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efficientnet.py
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efficientnet.py
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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
* 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 2021 Ross Wightman
"""
from functools import partial
from typing import List
import torch
import torch.nn as nn
import torch.nn.functional as F
from 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, default_cfg_for_features
from .layers import create_conv2d, create_classifier
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=''),
'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=''),
'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
'eca_efficientnet_b0': _cfg(
url=''),
'gc_efficientnet_b0': _cfg(
url=''),
'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),
'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/DeGirum/pruned-models/releases/download/efficientnet_v1.0/efficientnet_el.pth',
input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904),
'efficientnet_es_pruned': _cfg(
url='https://github.com/DeGirum/pruned-models/releases/download/efficientnet_v1.0/efficientnet_es_pruned75.pth'),
'efficientnet_el_pruned': _cfg(
url='https://github.com/DeGirum/pruned-models/releases/download/efficientnet_v1.0/efficientnet_el_pruned70.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://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45403/outputs/effnetb1_pruned_9ebb3fe6.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://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45403/outputs/effnetb2_pruned_203f55bc.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://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45403/outputs/effnetb3_pruned_5abcc29f.pth',
input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904, mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
'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),
'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_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_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'),
}
class EfficientNet(nn.Module):
""" (Generic) EfficientNet
A flexible and performant PyTorch implementation of efficient network architectures, including:
* EfficientNet-V2 Small, Medium, Large & B0-B3
* EfficientNet B0-B8, L2
* EfficientNet-EdgeTPU
* EfficientNet-CondConv
* MixNet S, M, L, XL
* MnasNet A1, B1, and small
* FBNet C
* Single-Path NAS Pixel1
"""
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
se_layer = se_layer or SqueezeExcite
self.num_classes = num_classes
self.num_features = num_features
self.drop_rate = drop_rate
# Stem
if not fix_stem:
stem_size = round_chs_fn(stem_size)
self.conv_stem = create_conv2d(in_chans, stem_size, 3, stride=2, padding=pad_type)
self.bn1 = norm_layer(stem_size)
self.act1 = act_layer(inplace=True)
# Middle stages (IR/ER/DS Blocks)
builder = EfficientNetBuilder(
output_stride=output_stride, pad_type=pad_type, round_chs_fn=round_chs_fn,
act_layer=act_layer, norm_layer=norm_layer, se_layer=se_layer, drop_path_rate=drop_path_rate)
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_layer(self.num_features)
self.act2 = act_layer(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, self.act1]
layers.extend(self.blocks)
layers.extend([self.conv_head, self.bn2, self.act2, self.global_pool])
layers.extend([nn.Dropout(self.drop_rate), self.classifier])
return nn.Sequential(*layers)
def get_classifier(self):
return self.classifier
def reset_classifier(self, num_classes, global_pool='avg'):
self.num_classes = num_classes
self.global_pool, 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)
x = self.act1(x)
x = self.blocks(x)
x = self.conv_head(x)
x = self.bn2(x)
x = self.act2(x)
return x
def forward(self, x):
x = self.forward_features(x)
x = self.global_pool(x)
if self.drop_rate > 0.:
x = F.dropout(x, p=self.drop_rate, training=self.training)
return self.classifier(x)
class EfficientNetFeatures(nn.Module):
""" EfficientNet Feature Extractor
A work-in-progress feature extraction module for EfficientNet, to use as a backbone for segmentation
and object detection models.
