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- # Copyright 2021 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 glore_resnet50################################
- python eval.py
- """
- import os
- import ast
- import random
- import argparse
- import numpy as np
-
- from mindspore import context
- from mindspore import dataset as de
- from mindspore.train.model import Model
- from mindspore.train.serialization import load_checkpoint, load_param_into_net
-
- from src.glore_resnet50 import glore_resnet50
- from src.dataset import create_eval_dataset
- from src.loss import CrossEntropySmooth, SoftmaxCrossEntropyExpand
- from src.config import config
-
- parser = argparse.ArgumentParser(description='Image classification with glore_resnet50')
- parser.add_argument('--use_glore', type=ast.literal_eval, default=True, help='Enable GloreUnit')
- parser.add_argument('--data_url', type=str, default=None, help='Dataset path')
- parser.add_argument('--train_url', type=str, help='Train output in modelarts')
- parser.add_argument('--device_target', type=str, default='Ascend', help='Device target')
- parser.add_argument('--device_id', type=int, default=0)
- parser.add_argument('--ckpt_url', type=str, default=None)
- parser.add_argument('--is_modelarts', type=ast.literal_eval, default=True)
- parser.add_argument('--parameter_server', type=ast.literal_eval, default=False, help='Run parameter server train')
- args_opt = parser.parse_args()
-
- if args_opt.is_modelarts:
- import moxing as mox
-
- random.seed(1)
- np.random.seed(1)
- de.config.set_seed(1)
-
- if __name__ == '__main__':
- target = args_opt.device_target
- # init context
- device_id = args_opt.device_id
- context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False,
- device_id=device_id)
-
- # dataset
- eval_dataset_path = os.path.join(args_opt.data_url, 'val')
- if args_opt.is_modelarts:
- mox.file.copy_parallel(src_url=args_opt.data_url, dst_url='/cache/dataset')
- eval_dataset_path = '/cache/dataset/'
- predict_data = create_eval_dataset(dataset_path=eval_dataset_path, repeat_num=1, batch_size=config.batch_size)
- step_size = predict_data.get_dataset_size()
- if step_size == 0:
- raise ValueError("Please check dataset size > 0 and batch_size <= dataset size")
-
- # define net
- net = glore_resnet50(class_num=config.class_num, use_glore=args_opt.use_glore)
-
- # load checkpoint
- param_dict = load_checkpoint(args_opt.ckpt_url)
- load_param_into_net(net, param_dict)
-
- # define loss, model
- if config.use_label_smooth:
- loss = CrossEntropySmooth(sparse=True, reduction="mean", smooth_factor=config.label_smooth_factor,
- num_classes=config.class_num)
- else:
- loss = SoftmaxCrossEntropyExpand(sparse=True)
- model = Model(net, loss_fn=loss, metrics={'top_1_accuracy', 'top_5_accuracy'})
- print("============== Starting Testing ==============")
- print("ckpt path : {}".format(args_opt.ckpt_url))
- print("data path : {}".format(eval_dataset_path))
- acc = model.eval(predict_data)
- print("==============Acc: {} ==============".format(acc))
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