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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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from torch.autograd import Variable |
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import math |
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from functools import partial |
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__all__ = ['ResNeXt', 'resnet50', 'resnet101'] |
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def conv3x3x3(in_planes, out_planes, stride=1): |
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return nn.Conv3d(in_planes, out_planes, kernel_size=3, |
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stride=stride, padding=1, bias=False) |
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def downsample_basic_block(x, planes, stride): |
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out = F.avg_pool3d(x, kernel_size=1, stride=stride) |
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zero_pads = torch.Tensor(out.size(0), planes - out.size(1), |
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out.size(2), out.size(3), |
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out.size(4)).zero_() |
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if isinstance(out.data, torch.cuda.FloatTensor): |
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zero_pads = zero_pads.cuda() |
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out = Variable(torch.cat([out.data, zero_pads], dim=1)) |
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return out |
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class ResNeXtBottleneck(nn.Module): |
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expansion = 2 |
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def __init__(self, inplanes, planes, cardinality, stride=1, downsample=None, norm_layer=nn.BatchNorm3d): |
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super(ResNeXtBottleneck, self).__init__() |
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mid_planes = cardinality * int(planes / 32) |
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self.conv1 = nn.Conv3d(inplanes, mid_planes, kernel_size=1, bias=False) |
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self.bn1 = norm_layer(mid_planes) |
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self.conv2 = nn.Conv3d(mid_planes, mid_planes, kernel_size=3, stride=stride, |
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padding=1, groups=cardinality, bias=False) |
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self.bn2 = norm_layer(mid_planes) |
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self.conv3 = nn.Conv3d(mid_planes, planes * self.expansion, kernel_size=1, bias=False) |
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self.bn3 = norm_layer(planes * self.expansion) |
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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.bn1(out) |
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out = self.relu(out) |
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out = self.conv2(out) |
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out = self.bn2(out) |
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out = self.relu(out) |
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out = self.conv3(out) |
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out = self.bn3(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 ResNeXt3D(nn.Module): |
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def __init__(self, block, layers, sample_size=16, sample_duration=112, shortcut_type='B', cardinality=32, num_classes=400, last_fc=True, norm_layer=None): |
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self.last_fc = last_fc |
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self.inplanes = 64 |
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super(ResNeXt3D, self).__init__() |
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self.conv1 = nn.Conv3d(3, 64, kernel_size=7, stride=(1, 2, 2), |
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padding=(3, 3, 3), bias=False) |
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if norm_layer is None: |
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norm_layer = nn.BatchNorm3d |
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print("use bn:", norm_layer) |
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self.bn1 = norm_layer(64) |
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self.relu = nn.ReLU(inplace=True) |
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self.maxpool = nn.MaxPool3d(kernel_size=(3, 3, 3), stride=2, padding=1) |
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self.layer1 = self._make_layer(block, 128, layers[0], shortcut_type, cardinality, norm_layer=norm_layer) |
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self.layer2 = self._make_layer(block, 256, layers[1], shortcut_type, cardinality, stride=2, norm_layer=norm_layer) |
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self.layer3 = self._make_layer(block, 512, layers[2], shortcut_type, cardinality, stride=2, norm_layer=norm_layer) |
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if len(layers) > 3: |
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self.layer4 = self._make_layer(block, 1024, layers[3], shortcut_type, cardinality, stride=2, norm_layer=norm_layer) |
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self.all_layers = True |
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else: |
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self.all_layers = False |
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last_duration = math.ceil(sample_duration / 16) |
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last_size = math.ceil(sample_size / 32) |
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self.avgpool = nn.AvgPool3d((last_duration, last_size, last_size), stride=1) |
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for m in self.modules(): |
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if isinstance(m, nn.Conv3d): |
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n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels |
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m.weight.data.normal_(0, math.sqrt(2. / n)) |
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elif isinstance(m, norm_layer): |
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m.weight.data.fill_(1) |
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m.bias.data.zero_() |
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def _make_layer(self, block, planes, blocks, shortcut_type, cardinality, stride=1, norm_layer=nn.BatchNorm3d): |
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downsample = None |
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if stride != 1 or self.inplanes != planes * block.expansion: |
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if shortcut_type == 'A': |
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downsample = partial(downsample_basic_block, |
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planes=planes * block.expansion, |
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stride=stride) |
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else: |
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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=stride, bias=False), |
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norm_layer(planes * block.expansion) |
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) |
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layers = [] |
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layers.append(block(self.inplanes, planes, cardinality, stride, downsample, norm_layer=norm_layer)) |
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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, cardinality, norm_layer=norm_layer)) |
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return nn.Sequential(*layers) |
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def forward(self, x): |
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x = self.conv1(x) |
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x = self.bn1(x) |
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x = self.relu(x) |
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x = self.maxpool(x) |
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x = self.layer1(x) |
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x = self.layer2(x) |
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x = self.layer3(x) |
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if self.all_layers: |
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x = self.layer4(x) |
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return x, x |
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def get_fine_tuning_parameters(model, ft_begin_index): |
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if ft_begin_index == 0: |
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return model.parameters() |
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ft_module_names = [] |
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for i in range(ft_begin_index, 5): |
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ft_module_names.append('layer{}'.format(ft_begin_index)) |
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ft_module_names.append('fc') |
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parameters = [] |
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for k, v in model.named_parameters(): |
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for ft_module in ft_module_names: |
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if ft_module in k: |
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parameters.append({'params': v}) |
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break |
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else: |
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parameters.append({'params': v, 'lr': 0.0}) |
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return parameters |
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def resnet50(**kwargs): |
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"""Constructs a ResNet-50 model. |
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""" |
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model = ResNeXt3D(ResNeXtBottleneck, [3, 4, 6, 3], **kwargs) |
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return model |
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def resnet101(**kwargs): |
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"""Constructs a ResNet-101 model. |
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""" |
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model = ResNeXt3D(ResNeXtBottleneck, [3, 4, 23, 3], **kwargs) |
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return model |
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def resnet152(**kwargs): |
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"""Constructs a ResNet-101 model. |
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""" |
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model = ResNeXt3D(ResNeXtBottleneck, [3, 8, 36, 3], **kwargs) |
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return model |
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