resnet with multigrid

resnet.py

class ResNet(nn.Module):
    def __init__(self,
                 block,
                 layers,
                 num_classes=1000,
                 fully_conv=False,
                 remove_avg_pool_layer=False,
                 output_stride=32,
                 additional_blocks=0,
                 multi_grid=(1,1,1) ):
        # Add additional variables to track
        # output stride. Necessary to achieve
        # specified output stride.
        self.output_stride = output_stride
        self.current_stride = 4
        self.current_dilation = 1
        self.remove_avg_pool_layer = remove_avg_pool_layer
        self.inplanes = 64
        self.fully_conv = fully_conv
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2, multi_grid=multi_grid)
        self.additional_blocks = additional_blocks
        if additional_blocks == 1:
            self.layer5 = self._make_layer(block, 512, layers[3], stride=2, multi_grid=multi_grid)
        if additional_blocks == 2:
            self.layer5 = self._make_layer(block, 512, layers[3], stride=2, multi_grid=multi_grid)
            self.layer6 = self._make_layer(block, 512, layers[3], stride=2, multi_grid=multi_grid)
        if additional_blocks == 3:
            self.layer5 = self._make_layer(block, 512, layers[3], stride=2, multi_grid=multi_grid)
            self.layer6 = self._make_layer(block, 512, layers[3], stride=2, multi_grid=multi_grid)
            self.layer7 = self._make_layer(block, 512, layers[3], stride=2, multi_grid=multi_grid)
        self.avgpool = nn.AvgPool2d(7)
        self.fc = nn.Linear(512 * block.expansion, num_classes)
        if self.fully_conv:
            self.avgpool = nn.AvgPool2d(7, padding=3, stride=1)
            # In the latest unstable torch 4.0 the tensor.copy_
            # method was changed and doesn't work as it used to be
            #self.fc = nn.Conv2d(512 * block.expansion, num_classes, 1)
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
    def _make_layer(self,
                    block,
                    planes,
                    blocks,
                    stride=1,
                    multi_grid=None):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            # Check if we already achieved desired output stride.
            if self.current_stride == self.output_stride:
                # If so, replace subsampling with a dilation to preserve
                # current spatial resolution.
                self.current_dilation = self.current_dilation * stride
                stride = 1
            else:
                # If not, perform subsampling and update current
                # new output stride.
                self.current_stride = self.current_stride * stride
            # We don't dilate 1x1 convolution.
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )
        layers = []
        dilation = multi_grid[0] * self.current_dilation if multi_grid else self.current_dilation
        layers.append(block(self.inplanes, planes, stride, downsample, dilation=dilation))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            dilation = multi_grid[i] * self.current_dilation if multi_grid else self.current_dilation
            layers.append(block(self.inplanes, planes, dilation=dilation))
        return nn.Sequential(*layers)
    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        if self.additional_blocks == 1:
            x = self.layer5(x)
        if self.additional_blocks == 2:
            x = self.layer5(x)
            x = self.layer6(x)
        if self.additional_blocks == 3:
            x = self.layer5(x)
            x = self.layer6(x)
            x = self.layer7(x)
        if not self.remove_avg_pool_layer:
            x = self.avgpool(x)
        if not self.fully_conv:
            x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x