Keras EfficientNetV2

As EfficientNetV2 is included in keras.application now, merged this project into Github leondgarse/keras_cv_attention_models/efficientnet. Package keras-efficientnet-v2 moved into stable status.

Summary

My own keras implementation of Official efficientnetv2. Article arXiv 2104.00298 EfficientNetV2: Smaller Models and Faster Training by Mingxing Tan, Quoc V. Le.
h5 model weights converted from official publication.
effv2-t-imagenet.h5 model weights converted from Github rwightman/pytorch-image-models. which claimed both faster and better accuracy than b3. Please notice that PyTorch using different bn_epsilon and padding strategy.

V2 Model	Params	Top1	Input	ImageNet21K	Imagenet21k-ft1k	Imagenet
EffV2B0	7.1M	78.7	224	v2b0-21k.h5	v2b0-21k-ft1k.h5	v2b0-imagenet.h5
EffV2B1	8.1M	79.8	240	v2b1-21k.h5	v2b1-21k-ft1k.h5	v2b1-imagenet.h5
EffV2B2	10.1M	80.5	260	v2b2-21k.h5	v2b2-21k-ft1k.h5	v2b2-imagenet.h5
EffV2B3	14.4M	82.1	300	v2b3-21k.h5	v2b3-21k-ft1k.h5	v2b3-imagenet.h5
EffV2T	13.6M	82.5	320			v2t-imagenet.h5
EffV2S	21.5M	84.9	384	v2s-21k.h5	v2s-21k-ft1k.h5	v2s-imagenet.h5
EffV2M	54.1M	86.2	480	v2m-21k.h5	v2m-21k-ft1k.h5	v2m-imagenet.h5
EffV2L	119.5M	86.9	480	v2l-21k.h5	v2l-21k-ft1k.h5	v2l-imagenet.h5
EffV2XL	206.8M	87.2	512	v2xl-21k.h5	v2xl-21k-ft1k.h5

EfficientNetV1 noisy_student models from Github tensorflow/tpu/efficientnet. Paper PDF 1911.04252 Self-training with Noisy Student improves ImageNet classification. Parameter include_preprocessing=False is added, and the default False value makes expecting input value in range [-1, 1], same with EfficientNetV2. Default pretrained is noisy_student.

V1 Model	Params	Top1	Input	noisy_student	Imagenet
EffV1B0	5.3M	78.8	224	v1-b0-noisy_student.h5	v1-b0-imagenet.h5
EffV1B1	7.8M	81.5	240	v1-b1-noisy_student.h5	v1-b1-imagenet.h5
EffV1B2	9.1M	82.4	260	v1-b2-noisy_student.h5	v1-b2-imagenet.h5
EffV1B3	12.2M	84.1	300	v1-b3-noisy_student.h5	v1-b3-imagenet.h5
EffV1B4	19.3M	85.3	380	v1-b4-noisy_student.h5	v1-b4-imagenet.h5
EffV1B5	30.4M	86.1	456	v1-b5-noisy_student.h5	v1-b5-imagenet.h5
EffV1B6	43.0M	86.4	528	v1-b6-noisy_student.h5	v1-b6-imagenet.h5
EffV1B7	66.3M	86.9	600	v1-b7-noisy_student.h5	v1-b7-imagenet.h5
EffV1L2	480.3M	88.4	800	v1-l2-noisy_student.h5

Self tested imagenet accuracy
- rescale_mode torch means (image - [0.485, 0.456, 0.406]) / [[0.229, 0.224, 0.225]], tf means (image - 0.5) / 0.5
- All resize_method is bicubic.
- Some testing detail is not clear, so not exactly matching official reported results.
- Testing Detail is EfficientNetV2 self tested imagenet accuracy.

