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Create Castor-level SST dataset (#123)

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* Move SST to root directory datasets

* Fix bugs
This commit is contained in:
Michael Tu
2018-06-09 17:32:32 -04:00
committed by GitHub
parent cb9fad9f97
commit 5cc027f03e
7 changed files with 95 additions and 116 deletions
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datasets/sst.py
+57
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@@ -0,0 +1,57 @@
import re
import torch
from torchtext.data import Field, TabularDataset
from torchtext.data.iterator import BucketIterator
from torchtext.vocab import Vectors
def clean_str_sst(string):
"""
Tokenization/string cleaning for the SST dataset
"""
string = re.sub(r"[^A-Za-z0-9(),!?\'\`]", " ", string)
string = re.sub(r"\s{2,}", " ", string)
return string.lower().strip().split()
class SST1(TabularDataset):
NAME = 'sst-1'
NUM_CLASSES = 5
TEXT_FIELD = Field(batch_first=True, tokenize=clean_str_sst)
LABEL_FIELD = Field(sequential=False, use_vocab=False, batch_first=True)
@staticmethod
def sort_key(ex):
return len(ex.text)
@classmethod
def splits(cls, path, train='stsa.fine.phrases.train', validation='stsa.fine.dev', test='stsa.fine.test', **kwargs):
return super(SST1, cls).splits(
path, train=train, validation=validation, test=test,
format='tsv', fields=[('label', cls.LABEL_FIELD), ('text', cls.TEXT_FIELD)]
)
@classmethod
def iters(cls, path, vectors_name, vectors_cache, batch_size=64, shuffle=True, device=0, vectors=None,
unk_init=torch.Tensor.zero_):
"""
:param path: directory containing train, test, dev files
:param vectors_name: name of word vectors file
:param vectors_cache: path to directory containing word vectors file
:param batch_size: batch size
:param device: GPU device
:param vectors: custom vectors - either predefined torchtext vectors or your own custom Vector classes
:param unk_init: function used to generate vector for OOV words
:return:
"""
if vectors is None:
vectors = Vectors(name=vectors_name, cache=vectors_cache, unk_init=unk_init)
train, val, test = cls.splits(path)
cls.TEXT_FIELD.build_vocab(train, val, test, min_freq=2, vectors=vectors)
return BucketIterator.splits((train, val, test), batch_size=batch_size, repeat=False, shuffle=shuffle,
sort_within_batch=True, device=device)
kim_cnn/__init__.py
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kim_cnn/args.py
+5 -2
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@@ -22,10 +22,13 @@ def get_args():
parser.add_argument('--embed_dim', type=int, default=300)
parser.add_argument('--dropout', type=float, default=0.5)
parser.add_argument('--epoch_decay', type=int, default=15)
parser.add_argument('--vector_cache', type=str, default="data/word2vec.sst-1.pt")
parser.add_argument('--data_dir', help='word vectors directory',
default=os.path.join(os.pardir, os.pardir, 'Castor-data', 'datasets', 'SST'))
parser.add_argument('--word_vectors_dir', help='word vectors directory',
default=os.path.join(os.pardir, os.pardir, 'Castor-data', 'embeddings', 'word2vec'))
parser.add_argument('--word_vectors_file', help='word vectors filename', default='GoogleNews-vectors-negative300.txt')
parser.add_argument('--trained_model', type=str, default="")
parser.add_argument('--weight_decay',type=float, default=0)
args = parser.parse_args()
return args
kim_cnn/main.py
+10 -25
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@@ -4,9 +4,8 @@ import numpy as np
import torch
from torchtext import data
from args import get_args
from SST1 import SST1Dataset
from utils import clean_str_sst
from datasets.sst import SST1
args = get_args()
torch.manual_seed(args.seed)
@@ -26,26 +25,12 @@ if not args.trained_model:
sys.exit(1)
if args.dataset == 'SST-1':
TEXT = data.Field(batch_first=True, lower=True, tokenize=clean_str_sst)
LABEL = data.Field(sequential=False)
train, dev, test = SST1Dataset.splits(TEXT, LABEL)
TEXT.build_vocab(train, min_freq=2)
LABEL.build_vocab(train)
