mirror of
https://github.com/Wan-Video/Wan2.1.git
synced 2025-11-04 06:15:17 +00:00
268 lines
8.6 KiB
Python
268 lines
8.6 KiB
Python
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
|
|
|
|
class ResidualBlock(nn.Module):
|
|
def __init__(self, in_planes, planes, norm_fn='group', stride=1):
|
|
super(ResidualBlock, self).__init__()
|
|
|
|
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, padding=1, stride=stride)
|
|
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1)
|
|
self.relu = nn.ReLU(inplace=True)
|
|
|
|
num_groups = planes // 8
|
|
|
|
if norm_fn == 'group':
|
|
self.norm1 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
|
|
self.norm2 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
|
|
if not stride == 1:
|
|
self.norm3 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
|
|
|
|
elif norm_fn == 'batch':
|
|
self.norm1 = nn.BatchNorm2d(planes)
|
|
self.norm2 = nn.BatchNorm2d(planes)
|
|
if not stride == 1:
|
|
self.norm3 = nn.BatchNorm2d(planes)
|
|
|
|
elif norm_fn == 'instance':
|
|
self.norm1 = nn.InstanceNorm2d(planes)
|
|
self.norm2 = nn.InstanceNorm2d(planes)
|
|
if not stride == 1:
|
|
self.norm3 = nn.InstanceNorm2d(planes)
|
|
|
|
elif norm_fn == 'none':
|
|
self.norm1 = nn.Sequential()
|
|
self.norm2 = nn.Sequential()
|
|
if not stride == 1:
|
|
self.norm3 = nn.Sequential()
|
|
|
|
if stride == 1:
|
|
self.downsample = None
|
|
|
|
else:
|
|
self.downsample = nn.Sequential(
|
|
nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride), self.norm3)
|
|
|
|
|
|
def forward(self, x):
|
|
y = x
|
|
y = self.relu(self.norm1(self.conv1(y)))
|
|
y = self.relu(self.norm2(self.conv2(y)))
|
|
|
|
if self.downsample is not None:
|
|
x = self.downsample(x)
|
|
|
|
return self.relu(x+y)
|
|
|
|
|
|
|
|
class BottleneckBlock(nn.Module):
|
|
def __init__(self, in_planes, planes, norm_fn='group', stride=1):
|
|
super(BottleneckBlock, self).__init__()
|
|
|
|
self.conv1 = nn.Conv2d(in_planes, planes//4, kernel_size=1, padding=0)
|
|
self.conv2 = nn.Conv2d(planes//4, planes//4, kernel_size=3, padding=1, stride=stride)
|
|
self.conv3 = nn.Conv2d(planes//4, planes, kernel_size=1, padding=0)
|
|
self.relu = nn.ReLU(inplace=True)
|
|
|
|
num_groups = planes // 8
|
|
|
|
if norm_fn == 'group':
|
|
self.norm1 = nn.GroupNorm(num_groups=num_groups, num_channels=planes//4)
|
|
self.norm2 = nn.GroupNorm(num_groups=num_groups, num_channels=planes//4)
|
|
self.norm3 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
|
|
if not stride == 1:
|
|
self.norm4 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
|
|
|
|
elif norm_fn == 'batch':
|
|
self.norm1 = nn.BatchNorm2d(planes//4)
|
|
self.norm2 = nn.BatchNorm2d(planes//4)
|
|
self.norm3 = nn.BatchNorm2d(planes)
|
|
if not stride == 1:
|
|
self.norm4 = nn.BatchNorm2d(planes)
|
|
|
|
elif norm_fn == 'instance':
|
|
self.norm1 = nn.InstanceNorm2d(planes//4)
|
|
self.norm2 = nn.InstanceNorm2d(planes//4)
|
|
self.norm3 = nn.InstanceNorm2d(planes)
|
|
if not stride == 1:
|
|
self.norm4 = nn.InstanceNorm2d(planes)
|
|
|
|
elif norm_fn == 'none':
|
|
self.norm1 = nn.Sequential()
|
|
self.norm2 = nn.Sequential()
|
|
self.norm3 = nn.Sequential()
|
|
if not stride == 1:
|
|
self.norm4 = nn.Sequential()
|
|
|
|
if stride == 1:
|
|
self.downsample = None
|
|
|
|
else:
|
|
self.downsample = nn.Sequential(
|
|
nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride), self.norm4)
|
|
|
|
|
|
def forward(self, x):
|
|
y = x
|
|
y = self.relu(self.norm1(self.conv1(y)))
|
|
y = self.relu(self.norm2(self.conv2(y)))
|
|
y = self.relu(self.norm3(self.conv3(y)))
|
|
|
|
if self.downsample is not None:
|
|
x = self.downsample(x)
|
|
|
|
return self.relu(x+y)
|
|
|
|
class BasicEncoder(nn.Module):
|
|
def __init__(self, output_dim=128, norm_fn='batch', dropout=0.0):
|
|
super(BasicEncoder, self).__init__()
|
|
self.norm_fn = norm_fn
|
|
|
|
if self.norm_fn == 'group':
|
|
self.norm1 = nn.GroupNorm(num_groups=8, num_channels=64)
