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1468 lines
58 KiB
1468 lines
58 KiB
""" PyTorch EfficientNet Family
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An implementation of EfficienNet that covers variety of related models with efficient architectures:
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* EfficientNet (B0-B8, L2 + Tensorflow pretrained AutoAug/RandAug/AdvProp/NoisyStudent weight ports)
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- EfficientNet: Rethinking Model Scaling for CNNs - https://arxiv.org/abs/1905.11946
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- CondConv: Conditionally Parameterized Convolutions for Efficient Inference - https://arxiv.org/abs/1904.04971
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- Adversarial Examples Improve Image Recognition - https://arxiv.org/abs/1911.09665
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- Self-training with Noisy Student improves ImageNet classification - https://arxiv.org/abs/1911.04252
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* MixNet (Small, Medium, and Large)
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- MixConv: Mixed Depthwise Convolutional Kernels - https://arxiv.org/abs/1907.09595
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* MNasNet B1, A1 (SE), Small
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- MnasNet: Platform-Aware Neural Architecture Search for Mobile - https://arxiv.org/abs/1807.11626
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* FBNet-C
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- FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable NAS - https://arxiv.org/abs/1812.03443
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* Single-Path NAS Pixel1
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- Single-Path NAS: Designing Hardware-Efficient ConvNets - https://arxiv.org/abs/1904.02877
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* And likely more...
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Hacked together by Ross Wightman
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"""
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from .efficientnet_builder import *
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from .feature_hooks import FeatureHooks
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from .registry import register_model
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from .helpers import load_pretrained
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from .layers import SelectAdaptivePool2d
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from timm.models.layers import create_conv2d
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
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__all__ = ['EfficientNet']
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def _cfg(url='', **kwargs):
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return {
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'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
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'crop_pct': 0.875, 'interpolation': 'bicubic',
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'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
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'first_conv': 'conv_stem', 'classifier': 'classifier',
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**kwargs
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}
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default_cfgs = {
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'mnasnet_050': _cfg(url=''),
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'mnasnet_075': _cfg(url=''),
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'mnasnet_100': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_b1-74cb7081.pth'),
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'mnasnet_140': _cfg(url=''),
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'semnasnet_050': _cfg(url=''),
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'semnasnet_075': _cfg(url=''),
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'semnasnet_100': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_a1-d9418771.pth'),
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'semnasnet_140': _cfg(url=''),
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'mnasnet_small': _cfg(url=''),
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'mobilenetv2_100': _cfg(url=''),
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'fbnetc_100': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/fbnetc_100-c345b898.pth',
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interpolation='bilinear'),
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'spnasnet_100': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/spnasnet_100-048bc3f4.pth',
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interpolation='bilinear'),
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'efficientnet_b0': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b0_ra-3dd342df.pth'),
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'efficientnet_b1': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b1-533bc792.pth',
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input_size=(3, 240, 240), pool_size=(8, 8)),
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'efficientnet_b2': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b2_ra-bcdf34b7.pth',
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input_size=(3, 260, 260), pool_size=(9, 9)),
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'efficientnet_b2a': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b2_ra-bcdf34b7.pth',
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input_size=(3, 288, 288), pool_size=(9, 9), crop_pct=1.0),
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'efficientnet_b3': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b3_ra-a5e2fbc7.pth',
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input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904),
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'efficientnet_b3a': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b3_ra-a5e2fbc7.pth',
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input_size=(3, 320, 320), pool_size=(10, 10), crop_pct=1.0),
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'efficientnet_b4': _cfg(
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url='', input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.922),
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'efficientnet_b5': _cfg(
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url='', input_size=(3, 456, 456), pool_size=(15, 15), crop_pct=0.934),
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'efficientnet_b6': _cfg(
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url='', input_size=(3, 528, 528), pool_size=(17, 17), crop_pct=0.942),
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'efficientnet_b7': _cfg(
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url='', input_size=(3, 600, 600), pool_size=(19, 19), crop_pct=0.949),
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'efficientnet_b8': _cfg(
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url='', input_size=(3, 672, 672), pool_size=(21, 21), crop_pct=0.954),
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'efficientnet_l2': _cfg(
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url='', input_size=(3, 800, 800), pool_size=(25, 25), crop_pct=0.961),
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'efficientnet_es': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_es_ra-f111e99c.pth'),
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'efficientnet_em': _cfg(
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url='', input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882),
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'efficientnet_el': _cfg(
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url='', input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904),
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'efficientnet_cc_b0_4e': _cfg(url=''),
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'efficientnet_cc_b0_8e': _cfg(url=''),
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'efficientnet_cc_b1_8e': _cfg(url='', input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882),
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'tf_efficientnet_b0': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_aa-827b6e33.pth',
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input_size=(3, 224, 224)),
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'tf_efficientnet_b1': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b1_aa-ea7a6ee0.pth',
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input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882),
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'tf_efficientnet_b2': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b2_aa-60c94f97.pth',
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input_size=(3, 260, 260), pool_size=(9, 9), crop_pct=0.890),
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'tf_efficientnet_b3': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3_aa-84b4657e.pth',
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input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904),
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'tf_efficientnet_b4': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b4_aa-818f208c.pth',
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input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.922),
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'tf_efficientnet_b5': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b5_ra-9a3e5369.pth',
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input_size=(3, 456, 456), pool_size=(15, 15), crop_pct=0.934),
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'tf_efficientnet_b6': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b6_aa-80ba17e4.pth',
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input_size=(3, 528, 528), pool_size=(17, 17), crop_pct=0.942),
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'tf_efficientnet_b7': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b7_ra-6c08e654.pth',
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input_size=(3, 600, 600), pool_size=(19, 19), crop_pct=0.949),
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'tf_efficientnet_b8': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b8_ra-572d5dd9.pth',
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input_size=(3, 672, 672), pool_size=(21, 21), crop_pct=0.954),
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'tf_efficientnet_b0_ap': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_ap-f262efe1.pth',
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mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, input_size=(3, 224, 224)),
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'tf_efficientnet_b1_ap': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b1_ap-44ef0a3d.pth',
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mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
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input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882),
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'tf_efficientnet_b2_ap': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b2_ap-2f8e7636.pth',
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mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
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input_size=(3, 260, 260), pool_size=(9, 9), crop_pct=0.890),
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'tf_efficientnet_b3_ap': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3_ap-aad25bdd.pth',
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mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
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input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904),
