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import torch
import torch.nn as nn
import torch.nn.init as init
def initialize_weights(net_l, scale=1):
if not isinstance(net_l, list):
net_l = [net_l]
for net in net_l:
for m in net.modules():
if isinstance(m, nn.Conv2d):
init.kaiming_normal_(m.weight, a=0, mode='fan_in')
m.weight.data *= scale # for residual block
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
init.kaiming_normal_(m.weight, a=0, mode='fan_in')
m.weight.data *= scale
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
init.constant_(m.weight, 1)
init.constant_(m.bias.data, 0.0)
def make_layer(block, n_layers):
layers = []
for _ in range(n_layers):
layers.append(block())
return nn.Sequential(*layers)
class ResidualBlock_noBN(nn.Module):
"""Residual block w/o BN
---Conv-ReLU-Conv-+-
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"""
def __init__(self, nf=64):
super(ResidualBlock_noBN, self).__init__()
self.conv1 = nn.Conv2d(nf, nf, kernel_size=3, stride=1, padding=1, bias=True)
self.conv2 = nn.Conv2d(nf, nf, kernel_size=3, stride=1, padding=1, bias=True)
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
def forward(self, x):
"""
Args:
x: with shape of [b, c, t, h, w]
Returns: processed features with shape [b, c, t, h, w]
"""
identity = x
out = self.lrelu(self.conv1(x))
out = self.conv2(out)
out = identity + out
# Remove ReLU at the end of the residual block
# http://torch.ch/blog/2016/02/04/resnets.html
return out
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