mirror of
https://git.datalinker.icu/comfyanonymous/ComfyUI
synced 2026-08-25 00:45:45 +08:00
288 lines
9.0 KiB
Python
288 lines
9.0 KiB
Python
# Modified from https://github.com/pytorch/vision/tree/master/torchvision/models/video
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import torch
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import torch.nn as nn
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__all__ = ['unet_18', 'unet_34']
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useBias = False
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class identity(nn.Module):
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def __init__(self , *args , **kwargs):
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super().__init__()
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def forward(self , x):
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return x
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class Conv3DSimple(nn.Conv3d):
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def __init__(self,
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in_planes,
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out_planes,
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midplanes=None,
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stride=1,
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padding=1):
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super(Conv3DSimple, self).__init__(
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in_channels=in_planes,
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out_channels=out_planes,
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kernel_size=(3, 3, 3),
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stride=stride,
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padding=padding,
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bias=useBias)
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@staticmethod
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def get_downsample_stride(stride , temporal_stride):
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if temporal_stride:
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return (temporal_stride, stride, stride)
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else:
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return (stride , stride , stride)
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class BasicStem(nn.Sequential):
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"""The default conv-batchnorm-relu stem
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"""
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def __init__(self):
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super().__init__(
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nn.Conv3d(3, 64, kernel_size=(3, 7, 7), stride=(1, 2, 2),
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padding=(1, 3, 3), bias=useBias),
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batchnorm(64),
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nn.ReLU(inplace=False))
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class Conv2Plus1D(nn.Sequential):
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def __init__(self,
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in_planes,
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out_planes,
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midplanes,
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stride=1,
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padding=1):
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if not isinstance(stride , int):
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temporal_stride , stride , stride = stride
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else:
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temporal_stride = stride
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super(Conv2Plus1D, self).__init__(
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nn.Conv3d(in_planes, midplanes, kernel_size=(1, 3, 3),
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stride=(1, stride, stride), padding=(0, padding, padding),
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bias=False),
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# batchnorm(midplanes),
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nn.ReLU(inplace=True),
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nn.Conv3d(midplanes, out_planes, kernel_size=(3, 1, 1),
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stride=(temporal_stride, 1, 1), padding=(padding, 0, 0),
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bias=False))
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@staticmethod
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def get_downsample_stride(stride , temporal_stride):
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if temporal_stride:
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return (temporal_stride, stride, stride)
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else:
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return (stride , stride , stride)
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class R2Plus1dStem(nn.Sequential):
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"""R(2+1)D stem is different than the default one as it uses separated 3D convolution
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"""
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def __init__(self):
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super().__init__(
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nn.Conv3d(3, 45, kernel_size=(1, 7, 7),
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stride=(1, 2, 2), padding=(0, 3, 3),
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bias=False),
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batchnorm(45),
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nn.ReLU(inplace=True),
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nn.Conv3d(45, 64, kernel_size=(3, 1, 1),
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stride=(1, 1, 1), padding=(1, 0, 0),
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bias=False),
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batchnorm(64),
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nn.ReLU(inplace=True))
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class SEGating(nn.Module):
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def __init__(self , inplanes , reduction=16):
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super().__init__()
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self.pool = nn.AdaptiveAvgPool3d(1)
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self.attn_layer = nn.Sequential(
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nn.Conv3d(inplanes , inplanes , kernel_size=1 , stride=1 , bias=True),
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nn.Sigmoid()
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)
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def forward(self , x):
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out = self.pool(x)
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y = self.attn_layer(out)
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return x * y
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class BasicBlock(nn.Module):
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expansion = 1
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def __init__(self, inplanes, planes, conv_builder, stride=1, downsample=None):
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midplanes = (inplanes * planes * 3 * 3 * 3) // (inplanes * 3 * 3 + 3 * planes)
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super(BasicBlock, self).__init__()
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self.conv1 = nn.Sequential(
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conv_builder(inplanes, planes, midplanes, stride),
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batchnorm(planes),
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nn.ReLU(inplace=True)
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)
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self.conv2 = nn.Sequential(
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conv_builder(planes, planes, midplanes),
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batchnorm(planes)
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)
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self.fg = SEGating(planes) ## Feature Gating
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self.relu = nn.ReLU(inplace=True)
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self.downsample = downsample
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self.stride = stride
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def forward(self, x):
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residual = x
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out = self.conv1(x)
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out = self.conv2(out)
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out = self.fg(out)
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if self.downsample is not None:
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residual = self.downsample(x)
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out += residual
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out = self.relu(out)
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return out
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class VideoResNet(nn.Module):
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def __init__(self, block, conv_makers, layers,
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stem, zero_init_residual=False):
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"""Generic resnet video generator.
