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| """ The EfficientNet Family in PyTorch | |
| An implementation of EfficienNet that covers variety of related models with efficient architectures: | |
| * EfficientNet-V2 | |
| - `EfficientNetV2: Smaller Models and Faster Training` - https://arxiv.org/abs/2104.00298 | |
| * EfficientNet (B0-B8, L2 + Tensorflow pretrained AutoAug/RandAug/AdvProp/NoisyStudent weight ports) | |
| - EfficientNet: Rethinking Model Scaling for CNNs - https://arxiv.org/abs/1905.11946 | |
| - CondConv: Conditionally Parameterized Convolutions for Efficient Inference - https://arxiv.org/abs/1904.04971 | |
| - Adversarial Examples Improve Image Recognition - https://arxiv.org/abs/1911.09665 | |
| - Self-training with Noisy Student improves ImageNet classification - https://arxiv.org/abs/1911.04252 | |
| * MixNet (Small, Medium, and Large) | |
| - MixConv: Mixed Depthwise Convolutional Kernels - https://arxiv.org/abs/1907.09595 | |
| * MNasNet B1, A1 (SE), Small | |
| - MnasNet: Platform-Aware Neural Architecture Search for Mobile - https://arxiv.org/abs/1807.11626 | |
| * FBNet-C | |
| - FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable NAS - https://arxiv.org/abs/1812.03443 | |
| * Single-Path NAS Pixel1 | |
| - Single-Path NAS: Designing Hardware-Efficient ConvNets - https://arxiv.org/abs/1904.02877 | |
| * TinyNet | |
| - Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets - https://arxiv.org/abs/2010.14819 | |
| - Definitions & weights borrowed from https://github.com/huawei-noah/CV-Backbones/tree/master/tinynet_pytorch | |
| * And likely more... | |
| The majority of the above models (EfficientNet*, MixNet, MnasNet) and original weights were made available | |
| by Mingxing Tan, Quoc Le, and other members of their Google Brain team. Thanks for consistently releasing | |
| the models and weights open source! | |
| Hacked together by / Copyright 2019, Ross Wightman | |
| """ | |
| from functools import partial | |
| from typing import List | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from custom_timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD | |
| from .efficientnet_blocks import SqueezeExcite | |
| from .efficientnet_builder import EfficientNetBuilder, decode_arch_def, efficientnet_init_weights,\ | |
| round_channels, resolve_bn_args, resolve_act_layer, BN_EPS_TF_DEFAULT | |
| from .features import FeatureInfo, FeatureHooks | |
| from .helpers import build_model_with_cfg, pretrained_cfg_for_features, checkpoint_seq | |
| from .layers import create_conv2d, create_classifier, get_norm_act_layer, EvoNorm2dS0, GroupNormAct | |
| from .registry import register_model | |
| __all__ = ['EfficientNet', 'EfficientNetFeatures'] | |
| def _cfg(url='', **kwargs): | |
| return { | |
| 'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7), | |
| 'crop_pct': 0.875, 'interpolation': 'bicubic', | |
| 'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, | |
| 'first_conv': 'conv_stem', 'classifier': 'classifier', | |
| **kwargs | |
| } | |
| default_cfgs = { | |
| 'mnasnet_050': _cfg(url=''), | |
| 'mnasnet_075': _cfg(url=''), | |
| 'mnasnet_100': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_b1-74cb7081.pth'), | |
| 'mnasnet_140': _cfg(url=''), | |
| 'semnasnet_050': _cfg(url=''), | |
| 'semnasnet_075': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/semnasnet_075-18710866.pth'), | |
| 'semnasnet_100': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_a1-d9418771.pth'), | |
| 'semnasnet_140': _cfg(url=''), | |
| 'mnasnet_small': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_small_lamb-aff75073.pth'), | |
| 'mobilenetv2_035': _cfg( | |
| url=''), | |
| 'mobilenetv2_050': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_050-3d30d450.pth', | |
| interpolation='bicubic', | |
| ), | |
| 'mobilenetv2_075': _cfg( | |
| url=''), | |
| 'mobilenetv2_100': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_100_ra-b33bc2c4.pth'), | |
| 'mobilenetv2_110d': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_110d_ra-77090ade.pth'), | |
| 'mobilenetv2_120d': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_120d_ra-5987e2ed.pth'), | |
| 'mobilenetv2_140': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_140_ra-21a4e913.pth'), | |
| 'fbnetc_100': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/fbnetc_100-c345b898.pth', | |
| interpolation='bilinear'), | |
| 'spnasnet_100': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/spnasnet_100-048bc3f4.pth', | |
| interpolation='bilinear'), | |
| # NOTE experimenting with alternate attention | |
| 'efficientnet_b0': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b0_ra-3dd342df.pth'), | |
| 'efficientnet_b1': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b1-533bc792.pth', | |
| test_input_size=(3, 256, 256), crop_pct=1.0), | |
