236 lines
7.9 KiB
Markdown
236 lines
7.9 KiB
Markdown
# PaddleClas wheel package
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## 1. Installation
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* installing from pypi
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```bash
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pip3 install paddleclas==2.2.0
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```
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* build own whl package and install
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```bash
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python3 setup.py bdist_wheel
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pip3 install dist/*
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```
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## 2. Quick Start
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* Using the `ResNet50` model provided by PaddleClas, the following image(`'docs/images/whl/demo.jpg'`) as an example.
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<div align="center">
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<img src="../images/whl/demo.jpg" width = "400" />
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</div>
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* Python
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```python
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from paddleclas import PaddleClas
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clas = PaddleClas(model_name='ResNet50')
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infer_imgs='docs/images/whl/demo.jpg'
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result=clas.predict(infer_imgs)
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print(next(result))
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```
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**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:
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```
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>>> result
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[{'class_ids': [8, 7, 136, 80, 84], 'scores': [0.79368, 0.16329, 0.01853, 0.00959, 0.00239], 'label_names': ['hen', 'cock', 'European gallinule, Porphyrio porphyrio', 'black grouse', 'peacock']}]
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```
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* CLI
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```bash
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paddleclas --model_name=ResNet50 --infer_imgs="docs/images/whl/demo.jpg"
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```
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```
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>>> result
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filename: docs/images/whl/demo.jpg, top-5, class_ids: [8, 7, 136, 80, 84], scores: [0.79368, 0.16329, 0.01853, 0.00959, 0.00239], label_names: ['hen', 'cock', 'European gallinule, Porphyrio porphyrio', 'black grouse', 'peacock']
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Predict complete!
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```
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## 3. Definition of Parameters
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The following parameters can be specified in Command Line or used as parameters of the constructor when instantiating the PaddleClas object in Python.
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* model_name(str): If using inference model based on ImageNet1k provided by Paddle, please specify the model's name by the parameter.
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* 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`.
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* 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.
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* use_gpu(bool): Whether to use GPU or not, default by `True`.
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* gpu_mem(int): GPU memory usages，default by `8000`。
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* use_tensorrt(bool): Whether to open TensorRT or not. Using it can greatly promote predict preformance, default by `False`.
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* enable_mkldnn(bool): Whether enable MKLDNN or not, default `False`.
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* cpu_num_threads(int): Assign number of cpu threads, valid when `--use_gpu` is `False` and `--enable_mkldnn` is `True`, default by `10`.
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* batch_size(int): Batch size, default by `1`.
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* resize_short(int): Resize the minima between height and width into `resize_short`, default by `256`.
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* crop_size(int): Center crop image to `crop_size`, default by `224`.
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* topk(int): Print (return) the `topk` prediction results, default by `5`.
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* class_id_map_file(str): The mapping file between class ID and label, default by `ImageNet1K` dataset's mapping.
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* pre_label_image(bool): whether prelabel or not, default=False.
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* save_dir(str): The directory to save the prediction results that can be used as pre-label, default by `None`, that is, not to save.
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**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.
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* CLI:
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```bash
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from paddleclas import PaddleClas, get_default_confg
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paddleclas --model_name=ViT_base_patch16_384 --infer_imgs='docs/images/whl/demo.jpg' --resize_short=384 --crop_size=384
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```
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* Python:
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```python
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from paddleclas import PaddleClas
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clas = PaddleClas(model_name='ViT_base_patch16_384', resize_short=384, crop_size=384)
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```
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## 4. Usage
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PaddleClas provides two ways to use:
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1. Python interative programming;
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2. Bash command line programming.
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### 4.1 View help information
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* CLI
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```bash
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paddleclas -h
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```
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### 4.2 Prediction using inference model provide by PaddleClas
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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/`.
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* Python
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```python
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from paddleclas import PaddleClas
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clas = PaddleClas(model_name='ResNet50')
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infer_imgs = 'docs/images/whl/demo.jpg'
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result=clas.predict(infer_imgs)
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print(next(result))
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```
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* CLI
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```bash
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paddleclas --model_name='ResNet50' --infer_imgs='docs/images/whl/demo.jpg'
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```
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### 4.3 Prediction using local model files
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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`.
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* Python
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```python
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from paddleclas import PaddleClas
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clas = PaddleClas(inference_model_dir='./inference/')
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infer_imgs = 'docs/images/whl/demo.jpg'
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result=clas.predict(infer_imgs)
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print(next(result))
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```
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* CLI
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```bash
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paddleclas --inference_model_dir='./inference/' --infer_imgs='docs/images/whl/demo.jpg'
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```
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### 4.4 Prediction by batch
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You can predict by batch, only need to specify `batch_size` when `infer_imgs` is direcotry contain image files.
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* Python
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```python
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from paddleclas import PaddleClas
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clas = PaddleClas(model_name='ResNet50', batch_size=2)
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infer_imgs = 'docs/images/'
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result=clas.predict(infer_imgs)
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for r in result:
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print(r)
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```
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* CLI
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```bash
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paddleclas --model_name='ResNet50' --infer_imgs='docs/images/' --batch_size 2
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```
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### 4.5 Prediction of Internet image
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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/`.
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* Python
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```python
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from paddleclas import PaddleClas
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clas = PaddleClas(model_name='ResNet50')
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infer_imgs = 'https://raw.githubusercontent.com/paddlepaddle/paddleclas/release/2.2/docs/images/whl/demo.jpg'
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result=clas.predict(infer_imgs)
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print(next(result))
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```
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* CLI
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```bash
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paddleclas --model_name='ResNet50' --infer_imgs='https://raw.githubusercontent.com/paddlepaddle/paddleclas/release/2.2/docs/images/whl/demo.jpg'
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```
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### 4.6 Prediction of NumPy.array format image
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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 image data must be 3 channels.
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* python
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```python
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import cv2
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from paddleclas import PaddleClas
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clas = PaddleClas(model_name='ResNet50')
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infer_imgs = cv2.imread("docs/images/whl/demo.jpg")
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result=clas.predict(infer_imgs)
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print(next(result))
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```
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### 4.7 Save the prediction result(s)
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You can save the prediction result(s) as pre-label, only need to use `pre_label_out_dir` to specify the directory to save.
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* python
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```python
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from paddleclas import PaddleClas
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clas = PaddleClas(model_name='ResNet50', save_dir='./output_pre_label/')
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infer_imgs = 'docs/images/whl/' # it can be infer_imgs folder path which contains all of images you want to predict.
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result=clas.predict(infer_imgs)
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print(next(result))
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```
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* CLI
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```bash
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paddleclas --model_name='ResNet50' --infer_imgs='docs/images/whl/' --save_dir='./output_pre_label/'
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```
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### 4.8 Specify the mapping between class id and label name
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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.
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The content format of mapping file shall be:
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```
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class_id<space>class_name<\n>
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```
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For example:
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```
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0 tench, Tinca tinca
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1 goldfish, Carassius auratus
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2 great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias
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......
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```
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* Python
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```python
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from paddleclas import PaddleClas
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clas = PaddleClas(model_name='ResNet50', class_id_map_file='./ppcls/utils/imagenet1k_label_list.txt')
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infer_imgs = 'docs/images/whl/demo.jpg'
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result=clas.predict(infer_imgs)
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print(next(result))
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```
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* CLI
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```bash
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paddleclas --model_name='ResNet50' --infer_imgs='docs/images/whl/demo.jpg' --class_id_map_file='./ppcls/utils/imagenet1k_label_list.txt'
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```
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