| | import math
|
| | import torch
|
| | 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
|
| |
|
| | @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:
|
| | 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()
|
| |
|
| | 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()
|
| | grid.requires_grad = False
|
| |
|
| | vgrid = grid + flow
|
| |
|
| | 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)
|
| |
|
| |
|
| | 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
|
| |
|
| |
|
| |
|
| | 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) |