|
|
|
|
elif self.norm_fn == 'batch':
|
|
self.norm1 = nn.BatchNorm2d(64)
|
|
|
|
elif self.norm_fn == 'instance':
|
|
self.norm1 = nn.InstanceNorm2d(64)
|
|
|
|
elif self.norm_fn == 'none':
|
|
self.norm1 = nn.Sequential()
|
|
|
|
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
|
|
self.relu1 = nn.ReLU(inplace=True)
|
|
|
|
self.in_planes = 64
|
|
self.layer1 = self._make_layer(64, stride=1)
|
|
self.layer2 = self._make_layer(96, stride=2)
|
|
self.layer3 = self._make_layer(128, stride=2)
|
|
|
|
# output convolution
|
|
self.conv2 = nn.Conv2d(128, output_dim, kernel_size=1)
|
|
|
|
self.dropout = None
|
|
if dropout > 0:
|
|
self.dropout = nn.Dropout2d(p=dropout)
|
|
|
|
for m in self.modules():
|
|
if isinstance(m, nn.Conv2d):
|
|
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
|
|
elif isinstance(m, (nn.BatchNorm2d, nn.InstanceNorm2d, nn.GroupNorm)):
|
|
if m.weight is not None:
|
|
nn.init.constant_(m.weight, 1)
|
|
if m.bias is not None:
|
|
nn.init.constant_(m.bias, 0)
|
|
|
|
def _make_layer(self, dim, stride=1):
|
|
layer1 = ResidualBlock(self.in_planes, dim, self.norm_fn, stride=stride)
|
|
layer2 = ResidualBlock(dim, dim, self.norm_fn, stride=1)
|
|
layers = (layer1, layer2)
|
|
|
|
self.in_planes = dim
|
|
return nn.Sequential(*layers)
|
|
|
|
|
|
def forward(self, x):
|
|
|
|
# if input is list, combine batch dimension
|
|
is_list = isinstance(x, tuple) or isinstance(x, list)
|
|
if is_list:
|
|
batch_dim = x[0].shape[0]
|
|
x = torch.cat(x, dim=0)
|
|
|
|
x = self.conv1(x)
|
|
x = self.norm1(x)
|
|
x = self.relu1(x)
|
|
|
|
x = self.layer1(x)
|
|
x = self.layer2(x)
|
|
x = self.layer3(x)
|
|
|
|
x = self.conv2(x)
|
|
|
|
if self.training and self.dropout is not None:
|
|
x = self.dropout(x)
|
|
|
|
if is_list:
|
|
x = torch.split(x, [batch_dim, batch_dim], dim=0)
|
|
|
|
return x
|
|
|
|
|
|
class SmallEncoder(nn.Module):
|
|
def __init__(self, output_dim=128, norm_fn='batch', dropout=0.0):
|
|
super(SmallEncoder, self).__init__()
|
|
self.norm_fn = norm_fn
|
|
|
|
if self.norm_fn == 'group':
|
|
self.norm1 = nn.GroupNorm(num_groups=8, num_channels=32)
|
|
|
|
elif self.norm_fn == 'batch':
|
|
self.norm1 = nn.BatchNorm2d(32)
|
|
|
|
elif self.norm_fn == 'instance':
|
|
self.norm1 = nn.InstanceNorm2d(32)
|
|
|
|
elif self.norm_fn == 'none':
|
|
self.norm1 = nn.Sequential()
|
|
|
|
self.conv1 = nn.Conv2d(3, 32, kernel_size=7, stride=2, padding=3)
|
|
self.relu1 = nn.ReLU(inplace=True)
|
|
|
|
self.in_planes = 32
|
|
self.layer1 = self._make_layer(32, stride=1)
|
|
self.layer2 = self._make_layer(64, stride=2)
|
|
self.layer3 = self._make_layer(96, stride=2)
|
|
|
|
self.dropout = None
|
|
if dropout > 0:
|
|
self.dropout = nn.Dropout2d(p=dropout)
|
|
|
|
self.conv2 = nn.Conv2d(96, output_dim, kernel_size=1)
|
|
|
|
for m in self.modules():
|
|
if isinstance(m, nn.Conv2d):
|
|
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
|
|
elif isinstance(m, (nn.BatchNorm2d, nn.InstanceNorm2d, nn.GroupNorm)):
|
|
if m.weight is not None:
|
|
nn.init.constant_(m.weight, 1)
|
|
if m.bias is not None:
|
|
nn.init.constant_(m.bias, 0)
|
|
|
|
def _make_layer(self, dim, stride=1):
|
|
layer1 = BottleneckBlock(self.in_planes, dim, self.norm_fn, stride=stride)
|
|
layer2 = BottleneckBlock(dim, dim, self.norm_fn, stride=1)
|
|
layers = (layer1, layer2)
|
|
|
|
self.in_planes = dim
|
|
return nn.Sequential(*layers)
|
|
|
|
|
|
def forward(self, x):
|
|
|
|
# if input is list, combine batch dimension
|
|
is_list = isinstance(x, tuple) or isinstance(x, list)
|
|
if is_list:
|
|
batch_dim = x[0].shape[0]
|
|
x = torch.cat(x, dim=0)
|
|
|
|
x = self.conv1(x)
|
|
x = self.norm1(x)
|
|
x = self.relu1(x)
|
|
|
|
x = self.layer1(x)
|
|
x = self.layer2(x)
|
|
x = self.layer3(x)
|
|
x = self.conv2(x)
|
|
|
|
if self.training and self.dropout is not None:
|
|
x = self.dropout(x)
|
|
|
|
if is_list:
|
|
x = torch.split(x, [batch_dim, batch_dim], dim=0)
|
|
|
|
return x
|