84 lines
4.1 KiB
Python
84 lines
4.1 KiB
Python
# 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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#################train lstm example on aclImdb########################
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python train.py --preprocess=true --aclimdb_path=your_imdb_path --glove_path=your_glove_path
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"""
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import argparse
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import os
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import numpy as np
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from src.config import lstm_cfg as cfg
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from src.dataset import convert_to_mindrecord
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from src.dataset import lstm_create_dataset
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from mindspore import Tensor, nn, Model, context
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from mindspore.model_zoo.lstm import SentimentNet
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from mindspore.nn import Accuracy
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from mindspore.train.callback import LossMonitor, CheckpointConfig, ModelCheckpoint, TimeMonitor
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='MindSpore LSTM Example')
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parser.add_argument('--preprocess', type=str, default='false', choices=['true', 'false'],
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help='whether to preprocess data.')
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parser.add_argument('--aclimdb_path', type=str, default="./aclImdb",
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help='path where the dataset is stored.')
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parser.add_argument('--glove_path', type=str, default="./glove",
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help='path where the GloVe is stored.')
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parser.add_argument('--preprocess_path', type=str, default="./preprocess",
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help='path where the pre-process data is stored.')
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parser.add_argument('--ckpt_path', type=str, default="./",
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help='the path to save the checkpoint file.')
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parser.add_argument('--device_target', type=str, default="GPU", choices=['GPU', 'CPU'],
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help='the target device to run, support "GPU", "CPU". Default: "GPU".')
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args = parser.parse_args()
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context.set_context(
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mode=context.GRAPH_MODE,
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save_graphs=False,
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device_target=args.device_target)
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if args.preprocess == "true":
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print("============== Starting Data Pre-processing ==============")
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convert_to_mindrecord(cfg.embed_size, args.aclimdb_path, args.preprocess_path, args.glove_path)
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embedding_table = np.loadtxt(os.path.join(args.preprocess_path, "weight.txt")).astype(np.float32)
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network = SentimentNet(vocab_size=embedding_table.shape[0],
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embed_size=cfg.embed_size,
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num_hiddens=cfg.num_hiddens,
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num_layers=cfg.num_layers,
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bidirectional=cfg.bidirectional,
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num_classes=cfg.num_classes,
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weight=Tensor(embedding_table),
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batch_size=cfg.batch_size)
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loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
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opt = nn.Momentum(network.trainable_params(), cfg.learning_rate, cfg.momentum)
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loss_cb = LossMonitor()
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model = Model(network, loss, opt, {'acc': Accuracy()})
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print("============== Starting Training ==============")
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ds_train = lstm_create_dataset(args.preprocess_path, cfg.batch_size, cfg.num_epochs)
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config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps,
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keep_checkpoint_max=cfg.keep_checkpoint_max)
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ckpoint_cb = ModelCheckpoint(prefix="lstm", directory=args.ckpt_path, config=config_ck)
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time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())
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if args.device_target == "CPU":
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model.train(cfg.num_epochs, ds_train, callbacks=[time_cb, ckpoint_cb, loss_cb], dataset_sink_mode=False)
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else:
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model.train(cfg.num_epochs, ds_train, callbacks=[time_cb, ckpoint_cb, loss_cb])
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print("============== Training Success ==============")
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