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""" The EfficientNet Family in PyTorch
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An implementation of EfficienNet that covers variety of related models with efficient architectures:
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* EfficientNet-V2
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- `EfficientNetV2: Smaller Models and Faster Training` - https://arxiv.org/abs/2104.00298
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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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* TinyNet
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- Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets - https://arxiv.org/abs/2010.14819
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- Definitions & weights borrowed from https://github.com/huawei-noah/CV-Backbones/tree/master/tinynet_pytorch
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* And likely more...
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The majority of the above models (EfficientNet*, MixNet, MnasNet) and original weights were made available
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by Mingxing Tan, Quoc Le, and other members of their Google Brain team. Thanks for consistently releasing
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the models and weights open source!
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Hacked together by / Copyright 2021 Ross Wightman
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"""
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from functools import partial
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from typing import List
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import torch
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Monster commit, activation refactor, VoVNet, norm_act improvements, more
* refactor activations into basic PyTorch, jit scripted, and memory efficient custom auto
* implement hard-mish, better grad for hard-swish
* add initial VovNet V1/V2 impl, fix #151
* VovNet and DenseNet first models to use NormAct layers (support BatchNormAct2d, EvoNorm, InplaceIABN)
* Wrap IABN for any models that use it
* make more models torchscript compatible (DPN, PNasNet, Res2Net, SelecSLS) and add tests
4 years ago
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import torch.nn as nn
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import torch.nn.functional as F
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
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from .efficientnet_blocks import SqueezeExcite
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from .efficientnet_builder import EfficientNetBuilder, decode_arch_def, efficientnet_init_weights,\
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round_channels, resolve_bn_args, resolve_act_layer, BN_EPS_TF_DEFAULT
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from .features import FeatureInfo, FeatureHooks
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from .helpers import build_model_with_cfg, default_cfg_for_features
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from .layers import create_conv2d, create_classifier
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from .registry import register_model
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__all__ = ['EfficientNet', 'EfficientNetFeatures']
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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_035': _cfg(
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url=''),
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'mobilenetv2_050': _cfg(
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url=''),
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'mobilenetv2_075': _cfg(
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url=''),
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'mobilenetv2_100': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_100_ra-b33bc2c4.pth'),
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'mobilenetv2_110d': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_110d_ra-77090ade.pth'),
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'mobilenetv2_120d': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_120d_ra-5987e2ed.pth'),
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'mobilenetv2_140': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_140_ra-21a4e913.pth'),
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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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# NOTE experimenting with alternate attention
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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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test_input_size=(3, 256, 256), crop_pct=1.0),
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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, 256, 256), pool_size=(8, 8), test_input_size=(3, 288, 288), 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_ra2-cf984f9c.pth',
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input_size=(3, 288, 288), pool_size=(9, 9), test_input_size=(3, 320, 320), crop_pct=1.0),
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'efficientnet_b4': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b4_ra2_320-7eb33cd5.pth',
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input_size=(3, 320, 320), pool_size=(10, 10), test_input_size=(3, 384, 384), crop_pct=1.0),
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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='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_em_ra2-66250f76.pth',
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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='https://github.com/DeGirum/pruned-models/releases/download/efficientnet_v1.0/efficientnet_el.pth',
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input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904),
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'efficientnet_es_pruned': _cfg(
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url='https://github.com/DeGirum/pruned-models/releases/download/efficientnet_v1.0/efficientnet_es_pruned75.pth'),
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'efficientnet_el_pruned': _cfg(
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url='https://github.com/DeGirum/pruned-models/releases/download/efficientnet_v1.0/efficientnet_el_pruned70.pth',
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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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'efficientnet_lite0': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_lite0_ra-37913777.pth'),
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'efficientnet_lite1': _cfg(
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url='',
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input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882),
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'efficientnet_lite2': _cfg(
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url='',
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input_size=(3, 260, 260), pool_size=(9, 9), crop_pct=0.890),
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'efficientnet_lite3': _cfg(
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url='',
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input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904),
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'efficientnet_lite4': _cfg(
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url='', input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.922),
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'efficientnet_b1_pruned': _cfg(
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url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45403/outputs/effnetb1_pruned_9ebb3fe6.pth',
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input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882, mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
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'efficientnet_b2_pruned': _cfg(
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url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45403/outputs/effnetb2_pruned_203f55bc.pth',
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input_size=(3, 260, 260), pool_size=(9, 9), crop_pct=0.890, mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
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'efficientnet_b3_pruned': _cfg(
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url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45403/outputs/effnetb3_pruned_5abcc29f.pth',
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input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904, mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
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'efficientnetv2_rw_t': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnetv2_t_agc-3620981a.pth',
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input_size=(3, 224, 224), test_input_size=(3, 288, 288), pool_size=(7, 7), crop_pct=1.0),
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'gc_efficientnetv2_rw_t': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gc_efficientnetv2_rw_t_agc-927a0bde.pth',
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input_size=(3, 224, 224), test_input_size=(3, 288, 288), pool_size=(7, 7), crop_pct=1.0),
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'efficientnetv2_rw_s': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_v2s_ra2_288-a6477665.pth',
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input_size=(3, 288, 288), test_input_size=(3, 384, 384), pool_size=(9, 9), crop_pct=1.0),
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'efficientnetv2_rw_m': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnetv2_rw_m_agc-3d90cb1e.pth',
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input_size=(3, 320, 320), test_input_size=(3, 416, 416), pool_size=(10, 10), crop_pct=1.0),
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'efficientnetv2_s': _cfg(
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url='',
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input_size=(3, 288, 288), test_input_size=(3, 384, 384), pool_size=(9, 9), crop_pct=1.0),
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'efficientnetv2_m': _cfg(
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url='',
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input_size=(3, 320, 320), test_input_size=(3, 416, 416), pool_size=(10, 10), crop_pct=1.0),
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'efficientnetv2_l': _cfg(
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url='',
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input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0),
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'efficientnetv2_xl': _cfg(
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url='',
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input_size=(3, 384, 384), test_input_size=(3, 512, 512), pool_size=(12, 12), crop_pct=1.0),
