forked from mindspore-Ecosystem/mindspore
!10507 Modify dataset preprocess of textcnn
From: @zhouyaqiang0 Reviewed-by: @linqingke,@oacjiewen Signed-off-by: @linqingke
This commit is contained in:
Gitee
commit
0903777d3f
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@ -104,6 +104,7 @@ Parameters for both training and evaluation can be set in config.py
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'checkpoint_path': './train_textcnn.ckpt' # the absolute full path to save the checkpoint file
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'word_len': 51 # The length of the word
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'vec_length': 40 # The length of the vector
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'base_lr': 1e-3 # The base learning rate
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```
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For more configuration details, please refer the script `config.py`.
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@ -24,20 +24,29 @@ 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
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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
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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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instance = MovieReview(root_dir=cfg.data_path, maxlen=cfg.word_len, split=0.9)
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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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@ -13,5 +13,14 @@
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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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python eval.py --checkpoint_path="$1" > eval.log 2>&1 &
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dataset_type='MR'
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if [ $# == 2 ]
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then
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if [ $2 != "MR" ] && [ $2 != "SUBJ" ] && [ $2 != "SST2" ]
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then
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echo "error: the selected dataset is not in supported set{MR, SUBJ, SST2}"
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exit 1
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fi
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dataset_type=$2
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fi
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python eval.py --checkpoint_path="$1" --dataset=$dataset_type > eval.log 2>&1 &
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@ -13,5 +13,15 @@
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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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python train.py > train.log 2>&1 &
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dataset_type='MR'
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if [ $# == 1 ]
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then
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if [ $1 != "MR" ] && [ $1 != "SUBJ" ] && [ $1 != "SST2" ]
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then
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echo "error: the selected dataset is not in supported set{MR, SUBJ, SST2}"
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exit 1
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fi
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dataset_type=$1
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fi
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rm ./ckpt_0 -rf
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python train.py --dataset=$dataset_type > train.log 2>&1 &
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@ -17,7 +17,7 @@ network config setting, will be used in main.py
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"""
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from easydict import EasyDict as edict
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cfg = edict({
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cfg_mr = edict({
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'name': 'movie review',
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'pre_trained': False,
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'num_classes': 2,
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@ -30,5 +30,40 @@ cfg = edict({
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'keep_checkpoint_max': 1,
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'checkpoint_path': './ckpt/train_textcnn-4_149.ckpt',
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'word_len': 51,
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'vec_length': 40
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'vec_length': 40,
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'base_lr': 1e-3
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})
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cfg_subj = edict({
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'name': 'subjectivity',
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'pre_trained': False,
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'num_classes': 2,
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'batch_size': 64,
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'epoch_size': 5,
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'weight_decay': 3e-5,
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'data_path': './Subj/',
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'device_target': 'Ascend',
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'device_id': 7,
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'keep_checkpoint_max': 1,
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'checkpoint_path': './ckpt/train_textcnn-4_149.ckpt',
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'word_len': 51,
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'vec_length': 40,
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'base_lr': 8e-4
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})
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cfg_sst2 = edict({
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'name': 'SST2',
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'pre_trained': False,
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'num_classes': 2,
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'batch_size': 64,
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'epoch_size': 4,
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'weight_decay': 3e-5,
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'data_path': './SST-2/',
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'device_target': 'Ascend',
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'device_id': 7,
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'keep_checkpoint_max': 1,
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'checkpoint_path': './ckpt/train_textcnn-4_149.ckpt',
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'word_len': 51,
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'vec_length': 40,
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'base_lr': 5e-3
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})
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@ -21,6 +21,7 @@ import random
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import codecs
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from pathlib import Path
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import numpy as np
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import pandas as pd
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import mindspore.dataset as ds
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@ -36,9 +37,56 @@ class Generator():
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return len(self.input_list)
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class MovieReview:
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class DataProcessor:
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"""
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preprocess MR dataset
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preprocess dataset
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"""
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def get_dict_len(self):
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"""
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get number of different words in the whole dataset
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"""
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if self.doConvert:
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return len(self.Vocab)
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return -1
