forked from mindspore-Ecosystem/mindspore
80 lines
3.4 KiB
Python
80 lines
3.4 KiB
Python
# Copyright 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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"""
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#################train lstm example on aclImdb########################
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"""
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import os
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import numpy as np
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from src.model_utils.config import config
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from src.model_utils.moxing_adapter import moxing_wrapper
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from src.dataset import lstm_create_dataset, convert_to_mindrecord
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from src.lstm import SentimentNet
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from mindspore import Tensor, nn, Model, context
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from mindspore.nn import Accuracy, Recall, F1
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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def modelarts_process():
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config.ckpt_file = os.path.join(config.output_path, config.ckpt_file)
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@moxing_wrapper(pre_process=modelarts_process)
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def eval_lstm():
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""" eval lstm """
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print('\neval.py config: \n', config)
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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=config.device_target)
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if config.preprocess == "true":
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print("============== Starting Data Pre-processing ==============")
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convert_to_mindrecord(config.embed_size, config.aclimdb_path, config.preprocess_path, config.glove_path)
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embedding_table = np.loadtxt(os.path.join(config.preprocess_path, "weight.txt")).astype(np.float32)
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# DynamicRNN in this network on Ascend platform only support the condition that the shape of input_size
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# and hiddle_size is multiples of 16, this problem will be solved later.
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if config.device_target == 'Ascend':
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pad_num = int(np.ceil(config.embed_size / 16) * 16 - config.embed_size)
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if pad_num > 0:
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embedding_table = np.pad(embedding_table, [(0, 0), (0, pad_num)], 'constant')
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config.embed_size = int(np.ceil(config.embed_size / 16) * 16)
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network = SentimentNet(vocab_size=embedding_table.shape[0],
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embed_size=config.embed_size,
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num_hiddens=config.num_hiddens,
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num_layers=config.num_layers,
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bidirectional=config.bidirectional,
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num_classes=config.num_classes,
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weight=Tensor(embedding_table),
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batch_size=config.batch_size)
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loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
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ds_eval = lstm_create_dataset(config.preprocess_path, config.batch_size, training=False)
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model = Model(network, loss, metrics={'acc': Accuracy(), 'recall': Recall(), 'f1': F1()})
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print("============== Starting Testing ==============")
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param_dict = load_checkpoint(config.ckpt_file)
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load_param_into_net(network, param_dict)
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if config.device_target == "CPU":
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acc = model.eval(ds_eval, dataset_sink_mode=False)
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else:
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acc = model.eval(ds_eval)
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print("============== {} ==============".format(acc))
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if __name__ == '__main__':
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eval_lstm()
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