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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),
|
|
|
|
|
'tf_efficientnet_b7': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b7_ra-6c08e654.pth',
|
|
|
|
|
input_size=(3, 600, 600), pool_size=(19, 19), crop_pct=0.949),
|
|
|
|
|
'tf_efficientnet_b8': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b8_ra-572d5dd9.pth',
|
|
|
|
|
input_size=(3, 672, 672), pool_size=(21, 21), crop_pct=0.954),
|
|
|
|
|
|
|
|
|
|
'tf_efficientnet_b0_ap': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_ap-f262efe1.pth',
|
|
|
|
|
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, input_size=(3, 224, 224)),
|
|
|
|
|
'tf_efficientnet_b1_ap': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b1_ap-44ef0a3d.pth',
|
|
|
|
|
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
|
|
|
|
|
input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882),
|
|
|
|
|
'tf_efficientnet_b2_ap': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b2_ap-2f8e7636.pth',
|
|
|
|
|
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
|
|
|
|
|
input_size=(3, 260, 260), pool_size=(9, 9), crop_pct=0.890),
|
|
|
|
|
'tf_efficientnet_b3_ap': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3_ap-aad25bdd.pth',
|
|
|
|
|
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
|
|
|
|
|
input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904),
|
|
|
|
|
'tf_efficientnet_b4_ap': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b4_ap-dedb23e6.pth',
|
|
|
|
|
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
|
|
|
|
|
input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.922),
|
|
|
|
|
'tf_efficientnet_b5_ap': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b5_ap-9e82fae8.pth',
|
|
|
|
|
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
|
|
|
|
|
input_size=(3, 456, 456), pool_size=(15, 15), crop_pct=0.934),
|
|
|
|
|
'tf_efficientnet_b6_ap': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b6_ap-4ffb161f.pth',
|
|
|
|
|
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
|
|
|
|
|
input_size=(3, 528, 528), pool_size=(17, 17), crop_pct=0.942),
|
|
|
|
|
'tf_efficientnet_b7_ap': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b7_ap-ddb28fec.pth',
|
|
|
|
|
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
|
|
|
|
|
input_size=(3, 600, 600), pool_size=(19, 19), crop_pct=0.949),
|
|
|
|
|
'tf_efficientnet_b8_ap': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b8_ap-00e169fa.pth',
|
|
|
|
|
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
|
|
|
|
|
input_size=(3, 672, 672), pool_size=(21, 21), crop_pct=0.954),
|
|
|
|
|
|
|
|
|
|
'tf_efficientnet_b0_ns': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_ns-c0e6a31c.pth',
|
|
|
|
|
input_size=(3, 224, 224)),
|
|
|
|
|
'tf_efficientnet_b1_ns': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b1_ns-99dd0c41.pth',
|
|
|
|
|
input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882),
|
|
|
|
|
'tf_efficientnet_b2_ns': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b2_ns-00306e48.pth',
|
|
|
|
|
input_size=(3, 260, 260), pool_size=(9, 9), crop_pct=0.890),
|
|
|
|
|
'tf_efficientnet_b3_ns': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3_ns-9d44bf68.pth',
|
|
|
|
|
input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904),
|
|
|
|
|
'tf_efficientnet_b4_ns': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b4_ns-d6313a46.pth',
|
|
|
|
|
input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.922),
|
|
|
|
|
'tf_efficientnet_b5_ns': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b5_ns-6f26d0cf.pth',
|
|
|
|
|
input_size=(3, 456, 456), pool_size=(15, 15), crop_pct=0.934),
|
|
|
|
|
'tf_efficientnet_b6_ns': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b6_ns-51548356.pth',
|
|
|
|
|
input_size=(3, 528, 528), pool_size=(17, 17), crop_pct=0.942),
|
|
|
|
|
'tf_efficientnet_b7_ns': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b7_ns-1dbc32de.pth',
|
|
|
|
|
input_size=(3, 600, 600), pool_size=(19, 19), crop_pct=0.949),
|
|
|
|
|
'tf_efficientnet_l2_ns_475': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_l2_ns_475-bebbd00a.pth',
|
|
|
|
|
input_size=(3, 475, 475), pool_size=(15, 15), crop_pct=0.936),
|
|
|
|
|
'tf_efficientnet_l2_ns': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_l2_ns-df73bb44.pth',
|
|
|
|
|
input_size=(3, 800, 800), pool_size=(25, 25), crop_pct=0.96),
|
|
|
|
|
|
|
|
|
|
'tf_efficientnet_es': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_es-ca1afbfe.pth',
|
|
|
|
|
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
|
|
|
|
|
input_size=(3, 224, 224), ),
|
|
|
|
|
'tf_efficientnet_em': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_em-e78cfe58.pth',
|
|
|
|
|
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
|
|
|
|
|
input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882),
|
|
|
|
|
'tf_efficientnet_el': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_el-5143854e.pth',
|
|
|
|
|
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
|
|
|
|
|
input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904),
|
|
|
|
|
|
|
|
|
|
'tf_efficientnet_cc_b0_4e': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_cc_b0_4e-4362b6b2.pth',
|
|
|
|
|
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
|
|
|
|
|
'tf_efficientnet_cc_b0_8e': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_cc_b0_8e-66184a25.pth',
|
|
|
|
|
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
|
|
|
|
|
'tf_efficientnet_cc_b1_8e': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_cc_b1_8e-f7c79ae1.pth',
|
|
|
|
|
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
|
|
|
|
|
input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882),
|
|
|
|
|
|
|
|
|
|
'tf_efficientnet_lite0': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite0-0aa007d2.pth',
|
|
|
|
|
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
|
|
|
|
|
interpolation='bicubic', # should be bilinear but bicubic better match for TF bilinear at low res
|
|
|
|
|
),
|
|
|
|
|
'tf_efficientnet_lite1': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite1-bde8b488.pth',
|
|
|
|
|
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
|
|
|
|
|
input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882,
|
|
|
|
|
interpolation='bicubic', # should be bilinear but bicubic better match for TF bilinear at low res
|
|
|
|
|
),
|
|
|
|
|
'tf_efficientnet_lite2': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite2-dcccb7df.pth',
|
|
|
|
|
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
|
|
|
|
|
input_size=(3, 260, 260), pool_size=(9, 9), crop_pct=0.890,
|
|
|
|
|
interpolation='bicubic', # should be bilinear but bicubic better match for TF bilinear at low res
|
|
|
|
|
),
|
|
|
|
|
'tf_efficientnet_lite3': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite3-b733e338.pth',
|
|
|
|
|
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
|
|
|
|
|
input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904, interpolation='bilinear'),
|
|
|
|
|
'tf_efficientnet_lite4': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite4-741542c3.pth',
|
|
|
|
|
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
|
|
|
|
|
input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.920, interpolation='bilinear'),
|
|
|
|
|
|
|
|
|
|
'tf_efficientnetv2_s': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_s-eb54923e.pth',
|
|
|
|
|
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
|
|
|
|
|
input_size=(3, 300, 300), test_input_size=(3, 384, 384), pool_size=(10, 10), crop_pct=1.0),
|
|
|
|
|
'tf_efficientnetv2_m': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_m-cc09e0cd.pth',
|
|
|
|
|
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
|
|
|
|
|
input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0),
|
|
|
|
|
'tf_efficientnetv2_l': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_l-d664b728.pth',
|
|
|
|
|
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
|
|
|
|
|
input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0),
|
|
|
|
|
|
|
|
|
|
'tf_efficientnetv2_s_in21ft1k': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_s_21ft1k-d7dafa41.pth',
|
|
|
|
|
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
|
|
|
|
|
input_size=(3, 300, 300), test_input_size=(3, 384, 384), pool_size=(10, 10), crop_pct=1.0),
|
|
|
|
|
'tf_efficientnetv2_m_in21ft1k': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_m_21ft1k-bf41664a.pth',
|
|
|
|
|
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
|
|
|
|
|
input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0),
|
|
|
|
|
'tf_efficientnetv2_l_in21ft1k': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_l_21ft1k-60127a9d.pth',
|
|
|
|
|
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
|
|
|
|
|
input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0),
|
|
|
|
|
'tf_efficientnetv2_xl_in21ft1k': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_xl_in21ft1k-06c35c48.pth',
|
|
|
|
|
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
|
|
|
|
|
input_size=(3, 384, 384), test_input_size=(3, 512, 512), pool_size=(12, 12), crop_pct=1.0),
|
|
|
|
|
|
|
|
|
|
'tf_efficientnetv2_s_in21k': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_s_21k-6337ad01.pth',
|
|
|
|
|
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), num_classes=21843,
|
|
|
|
|
input_size=(3, 300, 300), test_input_size=(3, 384, 384), pool_size=(10, 10), crop_pct=1.0),
|
|
|
|
|
'tf_efficientnetv2_m_in21k': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_m_21k-361418a2.pth',
|
|
|
|
|
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), num_classes=21843,
|
|
|
|
|
input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0),
|
|
|
|
|
'tf_efficientnetv2_l_in21k': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_l_21k-91a19ec9.pth',
|
|
|
|
|
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), num_classes=21843,
|
|
|
|
|
input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0),
|
|
|
|
|
'tf_efficientnetv2_xl_in21k': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_xl_in21k-fd7e8abf.pth',
|
|
|
|
|
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), num_classes=21843,
|
|
|
|
|
input_size=(3, 384, 384), test_input_size=(3, 512, 512), pool_size=(12, 12), crop_pct=1.0),
|
|
|
|
|
|
|
|
|
|
'tf_efficientnetv2_b0': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_b0-c7cc451f.pth',
|
|
|
|
|
input_size=(3, 192, 192), test_input_size=(3, 224, 224), pool_size=(6, 6)),
|
|
|
|
|
'tf_efficientnetv2_b1': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_b1-be6e41b0.pth',
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input_size=(3, 192, 192), test_input_size=(3, 240, 240), pool_size=(6, 6), crop_pct=0.882),
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'tf_efficientnetv2_b2': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_b2-847de54e.pth',
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input_size=(3, 208, 208), test_input_size=(3, 260, 260), pool_size=(7, 7), crop_pct=0.890),
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'tf_efficientnetv2_b3': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_b3-57773f13.pth',
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input_size=(3, 240, 240), test_input_size=(3, 300, 300), pool_size=(8, 8), crop_pct=0.904),
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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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"tinynet_a": _cfg(
