def get_upsampling_weight(self, in_channels, out_channels, kernel_size): Make a 2D bilinear kernel suitable for upsampling factor = (kernel_size 1) // 2 if kernel_size % 2 == 1: center = factor - 1 else: center = factor - 0.5 og = [:kernel_size, :kernel_size] filt = (1 - abs(og[0] - center) / factor) * (1 - abs(og[1] - center) / factor) weight = ((in_channels, out_channels, kernel_size, kernel_size), dtype=np.float64) weight[range(in_channels), range(out_channels), :, :] = filt return torch.from_numpy(weight).float()
a) Bilinear interpolation for image upsampling.
b) Construction of 2D convolutional filters.
c) Implementation of PyTorch upsampling layers.
d) Conversion of tensor data types in neural networks.