"""
def __init__(self, block_args, out_indices=(0, 1, 2, 3, 4), feature_location='bottleneck', in_chans=3,
stem_size=32, fix_stem=False, output_stride=32, pad_type='', round_chs_fn=round_channels,
act_layer=None, norm_layer=None, se_layer=None, drop_rate=0., drop_path_rate=0.):
super(EfficientNetFeatures, self).__init__()
act_layer = act_layer or nn.ReLU
norm_layer = norm_layer or nn.BatchNorm2d
se_layer = se_layer or SqueezeExcite
self.drop_rate = drop_rate
# Stem
if not fix_stem:
stem_size = round_chs_fn(stem_size)
self.conv_stem = create_conv2d(in_chans, stem_size, 3, stride=2, padding=pad_type)
self.bn1 = norm_layer(stem_size)
self.act1 = act_layer(inplace=True)
# Middle stages (IR/ER/DS Blocks)
builder = EfficientNetBuilder(
output_stride=output_stride, pad_type=pad_type, round_chs_fn=round_chs_fn,
act_layer=act_layer, norm_layer=norm_layer, se_layer=se_layer, drop_path_rate=drop_path_rate,
feature_location=feature_location)
self.blocks = nn.Sequential(*builder(stem_size, block_args))
self.feature_info = FeatureInfo(builder.features, out_indices)
self._stage_out_idx = {v['stage']: i for i, v in enumerate(self.feature_info) if i in out_indices}
efficientnet_init_weights(self)
# Register feature extraction hooks with FeatureHooks helper
self.feature_hooks = None
if feature_location != 'bottleneck':
hooks = self.feature_info.get_dicts(keys=('module', 'hook_type'))
self.feature_hooks = FeatureHooks(hooks, self.named_modules())
def forward(self, x) -> List[torch.Tensor]:
x = self.conv_stem(x)
x = self.bn1(x)
x = self.act1(x)
if self.feature_hooks is None:
features = []
if 0 in self._stage_out_idx:
features.append(x) # add stem out
for i, b in enumerate(self.blocks):
x = b(x)
if i + 1 in self._stage_out_idx:
features.append(x)
return features
else:
self.blocks(x)
out = self.feature_hooks.get_output(x.device)
return list(out.values())
def _create_effnet(variant, pretrained=False, **kwargs):
features_only = False
model_cls = EfficientNet
kwargs_filter = None
if kwargs.pop('features_only', False):
features_only = True
kwargs_filter = ('num_classes', 'num_features', 'head_conv', 'global_pool')
model_cls = EfficientNetFeatures
model = build_model_with_cfg(
model_cls, variant, pretrained,
default_cfg=default_cfgs[variant],
pretrained_strict=not features_only,
kwargs_filter=kwargs_filter,
**kwargs)
if features_only:
model.default_cfg = default_cfg_for_features(model.default_cfg)
return model
def _gen_mnasnet_a1(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
"""Creates a mnasnet-a1 model.
Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet
Paper: https://arxiv.org/pdf/1807.11626.pdf.
Args:
channel_multiplier: multiplier to number of channels per layer.
"""
arch_def = [
# stage 0, 112x112 in
['ds_r1_k3_s1_e1_c16_noskip'],
# stage 1, 112x112 in
['ir_r2_k3_s2_e6_c24'],
# stage 2, 56x56 in
['ir_r3_k5_s2_e3_c40_se0.25'],
# stage 3, 28x28 in
['ir_r4_k3_s2_e6_c80'],
# stage 4, 14x14in
['ir_r2_k3_s1_e6_c112_se0.25'],
# stage 5, 14x14in
['ir_r3_k5_s2_e6_c160_se0.25'],
# stage 6, 7x7 in
['ir_r1_k3_s1_e6_c320'],
]
model_kwargs = dict(
block_args=decode_arch_def(arch_def),
stem_size=32,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=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=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=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 round_chs_fn(1280),
stem_size=32,
fix_stem=fix_stem_head,
round_chs_fn=round_chs_fn,
norm_layer=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=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=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, 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)
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,
act_layer=resolve_act_layer(kwargs, 'swish'),
norm_layer=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, 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),
num_features=round_chs_fn(1280),
stem_size=32,
round_chs_fn=round_chs_fn,
norm_layer=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=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=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=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, 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),
num_features=round_chs_fn(num_features),
stem_size=24,
round_chs_fn=round_chs_fn,
norm_layer=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=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=resolve_act_layer(kwargs, 'silu'),
**kwargs,
)