def forward()

in src/image_gen_aux/preprocessors/teed/teed.py [0:0]

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

    def forward(self, x, single_test=False):
        assert x.ndim == 4, x.shape
        # supose the image size is 352x352

        # Block 1
        block_1 = self.block_1(x)  # [8,16,176,176]
        block_1_side = self.side_1(block_1)  # 16 [8,32,88,88]

        # Block 2
        block_2 = self.block_2(block_1)  # 32 # [8,32,176,176]
        block_2_down = self.maxpool(block_2)  # [8,32,88,88]
        block_2_add = block_2_down + block_1_side  # [8,32,88,88]

        # Block 3
        block_3_pre_dense = self.pre_dense_3(block_2_down)  # [8,64,88,88] block 3 L connection
        block_3, _ = self.dblock_3([block_2_add, block_3_pre_dense])  # [8,64,88,88]

        # upsampling blocks
        out_1 = self.up_block_1(block_1)
        out_2 = self.up_block_2(block_2)
        out_3 = self.up_block_3(block_3)

        results = [out_1, out_2, out_3]

        # concatenate multiscale outputs
        block_cat = torch.cat(results, dim=1)  # Bx6xHxW
        block_cat = self.block_cat(block_cat)  # Bx1xHxW DoubleFusion

        results.append(block_cat)
        return results