train_iter = data.Iterator(train, batch_size=args.batch_size, device=args.gpu, train=True, repeat=False,
sort=False, shuffle=True)
dev_iter = data.Iterator(dev, batch_size=args.batch_size, device=args.gpu, train=False, repeat=False,
sort=False, shuffle=False)
test_iter = data.Iterator(test, batch_size=args.batch_size, device=args.gpu, train=False, repeat=False,
sort=False, shuffle=False)
train_iter, dev_iter, test_iter = SST1.iters(args.data_dir, args.word_vectors_file, args.word_vectors_dir, batch_size=args.batch_size, device=args.gpu)
config = args
config.target_class = len(LABEL.vocab)
config.words_num = len(TEXT.vocab)
config.embed_num = len(TEXT.vocab)
print("Label dict:", LABEL.vocab.itos)
config.target_class = train_iter.dataset.NUM_CLASSES
config.words_num = len(train_iter.dataset.TEXT_FIELD.vocab)
config.embed_num = len(train_iter.dataset.TEXT_FIELD.vocab)
if args.cuda:
model = torch.load(args.trained_model, map_location=lambda storage, location: storage.cuda(args.gpu))
@@ -53,7 +38,7 @@ else:
model = torch.load(args.trained_model, map_location=lambda storage,location: storage)
def predict(dataset_iter, dataset, dataset_name):
def predict(dataset_iter, dataset_name):
print("Dataset: {}".format(dataset_name))
model.eval()
dataset_iter.init_epoch()
@@ -63,12 +48,12 @@ def predict(dataset_iter, dataset, dataset_name):
scores = model(data_batch)
n_correct += (torch.max(scores, 1)[1].view(data_batch.label.size()).data == data_batch.label.data).sum()
print("no. correct {} out of {}".format(n_correct, len(dataset)))
accuracy = 100. * n_correct / len(dataset)
print("no. correct {} out of {}".format(n_correct, len(dataset_iter.dataset.examples)))
accuracy = 100. * n_correct / len(dataset_iter.dataset.examples)
print("{} accuracy: {:8.6f}%".format(dataset_name, accuracy))
# Run the model on the dev set
predict(dataset_iter=dev_iter, dataset=dev, dataset_name="valid")
predict(dataset_iter=dev_iter, dataset_name="valid")
# Run the model on the test set
predict(dataset_iter=test_iter, dataset=test, dataset_name="test")
predict(dataset_iter=test_iter, dataset_name="test")
kim_cnn/model.py
+1 -1
View File
@@ -3,6 +3,7 @@ import torch.nn as nn
import torch.nn.functional as F
class KimCNN(nn.Module):
def __init__(self, config):
super(KimCNN, self).__init__()
@@ -30,7 +31,6 @@ class KimCNN(nn.Module):
self.dropout = nn.Dropout(config.dropout)
self.fc1 = nn.Linear(Ks * output_channel, target_class)
def forward(self, x):
x = x.text
if self.mode == 'rand':
kim_cnn/train.py
+22 -79
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@@ -4,17 +4,15 @@ import random
import torch
import torch.nn as nn
import numpy as np
from torchtext import data
from datasets.sst import SST1
from args import get_args
from model import KimCNN
from SST1 import SST1Dataset
from utils import clean_str_sst
# Set default configuration in : args.py
args = get_args()
# Set random seed for reproducibility
torch.manual_seed(args.seed)
torch.backends.cudnn.deterministic = True
if not args.cuda:
@@ -28,54 +26,21 @@ if torch.cuda.is_available() and not args.cuda:
np.random.seed(args.seed)
random.seed(args.seed)
# Set up the data for training
# SST-1
# Set up the data for training SST-1
if args.dataset == 'SST-1':
TEXT = data.Field(batch_first=True, tokenize=clean_str_sst)
LABEL = data.Field(sequential=False)
train, dev, test = SST1Dataset.splits(TEXT, LABEL)
TEXT.build_vocab(train, min_freq=2)
LABEL.build_vocab(train)
if os.path.isfile(args.vector_cache):
stoi, vectors, dim = torch.load(args.vector_cache)
TEXT.vocab.vectors = torch.Tensor(len(TEXT.vocab), dim)
for i, token in enumerate(TEXT.vocab.itos):
wv_index = stoi.get(token, None)
if wv_index is not None:
TEXT.vocab.vectors[i] = vectors[wv_index]
else:
TEXT.vocab.vectors[i] = torch.Tensor.zero_(TEXT.vocab.vectors[i])
else:
print("Error: Need word embedding pt file")
exit(1)
#print('len(TEXT.vocab)', len(TEXT.vocab))
#print('TEXT.vocab.vectors.size()', TEXT.vocab.vectors.size())
train_iter = data.Iterator(train, batch_size=args.batch_size, device=args.gpu, train=True, repeat=False,
