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Improve tests for Networks #74

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Warvito merged 4 commits into from
Nov 29, 2022
Merged

Improve tests for Networks #74

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fbb4447
Improve tests for AutoencoderKL
Warvito Nov 27, 2022
3f667d5
Improve tests for VQVAE (#74)
Warvito Nov 27, 2022
72f4f52
Merge branch 'main' into 72-improve-too-slow-too-many-overlapping-of-…
Warvito Nov 29, 2022
5593623
Update for new num_channels and num_res_channels format (#74)
Warvito Nov 29, 2022
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193 changes: 126 additions & 67 deletions tests/test_autoencoderkl.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,73 +20,108 @@

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

TEST_CASE_0 = [
{
"spatial_dims": 2,
"in_channels": 1,
"out_channels": 1,
"num_channels": 32,
"latent_channels": 8,
"ch_mult": [1, 1, 1],
"num_res_blocks": 1,
},
(2, 1, 64, 64),
(2, 1, 64, 64),
(2, 8, 16, 16),
CASES = [
[
{
"spatial_dims": 2,
"in_channels": 1,
"out_channels": 1,
"num_channels": 4,
"latent_channels": 4,
"ch_mult": [1, 1, 1],
"attention_levels": None,
"num_res_blocks": 1,
"norm_num_groups": 4,
},
(1, 1, 16, 16),
(1, 1, 16, 16),
(1, 4, 4, 4),
],
[
{
"spatial_dims": 2,
"in_channels": 1,
"out_channels": 1,
"num_channels": 4,
"latent_channels": 4,
"ch_mult": [1, 1, 1],
"attention_levels": (False, False, False),
"num_res_blocks": 1,
"norm_num_groups": 4,
},
(1, 1, 16, 16),
(1, 1, 16, 16),
(1, 4, 4, 4),
],
[
{
"spatial_dims": 2,
"in_channels": 1,
"out_channels": 1,
"num_channels": 4,
"latent_channels": 4,
"ch_mult": [1, 1, 1],
"attention_levels": (False, False, True),
"num_res_blocks": 1,
"norm_num_groups": 4,
},
(1, 1, 16, 16),
(1, 1, 16, 16),
(1, 4, 4, 4),
],
[
{
"spatial_dims": 2,
"in_channels": 1,
"out_channels": 1,
"num_channels": 4,
"latent_channels": 4,
"ch_mult": [1, 1, 1],
"attention_levels": (False, False, False),
"num_res_blocks": 1,
"norm_num_groups": 4,
"with_encoder_nonlocal_attn": False,
},
(1, 1, 16, 16),
(1, 1, 16, 16),
(1, 4, 4, 4),
],
[
{
"spatial_dims": 2,
"in_channels": 1,
"out_channels": 1,
"num_channels": 4,
"latent_channels": 4,
"ch_mult": [1, 1, 1],
"attention_levels": (False, False, False),
"num_res_blocks": 1,
"norm_num_groups": 4,
"with_encoder_nonlocal_attn": False,
"with_decoder_nonlocal_attn": False,
},
(1, 1, 16, 16),
(1, 1, 16, 16),
(1, 4, 4, 4),
],
[
{
"spatial_dims": 3,
"in_channels": 1,
"out_channels": 1,
"num_channels": 4,
"latent_channels": 4,
"ch_mult": [1, 1, 1],
"attention_levels": (False, False, True),
"num_res_blocks": 1,
"norm_num_groups": 4,
},
(1, 1, 16, 16, 16),
(1, 1, 16, 16, 16),
(1, 4, 4, 4, 4),
],
]

TEST_CASE_1 = [
{
"spatial_dims": 2,
"in_channels": 1,
"out_channels": 1,
"num_channels": 32,
"latent_channels": 32,
"ch_mult": [1, 1, 1, 1],
"num_res_blocks": 1,
},
(2, 1, 64, 64),
(2, 1, 64, 64),
(2, 32, 8, 8),
]

TEST_CASE_2 = [
{
"spatial_dims": 2,
"in_channels": 1,
"out_channels": 1,
"num_channels": 32,
"latent_channels": 32,
"ch_mult": [1, 1, 1, 1],
"num_res_blocks": 1,
"attention_levels": (False, False, False, True),
"with_encoder_nonlocal_attn": False,
},
(2, 1, 64, 64),
(2, 1, 64, 64),
(2, 32, 8, 8),
]

TEST_CASE_3 = [
{
"spatial_dims": 2,
"in_channels": 1,
"out_channels": 1,
"num_channels": 32,
"latent_channels": 32,
"ch_mult": [1, 1, 1, 1],
"num_res_blocks": 1,
"attention_levels": (True, True, True, True),
"with_encoder_nonlocal_attn": False,
"with_decoder_nonlocal_attn": False,
},
(2, 1, 64, 64),
(2, 1, 64, 64),
(2, 32, 8, 8),
]

CASES = [TEST_CASE_0, TEST_CASE_1, TEST_CASE_2, TEST_CASE_3]


class TestAutoEncoderKL(unittest.TestCase):
@parameterized.expand(CASES)
Expand Down Expand Up @@ -131,13 +166,37 @@ def test_model_ch_mult_not_same_size_of_attention_levels(self):
attention_levels=(True,),
)

@parameterized.expand(CASES)
def test_shape_reconstruction(self, input_param, input_shape, expected_shape, _):
def test_shape_reconstruction(self):
input_param, input_shape, expected_shape, _ = CASES[0]
net = AutoencoderKL(**input_param).to(device)
with eval_mode(net):
result = net.reconstruct(torch.randn(input_shape).to(device))
self.assertEqual(result.shape, expected_shape)

def test_shape_encode(self):
input_param, input_shape, _, expected_latent_shape = CASES[0]
net = AutoencoderKL(**input_param).to(device)
with eval_mode(net):
result = net.encode(torch.randn(input_shape).to(device))
self.assertEqual(result[0].shape, expected_latent_shape)
self.assertEqual(result[1].shape, expected_latent_shape)

def test_shape_sampling(self):
input_param, _, _, expected_latent_shape = CASES[0]
net = AutoencoderKL(**input_param).to(device)
with eval_mode(net):
result = net.sampling(
torch.randn(expected_latent_shape).to(device), torch.randn(expected_latent_shape).to(device)
)
self.assertEqual(result.shape, expected_latent_shape)

def test_shape_decode(self):
input_param, expected_input_shape, _, latent_shape = CASES[0]
net = AutoencoderKL(**input_param).to(device)
with eval_mode(net):
result = net.decode(torch.randn(latent_shape).to(device))
self.assertEqual(result.shape, expected_input_shape)


if __name__ == "__main__":
unittest.main()
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