Convolution test - torch

conv_test.lua

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()

conv_test.py

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()