| 'efficientnet_b2': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b2_ra-bcdf34b7.pth', | |
| input_size=(3, 256, 256), pool_size=(8, 8), test_input_size=(3, 288, 288), crop_pct=1.0), | |
| 'efficientnet_b3': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b3_ra2-cf984f9c.pth', | |
| input_size=(3, 288, 288), pool_size=(9, 9), test_input_size=(3, 320, 320), crop_pct=1.0), | |
| 'efficientnet_b4': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b4_ra2_320-7eb33cd5.pth', | |
| input_size=(3, 320, 320), pool_size=(10, 10), test_input_size=(3, 384, 384), crop_pct=1.0), | |
| 'efficientnet_b5': _cfg( | |
| url='', input_size=(3, 456, 456), pool_size=(15, 15), crop_pct=0.934), | |
| 'efficientnet_b6': _cfg( | |
| url='', input_size=(3, 528, 528), pool_size=(17, 17), crop_pct=0.942), | |
| 'efficientnet_b7': _cfg( | |
| url='', input_size=(3, 600, 600), pool_size=(19, 19), crop_pct=0.949), | |
| 'efficientnet_b8': _cfg( | |
| url='', input_size=(3, 672, 672), pool_size=(21, 21), crop_pct=0.954), | |
| 'efficientnet_l2': _cfg( | |
| url='', input_size=(3, 800, 800), pool_size=(25, 25), crop_pct=0.961), | |
| # FIXME experimental | |
| 'efficientnet_b0_gn': _cfg( | |
| url=''), | |
| 'efficientnet_b0_g8_gn': _cfg( | |
| url=''), | |
| 'efficientnet_b0_g16_evos': _cfg( | |
| url=''), | |
| 'efficientnet_b3_gn': _cfg( | |
| url='', | |
| input_size=(3, 288, 288), pool_size=(9, 9), test_input_size=(3, 320, 320), crop_pct=1.0), | |
| 'efficientnet_b3_g8_gn': _cfg( | |
| url='', | |
| input_size=(3, 288, 288), pool_size=(9, 9), test_input_size=(3, 320, 320), crop_pct=1.0), | |
| 'efficientnet_es': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_es_ra-f111e99c.pth'), | |
| 'efficientnet_em': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_em_ra2-66250f76.pth', | |
| input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882), | |
| 'efficientnet_el': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_el-3b455510.pth', | |
| input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904), | |
| 'efficientnet_es_pruned': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_es_pruned75-1b7248cf.pth'), | |
| 'efficientnet_el_pruned': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_el_pruned70-ef2a2ccf.pth', | |
| input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904), | |
| 'efficientnet_cc_b0_4e': _cfg(url=''), | |
| 'efficientnet_cc_b0_8e': _cfg(url=''), | |
| 'efficientnet_cc_b1_8e': _cfg(url='', input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882), | |
| 'efficientnet_lite0': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_lite0_ra-37913777.pth'), | |
| 'efficientnet_lite1': _cfg( | |
| url='', | |
| input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882), | |
| 'efficientnet_lite2': _cfg( | |
| url='', | |
| input_size=(3, 260, 260), pool_size=(9, 9), crop_pct=0.890), | |
| 'efficientnet_lite3': _cfg( | |
| url='', | |
| input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904), | |
| 'efficientnet_lite4': _cfg( | |
| url='', input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.922), | |
| 'efficientnet_b1_pruned': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/effnetb1_pruned-bea43a3a.pth', | |
| input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882, mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD), | |
| 'efficientnet_b2_pruned': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/effnetb2_pruned-08c1b27c.pth', | |
| input_size=(3, 260, 260), pool_size=(9, 9), crop_pct=0.890, mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD), | |
| 'efficientnet_b3_pruned': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/effnetb3_pruned-59ecf72d.pth', | |
| input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904, mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD), | |
| 'efficientnetv2_rw_t': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnetv2_t_agc-3620981a.pth', | |
| input_size=(3, 224, 224), test_input_size=(3, 288, 288), pool_size=(7, 7), crop_pct=1.0), | |
| 'gc_efficientnetv2_rw_t': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gc_efficientnetv2_rw_t_agc-927a0bde.pth', | |
| input_size=(3, 224, 224), test_input_size=(3, 288, 288), pool_size=(7, 7), crop_pct=1.0), | |
| 'efficientnetv2_rw_s': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_v2s_ra2_288-a6477665.pth', | |
| input_size=(3, 288, 288), test_input_size=(3, 384, 384), pool_size=(9, 9), crop_pct=1.0), | |
| 'efficientnetv2_rw_m': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnetv2_rw_m_agc-3d90cb1e.pth', | |
| input_size=(3, 320, 320), test_input_size=(3, 416, 416), pool_size=(10, 10), crop_pct=1.0), | |
| 'efficientnetv2_s': _cfg( | |
| url='', | |
| input_size=(3, 288, 288), test_input_size=(3, 384, 384), pool_size=(9, 9), crop_pct=1.0), | |
| 'efficientnetv2_m': _cfg( | |
| url='', | |