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'tf_efficientnet_b4_ap': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b4_ap-dedb23e6.pth',
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mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
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input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.922),
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'tf_efficientnet_b5_ap': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b5_ap-9e82fae8.pth',
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mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
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input_size=(3, 456, 456), pool_size=(15, 15), crop_pct=0.934),
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'tf_efficientnet_b6_ap': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b6_ap-4ffb161f.pth',
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mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
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input_size=(3, 528, 528), pool_size=(17, 17), crop_pct=0.942),
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'tf_efficientnet_b7_ap': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b7_ap-ddb28fec.pth',
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mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
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input_size=(3, 600, 600), pool_size=(19, 19), crop_pct=0.949),
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'tf_efficientnet_b8_ap': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b8_ap-00e169fa.pth',
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mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
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input_size=(3, 672, 672), pool_size=(21, 21), crop_pct=0.954),
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'tf_efficientnet_b0_ns': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_ns-c0e6a31c.pth',
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input_size=(3, 224, 224)),
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'tf_efficientnet_b1_ns': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b1_ns-99dd0c41.pth',
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input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882),
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'tf_efficientnet_b2_ns': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b2_ns-00306e48.pth',
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input_size=(3, 260, 260), pool_size=(9, 9), crop_pct=0.890),
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'tf_efficientnet_b3_ns': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3_ns-9d44bf68.pth',
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input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904),
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'tf_efficientnet_b4_ns': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b4_ns-d6313a46.pth',
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input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.922),
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'tf_efficientnet_b5_ns': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b5_ns-6f26d0cf.pth',
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input_size=(3, 456, 456), pool_size=(15, 15), crop_pct=0.934),
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'tf_efficientnet_b6_ns': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b6_ns-51548356.pth',
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input_size=(3, 528, 528), pool_size=(17, 17), crop_pct=0.942),
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'tf_efficientnet_b7_ns': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b7_ns-1dbc32de.pth',
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input_size=(3, 600, 600), pool_size=(19, 19), crop_pct=0.949),
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'tf_efficientnet_l2_ns_475': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_l2_ns_475-bebbd00a.pth',
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input_size=(3, 475, 475), pool_size=(15, 15), crop_pct=0.936),
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'tf_efficientnet_l2_ns': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_l2_ns-df73bb44.pth',
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input_size=(3, 800, 800), pool_size=(25, 25), crop_pct=0.96),
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'tf_efficientnet_es': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_es-ca1afbfe.pth',
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mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
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input_size=(3, 224, 224), ),
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'tf_efficientnet_em': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_em-e78cfe58.pth',
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mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
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input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882),
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'tf_efficientnet_el': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_el-5143854e.pth',
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mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
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input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904),
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'tf_efficientnet_cc_b0_4e': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_cc_b0_4e-4362b6b2.pth',
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mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
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'tf_efficientnet_cc_b0_8e': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_cc_b0_8e-66184a25.pth',
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mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
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'tf_efficientnet_cc_b1_8e': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_cc_b1_8e-f7c79ae1.pth',
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mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
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input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882),
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'mixnet_s': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_s-a907afbc.pth'),
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'mixnet_m': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_m-4647fc68.pth'),
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'mixnet_l': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_l-5a9a2ed8.pth'),
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'mixnet_xl': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_xl_ra-aac3c00c.pth'),
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'mixnet_xxl': _cfg(),
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'tf_mixnet_s': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_s-89d3354b.pth'),
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'tf_mixnet_m': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_m-0f4d8805.pth'),
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'tf_mixnet_l': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_l-6c92e0c8.pth'),
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}
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_DEBUG = False
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class EfficientNet(nn.Module):
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""" (Generic) EfficientNet
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A flexible and performant PyTorch implementation of efficient network architectures, including:
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* EfficientNet B0-B8, L2
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* EfficientNet-EdgeTPU
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* EfficientNet-CondConv
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* MixNet S, M, L, XL
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* MnasNet A1, B1, and small
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* FBNet C
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* Single-Path NAS Pixel1
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"""
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def __init__(self, block_args, num_classes=1000, num_features=1280, in_chans=3, stem_size=32,
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channel_multiplier=1.0, channel_divisor=8, channel_min=None,
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output_stride=32, pad_type='', act_layer=nn.ReLU, drop_rate=0., drop_path_rate=0.,
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se_kwargs=None, norm_layer=nn.BatchNorm2d, norm_kwargs=None, global_pool='avg'):
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super(EfficientNet, self).__init__()
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norm_kwargs = norm_kwargs or {}
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self.num_classes = num_classes
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self.num_features = num_features
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self.drop_rate = drop_rate
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self._in_chs = in_chans
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# Stem
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stem_size = round_channels(stem_size, channel_multiplier, channel_divisor, channel_min)
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self.conv_stem = create_conv2d(self._in_chs, stem_size, 3, stride=2, padding=pad_type)
|
|
self.bn1 = norm_layer(stem_size, **norm_kwargs)
|
|
self.act1 = act_layer(inplace=True)
|
|
self._in_chs = stem_size
|
|
|
|
# Middle stages (IR/ER/DS Blocks)
|
|
builder = EfficientNetBuilder(
|
|
channel_multiplier, channel_divisor, channel_min, output_stride, pad_type, act_layer, se_kwargs,
|
|
norm_layer, norm_kwargs, drop_path_rate, verbose=_DEBUG)
|
|
self.blocks = nn.Sequential(*builder(self._in_chs, block_args))
|
|
self.feature_info = builder.features
|
|
self._in_chs = builder.in_chs
|
|
|
|
# Head + Pooling
|
|
self.conv_head = create_conv2d(self._in_chs, self.num_features, 1, padding=pad_type)
|
|
self.bn2 = norm_layer(self.num_features, **norm_kwargs)
|
|
self.act2 = act_layer(inplace=True)
|
|
self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
|
|
|
|
# Classifier
|
|
self.classifier = nn.Linear(self.num_features * self.global_pool.feat_mult(), self.num_classes)
|
|
|
|
efficientnet_init_weights(self)
|
|
|
|
def as_sequential(self):
|
|
layers = [self.conv_stem, self.bn1, self.act1]
|
|
layers.extend(self.blocks)
|
|
layers.extend([self.conv_head, self.bn2, self.act2, self.global_pool])
|
|
layers.extend([nn.Flatten(), nn.Dropout(self.drop_rate), self.classifier])
|
|
return nn.Sequential(*layers)
|
|
|
|
def get_classifier(self):
|
|
return self.classifier
|
|
|
|
def reset_classifier(self, num_classes, global_pool='avg'):
|
|
self.num_classes = num_classes
|
|
self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
|
|
self.classifier = nn.Linear(
|
|
self.num_features * self.global_pool.feat_mult(), num_classes) if num_classes else None
|
|
|
|
def forward_features(self, x):
|
|
x = self.conv_stem(x)
|
|
x = self.bn1(x)
|
|
x = self.act1(x)
|
|
x = self.blocks(x)
|
|
x = self.conv_head(x)
|
|
x = self.bn2(x)
|
|
x = self.act2(x)
|
|
return x
|
|
|
|
def forward(self, x):
|
|
x = self.forward_features(x)
|
|
x = self.global_pool(x)
|
|
x = x.flatten(1)
|
|
if self.drop_rate > 0.:
|
|
x = F.dropout(x, p=self.drop_rate, training=self.training)
|
|
return self.classifier(x)
|
|
|
|
|
|
class EfficientNetFeatures(nn.Module):
|
|
""" EfficientNet Feature Extractor
|
|
|
|
A work-in-progress feature extraction module for EfficientNet, to use as a backbone for segmentation
|
|
and object detection models.
