Asymmetric Complement Trends

Question

Why did TensorFlow prefer padding to the bottom right?

WITH SAMEIMAGE, it would be logical for me to start snapping the center of the kernel to the first real pixel. Due to the use of asymmetric padding, this leads to a mismatch with some other structures. I understand that asymmetric padding is basically good, because otherwise you could leave an unused row / column of padding.

If TensorFlow would provide an advantage for filling left and top, it would make convolutions and weights the same as Caffe/cudnn/$frameworks, and weight conversion would be compatible regardless of filling.

The code:

import numpy as np
import tensorflow as tf
import torch
import torch.nn as nn

tf.enable_eager_execution()

def conv1d_tf(data, kernel_weights, stride):
    filters = np.reshape(kernel_weights, [len(kernel_weights), 1, 1])
    out = tf.nn.conv1d(
        value=data,
        filters=filters,
        stride=stride,
        padding='SAME',
        data_format='NCW',
        )
    return out


def conv1d_pytorch(data, kernel_weights, stride):
    filters = np.reshape(kernel_weights, [1, 1, len(kernel_weights)])
    kernel_size = len(kernel_weights)
    size = data.shape[-1]
    def same_padding(size, kernel_size, stride, dilation):
        padding = ((size - 1) * (stride - 1) + dilation * (kernel_size - 1)) //2
        return padding
    padding = same_padding(size=size, kernel_size=kernel_size, stride=stride, dilation=0)
    conv = nn.Conv1d(
        in_channels=1,
        out_channels=1,
        kernel_size=kernel_size,
        stride=stride,
        bias=False,
        padding=padding,
        )
    conv.weight = torch.nn.Parameter(torch.from_numpy(filters))
    return conv(torch.from_numpy(data))


data = np.array([[[1, 2, 3, 4]]], dtype=np.float32)
kernel_weights = np.array([0, 1], dtype=np.float32)
stride = 2

out_tf = conv1d_tf(data=data, kernel_weights=kernel_weights, stride=stride)
out_pytorch = conv1d_pytorch(data=data, kernel_weights=kernel_weights, stride=stride)

print('TensorFlow: %s' % out_tf)
print('pyTorch: %s' % out_pytorch)

Output:

TensorFlow: tf.Tensor([[[2. 4.]]], shape=(1, 1, 2), dtype=float32)
pyTorch: tensor([[[1., 3.]]], grad_fn=<SqueezeBackward1>)

Answer 1

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