gaea-paddleclas 2.5.0.2

A treasure chest for visual recognition powered by PaddlePaddle.

PaddleClas wheel package

PaddleClas supports Python wheel package for prediction. At present, PaddleClas wheel supports image classification including ImagetNet1k models and PULC models, but does not support mainbody detection, feature extraction and vector retrieval.

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Catalogue

• 1. Installation
• 2. Quick Start
  • 2.1 ImageNet1k models
  • 2.2 PULC models
• 3. Definition of Parameters
• 4. More usage
  • 4.1 View help information
  • 4.2 Prediction using inference model provide by PaddleClas
  • 4.3 Prediction using local model files
  • 4.4 Prediction by batch
  • 4.5 Prediction of Internet image
  • 4.6 Prediction of NumPy.array format image
  • 4.7 Save the prediction result(s)
  • 4.8 Specify the mapping between class id and label name

1. Installation

• [Recommended] Installing from PyPI:

pip3 install paddleclas

• Please build and install locally if you need to use the develop branch of PaddleClas to experience the latest functions, or need to redevelop based on PaddleClas. The command is as follows:

python3 setup.py install

2. Quick Start

2.1 ImageNet1k models

Using the ResNet50 model provided by PaddleClas, the following image('docs/images/inference_deployment/whl_demo.jpg') as an example.

• Python

from paddleclas import PaddleClas
clas = PaddleClas(model_name='ResNet50')
infer_imgs='docs/images/inference_deployment/whl_demo.jpg'
result=clas.predict(infer_imgs)
print(next(result))

Note: PaddleClas.predict() is a generator. Therefore you need to use next() or for call it iteratively. It will perform a prediction by batch_size and return the prediction result(s) when called. Examples of returned results are as follows:

>>> result
[{'class_ids': [8, 7, 86, 82, 80], 'scores': [0.97968, 0.02028, 3e-05, 1e-05, 0.0], 'label_names': ['hen', 'cock', 'partridge', 'ruffed grouse, partridge, Bonasa umbellus', 'black grouse'], 'filename': 'docs/images/inference_deployment/whl_demo.jpg'}]

• CLI

paddleclas --model_name=ResNet50  --infer_imgs="docs/images/inference_deployment/whl_demo.jpg"

>>> result
class_ids: [8, 7, 86, 82, 80], scores: [0.97968, 0.02028, 3e-05, 1e-05, 0.0], label_names: ['hen', 'cock', 'partridge', 'ruffed grouse, partridge, Bonasa umbellus', 'black grouse'], filename: docs/images/inference_deployment/whl_demo.jpg
Predict complete!

2.2 PULC models

PULC integrates various state-of-the-art algorithms such as backbone network, data augmentation and distillation, etc., and finally can automatically obtain a lightweight and high-precision image classification model.

PaddleClas provides a series of test cases, which contain demos of different scenes about people, cars, OCR, etc. Click here to download the data.

Prection using the PULC "Human Exists Classification" model provided by PaddleClas:

• Python

import paddleclas
model = paddleclas.PaddleClas(model_name="person_exists")
result = model.predict(input_data="pulc_demo_imgs/person_exists/objects365_01780782.jpg")
print(next(result))

>>> result
[{'class_ids': [0], 'scores': [0.9955421453341842], 'label_names': ['nobody'], 'filename': 'pulc_demo_imgs/person_exists/objects365_01780782.jpg'}]

Nobody means there is no one in the image, someone means there is someone in the image. Therefore, the prediction result indicates that there is no one in the figure.

Note: model.predict() is a generator, so next() or for is needed to call it. This would to predict by batch that length is batch_size, default by 1. You can specify the argument batch_size and model_name when instantiating PaddleClas object, for example: model = paddleclas.PaddleClas(model_name="person_exists", batch_size=2). Please refer to Supported Model List for the supported model list.

• CLI

paddleclas --model_name=person_exists --infer_imgs=pulc_demo_imgs/person_exists/objects365_01780782.jpg

>>> result
class_ids: [0], scores: [0.9955421453341842], label_names: ['nobody'], filename: pulc_demo_imgs/person_exists/objects365_01780782.jpg
Predict complete!

Note: The "--infer_imgs" argument specify the image(s) to be predict, and you can also specify a directoy contains images. If use other model, you can specify the --model_name argument. Please refer to Supported Model List for the supported model list.

Supported Model List

The name of PULC series models are as follows:

Name	Intro
person_exists	Human Exists Classification
person_attribute	Pedestrian Attribute Classification
safety_helmet	Classification of Wheather Wearing Safety Helmet
traffic_sign	Traffic Sign Classification
vehicle_attribute	Vehicle Attribute Classification
car_exists	Car Exists Classification
text_image_orientation	Text Image Orientation Classification
textline_orientation	Text-line Orientation Classification
language_classification	Language Classification

Please refer to Human Exists Classification、Pedestrian Attribute Classification、Classification of Wheather Wearing Safety Helmet、Traffic Sign Classification、Vehicle Attribute Classification、Car Exists Classification、Text Image Orientation Classification、Text-line Orientation Classification、Language Classification for more information about different scenarios.

3. Definition of Parameters

The following parameters can be specified in Command Line or used as parameters of the constructor when instantiating the PaddleClas object in Python.