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input_size=(3, 192, 192), pool_size=(6, 6), # int(224 * 0.86)
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|
url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_a.pth'),
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"tinynet_b": _cfg(
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input_size=(3, 188, 188), pool_size=(6, 6), # int(224 * 0.84)
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|
url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_b.pth'),
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"tinynet_c": _cfg(
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input_size=(3, 184, 184), pool_size=(6, 6), # int(224 * 0.825)
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|
url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_c.pth'),
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|
"tinynet_d": _cfg(
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|
input_size=(3, 152, 152), pool_size=(5, 5), # int(224 * 0.68)
|
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|
url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_d.pth'),
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|
"tinynet_e": _cfg(
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|
input_size=(3, 106, 106), pool_size=(4, 4), # int(224 * 0.475)
|
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|
url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_e.pth'),
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|
}
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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-V2 Small, Medium, Large, XL & B0-B3
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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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|
* MobileNet-V2
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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, fix_stem=False,
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output_stride=32, pad_type='', round_chs_fn=round_channels, act_layer=None, norm_layer=None,
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se_layer=None, drop_rate=0., drop_path_rate=0., global_pool='avg'):
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super(EfficientNet, self).__init__()
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act_layer = act_layer or nn.ReLU
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norm_layer = norm_layer or nn.BatchNorm2d
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se_layer = se_layer or SqueezeExcite
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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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# Stem
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if not fix_stem:
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stem_size = round_chs_fn(stem_size)
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self.conv_stem = create_conv2d(in_chans, stem_size, 3, stride=2, padding=pad_type)
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self.bn1 = norm_layer(stem_size)
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self.act1 = act_layer(inplace=True)
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# Middle stages (IR/ER/DS Blocks)
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builder = EfficientNetBuilder(
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|
output_stride=output_stride, pad_type=pad_type, round_chs_fn=round_chs_fn,
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act_layer=act_layer, norm_layer=norm_layer, se_layer=se_layer, drop_path_rate=drop_path_rate)
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self.blocks = nn.Sequential(*builder(stem_size, block_args))
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self.feature_info = builder.features
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head_chs = builder.in_chs
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# Head + Pooling
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self.conv_head = create_conv2d(head_chs, self.num_features, 1, padding=pad_type)
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self.bn2 = norm_layer(self.num_features)
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self.act2 = act_layer(inplace=True)
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self.global_pool, self.classifier = create_classifier(
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|
|
self.num_features, self.num_classes, pool_type=global_pool)
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|
efficientnet_init_weights(self)
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def as_sequential(self):
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|
layers = [self.conv_stem, self.bn1, self.act1]
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layers.extend(self.blocks)
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|
layers.extend([self.conv_head, self.bn2, self.act2, self.global_pool])
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layers.extend([nn.Dropout(self.drop_rate), self.classifier])
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|
return nn.Sequential(*layers)
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def get_classifier(self):
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|
return self.classifier
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|
def reset_classifier(self, num_classes, global_pool='avg'):
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|
self.num_classes = num_classes
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|
self.global_pool, self.classifier = create_classifier(
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|
|
self.num_features, self.num_classes, pool_type=global_pool)
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|
def forward_features(self, x):
|
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|
|
x = self.conv_stem(x)
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|
x = self.bn1(x)
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|
|
x = self.act1(x)
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|
|
x = self.blocks(x)
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|
x = self.conv_head(x)
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|
|
x = self.bn2(x)
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|
x = self.act2(x)
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|
return x
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|
def forward(self, x):
|
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|
|
x = self.forward_features(x)
|
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|
|
x = self.global_pool(x)
|
|
|
|
|
if self.drop_rate > 0.:
|
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|
|
|
x = F.dropout(x, p=self.drop_rate, training=self.training)
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|
|
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='bottleneck', in_chans=3,
|
|
|
|
|
stem_size=32, fix_stem=False, output_stride=32, pad_type='', round_chs_fn=round_channels,
|
|
|
|
|
act_layer=None, norm_layer=None, se_layer=None, drop_rate=0., drop_path_rate=0.):
|
|
|
|
|
super(EfficientNetFeatures, self).__init__()
|
|
|
|
|
act_layer = act_layer or nn.ReLU
|
|
|
|
|
norm_layer = norm_layer or nn.BatchNorm2d
|
|
|
|
|
se_layer = se_layer or SqueezeExcite
|
|
|
|
|
self.drop_rate = drop_rate
|
|
|
|
|
|
|
|
|
|
# Stem
|
|
|
|
|
if not fix_stem:
|
|
|
|
|
stem_size = round_chs_fn(stem_size)
|
|
|
|
|
self.conv_stem = create_conv2d(in_chans, stem_size, 3, stride=2, padding=pad_type)
|
|
|
|
|
self.bn1 = norm_layer(stem_size)
|
|
|
|
|
self.act1 = act_layer(inplace=True)
|
|
|
|
|
|
|
|
|
|
# Middle stages (IR/ER/DS Blocks)
|
|
|
|
|
builder = EfficientNetBuilder(
|
|
|
|
|
output_stride=output_stride, pad_type=pad_type, round_chs_fn=round_chs_fn,
|
|
|
|
|
act_layer=act_layer, norm_layer=norm_layer, se_layer=se_layer, drop_path_rate=drop_path_rate,
|
|
|
|
|
feature_location=feature_location)
|
|
|
|
|
self.blocks = nn.Sequential(*builder(stem_size, block_args))
|
|
|
|
|
self.feature_info = FeatureInfo(builder.features, out_indices)
|
|
|
|
|
self._stage_out_idx = {v['stage']: i for i, v in enumerate(self.feature_info) if i in out_indices}
|
|
|
|
|
|
|
|
|
|
efficientnet_init_weights(self)
|
|
|
|
|
|
|
|
|
|
# Register feature extraction hooks with FeatureHooks helper
|
|
|
|
|
self.feature_hooks = None
|
|
|
|
|
if feature_location != 'bottleneck':
|
|
|
|
|
hooks = self.feature_info.get_dicts(keys=('module', 'hook_type'))
|
|
|
|
|
self.feature_hooks = FeatureHooks(hooks, self.named_modules())
|
|
|
|
|
|
|
|
|
|
def forward(self, x) -> List[torch.Tensor]:
|
|
|
|
|
x = self.conv_stem(x)
|
|
|
|
|
x = self.bn1(x)
|
|
|
|
|
x = self.act1(x)
|
|
|
|
|
if self.feature_hooks is None:
|
|
|
|
|
features = []
|
|
|
|
|
if 0 in self._stage_out_idx:
|
|
|
|
|
features.append(x) # add stem out
|
|
|
|
|
for i, b in enumerate(self.blocks):
|
|
|
|
|
x = b(x)
|
|
|
|
|
if i + 1 in self._stage_out_idx:
|
|
|
|
|
features.append(x)
|
|
|
|
|
return features
|
|
|
|
|
else:
|
|
|
|
|
self.blocks(x)
|
|
|
|
|
out = self.feature_hooks.get_output(x.device)
|
|
|
|
|
return list(out.values())
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def _create_effnet(variant, pretrained=False, **kwargs):
|
|
|
|
|
features_only = False
|
|
|
|
|
model_cls = EfficientNet
|
|
|
|
|
kwargs_filter = None
|
|
|
|
|
if kwargs.pop('features_only', False):
|
|
|
|
|
features_only = True
|
|
|
|
|
kwargs_filter = ('num_classes', 'num_features', 'head_conv', 'global_pool')
|
|
|
|
|
model_cls = EfficientNetFeatures
|
|
|
|
|
model = build_model_with_cfg(
|
|
|
|
|
model_cls, variant, pretrained,
|
|
|
|
|
default_cfg=default_cfgs[variant],
|
|
|
|
|
pretrained_strict=not features_only,
|
|
|
|
|
kwargs_filter=kwargs_filter,
|
|
|
|
|
**kwargs)
|
|
|
|
|
if features_only:
|
|
|
|
|
model.default_cfg = default_cfg_for_features(model.default_cfg)
|
|
|
|
|
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,
|
|
|
|
|
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
|
|
|
|
|
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
|
|
|
|
|
**kwargs
|
|
|
|
|
)
|
|
|
|
|
model = _create_effnet(variant, pretrained, **model_kwargs)
|
|
|
|
|
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,
|
|
|
|
|
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
|
|
|
|
|
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
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**kwargs
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)
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model = _create_effnet(variant, pretrained, **model_kwargs)
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return model
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def _gen_mnasnet_small(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
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"""Creates a mnasnet-b1 model.
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Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet
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Paper: https://arxiv.org/pdf/1807.11626.pdf.
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Args:
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channel_multiplier: multiplier to number of channels per layer.
