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Merged
merged 8 commits into from
Oct 22, 2021
Merged

Simplify efficientnet code by removing _efficientnet_conf #4690

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a5d872d
adding efficientnet to prototype
jdsgomes Oct 21, 2021
fed5000
Removing _efficientnet_conf from prototype and main efficientnet clas…
jdsgomes Oct 21, 2021
865b4c0
merging main
jdsgomes Oct 22, 2021
3dac3fc
fixing merge conflicts
jdsgomes Oct 22, 2021
82d62ff
fixing merge conflicts
jdsgomes Oct 22, 2021
8323d2e
fixing lint errors
jdsgomes Oct 22, 2021
7ea8819
fixing lint errors
jdsgomes Oct 22, 2021
4212a1c
fixing lint errors
jdsgomes Oct 22, 2021
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50 changes: 21 additions & 29 deletions torchvision/models/efficientnet.py
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Open in desktop
Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
import copy
import math
from functools import partial
from typing import Any, Callable, List, Optional, Sequence
from typing import Any, Callable, Optional, List, Sequence

import torch
from torch import nn, Tensor
Expand Down Expand Up @@ -263,7 +263,15 @@ def forward(self, x: Tensor) -> Tensor:
return self._forward_impl(x)


def _efficientnet_conf(width_mult: float, depth_mult: float, **kwargs: Any) -> List[MBConvConfig]:
def _efficientnet(
arch: str,
width_mult: float,
depth_mult: float,
dropout: float,
pretrained: bool,
progress: bool,
**kwargs: Any,
) -> EfficientNet:
bneck_conf = partial(MBConvConfig, width_mult=width_mult, depth_mult=depth_mult)
inverted_residual_setting = [
bneck_conf(1, 3, 1, 32, 16, 1),
Expand All @@ -274,17 +282,6 @@ def _efficientnet_conf(width_mult: float, depth_mult: float, **kwargs: Any) -> L
bneck_conf(6, 5, 2, 112, 192, 4),
bneck_conf(6, 3, 1, 192, 320, 1),
]
return inverted_residual_setting


def _efficientnet(
arch: str,
inverted_residual_setting: List[MBConvConfig],
dropout: float,
pretrained: bool,
progress: bool,
**kwargs: Any,
) -> EfficientNet:
model = EfficientNet(inverted_residual_setting, dropout, **kwargs)
if pretrained:
if model_urls.get(arch, None) is None:
Expand All @@ -303,8 +300,7 @@ def efficientnet_b0(pretrained: bool = False, progress: bool = True, **kwargs: A
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
inverted_residual_setting = _efficientnet_conf(width_mult=1.0, depth_mult=1.0, **kwargs)
return _efficientnet("efficientnet_b0", inverted_residual_setting, 0.2, pretrained, progress, **kwargs)
return _efficientnet("efficientnet_b0", 1.0, 1.0, 0.2, pretrained, progress, **kwargs)


def efficientnet_b1(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet:
Expand All @@ -316,8 +312,7 @@ def efficientnet_b1(pretrained: bool = False, progress: bool = True, **kwargs: A
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
inverted_residual_setting = _efficientnet_conf(width_mult=1.0, depth_mult=1.1, **kwargs)
return _efficientnet("efficientnet_b1", inverted_residual_setting, 0.2, pretrained, progress, **kwargs)
return _efficientnet("efficientnet_b1", 1.0, 1.1, 0.2, pretrained, progress, **kwargs)


def efficientnet_b2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet:
Expand All @@ -329,8 +324,7 @@ def efficientnet_b2(pretrained: bool = False, progress: bool = True, **kwargs: A
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
inverted_residual_setting = _efficientnet_conf(width_mult=1.1, depth_mult=1.2, **kwargs)
return _efficientnet("efficientnet_b2", inverted_residual_setting, 0.3, pretrained, progress, **kwargs)
return _efficientnet("efficientnet_b2", 1.1, 1.2, 0.3, pretrained, progress, **kwargs)


def efficientnet_b3(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet:
Expand All @@ -342,8 +336,7 @@ def efficientnet_b3(pretrained: bool = False, progress: bool = True, **kwargs: A
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
inverted_residual_setting = _efficientnet_conf(width_mult=1.2, depth_mult=1.4, **kwargs)
return _efficientnet("efficientnet_b3", inverted_residual_setting, 0.3, pretrained, progress, **kwargs)
return _efficientnet("efficientnet_b3", 1.2, 1.4, 0.3, pretrained, progress, **kwargs)