|
|
"""
|
|
|
|
def __init__(self, block_args, out_indices=(0, 1, 2, 3, 4), feature_location='pre_pwl',
|
|
in_chans=3, stem_size=32, channel_multiplier=1.0, channel_divisor=8, channel_min=None,
|
|
output_stride=32, pad_type='', act_layer=nn.ReLU, drop_rate=0., drop_path_rate=0.,
|
|
se_kwargs=None, norm_layer=nn.BatchNorm2d, norm_kwargs=None):
|
|
super(EfficientNetFeatures, self).__init__()
|
|
norm_kwargs = norm_kwargs or {}
|
|
|
|
# TODO only create stages needed, currently all stages are created regardless of out_indices
|
|
num_stages = max(out_indices) + 1
|
|
|
|
self.out_indices = out_indices
|
|
self.drop_rate = drop_rate
|
|
self._in_chs = in_chans
|
|
|
|
# Stem
|
|
stem_size = round_channels(stem_size, channel_multiplier, channel_divisor, channel_min)
|
|
self.conv_stem = create_conv2d(self._in_chs, stem_size, 3, stride=2, padding=pad_type)
|
|
self.bn1 = norm_layer(stem_size, **norm_kwargs)
|
|
self.act1 = act_layer(inplace=True)
|
|
self._in_chs = stem_size
|
|
|
|
# Middle stages (IR/ER/DS Blocks)
|
|
builder = EfficientNetBuilder(
|
|
channel_multiplier, channel_divisor, channel_min, output_stride, pad_type, act_layer, se_kwargs,
|
|
norm_layer, norm_kwargs, drop_path_rate, feature_location=feature_location, verbose=_DEBUG)
|
|
self.blocks = nn.Sequential(*builder(self._in_chs, block_args))
|
|
self.feature_info = builder.features # builder provides info about feature channels for each block
|
|
self._in_chs = builder.in_chs
|
|
|
|
efficientnet_init_weights(self)
|
|
if _DEBUG:
|
|
for k, v in self.feature_info.items():
|
|
print('Feature idx: {}: Name: {}, Channels: {}'.format(k, v['name'], v['num_chs']))
|
|
|
|
# Register feature extraction hooks with FeatureHooks helper
|
|
hook_type = 'forward_pre' if feature_location == 'pre_pwl' else 'forward'
|
|
hooks = [dict(name=self.feature_info[idx]['name'], type=hook_type) for idx in out_indices]
|
|
self.feature_hooks = FeatureHooks(hooks, self.named_modules())
|
|
|
|
def feature_channels(self, idx=None):
|
|
""" Feature Channel Shortcut
|
|
Returns feature channel count for each output index if idx == None. If idx is an integer, will
|
|
return feature channel count for that feature block index (independent of out_indices setting).
|
|
"""
|
|
if isinstance(idx, int):
|
|
return self.feature_info[idx]['num_chs']
|
|
return [self.feature_info[i]['num_chs'] for i in self.out_indices]
|
|
|
|
def forward(self, x):
|
|
x = self.conv_stem(x)
|
|
x = self.bn1(x)
|
|
x = self.act1(x)
|
|
self.blocks(x)
|
|
return self.feature_hooks.get_output(x.device)
|
|
|
|
|
|
def _create_model(model_kwargs, default_cfg, pretrained=False):
|
|
if model_kwargs.pop('features_only', False):
|
|
load_strict = False
|
|
model_kwargs.pop('num_classes', 0)
|
|
model_kwargs.pop('num_features', 0)
|
|
model_kwargs.pop('head_conv', None)
|
|
model_class = EfficientNetFeatures
|
|
else:
|
|
load_strict = True
|
|
model_class = EfficientNet
|
|
|
|
model = model_class(**model_kwargs)
|
|
model.default_cfg = default_cfg
|
|
if pretrained:
|
|
load_pretrained(
|
|
model,
|
|
default_cfg,
|
|
num_classes=model_kwargs.get('num_classes', 0),
|
|
in_chans=model_kwargs.get('in_chans', 3),
|
|
strict=load_strict)
|
|
return model
|
|
|
|
|
|
def _gen_mnasnet_a1(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
|
|
"""Creates a mnasnet-a1 model.
|
|
|
|
Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet
|
|
Paper: https://arxiv.org/pdf/1807.11626.pdf.
|
|
|
|
Args:
|
|
channel_multiplier: multiplier to number of channels per layer.
|
|
"""
|
|
arch_def = [
|
|
# stage 0, 112x112 in
|
|
['ds_r1_k3_s1_e1_c16_noskip'],
|
|
# stage 1, 112x112 in
|
|
['ir_r2_k3_s2_e6_c24'],
|
|
# stage 2, 56x56 in
|
|
['ir_r3_k5_s2_e3_c40_se0.25'],
|
|
# stage 3, 28x28 in
|
|
['ir_r4_k3_s2_e6_c80'],
|
|
# stage 4, 14x14in
|
|
['ir_r2_k3_s1_e6_c112_se0.25'],
|
|
# stage 5, 14x14in
|
|
['ir_r3_k5_s2_e6_c160_se0.25'],
|
|
# stage 6, 7x7 in
|
|
['ir_r1_k3_s1_e6_c320'],
|
|
]
|
|
model_kwargs = dict(
|
|
block_args=decode_arch_def(arch_def),
|
|
stem_size=32,
|
|
channel_multiplier=channel_multiplier,
|
|
norm_kwargs=resolve_bn_args(kwargs),
|
|
**kwargs
|
|
)
|
|
model = _create_model(model_kwargs, default_cfgs[variant], pretrained)
|
|
return model
|
|
|
|
|
|
def _gen_mnasnet_b1(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
|
|
"""Creates a mnasnet-b1 model.
|
|
|
|
Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet
|
|
Paper: https://arxiv.org/pdf/1807.11626.pdf.
|
|
|
|
Args:
|
|
channel_multiplier: multiplier to number of channels per layer.
|
|
"""
|
|
arch_def = [
|
|
# stage 0, 112x112 in
|
|
['ds_r1_k3_s1_c16_noskip'],
|
|
# stage 1, 112x112 in
|
|
['ir_r3_k3_s2_e3_c24'],
|
|
# stage 2, 56x56 in
|
|
['ir_r3_k5_s2_e3_c40'],
|
|
# stage 3, 28x28 in
|
|
['ir_r3_k5_s2_e6_c80'],
|
|
# stage 4, 14x14in
|
|
['ir_r2_k3_s1_e6_c96'],
|
|
# stage 5, 14x14in
|
|
['ir_r4_k5_s2_e6_c192'],
|
|
# stage 6, 7x7 in
|
|
['ir_r1_k3_s1_e6_c320_noskip']
|
|
]
|
|
model_kwargs = dict(
|
|
block_args=decode_arch_def(arch_def),
|
|
stem_size=32,
|
|
channel_multiplier=channel_multiplier,
|
|
norm_kwargs=resolve_bn_args(kwargs),
|
|
**kwargs
|
|
)
|
|
model = _create_model(model_kwargs, default_cfgs[variant], pretrained)
|
|
return model
|
|
|
|
|
|
def _gen_mnasnet_small(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
|
|
"""Creates a mnasnet-b1 model.
|
|
|
|
Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet
|
|
Paper: https://arxiv.org/pdf/1807.11626.pdf.
|
|
|
|
Args:
|
|
channel_multiplier: multiplier to number of channels per layer.