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"""
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arch_def = [
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['ds_r1_k3_s1_c8'],
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['ir_r1_k3_s2_e3_c16'],
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['ir_r2_k3_s2_e6_c16'],
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['ir_r4_k5_s2_e6_c32_se0.25'],
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['ir_r3_k3_s1_e6_c32_se0.25'],
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['ir_r3_k5_s2_e6_c88_se0.25'],
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['ir_r1_k3_s1_e6_c144']
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]
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model_kwargs = dict(
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block_args=decode_arch_def(arch_def),
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stem_size=8,
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round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
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norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
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**kwargs
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)
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model = _create_effnet(variant, pretrained, **model_kwargs)
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return model
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def _gen_mobilenet_v2(
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variant, channel_multiplier=1.0, depth_multiplier=1.0, fix_stem_head=False, pretrained=False, **kwargs):
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""" Generate MobileNet-V2 network
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Ref impl: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v2.py
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Paper: https://arxiv.org/abs/1801.04381
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"""
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arch_def = [
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['ds_r1_k3_s1_c16'],
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['ir_r2_k3_s2_e6_c24'],
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['ir_r3_k3_s2_e6_c32'],
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['ir_r4_k3_s2_e6_c64'],
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['ir_r3_k3_s1_e6_c96'],
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['ir_r3_k3_s2_e6_c160'],
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['ir_r1_k3_s1_e6_c320'],
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]
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round_chs_fn = partial(round_channels, multiplier=channel_multiplier)
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model_kwargs = dict(
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block_args=decode_arch_def(arch_def, depth_multiplier=depth_multiplier, fix_first_last=fix_stem_head),
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num_features=1280 if fix_stem_head else round_chs_fn(1280),
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stem_size=32,
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fix_stem=fix_stem_head,
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round_chs_fn=round_chs_fn,
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norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
|
Monster commit, activation refactor, VoVNet, norm_act improvements, more
* refactor activations into basic PyTorch, jit scripted, and memory efficient custom auto
* implement hard-mish, better grad for hard-swish
* add initial VovNet V1/V2 impl, fix #151
* VovNet and DenseNet first models to use NormAct layers (support BatchNormAct2d, EvoNorm, InplaceIABN)
* Wrap IABN for any models that use it
* make more models torchscript compatible (DPN, PNasNet, Res2Net, SelecSLS) and add tests
4 years ago
|
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act_layer=resolve_act_layer(kwargs, 'relu6'),
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**kwargs
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)
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model = _create_effnet(variant, pretrained, **model_kwargs)
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return model
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def _gen_fbnetc(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
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""" FBNet-C
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Paper: https://arxiv.org/abs/1812.03443
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Ref Impl: https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/maskrcnn_benchmark/modeling/backbone/fbnet_modeldef.py
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NOTE: the impl above does not relate to the 'C' variant here, that was derived from paper,
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it was used to confirm some building block details
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"""
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arch_def = [
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['ir_r1_k3_s1_e1_c16'],
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['ir_r1_k3_s2_e6_c24', 'ir_r2_k3_s1_e1_c24'],
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['ir_r1_k5_s2_e6_c32', 'ir_r1_k5_s1_e3_c32', 'ir_r1_k5_s1_e6_c32', 'ir_r1_k3_s1_e6_c32'],
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['ir_r1_k5_s2_e6_c64', 'ir_r1_k5_s1_e3_c64', 'ir_r2_k5_s1_e6_c64'],
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['ir_r3_k5_s1_e6_c112', 'ir_r1_k5_s1_e3_c112'],
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['ir_r4_k5_s2_e6_c184'],
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['ir_r1_k3_s1_e6_c352'],
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]
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model_kwargs = dict(
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block_args=decode_arch_def(arch_def),
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stem_size=16,
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num_features=1984, # paper suggests this, but is not 100% clear
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round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
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norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
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**kwargs
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)
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model = _create_effnet(variant, pretrained, **model_kwargs)
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return model
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def _gen_spnasnet(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
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"""Creates the Single-Path NAS model from search targeted for Pixel1 phone.
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Paper: https://arxiv.org/abs/1904.02877
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Args:
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channel_multiplier: multiplier to number of channels per layer.
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"""
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arch_def = [
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# stage 0, 112x112 in
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['ds_r1_k3_s1_c16_noskip'],
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# stage 1, 112x112 in
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['ir_r3_k3_s2_e3_c24'],
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# stage 2, 56x56 in
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['ir_r1_k5_s2_e6_c40', 'ir_r3_k3_s1_e3_c40'],
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# stage 3, 28x28 in
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['ir_r1_k5_s2_e6_c80', 'ir_r3_k3_s1_e3_c80'],
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# stage 4, 14x14in
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['ir_r1_k5_s1_e6_c96', 'ir_r3_k5_s1_e3_c96'],
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# stage 5, 14x14in
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['ir_r4_k5_s2_e6_c192'],
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# stage 6, 7x7 in
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['ir_r1_k3_s1_e6_c320_noskip']
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]
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model_kwargs = dict(
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block_args=decode_arch_def(arch_def),
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stem_size=32,
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round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
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norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
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**kwargs
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)
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model = _create_effnet(variant, pretrained, **model_kwargs)
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return model
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def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
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|
"""Creates an EfficientNet model.
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Ref impl: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/efficientnet_model.py
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Paper: https://arxiv.org/abs/1905.11946
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EfficientNet params
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name: (channel_multiplier, depth_multiplier, resolution, dropout_rate)
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'efficientnet-b0': (1.0, 1.0, 224, 0.2),
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'efficientnet-b1': (1.0, 1.1, 240, 0.2),
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'efficientnet-b2': (1.1, 1.2, 260, 0.3),
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'efficientnet-b3': (1.2, 1.4, 300, 0.3),
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'efficientnet-b4': (1.4, 1.8, 380, 0.4),
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'efficientnet-b5': (1.6, 2.2, 456, 0.4),
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'efficientnet-b6': (1.8, 2.6, 528, 0.5),
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'efficientnet-b7': (2.0, 3.1, 600, 0.5),
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'efficientnet-b8': (2.2, 3.6, 672, 0.5),
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'efficientnet-l2': (4.3, 5.3, 800, 0.5),
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|
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|
Args:
|
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|
channel_multiplier: multiplier to number of channels per layer
|
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|
depth_multiplier: multiplier to number of repeats per stage
|
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"""
|
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|
arch_def = [
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['ds_r1_k3_s1_e1_c16_se0.25'],
|
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|
['ir_r2_k3_s2_e6_c24_se0.25'],
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['ir_r2_k5_s2_e6_c40_se0.25'],
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['ir_r3_k3_s2_e6_c80_se0.25'],
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|
['ir_r3_k5_s1_e6_c112_se0.25'],
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['ir_r4_k5_s2_e6_c192_se0.25'],
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['ir_r1_k3_s1_e6_c320_se0.25'],
|
|
|
|
|
]
|
|
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|
round_chs_fn = partial(round_channels, multiplier=channel_multiplier)
|
|
|
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|
model_kwargs = dict(
|
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block_args=decode_arch_def(arch_def, depth_multiplier),
|
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|
num_features=round_chs_fn(1280),
|
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|
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|
stem_size=32,
|
|
|
|
|
round_chs_fn=round_chs_fn,
