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efficientnet.py
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""" PyTorch EfficientNet Family
An implementation of EfficienNet that covers variety of related models with efficient architectures:
* 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...
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import List
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
from .efficientnet_blocks import round_channels, resolve_bn_args, resolve_act_layer, BN_EPS_TF_DEFAULT
from .efficientnet_builder import EfficientNetBuilder, decode_arch_def, efficientnet_init_weights
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']
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'),
'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',
input_size=(3, 240, 240), pool_size=(8, 8)),
'efficientnet_b2': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b2_ra-bcdf34b7.pth',
input_size=(3, 260, 260), pool_size=(9, 9)),
'efficientnet_b2a': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b2_ra-bcdf34b7.pth',
input_size=(3, 288, 288), pool_size=(9, 9), 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, 300, 300), pool_size=(10, 10), crop_pct=0.904),
'efficientnet_b3a': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b3_ra2-cf984f9c.pth',
input_size=(3, 320, 320), pool_size=(10, 10), crop_pct=1.0),
'efficientnet_b4': _cfg(
url='', input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.922),
'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),
'efficientnet_v2s': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_v2s_ra2-b265c1ba.pth',
input_size=(3, 224, 224), test_size=(3, 288, 288), pool_size=(7, 7), crop_pct=1.0), # FIXME WIP
'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'),
'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'),
}
_DEBUG = False
class EfficientNet(nn.Module):
""" (Generic) EfficientNet
A flexible and performant PyTorch implementation of efficient network architectures, including:
* 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,
channel_multiplier=1.0, channel_divisor=8, channel_min=None,
output_stride=32, pad_type='', fix_stem=False, act_layer=nn.ReLU, drop_rate=0., drop_path_rate=0.,
se_kwargs=None, norm_layer=nn.BatchNorm2d, norm_kwargs=None, global_pool='avg'):
super(EfficientNet, self).__init__()
norm_kwargs = norm_kwargs or {}
self.num_classes = num_classes
self.num_features = num_features
self.drop_rate = drop_rate
# Stem
if not fix_stem:
stem_size = round_channels(stem_size, channel_multiplier, channel_divisor, channel_min)
self.conv_stem = create_conv2d(in_chans, stem_size, 3, stride=2, padding=pad_type)
self.bn1 = norm_layer(stem_size, **norm_kwargs)
self.act1 = act_layer(inplace=True)
# Middle stages (IR/ER/DS Blocks)
builder = EfficientNetBuilder(
channel_multiplier, channel_divisor, channel_min, output_stride, pad_type, act_layer, se_kwargs,
norm_layer, norm_kwargs, drop_path_rate, verbose=_DEBUG)
self.blocks = nn.Sequential(*builder(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, **norm_kwargs)
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, channel_multiplier=1.0, channel_divisor=8, channel_min=None,
output_stride=32, pad_type='', fix_stem=False, act_layer=nn.ReLU, drop_rate=0., drop_path_rate=0.,
se_kwargs=None, norm_layer=nn.BatchNorm2d, norm_kwargs=None):
super(EfficientNetFeatures, self).__init__()
norm_kwargs = norm_kwargs or {}
self.drop_rate = drop_rate
# Stem
if not fix_stem:
stem_size = round_channels(stem_size, channel_multiplier, channel_divisor, channel_min)
self.conv_stem = create_conv2d(in_chans, stem_size, 3, stride=2, padding=pad_type)
self.bn1 = norm_layer(stem_size, **norm_kwargs)
self.act1 = act_layer(inplace=True)
# Middle stages (IR/ER/DS Blocks)
builder = EfficientNetBuilder(
channel_multiplier, channel_divisor, channel_min, output_stride, pad_type, act_layer, se_kwargs,
norm_layer, norm_kwargs, drop_path_rate, feature_location=feature_location, verbose=_DEBUG)
self.blocks = nn.Sequential(*builder(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,
channel_multiplier=channel_multiplier,
norm_kwargs=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,
channel_multiplier=channel_multiplier,
norm_kwargs=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,
channel_multiplier=channel_multiplier,
norm_kwargs=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'],
]
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_channels(1280, channel_multiplier, 8, None),
stem_size=32,
fix_stem=fix_stem_head,
channel_multiplier=channel_multiplier,
norm_kwargs=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
channel_multiplier=channel_multiplier,
norm_kwargs=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,
channel_multiplier=channel_multiplier,
norm_kwargs=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'],
]
model_kwargs = dict(
block_args=decode_arch_def(arch_def, depth_multiplier),
num_features=round_channels(1280, channel_multiplier, 8, None),
stem_size=32,
channel_multiplier=channel_multiplier,
act_layer=resolve_act_layer(kwargs, 'swish'),
norm_kwargs=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'],
]
model_kwargs = dict(
block_args=decode_arch_def(arch_def, depth_multiplier),
num_features=round_channels(1280, channel_multiplier, 8, None),
stem_size=32,
channel_multiplier=channel_multiplier,
norm_kwargs=resolve_bn_args(kwargs),
act_layer=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
model_kwargs = dict(
block_args=decode_arch_def(arch_def, depth_multiplier, experts_multiplier=experts_multiplier),
num_features=round_channels(1280, channel_multiplier, 8, None),
stem_size=32,
channel_multiplier=channel_multiplier,
norm_kwargs=resolve_bn_args(kwargs),
act_layer=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,
channel_multiplier=channel_multiplier,
act_layer=resolve_act_layer(kwargs, 'relu6'),
norm_kwargs=resolve_bn_args(kwargs),
**kwargs,
)
model = _create_effnet(variant, pretrained, **model_kwargs)
return model
def _gen_efficientnet_v2s(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
""" Creates an EfficientNet-V2s model
NOTE: this is a preliminary definition based on paper, awaiting official code release for details
and weights
Ref impl:
Paper: `EfficientNetV2: Smaller Models and Faster Training` - https://arxiv.org/abs/2104.00298
"""
