Model Zoo - ImageNet

ImageNet is a popular dataset for generic object classification. This tutorial provided convolutional neural network(CNN) models for ImageNet.

ResNet Introduction

ResNets from paper Deep Residual Learning for Image Recognition won the 1st place on the ILSVRC 2015 classification task. They present residual learning framework to ease the training of networks that are substantially deeper than those used previously. The residual connections are shown in following figure. The left building block is used in network of 34 layers and the right bottleneck building block is used in network of 50, 101, 152 layers .

Figure 1. ResNet Block

We present three ResNet models, which are converted from the models provided by the authors https://github.com/KaimingHe/deep-residual-networks. The classfication errors tested in PaddlePaddle on 50,000 ILSVRC validation set with input images channel order of BGR by single scale with the shorter side of 256 and single crop as following table.

ResNet	Top-1	Model Size
ResNet-50	24.9%	99M
ResNet-101	23.7%	173M
ResNet-152	23.2%	234M

ResNet Model

See demo/model_zoo/resnet/resnet.py. This confgiure contains network of 50, 101 and 152 layers. You can specify layer number by adding argument like this --config_args=layer_num=50 in command line arguments.

Network Visualization

You can get a diagram of ResNet network by running the following command. The script generates dot file and then converts dot file to PNG file, which uses installed draw_dot tool in our server. If you can not access the server, just install graphviz to convert dot file.

cd demo/model_zoo/resnet
./net_diagram.sh

Model Download

cd demo/model_zoo/resnet
./get_model.sh

You can run above command to download all models and mean file and save them in demo/model_zoo/resnet/model if downloading successfully.

mean_meta_224  resnet_101  resnet_152  resnet_50

• resnet_50: model of 50 layers.
• resnet_101: model of 101 layers.
• resnet_152: model of 152 layers.
• mean_meta_224: mean file with 3 x 224 x 224 size in BGR order. You also can use three mean values: 103.939, 116.779, 123.68.

Parameter Info

• Convolution Layer Weight

  As batch normalization layer is connected after each convolution layer, there is no parameter of bias and only one weight in this layer. shape: (Co, ky, kx, Ci)

  • Co: channle number of output feature map.
  • ky: filter size in vertical direction.
  • kx: filter size in horizontal direction.
  • Ci: channle number of input feature map.

  2-Dim matrix: (Co * ky * kx, Ci), saved in row-major order.

• Fully connected Layer Weight

  2-Dim matrix: (input layer size, this layer size), saved in row-major order.

• Batch Normalization Layer Weight

There are four parameters in this layer. In fact, only .w0 and .wbias are the learned parameters. The other two are therunning mean and variance respectively. They will be loaded in testing. Following table shows parameters of a batch normzalization layer.

Parameter Name	Number	Meaning
_res2_1_branch1_bn.w0	256	gamma, scale parameter
_res2_1_branch1_bn.w1	256	mean value of feature map
_res2_1_branch1_bn.w2	256	variance of feature map
_res2_1_branch1_bn.wbias	256	beta, shift parameter

Parameter Observation

Users who want to observe the parameters can use python to read:

import sys
import numpy as np

def load(file_name):
    with open(file_name, 'rb') as f:
        f.read(16) # skip header for float type.
        return np.fromfile(f, dtype=np.float32)

if __name__=='__main__':
    weight = load(sys.argv[1])

or simply use following shell command:

od -j 16 -f _res2_1_branch1_bn.w0

Feature Extraction

We provide both C++ and Python interfaces to extract features. The following examples use data in demo/model_zoo/resnet/example to show the extracting process in detail.

