paddleclas package

Get started quickly

install package

install by pypi

pip install paddleclas==2.0.0rc1

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',use_gpu=False,use_tensorrt=False)
image_file='docs/images/whl/demo.jpg'
result=clas.predict(image_file)
print(result)

    >>> result
    [{'filename': '/Users/mac/Downloads/PaddleClas/docs/images/whl/demo.jpg', 'class_ids': [8], 'scores': [0.9796774], 'label_names': ['hen']}]

• Using command line interactive programming

paddleclas --model_name='ResNet50' --image_file='docs/images/whl/demo.jpg'

    >>> result
    **********/Users/mac/Downloads/PaddleClas/docs/images/whl/demo.jpg**********
    [{'filename': '/Users/mac/Downloads/PaddleClas/docs/images/whl/demo.jpg', 'class_ids': [8], 'scores': [0.9796774], 'label_names': ['hen']}]

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): image's path. Support assigning single local image, internet image and folder containing series of images. Also Support numpy.ndarray.
• 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.
• 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.

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='user-specified model path',
    params_file='parmas path', use_gpu=False, use_tensorrt=False)
image_file = ''
result=clas.predict(image_file)
print(result)

bash

paddleclas --model_file='user-specified model path' --params_file='parmas path' --image_file='image path'

• Use inference model which PaddlePaddle provides to predict, you need to choose one of model when initializing PaddleClas to assign model_name. You may not assign model_file , and the model you chosen will be download in BASE_INFERENCE_MODEL_DIR ,which will be saved in folder named by model_name,avoiding overlay different inference model.

python

from paddleclas import PaddleClas
clas = PaddleClas(model_name='ResNet50',use_gpu=False, use_tensorrt=False)
image_file = ''
result=clas.predict(image_file)
print(result)

bash

paddleclas --model_name='ResNet50' --image_file='image path'

• You can assign input as formatnp.ndarray which has been preprocessed --image_file=np.ndarray.

python

from paddleclas import PaddleClas
clas = PaddleClas(model_name='ResNet50',use_gpu=False, use_tensorrt=False)
image_file =np.ndarray # image_file 可指定为前缀是https的网络图片，也可指定为本地图片
result=clas.predict(image_file)

bash

paddleclas --model_name='ResNet50' --image_file=np.ndarray

• You can assign image_file as a folder path containing series of images, also can assign top_k.

python

from paddleclas import PaddleClas
clas = PaddleClas(model_name='ResNet50',use_gpu=False, use_tensorrt=False,top_k=5)
image_file = '' # 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='image path' --top_k=5

• 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',use_gpu=False, use_tensorrt=False,top_k=5, pre_label_image=True,pre_label_out_idr='./output_pre_label/')
image_file = '' # 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='image path' --top_k=5 --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 Paddle 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= './inference.pdmodel',params_file = './inference.pdiparams',label_name_path='./ppcls/utils/imagenet1k_label_list.txt',use_gpu=False)
image_file = '' # 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= './inference.pdmodel' --params_file = './inference.pdiparams' --image_file='image path' --label_name_path='./ppcls/utils/imagenet1k_label_list.txt'

python

from paddleclas import PaddleClas
clas = PaddleClas(model_name='ResNet50',use_gpu=False)
image_file = '' # 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='image path'