How to use _smoke_test method in lisa

Best Python code snippet using lisa_python

tests.py

Source: tests.py Github

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...3import torch4from torch import distributions5from pytorch_generative import models6class ModelSmokeTestCase(unittest.TestCase):7 def _smoke_test(self, model, in_channels=1, test_sampling=True):8 shape = (2, in_channels, 5, 5)9 # Test forward().10 batch = torch.rand(shape)11 model(batch)12 if test_sampling:13 # Test unconditional sample().14 model.sample(out_shape=shape)15 # Test that conditional sample() only modifies pixels < 0.16 batch[:, :, 1:, :] = -117 sample = model.sample(conditioned_on=batch)18 self.assertTrue((sample[:, :, 0, :] == batch[:, :, 0, :]).all())19 # TODO(eugenhotaj): Use parameterized tests instead of creating a new method20 # for each model.21 def test_TinyCNN(self):22 model = models.TinyCNN(in_channels=3, out_channels=3)23 self._smoke_test(model, in_channels=3)24 def test_NADE(self):25 model = models.NADE(input_dim=25, hidden_dim=5)26 self._smoke_test(model)27 def test_MADE(self):28 model = models.MADE(input_dim=25, hidden_dims=[32, 32, 32], n_masks=8)29 self._smoke_test(model)30 def test_PixelCNN(self):31 model = models.PixelCNN(32 in_channels=3,33 out_channels=3,34 n_residual=1,35 residual_channels=1,36 head_channels=1,37 )38 self._smoke_test(model, in_channels=3)39 def test_GatedPixelCNN(self):40 model = models.GatedPixelCNN(41 in_channels=3, out_channels=3, n_gated=1, gated_channels=1, head_channels=142 )43 self._smoke_test(model, in_channels=3)44 def test_PixelSNAIL(self):45 model = models.PixelSNAIL(46 in_channels=3,47 out_channels=3,48 n_channels=2,49 n_pixel_snail_blocks=1,50 n_residual_blocks=1,51 attention_key_channels=1,52 attention_value_channels=1,53 )54 self._smoke_test(model, in_channels=3)55 def test_ImageGPT(self):56 model = models.ImageGPT(57 in_channels=3,58 out_channels=3,59 in_size=5,60 n_transformer_blocks=1,61 n_attention_heads=2,62 n_embedding_channels=4,63 )64 self._smoke_test(model, in_channels=3)65 def test_VAE(self):66 model = models.VAE(67 in_channels=3,68 out_channels=3,69 in_size=5,70 latent_dim=1,71 hidden_channels=2,72 n_residual_blocks=1,73 residual_channels=1,74 )75 self._smoke_test(model, in_channels=3, test_sampling=False)76 # TODO(eugenhotaj): Create a function to test VAEs sampling.77 model.sample(n_images=2)78 def test_VQVAE(self):79 model = models.VQVAE(80 in_channels=3,81 out_channels=3,82 hidden_channels=2,83 n_residual_blocks=1,84 residual_channels=1,85 n_embeddings=2,86 embedding_dim=2,87 )88 self._smoke_test(model, in_channels=3, test_sampling=False)89 def test_VQVAE2(self):90 model = models.VQVAE(91 in_channels=3,92 out_channels=3,93 hidden_channels=2,94 n_residual_blocks=1,95 residual_channels=1,96 n_embeddings=2,97 embedding_dim=2,98 )...

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