Function apply_convs

utils/resize_right/resize_right.py:251–277

(input, scale_factor, in_sz, out_sz, weights, dim, pad_sz,
                pad_mode, fw)

def apply_convs(input, scale_factor, in_sz, out_sz, weights, dim, pad_sz,
                pad_mode, fw):
    # for this operations we assume the resized dim is the last one.
    # so we transpose and will transpose back after multiplying
    input = fw_swapaxes(input, dim, -1, fw)

    # the stride for all convs is the denominator of the scale factor
    stride, num_convs = scale_factor.denominator, scale_factor.numerator

    # prepare an empty tensor for the output
    tmp_out_shape = list(input.shape)
    tmp_out_shape[-1] = out_sz
    tmp_output = fw_empty(tuple(tmp_out_shape), fw, input.device)

    # iterate over the conv operations. we have as many as the numerator
    # of the scale-factor. for each we need boundaries and a filter.
    for conv_ind, (pad_sz, filt) in enumerate(zip(pad_sz, weights)):
        # apply padding (we pad last dim, padding can be negative)
        pad_dim = input.ndim - 1
        tmp_input = fw_pad(input, fw, pad_sz, pad_mode, dim=pad_dim)

        # apply convolution over last dim. store in the output tensor with
        # positional strides so that when the loop is comlete conv results are
        # interwind
        tmp_output[..., conv_ind::num_convs] = fw_conv(tmp_input, filt, stride)

    return fw_swapaxes(tmp_output, -1, dim, fw)


def set_scale_and_out_sz(in_shape, out_shape, scale_factors, by_convs,