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[Enhance] Reduce the memory usage of unit tests for Swin-Transformer #759

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Apr 22, 2022
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[Enhance] Reduce the memory usage of unit tests for Swin-Transformer #759

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26 changes: 13 additions & 13 deletions tests/test_models/test_backbones/test_swin_transformer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -127,38 +127,38 @@ def test_init_weights(self):
os.remove(checkpoint_v2)

def test_forward(self):
imgs = torch.randn(3, 3, 224, 224)
imgs = torch.randn(1, 3, 224, 224)

cfg = deepcopy(self.cfg)
model = SwinTransformer(**cfg)
outs = model(imgs)
self.assertIsInstance(outs, tuple)
self.assertEqual(len(outs), 1)
feat = outs[-1]
self.assertEqual(feat.shape, (3, 1024, 7, 7))
self.assertEqual(feat.shape, (1, 1024, 7, 7))

# test with window_size=12
cfg = deepcopy(self.cfg)
cfg['window_size'] = 12
model = SwinTransformer(**cfg)
outs = model(torch.randn(3, 3, 384, 384))
outs = model(torch.randn(1, 3, 384, 384))
self.assertIsInstance(outs, tuple)
self.assertEqual(len(outs), 1)
feat = outs[-1]
self.assertEqual(feat.shape, (3, 1024, 12, 12))
self.assertEqual(feat.shape, (1, 1024, 12, 12))
with self.assertRaisesRegex(AssertionError, r'the window size \(12\)'):
model(torch.randn(3, 3, 224, 224))
model(torch.randn(1, 3, 224, 224))

# test with pad_small_map=True
cfg = deepcopy(self.cfg)
cfg['window_size'] = 12
cfg['pad_small_map'] = True
model = SwinTransformer(**cfg)
outs = model(torch.randn(3, 3, 224, 224))
outs = model(torch.randn(1, 3, 224, 224))
self.assertIsInstance(outs, tuple)
self.assertEqual(len(outs), 1)
feat = outs[-1]
self.assertEqual(feat.shape, (3, 1024, 7, 7))
self.assertEqual(feat.shape, (1, 1024, 7, 7))

# test multiple output indices
cfg = deepcopy(self.cfg)
Expand All @@ -169,7 +169,7 @@ def test_forward(self):
self.assertEqual(len(outs), 4)
for stride, out in zip([2, 4, 8, 8], outs):
self.assertEqual(out.shape,
(3, 128 * stride, 56 // stride, 56 // stride))
(1, 128 * stride, 56 // stride, 56 // stride))

# test with checkpoint forward
cfg = deepcopy(self.cfg)
Expand All @@ -185,12 +185,12 @@ def test_forward(self):
self.assertIsInstance(outs, tuple)
self.assertEqual(len(outs), 1)
feat = outs[-1]
self.assertEqual(feat.shape, (3, 1024, 7, 7))
self.assertEqual(feat.shape, (1, 1024, 7, 7))

# test with dynamic input shape
imgs1 = torch.randn(3, 3, 224, 224)
imgs2 = torch.randn(3, 3, 256, 256)
imgs3 = torch.randn(3, 3, 256, 309)
imgs1 = torch.randn(1, 3, 224, 224)
imgs2 = torch.randn(1, 3, 256, 256)
imgs3 = torch.randn(1, 3, 256, 309)
cfg = deepcopy(self.cfg)
model = SwinTransformer(**cfg)
for imgs in [imgs1, imgs2, imgs3]:
Expand All @@ -200,7 +200,7 @@ def test_forward(self):
feat = outs[-1]
expect_feat_shape = (math.ceil(imgs.shape[2] / 32),
math.ceil(imgs.shape[3] / 32))
self.assertEqual(feat.shape, (3, 1024, *expect_feat_shape))
self.assertEqual(feat.shape, (1, 1024, *expect_feat_shape))

def test_structure(self):
# test drop_path_rate decay
Expand Down