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73 lines
3.1 KiB
73 lines
3.1 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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"""Warpctc evaluation"""
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import os
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import argparse
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from mindspore import context
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from mindspore.common import set_seed
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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 src.loss import CTCLoss
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from src.dataset import create_dataset
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from src.crnn import crnn
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from src.metric import CRNNAccuracy
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set_seed(1)
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parser = argparse.ArgumentParser(description="CRNN eval")
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parser.add_argument("--dataset_path", type=str, default=None, help="Dataset, default is None.")
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parser.add_argument("--checkpoint_path", type=str, default=None, help="checkpoint file path, default is None")
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parser.add_argument('--platform', type=str, default='Ascend', choices=['Ascend', 'GPU'],
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help='Running platform, choose from Ascend, GPU, and default is Ascend.')
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parser.add_argument('--model', type=str, default='lowcase', help="Model type, default is uppercase")
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parser.add_argument('--dataset', type=str, default='synth', choices=['synth', 'ic03', 'ic13', 'svt', 'iiit5k'])
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args_opt = parser.parse_args()
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if args_opt.model == 'lowcase':
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from src.config import config1 as config
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else:
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from src.config import config2 as config
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context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.platform, save_graphs=False)
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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(device_id=device_id)
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if __name__ == '__main__':
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config.batch_size = 1
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max_text_length = config.max_text_length
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input_size = config.input_size
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# create dataset
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dataset = create_dataset(name=args_opt.dataset,
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dataset_path=args_opt.dataset_path,
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batch_size=config.batch_size,
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is_training=False,
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config=config)
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step_size = dataset.get_dataset_size()
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loss = CTCLoss(max_sequence_length=config.num_step,
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max_label_length=max_text_length,
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batch_size=config.batch_size)
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net = crnn(config)
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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 model
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model = Model(net, loss_fn=loss, metrics={'CRNNAccuracy': CRNNAccuracy(config)})
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# start evaluation
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res = model.eval(dataset, dataset_sink_mode=args_opt.platform == 'Ascend')
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print("result:", res, flush=True)
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