geffnet 0.9.0

(Generic) EfficientNets for PyTorch

(Generic) EfficientNets for PyTorch

A 'generic' implementation of EfficientNet, MixNet, MobileNetV3, etc. that covers most of the compute/parameter efficient architectures derived from the MobileNet V1/V2 block sequence, including those found via automated neural architecture search.

All models are implemented by GenEfficientNet or MobileNetV3 classes, with string based architecture definitions to configure the block layouts (idea from here)

What's New

Oct 27, 2019

• Add CondConv EfficientNet variants ported from https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/condconv
• Add RandAug weights for TF EfficientNet B5 and B7 from https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
• Bring over MixNet-XL model and depth scaling algo from my pytorch-image-models code base
• Switch activations and global pooling to modules
• Add memory-efficient Swish/Mish impl
• Add as_sequential() method to all models and allow as an argument in entrypoint fns
• Move MobileNetV3 into own file since it has a different head
• Remove ChamNet, MobileNet V2/V1 since they will likely never be used here

Models

Implemented models include:

• EfficientNet (B0-B7) (https://arxiv.org/abs/1905.11946) -- validated, compat with TF weights
• EfficientNet-EdgeTPU (S, M, L) (https://ai.googleblog.com/2019/08/efficientnet-edgetpu-creating.html) --validated w/ TF weights
• EfficientNet-CondConv (https://arxiv.org/abs/1904.04971)
• MixNet (https://arxiv.org/abs/1907.09595) -- validated, compat with TF weights
• MNASNet B1, A1 (Squeeze-Excite), and Small (https://arxiv.org/abs/1807.11626)
• MobileNet-V3 (https://arxiv.org/abs/1905.02244) -- native PyTorch model trained better than paper spec
• FBNet-C (https://arxiv.org/abs/1812.03443) -- TODO A/B variants
• Single-Path NAS (https://arxiv.org/abs/1904.02877) -- pixel1 variant

I originally implemented and trained some these models with code here, this repository contains just the GenEfficientNet models, validation, and associated ONNX/Caffe2 export code.

Pretrained

I've managed to train several of the models to accuracies close to or above the originating papers and official impl. My training code is here: https://github.com/rwightman/pytorch-image-models

Model	Prec@1 (Err)	Prec@5 (Err)	Param#(M)	MAdds(M)	Image Scaling	Resolution	Crop
mixnet_xl	80.120 (19.880)	95.022 (4.978)	11.90	TBD	bicubic	224	0.875
mixnet_l	78.976 (21.024	94.184 (5.816)	7.33	TBD	bicubic	224	0.875
efficientnet_b2	79.668 (20.332)	94.634 (5.366)	9.1	1003	bicubic	260	0.890
efficientnet_b1	78.692 (21.308)	94.086 (5.914)	7.8	694	bicubic	240	0.882
mixnet_m	77.256 (22.744)	93.418 (6.582)	5.01	353	bicubic	224	0.875
efficientnet_b0	76.912 (23.088)	93.210 (6.790)	5.3	390	bicubic	224	0.875
mixnet_s	75.988 (24.012)	92.794 (7.206)	4.13	TBD	bicubic	224	0.875
mobilenetv3_100	75.634 (24.366)	92.708 (7.292)	5.5	219	bicubic	224	0.875
mnasnet_a1	75.448 (24.552)	92.604 (7.396)	3.9	312	bicubic	224	0.875
fbnetc_100	75.124 (24.876)	92.386 (7.614)	5.6	385	bilinear	224	0.875
mnasnet_b1	74.658 (25.342)	92.114 (7.886)	4.4	315	bicubic	224	0.875
spnasnet_100	74.084 (25.916)	91.818 (8.182)	4.4	TBV	bilinear	224	0.875

More pretrained models to come...

Ported Weights

The weights ported from Tensorflow checkpoints for the EfficientNet models do pretty much match accuracy in Tensorflow once a SAME convolution padding equivalent is added, and the same crop factors, image scaling, etc (see table) are used via cmd line args.

Ex, to run validation for tf_efficientnet_b5: python validate.py /path/to/imagenet/validation/ --model tf_efficientnet_b5 -b 64 --img-size 456 --crop-pct 0.934 --interpolation bicubic

Enabling the Tensorflow preprocessing pipeline with --tf-preprocessing at validation time will improve these scores by 0.1-0.5% as it's closer to what these models were trained with.

Ex, to run validation w/ TF preprocessing for tf_efficientnet_b5: python validate.py /path/to/imagenet/validation/ --model tf_efficientnet_b5 -b 64 --img-size 456 --tf-preprocessing

EdgeTPU and EfficientNet-CondConv models use different normalization consts. Use Inception style 0.5, 0.5, 0.5 for mean and std.

