mirror of
https://github.com/Wan-Video/Wan2.1.git
synced 2025-11-04 06:15:17 +00:00
152 lines
4.0 KiB
Python
152 lines
4.0 KiB
Python
import torch.nn as nn
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def _make_scratch(in_shape, out_shape, groups=1, expand=False):
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scratch = nn.Module()
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out_shape1 = out_shape
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out_shape2 = out_shape
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out_shape3 = out_shape
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if len(in_shape) >= 4:
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out_shape4 = out_shape
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if expand:
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out_shape1 = out_shape
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out_shape2 = out_shape * 2
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out_shape3 = out_shape * 4
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if len(in_shape) >= 4:
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out_shape4 = out_shape * 8
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scratch.layer1_rn = nn.Conv2d(in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False,
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groups=groups)
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scratch.layer2_rn = nn.Conv2d(in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False,
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groups=groups)
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scratch.layer3_rn = nn.Conv2d(in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False,
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groups=groups)
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if len(in_shape) >= 4:
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scratch.layer4_rn = nn.Conv2d(in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False,
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groups=groups)
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return scratch
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class ResidualConvUnit(nn.Module):
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"""Residual convolution module.
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"""
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def __init__(self, features, activation, bn):
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"""Init.
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Args:
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features (int): number of features
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"""
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super().__init__()
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self.bn = bn
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self.groups = 1
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self.conv1 = nn.Conv2d(features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups)
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self.conv2 = nn.Conv2d(features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups)
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if self.bn == True:
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self.bn1 = nn.BatchNorm2d(features)
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self.bn2 = nn.BatchNorm2d(features)
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self.activation = activation
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self.skip_add = nn.quantized.FloatFunctional()
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def forward(self, x):
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"""Forward pass.
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Args:
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x (tensor): input
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Returns:
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tensor: output
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"""
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out = self.activation(x)
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out = self.conv1(out)
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if self.bn == True:
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out = self.bn1(out)
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out = self.activation(out)
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out = self.conv2(out)
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if self.bn == True:
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out = self.bn2(out)
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if self.groups > 1:
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out = self.conv_merge(out)
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return self.skip_add.add(out, x)
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class FeatureFusionBlock(nn.Module):
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"""Feature fusion block.
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"""
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def __init__(
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self,
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features,
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activation,
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deconv=False,
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bn=False,
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expand=False,
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align_corners=True,
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size=None
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):
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"""Init.
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Args:
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features (int): number of features
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"""
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super(FeatureFusionBlock, self).__init__()
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self.deconv = deconv
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self.align_corners = align_corners
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self.groups = 1
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self.expand = expand
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out_features = features
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if self.expand == True:
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out_features = features // 2
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self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1)
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self.resConfUnit1 = ResidualConvUnit(features, activation, bn)
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self.resConfUnit2 = ResidualConvUnit(features, activation, bn)
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self.skip_add = nn.quantized.FloatFunctional()
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self.size = size
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def forward(self, *xs, size=None):
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"""Forward pass.
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Returns:
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tensor: output
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"""
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output = xs[0]
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if len(xs) == 2:
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res = self.resConfUnit1(xs[1])
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output = self.skip_add.add(output, res)
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output = self.resConfUnit2(output)
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if (size is None) and (self.size is None):
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modifier = {"scale_factor": 2}
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elif size is None:
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modifier = {"size": self.size}
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else:
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modifier = {"size": size}
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output = nn.functional.interpolate(output, **modifier, mode="bilinear", align_corners=self.align_corners)
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output = self.out_conv(output)
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return output
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