• model_name(str): If using inference model based on ImageNet1k provided by Paddle, please specify the model's name by the parameter.
• inference_model_dir(str): Local model files directory, which is valid when model_name is not specified. The directory should contain inference.pdmodel and inference.pdiparams.
• infer_imgs(str): The path of image to be predicted, or the directory containing the image files, or the URL of the image from Internet.
• use_gpu(bool): Whether to use GPU or not.
• gpu_mem(int): GPU memory usages.
• use_tensorrt(bool): Whether to open TensorRT or not. Using it can greatly promote predict preformance.
• enable_mkldnn(bool): Whether enable MKLDNN or not.
• cpu_num_threads(int): Assign number of cpu threads, valid when --use_gpu is False and --enable_mkldnn is True.
• batch_size(int): Batch size.
• resize_short(int): Resize the minima between height and width into resize_short.
• crop_size(int): Center crop image to crop_size.
• topk(int): Print (return) the topk prediction results when Topk postprocess is used.
• threshold(float): The threshold of ThreshOutput when postprocess is used.
• class_id_map_file(str): The mapping file between class ID and label.
• save_dir(str): The directory to save the prediction results that can be used as pre-label.

Note: If you want to use Transformer series models, such as DeiT_***_384, ViT_***_384, etc., please pay attention to the input size of model, and need to set resize_short=384, resize=384. The following is a demo.

• CLI:

from paddleclas import PaddleClas, get_default_confg
paddleclas --model_name=ViT_base_patch16_384 --infer_imgs='docs/images/inference_deployment/whl_demo.jpg' --resize_short=384 --crop_size=384

• Python:

from paddleclas import PaddleClas
clas = PaddleClas(model_name='ViT_base_patch16_384', resize_short=384, crop_size=384)

4. Usage

PaddleClas provides two ways to use:

1. Python interative programming;
2. Bash command line programming.

4.1 View help information

• CLI

paddleclas -h

4.2 Prediction using inference model provide by PaddleClas

You can use the inference model provided by PaddleClas to predict, and only need to specify model_name. In this case, PaddleClas will automatically download files of specified model and save them in the directory ~/.paddleclas/.

• Python

from paddleclas import PaddleClas
clas = PaddleClas(model_name='ResNet50')
infer_imgs = 'docs/images/inference_deployment/whl_demo.jpg'
result=clas.predict(infer_imgs)
print(next(result))

• CLI

paddleclas --model_name='ResNet50' --infer_imgs='docs/images/inference_deployment/whl_demo.jpg'

4.3 Prediction using local model files

You can use the local model files trained by yourself to predict, and only need to specify inference_model_dir. Note that the directory must contain inference.pdmodel and inference.pdiparams.

• Python

from paddleclas import PaddleClas
clas = PaddleClas(inference_model_dir='./inference/')
infer_imgs = 'docs/images/inference_deployment/whl_demo.jpg'
result=clas.predict(infer_imgs)
print(next(result))

• CLI

paddleclas --inference_model_dir='./inference/' --infer_imgs='docs/images/inference_deployment/whl_demo.jpg'

4.4 Prediction by batch

You can predict by batch, only need to specify batch_size when infer_imgs is direcotry contain image files.

• Python

from paddleclas import PaddleClas
clas = PaddleClas(model_name='ResNet50', batch_size=2)
infer_imgs = 'docs/images/'
result=clas.predict(infer_imgs)
for r in result:
    print(r)

• CLI

paddleclas --model_name='ResNet50' --infer_imgs='docs/images/' --batch_size 2

4.5 Prediction of Internet image

You can predict the Internet image, only need to specify URL of Internet image by infer_imgs. In this case, the image file will be downloaded and saved in the directory ~/.paddleclas/images/.

• Python

from paddleclas import PaddleClas
clas = PaddleClas(model_name='ResNet50')
infer_imgs = 'https://raw.githubusercontent.com/paddlepaddle/paddleclas/release/2.2/docs/images/inference_deployment/whl_demo.jpg'
result=clas.predict(infer_imgs)
print(next(result))

• CLI

paddleclas --model_name='ResNet50' --infer_imgs='https://raw.githubusercontent.com/paddlepaddle/paddleclas/release/2.2/docs/images/inference_deployment/whl_demo.jpg'

4.6 Prediction of NumPy.array format image

In Python code, you can predict the NumPy.array format image, only need to use the infer_imgs to transfer variable of image data. Note that the models in PaddleClas only support to predict 3 channels image data, and channels order is RGB.

• python

import cv2
from paddleclas import PaddleClas
clas = PaddleClas(model_name='ResNet50')
infer_imgs = cv2.imread("docs/en/inference_deployment/whl_deploy_en.md")[:, :, ::-1]
result=clas.predict(infer_imgs)
print(next(result))

4.7 Save the prediction result(s)

You can save the prediction result(s) as pre-label, only need to use pre_label_out_dir to specify the directory to save.

• python

from paddleclas import PaddleClas
clas = PaddleClas(model_name='ResNet50', save_dir='./output_pre_label/')
infer_imgs = 'docs/images/' # it can be infer_imgs folder path which contains all of images you want to predict.
result=clas.predict(infer_imgs)
print(next(result))

• CLI

paddleclas --model_name='ResNet50' --infer_imgs='docs/images/' --save_dir='./output_pre_label/'

4.8 Specify the mapping between class id and label name

You can specify the mapping between class id and label name, only need to use class_id_map_file to specify the mapping file. PaddleClas uses ImageNet1K's mapping by default.

The content format of mapping file shall be:

class_id<space>class_name<\n>

For example:

0 tench, Tinca tinca
1 goldfish, Carassius auratus
2 great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias
......

• Python

from paddleclas import PaddleClas
clas = PaddleClas(model_name='ResNet50', class_id_map_file='./ppcls/utils/imagenet1k_label_list.txt')
infer_imgs = 'docs/images/inference_deployment/whl_demo.jpg'
result=clas.predict(infer_imgs)
print(next(result))

• CLI

paddleclas --model_name='ResNet50' --infer_imgs='docs/images/inference_deployment/whl_demo.jpg' --class_id_map_file='./ppcls/utils/imagenet1k_label_list.txt'