Model	Prec@1 (Err)	Prec@5 (Err)	Param #	Image Scaling	Image Size	Crop
tf_efficientnet_b7 *tfp	84.940 (15.060)	97.214 (2.786)	66.35	bicubic	600	N/A
tf_efficientnet_b7	84.932 (15.068)	97.208 (2.792)	66.35	bicubic	600	0.949
tf_efficientnet_b6 *tfp	84.140 (15.860)	96.852 (3.148)	43.04	bicubic	528	N/A
tf_efficientnet_b6	84.110 (15.890)	96.886 (3.114)	43.04	bicubic	528	0.942
tf_efficientnet_b5 *tfp	83.822 (16.178)	96.756 (3.244)	30.39	bicubic	456	N/A
tf_efficientnet_b5	83.812 (16.188)	96.748 (3.252)	30.39	bicubic	456	0.934
tf_efficientnet_b4	83.022 (16.978)	96.300 (3.700)	19.34	bicubic	380	0.922
tf_efficientnet_b4 *tfp	82.948 (17.052)	96.308 (3.692)	19.34	bicubic	380	N/A
tf_efficientnet_b3 *tfp	81.576 (18.424)	95.662 (4.338)	12.23	bicubic	300	N/A
tf_efficientnet_b3	81.636 (18.364)	95.718 (4.282)	12.23	bicubic	300	0.903
tf_efficientnet_el	80.534 (19.466)	95.190 (4.810)	10.59	bicubic	300	0.903
tf_efficientnet_el *tfp	80.476 (19.524)	95.200 (4.800)	10.59	bicubic	300	N/A
tf_efficientnet_b2 *tfp	80.188 (19.812)	94.974 (5.026)	9.11	bicubic	260	N/A
tf_efficientnet_b2	80.086 (19.914)	94.908 (5.092)	9.11	bicubic	260	0.890
tf_efficientnet_cc_b1_8e *tfp	79.464 (20.536)	94.492 (5.508)	39.7	bicubic	240	0.88
tf_efficientnet_cc_b1_8e	79.298 (20.702)	94.364 (5.636)	39.7	bicubic	240	0.888
tf_efficientnet_b1 *tfp	79.172 (20.828)	94.450 (5.550)	7.79	bicubic	240 N/A
tf_efficientnet_em *tfp	78.958 (21.042)	94.458 (5.542)	6.90	bicubic	240	N/A
tf_mixnet_l *tfp	78.846 (21.154)	94.212 (5.788)	7.33	bilinear	224	N/A
tf_efficientnet_b1	78.826 (21.174)	94.198 (5.802)	7.79	bicubic	240	0.88
tf_mixnet_l	78.770 (21.230)	94.004 (5.996)	7.33	bicubic	224	0.875
tf_efficientnet_em	78.742 (21.258)	94.332 (5.668)	6.90	bicubic	240	0.875
tf_efficientnet_cc_b0_8e *tfp	78.314 (21.686)	93.790 (6.210)	24.0	bicubic	224	0.875
tf_efficientnet_cc_b0_8e	77.908 (22.092)	93.656 (6.344)	24.0	bicubic	224	0.875
tf_efficientnet_cc_b0_4e *tfp	77.746 (22.254)	93.552 (6.448)	13.3	bicubic	224	0.875
tf_efficientnet_cc_b0_4e	77.304 (22.696)	93.332 (6.668)	13.3	bicubic	224	0.875
tf_efficientnet_es *tfp	77.616 (22.384)	93.750 (6.250)	5.44	bicubic	224	N/A
tf_efficientnet_es	77.264 (22.736)	93.600 (6.400)	5.44	bicubic	224	N/A
tf_efficientnet_b0 *tfp	77.258 (22.742)	93.478 (6.522)	5.29	bicubic	224	N/A
tf_mixnet_m *tfp	77.072 (22.928)	93.368 (6.632)	5.01	bilinear	224	N/A
tf_mixnet_m	76.950 (23.050)	93.156 (6.844)	5.01	bicubic	224	0.875
tf_efficientnet_b0	76.848 (23.152)	93.228 (6.772)	5.29	bicubic	224	0.875
tf_mixnet_s *tfp	75.800 (24.200)	92.788 (7.212)	4.13	bilinear	224	N/A
tf_mixnet_s	75.648 (24.352)	92.636 (7.364)	4.13	bicubic	224	0.875

*tfp models validated with tf-preprocessing pipeline

Google tf and tflite weights ported from official Tensorflow repositories

• https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet
• https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet

PyTorch Hub

Models can be accessed via the PyTorch Hub API

>>> torch.hub.list('rwightman/gen-efficientnet-pytorch')
['efficientnet_b0', ...]
>>> model = torch.hub.load('rwightman/gen-efficientnet-pytorch', 'efficientnet_b0', pretrained=True)
>>> model.eval()
>>> output = model(torch.randn(1,3,224,224))

Exporting

Scripts to export models to ONNX and then to Caffe2 are included, along with a Caffe2 script to verify.

As an example, to export the MobileNet-V3 pretrained model and then run an Imagenet validation:

python onnx_export.py --model mobilenetv3_100 ./mobilenetv3_100.onnx
python onnx_to_caffe.py ./mobilenetv3_100.onnx --c2-prefix mobilenetv3
python caffe2_validate.py /imagenet/validation/ --c2-init ./mobilenetv3.init.pb --c2-predict ./mobilenetv3.predict.pb --interpolation bicubic

NOTE the TF ported weights with the 'SAME' conv padding activated cannot be exported to ONNX unless _EXPORTABLE flag in exportable.py is set to True.