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Running
on
Zero
| import torch.nn as nn | |
| from torch.hub import load_state_dict_from_url | |
| __all__ = ['r3d_18', 'mc3_18', 'r2plus1d_18'] | |
| model_urls = { | |
| 'r3d_18': 'https://download.pytorch.org/models/r3d_18-b3b3357e.pth', | |
| 'mc3_18': 'https://download.pytorch.org/models/mc3_18-a90a0ba3.pth', | |
| 'r2plus1d_18': 'https://download.pytorch.org/models/r2plus1d_18-91a641e6.pth', | |
| } | |
| class Conv3DSimple(nn.Conv3d): | |
| def __init__(self, | |
| in_planes, | |
| out_planes, | |
| midplanes=None, | |
| stride=1, | |
| padding=1): | |
| super(Conv3DSimple, self).__init__( | |
| in_channels=in_planes, | |
| out_channels=out_planes, | |
| kernel_size=(3, 3, 3), | |
| stride=stride, | |
| padding=padding, | |
| bias=False) | |
| def get_downsample_stride(stride): | |
| return stride, stride, stride | |
| class Conv2Plus1D(nn.Sequential): | |
| def __init__(self, | |
| in_planes, | |
| out_planes, | |
| midplanes, | |
| stride=1, | |
| padding=1): | |
| super(Conv2Plus1D, self).__init__( | |
| nn.Conv3d(in_planes, midplanes, kernel_size=(1, 3, 3), | |
| stride=(1, stride, stride), padding=(0, padding, padding), | |
| bias=False), | |
| nn.BatchNorm3d(midplanes), | |
| nn.ReLU(inplace=True), | |
| nn.Conv3d(midplanes, out_planes, kernel_size=(3, 1, 1), | |
| stride=(stride, 1, 1), padding=(padding, 0, 0), | |
| bias=False)) | |
| def get_downsample_stride(stride): | |
| return stride, stride, stride | |
| class Conv3DNoTemporal(nn.Conv3d): | |
| def __init__(self, | |
| in_planes, | |
| out_planes, | |
| midplanes=None, | |
| stride=1, | |
| padding=1): | |
| super(Conv3DNoTemporal, self).__init__( | |
| in_channels=in_planes, | |
| out_channels=out_planes, | |
| kernel_size=(1, 3, 3), | |
| stride=(1, stride, stride), | |
| padding=(0, padding, padding), | |
| bias=False) | |
| def get_downsample_stride(stride): | |
| return 1, stride, stride | |
| class BasicBlock(nn.Module): | |
| expansion = 1 | |
| def __init__(self, inplanes, planes, conv_builder, stride=1, downsample=None): | |
| midplanes = (inplanes * planes * 3 * 3 * | |
| 3) // (inplanes * 3 * 3 + 3 * planes) | |
| super(BasicBlock, self).__init__() | |
| self.conv1 = nn.Sequential( | |
| conv_builder(inplanes, planes, midplanes, stride), | |
| nn.BatchNorm3d(planes), | |
| nn.ReLU(inplace=True) | |
| ) | |
| self.conv2 = nn.Sequential( | |
| conv_builder(planes, planes, midplanes), | |
| nn.BatchNorm3d(planes) | |
| ) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.downsample = downsample | |
| self.stride = stride | |
| def forward(self, x): | |
| residual = x | |
| out = self.conv1(x) | |
| out = self.conv2(out) | |
| if self.downsample is not None: | |
| residual = self.downsample(x) | |
| out += residual | |
| out = self.relu(out) | |
| return out | |
| class Bottleneck(nn.Module): | |
| expansion = 4 | |
| def __init__(self, inplanes, planes, conv_builder, stride=1, downsample=None): | |
| super(Bottleneck, self).__init__() | |
| midplanes = (inplanes * planes * 3 * 3 * | |
| 3) // (inplanes * 3 * 3 + 3 * planes) | |
| # 1x1x1 | |
| self.conv1 = nn.Sequential( | |
| nn.Conv3d(inplanes, planes, kernel_size=1, bias=False), | |
| nn.BatchNorm3d(planes), | |
| nn.ReLU(inplace=True) | |
| ) | |