| input_size=(3, 320, 320), test_input_size=(3, 416, 416), pool_size=(10, 10), crop_pct=1.0), | |
| 'efficientnetv2_l': _cfg( | |
| url='', | |
| input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0), | |
| 'efficientnetv2_xl': _cfg( | |
| url='', | |
| input_size=(3, 384, 384), test_input_size=(3, 512, 512), pool_size=(12, 12), crop_pct=1.0), | |
| 'tf_efficientnet_b0': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_aa-827b6e33.pth', | |
| input_size=(3, 224, 224)), | |
| 'tf_efficientnet_b1': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b1_aa-ea7a6ee0.pth', | |
| input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882), | |
| 'tf_efficientnet_b2': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b2_aa-60c94f97.pth', | |
| input_size=(3, 260, 260), pool_size=(9, 9), crop_pct=0.890), | |
| 'tf_efficientnet_b3': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3_aa-84b4657e.pth', | |
| input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904), | |
| 'tf_efficientnet_b4': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b4_aa-818f208c.pth', | |
| input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.922), | |
| 'tf_efficientnet_b5': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b5_ra-9a3e5369.pth', | |
| input_size=(3, 456, 456), pool_size=(15, 15), crop_pct=0.934), | |
| 'tf_efficientnet_b6': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b6_aa-80ba17e4.pth', | |
| 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', | |
| input_size=(3, 192, 192), test_input_size=(3, 240, 240), pool_size=(6, 6), crop_pct=0.882), | |
| 'tf_efficientnetv2_b2': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_b2-847de54e.pth', | |
| input_size=(3, 208, 208), test_input_size=(3, 260, 260), pool_size=(7, 7), crop_pct=0.890), | |
| 'tf_efficientnetv2_b3': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_b3-57773f13.pth', | |
| input_size=(3, 240, 240), test_input_size=(3, 300, 300), pool_size=(8, 8), crop_pct=0.904), | |
| 'mixnet_s': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_s-a907afbc.pth'), | |
| 'mixnet_m': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_m-4647fc68.pth'), | |
| 'mixnet_l': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_l-5a9a2ed8.pth'), | |
| 'mixnet_xl': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_xl_ra-aac3c00c.pth'), | |
| 'mixnet_xxl': _cfg(), | |
| 'tf_mixnet_s': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_s-89d3354b.pth'), | |
| 'tf_mixnet_m': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_m-0f4d8805.pth'), | |
| 'tf_mixnet_l': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_l-6c92e0c8.pth'), | |
| "tinynet_a": _cfg( | |
| input_size=(3, 192, 192), pool_size=(6, 6), # int(224 * 0.86) | |
| url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_a.pth'), | |
| "tinynet_b": _cfg( | |
| input_size=(3, 188, 188), pool_size=(6, 6), # int(224 * 0.84) | |
| url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_b.pth'), | |
| "tinynet_c": _cfg( | |
| input_size=(3, 184, 184), pool_size=(6, 6), # int(224 * 0.825) | |
| url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_c.pth'), | |
| "tinynet_d": _cfg( | |
| input_size=(3, 152, 152), pool_size=(5, 5), # int(224 * 0.68) | |
| url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_d.pth'), | |
| "tinynet_e": _cfg( | |
| input_size=(3, 106, 106), pool_size=(4, 4), # int(224 * 0.475) | |
| url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_e.pth'), | |
| } | |
| class EfficientNet(nn.Module): | |
| """ EfficientNet | |
| A flexible and performant PyTorch implementation of efficient network architectures, including: | |
| * EfficientNet-V2 Small, Medium, Large, XL & B0-B3 | |
| * EfficientNet B0-B8, L2 | |
| * EfficientNet-EdgeTPU | |
| * EfficientNet-CondConv | |
| * MixNet S, M, L, XL | |
| * MnasNet A1, B1, and small | |
| * MobileNet-V2 | |
| * FBNet C | |
| * Single-Path NAS Pixel1 | |
| * TinyNet | |
| """ | |
| def __init__( | |
| self, block_args, num_classes=1000, num_features=1280, 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., global_pool='avg'): | |
| super(EfficientNet, self).__init__() | |
| act_layer = act_layer or nn.ReLU | |
| norm_layer = norm_layer or nn.BatchNorm2d | |
| norm_act_layer = get_norm_act_layer(norm_layer, act_layer) | |
| se_layer = se_layer or SqueezeExcite | |
| self.num_classes = num_classes | |
| self.num_features = num_features | |
| self.drop_rate = drop_rate | |
| self.grad_checkpointing = False | |
| # 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_act_layer(stem_size, 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) | |
| self.blocks = nn.Sequential(*builder(stem_size, block_args)) | |
| self.feature_info = builder.features | |
| head_chs = builder.in_chs | |
| # Head + Pooling | |
| self.conv_head = create_conv2d(head_chs, self.num_features, 1, padding=pad_type) | |