|
Monster commit, activation refactor, VoVNet, norm_act improvements, more
* refactor activations into basic PyTorch, jit scripted, and memory efficient custom auto
* implement hard-mish, better grad for hard-swish
* add initial VovNet V1/V2 impl, fix #151
* VovNet and DenseNet first models to use NormAct layers (support BatchNormAct2d, EvoNorm, InplaceIABN)
* Wrap IABN for any models that use it
* make more models torchscript compatible (DPN, PNasNet, Res2Net, SelecSLS) and add tests
4 years ago
|
|
|
act_layer=resolve_act_layer(kwargs, 'swish'),
|
|
|
|
|
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
|
|
|
|
|
**kwargs,
|
|
|
|
|
)
|
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|
|
model = _create_effnet(variant, pretrained, **model_kwargs)
|
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|
return model
|
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def _gen_efficientnet_edge(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
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|
|
""" Creates an EfficientNet-EdgeTPU model
|
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Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/edgetpu
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|
"""
|
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|
arch_def = [
|
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|
|
# NOTE `fc` is present to override a mismatch between stem channels and in chs not
|
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|
# present in other models
|
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['er_r1_k3_s1_e4_c24_fc24_noskip'],
|
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['er_r2_k3_s2_e8_c32'],
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['er_r4_k3_s2_e8_c48'],
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['ir_r5_k5_s2_e8_c96'],
|
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|
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|
['ir_r4_k5_s1_e8_c144'],
|
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|
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['ir_r2_k5_s2_e8_c192'],
|
|
|
|
|
]
|
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round_chs_fn = partial(round_channels, multiplier=channel_multiplier)
|
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|
model_kwargs = dict(
|
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|
block_args=decode_arch_def(arch_def, depth_multiplier),
|
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num_features=round_chs_fn(1280),
|
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|
stem_size=32,
|
|
|
|
|
round_chs_fn=round_chs_fn,
|
|
|
|
|
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
|
Monster commit, activation refactor, VoVNet, norm_act improvements, more
* refactor activations into basic PyTorch, jit scripted, and memory efficient custom auto
* implement hard-mish, better grad for hard-swish
* add initial VovNet V1/V2 impl, fix #151
* VovNet and DenseNet first models to use NormAct layers (support BatchNormAct2d, EvoNorm, InplaceIABN)
* Wrap IABN for any models that use it
* make more models torchscript compatible (DPN, PNasNet, Res2Net, SelecSLS) and add tests
4 years ago
|
|
|
act_layer=resolve_act_layer(kwargs, 'relu'),
|
|
|
|
|
**kwargs,
|
|
|
|
|
)
|
|
|
|
|
model = _create_effnet(variant, pretrained, **model_kwargs)
|
|
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|
return model
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
def _gen_efficientnet_condconv(
|
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|
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
|
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|
"""
|
|
|
|
|
arch_def = [
|
|
|
|
|
['ds_r1_k3_s1_e1_c16_se0.25'],
|
|
|
|
|
['ir_r2_k3_s2_e6_c24_se0.25'],
|
|
|
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['ir_r2_k5_s2_e6_c40_se0.25'],
|
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['ir_r3_k3_s2_e6_c80_se0.25'],
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['ir_r3_k5_s1_e6_c112_se0.25_cc4'],
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['ir_r4_k5_s2_e6_c192_se0.25_cc4'],
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['ir_r1_k3_s1_e6_c320_se0.25_cc4'],
|
|
|
|
|
]
|
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|
|
|
# NOTE unlike official impl, this one uses `cc<x>` option where x is the base number of experts for each stage and
|
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|
# the expert_multiplier increases that on a per-model basis as with depth/channel multipliers
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|
round_chs_fn = partial(round_channels, multiplier=channel_multiplier)
|
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|
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|
model_kwargs = dict(
|
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|
block_args=decode_arch_def(arch_def, depth_multiplier, experts_multiplier=experts_multiplier),
|
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|
|
|
num_features=round_chs_fn(1280),
|
|
|
|
|
stem_size=32,
|
|
|
|
|
round_chs_fn=round_chs_fn,
|
|
|
|
|
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
|
Monster commit, activation refactor, VoVNet, norm_act improvements, more
* refactor activations into basic PyTorch, jit scripted, and memory efficient custom auto
* implement hard-mish, better grad for hard-swish
* add initial VovNet V1/V2 impl, fix #151
* VovNet and DenseNet first models to use NormAct layers (support BatchNormAct2d, EvoNorm, InplaceIABN)
* Wrap IABN for any models that use it
* make more models torchscript compatible (DPN, PNasNet, Res2Net, SelecSLS) and add tests
4 years ago
|
|
|
act_layer=resolve_act_layer(kwargs, 'swish'),
|
|
|
|
|
**kwargs,
|
|
|
|
|
)
|
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|
|
|
model = _create_effnet(variant, pretrained, **model_kwargs)
|
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|
return model
|
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def _gen_efficientnet_lite(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
|
|
|
|
|
"""Creates an EfficientNet-Lite model.
|
|
|
|
|
|
|
|
|
|
Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/lite
|
|
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|
Paper: https://arxiv.org/abs/1905.11946
|
|
|
|
|
|
|
|
|
|
EfficientNet params
|
|
|
|
|
name: (channel_multiplier, depth_multiplier, resolution, dropout_rate)
|
|
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|
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'efficientnet-lite0': (1.0, 1.0, 224, 0.2),
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'efficientnet-lite1': (1.0, 1.1, 240, 0.2),
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'efficientnet-lite2': (1.1, 1.2, 260, 0.3),
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'efficientnet-lite3': (1.2, 1.4, 280, 0.3),
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'efficientnet-lite4': (1.4, 1.8, 300, 0.3),
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Args:
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channel_multiplier: multiplier to number of channels per layer
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depth_multiplier: multiplier to number of repeats per stage
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"""
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arch_def = [
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['ds_r1_k3_s1_e1_c16'],
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['ir_r2_k3_s2_e6_c24'],
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['ir_r2_k5_s2_e6_c40'],
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['ir_r3_k3_s2_e6_c80'],
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['ir_r3_k5_s1_e6_c112'],
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['ir_r4_k5_s2_e6_c192'],
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['ir_r1_k3_s1_e6_c320'],
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]
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model_kwargs = dict(
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block_args=decode_arch_def(arch_def, depth_multiplier, fix_first_last=True),
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num_features=1280,
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stem_size=32,
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fix_stem=True,
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round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
|
Monster commit, activation refactor, VoVNet, norm_act improvements, more
* refactor activations into basic PyTorch, jit scripted, and memory efficient custom auto
* implement hard-mish, better grad for hard-swish
* add initial VovNet V1/V2 impl, fix #151
* VovNet and DenseNet first models to use NormAct layers (support BatchNormAct2d, EvoNorm, InplaceIABN)
* Wrap IABN for any models that use it
* make more models torchscript compatible (DPN, PNasNet, Res2Net, SelecSLS) and add tests
4 years ago
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act_layer=resolve_act_layer(kwargs, 'relu6'),
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norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
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**kwargs,
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)
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model = _create_effnet(variant, pretrained, **model_kwargs)
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return model
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def _gen_efficientnetv2_base(
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variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
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""" Creates an EfficientNet-V2 base model
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Ref impl: https://github.com/google/automl/tree/master/efficientnetv2
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Paper: `EfficientNetV2: Smaller Models and Faster Training` - https://arxiv.org/abs/2104.00298
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"""
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arch_def = [
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['cn_r1_k3_s1_e1_c16_skip'],
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['er_r2_k3_s2_e4_c32'],
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['er_r2_k3_s2_e4_c48'],
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['ir_r3_k3_s2_e4_c96_se0.25'],
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['ir_r5_k3_s1_e6_c112_se0.25'],
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['ir_r8_k3_s2_e6_c192_se0.25'],
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]
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round_chs_fn = partial(round_channels, multiplier=channel_multiplier, round_limit=0.)
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model_kwargs = dict(
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block_args=decode_arch_def(arch_def, depth_multiplier),
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num_features=round_chs_fn(1280),
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stem_size=32,
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round_chs_fn=round_chs_fn,
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norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
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act_layer=resolve_act_layer(kwargs, 'silu'),
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**kwargs,
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)
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model = _create_effnet(variant, pretrained, **model_kwargs)
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return model
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def _gen_efficientnetv2_s(
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variant, channel_multiplier=1.0, depth_multiplier=1.0, rw=False, pretrained=False, **kwargs):
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""" Creates an EfficientNet-V2 Small model
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Ref impl: https://github.com/google/automl/tree/master/efficientnetv2
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Paper: `EfficientNetV2: Smaller Models and Faster Training` - https://arxiv.org/abs/2104.00298
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NOTE: `rw` flag sets up 'small' variant to behave like my initial v2 small model,
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before ref the impl was released.
