Hi,

I tried to use convolution in Pytorch and torch7 (lua).

Operations with the same tensors (with float type) produce different results.

Python code:

```
import torch
import torch.nn as nn
torch.set_default_tensor_type('torch.FloatTensor')
def test_conv():
layer = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=True)
layer.weight.data.fill_(2.2)
layer.bias.data.fill_(1.2)
tensor = torch.zeros((1, 64, 256, 256))
tensor.fill_(1.3)
# print(tensor)
result = layer(tensor)
print("[test_conv] result: shape={ %s }, type='%s'\n" % (result.shape, result.type()) )
result_flatten = result.flatten()
i = 0
for n in result_flatten:
if i >= 30: break
number = n.item()
print("[%d] %f" % (i+1, number) )
i += 1
test_conv()
```

Result:

```
[1] 733.357605
[2] 1099.435791
[3] 1099.435791
...
[30] 1099.435791
```

Torch7 lua code:

```
require 'nn'
torch.setdefaulttensortype('torch.FloatTensor')
function test_conv()
local kernel_size = 3
local stride = 1
local padding = 1
local layer = nn.SpatialConvolutionMM(64, 64, kernel_size, kernel_size, stride, stride, padding, padding)
layer.weight:fill(2.2) -- fill weigths with 2.2
layer.bias:fill(1.2) -- fill weigths with 1.2
local tensor = torch.Tensor(1, 64, 256, 256)
tensor:fill(1.3) -- fill tesnor with 1.3
-- print(tensor)
local result = layer(tensor)
print(string.format("result: shape={ %s }, type='%s'\n", result:size(), result:type()) )
local result_flatten = result:view(result:nElement())
for i = 1, 30 do
print(string.format("[%d] %f", i, result_flatten[i]))
end
end
test_conv()
```

Result:

```
[1] 733.360107
[2] 1099.439697
[3] 1099.439697
....
[30] 1099.437012
```

Difference between results:

```
pytorch torch7
[1] 733.357605 [1] 733.360107
[2] 1099.435791 [2] 1099.439697
[3] 1099.435791 [3] 1099.439697
....
[30] 1099.435791 [30] 1099.437012
```

Maybe it was caused by different float-point arithmetic in Pytorch and torch7?

or should use a different convolution operator?