model	input	rescale_mode	central_crop	top 1	top 5	Reported top1
EffV2B0	224	torch	0.875	0.78748	0.94386	0.787
EffV2B1	240	torch	0.95	0.7987	0.94936	0.798
EffV2B2	260	torch	0.95	0.80642	0.95262	0.805
EffV2B3	300	torch	0.95	0.82098	0.95896	0.821
EffV2T	320	torch	0.99	0.82506	0.96228	0.823 (input 288)
EffV2S	384	tf	0.99	0.8386	0.967	0.839
EffV2M	480	tf	0.99	0.8509	0.973	0.852
EffV2L	480	tf	0.99	0.855	0.97324	0.857
EffV2S ft1k	384	tf	0.99	0.84328	0.97254	0.849
EffV2M ft1k	480	tf	0.99	0.85606	0.9775	0.862
EffV2L ft1k	480	tf	0.99	0.86294	0.9799	0.869
EffV2XL ft1k	512	tf	0.99	0.86532	0.97866	0.872

Usage

This repo can be installed as a pip package, or just git clone it.

pip install -U keras-efficientnet-v2
# Or
pip install -U git+https://github.com/leondgarse/keras_efficientnet_v2

Define model and load pretrained weights Parameter pretrained is added in value [None, "imagenet", "imagenet21k", "imagenet21k-ft1k"], default is imagenet. Model input value should be in range [-1, 1].

# Will download and load `imagenet` pretrained weights.
# Model weight is loaded with `by_name=True, skip_mismatch=True`.
import keras_efficientnet_v2
model = keras_efficientnet_v2.EfficientNetV2S(pretrained="imagenet")

# Run prediction
import tensorflow as tf
from tensorflow import keras
from skimage.data import chelsea
imm = tf.image.resize(chelsea(), model.input_shape[1:3]) # Chelsea the cat
pred = model(tf.expand_dims(imm / 128. - 1., 0)).numpy()
print(keras.applications.imagenet_utils.decode_predictions(pred)[0])
# [('n02124075', 'Egyptian_cat', 0.8642886), ('n02123159', 'tiger_cat', 0.030793495), ...]

Or download h5 model and load directly

mm = keras.models.load_model('efficientnetv2-b3-21k-ft1k.h5')

For "imagenet21k" pre-trained model, actual num_classes is 21843.

Exclude model top layers by set num_classes=0.

import keras_efficientnet_v2
model = keras_efficientnet_v2.EfficientNetV2B0(dropout=1e-6, num_classes=0, pretrained="imagenet21k")
print(model.output_shape)
# (None, 7, 7, 1280)

model.save('efficientnetv2-b0-21k-notop.h5')

Use dynamic input resolution by set input_shape=(None, None, 3).

import keras_efficientnet_v2
model = keras_efficientnet_v2.EfficientNetV2M(input_shape=(None, None, 3), drop_connect_rate=0.2, num_classes=0, pretrained="imagenet21k-ft1k")

print(model(np.ones([1, 224, 224, 3])).shape)
# (1, 7, 7, 1280)
print(model(np.ones([1, 512, 512, 3])).shape)
# (1, 16, 16, 1280)

include_preprocessing set True will add pre-processing Rescale + Normalization after Input. Means using input value in range [0, 255]. Default value False means in range [-1, 1]. Works both for EfficientNetV2 and EfficientNetV1.

import keras_efficientnet_v2
model = keras_efficientnet_v2.EfficientNetV1B4(pretrained="noisy_student", include_preprocessing=True)

from skimage.data import chelsea
imm = tf.image.resize(chelsea(), model.input_shape[1:3]) # Chelsea the cat
pred = model(tf.expand_dims(imm, 0)).numpy()  # value in range [0, 255]
print(keras.applications.imagenet_utils.decode_predictions(pred)[0])
# [('n02124075', 'Egyptian_cat', 0.68414235), ('n02123159', 'tiger_cat', 0.04486668), ...]

Training detail from article

Training configures, Eval size is used as the default input_shape for each model type.

Model	Train size	Eval size	Dropout	Randaug	Mixup
EffV2B0	192	224	0.2	0	0
EffV2B1	192	240	0.2	0	0
EffV2B2	208	260	0.3	0	0
EffV2B3	240	300	0.3	0	0
EffV2S	300	384	0.2	10	0
EffV2M	384	480	0.3	15	0.2
EffV2L	384	480	0.4	20	0.5
EffV2XL	384	512	0.4	20	0.5