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"""
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arch_def = [
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['cn_r2_k3_s1_e1_c24_skip'],
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['er_r4_k3_s2_e4_c48'],
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['er_r4_k3_s2_e4_c64'],
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['ir_r6_k3_s2_e4_c128_se0.25'],
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['ir_r9_k3_s1_e6_c160_se0.25'],
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['ir_r15_k3_s2_e6_c256_se0.25'],
|
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]
|
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num_features = 1280
|
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if rw:
|
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# my original variant, based on paper figure differs from the official release
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arch_def[0] = ['er_r2_k3_s1_e1_c24']
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arch_def[-1] = ['ir_r15_k3_s2_e6_c272_se0.25']
|
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num_features = 1792
|
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round_chs_fn = partial(round_channels, multiplier=channel_multiplier)
|
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|
model_kwargs = dict(
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block_args=decode_arch_def(arch_def, depth_multiplier),
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num_features=round_chs_fn(num_features),
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stem_size=24,
|
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|
round_chs_fn=round_chs_fn,
|
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norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
|
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act_layer=resolve_act_layer(kwargs, 'silu'),
|
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**kwargs,
|
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|
)
|
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model = _create_effnet(variant, pretrained, **model_kwargs)
|
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return model
|
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def _gen_efficientnetv2_m(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
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|
""" Creates an EfficientNet-V2 Medium model
|
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Ref impl: https://github.com/google/automl/tree/master/efficientnetv2
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Paper: `EfficientNetV2: Smaller Models and Faster Training` - https://arxiv.org/abs/2104.00298
|
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|
"""
|
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|
|
|
|
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|
arch_def = [
|
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|
['cn_r3_k3_s1_e1_c24_skip'],
|
|
|
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['er_r5_k3_s2_e4_c48'],
|
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['er_r5_k3_s2_e4_c80'],
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['ir_r7_k3_s2_e4_c160_se0.25'],
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['ir_r14_k3_s1_e6_c176_se0.25'],
|
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['ir_r18_k3_s2_e6_c304_se0.25'],
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['ir_r5_k3_s1_e6_c512_se0.25'],
|
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|
|
]
|
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|
|
|
|
|
|
|
|
model_kwargs = dict(
|
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|
block_args=decode_arch_def(arch_def, depth_multiplier),
|
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|
num_features=1280,
|
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|
|
|
stem_size=24,
|
|
|
|
|
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
|
|
|
|
|
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
|
|
|
|
|
act_layer=resolve_act_layer(kwargs, 'silu'),
|
|
|
|
|
**kwargs,
|
|
|
|
|
)
|
|
|
|
|
model = _create_effnet(variant, pretrained, **model_kwargs)
|
|
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|
|
return model
|
|
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|
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|
|
|
|
|
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|
|
def _gen_efficientnetv2_l(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
|
|
|
|
|
""" Creates an EfficientNet-V2 Large model
|
|
|
|
|
|
|
|
|
|
Ref impl: https://github.com/google/automl/tree/master/efficientnetv2
|
|
|
|
|
Paper: `EfficientNetV2: Smaller Models and Faster Training` - https://arxiv.org/abs/2104.00298
|
|
|
|
|
"""
|
|
|
|
|
|
|
|
|
|
arch_def = [
|
|
|
|
|
['cn_r4_k3_s1_e1_c32_skip'],
|
|
|
|
|
['er_r7_k3_s2_e4_c64'],
|
|
|
|
|
['er_r7_k3_s2_e4_c96'],
|
|
|
|
|
['ir_r10_k3_s2_e4_c192_se0.25'],
|
|
|
|
|
['ir_r19_k3_s1_e6_c224_se0.25'],
|
|
|
|
|
['ir_r25_k3_s2_e6_c384_se0.25'],
|
|
|
|
|
['ir_r7_k3_s1_e6_c640_se0.25'],
|
|
|
|
|
]
|
|
|
|
|
|
|
|
|
|
model_kwargs = dict(
|
|
|
|
|
block_args=decode_arch_def(arch_def, depth_multiplier),
|
|
|
|
|
num_features=1280,
|
|
|
|
|
stem_size=32,
|
|
|
|
|
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
|
|
|
|
|
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
|
|
|
|
|
act_layer=resolve_act_layer(kwargs, 'silu'),
|
|
|
|
|
**kwargs,
|
|
|
|
|
)
|
|
|
|
|
model = _create_effnet(variant, pretrained, **model_kwargs)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def _gen_efficientnetv2_xl(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
|
|
|
|
|
""" Creates an EfficientNet-V2 Xtra-Large model
|
|
|
|
|
|
|
|
|
|
Ref impl: https://github.com/google/automl/tree/master/efficientnetv2
|
|
|
|
|
Paper: `EfficientNetV2: Smaller Models and Faster Training` - https://arxiv.org/abs/2104.00298
|
|
|
|
|
"""
|
|
|
|
|
|
|
|
|
|
arch_def = [
|
|
|
|
|
['cn_r4_k3_s1_e1_c32_skip'],
|
|
|
|
|
['er_r8_k3_s2_e4_c64'],
|
|
|
|
|
['er_r8_k3_s2_e4_c96'],
|
|
|
|
|
['ir_r16_k3_s2_e4_c192_se0.25'],
|
|
|
|
|
['ir_r24_k3_s1_e6_c256_se0.25'],
|
|
|
|
|
['ir_r32_k3_s2_e6_c512_se0.25'],
|
|
|
|
|
['ir_r8_k3_s1_e6_c640_se0.25'],
|
|
|
|
|
]
|
|
|
|
|
|
|
|
|
|
model_kwargs = dict(
|
|
|
|
|
block_args=decode_arch_def(arch_def, depth_multiplier),
|
|
|
|
|
num_features=1280,
|
|
|
|
|
stem_size=32,
|
|
|
|
|
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
|
|
|
|
|
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
|
|
|
|
|
act_layer=resolve_act_layer(kwargs, 'silu'),
|
|
|
|
|
**kwargs,
|
|
|
|
|
)
|
|
|
|
|
model = _create_effnet(variant, pretrained, **model_kwargs)
|
|
|
|
|
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,
|
|
|
|
|
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
|
|
|
|
|
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
|
|
|
|
|
**kwargs
|
|
|
|
|
)
|
|
|
|
|
model = _create_effnet(variant, pretrained, **model_kwargs)
|
|
|
|
|
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,
|
|
|
|
|
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
|
|
|
|
|
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
|
|
|
|
|
**kwargs
|
|
|
|
|
)
|
|
|
|
|
model = _create_effnet(variant, pretrained, **model_kwargs)
|
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|
return model
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|
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|
|
def _gen_tinynet(
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|
variant, model_width=1.0, depth_multiplier=1.0, pretrained=False, **kwargs
|
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|
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|
):
|
|
|
|
|
"""Creates a TinyNet model.
|
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|
|
|
"""
|
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|
|
|
arch_def = [
|
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|
['ds_r1_k3_s1_e1_c16_se0.25'], ['ir_r2_k3_s2_e6_c24_se0.25'],
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|
|
|
|
['ir_r2_k5_s2_e6_c40_se0.25'], ['ir_r3_k3_s2_e6_c80_se0.25'],
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|
|
|
['ir_r3_k5_s1_e6_c112_se0.25'], ['ir_r4_k5_s2_e6_c192_se0.25'],
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|
|
|
|
['ir_r1_k3_s1_e6_c320_se0.25'],
|
|
|
|
|
]
|
|
|
|
|
model_kwargs = dict(
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|
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|
block_args=decode_arch_def(arch_def, depth_multiplier, depth_trunc='round'),
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|
|
|
num_features=max(1280, round_channels(1280, model_width, 8, None)),
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|
|
stem_size=32,
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|
|
|
fix_stem=True,
|
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|
|
|
round_chs_fn=partial(round_channels, multiplier=model_width),
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|
|
|
act_layer=resolve_act_layer(kwargs, 'swish'),