|
|
"""
|
|
arch_def = [
|
|
['ds_r1_k3_s1_c8'],
|
|
['ir_r1_k3_s2_e3_c16'],
|
|
['ir_r2_k3_s2_e6_c16'],
|
|
['ir_r4_k5_s2_e6_c32_se0.25'],
|
|
['ir_r3_k3_s1_e6_c32_se0.25'],
|
|
['ir_r3_k5_s2_e6_c88_se0.25'],
|
|
['ir_r1_k3_s1_e6_c144']
|
|
]
|
|
model_kwargs = dict(
|
|
block_args=decode_arch_def(arch_def),
|
|
stem_size=8,
|
|
channel_multiplier=channel_multiplier,
|
|
norm_kwargs=resolve_bn_args(kwargs),
|
|
**kwargs
|
|
)
|
|
model = _create_model(model_kwargs, default_cfgs[variant], pretrained)
|
|
return model
|
|
|
|
|
|
def _gen_mobilenet_v2(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
|
|
""" Generate MobileNet-V2 network
|
|
Ref impl: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v2.py
|
|
Paper: https://arxiv.org/abs/1801.04381
|
|
"""
|
|
arch_def = [
|
|
['ds_r1_k3_s1_c16'],
|
|
['ir_r2_k3_s2_e6_c24'],
|
|
['ir_r3_k3_s2_e6_c32'],
|
|
['ir_r4_k3_s2_e6_c64'],
|
|
['ir_r3_k3_s1_e6_c96'],
|
|
['ir_r3_k3_s2_e6_c160'],
|
|
['ir_r1_k3_s1_e6_c320'],
|
|
]
|
|
model_kwargs = dict(
|
|
block_args=decode_arch_def(arch_def),
|
|
stem_size=32,
|
|
channel_multiplier=channel_multiplier,
|
|
norm_kwargs=resolve_bn_args(kwargs),
|
|
act_layer=nn.ReLU6,
|
|
**kwargs
|
|
)
|
|
model = _create_model(model_kwargs, default_cfgs[variant], pretrained)
|
|
return model
|
|
|
|
|
|
def _gen_fbnetc(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
|
|
""" FBNet-C
|
|
|
|
Paper: https://arxiv.org/abs/1812.03443
|
|
Ref Impl: https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/maskrcnn_benchmark/modeling/backbone/fbnet_modeldef.py
|
|
|
|
NOTE: the impl above does not relate to the 'C' variant here, that was derived from paper,
|
|
it was used to confirm some building block details
|
|
"""
|
|
arch_def = [
|
|
['ir_r1_k3_s1_e1_c16'],
|
|
['ir_r1_k3_s2_e6_c24', 'ir_r2_k3_s1_e1_c24'],
|
|
['ir_r1_k5_s2_e6_c32', 'ir_r1_k5_s1_e3_c32', 'ir_r1_k5_s1_e6_c32', 'ir_r1_k3_s1_e6_c32'],
|
|
['ir_r1_k5_s2_e6_c64', 'ir_r1_k5_s1_e3_c64', 'ir_r2_k5_s1_e6_c64'],
|
|
['ir_r3_k5_s1_e6_c112', 'ir_r1_k5_s1_e3_c112'],
|
|
['ir_r4_k5_s2_e6_c184'],
|
|
['ir_r1_k3_s1_e6_c352'],
|
|
]
|
|
model_kwargs = dict(
|
|
block_args=decode_arch_def(arch_def),
|
|
stem_size=16,
|
|
num_features=1984, # paper suggests this, but is not 100% clear
|
|
channel_multiplier=channel_multiplier,
|
|
norm_kwargs=resolve_bn_args(kwargs),
|
|
**kwargs
|
|
)
|
|
model = _create_model(model_kwargs, default_cfgs[variant], pretrained)
|
|
return model
|
|
|
|
|
|
def _gen_spnasnet(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
|
|
"""Creates the Single-Path NAS model from search targeted for Pixel1 phone.
|
|
|
|
Paper: https://arxiv.org/abs/1904.02877
|
|
|
|
Args:
|
|
channel_multiplier: multiplier to number of channels per layer.
|
|
"""
|
|
arch_def = [
|
|
# stage 0, 112x112 in
|
|
['ds_r1_k3_s1_c16_noskip'],
|
|
# stage 1, 112x112 in
|
|
['ir_r3_k3_s2_e3_c24'],
|
|
# stage 2, 56x56 in
|
|
['ir_r1_k5_s2_e6_c40', 'ir_r3_k3_s1_e3_c40'],
|
|
# stage 3, 28x28 in
|
|
['ir_r1_k5_s2_e6_c80', 'ir_r3_k3_s1_e3_c80'],
|
|
# stage 4, 14x14in
|
|
['ir_r1_k5_s1_e6_c96', 'ir_r3_k5_s1_e3_c96'],
|
|
# stage 5, 14x14in
|
|
['ir_r4_k5_s2_e6_c192'],
|
|
# stage 6, 7x7 in
|
|
['ir_r1_k3_s1_e6_c320_noskip']
|
|
]
|
|
model_kwargs = dict(
|
|
block_args=decode_arch_def(arch_def),
|
|
stem_size=32,
|
|
channel_multiplier=channel_multiplier,
|
|
norm_kwargs=resolve_bn_args(kwargs),
|
|
**kwargs
|
|
)
|
|
model = _create_model(model_kwargs, default_cfgs[variant], pretrained)
|
|
return model
|
|
|
|
|
|
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
|
|
"""Creates an EfficientNet model.
|
|
|
|
Ref impl: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/efficientnet_model.py
|
|
Paper: https://arxiv.org/abs/1905.11946
|
|
|
|
EfficientNet params
|
|
name: (channel_multiplier, depth_multiplier, resolution, dropout_rate)
|
|
'efficientnet-b0': (1.0, 1.0, 224, 0.2),
|
|
'efficientnet-b1': (1.0, 1.1, 240, 0.2),
|
|
'efficientnet-b2': (1.1, 1.2, 260, 0.3),
|
|
'efficientnet-b3': (1.2, 1.4, 300, 0.3),
|
|
'efficientnet-b4': (1.4, 1.8, 380, 0.4),
|
|
'efficientnet-b5': (1.6, 2.2, 456, 0.4),
|
|
'efficientnet-b6': (1.8, 2.6, 528, 0.5),
|
|
'efficientnet-b7': (2.0, 3.1, 600, 0.5),
|
|
'efficientnet-b8': (2.2, 3.6, 672, 0.5),
|
|
'efficientnet-l2': (4.3, 5.3, 800, 0.5),
|
|
|
|
Args:
|
|
channel_multiplier: multiplier to number of channels per layer
|
|
depth_multiplier: multiplier to number of repeats per stage
|
|
|
|
"""
|
|
arch_def = [
|
|
['ds_r1_k3_s1_e1_c16_se0.25'],
|
|
['ir_r2_k3_s2_e6_c24_se0.25'],
|
|
['ir_r2_k5_s2_e6_c40_se0.25'],
|
|
['ir_r3_k3_s2_e6_c80_se0.25'],
|
|
['ir_r3_k5_s1_e6_c112_se0.25'],
|
|
['ir_r4_k5_s2_e6_c192_se0.25'],
|
|
['ir_r1_k3_s1_e6_c320_se0.25'],
|
|
]
|
|
model_kwargs = dict(
|
|
block_args=decode_arch_def(arch_def, depth_multiplier),
|
|
num_features=round_channels(1280, channel_multiplier, 8, None),
|
|
stem_size=32,
|
|
channel_multiplier=channel_multiplier,
|
|
act_layer=Swish,
|
|
norm_kwargs=resolve_bn_args(kwargs),
|
|
**kwargs,
|
|
)
|
|
model = _create_model(model_kwargs, default_cfgs[variant], pretrained)
|
|
return model
|
|
|
|
|
|
def _gen_efficientnet_edge(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
|
|
""" Creates an EfficientNet-EdgeTPU model
|
|
|
|
Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/edgetpu
|
|
"""
|
|
|
|
arch_def = [
|
|
# NOTE `fc` is present to override a mismatch between stem channels and in chs not
|
|
# present in other models
|
|
['er_r1_k3_s1_e4_c24_fc24_noskip'],
|
|
['er_r2_k3_s2_e8_c32'],
|
|
['er_r4_k3_s2_e8_c48'],
|
|
['ir_r5_k5_s2_e8_c96'],
|
|
['ir_r4_k5_s1_e8_c144'],
|
|
['ir_r2_k5_s2_e8_c192'],
|
|
]
|
|
model_kwargs = dict(
|
|
block_args=decode_arch_def(arch_def, depth_multiplier),
|
|
num_features=round_channels(1280, channel_multiplier, 8, None),
|
|
stem_size=32,
|
|
channel_multiplier=channel_multiplier,
|
|
norm_kwargs=resolve_bn_args(kwargs),
|
|
act_layer=nn.ReLU,
|
|
**kwargs,
|
|
)
|
|
model = _create_model(model_kwargs, default_cfgs[variant], pretrained)
|
|
return model
|
|
|
|
|
|
def _gen_efficientnet_condconv(
|
|
variant, channel_multiplier=1.0, depth_multiplier=1.0, experts_multiplier=1, pretrained=False, **kwargs):
|
|
"""Creates an EfficientNet-CondConv model.
