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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.
- # ============================================================================
- """
- ######################## train alexnet example ########################
- train alexnet and get network model files(.ckpt) :
- python finetune.py --data_path /YourDataPath
- """
-
- import ast
- import argparse
- import copy
- import os
- import time
- import mindspore
- from src.finetune.config import alexnet_imagenet_cfg
- from src.dataset import create_finetune_dataset
- from src.alexnet.generator_lr import get_lr_imagenet, Finetune_Loss
- from src.alexnet.alexnet import alexnet_finetune
- from src.alexnet.get_param_groups import get_param_groups
- import mindspore.nn as nn
- from mindspore.communication.management import init, get_rank
- from mindspore import dataset as de
- from mindspore import context
- from mindspore.train.serialization import load_checkpoint, load_param_into_net
- from mindspore import Tensor
- from mindspore.train import Model
- from mindspore.context import ParallelMode
- from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor, Callback
- from mindspore.common import set_seed
-
- set_seed(1)
- de.config.set_seed(1)
-
- class Monitor(Callback):
- def __init__(self,lr_init=None):
- super(Monitor, self).__init__()
- self.lr_init=lr_init
- self.lr_init_len=len(lr_init)
- def step_end(self, run_context):
- cb_params=run_context.original_args()
- print("lr:[{:8.6f}]".format(self.lr_init[cb_params.cur_step_num-1]),flush=True)
-
- if __name__ == "__main__":
- parser = argparse.ArgumentParser(description='MindSpore AlexNet Example')
- parser.add_argument('--device_target', type=str, default="CPU", choices=['Ascend', 'GPU','CPU'],
- help='device where the code will be implemented (default: Ascend)')
- parser.add_argument('--sink_size', type=int, default=-1, help='control the amount of data in each sink')
- parser.add_argument('--predata_path', type=str, default="src/data/finetune/train", help='path where the dataset is saved')
- parser.add_argument('--jpeg_root_path', type=str, default="src/data/VOCdevkit/VOC2007/JPEGImages",
- 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=6, help='device id of GPU or Ascend. (Default: 0)')
- args_opt = parser.parse_args()
- #看是否多卡
- device_num=1
- device_id=0
- #device_num = int(os.getenv("RANK_SIZE"))
- #device_id = int(os.getenv("DEVICE_ID"))
- #rank
-
- cfg = alexnet_imagenet_cfg
- #运行平台
- device_target = args_opt.device_target
- context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
- context.set_context(save_graphs=False)
- #多卡的代码
- if device_target == "Ascend":
- context.set_context(device_id=args_opt.device_id)
-
- if device_num > 1:
- context.reset_auto_parallel_context()
- context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
- gradients_mean=True)
- init()
- elif device_target == "GPU":
- if device_num > 1:
- init()
- context.reset_auto_parallel_context()
- context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
- gradients_mean=True)
-
- #训练数据集创建###############################数据集传入这里
- ds_eval = create_finetune_dataset(predata_path=args_opt.predata_path, jpeg_root_path=args_opt.jpeg_root_path, batch_size=cfg.batch_size,
- num_shards=device_num, shard_id=device_id, device_target=args_opt.device_target)
- #判断数据集是否为空
- if ds_eval.get_dataset_size() == 0:
- raise ValueError("Please check dataset size > 0 and batch_size <= dataset size")
-
- network,_,_=alexnet_finetune(cfg.num_classes, phase='test')
-
- loss_scale_manager = None
- metrics = None
-
- loss = Finetune_Loss()
-
- for dir in os.listdir('./ckpt'):
- param_dict = load_checkpoint(dir)
- 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)
-
- res = model.eval(ds_eval, dataset_sink_mode=False)
- print("result:", res, "ckpt=", dir)
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