I’m also interested in that topic. I think by combining asymmetric padding and conv2D, one can mimic ‘SAME’ in tensorflow for  tflearn.layers.conv.conv_2d and  tflearn.layers.conv.conv_2d_transpose of stride 1. But I couldn’t find a way to translate   tflearn.layers.conv.conv_2d_transpose with asymmetric padding and stride > 1.
After looking at “Padding, strides, transposed (odd)” from https://github.com/vdumoulin/conv_arithmetic 128, I’m convinced that you can’t reproduce   tflearn.layers.conv.conv_2d_transpose with asymmetric padding and stride > 1 using a cascade of  asymmetric padding and convolutions in pytorch.
Anyone has a solution for this ?
Toy exemple :
Trying to translate to pytorch : tflearn.layers.conv.conv_2d_transpose(x, nb_filter = 1, filter_size = [5, 5] , output_shape = [6,8], strides = 2, activation = 'linear')  with x.size = (1, 1, 3, 4) = (BATCH, INPUT_FILTERS, HEIGHT, WIDTH).