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|
|
|
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
|
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|
|
|
**kwargs,
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|
|
|
)
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|
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|
model = _create_effnet(variant, pretrained, **model_kwargs)
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|
|
|
return model
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|
|
|
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|
|
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|
|
|
|
|
|
@register_model
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|
|
|
|
def mnasnet_050(pretrained=False, **kwargs):
|
|
|
|
|
""" MNASNet B1, depth multiplier of 0.5. """
|
|
|
|
|
model = _gen_mnasnet_b1('mnasnet_050', 0.5, pretrained=pretrained, **kwargs)
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|
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|
return model
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|
|
|
|
|
|
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|
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|
|
|
|
@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)
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|
|
|
return model
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|
|
|
|
|
|
|
|
|
|
|
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|
@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)
|
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|
|
|
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_035(pretrained=False, **kwargs):
|
|
|
|
|
""" MobileNet V2 w/ 0.35 channel multiplier """
|
|
|
|
|
model = _gen_mobilenet_v2('mobilenetv2_035', 0.35, pretrained=pretrained, **kwargs)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def mobilenetv2_050(pretrained=False, **kwargs):
|
|
|
|
|
""" MobileNet V2 w/ 0.5 channel multiplier """
|
|
|
|
|
model = _gen_mobilenet_v2('mobilenetv2_050', 0.5, pretrained=pretrained, **kwargs)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def mobilenetv2_075(pretrained=False, **kwargs):
|
|
|
|
|
""" MobileNet V2 w/ 0.75 channel multiplier """
|
|
|
|
|
model = _gen_mobilenet_v2('mobilenetv2_075', 0.75, pretrained=pretrained, **kwargs)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def mobilenetv2_100(pretrained=False, **kwargs):
|
|
|
|
|
""" MobileNet V2 w/ 1.0 channel multiplier """
|
|
|
|
|
model = _gen_mobilenet_v2('mobilenetv2_100', 1.0, pretrained=pretrained, **kwargs)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def mobilenetv2_140(pretrained=False, **kwargs):
|
|
|
|
|
""" MobileNet V2 w/ 1.4 channel multiplier """
|
|
|
|
|
model = _gen_mobilenet_v2('mobilenetv2_140', 1.4, pretrained=pretrained, **kwargs)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def mobilenetv2_110d(pretrained=False, **kwargs):
|
|
|
|
|
""" MobileNet V2 w/ 1.1 channel, 1.2 depth multipliers"""
|
|
|
|
|
model = _gen_mobilenet_v2(
|
|
|
|
|
'mobilenetv2_110d', 1.1, depth_multiplier=1.2, fix_stem_head=True, pretrained=pretrained, **kwargs)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def mobilenetv2_120d(pretrained=False, **kwargs):
|
|
|
|
|
""" MobileNet V2 w/ 1.2 channel, 1.4 depth multipliers """
|
|
|
|
|
model = _gen_mobilenet_v2(
|
|
|
|
|
'mobilenetv2_120d', 1.2, depth_multiplier=1.4, fix_stem_head=True, 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"""
|
|
|
|
|
# WARN this model def is deprecated, different train/test res + test crop handled by default_cfg now
|
|
|
|
|
return efficientnet_b2(pretrained=pretrained, **kwargs)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@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 """
|
|
|
|
|
# WARN this model def is deprecated, different train/test res + test crop handled by default_cfg now
|
|
|
|
|
return efficientnet_b3(pretrained=pretrained, **kwargs)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@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_es_pruned(pretrained=False, **kwargs):
|
|
|
|
|
""" EfficientNet-Edge Small Pruned. For more info: https://github.com/DeGirum/pruned-models/releases/tag/efficientnet_v1.0"""
|
|
|
|
|
model = _gen_efficientnet_edge(
|
|
|
|
|
'efficientnet_es_pruned', 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_el_pruned(pretrained=False, **kwargs):
|
|
|
|
|
""" EfficientNet-Edge-Large pruned. For more info: https://github.com/DeGirum/pruned-models/releases/tag/efficientnet_v1.0"""
|
|
|
|
|
model = _gen_efficientnet_edge(
|
|
|
|
|
'efficientnet_el_pruned', 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 efficientnet_lite0(pretrained=False, **kwargs):
|
|
|
|
|
""" EfficientNet-Lite0 """
|
|
|
|
|
# NOTE for train, drop_rate should be 0.2, drop_path_rate should be 0.2
|
|
|
|
|
model = _gen_efficientnet_lite(
|
|
|
|
|
'efficientnet_lite0', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def efficientnet_lite1(pretrained=False, **kwargs):
|
|
|
|
|
""" EfficientNet-Lite1 """
|
|
|
|
|
# NOTE for train, drop_rate should be 0.2, drop_path_rate should be 0.2
|
|
|
|
|
model = _gen_efficientnet_lite(
|
|
|
|
|
'efficientnet_lite1', channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def efficientnet_lite2(pretrained=False, **kwargs):
|
|
|
|
|
""" EfficientNet-Lite2 """
|
|
|
|
|
# NOTE for train, drop_rate should be 0.3, drop_path_rate should be 0.2
|
|
|
|
|
model = _gen_efficientnet_lite(
|
|
|
|
|
'efficientnet_lite2', channel_multiplier=1.1, depth_multiplier=1.2, pretrained=pretrained, **kwargs)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def efficientnet_lite3(pretrained=False, **kwargs):
|
|
|
|
|
""" EfficientNet-Lite3 """
|
|
|
|
|
# NOTE for train, drop_rate should be 0.3, drop_path_rate should be 0.2
|
|
|
|
|
model = _gen_efficientnet_lite(
|
|
|
|
|
'efficientnet_lite3', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def efficientnet_lite4(pretrained=False, **kwargs):
|
|
|
|
|
""" EfficientNet-Lite4 """
|
|
|
|
|
# NOTE for train, drop_rate should be 0.4, drop_path_rate should be 0.2
|
|
|
|
|
model = _gen_efficientnet_lite(
|
|
|
|
|
'efficientnet_lite4', channel_multiplier=1.4, depth_multiplier=1.8, pretrained=pretrained, **kwargs)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def efficientnet_b1_pruned(pretrained=False, **kwargs):
|
|
|
|
|
""" EfficientNet-B1 Pruned. The pruning has been obtained using https://arxiv.org/pdf/2002.08258.pdf """
|
|
|
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
|
|
|
kwargs['pad_type'] = 'same'
|
|
|
|
|
variant = 'efficientnet_b1_pruned'
|
|
|
|
|
model = _gen_efficientnet(
|
|
|
|
|
variant, channel_multiplier=1.0, depth_multiplier=1.1, pruned=True, pretrained=pretrained, **kwargs)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def efficientnet_b2_pruned(pretrained=False, **kwargs):
|
|
|
|
|
""" EfficientNet-B2 Pruned. The pruning has been obtained using https://arxiv.org/pdf/2002.08258.pdf """
|
|
|
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
|
|
|
kwargs['pad_type'] = 'same'
|
|
|
|
|
model = _gen_efficientnet(
|
|
|
|
|
'efficientnet_b2_pruned', channel_multiplier=1.1, depth_multiplier=1.2, pruned=True,
|
|
|
|
|
pretrained=pretrained, **kwargs)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def efficientnet_b3_pruned(pretrained=False, **kwargs):
|
|
|
|
|
""" EfficientNet-B3 Pruned. The pruning has been obtained using https://arxiv.org/pdf/2002.08258.pdf """
|
|
|
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
|
|
|
kwargs['pad_type'] = 'same'
|
|
|
|
|
model = _gen_efficientnet(
|
|
|
|
|
'efficientnet_b3_pruned', channel_multiplier=1.2, depth_multiplier=1.4, pruned=True,
|
|
|
|
|
pretrained=pretrained, **kwargs)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def efficientnetv2_rw_t(pretrained=False, **kwargs):
|
|
|
|
|
""" EfficientNet-V2 Tiny (Custom variant, tiny not in paper). """
|
|
|
|
|
model = _gen_efficientnetv2_s(
|
|
|
|
|
'efficientnetv2_rw_t', channel_multiplier=0.8, depth_multiplier=0.9, rw=False, pretrained=pretrained, **kwargs)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def gc_efficientnetv2_rw_t(pretrained=False, **kwargs):
|
|
|
|
|
""" EfficientNet-V2 Tiny w/ Global Context Attn (Custom variant, tiny not in paper). """
|
|
|
|
|
model = _gen_efficientnetv2_s(
|
|
|
|
|
'gc_efficientnetv2_rw_t', channel_multiplier=0.8, depth_multiplier=0.9,
|
|
|
|
|
rw=False, se_layer='gc', pretrained=pretrained, **kwargs)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def efficientnetv2_rw_s(pretrained=False, **kwargs):
|
|
|
|
|
""" EfficientNet-V2 Small (RW variant).
|
|
|
|
|
NOTE: This is my initial (pre official code release) w/ some differences.
|
|
|
|
|
See efficientnetv2_s and tf_efficientnetv2_s for versions that match the official w/ PyTorch vs TF padding
|
|
|
|
|
"""
|
|
|
|
|
model = _gen_efficientnetv2_s('efficientnetv2_rw_s', rw=True, pretrained=pretrained, **kwargs)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def efficientnetv2_rw_m(pretrained=False, **kwargs):
|
|
|
|
|
""" EfficientNet-V2 Medium (RW variant).
