Tensorflow的非对称填充假设

为什么 TensorFlow 选择在右下角填充？

With SAME填充，对我来说，在第一个真实像素处启动内核的中心锚点是合乎逻辑的。由于使用了不对称填充，这导致与其他一些框架存在差异。我确实明白，原则上不对称填充是好的，因为否则会留下未使用的填充行/列。

如果 TensorFlow 在左侧和顶部提供填充，它将执行与以下相同的卷积和权重Caffe/cudnn/$frameworks，并且无论填充如何，重量转换都将兼容。

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>)

这是出于与以前（非公开）框架的历史兼容性原因。不幸的是，定义并不清晰，因为它是在不同库之间移植时的常见障碍。