def efficientnet_b4(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet:
Expand All @@ -355,8 +348,7 @@ def efficientnet_b4(pretrained: bool = False, progress: bool = True, **kwargs: A
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
inverted_residual_setting = _efficientnet_conf(width_mult=1.4, depth_mult=1.8, **kwargs)
return _efficientnet("efficientnet_b4", inverted_residual_setting, 0.4, pretrained, progress, **kwargs)
return _efficientnet("efficientnet_b4", 1.4, 1.8, 0.4, pretrained, progress, **kwargs)


def efficientnet_b5(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet:
Expand All @@ -368,10 +360,10 @@ def efficientnet_b5(pretrained: bool = False, progress: bool = True, **kwargs: A
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
inverted_residual_setting = _efficientnet_conf(width_mult=1.6, depth_mult=2.2, **kwargs)
return _efficientnet(
"efficientnet_b5",
inverted_residual_setting,
1.6,
2.2,
0.4,
pretrained,
progress,
Expand All @@ -389,10 +381,10 @@ def efficientnet_b6(pretrained: bool = False, progress: bool = True, **kwargs: A
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
inverted_residual_setting = _efficientnet_conf(width_mult=1.8, depth_mult=2.6, **kwargs)
return _efficientnet(
"efficientnet_b6",
inverted_residual_setting,
1.8,
2.6,
0.5,
pretrained,
progress,
Expand All @@ -410,10 +402,10 @@ def efficientnet_b7(pretrained: bool = False, progress: bool = True, **kwargs: A
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
inverted_residual_setting = _efficientnet_conf(width_mult=2.0, depth_mult=3.1, **kwargs)
return _efficientnet(
"efficientnet_b7",
inverted_residual_setting,
2.0,
3.1,
0.5,
pretrained,
progress,
Expand Down
45 changes: 26 additions & 19 deletions torchvision/prototype/models/efficientnet.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,11 +1,11 @@
import warnings
from functools import partial
from typing import Any, List, Optional
from typing import Any, Optional

from torch import nn
from torchvision.transforms.functional import InterpolationMode

from ...models.efficientnet import EfficientNet, MBConvConfig, _efficientnet_conf
from ...models.efficientnet import EfficientNet, MBConvConfig
from ..transforms.presets import ImageNetEval
from ._api import Weights, WeightEntry
from ._meta import _IMAGENET_CATEGORIES
Expand Down Expand Up @@ -33,7 +33,8 @@


def _efficientnet(
inverted_residual_setting: List[MBConvConfig],
width_mult: float,
depth_mult: float,
dropout: float,
weights: Optional[Weights],
progress: bool,
Expand All @@ -42,6 +43,17 @@ def _efficientnet(
if weights is not None:
kwargs["num_classes"] = len(weights.meta["categories"])

bneck_conf = partial(MBConvConfig, width_mult=width_mult, depth_mult=depth_mult)
inverted_residual_setting = [
bneck_conf(1, 3, 1, 32, 16, 1),
bneck_conf(6, 3, 2, 16, 24, 2),
bneck_conf(6, 5, 2, 24, 40, 2),
bneck_conf(6, 3, 2, 40, 80, 3),
bneck_conf(6, 5, 1, 80, 112, 3),
bneck_conf(6, 5, 2, 112, 192, 4),
bneck_conf(6, 3, 1, 192, 320, 1),
]

model = EfficientNet(inverted_residual_setting, dropout, **kwargs)

if weights is not None:
Expand Down Expand Up @@ -172,8 +184,7 @@ def efficientnet_b0(
warnings.warn("The argument pretrained is deprecated, please use weights instead.")
weights = EfficientNetB0Weights.ImageNet1K_TimmV1 if kwargs.pop("pretrained") else None
weights = EfficientNetB0Weights.verify(weights)
inverted_residual_setting = _efficientnet_conf(width_mult=1.0, depth_mult=1.0, **kwargs)
return _efficientnet(inverted_residual_setting, dropout=0.2, weights=weights, progress=progress, **kwargs)
return _efficientnet(width_mult=1.0, depth_mult=1.0, dropout=0.2, weights=weights, progress=progress, **kwargs)