arch_def = [
# FIXME it's not clear if the FusedMBConv layers have SE enabled for the Small variant,
# Table 4 suggests no. 23.94M params w/o, 23.98 with which is closer to 24M.
# ['er_r2_k3_s1_e1_c24_se0.25'],
# ['er_r4_k3_s2_e4_c48_se0.25'],
# ['er_r4_k3_s2_e4_c64_se0.25'],
['er_r2_k3_s1_e1_c24'],
['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_c272_se0.25'],
]
model_kwargs = dict(
block_args=decode_arch_def(arch_def, depth_multiplier),
num_features=round_channels(1792, channel_multiplier, 8, None),
stem_size=24,
channel_multiplier=channel_multiplier,
norm_kwargs=resolve_bn_args(kwargs),
act_layer=resolve_act_layer(kwargs, 'silu'), # FIXME this is an assumption, paper does not mention
**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,
channel_multiplier=channel_multiplier,
norm_kwargs=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,
channel_multiplier=channel_multiplier,
norm_kwargs=resolve_bn_args(kwargs),
**kwargs
)
model = _create_effnet(variant, pretrained, **model_kwargs)
return model
@register_model
def mnasnet_050(pretrained=False, **kwargs):
""" MNASNet B1, depth multiplier of 0.5. """
model = _gen_mnasnet_b1('mnasnet_050', 0.5, pretrained=pretrained, **kwargs)
return model
@register_model
def mnasnet_075(pretrained=False, **kwargs):
""" MNASNet B1, depth multiplier of 0.75. """
model = _gen_mnasnet_b1('mnasnet_075', 0.75, pretrained=pretrained, **kwargs)
return model
@register_model
def mnasnet_100(pretrained=False, **kwargs):
""" MNASNet B1, depth multiplier of 1.0. """
model = _gen_mnasnet_b1('mnasnet_100', 1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def mnasnet_b1(pretrained=False, **kwargs):
""" MNASNet B1, depth multiplier of 1.0. """
return mnasnet_100(pretrained, **kwargs)
@register_model
def mnasnet_140(pretrained=False, **kwargs):
""" MNASNet B1, depth multiplier of 1.4 """
model = _gen_mnasnet_b1('mnasnet_140', 1.4, pretrained=pretrained, **kwargs)
return model
@register_model
def semnasnet_050(pretrained=False, **kwargs):
""" MNASNet A1 (w/ SE), depth multiplier of 0.5 """
model = _gen_mnasnet_a1('semnasnet_050', 0.5, pretrained=pretrained, **kwargs)
return model
@register_model
def semnasnet_075(pretrained=False, **kwargs):
""" MNASNet A1 (w/ SE), depth multiplier of 0.75. """
model = _gen_mnasnet_a1('semnasnet_075', 0.75, pretrained=pretrained, **kwargs)
return model
@register_model
def semnasnet_100(pretrained=False, **kwargs):
""" MNASNet A1 (w/ SE), depth multiplier of 1.0. """
model = _gen_mnasnet_a1('semnasnet_100', 1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def mnasnet_a1(pretrained=False, **kwargs):
""" MNASNet A1 (w/ SE), depth multiplier of 1.0. """
return semnasnet_100(pretrained, **kwargs)
@register_model
def semnasnet_140(pretrained=False, **kwargs):
""" MNASNet A1 (w/ SE), depth multiplier of 1.4. """
model = _gen_mnasnet_a1('semnasnet_140', 1.4, pretrained=pretrained, **kwargs)
return model
@register_model
def mnasnet_small(pretrained=False, **kwargs):
""" MNASNet Small, depth multiplier of 1.0. """
model = _gen_mnasnet_small('mnasnet_small', 1.0, pretrained=pretrained, **kwargs)
return model