|
|
|
|
Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/condconv
|
|
"""
|
|
arch_def = [
|
|
['ds_r1_k3_s1_e1_c16_se0.25'],
|
|
['ir_r2_k3_s2_e6_c24_se0.25'],
|
|
['ir_r2_k5_s2_e6_c40_se0.25'],
|
|
['ir_r3_k3_s2_e6_c80_se0.25'],
|
|
['ir_r3_k5_s1_e6_c112_se0.25_cc4'],
|
|
['ir_r4_k5_s2_e6_c192_se0.25_cc4'],
|
|
['ir_r1_k3_s1_e6_c320_se0.25_cc4'],
|
|
]
|
|
# NOTE unlike official impl, this one uses `cc<x>` option where x is the base number of experts for each stage and
|
|
# the expert_multiplier increases that on a per-model basis as with depth/channel multipliers
|
|
model_kwargs = dict(
|
|
block_args=decode_arch_def(arch_def, depth_multiplier, experts_multiplier=experts_multiplier),
|
|
num_features=round_channels(1280, channel_multiplier, 8, None),
|
|
stem_size=32,
|
|
channel_multiplier=channel_multiplier,
|
|
norm_kwargs=resolve_bn_args(kwargs),
|
|
act_layer=Swish,
|
|
**kwargs,
|
|
)
|
|
model = _create_model(model_kwargs, default_cfgs[variant], pretrained)
|
|
return model
|
|
|
|
|
|
def _gen_mixnet_s(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
|
|
"""Creates a MixNet Small model.
|
|
|
|
Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet
|
|
Paper: https://arxiv.org/abs/1907.09595
|
|
"""
|
|
arch_def = [
|
|
# stage 0, 112x112 in
|
|
['ds_r1_k3_s1_e1_c16'], # relu
|
|
# stage 1, 112x112 in
|
|
['ir_r1_k3_a1.1_p1.1_s2_e6_c24', 'ir_r1_k3_a1.1_p1.1_s1_e3_c24'], # relu
|
|
# stage 2, 56x56 in
|
|
['ir_r1_k3.5.7_s2_e6_c40_se0.5_nsw', 'ir_r3_k3.5_a1.1_p1.1_s1_e6_c40_se0.5_nsw'], # swish
|
|
# stage 3, 28x28 in
|
|
['ir_r1_k3.5.7_p1.1_s2_e6_c80_se0.25_nsw', 'ir_r2_k3.5_p1.1_s1_e6_c80_se0.25_nsw'], # swish
|
|
# stage 4, 14x14in
|
|
['ir_r1_k3.5.7_a1.1_p1.1_s1_e6_c120_se0.5_nsw', 'ir_r2_k3.5.7.9_a1.1_p1.1_s1_e3_c120_se0.5_nsw'], # swish
|
|
# stage 5, 14x14in
|
|
['ir_r1_k3.5.7.9.11_s2_e6_c200_se0.5_nsw', 'ir_r2_k3.5.7.9_p1.1_s1_e6_c200_se0.5_nsw'], # swish
|
|
# 7x7
|
|
]
|
|
model_kwargs = dict(
|
|
block_args=decode_arch_def(arch_def),
|
|
num_features=1536,
|
|
stem_size=16,
|
|
channel_multiplier=channel_multiplier,
|
|
norm_kwargs=resolve_bn_args(kwargs),
|
|
**kwargs
|
|
)
|
|
model = _create_model(model_kwargs, default_cfgs[variant], pretrained)
|
|
return model
|
|
|
|
|
|
def _gen_mixnet_m(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
|
|
"""Creates a MixNet Medium-Large model.
|
|
|
|
Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet
|
|
Paper: https://arxiv.org/abs/1907.09595
|
|
"""
|
|
arch_def = [
|
|
# stage 0, 112x112 in
|
|
['ds_r1_k3_s1_e1_c24'], # relu
|
|
# stage 1, 112x112 in
|
|
['ir_r1_k3.5.7_a1.1_p1.1_s2_e6_c32', 'ir_r1_k3_a1.1_p1.1_s1_e3_c32'], # relu
|
|
# stage 2, 56x56 in
|
|
['ir_r1_k3.5.7.9_s2_e6_c40_se0.5_nsw', 'ir_r3_k3.5_a1.1_p1.1_s1_e6_c40_se0.5_nsw'], # swish
|
|
# stage 3, 28x28 in
|
|
['ir_r1_k3.5.7_s2_e6_c80_se0.25_nsw', 'ir_r3_k3.5.7.9_a1.1_p1.1_s1_e6_c80_se0.25_nsw'], # swish
|
|
# stage 4, 14x14in
|
|
['ir_r1_k3_s1_e6_c120_se0.5_nsw', 'ir_r3_k3.5.7.9_a1.1_p1.1_s1_e3_c120_se0.5_nsw'], # swish
|
|
# stage 5, 14x14in
|
|
['ir_r1_k3.5.7.9_s2_e6_c200_se0.5_nsw', 'ir_r3_k3.5.7.9_p1.1_s1_e6_c200_se0.5_nsw'], # swish
|
|
# 7x7
|
|
]
|
|
model_kwargs = dict(
|
|
block_args=decode_arch_def(arch_def, depth_multiplier, depth_trunc='round'),
|
|
num_features=1536,
|
|
stem_size=24,
|
|
channel_multiplier=channel_multiplier,
|
|
norm_kwargs=resolve_bn_args(kwargs),
|
|
**kwargs
|
|
)
|
|
model = _create_model(model_kwargs, default_cfgs[variant], pretrained)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def mnasnet_050(pretrained=False, **kwargs):
|
|
""" MNASNet B1, depth multiplier of 0.5. """
|
|
model = _gen_mnasnet_b1('mnasnet_050', 0.5, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def mnasnet_075(pretrained=False, **kwargs):
|
|
""" MNASNet B1, depth multiplier of 0.75. """
|
|
model = _gen_mnasnet_b1('mnasnet_075', 0.75, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def mnasnet_100(pretrained=False, **kwargs):
|
|
""" MNASNet B1, depth multiplier of 1.0. """
|
|
model = _gen_mnasnet_b1('mnasnet_100', 1.0, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def mnasnet_b1(pretrained=False, **kwargs):
|
|
""" MNASNet B1, depth multiplier of 1.0. """
|
|
return mnasnet_100(pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def mnasnet_140(pretrained=False, **kwargs):
|
|
""" MNASNet B1, depth multiplier of 1.4 """
|
|
model = _gen_mnasnet_b1('mnasnet_140', 1.4, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def semnasnet_050(pretrained=False, **kwargs):
|
|
""" MNASNet A1 (w/ SE), depth multiplier of 0.5 """
|
|
model = _gen_mnasnet_a1('semnasnet_050', 0.5, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def semnasnet_075(pretrained=False, **kwargs):
|
|
""" MNASNet A1 (w/ SE), depth multiplier of 0.75. """
|
|