def efficientnet_b1(
Expand All @@ -183,8 +194,7 @@ def efficientnet_b1(
warnings.warn("The argument pretrained is deprecated, please use weights instead.")
weights = EfficientNetB1Weights.ImageNet1K_TimmV1 if kwargs.pop("pretrained") else None
weights = EfficientNetB1Weights.verify(weights)
inverted_residual_setting = _efficientnet_conf(width_mult=1.0, depth_mult=1.1, **kwargs)
return _efficientnet(inverted_residual_setting, dropout=0.2, weights=weights, progress=progress, **kwargs)
return _efficientnet(width_mult=1.0, depth_mult=1.1, dropout=0.2, weights=weights, progress=progress, **kwargs)


def efficientnet_b2(
Expand All @@ -194,8 +204,7 @@ def efficientnet_b2(
warnings.warn("The argument pretrained is deprecated, please use weights instead.")
weights = EfficientNetB2Weights.ImageNet1K_TimmV1 if kwargs.pop("pretrained") else None
weights = EfficientNetB2Weights.verify(weights)
inverted_residual_setting = _efficientnet_conf(width_mult=1.1, depth_mult=1.2, **kwargs)
return _efficientnet(inverted_residual_setting, dropout=0.3, weights=weights, progress=progress, **kwargs)
return _efficientnet(width_mult=1.1, depth_mult=1.2, dropout=0.3, weights=weights, progress=progress, **kwargs)


def efficientnet_b3(
Expand All @@ -205,8 +214,7 @@ def efficientnet_b3(
warnings.warn("The argument pretrained is deprecated, please use weights instead.")
weights = EfficientNetB3Weights.ImageNet1K_TimmV1 if kwargs.pop("pretrained") else None
weights = EfficientNetB3Weights.verify(weights)
inverted_residual_setting = _efficientnet_conf(width_mult=1.2, depth_mult=1.4, **kwargs)
return _efficientnet(inverted_residual_setting, dropout=0.3, weights=weights, progress=progress, **kwargs)
return _efficientnet(width_mult=1.2, depth_mult=1.4, dropout=0.3, weights=weights, progress=progress, **kwargs)


def efficientnet_b4(
Expand All @@ -216,8 +224,7 @@ def efficientnet_b4(
warnings.warn("The argument pretrained is deprecated, please use weights instead.")
weights = EfficientNetB4Weights.ImageNet1K_TimmV1 if kwargs.pop("pretrained") else None
weights = EfficientNetB4Weights.verify(weights)
inverted_residual_setting = _efficientnet_conf(width_mult=1.4, depth_mult=1.8, **kwargs)
return _efficientnet(inverted_residual_setting, dropout=0.4, weights=weights, progress=progress, **kwargs)
return _efficientnet(width_mult=1.4, depth_mult=1.8, dropout=0.4, weights=weights, progress=progress, **kwargs)


def efficientnet_b5(
Expand All @@ -227,9 +234,9 @@ def efficientnet_b5(
warnings.warn("The argument pretrained is deprecated, please use weights instead.")
weights = EfficientNetB5Weights.ImageNet1K_TFV1 if kwargs.pop("pretrained") else None
weights = EfficientNetB5Weights.verify(weights)
inverted_residual_setting = _efficientnet_conf(width_mult=1.6, depth_mult=2.2, **kwargs)
return _efficientnet(
inverted_residual_setting,
width_mult=1.6,
depth_mult=2.2,
dropout=0.4,
weights=weights,
progress=progress,
Expand All @@ -245,9 +252,9 @@ def efficientnet_b6(
warnings.warn("The argument pretrained is deprecated, please use weights instead.")
weights = EfficientNetB6Weights.ImageNet1K_TFV1 if kwargs.pop("pretrained") else None
weights = EfficientNetB6Weights.verify(weights)
inverted_residual_setting = _efficientnet_conf(width_mult=1.8, depth_mult=2.6, **kwargs)
return _efficientnet(
inverted_residual_setting,
width_mult=1.8,
depth_mult=2.6,
dropout=0.5,
weights=weights,
progress=progress,
Expand All @@ -263,9 +270,9 @@ def efficientnet_b7(
warnings.warn("The argument pretrained is deprecated, please use weights instead.")
weights = EfficientNetB7Weights.ImageNet1K_TFV1 if kwargs.pop("pretrained") else None
weights = EfficientNetB7Weights.verify(weights)
inverted_residual_setting = _efficientnet_conf(width_mult=2.0, depth_mult=3.1, **kwargs)
return _efficientnet(
inverted_residual_setting,
width_mult=2.0,
depth_mult=3.1,
dropout=0.5,
weights=weights,
progress=progress,
Expand Down