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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. ' 'Supported scales: 2^n and 3.') |
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super(Upsample, self).__init__(*m) |
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def flow_warp(x, flow, interp_mode='bilinear', padding_mode='zeros', align_corners=True): |
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"""Warp an image or feature map with optical flow. |
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Args: |
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x (Tensor): Tensor with size (n, c, h, w). |
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flow (Tensor): Tensor with size (n, h, w, 2), normal value. |
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interp_mode (str): 'nearest' or 'bilinear'. Default: 'bilinear'. |
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padding_mode (str): 'zeros' or 'border' or 'reflection'. |
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Default: 'zeros'. |
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align_corners (bool): Before pytorch 1.3, the default value is |
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align_corners=True. After pytorch 1.3, the default value is |
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align_corners=False. Here, we use the True as default. |
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Returns: |
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Tensor: Warped image or feature map. |
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""" |
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assert x.size()[-2:] == flow.size()[1:3] |
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_, _, h, w = x.size() |
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grid_y, grid_x = torch.meshgrid(torch.arange(0, h).type_as(x), torch.arange(0, w).type_as(x)) |
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grid = torch.stack((grid_x, grid_y), 2).float() |
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grid.requires_grad = False |
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vgrid = grid + flow |
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vgrid_x = 2.0 * vgrid[:, :, :, 0] / max(w - 1, 1) - 1.0 |
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vgrid_y = 2.0 * vgrid[:, :, :, 1] / max(h - 1, 1) - 1.0 |
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vgrid_scaled = torch.stack((vgrid_x, vgrid_y), dim=3) |
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output = F.grid_sample(x, vgrid_scaled, mode=interp_mode, padding_mode=padding_mode, align_corners=align_corners) |
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return output |
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def resize_flow(flow, size_type, sizes, interp_mode='bilinear', align_corners=False): |
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"""Resize a flow according to ratio or shape. |
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Args: |
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flow (Tensor): Precomputed flow. shape [N, 2, H, W]. |
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size_type (str): 'ratio' or 'shape'. |
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sizes (list[int | float]): the ratio for resizing or the final output |
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shape. |
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1) The order of ratio should be [ratio_h, ratio_w]. For |
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downsampling, the ratio should be smaller than 1.0 (i.e., ratio |
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< 1.0). For upsampling, the ratio should be larger than 1.0 (i.e., |
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ratio > 1.0). |
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2) The order of output_size should be [out_h, out_w]. |
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interp_mode (str): The mode of interpolation for resizing. |
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Default: 'bilinear'. |
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align_corners (bool): Whether align corners. Default: False. |
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Returns: |
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Tensor: Resized flow. |
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""" |
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_, _, flow_h, flow_w = flow.size() |
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if size_type == 'ratio': |
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output_h, output_w = int(flow_h * sizes[0]), int(flow_w * sizes[1]) |
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elif size_type == 'shape': |
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output_h, output_w = sizes[0], sizes[1] |
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else: |
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raise ValueError(f'Size type should be ratio or shape, but got type {size_type}.') |
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input_flow = flow.clone() |
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ratio_h = output_h / flow_h |
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ratio_w = output_w / flow_w |
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input_flow[:, 0, :, :] *= ratio_w |
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input_flow[:, 1, :, :] *= ratio_h |
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resized_flow = F.interpolate( |
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input=input_flow, size=(output_h, output_w), mode=interp_mode, align_corners=align_corners) |
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return resized_flow |
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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) |
