def test_concatdownsample2d(self):
num_batches = 2
num_chan = 4
scale_factor = 2
x = torch.arange(num_batches * num_chan * 4**2).view(
num_batches, num_chan, 4, 4)
# Test functional API
self.assertRaises(AssertionError, F.concat_downsample2d, x, 3)
out = F.concat_downsample2d(x, scale_factor)
self.assertEqual(
out.shape,
(num_batches, num_chan * scale_factor**2,
x.shape[2] // scale_factor, x.shape[3] // scale_factor))
# Check first and last values
self.assertTrue(torch.equal(out[0][0], torch.tensor([[0, 2], [8,
10]])))
self.assertTrue(
torch.equal(out[0][-num_chan], torch.tensor([[5, 7], [13, 15]])))
# Test module
mod = downsample.ConcatDownsample2d(scale_factor)
self.assertTrue(torch.equal(mod(x), out))
# Test JIT module
mod = downsample.ConcatDownsample2dJit(scale_factor)
self.assertTrue(torch.equal(mod(x), out))
def test_concatdownsample2d(self):
num_batches = 2
num_chan = 4
x = torch.rand(num_batches, num_chan, 4, 4)
# Test functional API
self.assertRaises(AssertionError, F.concat_downsample2d, x, 3)
out = F.concat_downsample2d(x, 2)
self.assertEqual(
out.shape,
(num_batches, num_chan * 2**2, x.shape[2] // 2, x.shape[3] // 2))
self.assertTrue(
torch.equal(
out,
torch.stack((x[..., ::2, ::2], x[..., ::2, 1::2],
x[..., 1::2, ::2], x[..., 1::2, 1::2]),
dim=2).view(num_batches, -1, x.shape[2] // 2,
x.shape[3] // 2)))
# Test module
mod = downsample.ConcatDownsample2d(2)
self.assertTrue(torch.equal(mod(x), out))