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.

unknown

python

13 days ago

1.4 kB

Indexable

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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