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Allow dropout overwrites on EfficientNet #7031

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merged 2 commits into from
Dec 12, 2022
Merged

Allow dropout overwrites on EfficientNet #7031

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643e3d2
Allow dropout overwrites on EfficientNet
datumbox Dec 12, 2022
bb88133
Merge branch 'main' into models/efficientnet_dropout
datumbox Dec 12, 2022
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8 changes: 5 additions & 3 deletions test/smoke_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,7 @@ def smoke_test_torchvision() -> None:
all(x is not None for x in [torch.ops.image.decode_png, torch.ops.torchvision.roi_align]),
)


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def smoke_test_torchvision_read_decode() -> None:
img_jpg = read_image(str(SCRIPT_DIR / "assets" / "encode_jpeg" / "grace_hopper_517x606.jpg"))
if img_jpg.ndim != 3 or img_jpg.numel() < 100:
Expand All @@ -25,6 +26,7 @@ def smoke_test_torchvision_read_decode() -> None:
if img_png.ndim != 3 or img_png.numel() < 100:
raise RuntimeError(f"Unexpected shape of img_png: {img_png.shape}")


def smoke_test_torchvision_resnet50_classify(device: str = "cpu") -> None:
img = read_image(str(SCRIPT_DIR / ".." / "gallery" / "assets" / "dog2.jpg")).to(device)

Expand All @@ -47,9 +49,8 @@ def smoke_test_torchvision_resnet50_classify(device: str = "cpu") -> None:
expected_category = "German shepherd"
print(f"{category_name} ({device}): {100 * score:.1f}%")
if category_name != expected_category:
raise RuntimeError(
f"Failed ResNet50 classify {category_name} Expected: {expected_category}"
)
raise RuntimeError(f"Failed ResNet50 classify {category_name} Expected: {expected_category}")


def main() -> None:
print(f"torchvision: {torchvision.__version__}")
Expand All @@ -59,5 +60,6 @@ def main() -> None:
if torch.cuda.is_available():
smoke_test_torchvision_resnet50_classify("cuda")


if __name__ == "__main__":
main()
42 changes: 31 additions & 11 deletions torchvision/models/efficientnet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -779,7 +779,9 @@ def efficientnet_b0(
weights = EfficientNet_B0_Weights.verify(weights)

inverted_residual_setting, last_channel = _efficientnet_conf("efficientnet_b0", width_mult=1.0, depth_mult=1.0)
return _efficientnet(inverted_residual_setting, 0.2, last_channel, weights, progress, **kwargs)
return _efficientnet(
inverted_residual_setting, kwargs.pop("dropout", 0.2), last_channel, weights, progress, **kwargs
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)


@register_model()
Expand Down Expand Up @@ -808,7 +810,9 @@ def efficientnet_b1(
weights = EfficientNet_B1_Weights.verify(weights)

inverted_residual_setting, last_channel = _efficientnet_conf("efficientnet_b1", width_mult=1.0, depth_mult=1.1)
return _efficientnet(inverted_residual_setting, 0.2, last_channel, weights, progress, **kwargs)
return _efficientnet(
inverted_residual_setting, kwargs.pop("dropout", 0.2), last_channel, weights, progress, **kwargs
)


@register_model()
Expand Down Expand Up @@ -837,7 +841,9 @@ def efficientnet_b2(
weights = EfficientNet_B2_Weights.verify(weights)

inverted_residual_setting, last_channel = _efficientnet_conf("efficientnet_b2", width_mult=1.1, depth_mult=1.2)
return _efficientnet(inverted_residual_setting, 0.3, last_channel, weights, progress, **kwargs)
return _efficientnet(
inverted_residual_setting, kwargs.pop("dropout", 0.3), last_channel, weights, progress, **kwargs
)


@register_model()
Expand Down Expand Up @@ -866,7 +872,14 @@ def efficientnet_b3(
weights = EfficientNet_B3_Weights.verify(weights)

inverted_residual_setting, last_channel = _efficientnet_conf("efficientnet_b3", width_mult=1.2, depth_mult=1.4)
return _efficientnet(inverted_residual_setting, 0.3, last_channel, weights, progress, **kwargs)
return _efficientnet(
inverted_residual_setting,
kwargs.pop("dropout", 0.3),
last_channel,
weights,
progress,
**kwargs,
)


@register_model()
Expand Down Expand Up @@ -895,7 +908,14 @@ def efficientnet_b4(
weights = EfficientNet_B4_Weights.verify(weights)

inverted_residual_setting, last_channel = _efficientnet_conf("efficientnet_b4", width_mult=1.4, depth_mult=1.8)
return _efficientnet(inverted_residual_setting, 0.4, last_channel, weights, progress, **kwargs)
return _efficientnet(
inverted_residual_setting,
kwargs.pop("dropout", 0.4),
last_channel,
weights,
progress,
**kwargs,
)


@register_model()
Expand Down Expand Up @@ -926,7 +946,7 @@ def efficientnet_b5(
inverted_residual_setting, last_channel = _efficientnet_conf("efficientnet_b5", width_mult=1.6, depth_mult=2.2)
return _efficientnet(
inverted_residual_setting,
0.4,
kwargs.pop("dropout", 0.4),
last_channel,
weights,
progress,
Expand Down Expand Up @@ -963,7 +983,7 @@ def efficientnet_b6(
inverted_residual_setting, last_channel = _efficientnet_conf("efficientnet_b6", width_mult=1.8, depth_mult=2.6)
return _efficientnet(
inverted_residual_setting,
0.5,
kwargs.pop("dropout", 0.5),
last_channel,
weights,
progress,
Expand Down Expand Up @@ -1000,7 +1020,7 @@ def efficientnet_b7(
inverted_residual_setting, last_channel = _efficientnet_conf("efficientnet_b7", width_mult=2.0, depth_mult=3.1)
return _efficientnet(
inverted_residual_setting,
0.5,
kwargs.pop("dropout", 0.5),
last_channel,
weights,
progress,
Expand Down Expand Up @@ -1038,7 +1058,7 @@ def efficientnet_v2_s(
inverted_residual_setting, last_channel = _efficientnet_conf("efficientnet_v2_s")
return _efficientnet(
inverted_residual_setting,
0.2,
kwargs.pop("dropout", 0.2),
last_channel,
weights,
progress,
Expand Down Expand Up @@ -1076,7 +1096,7 @@ def efficientnet_v2_m(
inverted_residual_setting, last_channel = _efficientnet_conf("efficientnet_v2_m")
return _efficientnet(
inverted_residual_setting,
0.3,
kwargs.pop("dropout", 0.3),
last_channel,
weights,
progress,
Expand Down Expand Up @@ -1114,7 +1134,7 @@ def efficientnet_v2_l(
inverted_residual_setting, last_channel = _efficientnet_conf("efficientnet_v2_l")
return _efficientnet(
inverted_residual_setting,
0.4,
kwargs.pop("dropout", 0.4),
last_channel,
weights,
progress,
Expand Down