forked from mindspore-Ecosystem/mindspore
66 lines
2.5 KiB
Python
Executable File
66 lines
2.5 KiB
Python
Executable File
# 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 math as m
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import random
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import argparse
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import numpy as np
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from mindspore import context
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from mindspore import dataset as de
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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.config import config as cf
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from src.dataset import create_dataset
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from src.warpctc import StackedRNN
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from src.metric import WarpCTCAccuracy
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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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parser = argparse.ArgumentParser(description="Warpctc training")
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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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args_opt = parser.parse_args()
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device_id = int(os.getenv('DEVICE_ID'))
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context.set_context(mode=context.GRAPH_MODE,
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device_target="Ascend",
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save_graphs=False,
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device_id=device_id)
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if __name__ == '__main__':
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max_captcha_digits = cf.max_captcha_digits
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input_size = m.ceil(cf.captcha_height / 64) * 64 * 3
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# create dataset
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dataset = create_dataset(dataset_path=args_opt.dataset_path, repeat_num=1, batch_size=cf.batch_size)
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step_size = dataset.get_dataset_size()
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# define loss
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loss = CTCLoss(max_sequence_length=cf.captcha_width, max_label_length=max_captcha_digits, batch_size=cf.batch_size)
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# define net
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net = StackedRNN(input_size=input_size, batch_size=cf.batch_size, hidden_size=cf.hidden_size)
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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={'WarpCTCAccuracy': WarpCTCAccuracy()})
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# start evaluation
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res = model.eval(dataset)
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print("result:", res, flush=True)
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