| self.bn2 = norm_act_layer(self.num_features, inplace=True) | |
| self.global_pool, self.classifier = create_classifier( | |
| self.num_features, self.num_classes, pool_type=global_pool) | |
| efficientnet_init_weights(self) | |
| def as_sequential(self): | |
| layers = [self.conv_stem, self.bn1] | |
| layers.extend(self.blocks) | |
| layers.extend([self.conv_head, self.bn2, self.global_pool]) | |
| layers.extend([nn.Dropout(self.drop_rate), self.classifier]) | |
| return nn.Sequential(*layers) | |
| def group_matcher(self, coarse=False): | |
| return dict( | |
| stem=r'^conv_stem|bn1', | |
| blocks=[ | |
| (r'^blocks\.(\d+)' if coarse else r'^blocks\.(\d+)\.(\d+)', None), | |
| (r'conv_head|bn2', (99999,)) | |
| ] | |
| ) | |
| def set_grad_checkpointing(self, enable=True): | |
| self.grad_checkpointing = enable | |
| def get_classifier(self): | |
| return self.classifier | |
| def reset_classifier(self, num_classes, global_pool='avg'): | |
| self.num_classes = num_classes | |
| self.global_pool, self.classifier = create_classifier( | |
| self.num_features, self.num_classes, pool_type=global_pool) | |
| def forward_features(self, x): | |
| x = self.conv_stem(x) | |
| x = self.bn1(x) | |
| if self.grad_checkpointing and not torch.jit.is_scripting(): | |
| x = checkpoint_seq(self.blocks, x, flatten=True) | |
| else: | |
| x = self.blocks(x) | |
| x = self.conv_head(x) | |
| x = self.bn2(x) | |
| return x | |
| def forward_head(self, x, pre_logits: bool = False): | |
| x = self.global_pool(x) | |
| if self.drop_rate > 0.: | |
| x = F.dropout(x, p=self.drop_rate, training=self.training) | |
| return x if pre_logits else self.classifier(x) | |
| def forward(self, x): | |
| x = self.forward_features(x) | |
| x = self.forward_head(x) | |
| return 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 | |
| norm_act_layer = get_norm_act_layer(norm_layer, act_layer) | |
| 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_act_layer(stem_size, 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) | |
| 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, | |
| pretrained_strict=not features_only, | |
| kwargs_filter=kwargs_filter, | |
| **kwargs) | |
| if features_only: | |
| model.default_cfg = pretrained_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)), | |
| **kwargs | |
| ) | |
| model = _create_effnet(variant, pretrained, **model_kwargs) | |
| return model | |
| def _gen_mnasnet_small(variant, channel_multiplier=1.0, pretrained=False, **kwargs): | |
| """Creates a mnasnet-b1 model. | |
| Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet | |
| Paper: https://arxiv.org/pdf/1807.11626.pdf. | |
| Args: | |
| channel_multiplier: multiplier to number of channels per layer. | |
| """ | |
| arch_def = [ | |
| ['ds_r1_k3_s1_c8'], | |
| ['ir_r1_k3_s2_e3_c16'], | |
| ['ir_r2_k3_s2_e6_c16'], | |
| ['ir_r4_k5_s2_e6_c32_se0.25'], | |
| ['ir_r3_k3_s1_e6_c32_se0.25'], | |
| ['ir_r3_k5_s2_e6_c88_se0.25'], | |
| ['ir_r1_k3_s1_e6_c144'] | |
| ] | |
| model_kwargs = dict( | |
| block_args=decode_arch_def(arch_def), | |
| stem_size=8, | |
| 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_mobilenet_v2( | |
| variant, channel_multiplier=1.0, depth_multiplier=1.0, fix_stem_head=False, pretrained=False, **kwargs): | |
| """ Generate MobileNet-V2 network | |
| Ref impl: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v2.py | |
| Paper: https://arxiv.org/abs/1801.04381 | |
| """ | |
| arch_def = [ | |
| ['ds_r1_k3_s1_c16'], | |
| ['ir_r2_k3_s2_e6_c24'], | |
| ['ir_r3_k3_s2_e6_c32'], | |
| ['ir_r4_k3_s2_e6_c64'], | |
| ['ir_r3_k3_s1_e6_c96'], | |
| ['ir_r3_k3_s2_e6_c160'], | |
| ['ir_r1_k3_s1_e6_c320'], | |
| ] | |
| round_chs_fn = partial(round_channels, multiplier=channel_multiplier) | |
| model_kwargs = dict( | |
| block_args=decode_arch_def(arch_def, depth_multiplier=depth_multiplier, fix_first_last=fix_stem_head), | |
| num_features=1280 if fix_stem_head else max(1280, round_chs_fn(1280)), | |
| stem_size=32, | |
| fix_stem=fix_stem_head, | |
| round_chs_fn=round_chs_fn, | |
| norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)), | |
| act_layer=resolve_act_layer(kwargs, 'relu6'), | |
| **kwargs | |
| ) | |
| model = _create_effnet(variant, pretrained, **model_kwargs) | |
| return model | |
| def _gen_fbnetc(variant, channel_multiplier=1.0, pretrained=False, **kwargs): | |
| """ FBNet-C | |
| Paper: https://arxiv.org/abs/1812.03443 | |
| Ref Impl: https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/maskrcnn_benchmark/modeling/backbone/fbnet_modeldef.py | |
| NOTE: the impl above does not relate to the 'C' variant here, that was derived from paper, | |
| it was used to confirm some building block details | |
| """ | |
| arch_def = [ | |
| ['ir_r1_k3_s1_e1_c16'], | |
| ['ir_r1_k3_s2_e6_c24', 'ir_r2_k3_s1_e1_c24'], | |
