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Fix warnings in unit-tests tests #6159

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Fix warnings in unit-tests tests #6159

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93b3e84
To resolve issue #6155
puhuk Jun 13, 2022
5b2049b
To fix CI error
puhuk Jun 14, 2022
5ca18fe
Update resnet.py
puhuk Jun 14, 2022
d8d5b84
Remove unrelevant init_weights
puhuk Jun 14, 2022
4813241
To resolve warnings in models
puhuk Jun 14, 2022
16ddd6c
To resolve warnings in models
puhuk Jun 14, 2022
10595d8
Update test_models.py
puhuk Jun 14, 2022
ae06b1b
Update test_models.py _model_params
puhuk Jun 15, 2022
f187760
Update test_models.py
puhuk Jun 15, 2022
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1 change: 0 additions & 1 deletion test/test_datasets.py
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Original file line number Diff line number Diff line change
Expand Up @@ -616,7 +616,6 @@ class VOCSegmentationTestCase(datasets_utils.ImageDatasetTestCase):
year=[f"20{year:02d}" for year in range(7, 13)], image_set=("train", "val", "trainval")
),
dict(year="2007", image_set="test"),
dict(year="2007-test", image_set="test"),
)

def inject_fake_data(self, tmpdir, config):
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2 changes: 1 addition & 1 deletion torchvision/models/googlenet.py
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Expand Up @@ -34,7 +34,7 @@ def __init__(
num_classes: int = 1000,
aux_logits: bool = True,
transform_input: bool = False,
init_weights: Optional[bool] = None,
init_weights: bool = True,
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This is not good. Check #2170 why they switched from True to None

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Hmm, I don't have good idea. Is there better method than I did here.
https://github.com/pytorch/vision/pull/6159/commits/16ddd6c0f23fe56e8029f336c5e3f3c69e9c534f

blocks: Optional[List[Callable[..., nn.Module]]] = None,
dropout: float = 0.2,
dropout_aux: float = 0.7,
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2 changes: 1 addition & 1 deletion torchvision/models/inception.py
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Expand Up @@ -32,7 +32,7 @@ def __init__(
aux_logits: bool = True,
transform_input: bool = False,
inception_blocks: Optional[List[Callable[..., nn.Module]]] = None,
init_weights: Optional[bool] = None,
init_weights: bool = True,
dropout: float = 0.5,
) -> None:
super().__init__()
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2 changes: 2 additions & 0 deletions torchvision/models/quantization/googlenet.py
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Expand Up @@ -74,6 +74,8 @@ def forward(self, x: Tensor) -> Tensor:
class QuantizableGoogLeNet(GoogLeNet):
# TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659
def __init__(self, *args: Any, **kwargs: Any) -> None:
if "init_weights" not in kwargs:
kwargs["init_weights"] = True
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I'm not sure why in order to fix the warning on unit-tests we modify the models themselves. My expectations without having all the details in my mind, is that the tests are the ones that need to be updated. What am I missing?

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At first I was thinking that QuantizableGoogLeNet does not properly set up its base class GoogLeNet. Thinking more about that, I think you are right, we have to try again to fix that from tests side

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@vfdev-5 vfdev-5 Jun 15, 2022 •
edited
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@puhuk can you check if we could use _model_params when we set kwargs = {**defaults, **_model_params.get(model_name, {})}. Looks like _model_params can define specific values for special models:

vision/test/test_models.py

Lines 246 to 247 in 93b3e84

_model_params = {
"inception_v3": {"input_shape": (1, 3, 299, 299)},

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Ok, let me check and send PR again.

super().__init__( # type: ignore[misc]
blocks=[QuantizableBasicConv2d, QuantizableInception, QuantizableInceptionAux], *args, **kwargs
)
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1 change: 1 addition & 0 deletions torchvision/models/quantization/inception.py
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Expand Up @@ -142,6 +142,7 @@ def __init__(
QuantizableInceptionE,
QuantizableInceptionAux,
],
init_weights=True,
)
self.quant = torch.ao.quantization.QuantStub()
self.dequant = torch.ao.quantization.DeQuantStub()
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