van-classification-tensorflow 1.0.3

TensorFlow 2.X reimplementation of Visual Attention Network, Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, Shi-Min Hu.

VAN-Classification-TensorFlow

TensorFlow 2.X reimplementation of Visual Attention Network, Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, Shi-Min Hu.

• Exact TensorFlow reimplementation of official PyTorch repo, including timm modules used by authors, preserving models and layers structure.
• ImageNet pretrained weights ported from PyTorch official implementation.

Table of contents

• Abstract
• Results
• Installation
• Usage
• Acknowledgement
• Citations
• License

Abstract

While originally designed for natural language processing (NLP) tasks, the self-attention mechanism has recently taken various computer vision areas by storm. However, the 2D nature of images brings three challenges for applying self-attention in computer vision. (1) Treating images as 1D sequences neglects their 2D structures. (2) The quadratic complexity is too expensive for high-resolution images. (3) It only captures spatial adaptability but ignores channel adaptability. In this paper, the authors propose a novel large kernel attention (LKA) module to enable self-adaptive and long-range correlations in self-attention while avoiding the above issues. The authors further introduce a novel neural network based on LKA, namely Visual Attention Network (VAN). While extremely simple and efficient, VAN outperforms the state-of-the-art vision transformers (ViTs) and convolutional neural networks (CNNs) with a large margin in extensive experiments, including image classification, object detection, semantic segmentation, instance segmentation, etc.

_{Figure 1. Compare with different vision backbones on ImageNet-1K validation set.}

_{Figure 2. Decomposition diagram of large-kernel convolution. A standard convolution can be decomposed into three parts: a depth-wise convolution (DW-Conv), a depth-wise dilation convolution (DW-D-Conv) and a 1×1 convolution (1×1 Conv).}

_{Figure 3. The structure of different modules: (a) the proposed Large Kernel Attention (LKA); (b) non-attention module; (c) the self-attention module (d) a stage of our Visual Attention Network (VAN). CFF means convolutional feed-forward network. The difference between (a) and (b) is the element-wise multiply. It is worth noting that (c) is designed for 1D sequences.}

Results

TensorFlow implementation and ImageNet ported weights have been compared to the official PyTorch implementation on ImageNet-V2 test set.

Models pre-trained on ImageNet-1K

Configuration	Resolution	Top-1 (Original)	Top-1 (Ported)	Top-5 (Original)	Top-5 (Ported)	#Params
VAN-B0	224x224	0.59	0.59	0.81	0.81	4.1M
VAN-B1	224x224	0.64	0.64	0.84	0.84	13.9M
VAN-B2	224x224	0.69	0.69	0.88	0.88	26.6M
VAN-B3	224x224	0.71	0.71	0.89	0.89	44.8M

Metrics difference: 0.

Installation

• Install from PyPI.

pip install van-classification-tensorflow

• Install from GitHub.

pip install git+https://github.com/EMalagoli92/VAN-Classification-TensorFlow

• Clone the repo and install necessary packages.

git clone https://github.com/EMalagoli92/VAN-Classification-TensorFlow.git
pip install -r requirements.txt

Tested on Ubuntu 20.04.4 LTS x86_64, python 3.9.7.

Usage

• Define a custom VAN configuration.

from van_classification_tensorflow import VAN

# Define a custom VAN configuration
model = VAN(
    in_chans=3,
    num_classes=1000,
    embed_dims=[64, 128, 256, 512],
    mlp_ratios=[4, 4, 4, 4],
    drop_rate=0.0,
    drop_path_rate=0.0,
    depths=[3, 4, 6, 3],
    num_stages=4,
    include_top=True,
    classifier_activation="softmax",
    data_format="channels_last",
)

• Use a predefined VAN configuration.

from van_classification_tensorflow import VAN

model = VAN(
    configuration="van_b0", data_format="channels_last", classifier_activation="softmax"
)

model.build((None, 224, 224, 3))
print(model.summary())

Model: "van_b0"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 patch_embed1 (OverlapPatchE  ((None, 32, 56, 56),     4864      
 mbed)                        (),                                
                              ())                                
                                                                 
 block1/0 (Block)            (None, 32, 56, 56)        25152     
                                                                 
 block1/1 (Block)            (None, 32, 56, 56)        25152     
                                                                 
 block1/2 (Block)            (None, 32, 56, 56)        25152     
                                                                 
 norm1 (LayerNorm_)          (None, 3136, 32)          64        
                                                                 
 patch_embed2 (OverlapPatchE  ((None, 64, 28, 28),     18752     
 mbed)                        (),                                
                              ())                                
                                                                 
 block2/0 (Block)            (None, 64, 28, 28)        89216     
                                                                 
 block2/1 (Block)            (None, 64, 28, 28)        89216     
                                                                 
 block2/2 (Block)            (None, 64, 28, 28)        89216     
                                                                 
 norm2 (LayerNorm_)          (None, 784, 64)           128       
                                                                 
 patch_embed3 (OverlapPatchE  ((None, 160, 14, 14),    92960     
 mbed)                        (),                                
                              ())                                
                                                                 
 block3/0 (Block)            (None, 160, 14, 14)       303040    
                                                                 
 block3/1 (Block)            (None, 160, 14, 14)       303040    
                                                                 
 block3/2 (Block)            (None, 160, 14, 14)       303040    
                                                                 
 block3/3 (Block)            (None, 160, 14, 14)       303040    
                                                                 
 block3/4 (Block)            (None, 160, 14, 14)       303040    
                                                                 
 norm3 (LayerNorm_)          (None, 196, 160)          320       
                                                                 
 patch_embed4 (OverlapPatchE  ((None, 256, 7, 7),      369920    
 mbed)                        (),                                
                              ())                                
                                                                 
 block4/0 (Block)            (None, 256, 7, 7)         755200    
                                                                 
 block4/1 (Block)            (None, 256, 7, 7)         755200    
                                                                 
 norm4 (LayerNorm_)          (None, 49, 256)           512       
                                                                 
 head (Linear_)              (None, 1000)              257000    
                                                                 
 pred (Activation)           (None, 1000)              0         
                                                                 
=================================================================
Total params: 4,113,224
Trainable params: 4,105,800
Non-trainable params: 7,424
_________________________________________________________________

• Train from scratch the model.

# Example
model.compile(
    optimizer="sgd",
    loss="sparse_categorical_crossentropy",
    metrics=["accuracy", "sparse_top_k_categorical_accuracy"],
)
model.fit(x, y)

• Use ported ImageNet pretrained weights.

# Example
from van_classification_tensorflow import VAN

model = VAN(
    configuration="van_b1",
    pretrained=True,
    include_top=True,
    classifier_activation="softmax",
)
y_pred = model(image)

• Use ported ImageNet pretrained weights for feature extraction (include_top=False).

import tensorflow as tf

from van_classification_tensorflow import VAN

# Get Features
inputs = tf.keras.layers.Input(shape=(224, 224, 3), dtype="float32")
features = VAN(configuration="van_b0", pretrained=True, include_top=False)(inputs)


# Custom classification
num_classes = 10
outputs = tf.keras.layers.Dense(num_classes, activation="softmax")(features)
model = tf.keras.models.Model(inputs=inputs, outputs=outputs)

Acknowledgement

VAN-Classification (Official PyTorch implementation).

Citations

@article{guo2022visual,
  title={Visual Attention Network},
  author={Guo, Meng-Hao and Lu, Cheng-Ze and Liu, Zheng-Ning and Cheng, Ming-Ming and Hu, Shi-Min},
  journal={arXiv preprint arXiv:2202.09741},
  year={2022}
}

License

This work is made available under the MIT License.