https://github.com/szagoruyko/diracnets
Tip revision: 3c6f9863e983fdd56db2372617d9fd0d2c838125 authored by Sergey Zagoruyko on 09 June 2018, 17:46:56 UTC
Merge pull request #19 from szagoruyko/pytorch0.4
Merge pull request #19 from szagoruyko/pytorch0.4
Tip revision: 3c6f986
test.py
import unittest
import torch
from diracconv import DiracConv1d, DiracConv2d, DiracConv3d
from torch.autograd import Variable
from diracnet import define_diracnet
class TestDirac(unittest.TestCase):
def test_dirac_property1d(self):
ni, no, k, pad = 4, 4, 3, 1
module = DiracConv1d(in_channels=ni, out_channels=no, kernel_size=k, padding=pad, bias=False)
module.alpha.data.fill_(1)
module.beta.data.fill_(0)
x = Variable(torch.randn(4, ni, 5))
y = module(x)
self.assertEqual(y.size(), x.size(), 'shape check')
self.assertEqual((y - x).data.abs().sum(), 0, 'dirac delta property check')
def test_dirac_property2d(self):
ni, no, k, pad = 4, 4, 3, 1
module = DiracConv2d(in_channels=ni, out_channels=no, kernel_size=k, padding=pad, bias=False)
module.alpha.data.fill_(1)
module.beta.data.fill_(0)
x = Variable(torch.randn(4, ni, 5, 5))
y = module(x)
self.assertEqual(y.size(), x.size(), 'shape check')
self.assertEqual((y - x).data.abs().sum(), 0, 'dirac delta property check')
def test_dirac_property3d(self):
ni, no, k, pad = 4, 4, 3, 1
module = DiracConv3d(in_channels=ni, out_channels=no, kernel_size=k, padding=pad, bias=False)
module.alpha.data.fill_(1)
module.beta.data.fill_(0)
x = Variable(torch.randn(4, ni, 5, 5, 5))
y = module(x)
self.assertEqual(y.size(), x.size(), 'shape check')
self.assertEqual((y - x).data.abs().sum(), 0, 'dirac delta property check')
def test_nonsquare(self):
ni, no, k, pad = 8, 4, 3, 1
module = DiracConv2d(in_channels=ni, out_channels=no, kernel_size=k, padding=pad, bias=False)
x = Variable(torch.randn(4, ni, 5, 5))
y = module(x)
def test_cifar10(self):
inputs = Variable(torch.randn(1,3,32,32))
f, params, stats = define_diracnet(34, 1, 'CIFAR10')
outputs = f(inputs, params, stats, mode=False)
self.assertEqual(outputs.size(), torch.Size((1, 10)))
def test_imagenet(self):
inputs = Variable(torch.randn(1,3,224,224))
f, params, stats = define_diracnet(18, 1, 'ImageNet')
outputs = f(inputs, params, stats, mode=False)
self.assertEqual(outputs.size(), torch.Size((1, 1000)))
if __name__ == '__main__':
unittest.main()