model = _gen_mnasnet_a1('semnasnet_075', 0.75, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def semnasnet_100(pretrained=False, **kwargs):
|
|
""" MNASNet A1 (w/ SE), depth multiplier of 1.0. """
|
|
model = _gen_mnasnet_a1('semnasnet_100', 1.0, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def mnasnet_a1(pretrained=False, **kwargs):
|
|
""" MNASNet A1 (w/ SE), depth multiplier of 1.0. """
|
|
return semnasnet_100(pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def semnasnet_140(pretrained=False, **kwargs):
|
|
""" MNASNet A1 (w/ SE), depth multiplier of 1.4. """
|
|
model = _gen_mnasnet_a1('semnasnet_140', 1.4, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def mnasnet_small(pretrained=False, **kwargs):
|
|
""" MNASNet Small, depth multiplier of 1.0. """
|
|
model = _gen_mnasnet_small('mnasnet_small', 1.0, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def mobilenetv2_100(pretrained=False, **kwargs):
|
|
""" MobileNet V2 """
|
|
model = _gen_mobilenet_v2('mobilenetv2_100', 1.0, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def fbnetc_100(pretrained=False, **kwargs):
|
|
""" FBNet-C """
|
|
if pretrained:
|
|
# pretrained model trained with non-default BN epsilon
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
model = _gen_fbnetc('fbnetc_100', 1.0, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def spnasnet_100(pretrained=False, **kwargs):
|
|
""" Single-Path NAS Pixel1"""
|
|
model = _gen_spnasnet('spnasnet_100', 1.0, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def efficientnet_b0(pretrained=False, **kwargs):
|
|
""" EfficientNet-B0 """
|
|
# NOTE for train, drop_rate should be 0.2, drop_path_rate should be 0.2
|
|
model = _gen_efficientnet(
|
|
'efficientnet_b0', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def efficientnet_b1(pretrained=False, **kwargs):
|
|
""" EfficientNet-B1 """
|
|
# NOTE for train, drop_rate should be 0.2, drop_path_rate should be 0.2
|
|
model = _gen_efficientnet(
|
|
'efficientnet_b1', channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def efficientnet_b2(pretrained=False, **kwargs):
|
|
""" EfficientNet-B2 """
|
|
# NOTE for train, drop_rate should be 0.3, drop_path_rate should be 0.2
|
|
model = _gen_efficientnet(
|
|
'efficientnet_b2', channel_multiplier=1.1, depth_multiplier=1.2, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def efficientnet_b2a(pretrained=False, **kwargs):
|
|
""" EfficientNet-B2 @ 288x288 w/ 1.0 test crop"""
|
|
# NOTE for train, drop_rate should be 0.3, drop_path_rate should be 0.2
|
|
model = _gen_efficientnet(
|
|
'efficientnet_b2a', channel_multiplier=1.1, depth_multiplier=1.2, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def efficientnet_b3(pretrained=False, **kwargs):
|
|
""" EfficientNet-B3 """
|
|
# NOTE for train, drop_rate should be 0.3, drop_path_rate should be 0.2
|
|
model = _gen_efficientnet(
|
|
'efficientnet_b3', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def efficientnet_b3a(pretrained=False, **kwargs):
|
|
""" EfficientNet-B3 @ 320x320 w/ 1.0 test crop-pct """
|
|
# NOTE for train, drop_rate should be 0.3, drop_path_rate should be 0.2
|
|
model = _gen_efficientnet(
|
|
'efficientnet_b3a', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def efficientnet_b4(pretrained=False, **kwargs):
|
|
""" EfficientNet-B4 """
|
|
# NOTE for train, drop_rate should be 0.4, drop_path_rate should be 0.2
|
|
model = _gen_efficientnet(
|
|
'efficientnet_b4', channel_multiplier=1.4, depth_multiplier=1.8, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def efficientnet_b5(pretrained=False, **kwargs):
|
|
""" EfficientNet-B5 """
|
|
# NOTE for train, drop_rate should be 0.4, drop_path_rate should be 0.2
|
|
model = _gen_efficientnet(
|
|
'efficientnet_b5', channel_multiplier=1.6, depth_multiplier=2.2, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def efficientnet_b6(pretrained=False, **kwargs):
|
|
""" EfficientNet-B6 """
|
|
# NOTE for train, drop_rate should be 0.5, drop_path_rate should be 0.2
|
|
model = _gen_efficientnet(
|
|
'efficientnet_b6', channel_multiplier=1.8, depth_multiplier=2.6, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def efficientnet_b7(pretrained=False, **kwargs):
|
|
""" EfficientNet-B7 """
|
|
# NOTE for train, drop_rate should be 0.5, drop_path_rate should be 0.2
|
|
model = _gen_efficientnet(
|
|
'efficientnet_b7', channel_multiplier=2.0, depth_multiplier=3.1, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def efficientnet_b8(pretrained=False, **kwargs):
|
|
""" EfficientNet-B8 """
|
|
# NOTE for train, drop_rate should be 0.5, drop_path_rate should be 0.2
|
|
model = _gen_efficientnet(
|
|
'efficientnet_b8', channel_multiplier=2.2, depth_multiplier=3.6, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def efficientnet_l2(pretrained=False, **kwargs):
|
|
""" EfficientNet-L2."""
