forked from mrq/DL-Art-School
80 lines
2.6 KiB
Python
80 lines
2.6 KiB
Python
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import torch
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import torch.nn as nn
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import torch.nn.init as init
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import torch.nn.functional as F
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def initialize_weights(net_l, scale=1):
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if not isinstance(net_l, list):
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net_l = [net_l]
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for net in net_l:
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for m in net.modules():
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if isinstance(m, nn.Conv2d):
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init.kaiming_normal_(m.weight, a=0, mode='fan_in')
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m.weight.data *= scale # for residual block
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if m.bias is not None:
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m.bias.data.zero_()
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elif isinstance(m, nn.Linear):
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init.kaiming_normal_(m.weight, a=0, mode='fan_in')
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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.zero_()
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elif isinstance(m, nn.BatchNorm2d):
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init.constant_(m.weight, 1)
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init.constant_(m.bias.data, 0.0)
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def make_layer(block, n_layers):
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layers = []
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for _ in range(n_layers):
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layers.append(block())
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return nn.Sequential(*layers)
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class ResidualBlock_noBN(nn.Module):
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'''Residual block w/o BN
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---Conv-ReLU-Conv-+-
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|________________|
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'''
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def __init__(self, nf=64):
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super(ResidualBlock_noBN, self).__init__()
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self.conv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
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self.conv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
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# initialization
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initialize_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 = F.relu(self.conv1(x), inplace=True)
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out = self.conv2(out)
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return identity + out
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def flow_warp(x, flow, interp_mode='bilinear', padding_mode='zeros'):
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"""Warp an image or feature map with optical flow
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Args:
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x (Tensor): size (N, C, H, W)
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flow (Tensor): size (N, H, W, 2), normal value
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interp_mode (str): 'nearest' or 'bilinear'
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padding_mode (str): 'zeros' or 'border' or 'reflection'
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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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B, C, H, W = x.size()
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# mesh grid
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grid_y, grid_x = torch.meshgrid(torch.arange(0, H), torch.arange(0, W))
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grid = torch.stack((grid_x, grid_y), 2).float() # W(x), H(y), 2
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grid.requires_grad = False
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grid = grid.type_as(x)
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vgrid = grid + flow
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# scale grid to [-1,1]
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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)
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return output
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