ResNet_v1_50 (Image Classification/TensorFlow)

OBS raw dataset format

Unsplitted dataset

your_local_path/flowers
    |- classA   ------------- Class A
        |- A_0.jpg
        |- A_1.jpg
        |- ...
        |- A_n.jpg
    |- classB    ------------ Class B
        |- B_0.jpg
        |- B_1.jpg
        |- ...
        |- B_n.jpg
    |- classC --------------- Class C
        |- C_0.jpg
        |- C_1.jpg
        |- ...
        |- C_n.jpg
The others follow the same rule.
    ...

Splitted dataset

your_local_path/flowers
    |- train --------------- Training set
  |- classA   ------------- Class A
   |- A_0.jpg
   |- A_1.jpg
   |- ...
   |- A_n.jpg
  |- classB    ------------ Class B
   |- B_0.jpg
   |- B_1.jpg
   |- ...
   |- B_n.jpg
  |- classC --------------- Class C
   |- C_0.jpg
   |- C_1.jpg
   |- ...
   |- C_n.jpg
The others follow the same rule.
  ...
		
 |- eval --------------- Test dataset
  |- classA   ------------- Class A
   |- A_0.jpg
   |- A_1.jpg
   |- ...
   |- A_n.jpg
  |- classB    ------------ Class B
   |- B_0.jpg
   |- B_1.jpg
   |- ...
   |- B_n.jpg
  |- classC --------------- Class C
   |- C_0.jpg
   |- C_1.jpg
   |- ...
   |- C_n.jpg
The others follow the same rule.
  ...

1. Overview

This is a ResNet model with 50 layers. It is implemented based on the model structure proposed in the Deep Residual Learning for Image Recognition (https://arxiv.org/abs/1512.03385). It can be used for image classification, such as cat and dog classification and flower classification. You provide a series of labeled datasets. The algorithm loads the pre-trained model of ImageNet-1000 (http://www.image-net.org/) for transfer learning on your datasets. The trained model can be directly deployed as a real-time service on the ModelArts platform. In addition, CPU and GPU flavors can be used for inference. You can also convert models into the Ascend type and deploy them on Ascend 310 for inference.

2. Training

2.1. Basic Algorithm Information

• Task type: image classification
• Supported engine: TensorFlow 1.13.1-Python 3.6-horovod
• Test card: NVIDIA V100
• Performance:

• Algorithm input:
  A training and validation ratio must be set, 0.8 or 0.9 is recommended, for the image classification dataset published on the ModelArts data management platform.
• Pre-trained ImageNet model whose top 1 accuracy rate is 74.6% and top 5 accuracy rate is 92.0%
• Algorithm output:
• Model saved_model for TF-Serving inference. The inference speed of P4 is 11 ms/image.
• Model frozen.pb used to be converted to the model in Ascend format. The inference speed of Ascend 310 is 3.2 ms/image.

2.2. Training Parameters

2.3. Training Output File

After training is complete, the output file is as follows:

Training output directory
  |- om
    |- model
      |- index
      |- customize_service_d310.py
  |- model
    |- variables
      |- variables.data-00000-of-00001
      |- variables.index
    |- customize_service.py
    |- index
    |- config.json
    |- saved_model.pb
  |- frozen_graph
    |- insert_op_conf.cfg
    |- model.pb
  |- checkpoint
  |- model.ckpt-xxx
  |- ...
  |- best_checkpoint
  |- best_model.ckpt-xxx
  |- ...
  |- events...
  |- graph.pbtxt

3. Ascend 310 Inference

3.1. Model Conversion Parameters

• Conversion Template: Select TF-FrozenGraph-To-Ascend-C32.
• Conversion Input Path: Select frozen_graph in the training output path.
• Conversion Output Path: Select om/model in the training output path.
• input_shape: Enter images:1,224,224,3.
• input_format: Select NHWC.
• out_nodes: Enter logits:0.

Retain the default values of other parameters.

3.2. Model import parameters

• Template: Select ARM-Ascend template.
• Model Directory: Select om/model in the training output path.
• Input and Output Mode: Select Built-in image processing.

4. GPU/CPU Inference

To deploy an inference service on CPUs or GPUs, set Meta Model Source to Training job and select a training job and version.

Note: In the inference configuration file model/config.json, the CPU inference image runtime:tf1.xx-python3.x-cpu is used by default.
To use GPU inference, change the runtime field to tf1.xx-python3.x-gpu in the model/config.json file before importing the model.