| ['ir_r1_k5_s2_e6_c32', 'ir_r1_k5_s1_e3_c32', 'ir_r1_k5_s1_e6_c32', 'ir_r1_k3_s1_e6_c32'], | |
| ['ir_r1_k5_s2_e6_c64', 'ir_r1_k5_s1_e3_c64', 'ir_r2_k5_s1_e6_c64'], | |
| ['ir_r3_k5_s1_e6_c112', 'ir_r1_k5_s1_e3_c112'], | |
| ['ir_r4_k5_s2_e6_c184'], | |
| ['ir_r1_k3_s1_e6_c352'], | |
| ] | |
| model_kwargs = dict( | |
| block_args=decode_arch_def(arch_def), | |
| stem_size=16, | |
| num_features=1984, # paper suggests this, but is not 100% clear | |
| 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_spnasnet(variant, channel_multiplier=1.0, pretrained=False, **kwargs): | |
| """Creates the Single-Path NAS model from search targeted for Pixel1 phone. | |
| Paper: https://arxiv.org/abs/1904.02877 | |
| Args: | |
| channel_multiplier: multiplier to number of channels per layer. | |
| """ | |
| arch_def = [ | |
| # stage 0, 112x112 in | |
| ['ds_r1_k3_s1_c16_noskip'], | |
| # stage 1, 112x112 in | |
| ['ir_r3_k3_s2_e3_c24'], | |
| # stage 2, 56x56 in | |
| ['ir_r1_k5_s2_e6_c40', 'ir_r3_k3_s1_e3_c40'], | |
| # stage 3, 28x28 in | |
| ['ir_r1_k5_s2_e6_c80', 'ir_r3_k3_s1_e3_c80'], | |
| # stage 4, 14x14in | |
| ['ir_r1_k5_s1_e6_c96', 'ir_r3_k5_s1_e3_c96'], | |
| # stage 5, 14x14in | |
| ['ir_r4_k5_s2_e6_c192'], | |
| # stage 6, 7x7 in | |
| ['ir_r1_k3_s1_e6_c320_noskip'] | |
| ] | |
| model_kwargs = dict( | |
| block_args=decode_arch_def(arch_def), | |
| stem_size=32, | |
| 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_efficientnet( | |
| variant, channel_multiplier=1.0, depth_multiplier=1.0, channel_divisor=8, | |
| group_size=None, pretrained=False, **kwargs): | |
| """Creates an EfficientNet model. | |
| Ref impl: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/efficientnet_model.py | |
| Paper: https://arxiv.org/abs/1905.11946 | |
| EfficientNet params | |
| name: (channel_multiplier, depth_multiplier, resolution, dropout_rate) | |
| 'efficientnet-b0': (1.0, 1.0, 224, 0.2), | |
| 'efficientnet-b1': (1.0, 1.1, 240, 0.2), | |
| 'efficientnet-b2': (1.1, 1.2, 260, 0.3), | |
| 'efficientnet-b3': (1.2, 1.4, 300, 0.3), | |
| 'efficientnet-b4': (1.4, 1.8, 380, 0.4), | |
| 'efficientnet-b5': (1.6, 2.2, 456, 0.4), | |
| 'efficientnet-b6': (1.8, 2.6, 528, 0.5), | |
| 'efficientnet-b7': (2.0, 3.1, 600, 0.5), | |
| 'efficientnet-b8': (2.2, 3.6, 672, 0.5), | |
| 'efficientnet-l2': (4.3, 5.3, 800, 0.5), | |
| Args: | |
| channel_multiplier: multiplier to number of channels per layer | |
| depth_multiplier: multiplier to number of repeats per stage | |
| """ | |
| arch_def = [ | |
| ['ds_r1_k3_s1_e1_c16_se0.25'], | |
| ['ir_r2_k3_s2_e6_c24_se0.25'], | |
| ['ir_r2_k5_s2_e6_c40_se0.25'], | |
| ['ir_r3_k3_s2_e6_c80_se0.25'], | |
| ['ir_r3_k5_s1_e6_c112_se0.25'], | |
| ['ir_r4_k5_s2_e6_c192_se0.25'], | |
| ['ir_r1_k3_s1_e6_c320_se0.25'], | |
| ] | |
| round_chs_fn = partial(round_channels, multiplier=channel_multiplier, divisor=channel_divisor) | |
| model_kwargs = dict( | |
| block_args=decode_arch_def(arch_def, depth_multiplier, group_size=group_size), | |
| num_features=round_chs_fn(1280), | |
| stem_size=32, | |
| round_chs_fn=round_chs_fn, | |
| act_layer=resolve_act_layer(kwargs, 'swish'), | |
| 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_efficientnet_edge( | |
| variant, channel_multiplier=1.0, depth_multiplier=1.0, group_size=None, pretrained=False, **kwargs): | |
| """ Creates an EfficientNet-EdgeTPU model | |
| Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/edgetpu | |
| """ | |
| arch_def = [ | |
| # NOTE `fc` is present to override a mismatch between stem channels and in chs not | |
| # present in other models | |
| ['er_r1_k3_s1_e4_c24_fc24_noskip'], | |
| ['er_r2_k3_s2_e8_c32'], | |
| ['er_r4_k3_s2_e8_c48'], | |
| ['ir_r5_k5_s2_e8_c96'], | |
| ['ir_r4_k5_s1_e8_c144'], | |
| ['ir_r2_k5_s2_e8_c192'], | |
| ] | |
| round_chs_fn = partial(round_channels, multiplier=channel_multiplier) | |
| model_kwargs = dict( | |
| block_args=decode_arch_def(arch_def, depth_multiplier, group_size=group_size), | |
| 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)), | |
| act_layer=resolve_act_layer(kwargs, 'relu'), | |
| **kwargs, | |
| ) | |
| model = _create_effnet(variant, pretrained, **model_kwargs) | |
| return model | |
| def _gen_efficientnet_condconv( | |
| variant, channel_multiplier=1.0, depth_multiplier=1.0, experts_multiplier=1, pretrained=False, **kwargs): | |
| """Creates an EfficientNet-CondConv model. | |
| Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/condconv | |
| """ | |
| arch_def = [ | |
| ['ds_r1_k3_s1_e1_c16_se0.25'], | |
| ['ir_r2_k3_s2_e6_c24_se0.25'], | |
| ['ir_r2_k5_s2_e6_c40_se0.25'], | |
| ['ir_r3_k3_s2_e6_c80_se0.25'], | |
| ['ir_r3_k5_s1_e6_c112_se0.25_cc4'], | |
| ['ir_r4_k5_s2_e6_c192_se0.25_cc4'], | |
| ['ir_r1_k3_s1_e6_c320_se0.25_cc4'], | |
| ] | |
| # NOTE unlike official impl, this one uses `cc<x>` option where x is the base number of experts for each stage and | |
