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70 lines
3.0 KiB
70 lines
3.0 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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"""
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##############test textcnn example on movie review#################
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python eval.py
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"""
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import argparse
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import mindspore.nn as nn
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from mindspore.nn.metrics import Accuracy
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from mindspore import context
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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.config import cfg_mr, cfg_subj, cfg_sst2
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from src.textcnn import TextCNN
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from src.dataset import MovieReview, SST2, Subjectivity
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parser = argparse.ArgumentParser(description='TextCNN')
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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', type=str, default="MR", choices=['MR', 'SUBJ', 'SST2'])
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args_opt = parser.parse_args()
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if __name__ == '__main__':
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if args_opt.dataset == 'MR':
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cfg = cfg_mr
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instance = MovieReview(root_dir=cfg.data_path, maxlen=cfg.word_len, split=0.9)
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elif args_opt.dataset == 'SUBJ':
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cfg = cfg_subj
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instance = Subjectivity(root_dir=cfg.data_path, maxlen=cfg.word_len, split=0.9)
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elif args_opt.dataset == 'SST2':
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cfg = cfg_sst2
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instance = SST2(root_dir=cfg.data_path, maxlen=cfg.word_len, split=0.9)
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device_target = cfg.device_target
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context.set_context(mode=context.GRAPH_MODE, device_target=cfg.device_target)
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if device_target == "Ascend":
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context.set_context(device_id=cfg.device_id)
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dataset = instance.create_test_dataset(batch_size=cfg.batch_size)
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loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True)
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net = TextCNN(vocab_len=instance.get_dict_len(), word_len=cfg.word_len,
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num_classes=cfg.num_classes, vec_length=cfg.vec_length)
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opt = nn.Adam(filter(lambda x: x.requires_grad, net.get_parameters()), learning_rate=0.001,
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weight_decay=cfg.weight_decay)
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if args_opt.checkpoint_path is not None:
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param_dict = load_checkpoint(args_opt.checkpoint_path)
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print("load checkpoint from [{}].".format(args_opt.checkpoint_path))
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else:
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param_dict = load_checkpoint(cfg.checkpoint_path)
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print("load checkpoint from [{}].".format(cfg.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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model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc': Accuracy()})
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acc = model.eval(dataset)
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print("accuracy: ", acc)
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