C++ Interface

First, specify image data list in define_py_data_sources in the config, see example demo/model_zoo/resnet/resnet.py.

    train_list = 'train.list' if not is_test else None
    # mean.meta is mean file of ImageNet dataset.
    # mean.meta size : 3 x 224 x 224.
    # If you use three mean value, set like:
    # "mean_value:103.939,116.779,123.68;"
    args={
        'mean_meta': "model/mean_meta_224/mean.meta",
        'image_size': 224, 'crop_size': 224,
        'color': True,'swap_channel:': [2, 1, 0]}
    define_py_data_sources2(train_list,
                           'example/test.list',
                           module="example.image_list_provider",
                           obj="processData",
                           args=args)

Second, specify layers to extract features in Outputs() of resnet.py. For example,

Outputs("res5_3_branch2c_conv", "res5_3_branch2c_bn")

Third, specify model path and output directory in extract_fea_c++.sh, and then run following commands

cd demo/model_zoo/resnet
./extract_fea_c++.sh

If successful, features are saved in fea_output/rank-00000 as follows. And you can use load_feature_c interface in load_feature.py to load such a file.

-0.115318 -0.108358 ... -0.087884;-1.27664 ... -1.11516 -2.59123;
-0.126383 -0.116248 ... -0.00534909;-1.42593 ... -1.04501 -1.40769;

• Each line stores features of a sample. Here, the first line stores features of example/dog.jpg and second line stores features of example/cat.jpg.
• Features of different layers are splitted by ;, and their order is consistent with the layer order in Outputs(). Here, the left features are res5_3_branch2c_conv layer and right features are res5_3_branch2c_bn layer.

Python Interface

demo/model_zoo/resnet/classify.py is an example to show how to use python to extract features. Following example still uses data of ./example/test.list. Command is as follows:

cd demo/model_zoo/resnet
./extract_fea_py.sh

extract_fea_py.sh:

python classify.py \
     --job=extract \
     --conf=resnet.py\
     --mean=model/mean_meta_224/mean.meta \
     --model=model/resnet_50 \
     --data=./example/test.list \
     --output_layer="res5_3_branch2c_conv,res5_3_branch2c_bn" \
     --output_dir=features

• –job=extract: specify job mode to extract feature.
• –conf=resnet.py: network configure.
• –model=model/resnet_5: model path.
• –data=./example/test.list: data list.
• –output_layer=”xxx,xxx”: specify layers to extract features.
• –output_dir=features: output diretcoty.

If run successfully, you will see features saved in features/batch_0, this file is produced with cPickle. You can use load_feature_py interface in load_feature.py to open the file, and it returns a dictionary as follows:

{
'cat.jpg': {'res5_3_branch2c_conv': array([[-0.12638293, -0.116248  , -0.11883899, ..., -0.00895038, 0.01994277, -0.00534909]], dtype=float32), 'res5_3_branch2c_bn': array([[-1.42593431, -1.28918779, -1.32414699, ..., -1.45933616, -1.04501402, -1.40769434]], dtype=float32)},
'dog.jpg': {'res5_3_branch2c_conv': array([[-0.11531784, -0.10835785, -0.08809858, ...,0.0055237, 0.01505112, -0.08788397]], dtype=float32), 'res5_3_branch2c_bn': array([[-1.27663755, -1.18272924, -0.90937918, ..., -1.25178063, -1.11515927, -2.59122872]], dtype=float32)}
}

Observed carefully, these feature values are consistent with the above results extracted by C++ interface.

Prediction

classify.py also can be used to predict. We provide an example script predict.sh to predict data in example/test.list using a ResNet model with 50 layers.

cd demo/model_zoo/resnet
./predict.sh

predict.sh calls the classify.py:

python classify.py \
     --job=predict \
     --conf=resnet.py\
     --multi_crop \
     --model=model/resnet_50 \
     --data=./example/test.list

• –job=extract: speficy job mode to predict.
• –conf=resnet.py: network configure.
• –multi_crop: use 10 crops and average predicting probability.
• –model=model/resnet_50: model path.
• –data=./example/test.list: data list.

If run successfully, you will see following results, where 156 and 285 are labels of the images.

Label of example/dog.jpg is: 156
Label of example/cat.jpg is: 282