| # the expert_multiplier increases that on a per-model basis as with depth/channel multipliers | |
| round_chs_fn = partial(round_channels, multiplier=channel_multiplier) | |
| model_kwargs = dict( | |
| block_args=decode_arch_def(arch_def, depth_multiplier, experts_multiplier=experts_multiplier), | |
| 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)), | |
| act_layer=resolve_act_layer(kwargs, 'swish'), | |
| **kwargs, | |
| ) | |
| model = _create_effnet(variant, pretrained, **model_kwargs) | |
| return model | |
| 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 | |
| Paper: https://arxiv.org/abs/1905.11946 | |
| EfficientNet params | |
| name: (channel_multiplier, depth_multiplier, resolution, dropout_rate) | |
| 'efficientnet-lite0': (1.0, 1.0, 224, 0.2), | |
| 'efficientnet-lite1': (1.0, 1.1, 240, 0.2), | |
| 'efficientnet-lite2': (1.1, 1.2, 260, 0.3), | |
| 'efficientnet-lite3': (1.2, 1.4, 280, 0.3), | |
| 'efficientnet-lite4': (1.4, 1.8, 300, 0.3), | |
| Args: | |
| channel_multiplier: multiplier to number of channels per layer | |
| depth_multiplier: multiplier to number of repeats per stage | |
| """ | |
| arch_def = [ | |
| ['ds_r1_k3_s1_e1_c16'], | |
| ['ir_r2_k3_s2_e6_c24'], | |
| ['ir_r2_k5_s2_e6_c40'], | |
| ['ir_r3_k3_s2_e6_c80'], | |
| ['ir_r3_k5_s1_e6_c112'], | |
| ['ir_r4_k5_s2_e6_c192'], | |
| ['ir_r1_k3_s1_e6_c320'], | |
| ] | |
| model_kwargs = dict( | |
| block_args=decode_arch_def(arch_def, depth_multiplier, fix_first_last=True), | |
| num_features=1280, | |
| stem_size=32, | |
| fix_stem=True, | |
| round_chs_fn=partial(round_channels, multiplier=channel_multiplier), | |
| act_layer=resolve_act_layer(kwargs, 'relu6'), | |
| 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_efficientnetv2_base( | |
| variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs): | |
| """ Creates an EfficientNet-V2 base 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_r1_k3_s1_e1_c16_skip'], | |
| ['er_r2_k3_s2_e4_c32'], | |
| ['er_r2_k3_s2_e4_c48'], | |
| ['ir_r3_k3_s2_e4_c96_se0.25'], | |
| ['ir_r5_k3_s1_e6_c112_se0.25'], | |
| ['ir_r8_k3_s2_e6_c192_se0.25'], | |
| ] | |
| round_chs_fn = partial(round_channels, multiplier=channel_multiplier, round_limit=0.) | |
| model_kwargs = dict( | |
| block_args=decode_arch_def(arch_def, depth_multiplier), | |
| 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)), | |
| act_layer=resolve_act_layer(kwargs, 'silu'), | |
| **kwargs, | |
| ) | |
| model = _create_effnet(variant, pretrained, **model_kwargs) | |
| return model | |
| def _gen_efficientnetv2_s( | |
| variant, channel_multiplier=1.0, depth_multiplier=1.0, group_size=None, rw=False, pretrained=False, **kwargs): | |
| """ Creates an EfficientNet-V2 Small model | |
| Ref impl: https://github.com/google/automl/tree/master/efficientnetv2 | |
| Paper: `EfficientNetV2: Smaller Models and Faster Training` - https://arxiv.org/abs/2104.00298 | |
| NOTE: `rw` flag sets up 'small' variant to behave like my initial v2 small model, | |
| before ref the impl was released. | |
| """ | |
| arch_def = [ | |
| ['cn_r2_k3_s1_e1_c24_skip'], | |
| ['er_r4_k3_s2_e4_c48'], | |
| ['er_r4_k3_s2_e4_c64'], | |
| ['ir_r6_k3_s2_e4_c128_se0.25'], | |
| ['ir_r9_k3_s1_e6_c160_se0.25'], | |
| ['ir_r15_k3_s2_e6_c256_se0.25'], | |
| ] | |
| num_features = 1280 | |
| if rw: | |
| # my original variant, based on paper figure differs from the official release | |
| arch_def[0] = ['er_r2_k3_s1_e1_c24'] | |
| arch_def[-1] = ['ir_r15_k3_s2_e6_c272_se0.25'] | |
| num_features = 1792 | |
| round_chs_fn = partial(round_channels, multiplier=channel_multiplier) | |
| model_kwargs = dict( | |
| block_args=decode_arch_def(arch_def, depth_multiplier, group_size=group_size), | |
| num_features=round_chs_fn(num_features), | |
| stem_size=24, | |
| round_chs_fn=round_chs_fn, | |
| 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_m(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs): | |
| """ Creates an EfficientNet-V2 Medium 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_r3_k3_s1_e1_c24_skip'], | |
| ['er_r5_k3_s2_e4_c48'], | |
| ['er_r5_k3_s2_e4_c80'], | |
| ['ir_r7_k3_s2_e4_c160_se0.25'], | |
| ['ir_r14_k3_s1_e6_c176_se0.25'], | |
| ['ir_r18_k3_s2_e6_c304_se0.25'], | |
| ['ir_r5_k3_s1_e6_c512_se0.25'], | |
| ] | |
| model_kwargs = dict( | |
| block_args=decode_arch_def(arch_def, depth_multiplier), | |
| num_features=1280, | |
| 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) | |
| return model | |
| 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) | |
| return model | |
| def _gen_tinynet( | |
| variant, model_width=1.0, depth_multiplier=1.0, pretrained=False, **kwargs | |
| ): | |
| """Creates a TinyNet model. | |
| """ | |
| arch_def = [ | |
| ['ds_r1_k3_s1_e1_c16_se0.25'], ['ir_r2_k3_s2_e6_c24_se0.25'], | |
| ['ir_r2_k5_s2_e6_c40_se0.25'], ['ir_r3_k3_s2_e6_c80_se0.25'], | |