sort=False, shuffle=True)
dev_iter = data.Iterator(dev, batch_size=args.batch_size, device=args.gpu, train=False, repeat=False,
sort=False, shuffle=False)
test_iter = data.Iterator(test, batch_size=args.batch_size, device=args.gpu, train=False, repeat=False,
sort=False, shuffle=False)
train_iter, dev_iter, test_iter = SST1.iters(args.data_dir, args.word_vectors_file, args.word_vectors_dir, batch_size=args.batch_size, device=args.gpu)
config = args
config.target_class = len(LABEL.vocab)
config.words_num = len(TEXT.vocab)
config.embed_num = len(TEXT.vocab)
config.target_class = train_iter.dataset.NUM_CLASSES
config.words_num = len(train_iter.dataset.TEXT_FIELD.vocab)
config.embed_num = len(train_iter.dataset.TEXT_FIELD.vocab)
#print(config)
print("Dataset {} Mode {}".format(args.dataset, args.mode))
print("VOCAB num",len(TEXT.vocab))
print("LABEL.target_class:", len(LABEL.vocab))
print("LABELS:",LABEL.vocab.itos)
print("Train instance", len(train))
print("Dev instance", len(dev))
print("Test instance", len(test))
print("VOCAB num",len(train_iter.dataset.TEXT_FIELD.vocab))
print("LABEL.target_class:", train_iter.dataset.NUM_CLASSES)
print("Train instance", len(train_iter.dataset))
print("Dev instance", len(dev_iter.dataset))
print("Test instance", len(test_iter.dataset))
if args.resume_snapshot:
if args.cuda:
@@ -84,8 +49,8 @@ if args.resume_snapshot:
model = torch.load(args.resume_snapshot, map_location=lambda storage, location: storage)
else:
model = KimCNN(config)
model.static_embed.weight.data.copy_(TEXT.vocab.vectors)
model.non_static_embed.weight.data.copy_(TEXT.vocab.vectors)
model.static_embed.weight.data.copy_(train_iter.dataset.TEXT_FIELD.vocab.vectors)
model.non_static_embed.weight.data.copy_(train_iter.dataset.TEXT_FIELD.vocab.vectors)
if args.cuda:
model.cuda()
print("Shift model to GPU")
@@ -119,11 +84,9 @@ while True:
n_correct, n_total = 0, 0
for batch_idx, batch in enumerate(train_iter):
# Batch size : (Sentence Length, Batch_size)
iterations += 1
model.train(); optimizer.zero_grad()
#print("Text Size:", batch.text.size())
#print("Label Size:", batch.label.size())
model.train()
optimizer.zero_grad()
scores = model(batch)
n_correct += (torch.max(scores, 1)[1].view(batch.label.size()).data == batch.label.data).sum()
n_total += batch.batch_size
@@ -134,22 +97,22 @@ while True:
optimizer.step()
# Evaluate performance on validation set
if iterations % args.dev_every == 1:
# switch model into evalutaion mode
model.eval(); dev_iter.init_epoch()
model.eval()
dev_iter.init_epoch()
n_dev_correct = 0
dev_losses = []
for dev_batch_idx, dev_batch in enumerate(dev_iter):
scores = model(dev_batch)
n_dev_correct += (torch.max(scores, 1)[1].view(dev_batch.label.size()).data == dev_batch.label.data).sum()
dev_loss = criterion(scores, dev_batch.label)
dev_losses.append(dev_loss.data[0])
dev_acc = 100. * n_dev_correct / len(dev)
dev_losses.append(dev_loss.item())
dev_acc = 100. * n_dev_correct / len(dev_iter.dataset)
print(dev_log_template.format(time.time() - start,
epoch, iterations, 1 + batch_idx, len(train_iter),
100. * (1 + batch_idx) / len(train_iter), loss.data[0],
100. * (1 + batch_idx) / len(train_iter), loss.item(),
sum(dev_losses) / len(dev_losses), train_acc, dev_acc))
# Update validation results
@@ -168,25 +131,5 @@ while True:
# print progress message
print(log_template.format(time.time() - start,
epoch, iterations, 1 + batch_idx, len(train_iter),
100. * (1 + batch_idx) / len(train_iter), loss.data[0], ' ' * 8,
100. * (1 + batch_idx) / len(train_iter), loss.item(), ' ' * 8,
n_correct / n_total * 100, ' ' * 12))
kim_cnn/utils.py
-9
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@@ -19,12 +19,3 @@ def clean_str(string):
string = re.sub(r"\?", " ? ", string)
string = re.sub(r"\s{2,}", " ", string)
return string.lower().strip().split()
def clean_str_sst(string):
"""
Tokenization/string cleaning for the SST dataset
"""
string = re.sub(r"[^A-Za-z0-9(),!?\'\`]", " ", string)
string = re.sub(r"\s{2,}", " ", string)
return string.lower().strip().split()