(Conv1D) Tensorflow and Jax Resulting Different Outputs for The Same Input

I am trying to use conv1d functions to make a transposed convlotion repectively at jax and tensorflow. I read the documentation of both of jax and tensorflow for the con1d_transposed operation but they are resulting with different outputs for the same input.

I can not find out what the problem is. And I don’t know which one produces the correct results. Help me please.

My Jax Implementation (Jax Code)

x = np.asarray([[[1, 2, 3, 4, -5], [1, 2, 3, 4, 5]]], dtype=np.float32).transpose((0, 2, 1))
filters = np.array([[[1, 0, -1], [-1,  0,  1]], 
                    [[1, 1,  1], [-1, -1, -1]]], 
                    dtype=np.float32).transpose((2, 1, 0))

kernel_rot = np.rot90(np.rot90(filters))

print(f"x strides:  {x.strides}nfilters strides: {kernel_rot.strides}nx shape: {x.shape}nfilters shape: {filters.shape}nx: n{x}nfilters: n{filters}n")

dn1 = lax.conv_dimension_numbers(x.shape, filters.shape,('NWC', 'WIO', 'NWC'))
print(dn1)

res = lax.conv_general_dilated(x,kernel_rot,(1,),'SAME',(1,),(1,),dn1)     

res = np.asarray(res)
print(f"result strides: {res.strides}nresult shape: {res.shape}nresult: n{res}n")

My TensorFlow Implementation (TensorFlow Code)

x = np.asarray([[[1, 2, 3, 4, -5], [1, 2, 3, 4, 5]]], dtype=np.float32).transpose((0, 2, 1))
filters = np.array([[[1, 0, -1], [-1,  0,  1]], 
                    [[1, 1,  1], [-1, -1, -1]]], 
                    dtype=np.float32).transpose((2, 1, 0))

print(f"x strides:  {x.strides}nfilters strides: {filters.strides}nx shape: {x.shape}nfilters shape: {filters.shape}nx: n{x}nfilters: n{filters}n")
    
res = tf.nn.conv1d_transpose(x, filters, output_shape = x.shape, strides = (1, 1, 1), padding = 'SAME', data_format='NWC', dilations=1)

res = np.asarray(res)
print(f"result strides: {res.strides}nresult shape: {res.shape}nresult: n{res}n")

Output from the Jax

result strides: (40, 8, 4)
result shape: (1, 5, 2)
result: 
[[[ 0.  0.]
  [ 0.  0.]
  [ 0.  0.]
  [10. 10.]
  [ 0. 10.]]]

Output from the TensorFlow

result strides: (40, 8, 4)
result shape: (1, 5, 2)
result: 
[[[  5.  -5.]
  [  8.  -8.]
  [ 11. -11.]
  [  4.  -4.]
  [  5.  -5.]]]

Answer

Function conv1d_transpose expects filters in shape [filter_width, output_channels, in_channels]. If filters in snippet above were transposed to satisfy this shape, then for jax to return correct results, while computing dn1 parameter should be WOI (Width – Output_channels – Input_channels) and not WIO (Width – Input_channels – Output_channels). After that:

result.strides = (40, 8, 4)
result.shape = (1, 5, 2)
result: 
[[[ -5.,   5.],
  [ -8.,   8.],
  [-11.,  11.],
  [ -4.,   4.],
  [ -5.,   5.]]]

Results not same as with tensorflow, but kernels for jax were flipped, so actually that was expected.