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87 lines
3.2 KiB
87 lines
3.2 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""
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eval.
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"""
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import os
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import argparse
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import numpy as np
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from mindspore import context, Tensor
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from mindspore import nn
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from mindspore.train.model import Model
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from mindspore.common import dtype as mstype
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from src.pet_dataset import create_dataset
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from src.config import cfg
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from src.resnet_imgnet import resnet50
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parser = argparse.ArgumentParser(description='Image classification')
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parser.add_argument('--checkpoint_path', type=str,
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default='resnet50-imgnet-0.65x-80.24.ckpt', help='Checkpoint file path')
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parser.add_argument('--dataset_path', type=str,
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default='/home/hankai/xiaoan/data/test.mindrecord', help='Dataset path')
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parser.add_argument('--platform', type=str, default='GPU', help='run platform')
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args_opt = parser.parse_args()
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if __name__ == '__main__':
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config_platform = cfg
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if args_opt.platform == "Ascend":
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device_id = int(os.getenv('DEVICE_ID'))
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend",
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device_id=device_id, save_graphs=False)
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elif args_opt.platform == "GPU":
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context.set_context(mode=context.GRAPH_MODE,
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device_target="GPU", save_graphs=False)
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else:
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raise ValueError("Unsupported platform.")
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loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
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dataset = create_dataset(dataset_path=args_opt.dataset_path,
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do_train=False,
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config=config_platform,
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platform=args_opt.platform,
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batch_size=config_platform.batch_size)
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step_size = dataset.get_dataset_size()
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index = []
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with open('index.txt', 'r') as f:
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for line in f:
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ind = Tensor((np.array(line.strip('\n').split(' ')[:-1])).astype(np.int32).reshape(-1, 1))
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index.append(ind)
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net = resnet50(
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rate=0.65, class_num=config_platform.num_classes, index=index)
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if args_opt.platform == "Ascend":
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net.to_float(mstype.float16)
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for _, cell in net.cells_and_names():
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if isinstance(cell, nn.Dense):
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cell.to_float(mstype.float32)
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if args_opt.checkpoint_path:
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param_dict = load_checkpoint(args_opt.checkpoint_path)
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load_param_into_net(net, param_dict)
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net.set_train(False)
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model = Model(net, loss_fn=loss, metrics={'acc'})
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res = model.eval(dataset)
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print("result:", res, "ckpt=", args_opt.checkpoint_path)
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