File size: 11,431 Bytes
8e542dc
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
import collections.abc
import math
import torch
import torchvision
import warnings
from distutils.version import LooseVersion
from itertools import repeat
from torch import nn as nn
from torch.nn import functional as F
from torch.nn import init as init
from torch.nn.modules.batchnorm import _BatchNorm

from basicsr.ops.dcn import ModulatedDeformConvPack, modulated_deform_conv
from basicsr.utils import get_root_logger


@torch.no_grad()
def default_init_weights(module_list, scale=1, bias_fill=0, **kwargs):
    """Initialize network weights.

    Args:
        module_list (list[nn.Module] | nn.Module): Modules to be initialized.
        scale (float): Scale initialized weights, especially for residual
            blocks. Default: 1.
        bias_fill (float): The value to fill bias. Default: 0
        kwargs (dict): Other arguments for initialization function.
    """
    if not isinstance(module_list, list):
        module_list = [module_list]
    for module in module_list:
        for m in module.modules():
            if isinstance(m, nn.Conv2d):
                init.kaiming_normal_(m.weight, **kwargs)
                m.weight.data *= scale
                if m.bias is not None:
                    m.bias.data.fill_(bias_fill)
            elif isinstance(m, nn.Linear):
                init.kaiming_normal_(m.weight, **kwargs)
                m.weight.data *= scale
                if m.bias is not None:
                    m.bias.data.fill_(bias_fill)
            elif isinstance(m, _BatchNorm):
                init.constant_(m.weight, 1)
                if m.bias is not None:
                    m.bias.data.fill_(bias_fill)


def make_layer(basic_block, num_basic_block, **kwarg):
    """Make layers by stacking the same blocks.

    Args:
        basic_block (nn.module): nn.module class for basic block.
        num_basic_block (int): number of blocks.

    Returns:
        nn.Sequential: Stacked blocks in nn.Sequential.
    """
    layers = []
    for _ in range(num_basic_block):
        layers.append(basic_block(**kwarg))
    return nn.Sequential(*layers)


class ResidualBlockNoBN(nn.Module):
    """Residual block without BN.

    It has a style of:
        ---Conv-ReLU-Conv-+-
         |________________|

    Args:
        num_feat (int): Channel number of intermediate features.
            Default: 64.
        res_scale (float): Residual scale. Default: 1.
        pytorch_init (bool): If set to True, use pytorch default init,
            otherwise, use default_init_weights. Default: False.
    """

    def __init__(self, num_feat=64, res_scale=1, pytorch_init=False):
        super(ResidualBlockNoBN, self).__init__()
        self.res_scale = res_scale
        self.conv1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)
        self.conv2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)
        self.relu = nn.ReLU(inplace=True)

        if not pytorch_init:
            default_init_weights([self.conv1, self.conv2], 0.1)

    def forward(self, x):
        identity = x
        out = self.conv2(self.relu(self.conv1(x)))
        return identity + out * self.res_scale


class Upsample(nn.Sequential):
    """Upsample module.

    Args:
        scale (int): Scale factor. Supported scales: 2^n and 3.
        num_feat (int): Channel number of intermediate features.
    """

    def __init__(self, scale, num_feat):
        m = []
        if (scale & (scale - 1)) == 0:  # scale = 2^n
            for _ in range(int(math.log(scale, 2))):
                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
                m.append(nn.PixelShuffle(2))
        elif scale == 3:
            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
            m.append(nn.PixelShuffle(3))
        else:
            raise ValueError(f'scale {scale} is not supported. Supported scales: 2^n and 3.')
        super(Upsample, self).__init__(*m)


def flow_warp(x, flow, interp_mode='bilinear', padding_mode='zeros', align_corners=True):
    """Warp an image or feature map with optical flow.

    Args:
        x (Tensor): Tensor with size (n, c, h, w).
        flow (Tensor): Tensor with size (n, h, w, 2), normal value.
        interp_mode (str): 'nearest' or 'bilinear'. Default: 'bilinear'.
        padding_mode (str): 'zeros' or 'border' or 'reflection'.
            Default: 'zeros'.
        align_corners (bool): Before pytorch 1.3, the default value is
            align_corners=True. After pytorch 1.3, the default value is
            align_corners=False. Here, we use the True as default.

    Returns:
        Tensor: Warped image or feature map.
    """
    assert x.size()[-2:] == flow.size()[1:3]
    _, _, h, w = x.size()
    # create mesh grid
    grid_y, grid_x = torch.meshgrid(torch.arange(0, h).type_as(x), torch.arange(0, w).type_as(x))
    grid = torch.stack((grid_x, grid_y), 2).float()  # W(x), H(y), 2
    grid.requires_grad = False

    vgrid = grid + flow
    # scale grid to [-1,1]
    vgrid_x = 2.0 * vgrid[:, :, :, 0] / max(w - 1, 1) - 1.0
    vgrid_y = 2.0 * vgrid[:, :, :, 1] / max(h - 1, 1) - 1.0
    vgrid_scaled = torch.stack((vgrid_x, vgrid_y), dim=3)
    output = F.grid_sample(x, vgrid_scaled, mode=interp_mode, padding_mode=padding_mode, align_corners=align_corners)

    # TODO, what if align_corners=False
    return output


def resize_flow(flow, size_type, sizes, interp_mode='bilinear', align_corners=False):
    """Resize a flow according to ratio or shape.

