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- """
- ######################## inference lenet example ########################
- inference lenet according to model file
- """
-
- """
- ######################## 推理环境使用说明 ########################
- 1、在推理环境中,需要将数据集从obs拷贝到推理镜像中,推理完以后,需要将输出的结果拷贝到obs.
- (1)将数据集从obs拷贝到推理镜像中:
- obs_data_url = args.data_url
- args.data_url = '/home/work/user-job-dir/data/'
- if not os.path.exists(args.data_url):
- os.mkdir(args.data_url)
- try:
- mox.file.copy_parallel(obs_data_url, args.data_url)
- print("Successfully Download {} to {}".format(obs_data_url,
- args.data_url))
- except Exception as e:
- print('moxing download {} to {} failed: '.format(
- obs_data_url, args.data_url) + str(e))
-
- (2)将模型文件从obs拷贝到推理镜像中:
- obs_ckpt_url = args.ckpt_url
- args.ckpt_url = '/home/work/user-job-dir/checkpoint.ckpt'
- try:
- mox.file.copy(obs_ckpt_url, args.ckpt_url)
- print("Successfully Download {} to {}".format(obs_ckpt_url,
- args.ckpt_url))
- except Exception as e:
- print('moxing download {} to {} failed: '.format(
- obs_ckpt_url, args.ckpt_url) + str(e))
-
- (3)将输出的结果拷贝回obs:
- obs_result_url = args.result_url
- args.result_url = '/home/work/user-job-dir/result/'
- if not os.path.exists(args.result_url):
- os.mkdir(args.result_url)
- try:
- mox.file.copy_parallel(args.result_url, obs_result_url)
- print("Successfully Upload {} to {}".format(args.result_url, obs_result_url))
- except Exception as e:
- print('moxing upload {} to {} failed: '.format(args.result_url, obs_result_url) + str(e))
- 详细代码可参考以下示例代码:
- """
- import os
-
- import cv2
- import numpy as np
- import pandas as pd
- import os
- import argparse
- import sys
- import moxing as mox
- import mindspore.nn as nn
- from mindspore import context
- from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
- from mindspore.train import Model
- from mindspore.nn.metrics import Accuracy
- from mindspore.common import set_seed
- import mindspore.numpy as msnp
- from mindspore import Model, dataset, nn
- from mindspore.nn import learning_rate_schedule
- from mindspore.train.callback import LossMonitor
- from mindspore.common import set_seed
- from dataloader import get_dataloader
- from Loss import Tripletloss, SoftMaxCE
- from mindspore.train.serialization import load_checkpoint, load_param_into_net
-
- from Resnet import resnet50
- from mindspore.communication import init, get_rank, get_group_size
-
- from mindspore import Model, dataset, nn
- from mindspore.communication import get_rank
- from mindspore.nn import learning_rate_schedule, TrainOneStepCell
- from mindspore.train.callback import LossMonitor, CheckpointConfig, ModelCheckpoint
- from mindspore.common import set_seed
-
- from Multigrain_net import Layer
- from dataloader import get_dataloader
-
- from Resnet import resnet50
-
- import os
- import argparse
- import moxing as mox
- import mindspore.nn as nn
- from mindspore import context
- from mindspore.train.serialization import load_checkpoint, load_param_into_net
- from mindspore.train import Model
- from mindspore.nn.metrics import Accuracy
- from mindspore import Tensor
- import numpy as np
- from config import mnist_cfg as cfg
- from Resnet import resnet50
-
- NUM_CLASSES = 2048
-
- workroot = '/home/work/user-job-dir' # 训练任务使用该参数
-
- if __name__ == "__main__":
- parser = argparse.ArgumentParser(description='MindSpore Lenet Example')
- parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU', 'CPU'],
- help='device where the code will be implemented (default: Ascend)')
- parser.add_argument('--data_url',
- type=str,
- default="./Data",
- help='path where the dataset is saved')
- parser.add_argument('--ckpt_url',
- help='model to save/load',
- default='./ckpt_url')
