手写数字识别-paddle版

平台

https://www.paddlepaddle.org.cn/

环境变量

# 路径
data_dir = '../data'
model_dir = 'inference_model'
base_dir = '{}/{}'.format(data_dir, model_dir)

# 模型名称
model_name = 'minist'
model_file = '{}/{}'.format(base_dir, model_name)
model_dynamic_file = '{}/{}/{}'.format(data_dir,'dynamic_model', model_name)

加载模型，并推理

import paddle
import numpy as np
# 引用 paddle inference 预测库
import paddle.inference as paddle_infer
from paddle.vision.transforms import Normalize


def main():
    # 归一化函数，对[0-255]数据进行归一化，这样好处理
    transform = Normalize(mean=[127.5], std=[127.5], data_format='CHW')
    test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=transform)

    # 设置输入
    fake_input = np.asarray(test_dataset[1][0]).reshape([1,1,28,28])
#     print(fake_input)
    
    # 设置Config
    config = set_config()

    # 创建Predictor
    predictor = paddle_infer.create_predictor(config)

    # 获取输入的名称
    input_names = predictor.get_input_names()
    input_tensor = predictor.get_input_handle(input_names[0])

    # 设置输入
#     fake_input = np.random.randn(1,784).astype("float32")
    input_tensor.copy_from_cpu(fake_input)

    # 运行predictor
    predictor.run()

    # 获取输出
    output_names = predictor.get_output_names()
    output_tensor = predictor.get_output_handle(output_names[0])
    output_data = output_tensor.copy_to_cpu() # numpy.ndarray类型
    print("输出的形状如下: ")
    print(output_data.shape)
    print(output_data.argmax())

def set_config():
    pdmodel_file = '{}.pdmodel'.format(model_file)
    pdiparams_file = '{}.pdiparams'.format(model_file)
    print('模型: {}'.format(pdmodel_file))
    config = paddle_infer.Config(pdmodel_file, pdiparams_file)
    config.disable_gpu()
    return config

if __name__ == "__main__":
    main()

可视化图片

import paddle
# 可视化图片
from matplotlib import pyplot as plt

test_dataset = paddle.vision.datasets.MNIST(mode='test')
# 从测试集中取出一张图片
img, label = test_dataset[1]
print(img)
plt.imshow(img)
plt.show()