Tensorflow vs PyTorch：卷积不起作用

Question

我正在尝试测试 Tensorflow 卷积输出是否与具有相同权重的 PyTorch 卷积输出匹配。

这是我将权重从 Tensorflow 复制到 Torch、卷积和比较输出的代码：

import tensorflow as tf
import numpy as np
import math
from math import floor, ceil
import os
import math
import datetime
from scipy import misc
from PIL import Image
import model
import torch
from torch import nn
import common
import torch.nn.functional as F


sess = tf.Session()
np.random.seed(1)
tf.set_random_seed(1)

#parameters
kernel_size = 3
input_feat = 4
output_feat = 4

#inputs
npo = np.random.random((1,5,5, input_feat))
x = tf.convert_to_tensor(npo, tf.float32)
x2 = torch.tensor(np.transpose(npo, [0, 3, 1, 2])).double()

#the same weights
weights = np.random.random((kernel_size,kernel_size,input_feat,output_feat))
weights_torch = np.transpose(weights, [3, 2, 1, 0])

#convolving with tensorflow
w = tf.Variable(weights, name="testconv_W", dtype=tf.float32)
res = tf.nn.conv2d(x, w, strides=[1, 1, 1, 1], padding="VALID")

sess.run(tf.global_variables_initializer())


#convolving with torch
torchres = F.conv2d(x2, torch.tensor(weights_torch), padding=0, bias=torch.zeros((output_feat)).double())

#comparing the results
print(np.mean(np.transpose(sess.run(res), [0, 3, 1, 2])) - torch.mean(torchres).detach().numpy())

输出

0.15440369065716908

为什么？为什么会有这么大的差异？ Tensorflow conv2d 实现不正确吗？为什么它不匹配 PyTorch？难道我做错了什么？在内核大小 1 上一切正常。 请帮忙。

Answer 1

你可以试试x2 = torch.tensor(np.transpose(npo, [0, 3, 2, 1])).double() 而不是x2 = torch.tensor(np.transpose(npo, [0, 3, 1, 2])).double()