|
|
|
|
|
"""
|
|
|
|
|
model = _gen_efficientnetv2_s(
|
|
|
|
|
'efficientnetv2_rw_m', channel_multiplier=1.2, depth_multiplier=(1.2,) * 4 + (1.6,) * 2, rw=True,
|
|
|
|
|
pretrained=pretrained, **kwargs)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def efficientnetv2_s(pretrained=False, **kwargs):
|
|
|
|
|
""" EfficientNet-V2 Small. """
|
|
|
|
|
model = _gen_efficientnetv2_s('efficientnetv2_s', pretrained=pretrained, **kwargs)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def efficientnetv2_m(pretrained=False, **kwargs):
|
|
|
|
|
""" EfficientNet-V2 Medium. """
|
|
|
|
|
model = _gen_efficientnetv2_m('efficientnetv2_m', pretrained=pretrained, **kwargs)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def efficientnetv2_l(pretrained=False, **kwargs):
|
|
|
|
|
""" EfficientNet-V2 Large. """
|
|
|
|
|
model = _gen_efficientnetv2_l('efficientnetv2_l', pretrained=pretrained, **kwargs)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def efficientnetv2_xl(pretrained=False, **kwargs):
|
|
|
|
|
""" EfficientNet-V2 Xtra-Large. """
|
|
|
|
|
model = _gen_efficientnetv2_xl('efficientnetv2_xl', 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 tf_efficientnet_lite0(pretrained=False, **kwargs):
|
|
|
|
|
""" EfficientNet-Lite0 """
|
|
|
|
|
# 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_lite(
|
|
|
|
|
'tf_efficientnet_lite0', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def tf_efficientnet_lite1(pretrained=False, **kwargs):
|
|
|
|
|
""" EfficientNet-Lite1 """
|
|
|
|
|
# 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_lite(
|
|
|
|
|
'tf_efficientnet_lite1', channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def tf_efficientnet_lite2(pretrained=False, **kwargs):
|
|
|
|
|
""" EfficientNet-Lite2 """
|
|
|
|
|
# NOTE for train, drop_rate should be 0.3, drop_path_rate should be 0.2
|
|
|
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
|
|
|
kwargs['pad_type'] = 'same'
|
|
|
|
|
model = _gen_efficientnet_lite(
|
|
|
|
|
'tf_efficientnet_lite2', channel_multiplier=1.1, depth_multiplier=1.2, pretrained=pretrained, **kwargs)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def tf_efficientnet_lite3(pretrained=False, **kwargs):
|
|
|
|
|
""" EfficientNet-Lite3 """
|
|
|
|
|
# NOTE for train, drop_rate should be 0.3, drop_path_rate should be 0.2
|
|
|
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
|
|
|
kwargs['pad_type'] = 'same'
|
|
|
|
|
model = _gen_efficientnet_lite(
|
|
|
|
|
'tf_efficientnet_lite3', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
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@register_model
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def tf_efficientnet_lite4(pretrained=False, **kwargs):
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""" EfficientNet-Lite4 """
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# NOTE for train, drop_rate should be 0.4, drop_path_rate should be 0.2
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kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
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kwargs['pad_type'] = 'same'
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model = _gen_efficientnet_lite(
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'tf_efficientnet_lite4', channel_multiplier=1.4, depth_multiplier=1.8, pretrained=pretrained, **kwargs)
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return model
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@register_model
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def tf_efficientnetv2_s(pretrained=False, **kwargs):
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""" EfficientNet-V2 Small. Tensorflow compatible variant """
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kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
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kwargs['pad_type'] = 'same'
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model = _gen_efficientnetv2_s('tf_efficientnetv2_s', pretrained=pretrained, **kwargs)
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return model
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@register_model
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def tf_efficientnetv2_m(pretrained=False, **kwargs):
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""" EfficientNet-V2 Medium. Tensorflow compatible variant """
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kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
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kwargs['pad_type'] = 'same'
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model = _gen_efficientnetv2_m('tf_efficientnetv2_m', pretrained=pretrained, **kwargs)
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return model
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@register_model
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def tf_efficientnetv2_l(pretrained=False, **kwargs):
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""" EfficientNet-V2 Large. Tensorflow compatible variant """
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kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
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kwargs['pad_type'] = 'same'
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model = _gen_efficientnetv2_l('tf_efficientnetv2_l', pretrained=pretrained, **kwargs)
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return model
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@register_model
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def tf_efficientnetv2_s_in21ft1k(pretrained=False, **kwargs):
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""" EfficientNet-V2 Small. Pretrained on ImageNet-21k, fine-tuned on 1k. Tensorflow compatible variant
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"""
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kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
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kwargs['pad_type'] = 'same'
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model = _gen_efficientnetv2_s('tf_efficientnetv2_s_in21ft1k', pretrained=pretrained, **kwargs)
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return model
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@register_model
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def tf_efficientnetv2_m_in21ft1k(pretrained=False, **kwargs):
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""" EfficientNet-V2 Medium. Pretrained on ImageNet-21k, fine-tuned on 1k. Tensorflow compatible variant
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"""
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kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
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kwargs['pad_type'] = 'same'
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model = _gen_efficientnetv2_m('tf_efficientnetv2_m_in21ft1k', pretrained=pretrained, **kwargs)
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return model
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@register_model
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|
def tf_efficientnetv2_l_in21ft1k(pretrained=False, **kwargs):
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|
""" EfficientNet-V2 Large. Pretrained on ImageNet-21k, fine-tuned on 1k. Tensorflow compatible variant
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"""
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kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
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kwargs['pad_type'] = 'same'
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model = _gen_efficientnetv2_l('tf_efficientnetv2_l_in21ft1k', pretrained=pretrained, **kwargs)
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return model
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@register_model
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|
def tf_efficientnetv2_xl_in21ft1k(pretrained=False, **kwargs):
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|
""" EfficientNet-V2 Xtra-Large. Pretrained on ImageNet-21k, fine-tuned on 1k. Tensorflow compatible variant
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|
"""
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|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
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|
kwargs['pad_type'] = 'same'
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model = _gen_efficientnetv2_xl('tf_efficientnetv2_xl_in21ft1k', pretrained=pretrained, **kwargs)
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return model
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|
@register_model
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|
def tf_efficientnetv2_s_in21k(pretrained=False, **kwargs):
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|
""" EfficientNet-V2 Small w/ ImageNet-21k pretrained weights. Tensorflow compatible variant
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|
"""
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|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
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|
kwargs['pad_type'] = 'same'
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|
model = _gen_efficientnetv2_s('tf_efficientnetv2_s_in21k', pretrained=pretrained, **kwargs)
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|
return model
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|
@register_model
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|
|
|
|
def tf_efficientnetv2_m_in21k(pretrained=False, **kwargs):
|
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|
|
""" EfficientNet-V2 Medium w/ ImageNet-21k pretrained weights. Tensorflow compatible variant
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|
"""
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|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
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|
|
kwargs['pad_type'] = 'same'
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|
|
model = _gen_efficientnetv2_m('tf_efficientnetv2_m_in21k', pretrained=pretrained, **kwargs)
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|
return model
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|
|
|
|
|
|
|
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|
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|
|
@register_model
|
|
|
|
|
def tf_efficientnetv2_l_in21k(pretrained=False, **kwargs):
|
|
|
|
|
""" EfficientNet-V2 Large w/ ImageNet-21k pretrained weights. Tensorflow compatible variant
|
|
|
|
|
"""
|
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|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
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|
|
kwargs['pad_type'] = 'same'
|
|
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|
|
model = _gen_efficientnetv2_l('tf_efficientnetv2_l_in21k', pretrained=pretrained, **kwargs)
|
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|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def tf_efficientnetv2_xl_in21k(pretrained=False, **kwargs):
|
|
|
|
|
""" EfficientNet-V2 Xtra-Large w/ ImageNet-21k pretrained weights. Tensorflow compatible variant
|
|
|
|
|
"""
|
|
|
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
|
|
|
kwargs['pad_type'] = 'same'
|
|
|
|
|
model = _gen_efficientnetv2_xl('tf_efficientnetv2_xl_in21k', pretrained=pretrained, **kwargs)
|
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|
|
return model
|
|
|
|
|
|
|
|
|
|
|
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|
|
@register_model
|
|
|
|
|
def tf_efficientnetv2_b0(pretrained=False, **kwargs):
|
|
|
|
|
""" EfficientNet-V2-B0. Tensorflow compatible variant """
|
|
|
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
|
|
|
kwargs['pad_type'] = 'same'
|
|
|
|
|
model = _gen_efficientnetv2_base('tf_efficientnetv2_b0', pretrained=pretrained, **kwargs)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def tf_efficientnetv2_b1(pretrained=False, **kwargs):
|
|
|
|
|
""" EfficientNet-V2-B1. Tensorflow compatible variant """
|
|
|
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
|
|
|
kwargs['pad_type'] = 'same'
|
|
|
|
|
model = _gen_efficientnetv2_base(
|
|
|
|
|
'tf_efficientnetv2_b1', channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def tf_efficientnetv2_b2(pretrained=False, **kwargs):
|
|
|
|
|
""" EfficientNet-V2-B2. Tensorflow compatible variant """
|
|
|
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
|
|
|
kwargs['pad_type'] = 'same'
|
|
|
|
|
model = _gen_efficientnetv2_base(
|
|
|
|
|
'tf_efficientnetv2_b2', channel_multiplier=1.1, depth_multiplier=1.2, pretrained=pretrained, **kwargs)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def tf_efficientnetv2_b3(pretrained=False, **kwargs):
|
|
|
|
|
""" EfficientNet-V2-B3. Tensorflow compatible variant """
|
|
|
|
|
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
|
|
|
|
|
kwargs['pad_type'] = 'same'
|
|
|
|
|
model = _gen_efficientnetv2_base(
|
|
|
|
|
'tf_efficientnetv2_b3', channel_multiplier=1.2, depth_multiplier=1.4, 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
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def tinynet_a(pretrained=False, **kwargs):
|
|
|
|
|
model = _gen_tinynet('tinynet_a', 1.0, 1.2, pretrained=pretrained, **kwargs)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def tinynet_b(pretrained=False, **kwargs):
|
|
|
|
|
model = _gen_tinynet('tinynet_b', 0.75, 1.1, pretrained=pretrained, **kwargs)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def tinynet_c(pretrained=False, **kwargs):
|
|
|
|
|
model = _gen_tinynet('tinynet_c', 0.54, 0.85, pretrained=pretrained, **kwargs)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def tinynet_d(pretrained=False, **kwargs):
|
|
|
|
|
model = _gen_tinynet('tinynet_d', 0.54, 0.695, pretrained=pretrained, **kwargs)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def tinynet_e(pretrained=False, **kwargs):
|
|
|
|
|
model = _gen_tinynet('tinynet_e', 0.51, 0.6, pretrained=pretrained, **kwargs)
|
|
|
|
|
return model
|
