Run all tests

tests/test_models.py

@@ -75,54 +75,54 @@ def test_model_backward(model_name, batch_size):
     assert not torch.isnan(outputs).any(), 'Output included NaNs'
 
 
-# @pytest.mark.timeout(120)
+@pytest.mark.timeout(120)
-# @pytest.mark.parametrize('model_name', list_models(exclude_filters=NON_STD_FILTERS))
+@pytest.mark.parametrize('model_name', list_models(exclude_filters=NON_STD_FILTERS))
-# @pytest.mark.parametrize('batch_size', [1])
+@pytest.mark.parametrize('batch_size', [1])
-# def test_model_default_cfgs(model_name, batch_size):
+def test_model_default_cfgs(model_name, batch_size):
-#     """Run a single forward pass with each model"""
+    """Run a single forward pass with each model"""
-#     model = create_model(model_name, pretrained=False)
+    model = create_model(model_name, pretrained=False)
-#     model.eval()
+    model.eval()
-#     state_dict = model.state_dict()
+    state_dict = model.state_dict()
-#     cfg = model.default_cfg
+    cfg = model.default_cfg
-
+
-#     classifier = cfg['classifier']
+    classifier = cfg['classifier']
-#     pool_size = cfg['pool_size']
+    pool_size = cfg['pool_size']
-#     input_size = model.default_cfg['input_size']
+    input_size = model.default_cfg['input_size']
-
+
-#     if all([x <= MAX_FWD_FEAT_SIZE for x in input_size]) and \
+    if all([x <= MAX_FWD_FEAT_SIZE for x in input_size]) and \
-#             not any([fnmatch.fnmatch(model_name, x) for x in EXCLUDE_FILTERS]):
+            not any([fnmatch.fnmatch(model_name, x) for x in EXCLUDE_FILTERS]):
-#         # output sizes only checked if default res <= 448 * 448 to keep resource down
+        # output sizes only checked if default res <= 448 * 448 to keep resource down
-#         input_size = tuple([min(x, MAX_FWD_FEAT_SIZE) for x in input_size])
+        input_size = tuple([min(x, MAX_FWD_FEAT_SIZE) for x in input_size])
-#         input_tensor = torch.randn((batch_size, *input_size))
+        input_tensor = torch.randn((batch_size, *input_size))
-
+
-#         # test forward_features (always unpooled)
+        # test forward_features (always unpooled)
-#         outputs = model.forward_features(input_tensor)
+        outputs = model.forward_features(input_tensor)
-#         assert outputs.shape[-1] == pool_size[-1] and outputs.shape[-2] == pool_size[-2]
+        assert outputs.shape[-1] == pool_size[-1] and outputs.shape[-2] == pool_size[-2]
-
+
-#         # test forward after deleting the classifier, output should be poooled, size(-1) == model.num_features
+        # test forward after deleting the classifier, output should be poooled, size(-1) == model.num_features
-#         model.reset_classifier(0)
+        model.reset_classifier(0)
-#         outputs = model.forward(input_tensor)
+        outputs = model.forward(input_tensor)
-#         assert len(outputs.shape) == 2
+        assert len(outputs.shape) == 2
-#         assert outputs.shape[-1] == model.num_features
+        assert outputs.shape[-1] == model.num_features
-
+
-#         # test model forward without pooling and classifier
+        # test model forward without pooling and classifier
-#         model.reset_classifier(0, '')  # reset classifier and set global pooling to pass-through
+        model.reset_classifier(0, '')  # reset classifier and set global pooling to pass-through
-#         outputs = model.forward(input_tensor)
+        outputs = model.forward(input_tensor)
-#         assert len(outputs.shape) == 4
+        assert len(outputs.shape) == 4
-#         if not isinstance(model, timm.models.MobileNetV3):
+        if not isinstance(model, timm.models.MobileNetV3):
-#             # FIXME mobilenetv3 forward_features vs removed pooling differ
+            # FIXME mobilenetv3 forward_features vs removed pooling differ
-#             assert outputs.shape[-1] == pool_size[-1] and outputs.shape[-2] == pool_size[-2]
+            assert outputs.shape[-1] == pool_size[-1] and outputs.shape[-2] == pool_size[-2]
-
+
-#     # check classifier name matches default_cfg
+    # check classifier name matches default_cfg
-#     assert classifier + ".weight" in state_dict.keys(), f'{classifier} not in model params'
+    assert classifier + ".weight" in state_dict.keys(), f'{classifier} not in model params'
-
+
-#     # check first conv(s) names match default_cfg
+    # check first conv(s) names match default_cfg
-#     first_conv = cfg['first_conv']
+    first_conv = cfg['first_conv']
-#     if isinstance(first_conv, str):
+    if isinstance(first_conv, str):
-#         first_conv = (first_conv,)
+        first_conv = (first_conv,)
-#     assert isinstance(first_conv, (tuple, list))
+    assert isinstance(first_conv, (tuple, list))
-#     for fc in first_conv:
+    for fc in first_conv:
-#         assert fc + ".weight" in state_dict.keys(), f'{fc} not in model params'
+        assert fc + ".weight" in state_dict.keys(), f'{fc} not in model params'
 
 
 if 'GITHUB_ACTIONS' not in os.environ:
@@ -147,20 +147,20 @@ EXCLUDE_JIT_FILTERS = [
 ]
 
 
-# @pytest.mark.timeout(120)
+@pytest.mark.timeout(120)
-# @pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_JIT_FILTERS))
+@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_JIT_FILTERS))
-# @pytest.mark.parametrize('batch_size', [1])
+@pytest.mark.parametrize('batch_size', [1])
-# def test_model_forward_torchscript(model_name, batch_size):
+def test_model_forward_torchscript(model_name, batch_size):
-#     """Run a single forward pass with each model"""
+    """Run a single forward pass with each model"""
-#     with set_scriptable(True):
+    with set_scriptable(True):
-#         model = create_model(model_name, pretrained=False)
+        model = create_model(model_name, pretrained=False)
-#     model.eval()
+    model.eval()
-#     input_size = (3, 128, 128)  # jit compile is already a bit slow and we've tested normal res already...
+    input_size = (3, 128, 128)  # jit compile is already a bit slow and we've tested normal res already...
-#     model = torch.jit.script(model)
+    model = torch.jit.script(model)
-#     outputs = model(torch.randn((batch_size, *input_size)))
+    outputs = model(torch.randn((batch_size, *input_size)))
-
+
-#     assert outputs.shape[0] == batch_size
+    assert outputs.shape[0] == batch_size
-#     assert not torch.isnan(outputs).any(), 'Output included NaNs'
+    assert not torch.isnan(outputs).any(), 'Output included NaNs'
 
 
 EXCLUDE_FEAT_FILTERS = [