def forward()

in src/controlnet_aux/open_pose/face.py [0:0]

• 70 lines of code
• 1 McCabe index (conditional complexity)

    def forward(self, x):
        """Return a list of heatmaps."""
        heatmaps = []

        h = self.relu(self.conv1_1(x))
        h = self.relu(self.conv1_2(h))
        h = self.max_pooling_2d(h)
        h = self.relu(self.conv2_1(h))
        h = self.relu(self.conv2_2(h))
        h = self.max_pooling_2d(h)
        h = self.relu(self.conv3_1(h))
        h = self.relu(self.conv3_2(h))
        h = self.relu(self.conv3_3(h))
        h = self.relu(self.conv3_4(h))
        h = self.max_pooling_2d(h)
        h = self.relu(self.conv4_1(h))
        h = self.relu(self.conv4_2(h))
        h = self.relu(self.conv4_3(h))
        h = self.relu(self.conv4_4(h))
        h = self.relu(self.conv5_1(h))
        h = self.relu(self.conv5_2(h))
        h = self.relu(self.conv5_3_CPM(h))
        feature_map = h

        # stage1
        h = self.relu(self.conv6_1_CPM(h))
        h = self.conv6_2_CPM(h)
        heatmaps.append(h)

        # stage2
        h = torch.cat([h, feature_map], dim=1)  # channel concat
        h = self.relu(self.Mconv1_stage2(h))
        h = self.relu(self.Mconv2_stage2(h))
        h = self.relu(self.Mconv3_stage2(h))
        h = self.relu(self.Mconv4_stage2(h))
        h = self.relu(self.Mconv5_stage2(h))
        h = self.relu(self.Mconv6_stage2(h))
        h = self.Mconv7_stage2(h)
        heatmaps.append(h)

        # stage3
        h = torch.cat([h, feature_map], dim=1)  # channel concat
        h = self.relu(self.Mconv1_stage3(h))
        h = self.relu(self.Mconv2_stage3(h))
        h = self.relu(self.Mconv3_stage3(h))
        h = self.relu(self.Mconv4_stage3(h))
        h = self.relu(self.Mconv5_stage3(h))
        h = self.relu(self.Mconv6_stage3(h))
        h = self.Mconv7_stage3(h)
        heatmaps.append(h)

        # stage4
        h = torch.cat([h, feature_map], dim=1)  # channel concat
        h = self.relu(self.Mconv1_stage4(h))
        h = self.relu(self.Mconv2_stage4(h))
        h = self.relu(self.Mconv3_stage4(h))
        h = self.relu(self.Mconv4_stage4(h))
        h = self.relu(self.Mconv5_stage4(h))
        h = self.relu(self.Mconv6_stage4(h))
        h = self.Mconv7_stage4(h)
        heatmaps.append(h)

        # stage5
        h = torch.cat([h, feature_map], dim=1)  # channel concat
        h = self.relu(self.Mconv1_stage5(h))
        h = self.relu(self.Mconv2_stage5(h))
        h = self.relu(self.Mconv3_stage5(h))
        h = self.relu(self.Mconv4_stage5(h))
        h = self.relu(self.Mconv5_stage5(h))
        h = self.relu(self.Mconv6_stage5(h))
        h = self.Mconv7_stage5(h)
        heatmaps.append(h)

        # stage6
        h = torch.cat([h, feature_map], dim=1)  # channel concat
        h = self.relu(self.Mconv1_stage6(h))
        h = self.relu(self.Mconv2_stage6(h))
        h = self.relu(self.Mconv3_stage6(h))
        h = self.relu(self.Mconv4_stage6(h))
        h = self.relu(self.Mconv5_stage6(h))
        h = self.relu(self.Mconv6_stage6(h))
        h = self.Mconv7_stage6(h)
        heatmaps.append(h)

        return heatmaps