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- # Copyright 2020 Huawei Technologies Co., Ltd
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- # ============================================================================
- """
- ######################## eval alexnet example ########################
- eval alexnet according to model file:
- python eval.py --data_path /YourDataPath --ckpt_path Your.ckpt
- """
-
- import ast
- import argparse
- from src.config import alexnet_cifar10_cfg, alexnet_imagenet_cfg
- from src.dataset import create_dataset_cifar10, create_dataset_imagenet
- from src.alexnet import AlexNet
- 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
- import moxing as mox
- import os
- local_data_url='./cache/data'
- local_ckpt_url='./cache/ckpt'
-
- if __name__ == "__main__":
- parser = argparse.ArgumentParser(description='MindSpore AlexNet Example')
- parser.add_argument('--dataset_name', type=str, default='imagenet', choices=['imagenet', 'cifar10'],
- help='dataset name.')
- parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU'],
- help='device where the code will be implemented (default: Ascend)')
- parser.add_argument('--data_url',type=str,default="None",help='Datapath')
- parser.add_argument('--train_url',type=str,default="None", help='Train output path')
- #parser.add_argument('--data_path', type=str, default="./", help='path where the dataset is saved')
- parser.add_argument('--ckpt_path', type=str, default="./ckpt", help='if is test, must provide\
- path where the trained ckpt file')
- parser.add_argument('--dataset_sink_mode', type=ast.literal_eval,
- default=True, help='dataset_sink_mode is False or True')
- parser.add_argument('--device_id', type=int, default=0, help='device id of GPU or Ascend. (Default: 0)')
- args = parser.parse_args()
-
- context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
-
- print("============== Starting Testing ==============")
-
- #if args.ckpt_path:
- # checkpoint_file = os.path.join(local_ckpt_url, os.path.split(args.ckpt_path)[1])
- ckpt_path='obs://hit-lmx/lmx/alexnetimagenet/alexnetcifar10/ckpt/final/'
- mox.file.copy_parallel(args.data_url, local_data_url)
- #mox.file.copy_parallel(args.ckpt_path, checkpoint_file)
- mox.file.copy_parallel(ckpt_path,local_ckpt_url)
-
- if args.dataset_name == 'cifar10':
- cfg = alexnet_cifar10_cfg
-
-
- #create dataset
- ds_eval = create_dataset_cifar10(local_data_url, cfg.batch_size, status="test", target=args.device_target)
- step_size=ds_eval.get_dataset_size() #不是真正数据集大小,是一次batch的步数
-
- #define net
- network = AlexNet(cfg.num_classes, phase='test')
-
- #define loss
- loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
-
- for i in range(6):
- #load checkpoint
- ckpt_num=local_ckpt_url+'/checkpoint_alexnet-'+str(9+i*9)+'_195.ckpt'
- print(ckpt_num)
- param_dict=load_checkpoint(ckpt_num)
- load_param_into_net(network,param_dict)
- network.set_train(False)
-
- eval_metrics={'Loss:':nn.Loss(),'Top_1_Acc':nn.Top1CategoricalAccuracy(),
- 'Top_5_Acc:':nn.Top5CategoricalAccuracy()}
-
- model=Model(network,loss_fn=loss,metrics=eval_metrics)
-
- #eval model
- res=model.eval(ds_eval,dataset_sink_mode=False)
- print("result:",res,"ckpt=",ckpt_num)
- #?????????????
- #opt = nn.Momentum(network.trainable_params(), cfg.learning_rate, cfg.momentum)
-
-
- #param_dict = load_checkpoint(checkpoint_file)
- #print("load checkpoint from [{}].".format(args.ckpt_path))
- #load_param_into_net(network, param_dict)
- #network.set_train(False)
-
- #model = Model(network, loss, opt, metrics={"Accuracy": Accuracy()})
- #model = Model(network, loss, opt, metrics={"Accuracy": Accuracy()})
-
- elif args.dataset_name == 'imagenet':
-
- cfg = alexnet_imagenet_cfg
-
- # create dataset
- ds_eval = create_dataset_imagenet(local_data_url, cfg.batch_size, training=False)
- step_size = ds_eval.get_dataset_size() # 不是真正数据集大小,是一次batch的步数
-
- # define net
- network = AlexNet(cfg.num_classes, phase='test')
-
- # define loss
- loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
-
-
- for i in range(9):
- #load checkpoint
- ckpt_num=local_ckpt_url+'/checkpoint_alexnet-'+str(141+i*1)+'_625.ckpt'
- print(ckpt_num)
- param_dict=load_checkpoint(ckpt_num)
- load_param_into_net(network,param_dict)
- network.set_train(False)
-
- eval_metrics={'Loss:':nn.Loss(),'Top_1_Acc':nn.Top1CategoricalAccuracy(),
- 'Top_5_Acc:':nn.Top5CategoricalAccuracy()}
-
- model=Model(network,loss_fn=loss,metrics=eval_metrics)
-
- #eval model
- res=model.eval(ds_eval,dataset_sink_mode=False)
- print("result:",res,"ckpt=",ckpt_num)
-
- #network = AlexNet(cfg.num_classes, phase='test')
- #loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
- #ds_eval = create_dataset_imagenet(local_data_url, cfg.batch_size, training=False)
-
- #param_dict = load_checkpoint(checkpoint_file)
- #print("load checkpoint from [{}].".format(args.ckpt_path))
- #load_param_into_net(network, param_dict)
- #network.set_train(False)
-
- #model = Model(network, loss_fn=loss, metrics={'top_1_accuracy', 'top_5_accuracy'})
-
- #eval_metrics = {'Loss': nn.Loss(), 'Top_1_Acc': nn.Top1CategoricalAccuracy(),
- # 'Top_5_Acc': nn.Top5CategoricalAccuracy()}
- #model = Model(network, loss_fn=loss, metrics=eval_metrics)
-
- else:
- raise ValueError("Unsupport dataset.")
-
- if ds_eval.get_dataset_size() == 0:
- raise ValueError("Please check dataset size > 0 and batch_size <= dataset size")
-
- #result = model.eval(ds_eval, dataset_sink_mode=args.dataset_sink_mode)
- #print("result : {}".format(result))
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