| # Second kernel | |
| self.conv2 = nn.Sequential( | |
| conv_builder(planes, planes, midplanes, stride), | |
| nn.BatchNorm3d(planes), | |
| nn.ReLU(inplace=True) | |
| ) | |
| # 1x1x1 | |
| self.conv3 = nn.Sequential( | |
| nn.Conv3d(planes, planes * self.expansion, | |
| kernel_size=1, bias=False), | |
| nn.BatchNorm3d(planes * self.expansion) | |
| ) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.downsample = downsample | |
| self.stride = stride | |
| def forward(self, x): | |
| residual = x | |
| out = self.conv1(x) | |
| out = self.conv2(out) | |
| out = self.conv3(out) | |
| if self.downsample is not None: | |
| residual = self.downsample(x) | |
| out += residual | |
| out = self.relu(out) | |
| return out | |
| class BasicStem(nn.Sequential): | |
| """The default conv-batchnorm-relu stem | |
| """ | |
| def __init__(self): | |
| super(BasicStem, self).__init__( | |
| nn.Conv3d(3, 64, kernel_size=(3, 7, 7), stride=(1, 2, 2), | |
| padding=(1, 3, 3), bias=False), | |
| nn.BatchNorm3d(64), | |
| nn.ReLU(inplace=True)) | |
| class R2Plus1dStem(nn.Sequential): | |
| """R(2+1)D stem is different than the default one as it uses separated 3D convolution | |
| """ | |
| def __init__(self): | |
| super(R2Plus1dStem, self).__init__( | |
| nn.Conv3d(3, 45, kernel_size=(1, 7, 7), | |
| stride=(1, 2, 2), padding=(0, 3, 3), | |
| bias=False), | |
| nn.BatchNorm3d(45), | |
| nn.ReLU(inplace=True), | |
| nn.Conv3d(45, 64, kernel_size=(3, 1, 1), | |
| stride=(1, 1, 1), padding=(1, 0, 0), | |
| bias=False), | |
| nn.BatchNorm3d(64), | |
| nn.ReLU(inplace=True)) | |
| class VideoResNet(nn.Module): | |
| def __init__(self, block, conv_makers, layers, | |
| stem, num_classes=400, | |
| zero_init_residual=False): | |
| """Generic resnet video generator. | |
| Args: | |
| block (nn.Module): resnet building block | |
| conv_makers (list(functions)): generator function for each layer | |
| layers (List[int]): number of blocks per layer | |
| stem (nn.Module, optional): Resnet stem, if None, defaults to conv-bn-relu. Defaults to None. | |
| num_classes (int, optional): Dimension of the final FC layer. Defaults to 400. | |
| zero_init_residual (bool, optional): Zero init bottleneck residual BN. Defaults to False. | |
| """ | |
| super(VideoResNet, self).__init__() | |
| self.inplanes = 64 | |
| self.stem = stem() | |
| self.layer1 = self._make_layer( | |
| block, conv_makers[0], 64, layers[0], stride=1) | |
| self.layer2 = self._make_layer( | |
| block, conv_makers[1], 128, layers[1], stride=2) | |
| self.layer3 = self._make_layer( | |
| block, conv_makers[2], 256, layers[2], stride=2) | |
| self.layer4 = self._make_layer( | |
| block, conv_makers[3], 512, layers[3], stride=2) | |
| self.avgpool = nn.AdaptiveAvgPool3d((1, 1, 1)) | |
| self.fc = nn.Linear(512 * block.expansion, num_classes) | |
| # init weights | |
| self._initialize_weights() | |
| if zero_init_residual: | |
| for m in self.modules(): | |
| if isinstance(m, Bottleneck): | |
| nn.init.constant_(m.bn3.weight, 0) | |
| def forward(self, x): | |
| x = self.stem(x) | |
| x = self.layer1(x) | |
| x = self.layer2(x) | |
| x = self.layer3(x) | |
| x = self.layer4(x) | |
| x = self.avgpool(x) | |
| # Flatten the layer to fc | |
| # x = x.flatten(1) | |
| # x = self.fc(x) | |
| N = x.shape[0] | |
