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import math |
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import torch |
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from torch import nn as nn |
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from torch.nn import functional as F |
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from torch.nn import init as init |
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from torch.nn.modules.batchnorm import _BatchNorm |
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@torch.no_grad() |
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def default_init_weights(module_list, scale=1, bias_fill=0, **kwargs): |
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"""Initialize network weights. |
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Args: |
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module_list (list[nn.Module] | nn.Module): Modules to be initialized. |
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scale (float): Scale initialized weights, especially for residual |
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blocks. Default: 1. |
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bias_fill (float): The value to fill bias. Default: 0 |
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kwargs (dict): Other arguments for initialization function. |
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""" |
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if not isinstance(module_list, list): |
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module_list = [module_list] |
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for module in module_list: |
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for m in module.modules(): |
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if isinstance(m, nn.Conv2d): |
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init.kaiming_normal_(m.weight, **kwargs) |
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m.weight.data *= scale |
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if m.bias is not None: |
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m.bias.data.fill_(bias_fill) |
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elif isinstance(m, nn.Linear): |
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init.kaiming_normal_(m.weight, **kwargs) |
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m.weight.data *= scale |
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if m.bias is not None: |
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m.bias.data.fill_(bias_fill) |
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elif isinstance(m, _BatchNorm): |
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init.constant_(m.weight, 1) |
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if m.bias is not None: |
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m.bias.data.fill_(bias_fill) |
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def make_layer(basic_block, num_basic_block, **kwarg): |
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"""Make layers by stacking the same blocks. |
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Args: |
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basic_block (nn.module): nn.module class for basic block. |
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num_basic_block (int): number of blocks. |
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Returns: |
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nn.Sequential: Stacked blocks in nn.Sequential. |
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""" |
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layers = [] |
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for _ in range(num_basic_block): |
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layers.append(basic_block(**kwarg)) |
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return nn.Sequential(*layers) |
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class ResidualBlockNoBN(nn.Module): |
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"""Residual block without BN. |
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It has a style of: |
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---Conv-ReLU-Conv-+- |
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Args: |
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num_feat (int): Channel number of intermediate features. |
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Default: 64. |
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res_scale (float): Residual scale. Default: 1. |
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pytorch_init (bool): If set to True, use pytorch default init, |
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otherwise, use default_init_weights. Default: False. |
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""" |
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def __init__(self, num_feat=64, res_scale=1, pytorch_init=False): |
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super(ResidualBlockNoBN, self).__init__() |
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self.res_scale = res_scale |
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self.conv1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True) |
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self.conv2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True) |
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self.relu = nn.ReLU(inplace=True) |
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if not pytorch_init: |
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default_init_weights([self.conv1, self.conv2], 0.1) |
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def forward(self, x): |
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identity = x |
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out = self.conv2(self.relu(self.conv1(x))) |
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return identity + out * self.res_scale |
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class Upsample(nn.Sequential): |
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"""Upsample module. |
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Args: |
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scale (int): Scale factor. Supported scales: 2^n and 3. |
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num_feat (int): Channel number of intermediate features. |
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""" |
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def __init__(self, scale, num_feat): |
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m = [] |
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if (scale & (scale - 1)) == 0: |
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for _ in range(int(math.log(scale, 2))): |
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m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) |
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m.append(nn.PixelShuffle(2)) |
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elif scale == 3: |
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m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) |
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m.append(nn.PixelShuffle(3)) |
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else: |
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raise ValueError(f'scale {scale} is not supported. ' |
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'Supported scales: 2^n and 3.') |
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super(Upsample, self).__init__(*m) |
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def pixel_unshuffle(x, scale): |
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""" Pixel unshuffle. |
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Args: |
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x (Tensor): Input feature with shape (b, c, hh, hw). |
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scale (int): Downsample ratio. |
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Returns: |
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Tensor: the pixel unshuffled feature. |
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""" |
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b, c, hh, hw = x.size() |
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out_channel = c * (scale**2) |
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assert hh % scale == 0 and hw % scale == 0 |
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h = hh // scale |
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w = hw // scale |
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x_view = x.view(b, c, h, scale, w, scale) |
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return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w) |