- parser.add_argument('--result_url',
- help='result folder to save/load',
- default='./result')
- args = parser.parse_args()
-
- #将数据集从obs拷贝到推理镜像中:
- obs_data_url = args.data_url
- args.data_url = '/home/work/user-job-dir/data/'
- data_dir = workroot + '/data' #数据集存放路径
- if not os.path.exists(args.data_url):
- os.mkdir(args.data_url)
- try:
- mox.file.copy_parallel(obs_data_url, args.data_url)
- print("Successfully Download {} to {}".format(obs_data_url,
- args.data_url))
- except Exception as e:
- print('moxing download {} to {} failed: '.format(
- obs_data_url, args.data_url) + str(e))
-
- #对文件夹进行操作,请使用mox.file.copy_parallel。如果拷贝一个文件。请使用mox.file.copy对文件操作,本次操作是对文件进行操作
- #将模型文件从obs拷贝到推理镜像中:
- obs_ckpt_url = args.ckpt_url
- args.ckpt_url = '/home/work/user-job-dir/checkpoint.ckpt'
- try:
- mox.file.copy(obs_ckpt_url, args.ckpt_url)
- print("Successfully Download {} to {}".format(obs_ckpt_url,
- args.ckpt_url))
- except Exception as e:
- print('moxing download {} to {} failed: '.format(
- obs_ckpt_url, args.ckpt_url) + str(e))
-
- #设置输出路径result_url
- obs_result_url = args.result_url
- args.result_url = '/home/work/user-job-dir/result/'
- if not os.path.exists(args.result_url):
- os.mkdir(args.result_url)
-
- args.dataset_path = args.data_url
- args.save_checkpoint_path = args.ckpt_url
-
- context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
-
- loss_cb = LossMonitor()
- global_net = Layer(NUM_CLASSES)
- lr = learning_rate_schedule.CosineDecayLR(min_lr=0.001, max_lr=0.1, decay_steps=4)
- global_optimizer = nn.SGD(global_net.trainable_params(), learning_rate=lr, momentum=0.9)
- global_net_with_grad = TrainOneStepCell(global_net, global_optimizer)
- model = Model(global_net_with_grad)
-
-
- print("============== Starting Testing ==============")
- args.load_ckpt_url = os.path.join(args.save_checkpoint_path)
- print("args.load_ckpt_url is:{}", args.load_ckpt_url )
- param_dict = load_checkpoint(args.load_ckpt_url )
- load_param_into_net(global_net, param_dict)
- load_checkpoint(args.load_ckpt_url, global_net)
-
- print("0--------------------------")
- dataset_generator = get_dataloader(os.path.join(data_dir, "ILSVRC2012_img_train"), 4, 4)
- print("0.1--------------------------")
- ds_train = dataset.GeneratorDataset(dataset_generator, ["image", "label"], shuffle=True)
- print("0.2--------------------------")
- ds_train = ds_train.batch(16).create_dict_iterator()
- print("0.3--------------------------")
- data = next(ds_train)
- print("1--------------------------")
- # images为测试图片,labels为测试图片的实际分类
- images = data["image"].asnumpy()
- labels = data["label"].asnumpy()
- print("2--------------------------")
- print('data')
-
- # 使用函数model.predict预测image对应分类
- output = model.predict(Tensor(data['image']))
- print("3--------------------------")
- predicted = np.argmax(output, axis=1)
- pred = np.argmax(output, axis=1)
- print('predicted:', predicted)
- print('pred:', pred)
- print("4--------------------------")
-
- # 输出预测分类与实际分类,并输出到result_url
- print(f'Predicted: "{predicted[0]}", Actual: "{labels[0]}"')
- filename = 'result.txt'
- file_path = os.path.join(args.result_url, filename)
- with open(file_path, 'a+') as file:
- file.write(" {}: {:.2f} \n".format("Predicted", predicted[0]))
-
-
- # Upload results to obs
- ######################## 将输出的结果拷贝到obs(固定写法) ########################
- # 把推理后的结果从本地的运行环境拷贝回obs,在启智平台相对应的推理任务中会提供下载
- try:
- mox.file.copy_parallel(args.result_url, obs_result_url)
- print("Successfully Upload {} to {}".format(args.result_url, obs_result_url))
- except Exception as e:
- print('moxing upload {} to {} failed: '.format(args.result_url, obs_result_url) + str(e))
- ######################## 将输出的模型拷贝到obs ########################
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