| x = x.squeeze() | |
| if N == 1: | |
| x = x[None] | |
| return x | |
| def _make_layer(self, block, conv_builder, planes, blocks, stride=1): | |
| downsample = None | |
| if stride != 1 or self.inplanes != planes * block.expansion: | |
| ds_stride = conv_builder.get_downsample_stride(stride) | |
| downsample = nn.Sequential( | |
| nn.Conv3d(self.inplanes, planes * block.expansion, | |
| kernel_size=1, stride=ds_stride, bias=False), | |
| nn.BatchNorm3d(planes * block.expansion) | |
| ) | |
| layers = [] | |
| layers.append(block(self.inplanes, planes, | |
| conv_builder, stride, downsample)) | |
| self.inplanes = planes * block.expansion | |
| for i in range(1, blocks): | |
| layers.append(block(self.inplanes, planes, conv_builder)) | |
| return nn.Sequential(*layers) | |
| def _initialize_weights(self): | |
| for m in self.modules(): | |
| if isinstance(m, nn.Conv3d): | |
| nn.init.kaiming_normal_(m.weight, mode='fan_out', | |
| nonlinearity='relu') | |
| if m.bias is not None: | |
| nn.init.constant_(m.bias, 0) | |
| elif isinstance(m, nn.BatchNorm3d): | |
| nn.init.constant_(m.weight, 1) | |
| nn.init.constant_(m.bias, 0) | |
| elif isinstance(m, nn.Linear): | |
| nn.init.normal_(m.weight, 0, 0.01) | |
| nn.init.constant_(m.bias, 0) | |
| def _video_resnet(arch, pretrained=False, progress=True, **kwargs): | |
| model = VideoResNet(**kwargs) | |
| if pretrained: | |
| state_dict = load_state_dict_from_url(model_urls[arch], | |
| progress=progress) | |
| model.load_state_dict(state_dict) | |
| return model | |
| def r3d_18(pretrained=False, progress=True, **kwargs): | |
| """Construct 18 layer Resnet3D model as in | |
| https://arxiv.org/abs/1711.11248 | |
| Args: | |
| pretrained (bool): If True, returns a model pre-trained on Kinetics-400 | |
| progress (bool): If True, displays a progress bar of the download to stderr | |
| Returns: | |
| nn.Module: R3D-18 network | |
| """ | |
| return _video_resnet('r3d_18', | |
| pretrained, progress, | |
| block=BasicBlock, | |
| conv_makers=[Conv3DSimple] * 4, | |
| layers=[2, 2, 2, 2], | |
| stem=BasicStem, **kwargs) | |
| def mc3_18(pretrained=False, progress=True, **kwargs): | |
| """Constructor for 18 layer Mixed Convolution network as in | |
| https://arxiv.org/abs/1711.11248 | |
| Args: | |
| pretrained (bool): If True, returns a model pre-trained on Kinetics-400 | |
| progress (bool): If True, displays a progress bar of the download to stderr | |
| Returns: | |
| nn.Module: MC3 Network definition | |
| """ | |
| return _video_resnet('mc3_18', | |
| pretrained, progress, | |
| block=BasicBlock, | |
| conv_makers=[Conv3DSimple] + [Conv3DNoTemporal] * 3, | |
| layers=[2, 2, 2, 2], | |
| stem=BasicStem, **kwargs) | |
| def r2plus1d_18(pretrained=False, progress=True, **kwargs): | |
| """Constructor for the 18 layer deep R(2+1)D network as in | |
| https://arxiv.org/abs/1711.11248 | |
| Args: | |
| pretrained (bool): If True, returns a model pre-trained on Kinetics-400 | |
| progress (bool): If True, displays a progress bar of the download to stderr | |
| Returns: | |
| nn.Module: R(2+1)D-18 network | |
| """ | |
| return _video_resnet('r2plus1d_18', | |
| pretrained, progress, | |
| block=BasicBlock, | |
| conv_makers=[Conv2Plus1D] * 4, | |
| layers=[2, 2, 2, 2], | |
| stem=R2Plus1dStem, **kwargs) |