|
|
# NOTE for train, drop_rate should be 0.5, drop_path_rate should be 0.2
|
|
model = _gen_efficientnet(
|
|
'efficientnet_l2', channel_multiplier=4.3, depth_multiplier=5.3, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def efficientnet_es(pretrained=False, **kwargs):
|
|
""" EfficientNet-Edge Small. """
|
|
model = _gen_efficientnet_edge(
|
|
'efficientnet_es', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def efficientnet_em(pretrained=False, **kwargs):
|
|
""" EfficientNet-Edge-Medium. """
|
|
model = _gen_efficientnet_edge(
|
|
'efficientnet_em', channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def efficientnet_el(pretrained=False, **kwargs):
|
|
""" EfficientNet-Edge-Large. """
|
|
model = _gen_efficientnet_edge(
|
|
'efficientnet_el', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def efficientnet_cc_b0_4e(pretrained=False, **kwargs):
|
|
""" EfficientNet-CondConv-B0 w/ 8 Experts """
|
|
# NOTE for train, drop_rate should be 0.2, drop_path_rate should be 0.2
|
|
model = _gen_efficientnet_condconv(
|
|
'efficientnet_cc_b0_4e', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def efficientnet_cc_b0_8e(pretrained=False, **kwargs):
|
|
""" EfficientNet-CondConv-B0 w/ 8 Experts """
|
|
# NOTE for train, drop_rate should be 0.2, drop_path_rate should be 0.2
|
|
model = _gen_efficientnet_condconv(
|
|
'efficientnet_cc_b0_8e', channel_multiplier=1.0, depth_multiplier=1.0, experts_multiplier=2,
|
|
pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
@register_model
|
|
def efficientnet_cc_b1_8e(pretrained=False, **kwargs):
|
|
""" EfficientNet-CondConv-B1 w/ 8 Experts """
|
|
# NOTE for train, drop_rate should be 0.2, drop_path_rate should be 0.2
|
|
model = _gen_efficientnet_condconv(
|
|
'efficientnet_cc_b1_8e', channel_multiplier=1.0, depth_multiplier=1.1, experts_multiplier=2,
|
|
pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_efficientnet_b0(pretrained=False, **kwargs):
|
|
""" EfficientNet-B0. Tensorflow compatible variant """
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
kwargs['pad_type'] = 'same'
|
|
model = _gen_efficientnet(
|
|
'tf_efficientnet_b0', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_efficientnet_b1(pretrained=False, **kwargs):
|
|
""" EfficientNet-B1. Tensorflow compatible variant """
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
kwargs['pad_type'] = 'same'
|
|
model = _gen_efficientnet(
|
|
'tf_efficientnet_b1', channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_efficientnet_b2(pretrained=False, **kwargs):
|
|
""" EfficientNet-B2. Tensorflow compatible variant """
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
kwargs['pad_type'] = 'same'
|
|
model = _gen_efficientnet(
|
|
'tf_efficientnet_b2', channel_multiplier=1.1, depth_multiplier=1.2, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_efficientnet_b3(pretrained=False, **kwargs):
|
|
""" EfficientNet-B3. Tensorflow compatible variant """
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
kwargs['pad_type'] = 'same'
|
|
model = _gen_efficientnet(
|
|
'tf_efficientnet_b3', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_efficientnet_b4(pretrained=False, **kwargs):
|
|
""" EfficientNet-B4. Tensorflow compatible variant """
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
kwargs['pad_type'] = 'same'
|
|
model = _gen_efficientnet(
|
|
'tf_efficientnet_b4', channel_multiplier=1.4, depth_multiplier=1.8, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_efficientnet_b5(pretrained=False, **kwargs):
|
|
""" EfficientNet-B5. Tensorflow compatible variant """
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
kwargs['pad_type'] = 'same'
|
|
model = _gen_efficientnet(
|
|
'tf_efficientnet_b5', channel_multiplier=1.6, depth_multiplier=2.2, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_efficientnet_b6(pretrained=False, **kwargs):
|
|
""" EfficientNet-B6. Tensorflow compatible variant """
|
|
# NOTE for train, drop_rate should be 0.5
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
kwargs['pad_type'] = 'same'
|
|
model = _gen_efficientnet(
|
|
'tf_efficientnet_b6', channel_multiplier=1.8, depth_multiplier=2.6, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_efficientnet_b7(pretrained=False, **kwargs):
|
|
""" EfficientNet-B7. Tensorflow compatible variant """
|
|
# NOTE for train, drop_rate should be 0.5
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
kwargs['pad_type'] = 'same'
|
|
model = _gen_efficientnet(
|
|
'tf_efficientnet_b7', channel_multiplier=2.0, depth_multiplier=3.1, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_efficientnet_b8(pretrained=False, **kwargs):
|
|
""" EfficientNet-B8. Tensorflow compatible variant """
|
|
# NOTE for train, drop_rate should be 0.5
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
kwargs['pad_type'] = 'same'
|
|
model = _gen_efficientnet(
|
|
'tf_efficientnet_b8', channel_multiplier=2.2, depth_multiplier=3.6, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_efficientnet_b0_ap(pretrained=False, **kwargs):
|
|
""" EfficientNet-B0 AdvProp. Tensorflow compatible variant """
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
kwargs['pad_type'] = 'same'
|
|
model = _gen_efficientnet(
|
|
'tf_efficientnet_b0_ap', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_efficientnet_b1_ap(pretrained=False, **kwargs):
|
|
""" EfficientNet-B1 AdvProp. Tensorflow compatible variant """
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
kwargs['pad_type'] = 'same'
|
|
model = _gen_efficientnet(
|
|
'tf_efficientnet_b1_ap', channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_efficientnet_b2_ap(pretrained=False, **kwargs):
|
|
""" EfficientNet-B2 AdvProp. Tensorflow compatible variant """
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
kwargs['pad_type'] = 'same'
|
|
model = _gen_efficientnet(
|
|
'tf_efficientnet_b2_ap', channel_multiplier=1.1, depth_multiplier=1.2, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_efficientnet_b3_ap(pretrained=False, **kwargs):
|
|
""" EfficientNet-B3 AdvProp. Tensorflow compatible variant """
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
kwargs['pad_type'] = 'same'
|
|
model = _gen_efficientnet(
|
|
'tf_efficientnet_b3_ap', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_efficientnet_b4_ap(pretrained=False, **kwargs):
|
|
""" EfficientNet-B4 AdvProp. Tensorflow compatible variant """
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
kwargs['pad_type'] = 'same'
|
|
model = _gen_efficientnet(
|
|
'tf_efficientnet_b4_ap', channel_multiplier=1.4, depth_multiplier=1.8, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_efficientnet_b5_ap(pretrained=False, **kwargs):
|
|
""" EfficientNet-B5 AdvProp. Tensorflow compatible variant """
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
kwargs['pad_type'] = 'same'
|
|
model = _gen_efficientnet(
|
|
'tf_efficientnet_b5_ap', channel_multiplier=1.6, depth_multiplier=2.2, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_efficientnet_b6_ap(pretrained=False, **kwargs):
|
|
""" EfficientNet-B6 AdvProp. Tensorflow compatible variant """
|
|
# NOTE for train, drop_rate should be 0.5
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
kwargs['pad_type'] = 'same'
|
|
model = _gen_efficientnet(
|
|
'tf_efficientnet_b6_ap', channel_multiplier=1.8, depth_multiplier=2.6, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_efficientnet_b7_ap(pretrained=False, **kwargs):
|
|
""" EfficientNet-B7 AdvProp. Tensorflow compatible variant """
|
|
# NOTE for train, drop_rate should be 0.5
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
kwargs['pad_type'] = 'same'
|
|
model = _gen_efficientnet(
|
|
'tf_efficientnet_b7_ap', channel_multiplier=2.0, depth_multiplier=3.1, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_efficientnet_b8_ap(pretrained=False, **kwargs):
|
|
""" EfficientNet-B8 AdvProp. Tensorflow compatible variant """
|
|
# NOTE for train, drop_rate should be 0.5
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
kwargs['pad_type'] = 'same'
|
|
model = _gen_efficientnet(
|
|
'tf_efficientnet_b8_ap', channel_multiplier=2.2, depth_multiplier=3.6, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_efficientnet_b0_ns(pretrained=False, **kwargs):