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Args:
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block (nn.Module): resnet building block
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conv_makers (list(functions)): generator function for each layer
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layers (List[int]): number of blocks per layer
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stem (nn.Module, optional): Resnet stem, if None, defaults to conv-bn-relu. Defaults to None.
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"""
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super(VideoResNet, self).__init__()
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self.inplanes = 64
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self.stem = stem()
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self.layer1 = self._make_layer(block, conv_makers[0], 64, layers[0], stride=1 )
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self.layer2 = self._make_layer(block, conv_makers[1], 128, layers[1], stride=2 , temporal_stride=1)
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self.layer3 = self._make_layer(block, conv_makers[2], 256, layers[2], stride=2 , temporal_stride=1)
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self.layer4 = self._make_layer(block, conv_makers[3], 512, layers[3], stride=1, temporal_stride=1)
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# init weights
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self._initialize_weights()
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if zero_init_residual:
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for m in self.modules():
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if isinstance(m, Bottleneck):
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nn.init.constant_(m.bn3.weight, 0)
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def forward(self, x):
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x_0 = self.stem(x)
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x_1 = self.layer1(x_0)
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x_2 = self.layer2(x_1)
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x_3 = self.layer3(x_2)
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x_4 = self.layer4(x_3)
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return x_0 , x_1 , x_2 , x_3 , x_4
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def _make_layer(self, block, conv_builder, planes, blocks, stride=1, temporal_stride=None):
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downsample = None
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if stride != 1 or self.inplanes != planes * block.expansion:
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ds_stride = conv_builder.get_downsample_stride(stride , temporal_stride)
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downsample = nn.Sequential(
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nn.Conv3d(self.inplanes, planes * block.expansion,
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kernel_size=1, stride=ds_stride, bias=False),
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batchnorm(planes * block.expansion)
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)
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stride = ds_stride
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layers = []
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layers.append(block(self.inplanes, planes, conv_builder, stride, downsample ))
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self.inplanes = planes * block.expansion
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for i in range(1, blocks):
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layers.append(block(self.inplanes, planes, conv_builder ))
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return nn.Sequential(*layers)
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def _initialize_weights(self):
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for m in self.modules():
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if isinstance(m, nn.Conv3d):
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nn.init.kaiming_normal_(m.weight, mode='fan_out',
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nonlinearity='relu')
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if m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.BatchNorm3d):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.Linear):
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nn.init.normal_(m.weight, 0, 0.01)
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nn.init.constant_(m.bias, 0)
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def _video_resnet(arch, pretrained=False, progress=True, **kwargs):
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model = VideoResNet(**kwargs)
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## TODO: Other 3D resnet models, like S3D, r(2+1)D.
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if pretrained:
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state_dict = load_state_dict_from_url(model_urls[arch],
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progress=progress)
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model.load_state_dict(state_dict)
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return model
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def unet_18(pretrained=False, bn=False, progress=True, **kwargs):
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"""
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Construct 18 layer Unet3D model as in
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https://arxiv.org/abs/1711.11248
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Args:
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pretrained (bool): If True, returns a model pre-trained on Kinetics-400
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progress (bool): If True, displays a progress bar of the download to stderr
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Returns:
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nn.Module: R3D-18 encoder
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"""
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global batchnorm
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if bn:
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batchnorm = nn.BatchNorm3d
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else:
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batchnorm = identity
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return _video_resnet('r3d_18',
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pretrained, progress,
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block=BasicBlock,
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conv_makers=[Conv3DSimple] * 4,
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layers=[2, 2, 2, 2],
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stem=BasicStem, **kwargs)
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def unet_34(pretrained=False, bn=False, progress=True, **kwargs):
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"""
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Construct 34 layer Unet3D model as in
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https://arxiv.org/abs/1711.11248
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Args:
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pretrained (bool): If True, returns a model pre-trained on Kinetics-400
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progress (bool): If True, displays a progress bar of the download to stderr
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Returns:
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nn.Module: R3D-18 encoder
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"""
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global batchnorm
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# bn = False
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if bn:
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batchnorm = nn.BatchNorm3d
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else:
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batchnorm = identity
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return _video_resnet('r3d_34',
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pretrained, progress,
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block=BasicBlock,
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conv_makers=[Conv3DSimple] * 4,
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layers=[3, 4, 6, 3],
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stem=BasicStem, **kwargs) |