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paddleclas package

Get started quickly

install package

install by pypi

pip install paddleclas==2.0.3

build own whl package and install

python3 setup.py bdist_wheel
pip3 install dist/paddleclas-x.x.x-py3-none-any.whl

1. Quick Start

  • Assign image_file='docs/images/whl/demo.jpg', Use inference model that Paddle provides model_name='ResNet50'

Here is demo.jpg

from paddleclas import PaddleClas
clas = PaddleClas(model_name='ResNet50', top_k=5)
image_file='docs/images/whl/demo.jpg'
result=clas.predict(image_file)
print(result)
    >>> result
    [{'class_ids': array([ 8,  7, 86, 82, 80]), 'scores': array([9.7967714e-01, 2.0280687e-02, 2.7053760e-05, 6.1860351e-06,
       2.6378802e-06], dtype=float32), 'label_names': ['hen', 'cock', 'partridge', 'ruffed grouse, partridge, Bonasa umbellus', 'black grouse'], 'filename': 'docs/images/whl/demo.jpg'}
  • Using command line interactive programming
paddleclas --model_name=ResNet50 --top_k=5 --image_file='docs/images/whl/demo.jpg'
    >>> result
    **********docs/images/whl/demo.jpg**********
    filename: docs/images/whl/demo.jpg; class id: 8, 7, 86, 82, 80; scores: 0.9797, 0.0203, 0.0000, 0.0000, 0.0000; label: hen, cock, partridge, ruffed grouse, partridge, Bonasa umbellus, black grouse
    Predict complete!

2. Definition of Parameters

  • model_name(str): model's name. If not assigning model_fileandparams_file, you can assign this param. If using inference model based on ImageNet1k provided by Paddle, set as default='ResNet50'.
  • image_file(str or numpy.ndarray): image's path. Support assigning single local image, internet image and folder containing series of images. Also Support numpy.ndarray, the channel order is [B, G, R].
  • use_gpu(bool): Whether to use GPU or not, defalut=False。
  • use_tensorrt(bool): whether to open tensorrt or not. Using it can greatly promote predict preformance, default=False.
  • is_preprocessed(bool): Assign the image data has been preprocessed or not when the image_file is numpy.ndarray.
  • resize_short(int): resize the minima between height and width into resize_short(int), default=256
  • resize(int): resize image into resize(int), default=224.
  • normalize(bool): whether normalize image or not, default=True.
  • batch_size(int): batch number, default=1.
  • model_file(str): path of inference.pdmodel. If not assign this param，you need assign model_name for downloading.
  • params_file(str): path of inference.pdiparams. If not assign this param，you need assign model_name for downloading.
  • ir_optim(bool): whether enable IR optimization or not, default=True.
  • gpu_mem(int): GPU memory usages，default=8000。
  • enable_profile(bool): whether enable profile or not,default=False.
  • top_k(int): Assign top_k, default=1.
  • enable_mkldnn(bool): whether enable MKLDNN or not, default=False.
  • cpu_num_threads(int): Assign number of cpu threads, default=10.
  • label_name_path(str): Assign path of label_name_dict you use. If using your own training model, you can assign this param. If using inference model based on ImageNet1k provided by Paddle, you may not assign this param.Defaults take ImageNet1k's label name.
  • pre_label_image(bool): whether prelabel or not, default=False.
  • pre_label_out_idr(str): If prelabeling, the path of output.

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 when building a PaddleClas object. The following is a demo.

  • Bash:

    paddleclas --model_name=ViT_base_patch16_384 --image_file='docs/images/whl/demo.jpg' --resize_short=384 --resize=384

  • Python:

    clas = PaddleClas(model_name='ViT_base_patch16_384', top_k=5, resize_short=384, resize=384)

3. Different Usages of Codes

We provide two ways to use: 1. Python interative programming 2. Bash command line programming

  • check help information
paddleclas -h
  • Use user-specified model, you need to assign model's path model_file and parameters's pathparams_file
python
from paddleclas import PaddleClas
clas = PaddleClas(model_file='the path of model file',
    params_file='the path of params file')
image_file = 'docs/images/whl/demo.jpg'
result=clas.predict(image_file)
print(result)
bash
paddleclas --model_file='user-specified model path' --params_file='parmas path' --image_file='docs/images/whl/demo.jpg'
  • Use inference model which PaddlePaddle provides to predict, you need to choose one of model proviede by PaddleClas to assign model_name. So there's no need to assign model_file. And the model you chosen will be download in ~/.paddleclas/, which will be saved in folder named by model_name.
python
from paddleclas import PaddleClas
clas = PaddleClas(model_name='ResNet50')
image_file = 'docs/images/whl/demo.jpg'
result=clas.predict(image_file)
print(result)
bash
paddleclas --model_name='ResNet50' --image_file='docs/images/whl/demo.jpg'
  • You can assign input as format numpy.ndarray which has been preprocessed image_file=np.ndarray. Note that the image data must be three channel. If need To preprocess the image, the image channels order must be [B, G, R].
python
import cv2
from paddleclas import PaddleClas
clas = PaddleClas(model_name='ResNet50')
image_file = cv2.imread("docs/images/whl/demo.jpg")
result=clas.predict(image_file)
  • You can assign image_file as a folder path containing series of images.
python
from paddleclas import PaddleClas
clas = PaddleClas(model_name='ResNet50')
image_file = 'docs/images/whl/' # it can be image_file folder path which contains all of images you want to predict.
result=clas.predict(image_file)
print(result)
bash
paddleclas --model_name='ResNet50' --image_file='docs/images/whl/'
  • You can assign --pre_label_image=True, --pre_label_out_idr= './output_pre_label/'. Then images will be copied into folder named by top-1 class_id.
python
from paddleclas import PaddleClas
clas = PaddleClas(model_name='ResNet50', pre_label_image=True, pre_label_out_idr='./output_pre_label/')
image_file = 'docs/images/whl/' # it can be image_file folder path which contains all of images you want to predict.
result=clas.predict(image_file)
print(result)
bash
paddleclas --model_name='ResNet50' --image_file='docs/images/whl/' --pre_label_image=True --pre_label_out_idr='./output_pre_label/'
  • You can assign --label_name_path as your own label_dict_file, format should be as(class_idclass_name<\n>).
0 tench, Tinca tinca
1 goldfish, Carassius auratus
2 great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias
......
  • If you use inference model that PaddleClas provides, you do not need assign label_name_path. Program will take ppcls/utils/imagenet1k_label_list.txt as defaults. If you hope using your own training model, you can provide label_name_path outputing 'label_name' and scores, otherwise no 'label_name' in output information.
python
from paddleclas import PaddleClas
clas = PaddleClas(model_file= 'the path of model file', params_file = 'the path of params file', label_name_path='./ppcls/utils/imagenet1k_label_list.txt')
image_file = 'docs/images/whl/demo.jpg' # it can be image_file folder path which contains all of images you want to predict.
result=clas.predict(image_file)
print(result)
bash
paddleclas --model_file='the path of model file' --params_file='the path of params file' --image_file='docs/images/whl/demo.jpg' --label_name_path='./ppcls/utils/imagenet1k_label_list.txt'
python
from paddleclas import PaddleClas
clas = PaddleClas(model_name='ResNet50')
image_file = 'docs/images/whl/' # it can be directory which contains all of images you want to predict.
result=clas.predict(image_file)
print(result)
bash
paddleclas --model_name='ResNet50' --image_file='docs/images/whl/'