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How to use _smoke_test method in lisa

Best Python code snippet using lisa_python

tests.py

Source:tests.py

...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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