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def collect_weight(self, glove_path, embed_size):
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""" collect weight """
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vocab_size = self.get_dict_len()
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embedding_index = {}
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f = open(glove_path)
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for line in f:
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values = line.split()
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word = values[0]
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vec = np.array(values[1:], dtype='float32')
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embedding_index[word] = vec
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weight_np = np.zeros((vocab_size, embed_size)).astype(np.float32)
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for word, vec in embedding_index.items():
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try:
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index = self.Vocab[word]
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except KeyError:
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continue
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weight_np[index, :] = vec
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return weight_np
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def create_train_dataset(self, epoch_size, batch_size, collect_weight=False, glove_path='', embed_size=50):
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if collect_weight:
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weight_np = self.collect_weight(glove_path, embed_size)
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np.savetxt('./weight.txt', weight_np)
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dataset = ds.GeneratorDataset(source=Generator(input_list=self.train),
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column_names=["data", "label"], shuffle=False)
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dataset = dataset.batch(batch_size=batch_size, drop_remainder=True)
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return dataset
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def create_test_dataset(self, batch_size):
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dataset = ds.GeneratorDataset(source=Generator(input_list=self.test),
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column_names=["data", "label"], shuffle=False)
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dataset = dataset.batch(batch_size=batch_size, drop_remainder=True)
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return dataset
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class MovieReview(DataProcessor):
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"""
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preprocess MovieReview dataset
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"""
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def __init__(self, root_dir, maxlen, split):
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"""
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@ -151,7 +199,6 @@ class MovieReview:
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# Vocab = {word : index}
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self.Vocab = dict()
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# self.Vocab['None']
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for SentenceLabel in self.Pos+self.Neg:
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vector = [0]*maxlen
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for index, word in enumerate(SentenceLabel[0]):
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@ -185,27 +232,256 @@ class MovieReview:
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self.train = [i for item in pos_temp+neg_temp for i in item]
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random.shuffle(self.train)
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# random.shuffle(self.test)
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def get_dict_len(self):
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class Subjectivity(DataProcessor):
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"""
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preprocess Subjectivity dataset
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"""
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def __init__(self, root_dir, maxlen, split):
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self.path = root_dir
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self.feelMap = {
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'neg': 0,
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'pos': 1
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}
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self.files = []
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self.doConvert = False
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mypath = Path(self.path)
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if not mypath.exists() or not mypath.is_dir():
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print("please check the root_dir!")
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raise ValueError
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# walk through the root_dir
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for root, _, filename in os.walk(self.path):
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for each in filename:
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self.files.append(os.path.join(root, each))
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break
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# begin to read data
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self.word_num = 0
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self.maxlen = 0
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self.minlen = float("inf")
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self.maxlen = float("-inf")
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self.Pos = []
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self.Neg = []
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for filename in self.files:
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self.read_data(filename)
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self.PosNeg = self.Pos + self.Neg
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self.text2vec(maxlen=maxlen)
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self.split_dataset(split=split)
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def read_data(self, filePath):
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"""
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get number of different words in the whole dataset
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read text into memory
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input:
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filePath: the path where the data is stored in
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"""
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if self.doConvert:
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return len(self.Vocab)
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return -1
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#else:
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# print("Haven't finished Text2Vec")
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# return -1
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with open(filePath, 'r', encoding="ISO-8859-1") as f:
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for sentence in f.readlines():
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sentence = sentence.replace('\n', '')\
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.replace('"', '')\
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.replace('\'', '')\
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.replace('.', '')\
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.replace(',', '')\
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.replace('[', '')\
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.replace(']', '')\
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.replace('(', '')\
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.replace(')', '')\
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.replace(':', '')\
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.replace('--', '')\
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.replace('-', '')\
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.replace('\\', '')\
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.replace('0', '')\