| ['ir_r3_k5_s1_e6_c112_se0.25'], ['ir_r4_k5_s2_e6_c192_se0.25'], | |
| ['ir_r1_k3_s1_e6_c320_se0.25'], | |
| ] | |
| model_kwargs = dict( | |
| block_args=decode_arch_def(arch_def, depth_multiplier, depth_trunc='round'), | |
| num_features=max(1280, round_channels(1280, model_width, 8, None)), | |
| stem_size=32, | |
| fix_stem=True, | |
| round_chs_fn=partial(round_channels, multiplier=model_width), | |
| act_layer=resolve_act_layer(kwargs, 'swish'), | |
| 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 mnasnet_050(pretrained=False, **kwargs): | |
| """ MNASNet B1, depth multiplier of 0.5. """ | |
| model = _gen_mnasnet_b1('mnasnet_050', 0.5, pretrained=pretrained, **kwargs) | |
| return model | |
| def mnasnet_075(pretrained=False, **kwargs): | |
| """ MNASNet B1, depth multiplier of 0.75. """ | |
| model = _gen_mnasnet_b1('mnasnet_075', 0.75, pretrained=pretrained, **kwargs) | |
| return model | |
| def mnasnet_100(pretrained=False, **kwargs): | |
| """ MNASNet B1, depth multiplier of 1.0. """ | |
| model = _gen_mnasnet_b1('mnasnet_100', 1.0, pretrained=pretrained, **kwargs) | |
| return model | |
| def mnasnet_b1(pretrained=False, **kwargs): | |
| """ MNASNet B1, depth multiplier of 1.0. """ | |
| return mnasnet_100(pretrained, **kwargs) | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| def mnasnet_a1(pretrained=False, **kwargs): | |
| """ MNASNet A1 (w/ SE), depth multiplier of 1.0. """ | |
| return semnasnet_100(pretrained, **kwargs) | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| def spnasnet_100(pretrained=False, **kwargs): | |
| """ Single-Path NAS Pixel1""" | |
| model = _gen_spnasnet('spnasnet_100', 1.0, pretrained=pretrained, **kwargs) | |
| return 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 | |
| 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 | |
| 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 | |
| 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) | |
| 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 | |
| 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) | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| # FIXME experimental group cong / GroupNorm / EvoNorm experiments | |
| def efficientnet_b0_gn(pretrained=False, **kwargs): | |
| """ EfficientNet-B0 + GroupNorm""" | |
| model = _gen_efficientnet( | |
| 'efficientnet_b0_gn', norm_layer=partial(GroupNormAct, group_size=8), pretrained=pretrained, **kwargs) | |
| return model | |
| def efficientnet_b0_g8_gn(pretrained=False, **kwargs): | |
| """ EfficientNet-B0 w/ group conv + GroupNorm""" | |
| model = _gen_efficientnet( | |
| 'efficientnet_b0_g8_gn', group_size=8, norm_layer=partial(GroupNormAct, group_size=8), | |
| pretrained=pretrained, **kwargs) | |
| return model | |
| def efficientnet_b0_g16_evos(pretrained=False, **kwargs): | |
| """ EfficientNet-B0 w/ group 16 conv + EvoNorm""" | |
| model = _gen_efficientnet( | |
| 'efficientnet_b0_g16_evos', group_size=16, channel_divisor=16, | |
| pretrained=pretrained, **kwargs) #norm_layer=partial(EvoNorm2dS0, group_size=16), | |
| return model | |
| def efficientnet_b3_gn(pretrained=False, **kwargs): | |
| """ EfficientNet-B3 w/ GroupNorm """ | |
| # NOTE for train, drop_rate should be 0.3, drop_path_rate should be 0.2 | |
| model = _gen_efficientnet( | |
| 'efficientnet_b3_gn', channel_multiplier=1.2, depth_multiplier=1.4, channel_divisor=16, | |
| norm_layer=partial(GroupNormAct, group_size=16), pretrained=pretrained, **kwargs) | |
| return model | |
| def efficientnet_b3_g8_gn(pretrained=False, **kwargs): | |
| """ EfficientNet-B3 w/ grouped conv + BN""" | |
| # NOTE for train, drop_rate should be 0.3, drop_path_rate should be 0.2 | |
| model = _gen_efficientnet( | |
| 'efficientnet_b3_g8_gn', channel_multiplier=1.2, depth_multiplier=1.4, group_size=8, channel_divisor=16, | |
| norm_layer=partial(GroupNormAct, group_size=16), pretrained=pretrained, **kwargs) | |
| return 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| def efficientnetv2_s(pretrained=False, **kwargs): | |
| """ EfficientNet-V2 Small. """ | |
| model = _gen_efficientnetv2_s('efficientnetv2_s', pretrained=pretrained, **kwargs) | |
| return model | |
| def efficientnetv2_m(pretrained=False, **kwargs): | |
| """ EfficientNet-V2 Medium. """ | |
| model = _gen_efficientnetv2_m('efficientnetv2_m', pretrained=pretrained, **kwargs) | |
| return model | |
| def efficientnetv2_l(pretrained=False, **kwargs): | |
| """ EfficientNet-V2 Large. """ | |
| model = _gen_efficientnetv2_l('efficientnetv2_l', pretrained=pretrained, **kwargs) | |
| return model | |
| def efficientnetv2_xl(pretrained=False, **kwargs): | |
| """ EfficientNet-V2 Xtra-Large. """ | |
| model = _gen_efficientnetv2_xl('efficientnetv2_xl', pretrained=pretrained, **kwargs) | |
| return 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| def tf_efficientnet_lite4(pretrained=False, **kwargs): | |
