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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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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) |
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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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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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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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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) |