EfficientNetV2-S architecture

Stage	Operator	Stride	#Channels	#Layers
0	Conv3x3	2	24	1
1	Fused-MBConv1, k3x3	1	24	2
2	Fused-MBConv4, k3x3	2	48	4
3	Fused-MBConv4, k3x3	2	64	4
4	MBConv4, k3x3, SE0.25	2	128	6
5	MBConv6, k3x3, SE0.25	1	160	9
6	MBConv6, k3x3, SE0.25	2	256	15
7	Conv1x1 & Pooling & FC	-	1280	1

Progressive training settings for EfficientNetV2

S min S max M min M max L min M max

Image Size 128 300 128 380 128 380

RandAugment 5 15 5 20 5 25

Mixup alpha 0 0 0 0.2 0 0.4

Dropout rate 0.1 0.3 0.1 0.4 0.1 0.5
Imagenet training detail
- RMSProp optimizer with decay 0.9 and momentum 0.9
- batch norm momentum 0.99; weight decay 1e-5
- Each model is trained for 350 epochs with total batch size 4096
- Learning rate is first warmed up from 0 to 0.256, and then decayed by 0.97 every 2.4 epochs
- We use exponential moving average with 0.9999 decay rate
- RandAugment (Cubuk et al., 2020)
- Mixup (Zhang et al., 2018)
- Dropout (Srivastava et al., 2014)
- and stochastic depth (Huang et al., 2016) with 0.8 survival probability

Detailed conversion procedure

convert_effnetv2_model.py is a modified version of the orignal effnetv2_model.py. Check detail by vimdiff convert_effnetv2_model.py ../automl/efficientnetv2/effnetv2_model.py
- Delete some names, as they may cause confliction in keras.
- Use .call directly calling se modules and other blocks, so they will not be blocks in model.summary()
- Just use Add layer instead of utils.drop_connect, as when is_training=False, utils.drop_connect functions like Add.
- Add a num_classes parameter outside of mconfig.
- Add __main__ part, which makes this can be run as a script. Refer to it for converting detail.

Depends on official repo

../
├── automl  # Official repo
├── keras_efficientnet_v2  # This one

Procedure

# See help info
CUDA_VISIBLE_DEVICES='-1' python convert_effnetv2_model.py -h

# Convert by specific model_type and dataset type
CUDA_VISIBLE_DEVICES='-1' python convert_effnetv2_model.py -m b0 -d imagenet21k

# Convert by specific model_type and all its datasets ['imagenet', 'imagenet21k', 'imagenetft']
CUDA_VISIBLE_DEVICES='-1' python convert_effnetv2_model.py -m s -d all

# Convert all model_types and and all datasets
CUDA_VISIBLE_DEVICES='-1' python convert_effnetv2_model.py -m all -d all

Progressive train test on cifar10

Colab efficientnetV2_basic_test.ipynb

import keras_efficientnet_v2
from tensorflow import keras
from keras_efficientnet_v2 import progressive_train_test

model = keras_efficientnet_v2.EfficientNetV2S(input_shape=(None, None, 3), num_classes=10, classifier_activation='softmax', dropout=0.1)
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])

hhs = progressive_train_test.progressive_with_dropout_randaug(
    model,
    data_name="cifar10",
    lr_scheduler=None,
    total_epochs=36,
    batch_size=64,
    dropout_layer=-2,
    target_shapes=[128, 160, 192, 224], # [128, 185, 242, 300] for final shape (300, 300)
    dropouts=[0.1, 0.2, 0.3, 0.4],
    magnitudes=[5, 8, 12, 15],
)

with open("history_ev2s_imagenet_progressive_224.json", "w") as ff:
    json.dump(hhs, ff)

Related Projects

tfa.layers.StochasticDepth
Official efficientnetv2
Github rwightman/pytorch-image-models

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README.md

README.md

Keras EfficientNetV2

Table of Contents

Summary

Usage

Training detail from article

Detailed conversion procedure

Progressive train test on cifar10

Related Projects

	S min	S max	M min	M max	L min	M max
Image Size	128	300	128	380	128	380
RandAugment	5	15	5	20	5	25
Mixup alpha	0	0	0	0.2	0	0.4
Dropout rate	0.1	0.3	0.1	0.4	0.1	0.5

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Keras EfficientNetV2

Table of Contents

Summary

Usage

Training detail from article

Detailed conversion procedure

Progressive train test on cifar10

Related Projects