| """ EfficientNet-Lite4 """ | |
| # NOTE for train, drop_rate should be 0.4, drop_path_rate should be 0.2 | |
| kwargs['bn_eps'] = BN_EPS_TF_DEFAULT | |
| kwargs['pad_type'] = 'same' | |
| model = _gen_efficientnet_lite( | |
| 'tf_efficientnet_lite4', channel_multiplier=1.4, depth_multiplier=1.8, pretrained=pretrained, **kwargs) | |
| return model | |
| def tf_efficientnetv2_s(pretrained=False, **kwargs): | |
| """ EfficientNet-V2 Small. Tensorflow compatible variant """ | |
| kwargs['bn_eps'] = BN_EPS_TF_DEFAULT | |
| kwargs['pad_type'] = 'same' | |
| model = _gen_efficientnetv2_s('tf_efficientnetv2_s', pretrained=pretrained, **kwargs) | |
| return model | |
| def tf_efficientnetv2_m(pretrained=False, **kwargs): | |
| """ EfficientNet-V2 Medium. Tensorflow compatible variant """ | |
| kwargs['bn_eps'] = BN_EPS_TF_DEFAULT | |
| kwargs['pad_type'] = 'same' | |
| model = _gen_efficientnetv2_m('tf_efficientnetv2_m', pretrained=pretrained, **kwargs) | |
| return model | |
| def tf_efficientnetv2_l(pretrained=False, **kwargs): | |
| """ EfficientNet-V2 Large. Tensorflow compatible variant """ | |
| kwargs['bn_eps'] = BN_EPS_TF_DEFAULT | |
| kwargs['pad_type'] = 'same' | |
| model = _gen_efficientnetv2_l('tf_efficientnetv2_l', pretrained=pretrained, **kwargs) | |
| return model | |
| def tf_efficientnetv2_s_in21ft1k(pretrained=False, **kwargs): | |
| """ EfficientNet-V2 Small. Pretrained on ImageNet-21k, fine-tuned on 1k. Tensorflow compatible variant | |
| """ | |
| kwargs['bn_eps'] = BN_EPS_TF_DEFAULT | |
| kwargs['pad_type'] = 'same' | |
| model = _gen_efficientnetv2_s('tf_efficientnetv2_s_in21ft1k', pretrained=pretrained, **kwargs) | |
| return model | |
| def tf_efficientnetv2_m_in21ft1k(pretrained=False, **kwargs): | |
| """ EfficientNet-V2 Medium. Pretrained on ImageNet-21k, fine-tuned on 1k. Tensorflow compatible variant | |
| """ | |
| kwargs['bn_eps'] = BN_EPS_TF_DEFAULT | |
| kwargs['pad_type'] = 'same' | |
| model = _gen_efficientnetv2_m('tf_efficientnetv2_m_in21ft1k', pretrained=pretrained, **kwargs) | |
| return model | |
| def tf_efficientnetv2_l_in21ft1k(pretrained=False, **kwargs): | |
| """ EfficientNet-V2 Large. Pretrained on ImageNet-21k, fine-tuned on 1k. Tensorflow compatible variant | |
| """ | |
| kwargs['bn_eps'] = BN_EPS_TF_DEFAULT | |
| kwargs['pad_type'] = 'same' | |
| model = _gen_efficientnetv2_l('tf_efficientnetv2_l_in21ft1k', pretrained=pretrained, **kwargs) | |
| return model | |
| def tf_efficientnetv2_xl_in21ft1k(pretrained=False, **kwargs): | |
| """ EfficientNet-V2 Xtra-Large. Pretrained on ImageNet-21k, fine-tuned on 1k. Tensorflow compatible variant | |
| """ | |
| kwargs['bn_eps'] = BN_EPS_TF_DEFAULT | |
| kwargs['pad_type'] = 'same' | |
| model = _gen_efficientnetv2_xl('tf_efficientnetv2_xl_in21ft1k', pretrained=pretrained, **kwargs) | |
| return model | |
| def tf_efficientnetv2_s_in21k(pretrained=False, **kwargs): | |
| """ EfficientNet-V2 Small w/ ImageNet-21k pretrained weights. Tensorflow compatible variant | |
| """ | |
| kwargs['bn_eps'] = BN_EPS_TF_DEFAULT | |
| kwargs['pad_type'] = 'same' | |
| model = _gen_efficientnetv2_s('tf_efficientnetv2_s_in21k', pretrained=pretrained, **kwargs) | |
| return model | |
| def tf_efficientnetv2_m_in21k(pretrained=False, **kwargs): | |
| """ EfficientNet-V2 Medium w/ ImageNet-21k pretrained weights. Tensorflow compatible variant | |
| """ | |
| kwargs['bn_eps'] = BN_EPS_TF_DEFAULT | |
| kwargs['pad_type'] = 'same' | |
| model = _gen_efficientnetv2_m('tf_efficientnetv2_m_in21k', pretrained=pretrained, **kwargs) | |
| return model | |
| def tf_efficientnetv2_l_in21k(pretrained=False, **kwargs): | |
| """ EfficientNet-V2 Large w/ ImageNet-21k pretrained weights. Tensorflow compatible variant | |
| """ | |
| kwargs['bn_eps'] = BN_EPS_TF_DEFAULT | |
| kwargs['pad_type'] = 'same' | |
| model = _gen_efficientnetv2_l('tf_efficientnetv2_l_in21k', pretrained=pretrained, **kwargs) | |
| return 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) | |
| return 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| def tinynet_a(pretrained=False, **kwargs): | |
| model = _gen_tinynet('tinynet_a', 1.0, 1.2, pretrained=pretrained, **kwargs) | |
| return model | |
| def tinynet_b(pretrained=False, **kwargs): | |
| model = _gen_tinynet('tinynet_b', 0.75, 1.1, pretrained=pretrained, **kwargs) | |
| return model | |
| def tinynet_c(pretrained=False, **kwargs): | |
| model = _gen_tinynet('tinynet_c', 0.54, 0.85, pretrained=pretrained, **kwargs) | |
| return model | |
| def tinynet_d(pretrained=False, **kwargs): | |
| model = _gen_tinynet('tinynet_d', 0.54, 0.695, pretrained=pretrained, **kwargs) | |
| return model | |
| def tinynet_e(pretrained=False, **kwargs): | |
| model = _gen_tinynet('tinynet_e', 0.51, 0.6, pretrained=pretrained, **kwargs) | |
| return model | |