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.

Table of Contents

• Keras EfficientNetV2
  • Table of Contents
  • Summary
  • Usage
  • Training detail from article
  • Detailed conversion procedure
  • Progressive train test on cifar10
  • Related Projects

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.

• 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.

• 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.

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