    Args:
        flow (Tensor): Precomputed flow. shape [N, 2, H, W].
        size_type (str): 'ratio' or 'shape'.
        sizes (list[int | float]): the ratio for resizing or the final output
            shape.
            1) The order of ratio should be [ratio_h, ratio_w]. For
            downsampling, the ratio should be smaller than 1.0 (i.e., ratio
            < 1.0). For upsampling, the ratio should be larger than 1.0 (i.e.,
            ratio > 1.0).
            2) The order of output_size should be [out_h, out_w].
        interp_mode (str): The mode of interpolation for resizing.
            Default: 'bilinear'.
        align_corners (bool): Whether align corners. Default: False.

    Returns:
        Tensor: Resized flow.
    """
    _, _, flow_h, flow_w = flow.size()
    if size_type == 'ratio':
        output_h, output_w = int(flow_h * sizes[0]), int(flow_w * sizes[1])
    elif size_type == 'shape':
        output_h, output_w = sizes[0], sizes[1]
    else:
        raise ValueError(f'Size type should be ratio or shape, but got type {size_type}.')

    input_flow = flow.clone()
    ratio_h = output_h / flow_h
    ratio_w = output_w / flow_w
    input_flow[:, 0, :, :] *= ratio_w
    input_flow[:, 1, :, :] *= ratio_h
    resized_flow = F.interpolate(
        input=input_flow, size=(output_h, output_w), mode=interp_mode, align_corners=align_corners)
    return resized_flow


# TODO: may write a cpp file
def pixel_unshuffle(x, scale):
    """ Pixel unshuffle.

    Args:
        x (Tensor): Input feature with shape (b, c, hh, hw).
        scale (int): Downsample ratio.

    Returns:
        Tensor: the pixel unshuffled feature.
    """
    b, c, hh, hw = x.size()
    out_channel = c * (scale**2)
    assert hh % scale == 0 and hw % scale == 0
    h = hh // scale
    w = hw // scale
    x_view = x.view(b, c, h, scale, w, scale)
    return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w)


class DCNv2Pack(ModulatedDeformConvPack):
    """Modulated deformable conv for deformable alignment.

    Different from the official DCNv2Pack, which generates offsets and masks
    from the preceding features, this DCNv2Pack takes another different
    features to generate offsets and masks.

    Ref:
        Delving Deep into Deformable Alignment in Video Super-Resolution.
    """

    def forward(self, x, feat):
        out = self.conv_offset(feat)
        o1, o2, mask = torch.chunk(out, 3, dim=1)
        offset = torch.cat((o1, o2), dim=1)
        mask = torch.sigmoid(mask)

        offset_absmean = torch.mean(torch.abs(offset))
        if offset_absmean > 50:
            logger = get_root_logger()
            logger.warning(f'Offset abs mean is {offset_absmean}, larger than 50.')

        if LooseVersion(torchvision.__version__) >= LooseVersion('0.9.0'):
            return torchvision.ops.deform_conv2d(x, offset, self.weight, self.bias, self.stride, self.padding,
                                                 self.dilation, mask)
        else:
            return modulated_deform_conv(x, offset, mask, self.weight, self.bias, self.stride, self.padding,
                                         self.dilation, self.groups, self.deformable_groups)


def _no_grad_trunc_normal_(tensor, mean, std, a, b):
    # From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/weight_init.py
    # Cut & paste from PyTorch official master until it's in a few official releases - RW
    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
    def norm_cdf(x):
        # Computes standard normal cumulative distribution function
        return (1. + math.erf(x / math.sqrt(2.))) / 2.

    if (mean < a - 2 * std) or (mean > b + 2 * std):
        warnings.warn(
            'mean is more than 2 std from [a, b] in nn.init.trunc_normal_. '
            'The distribution of values may be incorrect.',
            stacklevel=2)

    with torch.no_grad():
        # Values are generated by using a truncated uniform distribution and
        # then using the inverse CDF for the normal distribution.
        # Get upper and lower cdf values
        low = norm_cdf((a - mean) / std)
        up = norm_cdf((b - mean) / std)

        # Uniformly fill tensor with values from [low, up], then translate to
        # [2l-1, 2u-1].
        tensor.uniform_(2 * low - 1, 2 * up - 1)

        # Use inverse cdf transform for normal distribution to get truncated
        # standard normal
        tensor.erfinv_()

        # Transform to proper mean, std
        tensor.mul_(std * math.sqrt(2.))
        tensor.add_(mean)

        # Clamp to ensure it's in the proper range
        tensor.clamp_(min=a, max=b)
        return tensor


def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
    r"""Fills the input Tensor with values drawn from a truncated
    normal distribution.

    From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/weight_init.py

    The values are effectively drawn from the
    normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
    with values outside :math:`[a, b]` redrawn until they are within
    the bounds. The method used for generating the random values works
    best when :math:`a \leq \text{mean} \leq b`.

    Args:
        tensor: an n-dimensional `torch.Tensor`
        mean: the mean of the normal distribution
        std: the standard deviation of the normal distribution
        a: the minimum cutoff value
        b: the maximum cutoff value

    Examples:
        >>> w = torch.empty(3, 5)
        >>> nn.init.trunc_normal_(w)
    """
    return _no_grad_trunc_normal_(tensor, mean, std, a, b)


# From PyTorch
def _ntuple(n):

    def parse(x):
        if isinstance(x, collections.abc.Iterable):
            return x
        return tuple(repeat(x, n))

    return parse


to_1tuple = _ntuple(1)
to_2tuple = _ntuple(2)
to_3tuple = _ntuple(3)
to_4tuple = _ntuple(4)
to_ntuple = _ntuple