Forward method for neural network layer processing

This code snippet defines the forward method of a neural network layer, where it concatenates input with a grid, applies convolutional layers, and iterates through scale blocks for further processing. It utilizes Torch for tensor operations, making it suitable for deep learning applications.

def forward(self, x):
        grid = self.get_grid(x.shape, x.device)
        x = torch.cat((x, grid), dim=1)
        # x = x.permute(0, 3, 1, 2)
        x = self.conv_begin_0(x)
        x = self.prelu_begin(x)
        x = self.conv_begin_1(x)
        x_0 = x
        x_s = x_0
        x_t = x_0
        

        pointer = 0
        for i in range(len(self.scales_per_block)):
            # print(i)
            if self.share_block[i]:
                for _ in range(self.num_per_block[i]):
                    # print("\t",pointer)
                    x = self.SConv2d_list[pointer](x) + self.w_list[pointer](x)
                    x = self.prelu_list[pointer](x)
                    x = x + x_t
                    x_t = x
                pointer += 1
            else:
                for _ in range(self.num_per_block[i]):
                    # print("\t",pointer)
                    x = self.SConv2d_list[pointer](x) + self.w_list[pointer](x)
                    x = self.prelu_list[pointer](x)
                    x = x + x_t
                    x_t = x
                    pointer += 1
            x = self.ssc_list[i](x)
            x = x + x_s
            x_s = x
            x_t = x_s


        x = self.lsc(x)
        x = x + x_0

        x = self.conv_end1(x)
        x = self.prelu_end(x)
        x = self.conv_end2(x)
        # x = x.permute(0, 2, 3, 1)
        return x

Editor is loading...