|
|
""" EfficientNet-B0 NoisyStudent. Tensorflow compatible variant """
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
kwargs['pad_type'] = 'same'
|
|
model = _gen_efficientnet(
|
|
'tf_efficientnet_b0_ns', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_efficientnet_b1_ns(pretrained=False, **kwargs):
|
|
""" EfficientNet-B1 NoisyStudent. Tensorflow compatible variant """
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
kwargs['pad_type'] = 'same'
|
|
model = _gen_efficientnet(
|
|
'tf_efficientnet_b1_ns', channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_efficientnet_b2_ns(pretrained=False, **kwargs):
|
|
""" EfficientNet-B2 NoisyStudent. Tensorflow compatible variant """
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
kwargs['pad_type'] = 'same'
|
|
model = _gen_efficientnet(
|
|
'tf_efficientnet_b2_ns', channel_multiplier=1.1, depth_multiplier=1.2, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_efficientnet_b3_ns(pretrained=False, **kwargs):
|
|
""" EfficientNet-B3 NoisyStudent. Tensorflow compatible variant """
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
kwargs['pad_type'] = 'same'
|
|
model = _gen_efficientnet(
|
|
'tf_efficientnet_b3_ns', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_efficientnet_b4_ns(pretrained=False, **kwargs):
|
|
""" EfficientNet-B4 NoisyStudent. Tensorflow compatible variant """
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
kwargs['pad_type'] = 'same'
|
|
model = _gen_efficientnet(
|
|
'tf_efficientnet_b4_ns', channel_multiplier=1.4, depth_multiplier=1.8, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_efficientnet_b5_ns(pretrained=False, **kwargs):
|
|
""" EfficientNet-B5 NoisyStudent. Tensorflow compatible variant """
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
kwargs['pad_type'] = 'same'
|
|
model = _gen_efficientnet(
|
|
'tf_efficientnet_b5_ns', channel_multiplier=1.6, depth_multiplier=2.2, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_efficientnet_b6_ns(pretrained=False, **kwargs):
|
|
""" EfficientNet-B6 NoisyStudent. Tensorflow compatible variant """
|
|
# NOTE for train, drop_rate should be 0.5
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
kwargs['pad_type'] = 'same'
|
|
model = _gen_efficientnet(
|
|
'tf_efficientnet_b6_ns', channel_multiplier=1.8, depth_multiplier=2.6, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_efficientnet_b7_ns(pretrained=False, **kwargs):
|
|
""" EfficientNet-B7 NoisyStudent. Tensorflow compatible variant """
|
|
# NOTE for train, drop_rate should be 0.5
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
kwargs['pad_type'] = 'same'
|
|
model = _gen_efficientnet(
|
|
'tf_efficientnet_b7_ns', channel_multiplier=2.0, depth_multiplier=3.1, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_efficientnet_l2_ns_475(pretrained=False, **kwargs):
|
|
""" EfficientNet-L2 NoisyStudent @ 475x475. Tensorflow compatible variant """
|
|
# NOTE for train, drop_rate should be 0.5
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
kwargs['pad_type'] = 'same'
|
|
model = _gen_efficientnet(
|
|
'tf_efficientnet_l2_ns_475', channel_multiplier=4.3, depth_multiplier=5.3, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_efficientnet_l2_ns(pretrained=False, **kwargs):
|
|
""" EfficientNet-L2 NoisyStudent. Tensorflow compatible variant """
|
|
# NOTE for train, drop_rate should be 0.5
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
kwargs['pad_type'] = 'same'
|
|
model = _gen_efficientnet(
|
|
'tf_efficientnet_l2_ns', channel_multiplier=4.3, depth_multiplier=5.3, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_efficientnet_es(pretrained=False, **kwargs):
|
|
""" EfficientNet-Edge Small. Tensorflow compatible variant """
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
kwargs['pad_type'] = 'same'
|
|
model = _gen_efficientnet_edge(
|
|
'tf_efficientnet_es', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_efficientnet_em(pretrained=False, **kwargs):
|
|
""" EfficientNet-Edge-Medium. Tensorflow compatible variant """
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
kwargs['pad_type'] = 'same'
|
|
model = _gen_efficientnet_edge(
|
|
'tf_efficientnet_em', channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_efficientnet_el(pretrained=False, **kwargs):
|
|
""" EfficientNet-Edge-Large. Tensorflow compatible variant """
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
kwargs['pad_type'] = 'same'
|
|
model = _gen_efficientnet_edge(
|
|
'tf_efficientnet_el', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_efficientnet_cc_b0_4e(pretrained=False, **kwargs):
|
|
""" EfficientNet-CondConv-B0 w/ 4 Experts. Tensorflow compatible variant """
|
|
# NOTE for train, drop_rate should be 0.2, drop_path_rate should be 0.2
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
kwargs['pad_type'] = 'same'
|
|
model = _gen_efficientnet_condconv(
|
|
'tf_efficientnet_cc_b0_4e', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_efficientnet_cc_b0_8e(pretrained=False, **kwargs):
|
|
""" EfficientNet-CondConv-B0 w/ 8 Experts. Tensorflow compatible variant """
|
|
# NOTE for train, drop_rate should be 0.2, drop_path_rate should be 0.2
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
kwargs['pad_type'] = 'same'
|
|
model = _gen_efficientnet_condconv(
|
|
'tf_efficientnet_cc_b0_8e', channel_multiplier=1.0, depth_multiplier=1.0, experts_multiplier=2,
|
|
pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
@register_model
|
|
def tf_efficientnet_cc_b1_8e(pretrained=False, **kwargs):
|
|
""" EfficientNet-CondConv-B1 w/ 8 Experts. Tensorflow compatible variant """
|
|
# NOTE for train, drop_rate should be 0.2, drop_path_rate should be 0.2
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
kwargs['pad_type'] = 'same'
|
|
model = _gen_efficientnet_condconv(
|
|
'tf_efficientnet_cc_b1_8e', channel_multiplier=1.0, depth_multiplier=1.1, experts_multiplier=2,
|
|
pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def mixnet_s(pretrained=False, **kwargs):
|
|
"""Creates a MixNet Small model.
|
|
"""
|
|
model = _gen_mixnet_s(
|
|
'mixnet_s', channel_multiplier=1.0, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def mixnet_m(pretrained=False, **kwargs):
|
|
"""Creates a MixNet Medium model.
|
|
"""
|
|
model = _gen_mixnet_m(
|
|
'mixnet_m', channel_multiplier=1.0, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def mixnet_l(pretrained=False, **kwargs):
|
|
"""Creates a MixNet Large model.
|
|
"""
|
|
model = _gen_mixnet_m(
|
|
'mixnet_l', channel_multiplier=1.3, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def mixnet_xl(pretrained=False, **kwargs):
|
|
"""Creates a MixNet Extra-Large model.
|
|
Not a paper spec, experimental def by RW w/ depth scaling.
|
|
"""
|
|
model = _gen_mixnet_m(
|
|
'mixnet_xl', channel_multiplier=1.6, depth_multiplier=1.2, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def mixnet_xxl(pretrained=False, **kwargs):
|
|
"""Creates a MixNet Double Extra Large model.
|
|
Not a paper spec, experimental def by RW w/ depth scaling.
|
|
"""
|
|
model = _gen_mixnet_m(
|
|
'mixnet_xxl', channel_multiplier=2.4, depth_multiplier=1.3, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_mixnet_s(pretrained=False, **kwargs):
|
|
"""Creates a MixNet Small model. Tensorflow compatible variant
|
|
"""
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
kwargs['pad_type'] = 'same'
|
|
model = _gen_mixnet_s(
|
|
'tf_mixnet_s', channel_multiplier=1.0, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_mixnet_m(pretrained=False, **kwargs):
|
|
"""Creates a MixNet Medium model. Tensorflow compatible variant
|
|
"""
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
kwargs['pad_type'] = 'same'
|
|
model = _gen_mixnet_m(
|
|
'tf_mixnet_m', channel_multiplier=1.0, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_mixnet_l(pretrained=False, **kwargs):
|
|
"""Creates a MixNet Large model. Tensorflow compatible variant
|
|
"""
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
kwargs['pad_type'] = 'same'
|
|
model = _gen_mixnet_m(
|
|
'tf_mixnet_l', channel_multiplier=1.3, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|