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| """ "This module contains an implementation of ResNet model for video | |
| processing.""" | |
| from functools import partial | |
| import torch | |
| import torch.nn.functional as F | |
| from torch import nn | |
| def get_inplanes(): | |
| return [64, 128, 256, 512] | |
| def conv3x3x3(in_planes, out_planes, stride=1): | |
| return nn.Conv3d( | |
| in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False | |
| ) | |
| def conv1x1x1(in_planes, out_planes, stride=1): | |
| return nn.Conv3d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) | |
| class BasicBlock(nn.Module): | |
| expansion = 1 | |
| def __init__(self, in_planes, planes, stride=1, downsample=None): | |
| super().__init__() | |
| self.conv1 = conv3x3x3(in_planes, planes, stride) | |
| self.bn1 = nn.BatchNorm3d(planes) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.conv2 = conv3x3x3(planes, planes) | |
| self.bn2 = nn.BatchNorm3d(planes) | |
| self.downsample = downsample | |
| self.stride = stride | |
| def forward(self, x): | |
| residual = x | |
| out = self.conv1(x) | |
| out = self.bn1(out) | |
| out = self.relu(out) | |
| out = self.conv2(out) | |
| out = self.bn2(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, in_planes, planes, stride=1, downsample=None): | |
| super().__init__() | |
| self.conv1 = conv1x1x1(in_planes, planes) | |
| self.bn1 = nn.BatchNorm3d(planes) | |
| self.conv2 = conv3x3x3(planes, planes, stride) | |
| self.bn2 = nn.BatchNorm3d(planes) | |
| self.conv3 = conv1x1x1(planes, planes * self.expansion) | |
| self.bn3 = 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.bn1(out) | |
| out = self.relu(out) | |
| out = self.conv2(out) | |
| out = self.bn2(out) | |
| out = self.relu(out) | |
| out = self.conv3(out) | |
| out = self.bn3(out) | |
| if self.downsample is not None: | |
| residual = self.downsample(x) | |
| out += residual | |
| out = self.relu(out) | |
| return out | |
| class ResNet(nn.Module): | |
| def __init__( | |
| self, | |
| block, | |
| layers, | |
| block_inplanes, | |
| n_input_channels=3, | |
| conv1_t_size=7, | |
| conv1_t_stride=1, | |
| no_max_pool=False, | |
| shortcut_type="B", | |
| widen_factor=1.0, | |
| n_classes=1039, | |
| ): | |
| super().__init__() | |
| block_inplanes = [int(x * widen_factor) for x in block_inplanes] | |
| self.in_planes = block_inplanes[0] | |
| self.no_max_pool = no_max_pool | |
| self.conv1 = nn.Conv3d( | |
| n_input_channels, | |
| self.in_planes, | |
| kernel_size=(conv1_t_size, 7, 7), | |
| stride=(conv1_t_stride, 2, 2), | |
| padding=(conv1_t_size // 2, 3, 3), | |
| bias=False, | |
| ) | |
| self.bn1 = nn.BatchNorm3d(self.in_planes) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.maxpool = nn.MaxPool3d(kernel_size=3, stride=2, padding=1) | |
| self.layer1 = self._make_layer( | |
| block, block_inplanes[0], layers[0], shortcut_type | |
| ) | |
| self.layer2 = self._make_layer( | |
| block, block_inplanes[1], layers[1], shortcut_type, stride=2 | |
| ) | |
| self.layer3 = self._make_layer( | |
| block, block_inplanes[2], layers[2], shortcut_type, stride=2 | |
| ) | |
| self.layer4 = self._make_layer( | |
| block, block_inplanes[3], layers[3], shortcut_type, stride=2 | |
| ) | |
| self.avgpool = nn.AdaptiveAvgPool3d((1, 1, 1)) | |
| # self.fc = nn.Linear(block_inplanes[3] * block.expansion, n_classes) | |
| for m in self.modules(): | |
| if isinstance(m, nn.Conv3d): | |
| nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") | |
| elif isinstance(m, nn.BatchNorm3d): | |
| nn.init.constant_(m.weight, 1) | |
| nn.init.constant_(m.bias, 0) | |
| def _downsample_basic_block(self, x, planes, stride): | |
| out = F.avg_pool3d(x, kernel_size=1, stride=stride) | |
| zero_pads = torch.zeros( | |
| out.size(0), planes - out.size(1), out.size(2), out.size(3), out.size(4) | |
| ) | |
| if isinstance(out.data, torch.cuda.FloatTensor): | |
| zero_pads = zero_pads.cuda() | |
| out = torch.cat([out.data, zero_pads], dim=1) | |
| return out | |
| def _make_layer(self, block, planes, blocks, shortcut_type, stride=1): | |
| downsample = None | |
| if stride != 1 or self.in_planes != planes * block.expansion: | |
| if shortcut_type == "A": | |
| downsample = partial( | |
| self._downsample_basic_block, | |
| planes=planes * block.expansion, | |
| stride=stride, | |
| ) | |
| else: | |
| downsample = nn.Sequential( | |
| conv1x1x1(self.in_planes, planes * block.expansion, stride), | |
| nn.BatchNorm3d(planes * block.expansion), | |
| ) | |
| layers = [] | |
| layers.append( | |
| block( | |
| in_planes=self.in_planes, | |
| planes=planes, | |
| stride=stride, | |
| downsample=downsample, | |
| ) | |
| ) | |
| self.in_planes = planes * block.expansion | |
| for _ in range(1, blocks): | |
| layers.append(block(self.in_planes, planes)) | |
| return nn.Sequential(*layers) | |
| def forward(self, x): | |
| x = self.conv1(x) | |
| x = self.bn1(x) | |
| x = self.relu(x) | |
| if not self.no_max_pool: | |
| x = self.maxpool(x) | |
| x = self.layer1(x) | |
| x = self.layer2(x) | |
| x = self.layer3(x) | |
| x = self.layer4(x) | |
| x = self.avgpool(x) | |
| x = x.view(x.size(0), -1) | |
| # x = self.fc(x) | |
| return x | |
| def generate_model(model_depth, **kwargs): | |
| assert model_depth in [10, 18, 34, 50, 101, 152, 200] | |
| if model_depth == 10: | |
| model = ResNet(BasicBlock, [1, 1, 1, 1], get_inplanes(), **kwargs) | |
| elif model_depth == 18: | |
| model = ResNet(BasicBlock, [2, 2, 2, 2], get_inplanes(), **kwargs) | |
| elif model_depth == 34: | |
| model = ResNet(BasicBlock, [3, 4, 6, 3], get_inplanes(), **kwargs) | |
| elif model_depth == 50: | |
| model = ResNet(Bottleneck, [3, 4, 6, 3], get_inplanes(), **kwargs) | |
| elif model_depth == 101: | |
| model = ResNet(Bottleneck, [3, 4, 23, 3], get_inplanes(), **kwargs) | |
| elif model_depth == 152: | |
| model = ResNet(Bottleneck, [3, 8, 36, 3], get_inplanes(), **kwargs) | |
| elif model_depth == 200: | |
| model = ResNet(Bottleneck, [3, 24, 36, 3], get_inplanes(), **kwargs) | |
| return model | |