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-
- """
- 示例选用的数据集是MNISTData.zip
- 数据集结构是:
- MNISTData.zip
- ├── test
- │ ├── t10k-images-idx3-ubyte
- │ └── t10k-labels-idx1-ubyte
- └── train
- ├── train-images-idx3-ubyte
- └── train-labels-idx1-ubyte
-
- 示例选用的模型文件是:checkpoint_lenet-1_1875.ckpt
-
- 使用注意事项:
- 1、本示例需要用户定义的参数有--multi_data_url,--pretrain_url,--result_url,这3个参数任务中必须定义
- 具体的含义如下:
- --multi_data_url是启智平台上选择的数据集的obs路径
- --pretrain_url是启智平台上选择的预训练模型文件的obs路径
- --result_url是训练结果回传到启智平台的obs路径
- 2、用户需要调用OpenI.py下的DatasetToEnv,PretrainToEnv,UploadToOpenI等函数,来实现数据集、预训练模型文件、训练结果的拷贝和回传
- """
-
- import os
- import argparse
- import mindspore.nn as nn
- import numpy as np
- from mindspore import context
- from mindspore.train.serialization import load_checkpoint, load_param_into_net
- from mindspore.train import Model
- from mindspore import Tensor
- from dataset import create_dataset
- from config import mnist_cfg as cfg
- from lenet import LeNet5
-
- from openi import openi_multidataset_to_env as DatasetToEnv
- from openi import env_to_openi
- from openi import pretrain_to_env
-
-
- parser = argparse.ArgumentParser(description='MindSpore Lenet Example')
- parser.add_argument('--multi_data_url',
- type=str,
- default= '[{}]',
- help='path where the dataset is saved')
- parser.add_argument('--pretrain_url',
- help='model to save/load',
- default= '[{}]')
- parser.add_argument('--result_url',
- help='result folder to save/load',
- default= '')
- parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU', 'CPU'],
- help='device where the code will be implemented (default: Ascend)')
-
- if __name__ == "__main__":
- args, unknown = parser.parse_known_args()
-
- ###Initialize the data and result directories in the inference image###
- data_dir = '/cache/data'
- pretrain_dir = '/cache/pretrain'
- result_dir = '/cache/result'
- if not os.path.exists(data_dir):
- os.makedirs(data_dir)
- if not os.path.exists(pretrain_dir):
- os.makedirs(pretrain_dir)
- if not os.path.exists(result_dir):
- os.makedirs(result_dir)
-
- ###拷贝数据集到训练环境
- DatasetToEnv(args.multi_data_url, data_dir)
-
- ###拷贝预训练模型文件到训练环境
- pretrain_to_env(args.pretrain_url, pretrain_dir)
-
- context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
- network = LeNet5(cfg.num_classes)
- net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
- repeat_size = cfg.epoch_size
- net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
- model = Model(network, net_loss, net_opt, metrics={"Accuracy"})
-
- print("============== Starting Testing ==============")
-
- param_dict = load_checkpoint(os.path.join(pretrain_dir, "checkpoint_lenet-1_1875.ckpt"))
- load_param_into_net(network, param_dict)
- ds_test = create_dataset(os.path.join(data_dir + "/MNISTData", "test"), batch_size=1).create_dict_iterator()
- data = next(ds_test)
- images = data["image"].asnumpy()
- labels = data["label"].asnumpy()
- print('Tensor:', Tensor(data['image']))
- output = model.predict(Tensor(data['image']))
- predicted = np.argmax(output.asnumpy(), axis=1)
- pred = np.argmax(output.asnumpy(), axis=1)
- print('predicted:', predicted)
- print('pred:', pred)
-
- print(f'Predicted: "{predicted[0]}", Actual: "{labels[0]}"')
- filename = 'result.txt'
- file_path = os.path.join(result_dir, filename)
- with open(file_path, 'a+') as file:
- file.write(" {}: {:.2f} \n".format("Predicted", predicted[0]))
-
- ###上传训练结果到启智平台
- env_to_openi(result_dir, args.result_url)
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