="The evaluation metric(s) for generated images.")
parser.add_argument(
"--batch_size",
type=int,
default=10,
required=False,
help="The batch size for the video dataset.",
)
parser.add_argument("--saved_path", type=str, required=True, help="The save path to the output results (csv/jsonl).")
parser.add_argument("--saved_freq", type=int, default=1000, help="The frequency to save the output results.")
args = parser.parse_args()
return args
def main():
args = parse_args()
assert args.batch_size > 1
video_path_list = get_video_path_list(
video_folder=args.video_folder,
video_metadata_path=args.video_metadata_path,
video_path_column=args.video_path_column
)
if not (args.saved_path.endswith(".csv") or args.saved_path.endswith(".jsonl")):
raise ValueError("The saved_path must end with .csv or .jsonl.")
caption_list = None
if args.video_metadata_path is not None and args.caption_column is not None:
if args.video_metadata_path.endswith(".csv"):
video_metadata_df = pd.read_csv(args.video_metadata_path)
elif args.video_metadata_path.endswith(".jsonl"):
video_metadata_df = pd.read_json(args.video_metadata_path, lines=True)
else:
raise ValueError("The video_metadata_path must end with .csv or .jsonl.")
caption_list = video_metadata_df[args.caption_column].tolist()
if os.path.exists(args.saved_path):
if args.saved_path.endswith(".csv"):
saved_metadata_df = pd.read_csv(args.saved_path)
elif args.saved_path.endswith(".jsonl"):
saved_metadata_df = pd.read_json(args.saved_path, lines=True)
saved_video_path_list = saved_metadata_df[args.video_path_column].tolist()
saved_video_path_list = [os.path.join(args.video_folder, video_path) for video_path in saved_video_path_list]
video_path_list = list(set(video_path_list).difference(set(saved_video_path_list)))
# Sorting to guarantee the same result for each process.
video_path_list = natsorted(video_path_list)
logger.info(f"Resume from {args.saved_path}: {len(saved_video_path_list)} processed and {len(video_path_list)} to be processed.")
logger.info("Initializing evaluator metrics...")
state = PartialState()
metric_fns = [getattr(image_evaluator, metric)(device=state.device) for metric in args.metrics]
# The workaround can be removed after https://github.com/huggingface/accelerate/pull/2781 is released.
index = len(video_path_list) - len(video_path_list) % state.num_processes
logger.info(f"Drop {len(video_path_list) % state.num_processes} videos to avoid duplicates in state.split_between_processes.")
video_path_list = video_path_list[:index]
result_dict = {args.video_path_column: [], "sample_frame_idx": []}
for metric in args.metrics:
result_dict[camel2snake(metric)] = []
with state.split_between_processes(video_path_list) as splitted_video_path_list:
video_dataset = VideoDataset(
video_path_list=splitted_video_path_list,
sample_method="uniform",
num_sampled_frames=args.num_sampled_frames
)
video_loader = DataLoader(video_dataset, batch_size=args.batch_size, num_workers=4, collate_fn=collate_fn)
for idx, batch in enumerate(tqdm(video_loader)):
if len(batch) == 0:
continue
batch_video_path = batch[args.video_path_column]
result_dict["sample_frame_idx"].extend(batch["sampled_frame_idx"])
# [batch_size, num_sampled_frames, H, W, C] => [batch_size * num_sampled_frames, H, W, C].
batch_frame = []
for item_sampled_frame in batch["sampled_frame"]:
batch_frame.extend([frame for frame in item_sampled_frame])
batch_caption = None
if caption_list is not None:
batch_caption = caption_list[i : i + args.batch_size]
# Compute the frame quality.
for i, metric in enumerate(args.metrics):
# [batch_size * num_sampled_frames] => [batch_size, num_sampled_frames]
quality_scores = metric_fns[i](batch_frame, batch_caption)
quality_scores = [round(score, 5) for score in quality_scores]
quality_scores = [quality_scores[j:j + args.num_sampled_frames] for j in range(0, len(quality_scores), args.num_sampled_frames)]
result_dict[camel2snake(metric)].extend(quality_scores)
saved_video_path_list = [os.path.basename(video_path) for video_path in batch_video_path]
result_dict[args.video_path_column].extend(saved_video_path_list)
# Save the metadata in the main process every saved_freq.
if (idx != 0) and (idx % args.saved_freq == 0):
state.wait_for_everyone()
gathered_result_dict = {k: gather_object(v) for k, v in result_dict.items()}
if state.is_main_process:
result_df = pd.DataFrame(gathered_result_dict)
if args.saved_path.endswith(".csv"):
header = False if os.path.exists(args.saved_path) else True
result_df.to_csv(args.saved_path, header=header, index=False, mode="a")
elif args.saved_path.endswith(".jsonl"):
result_df.to_json(args.saved_path, orient="records", lines=True, mode="a")
logger.info(f"Save result to {args.saved_path}.")
for k in result_dict.keys():
result_dict[k] = []
# Wait for all processes to finish and gather the final result.
state.wait_for_everyone()
gathered_result_dict = {k: gather_object(v) for k, v in result_dict.items()}
# Save the metadata in the main process.
if state.is_main_process:
result_df = pd.DataFrame(gathered_result_dict)
if len(gathered_result_dict[args.video_path_column]) != 0:
result_df = pd.DataFrame(gathered_result_dict)
if args.saved_path.endswith(".csv"):
header = False if os.path.exists(args.saved_path) else True
result_df.to_csv(args.saved_path, header=header, index=False, mode="a")
elif args.saved_path.endswith(".jsonl"):
result_df.to_json(args.saved_path, orient="records", lines=True, mode="a")
logger.info(f"Save the final result to {args.saved_path}.")
if __name__ == "__main__":
main()
|