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74 lines
2.7 KiB
74 lines
2.7 KiB
# Copyright 2020-2021 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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"""test direction model."""
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import argparse
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import os
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import random
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import numpy as np
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from src.cnn_direction_model import CNNDirectionModel
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from src.config import config1 as config
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from src.dataset import create_dataset_eval
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from mindspore import context
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from mindspore import dataset as de
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from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
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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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parser = argparse.ArgumentParser(description='Image classification')
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parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
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parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
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args_opt = parser.parse_args()
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random.seed(1)
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np.random.seed(1)
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de.config.set_seed(1)
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if __name__ == '__main__':
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# init context
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False)
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device_id = int(os.getenv('DEVICE_ID'))
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context.set_context(device_id=device_id)
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# create dataset
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dataset_name = config.dataset_name
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dataset_lr, dataset_rl = create_dataset_eval(args_opt.dataset_path + "/" + dataset_name +
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".mindrecord0", config=config, dataset_name=dataset_name)
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step_size = dataset_lr.get_dataset_size()
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print("step_size ", step_size)
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# define net
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net = CNNDirectionModel([3, 64, 48, 48, 64], [64, 48, 48, 64, 64], [256, 64], [64, 512])
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# load checkpoint
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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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# define loss, model
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loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction="sum")
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# define model
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model = Model(net, loss_fn=loss, metrics={'top_1_accuracy'})
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# eval model
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res_lr = model.eval(dataset_lr, dataset_sink_mode=False)
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res_rl = model.eval(dataset_rl, dataset_sink_mode=False)
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print("result on upright images:", res_lr, "ckpt=", args_opt.checkpoint_path)
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print("result on 180 degrees rotated images:", res_rl, "ckpt=", args_opt.checkpoint_path)
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