139 lines
5.9 KiB
Python
139 lines
5.9 KiB
Python
"""
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######################## single-dataset inference lenet example ########################
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This example is a single-dataset inference tutorial.
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######################## Instructions for using the inference environment ########################
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1、Inference task requires predefined functions
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(1)Copy single dataset from obs to inference image.
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function ObsToEnv(obs_data_url, data_dir)
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(2)Copy ckpt file from obs to inference image.
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function ObsUrlToEnv(obs_ckpt_url, ckpt_url)
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(3)Copy the output result to obs.
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function EnvToObs(train_dir, obs_train_url)
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3、4 parameters need to be defined.
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--data_url is the dataset you selected on the Qizhi platform
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--ckpt_url is the weight file you choose on the Qizhi platform
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--data_url,--ckpt_url,--result_url,--device_target,These 4 parameters must be defined first in a single dataset,
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otherwise an error will be reported.
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There is no need to add these parameters to the running parameters of the Qizhi platform,
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because they are predefined in the background, you only need to define them in your code.
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4、How the dataset is used
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Inference task uses data_url as the input, and data_dir (ie: '/cache/data') as the calling method
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of the dataset in the image.
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For details, please refer to the following sample code.
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"""
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import os
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import argparse
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import moxing as mox
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import mindspore.nn as nn
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from mindspore import context
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from mindspore.train import Model
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from mindspore.nn.metrics import Accuracy
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from mindspore import Tensor
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import numpy as np
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from glob import glob
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from dataset import create_dataset
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from config import mnist_cfg as cfg
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from lenet import LeNet5
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### Copy single dataset from obs to inference image ###
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def ObsToEnv(obs_data_url, data_dir):
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try:
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mox.file.copy_parallel(obs_data_url, data_dir)
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print("Successfully Download {} to {}".format(obs_data_url, data_dir))
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except Exception as e:
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print('moxing download {} to {} failed: '.format(obs_data_url, data_dir) + str(e))
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return
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### Copy ckpt file from obs to inference image###
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### To operate on folders, use mox.file.copy_parallel. If copying a file.
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### Please use mox.file.copy to operate the file, this operation is to operate the file
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def ObsUrlToEnv(obs_ckpt_url, ckpt_url):
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try:
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mox.file.copy(obs_ckpt_url, ckpt_url)
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print("Successfully Download {} to {}".format(obs_ckpt_url,ckpt_url))
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except Exception as e:
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print('moxing download {} to {} failed: '.format(obs_ckpt_url, ckpt_url) + str(e))
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return
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### Copy the output result to obs###
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def EnvToObs(train_dir, obs_train_url):
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try:
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mox.file.copy_parallel(train_dir, obs_train_url)
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print("Successfully Upload {} to {}".format(train_dir,obs_train_url))
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except Exception as e:
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print('moxing upload {} to {} failed: '.format(train_dir,obs_train_url) + str(e))
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return
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### --data_url,--ckpt_url,--result_url,--device_target,These 4 parameters must be defined first in a inference task,
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### otherwise an error will be reported.
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### There is no need to add these parameters to the running parameters of the Qizhi platform,
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### because they are predefined in the background, you only need to define them in your code.
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parser = argparse.ArgumentParser(description='MindSpore Lenet Example')
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parser.add_argument('--data_url',
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type=str,
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default= '/cache/data/',
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help='path where the dataset is saved')
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parser.add_argument('--ckpt_url',
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help='model to save/load',
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default= '/cache/checkpoint.ckpt')
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parser.add_argument('--result_url',
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help='result folder to save/load',
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default= '/cache/result/')
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parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU', 'CPU'],
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help='device where the code will be implemented (default: Ascend)')
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if __name__ == "__main__":
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args, unknown = parser.parse_known_args()
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###Initialize the data and result directories in the inference image###
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data_dir = '/cache/data'
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result_dir = '/cache/result'
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ckpt_url = '/cache/checkpoint.ckpt'
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if not os.path.exists(data_dir):
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os.makedirs(data_dir)
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if not os.path.exists(result_dir):
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os.makedirs(result_dir)
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###Copy dataset from obs to inference image
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ObsToEnv(args.data_url, data_dir)
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###Copy ckpt file from obs to inference image
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ObsUrlToEnv(args.ckpt_url, ckpt_url)
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context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
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network = LeNet5(cfg.num_classes)
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net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
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repeat_size = cfg.epoch_size
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net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
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model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
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print("============== Starting Testing ==============")
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param_dict = load_checkpoint(os.path.join(ckpt_url))
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load_param_into_net(network, param_dict)
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ds_test = create_dataset(os.path.join(data_dir, "test"), batch_size=1).create_dict_iterator()
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data = next(ds_test)
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images = data["image"].asnumpy()
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labels = data["label"].asnumpy()
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print('Tensor:', Tensor(data['image']))
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output = model.predict(Tensor(data['image']))
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predicted = np.argmax(output.asnumpy(), axis=1)
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pred = np.argmax(output.asnumpy(), axis=1)
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print('predicted:', predicted)
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print('pred:', pred)
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print(f'Predicted: "{predicted[0]}", Actual: "{labels[0]}"')
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filename = 'result.txt'
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file_path = os.path.join(result_dir, filename)
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with open(file_path, 'a+') as file:
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file.write(" {}: {:.2f} \n".format("Predicted", predicted[0]))
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###Copy result data from the local running environment back to obs,
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###and download it in the inference task corresponding to the Qizhi platform
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EnvToObs(result_dir, args.result_url) |