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.replace('1', '')\
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.replace('2', '')\
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.replace('3', '')\
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.replace('4', '')\
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.replace('5', '')\
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.replace('6', '')\
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.replace('7', '')\
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.replace('8', '')\
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.replace('9', '')\
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.replace('`', '')\
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.replace('=', '')\
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.replace('$', '')\
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.replace('/', '')\
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.replace('*', '')\
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.replace(';', '')\
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.replace('<b>', '')\
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.replace('%', '')
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sentence = sentence.split(' ')
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sentence = list(filter(lambda x: x, sentence))
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if sentence:
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self.word_num += len(sentence)
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self.maxlen = self.maxlen if self.maxlen >= len(sentence) else len(sentence)
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self.minlen = self.minlen if self.minlen <= len(sentence) else len(sentence)
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if 'quote' in filePath:
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self.Pos.append([sentence, self.feelMap['pos']])
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elif 'plot' in filePath:
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self.Neg.append([sentence, self.feelMap['neg']])
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def create_train_dataset(self, epoch_size, batch_size):
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dataset = ds.GeneratorDataset(source=Generator(input_list=self.train),
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column_names=["data", "label"], shuffle=False)
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dataset = dataset.batch(batch_size=batch_size, drop_remainder=True)
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return dataset
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def text2vec(self, maxlen):
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"""
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convert the sentence into a vector in an int type
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def create_test_dataset(self, batch_size):
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dataset = ds.GeneratorDataset(source=Generator(input_list=self.test),
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column_names=["data", "label"], shuffle=False)
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dataset = dataset.batch(batch_size=batch_size, drop_remainder=True)
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return dataset
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input:
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maxlen: max length of the sentence
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"""
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# Vocab = {word : index}
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self.Vocab = dict()
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for SentenceLabel in self.Pos+self.Neg:
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vector = [0]*maxlen
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for index, word in enumerate(SentenceLabel[0]):
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if index >= maxlen:
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break
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if word not in self.Vocab.keys():
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self.Vocab[word] = len(self.Vocab)
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vector[index] = len(self.Vocab) - 1
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else:
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vector[index] = self.Vocab[word]
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SentenceLabel[0] = vector
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self.doConvert = True
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def split_dataset(self, split):
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"""
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split the dataset into training set and test set
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input:
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split: the ratio of training set to test set
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rank: logic order
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size: device num
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"""
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trunk_pos_size = math.ceil((1-split)*len(self.Pos))
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trunk_neg_size = math.ceil((1-split)*len(self.Neg))
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trunk_num = int(1/(1-split))
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pos_temp = list()
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neg_temp = list()
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for index in range(trunk_num):
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pos_temp.append(self.Pos[index*trunk_pos_size:(index+1)*trunk_pos_size])
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neg_temp.append(self.Neg[index*trunk_neg_size:(index+1)*trunk_neg_size])
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self.test = pos_temp.pop(2)+neg_temp.pop(2)
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self.train = [i for item in pos_temp+neg_temp for i in item]
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random.shuffle(self.train)
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class SST2(DataProcessor):
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"""
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preprocess SST2 dataset
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"""
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def __init__(self, root_dir, maxlen, split):
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self.path = root_dir
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self.files = []
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self.train = []
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self.test = []
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self.doConvert = False
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mypath = Path(self.path)
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if not mypath.exists() or not mypath.is_dir():
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print("please check the root_dir!")
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raise ValueError
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# walk through the root_dir
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for root, _, filename in os.walk(self.path):
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for each in filename:
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self.files.append(os.path.join(root, each))
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break
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# begin to read data
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self.word_num = 0
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self.maxlen = 0
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self.minlen = float("inf")
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self.maxlen = float("-inf")
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for filename in self.files:
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if 'train' in filename or 'dev' in filename:
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f = codecs.open(filename, 'r')
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ff = f.read()
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file_object = codecs.open(filename, 'w', 'utf-8')
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file_object.write(ff)
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self.read_data(filename)
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self.text2vec(maxlen=maxlen)
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self.split_dataset(split=split)
