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| import unittest |
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| import torch |
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| from src.models.vision_transformer import VIT_EMBED_DIMS, vit_tiny |
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| class TestImageViT(unittest.TestCase): |
| def setUp(self) -> None: |
| self._vit_tiny = vit_tiny() |
| self.height, self.width = 224, 224 |
| self.num_patches = (self.height // self._vit_tiny.patch_size) * (self.width // self._vit_tiny.patch_size) |
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| def test_model_image_nomask_batchsize_4(self): |
| BS = 4 |
| x = torch.rand((BS, 3, self.height, self.width)) |
| y = self._vit_tiny(x) |
| self.assertIsInstance(y, torch.Tensor) |
| self.assertEqual(y.size(), (BS, self.num_patches, VIT_EMBED_DIMS["vit_tiny"])) |
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| def test_model_image_nomask_batchsize_1(self): |
| BS = 1 |
| x = torch.rand((BS, 3, self.height, self.width)) |
| y = self._vit_tiny(x) |
| self.assertIsInstance(y, torch.Tensor) |
| self.assertEqual(y.size(), (BS, self.num_patches, VIT_EMBED_DIMS["vit_tiny"])) |
|
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| def test_model_image_masked_batchsize_4(self): |
| BS = 4 |
| mask_indices = [6, 7, 8] |
| masks = [torch.tensor(mask_indices, dtype=torch.int64) for _ in range(BS)] |
| x = torch.rand((BS, 3, self.height, self.width)) |
| y = self._vit_tiny(x, masks=masks) |
| self.assertIsInstance(y, torch.Tensor) |
| self.assertEqual(y.size(), (BS, len(mask_indices), VIT_EMBED_DIMS["vit_tiny"])) |
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| def test_model_image_masked_batchsize_1(self): |
| BS = 1 |
| mask_indices = [6, 7, 8] |
| masks = [torch.tensor(mask_indices, dtype=torch.int64) for _ in range(BS)] |
| x = torch.rand((BS, 3, self.height, self.width)) |
| y = self._vit_tiny(x, masks=masks) |
| self.assertIsInstance(y, torch.Tensor) |
| self.assertEqual(y.size(), (BS, len(mask_indices), VIT_EMBED_DIMS["vit_tiny"])) |
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|
| class TestVideoViT(unittest.TestCase): |
| def setUp(self) -> None: |
| self.num_frames = 8 |
| self._vit_tiny = vit_tiny(num_frames=8) |
| self.height, self.width = 224, 224 |
| self.num_patches = ( |
| (self.height // self._vit_tiny.patch_size) |
| * (self.width // self._vit_tiny.patch_size) |
| * (self.num_frames // self._vit_tiny.tubelet_size) |
| ) |
|
|
| def test_model_video_nomask_batchsize_4(self): |
| BS = 4 |
| x = torch.rand((BS, 3, self.num_frames, self.height, self.width)) |
| y = self._vit_tiny(x) |
| self.assertIsInstance(y, torch.Tensor) |
| self.assertEqual(y.size(), (BS, self.num_patches, VIT_EMBED_DIMS["vit_tiny"])) |
|
|
| def test_model_video_nomask_batchsize_1(self): |
| BS = 1 |
| x = torch.rand((BS, 3, self.num_frames, self.height, self.width)) |
| y = self._vit_tiny(x) |
| self.assertIsInstance(y, torch.Tensor) |
| self.assertEqual(y.size(), (BS, self.num_patches, VIT_EMBED_DIMS["vit_tiny"])) |
|
|
| def test_model_video_masked_batchsize_4(self): |
| BS = 4 |
| mask_indices = [6, 7, 8] |
| masks = [torch.tensor(mask_indices, dtype=torch.int64) for _ in range(BS)] |
| x = torch.rand((BS, 3, self.num_frames, self.height, self.width)) |
| y = self._vit_tiny(x, masks=masks) |
| self.assertIsInstance(y, torch.Tensor) |
| self.assertEqual(y.size(), (BS, len(mask_indices), VIT_EMBED_DIMS["vit_tiny"])) |
|
|
| def test_model_video_masked_batchsize_1(self): |
| BS = 1 |
| mask_indices = [6, 7, 8] |
| masks = [torch.tensor(mask_indices, dtype=torch.int64) for _ in range(BS)] |
| x = torch.rand((BS, 3, self.num_frames, self.height, self.width)) |
| y = self._vit_tiny(x, masks=masks) |
| self.assertIsInstance(y, torch.Tensor) |
| self.assertEqual(y.size(), (BS, len(mask_indices), VIT_EMBED_DIMS["vit_tiny"])) |
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