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def read_data(self, filePath):
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"""
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read text into memory
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input:
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filePath: the path where the data is stored in
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"""
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df = pd.read_csv(filePath, delimiter='\t')
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for sentence, label in zip(df['sentence'], df['label']):
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sentence = sentence.replace('\n', '')\
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.replace('"', '')\
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.replace('\'', '')\
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.replace('.', '')\
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.replace(',', '')\
|
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.replace('[', '')\
|
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.replace(']', '')\
|
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.replace('(', '')\
|
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.replace(')', '')\
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.replace(':', '')\
|
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.replace('--', '')\
|
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.replace('-', '')\
|
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.replace('\\', '')\
|
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.replace('0', '')\
|
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.replace('1', '')\
|
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.replace('2', '')\
|
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.replace('3', '')\
|
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.replace('4', '')\
|
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.replace('5', '')\
|
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.replace('6', '')\
|
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.replace('7', '')\
|
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.replace('8', '')\
|
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.replace('9', '')\
|
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.replace('`', '')\
|
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.replace('=', '')\
|
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.replace('$', '')\
|
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.replace('/', '')\
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.replace('*', '')\
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.replace(';', '')\
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.replace('<b>', '')\
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.replace('%', '')
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sentence = sentence.split(' ')
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sentence = list(filter(lambda x: x, sentence))
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if sentence:
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self.word_num += len(sentence)
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self.maxlen = self.maxlen if self.maxlen >= len(sentence) else len(sentence)
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self.minlen = self.minlen if self.minlen <= len(sentence) else len(sentence)
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if 'train' in filePath:
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self.train.append([sentence, label])
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elif 'dev' in filePath:
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self.test.append([sentence, label])
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def text2vec(self, maxlen):
|
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"""
|
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convert the sentence into a vector in an int type
|
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|
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input:
|
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maxlen: max length of the sentence
|
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"""
|
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# Vocab = {word : index}
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self.Vocab = dict()
|
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|
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for SentenceLabel in self.train+self.test:
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vector = [0]*maxlen
|
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for index, word in enumerate(SentenceLabel[0]):
|
||||
if index >= maxlen:
|
||||
break
|
||||
if word not in self.Vocab.keys():
|
||||
self.Vocab[word] = len(self.Vocab)
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vector[index] = len(self.Vocab) - 1
|
||||
else:
|
||||
vector[index] = self.Vocab[word]
|
||||
SentenceLabel[0] = vector
|
||||
self.doConvert = True
|
||||
|
||||
def split_dataset(self, split):
|
||||
"""
|
||||
split the dataset into training set and test set
|
||||
input:
|
||||
split: the ratio of training set to test set
|
||||
rank: logic order
|
||||
size: device num
|
||||
"""
|
||||
random.shuffle(self.train)
|
||||
|
|
|
|||
|
|
@ -97,14 +97,14 @@ class TextCNN(nn.Cell):
|
|||
"""
|
||||
TextCNN architecture
|
||||
"""
|
||||
def __init__(self, vocab_len, word_len, num_classes, vec_length):
|
||||
def __init__(self, vocab_len, word_len, num_classes, vec_length, embedding_table='uniform'):
|
||||
super(TextCNN, self).__init__()
|
||||
self.vec_length = vec_length
|
||||
self.word_len = word_len
|
||||
self.num_classes = num_classes
|
||||
|
||||
self.unsqueeze = P.ExpandDims()
|
||||
self.embedding = nn.Embedding(vocab_len, self.vec_length, embedding_table='uniform')
|
||||
self.embedding = nn.Embedding(vocab_len, self.vec_length, embedding_table=embedding_table)
|
||||
|
||||
self.slice = P.Slice()
|
||||
self.layer1 = self.make_layer(kernel_height=3)
|
||||
|
|
|
|||
|
|
@ -26,15 +26,16 @@ from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMoni
|
|||
from mindspore.train.model import Model
|
||||
from mindspore.train.serialization import load_checkpoint, load_param_into_net
|
||||
|
||||
from src.config import cfg
|
||||
from src.config import cfg_mr, cfg_subj, cfg_sst2
|
||||
from src.textcnn import TextCNN
|
||||
from src.textcnn import SoftmaxCrossEntropyExpand
|
||||
from src.dataset import MovieReview
|
||||
from src.dataset import MovieReview, SST2, Subjectivity
|
||||
|
||||
parser = argparse.ArgumentParser(description='TextCNN')
|
||||
parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU', 'CPU'],
|
||||
help='device where the code will be implemented (default: Ascend)')
|
||||
parser.add_argument('--device_id', type=int, default=5, help='device id of GPU or Ascend.')
|
||||
parser.add_argument('--dataset', type=str, default="MR", choices=['MR', 'SUBJ', 'SST2'])
|
||||
args_opt = parser.parse_args()
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
|
@ -42,16 +43,25 @@ if __name__ == '__main__':
|
|||
# set context
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
|
||||
context.set_context(device_id=args_opt.device_id)
|
||||
if args_opt.dataset == 'MR':
|
||||
cfg = cfg_mr
|
||||
instance = MovieReview(root_dir=cfg.data_path, maxlen=cfg.word_len, split=0.9)
|
||||
elif args_opt.dataset == 'SUBJ':
|
||||
cfg = cfg_subj
|
||||
instance = Subjectivity(root_dir=cfg.data_path, maxlen=cfg.word_len, split=0.9)
|
||||
elif args_opt.dataset == 'SST2':
|
||||
cfg = cfg_sst2
|
||||
instance = SST2(root_dir=cfg.data_path, maxlen=cfg.word_len, split=0.9)
|
||||
|
||||
instance = MovieReview(root_dir=cfg.data_path, maxlen=cfg.word_len, split=0.9)
|
||||
dataset = instance.create_train_dataset(batch_size=cfg.batch_size, epoch_size=cfg.epoch_size)
|
||||
batch_num = dataset.get_dataset_size()
|
||||
|
||||
base_lr = cfg.base_lr
|
||||
learning_rate = []
|
||||
warm_up = [1e-3 / math.floor(cfg.epoch_size / 5) * (i + 1) for _ in range(batch_num) for i in
|
||||
warm_up = [base_lr / math.floor(cfg.epoch_size / 5) * (i + 1) for _ in range(batch_num) for i in
|
||||
range(math.floor(cfg.epoch_size / 5))]
|
||||
shrink = [1e-3 / (16 * (i + 1)) for _ in range(batch_num) for i in range(math.floor(cfg.epoch_size * 3 / 5))]
|
||||
normal_run = [1e-3 for _ in range(batch_num) for i in
|
||||
shrink = [base_lr / (16 * (i + 1)) for _ in range(batch_num) for i in range(math.floor(cfg.epoch_size * 3 / 5))]
|
||||
normal_run = [base_lr for _ in range(batch_num) for i in
|
||||
range(cfg.epoch_size - math.floor(cfg.epoch_size / 5) - math.floor(cfg.epoch_size * 2 / 5))]
|
||||
learning_rate = learning_rate + warm_up + normal_run + shrink
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue