@@ -0,0 +1,80 @@ | |||||
from fastNLP.io.embed_loader import EmbeddingOption, EmbedLoader | |||||
from fastNLP.core.vocabulary import VocabularyOption | |||||
from fastNLP.io.base_loader import DataSetLoader, DataInfo | |||||
from typing import Union, Dict, List, Iterator | |||||
from fastNLP import DataSet | |||||
from fastNLP import Instance | |||||
from fastNLP import Vocabulary | |||||
from fastNLP import Const | |||||
# from reproduction.utils import check_dataloader_paths | |||||
from functools import partial | |||||
class IMDBLoader(DataSetLoader): | |||||
""" | |||||
读取IMDB数据集,DataSet包含以下fields: | |||||
words: list(str), 需要分类的文本 | |||||
target: str, 文本的标签 | |||||
""" | |||||
def __init__(self): | |||||
super(IMDBLoader, self).__init__() | |||||
def _load(self, path): | |||||
dataset = DataSet() | |||||
with open(path, 'r', encoding="utf-8") as f: | |||||
for line in f: | |||||
line = line.strip() | |||||
if not line: | |||||
continue | |||||
parts = line.split('\t') | |||||
target = parts[0] | |||||
words = parts[1].lower().split() | |||||
dataset.append(Instance(words=words, target=target)) | |||||
if len(dataset)==0: | |||||
raise RuntimeError(f"{path} has no valid data.") | |||||
return dataset | |||||
def process(self, | |||||
paths: Union[str, Dict[str, str]], | |||||
src_vocab_opt: VocabularyOption = None, | |||||
tgt_vocab_opt: VocabularyOption = None, | |||||
src_embed_opt: EmbeddingOption = None): | |||||
# paths = check_dataloader_paths(paths) | |||||
datasets = {} | |||||
info = DataInfo() | |||||
for name, path in paths.items(): | |||||
dataset = self.load(path) | |||||
datasets[name] = dataset | |||||
datasets["train"], datasets["dev"] = datasets["train"].split(0.1, shuffle=False) | |||||
src_vocab = Vocabulary() if src_vocab_opt is None else Vocabulary(**src_vocab_opt) | |||||
src_vocab.from_dataset(datasets['train'], field_name='words') | |||||
src_vocab.index_dataset(*datasets.values(), field_name='words') | |||||
tgt_vocab = Vocabulary(unknown=None, padding=None) \ | |||||
if tgt_vocab_opt is None else Vocabulary(**tgt_vocab_opt) | |||||
tgt_vocab.from_dataset(datasets['train'], field_name='target') | |||||
tgt_vocab.index_dataset(*datasets.values(), field_name='target') | |||||
info.vocabs = { | |||||
"words": src_vocab, | |||||
"target": tgt_vocab | |||||
} | |||||
info.datasets = datasets | |||||
if src_embed_opt is not None: | |||||
embed = EmbedLoader.load_with_vocab(**src_embed_opt, vocab=src_vocab) | |||||
info.embeddings['words'] = embed | |||||
for name, dataset in info.datasets.items(): | |||||
dataset.set_input("words") | |||||
dataset.set_target("target") | |||||
return info |
@@ -32,7 +32,7 @@ class MTL16Loader(DataSetLoader): | |||||
continue | continue | ||||
parts = line.split('\t') | parts = line.split('\t') | ||||
target = parts[0] | target = parts[0] | ||||
words = parts[1].split() | |||||
words = parts[1].lower().split() | |||||
dataset.append(Instance(words=words, target=target)) | dataset.append(Instance(words=words, target=target)) | ||||
if len(dataset)==0: | if len(dataset)==0: | ||||
raise RuntimeError(f"{path} has no valid data.") | raise RuntimeError(f"{path} has no valid data.") | ||||
@@ -72,4 +72,8 @@ class MTL16Loader(DataSetLoader): | |||||
embed = EmbedLoader.load_with_vocab(**src_embed_opt, vocab=src_vocab) | embed = EmbedLoader.load_with_vocab(**src_embed_opt, vocab=src_vocab) | ||||
info.embeddings['words'] = embed | info.embeddings['words'] = embed | ||||
for name, dataset in info.datasets.items(): | |||||
dataset.set_input("words") | |||||
dataset.set_target("target") | |||||
return info | return info |
@@ -0,0 +1,99 @@ | |||||
from typing import Iterable | |||||
from nltk import Tree | |||||
from fastNLP.io.base_loader import DataInfo, DataSetLoader | |||||
from fastNLP.core.vocabulary import VocabularyOption, Vocabulary | |||||
from fastNLP import DataSet | |||||
from fastNLP import Instance | |||||
from fastNLP.io.embed_loader import EmbeddingOption, EmbedLoader | |||||
class SSTLoader(DataSetLoader): | |||||
URL = 'https://nlp.stanford.edu/sentiment/trainDevTestTrees_PTB.zip' | |||||
DATA_DIR = 'sst/' | |||||
""" | |||||
别名::class:`fastNLP.io.SSTLoader` :class:`fastNLP.io.dataset_loader.SSTLoader` | |||||
读取SST数据集, DataSet包含fields:: | |||||
words: list(str) 需要分类的文本 | |||||
target: str 文本的标签 | |||||
数据来源: https://nlp.stanford.edu/sentiment/trainDevTestTrees_PTB.zip | |||||
:param subtree: 是否将数据展开为子树,扩充数据量. Default: ``False`` | |||||
:param fine_grained: 是否使用SST-5标准,若 ``False`` , 使用SST-2。Default: ``False`` | |||||
""" | |||||
def __init__(self, subtree=False, fine_grained=False): | |||||
self.subtree = subtree | |||||
tag_v = {'0': 'very negative', '1': 'negative', '2': 'neutral', | |||||
'3': 'positive', '4': 'very positive'} | |||||
if not fine_grained: | |||||
tag_v['0'] = tag_v['1'] | |||||
tag_v['4'] = tag_v['3'] | |||||
self.tag_v = tag_v | |||||
def _load(self, path): | |||||
""" | |||||
:param str path: 存储数据的路径 | |||||
:return: 一个 :class:`~fastNLP.DataSet` 类型的对象 | |||||
""" | |||||
datalist = [] | |||||
with open(path, 'r', encoding='utf-8') as f: | |||||
datas = [] | |||||
for l in f: | |||||
datas.extend([(s, self.tag_v[t]) | |||||
for s, t in self._get_one(l, self.subtree)]) | |||||
ds = DataSet() | |||||
for words, tag in datas: | |||||
ds.append(Instance(words=words, target=tag)) | |||||
return ds | |||||
@staticmethod | |||||
def _get_one(data, subtree): | |||||
tree = Tree.fromstring(data) | |||||
if subtree: | |||||
return [(t.leaves(), t.label()) for t in tree.subtrees()] | |||||
return [(tree.leaves(), tree.label())] | |||||
def process(self, | |||||
paths, | |||||
train_ds: Iterable[str] = None, | |||||
src_vocab_op: VocabularyOption = None, | |||||
tgt_vocab_op: VocabularyOption = None, | |||||
src_embed_op: EmbeddingOption = None): | |||||
input_name, target_name = 'words', 'target' | |||||
src_vocab = Vocabulary() if src_vocab_op is None else Vocabulary(**src_vocab_op) | |||||
tgt_vocab = Vocabulary(unknown=None, padding=None) \ | |||||
if tgt_vocab_op is None else Vocabulary(**tgt_vocab_op) | |||||
info = DataInfo(datasets=self.load(paths)) | |||||
_train_ds = [info.datasets[name] | |||||
for name in train_ds] if train_ds else info.datasets.values() | |||||
src_vocab.from_dataset(*_train_ds, field_name=input_name) | |||||
tgt_vocab.from_dataset(*_train_ds, field_name=target_name) | |||||
src_vocab.index_dataset( | |||||
*info.datasets.values(), | |||||
field_name=input_name, new_field_name=input_name) | |||||
tgt_vocab.index_dataset( | |||||
*info.datasets.values(), | |||||
field_name=target_name, new_field_name=target_name) | |||||
info.vocabs = { | |||||
input_name: src_vocab, | |||||
target_name: tgt_vocab | |||||
} | |||||
if src_embed_op is not None: | |||||
src_embed_op.vocab = src_vocab | |||||
init_emb = EmbedLoader.load_with_vocab(**src_embed_op) | |||||
info.embeddings[input_name] = init_emb | |||||
for name, dataset in info.datasets.items(): | |||||
dataset.set_input(input_name) | |||||
dataset.set_target(target_name) | |||||
return info | |||||
@@ -1,68 +1,77 @@ | |||||
import ast | |||||
from fastNLP import DataSet, Instance, Vocabulary | |||||
from fastNLP.io.embed_loader import EmbeddingOption, EmbedLoader | |||||
from fastNLP.core.vocabulary import VocabularyOption | from fastNLP.core.vocabulary import VocabularyOption | ||||
from fastNLP.io import JsonLoader | |||||
from fastNLP.io.base_loader import DataInfo | |||||
from fastNLP.io.embed_loader import EmbeddingOption | |||||
from fastNLP.io.file_reader import _read_json | |||||
from typing import Union, Dict | |||||
from reproduction.Star_transformer.datasets import EmbedLoader | |||||
from reproduction.utils import check_dataloader_paths | |||||
from fastNLP.io.base_loader import DataSetLoader, DataInfo | |||||
from typing import Union, Dict, List, Iterator | |||||
from fastNLP import DataSet | |||||
from fastNLP import Instance | |||||
from fastNLP import Vocabulary | |||||
from fastNLP import Const | |||||
# from reproduction.utils import check_dataloader_paths | |||||
from functools import partial | |||||
import pandas as pd | |||||
class yelpLoader(JsonLoader): | |||||
class yelpLoader(DataSetLoader): | |||||
""" | """ | ||||
读取Yelp数据集, DataSet包含fields: | |||||
review_id: str, 22 character unique review id | |||||
user_id: str, 22 character unique user id | |||||
business_id: str, 22 character business id | |||||
useful: int, number of useful votes received | |||||
funny: int, number of funny votes received | |||||
cool: int, number of cool votes received | |||||
date: str, date formatted YYYY-MM-DD | |||||
读取IMDB数据集,DataSet包含以下fields: | |||||
words: list(str), 需要分类的文本 | words: list(str), 需要分类的文本 | ||||
target: str, 文本的标签 | target: str, 文本的标签 | ||||
数据来源: https://www.yelp.com/dataset/download | |||||
:param fine_grained: 是否使用SST-5标准,若 ``False`` , 使用SST-2。Default: ``False`` | |||||
""" | """ | ||||
def __init__(self, fine_grained=False): | |||||
def __init__(self): | |||||
super(yelpLoader, self).__init__() | super(yelpLoader, self).__init__() | ||||
tag_v = {'1.0': 'very negative', '2.0': 'negative', '3.0': 'neutral', | |||||
'4.0': 'positive', '5.0': 'very positive'} | |||||
if not fine_grained: | |||||
tag_v['1.0'] = tag_v['2.0'] | |||||
tag_v['5.0'] = tag_v['4.0'] | |||||
self.fine_grained = fine_grained | |||||
self.tag_v = tag_v | |||||
def _load(self, path): | def _load(self, path): | ||||
ds = DataSet() | |||||
for idx, d in _read_json(path, fields=self.fields_list, dropna=self.dropna): | |||||
d = ast.literal_eval(d) | |||||
d["words"] = d.pop("text").split() | |||||
d["target"] = self.tag_v[str(d.pop("stars"))] | |||||
ds.append(Instance(**d)) | |||||
return ds | |||||
dataset = DataSet() | |||||
data = pd.read_csv(path, header=None, sep=",").values | |||||
for line in data: | |||||
target = str(line[0]) | |||||
words = str(line[1]).lower().split() | |||||
dataset.append(Instance(words=words, target=target)) | |||||
if len(dataset)==0: | |||||
raise RuntimeError(f"{path} has no valid data.") | |||||
def process(self, paths: Union[str, Dict[str, str]], vocab_opt: VocabularyOption = None, | |||||
embed_opt: EmbeddingOption = None): | |||||
paths = check_dataloader_paths(paths) | |||||
return dataset | |||||
def process(self, | |||||
paths: Union[str, Dict[str, str]], | |||||
src_vocab_opt: VocabularyOption = None, | |||||
tgt_vocab_opt: VocabularyOption = None, | |||||
src_embed_opt: EmbeddingOption = None): | |||||
# paths = check_dataloader_paths(paths) | |||||
datasets = {} | datasets = {} | ||||
info = DataInfo() | info = DataInfo() | ||||
vocab = Vocabulary(min_freq=2) if vocab_opt is None else Vocabulary(**vocab_opt) | |||||
for name, path in paths.items(): | for name, path in paths.items(): | ||||
dataset = self.load(path) | dataset = self.load(path) | ||||
datasets[name] = dataset | datasets[name] = dataset | ||||
vocab.from_dataset(dataset, field_name="words") | |||||
info.vocabs = vocab | |||||
datasets["train"], datasets["dev"] = datasets["train"].split(0.1, shuffle=False) | |||||
src_vocab = Vocabulary() if src_vocab_opt is None else Vocabulary(**src_vocab_opt) | |||||
src_vocab.from_dataset(datasets['train'], field_name='words') | |||||
src_vocab.index_dataset(*datasets.values(), field_name='words') | |||||
tgt_vocab = Vocabulary(unknown=None, padding=None) \ | |||||
if tgt_vocab_opt is None else Vocabulary(**tgt_vocab_opt) | |||||
tgt_vocab.from_dataset(datasets['train'], field_name='target') | |||||
tgt_vocab.index_dataset(*datasets.values(), field_name='target') | |||||
info.vocabs = { | |||||
"words": src_vocab, | |||||
"target": tgt_vocab | |||||
} | |||||
info.datasets = datasets | info.datasets = datasets | ||||
if embed_opt is not None: | |||||
embed = EmbedLoader.load_with_vocab(**embed_opt, vocab=vocab) | |||||
if src_embed_opt is not None: | |||||
embed = EmbedLoader.load_with_vocab(**src_embed_opt, vocab=src_vocab) | |||||
info.embeddings['words'] = embed | info.embeddings['words'] = embed | ||||
return info | |||||
for name, dataset in info.datasets.items(): | |||||
dataset.set_input("words") | |||||
dataset.set_target("target") | |||||
return info |
@@ -0,0 +1,31 @@ | |||||
import torch | |||||
import torch.nn as nn | |||||
from fastNLP.core.const import Const as C | |||||
from .awdlstm_module import LSTM | |||||
from fastNLP.modules import encoder | |||||
from fastNLP.modules.decoder.mlp import MLP | |||||
class AWDLSTMSentiment(nn.Module): | |||||
def __init__(self, init_embed, | |||||
num_classes, | |||||
hidden_dim=256, | |||||
num_layers=1, | |||||
nfc=128, | |||||
wdrop=0.5): | |||||
super(AWDLSTMSentiment,self).__init__() | |||||
self.embed = encoder.Embedding(init_embed) | |||||
self.lstm = LSTM(input_size=self.embed.embedding_dim, hidden_size=hidden_dim, num_layers=num_layers, bidirectional=True, wdrop=wdrop) | |||||
self.mlp = MLP(size_layer=[hidden_dim* 2, nfc, num_classes]) | |||||
def forward(self, words): | |||||
x_emb = self.embed(words) | |||||
output, _ = self.lstm(x_emb) | |||||
output = self.mlp(output[:,-1,:]) | |||||
return {C.OUTPUT: output} | |||||
def predict(self, words): | |||||
output = self(words) | |||||
_, predict = output[C.OUTPUT].max(dim=1) | |||||
return {C.OUTPUT: predict} | |||||
@@ -0,0 +1,86 @@ | |||||
""" | |||||
轻量封装的 Pytorch LSTM 模块. | |||||
可在 forward 时传入序列的长度, 自动对padding做合适的处理. | |||||
""" | |||||
__all__ = [ | |||||
"LSTM" | |||||
] | |||||
import torch | |||||
import torch.nn as nn | |||||
import torch.nn.utils.rnn as rnn | |||||
from fastNLP.modules.utils import initial_parameter | |||||
from torch import autograd | |||||
from .weight_drop import WeightDrop | |||||
class LSTM(nn.Module): | |||||
""" | |||||
别名::class:`fastNLP.modules.LSTM` :class:`fastNLP.modules.encoder.lstm.LSTM` | |||||
LSTM 模块, 轻量封装的Pytorch LSTM. 在提供seq_len的情况下,将自动使用pack_padded_sequence; 同时默认将forget gate的bias初始化 | |||||
为1; 且可以应对DataParallel中LSTM的使用问题。 | |||||
:param input_size: 输入 `x` 的特征维度 | |||||
:param hidden_size: 隐状态 `h` 的特征维度. | |||||
:param num_layers: rnn的层数. Default: 1 | |||||
:param dropout: 层间dropout概率. Default: 0 | |||||
:param bidirectional: 若为 ``True``, 使用双向的RNN. Default: ``False`` | |||||
:param batch_first: 若为 ``True``, 输入和输出 ``Tensor`` 形状为 | |||||
:(batch, seq, feature). Default: ``False`` | |||||
:param bias: 如果为 ``False``, 模型将不会使用bias. Default: ``True`` | |||||
""" | |||||
def __init__(self, input_size, hidden_size=100, num_layers=1, dropout=0.0, batch_first=True, | |||||
bidirectional=False, bias=True, wdrop=0.5): | |||||
super(LSTM, self).__init__() | |||||
self.batch_first = batch_first | |||||
self.lstm = nn.LSTM(input_size, hidden_size, num_layers, bias=bias, batch_first=batch_first, | |||||
dropout=dropout, bidirectional=bidirectional) | |||||
self.lstm = WeightDrop(self.lstm, ['weight_hh_l0'], dropout=wdrop) | |||||
self.init_param() | |||||
def init_param(self): | |||||
for name, param in self.named_parameters(): | |||||
if 'bias' in name: | |||||
# based on https://github.com/pytorch/pytorch/issues/750#issuecomment-280671871 | |||||
param.data.fill_(0) | |||||
n = param.size(0) | |||||
start, end = n // 4, n // 2 | |||||
param.data[start:end].fill_(1) | |||||
else: | |||||
nn.init.xavier_uniform_(param) | |||||
def forward(self, x, seq_len=None, h0=None, c0=None): | |||||
""" | |||||
:param x: [batch, seq_len, input_size] 输入序列 | |||||
:param seq_len: [batch, ] 序列长度, 若为 ``None``, 所有输入看做一样长. Default: ``None`` | |||||
:param h0: [batch, hidden_size] 初始隐状态, 若为 ``None`` , 设为全0向量. Default: ``None`` | |||||
:param c0: [batch, hidden_size] 初始Cell状态, 若为 ``None`` , 设为全0向量. Default: ``None`` | |||||
:return (output, ht) 或 output: 若 ``get_hidden=True`` [batch, seq_len, hidden_size*num_direction] 输出序列 | |||||
和 [batch, hidden_size*num_direction] 最后时刻隐状态. | |||||
""" | |||||
batch_size, max_len, _ = x.size() | |||||
if h0 is not None and c0 is not None: | |||||
hx = (h0, c0) | |||||
else: | |||||
hx = None | |||||
if seq_len is not None and not isinstance(x, rnn.PackedSequence): | |||||
sort_lens, sort_idx = torch.sort(seq_len, dim=0, descending=True) | |||||
if self.batch_first: | |||||
x = x[sort_idx] | |||||
else: | |||||
x = x[:, sort_idx] | |||||
x = rnn.pack_padded_sequence(x, sort_lens, batch_first=self.batch_first) | |||||
output, hx = self.lstm(x, hx) # -> [N,L,C] | |||||
output, _ = rnn.pad_packed_sequence(output, batch_first=self.batch_first, total_length=max_len) | |||||
_, unsort_idx = torch.sort(sort_idx, dim=0, descending=False) | |||||
if self.batch_first: | |||||
output = output[unsort_idx] | |||||
else: | |||||
output = output[:, unsort_idx] | |||||
else: | |||||
output, hx = self.lstm(x, hx) | |||||
return output, hx |
@@ -0,0 +1,30 @@ | |||||
import torch | |||||
import torch.nn as nn | |||||
from fastNLP.core.const import Const as C | |||||
from fastNLP.modules.encoder.lstm import LSTM | |||||
from fastNLP.modules import encoder | |||||
from fastNLP.modules.decoder.mlp import MLP | |||||
class BiLSTMSentiment(nn.Module): | |||||
def __init__(self, init_embed, | |||||
num_classes, | |||||
hidden_dim=256, | |||||
num_layers=1, | |||||
nfc=128): | |||||
super(BiLSTMSentiment,self).__init__() | |||||
self.embed = encoder.Embedding(init_embed) | |||||
self.lstm = LSTM(input_size=self.embed.embedding_dim, hidden_size=hidden_dim, num_layers=num_layers, bidirectional=True) | |||||
self.mlp = MLP(size_layer=[hidden_dim* 2, nfc, num_classes]) | |||||
def forward(self, words): | |||||
x_emb = self.embed(words) | |||||
output, _ = self.lstm(x_emb) | |||||
output = self.mlp(output[:,-1,:]) | |||||
return {C.OUTPUT: output} | |||||
def predict(self, words): | |||||
output = self(words) | |||||
_, predict = output[C.OUTPUT].max(dim=1) | |||||
return {C.OUTPUT: predict} | |||||
@@ -0,0 +1,35 @@ | |||||
import torch | |||||
import torch.nn as nn | |||||
from fastNLP.core.const import Const as C | |||||
from fastNLP.modules.encoder.lstm import LSTM | |||||
from fastNLP.modules import encoder | |||||
from fastNLP.modules.aggregator.attention import SelfAttention | |||||
from fastNLP.modules.decoder.mlp import MLP | |||||
class BiLSTM_SELF_ATTENTION(nn.Module): | |||||
def __init__(self, init_embed, | |||||
num_classes, | |||||
hidden_dim=256, | |||||
num_layers=1, | |||||
attention_unit=256, | |||||
attention_hops=1, | |||||
nfc=128): | |||||
super(BiLSTM_SELF_ATTENTION,self).__init__() | |||||
self.embed = encoder.Embedding(init_embed) | |||||
self.lstm = LSTM(input_size=self.embed.embedding_dim, hidden_size=hidden_dim, num_layers=num_layers, bidirectional=True) | |||||
self.attention = SelfAttention(input_size=hidden_dim * 2 , attention_unit=attention_unit, attention_hops=attention_hops) | |||||
self.mlp = MLP(size_layer=[hidden_dim* 2*attention_hops, nfc, num_classes]) | |||||
def forward(self, words): | |||||
x_emb = self.embed(words) | |||||
output, _ = self.lstm(x_emb) | |||||
after_attention, penalty = self.attention(output,words) | |||||
after_attention =after_attention.view(after_attention.size(0),-1) | |||||
output = self.mlp(after_attention) | |||||
return {C.OUTPUT: output} | |||||
def predict(self, words): | |||||
output = self(words) | |||||
_, predict = output[C.OUTPUT].max(dim=1) | |||||
return {C.OUTPUT: predict} |
@@ -0,0 +1,99 @@ | |||||
import torch | |||||
from torch.nn import Parameter | |||||
from functools import wraps | |||||
class WeightDrop(torch.nn.Module): | |||||
def __init__(self, module, weights, dropout=0, variational=False): | |||||
super(WeightDrop, self).__init__() | |||||
self.module = module | |||||
self.weights = weights | |||||
self.dropout = dropout | |||||
self.variational = variational | |||||
self._setup() | |||||
def widget_demagnetizer_y2k_edition(*args, **kwargs): | |||||
# We need to replace flatten_parameters with a nothing function | |||||
# It must be a function rather than a lambda as otherwise pickling explodes | |||||
# We can't write boring code though, so ... WIDGET DEMAGNETIZER Y2K EDITION! | |||||
# (╯°□°)╯︵ ┻━┻ | |||||
return | |||||
def _setup(self): | |||||
# Terrible temporary solution to an issue regarding compacting weights re: CUDNN RNN | |||||
if issubclass(type(self.module), torch.nn.RNNBase): | |||||
self.module.flatten_parameters = self.widget_demagnetizer_y2k_edition | |||||
for name_w in self.weights: | |||||
print('Applying weight drop of {} to {}'.format(self.dropout, name_w)) | |||||
w = getattr(self.module, name_w) | |||||
del self.module._parameters[name_w] | |||||
self.module.register_parameter(name_w + '_raw', Parameter(w.data)) | |||||
def _setweights(self): | |||||
for name_w in self.weights: | |||||
raw_w = getattr(self.module, name_w + '_raw') | |||||
w = None | |||||
if self.variational: | |||||
mask = torch.autograd.Variable(torch.ones(raw_w.size(0), 1)) | |||||
if raw_w.is_cuda: mask = mask.cuda() | |||||
mask = torch.nn.functional.dropout(mask, p=self.dropout, training=True) | |||||
w = mask.expand_as(raw_w) * raw_w | |||||
else: | |||||
w = torch.nn.functional.dropout(raw_w, p=self.dropout, training=self.training) | |||||
setattr(self.module, name_w, w) | |||||
def forward(self, *args): | |||||
self._setweights() | |||||
return self.module.forward(*args) | |||||
if __name__ == '__main__': | |||||
import torch | |||||
from weight_drop import WeightDrop | |||||
# Input is (seq, batch, input) | |||||
x = torch.autograd.Variable(torch.randn(2, 1, 10)).cuda() | |||||
h0 = None | |||||
### | |||||
print('Testing WeightDrop') | |||||
print('=-=-=-=-=-=-=-=-=-=') | |||||
### | |||||
print('Testing WeightDrop with Linear') | |||||
lin = WeightDrop(torch.nn.Linear(10, 10), ['weight'], dropout=0.9) | |||||
lin.cuda() | |||||
run1 = [x.sum() for x in lin(x).data] | |||||
run2 = [x.sum() for x in lin(x).data] | |||||
print('All items should be different') | |||||
print('Run 1:', run1) | |||||
print('Run 2:', run2) | |||||
assert run1[0] != run2[0] | |||||
assert run1[1] != run2[1] | |||||
print('---') | |||||
### | |||||
print('Testing WeightDrop with LSTM') | |||||
wdrnn = WeightDrop(torch.nn.LSTM(10, 10), ['weight_hh_l0'], dropout=0.9) | |||||
wdrnn.cuda() | |||||
run1 = [x.sum() for x in wdrnn(x, h0)[0].data] | |||||
run2 = [x.sum() for x in wdrnn(x, h0)[0].data] | |||||
print('First timesteps should be equal, all others should differ') | |||||
print('Run 1:', run1) | |||||
print('Run 2:', run2) | |||||
# First time step, not influenced by hidden to hidden weights, should be equal | |||||
assert run1[0] == run2[0] | |||||
# Second step should not | |||||
assert run1[1] != run2[1] | |||||
print('---') |
@@ -0,0 +1,102 @@ | |||||
# 这个模型需要在pytorch=0.4下运行,weight_drop不支持1.0 | |||||
# 首先需要加入以下的路径到环境变量,因为当前只对内部测试开放,所以需要手动申明一下路径 | |||||
import os | |||||
os.environ['FASTNLP_BASE_URL'] = 'http://10.141.222.118:8888/file/download/' | |||||
os.environ['FASTNLP_CACHE_DIR'] = '/remote-home/hyan01/fastnlp_caches' | |||||
import torch.nn as nn | |||||
from data.SSTLoader import SSTLoader | |||||
from data.IMDBLoader import IMDBLoader | |||||
from data.yelpLoader import yelpLoader | |||||
from fastNLP.modules.encoder.embedding import StaticEmbedding | |||||
from model.awd_lstm import AWDLSTMSentiment | |||||
from fastNLP.core.const import Const as C | |||||
from fastNLP import CrossEntropyLoss, AccuracyMetric | |||||
from fastNLP import Trainer, Tester | |||||
from torch.optim import Adam | |||||
from fastNLP.io.model_io import ModelLoader, ModelSaver | |||||
import argparse | |||||
class Config(): | |||||
train_epoch= 10 | |||||
lr=0.001 | |||||
num_classes=2 | |||||
hidden_dim=256 | |||||
num_layers=1 | |||||
nfc=128 | |||||
wdrop=0.5 | |||||
task_name = "IMDB" | |||||
datapath={"train":"IMDB_data/train.csv", "test":"IMDB_data/test.csv"} | |||||
load_model_path="./result_IMDB/best_BiLSTM_SELF_ATTENTION_acc_2019-07-07-04-16-51" | |||||
save_model_path="./result_IMDB_test/" | |||||
opt=Config | |||||
# load data | |||||
dataloaders = { | |||||
"IMDB":IMDBLoader(), | |||||
"YELP":yelpLoader(), | |||||
"SST-5":SSTLoader(subtree=True,fine_grained=True), | |||||
"SST-3":SSTLoader(subtree=True,fine_grained=False) | |||||
} | |||||
if opt.task_name not in ["IMDB", "YELP", "SST-5", "SST-3"]: | |||||
raise ValueError("task name must in ['IMDB', 'YELP, 'SST-5', 'SST-3']") | |||||
dataloader = dataloaders[opt.task_name] | |||||
datainfo=dataloader.process(opt.datapath) | |||||
# print(datainfo.datasets["train"]) | |||||
# print(datainfo) | |||||
# define model | |||||
vocab=datainfo.vocabs['words'] | |||||
embed = StaticEmbedding(vocab, model_dir_or_name='en-glove-840b-300', requires_grad=True) | |||||
model=AWDLSTMSentiment(init_embed=embed, num_classes=opt.num_classes, hidden_dim=opt.hidden_dim, num_layers=opt.num_layers, nfc=opt.nfc, wdrop=opt.wdrop) | |||||
# define loss_function and metrics | |||||
loss=CrossEntropyLoss() | |||||
metrics=AccuracyMetric() | |||||
optimizer= Adam([param for param in model.parameters() if param.requires_grad==True], lr=opt.lr) | |||||
def train(datainfo, model, optimizer, loss, metrics, opt): | |||||
trainer = Trainer(datainfo.datasets['train'], model, optimizer=optimizer, loss=loss, | |||||
metrics=metrics, dev_data=datainfo.datasets['dev'], device=0, check_code_level=-1, | |||||
n_epochs=opt.train_epoch, save_path=opt.save_model_path) | |||||
trainer.train() | |||||
def test(datainfo, metrics, opt): | |||||
# load model | |||||
model = ModelLoader.load_pytorch_model(opt.load_model_path) | |||||
print("model loaded!") | |||||
# Tester | |||||
tester = Tester(datainfo.datasets['test'], model, metrics, batch_size=4, device=0) | |||||
acc = tester.test() | |||||
print("acc=",acc) | |||||
parser = argparse.ArgumentParser() | |||||
parser.add_argument('--mode', required=True, dest="mode",help='set the model\'s model') | |||||
args = parser.parse_args() | |||||
if args.mode == 'train': | |||||
train(datainfo, model, optimizer, loss, metrics, opt) | |||||
elif args.mode == 'test': | |||||
test(datainfo, metrics, opt) | |||||
else: | |||||
print('no mode specified for model!') | |||||
parser.print_help() |
@@ -0,0 +1,99 @@ | |||||
# 首先需要加入以下的路径到环境变量,因为当前只对内部测试开放,所以需要手动申明一下路径 | |||||
import os | |||||
os.environ['FASTNLP_BASE_URL'] = 'http://10.141.222.118:8888/file/download/' | |||||
os.environ['FASTNLP_CACHE_DIR'] = '/remote-home/hyan01/fastnlp_caches' | |||||
import torch.nn as nn | |||||
from data.SSTLoader import SSTLoader | |||||
from data.IMDBLoader import IMDBLoader | |||||
from data.yelpLoader import yelpLoader | |||||
from fastNLP.modules.encoder.embedding import StaticEmbedding | |||||
from model.lstm import BiLSTMSentiment | |||||
from fastNLP.core.const import Const as C | |||||
from fastNLP import CrossEntropyLoss, AccuracyMetric | |||||
from fastNLP import Trainer, Tester | |||||
from torch.optim import Adam | |||||
from fastNLP.io.model_io import ModelLoader, ModelSaver | |||||
import argparse | |||||
class Config(): | |||||
train_epoch= 10 | |||||
lr=0.001 | |||||
num_classes=2 | |||||
hidden_dim=256 | |||||
num_layers=1 | |||||
nfc=128 | |||||
task_name = "IMDB" | |||||
datapath={"train":"IMDB_data/train.csv", "test":"IMDB_data/test.csv"} | |||||
load_model_path="./result_IMDB/best_BiLSTM_SELF_ATTENTION_acc_2019-07-07-04-16-51" | |||||
save_model_path="./result_IMDB_test/" | |||||
opt=Config | |||||
# load data | |||||
dataloaders = { | |||||
"IMDB":IMDBLoader(), | |||||
"YELP":yelpLoader(), | |||||
"SST-5":SSTLoader(subtree=True,fine_grained=True), | |||||
"SST-3":SSTLoader(subtree=True,fine_grained=False) | |||||
} | |||||
if opt.task_name not in ["IMDB", "YELP", "SST-5", "SST-3"]: | |||||
raise ValueError("task name must in ['IMDB', 'YELP, 'SST-5', 'SST-3']") | |||||
dataloader = dataloaders[opt.task_name] | |||||
datainfo=dataloader.process(opt.datapath) | |||||
# print(datainfo.datasets["train"]) | |||||
# print(datainfo) | |||||
# define model | |||||
vocab=datainfo.vocabs['words'] | |||||
embed = StaticEmbedding(vocab, model_dir_or_name='en-glove-840b-300', requires_grad=True) | |||||
model=BiLSTMSentiment(init_embed=embed, num_classes=opt.num_classes, hidden_dim=opt.hidden_dim, num_layers=opt.num_layers, nfc=opt.nfc) | |||||
# define loss_function and metrics | |||||
loss=CrossEntropyLoss() | |||||
metrics=AccuracyMetric() | |||||
optimizer= Adam([param for param in model.parameters() if param.requires_grad==True], lr=opt.lr) | |||||
def train(datainfo, model, optimizer, loss, metrics, opt): | |||||
trainer = Trainer(datainfo.datasets['train'], model, optimizer=optimizer, loss=loss, | |||||
metrics=metrics, dev_data=datainfo.datasets['dev'], device=0, check_code_level=-1, | |||||
n_epochs=opt.train_epoch, save_path=opt.save_model_path) | |||||
trainer.train() | |||||
def test(datainfo, metrics, opt): | |||||
# load model | |||||
model = ModelLoader.load_pytorch_model(opt.load_model_path) | |||||
print("model loaded!") | |||||
# Tester | |||||
tester = Tester(datainfo.datasets['test'], model, metrics, batch_size=4, device=0) | |||||
acc = tester.test() | |||||
print("acc=",acc) | |||||
parser = argparse.ArgumentParser() | |||||
parser.add_argument('--mode', required=True, dest="mode",help='set the model\'s model') | |||||
args = parser.parse_args() | |||||
if args.mode == 'train': | |||||
train(datainfo, model, optimizer, loss, metrics, opt) | |||||
elif args.mode == 'test': | |||||
test(datainfo, metrics, opt) | |||||
else: | |||||
print('no mode specified for model!') | |||||
parser.print_help() |
@@ -0,0 +1,101 @@ | |||||
# 首先需要加入以下的路径到环境变量,因为当前只对内部测试开放,所以需要手动申明一下路径 | |||||
import os | |||||
os.environ['FASTNLP_BASE_URL'] = 'http://10.141.222.118:8888/file/download/' | |||||
os.environ['FASTNLP_CACHE_DIR'] = '/remote-home/hyan01/fastnlp_caches' | |||||
import torch.nn as nn | |||||
from data.SSTLoader import SSTLoader | |||||
from data.IMDBLoader import IMDBLoader | |||||
from data.yelpLoader import yelpLoader | |||||
from fastNLP.modules.encoder.embedding import StaticEmbedding | |||||
from model.lstm_self_attention import BiLSTM_SELF_ATTENTION | |||||
from fastNLP.core.const import Const as C | |||||
from fastNLP import CrossEntropyLoss, AccuracyMetric | |||||
from fastNLP import Trainer, Tester | |||||
from torch.optim import Adam | |||||
from fastNLP.io.model_io import ModelLoader, ModelSaver | |||||
import argparse | |||||
class Config(): | |||||
train_epoch= 10 | |||||
lr=0.001 | |||||
num_classes=2 | |||||
hidden_dim=256 | |||||
num_layers=1 | |||||
attention_unit=256 | |||||
attention_hops=1 | |||||
nfc=128 | |||||
task_name = "IMDB" | |||||
datapath={"train":"IMDB_data/train.csv", "test":"IMDB_data/test.csv"} | |||||
load_model_path="./result_IMDB/best_BiLSTM_SELF_ATTENTION_acc_2019-07-07-04-16-51" | |||||
save_model_path="./result_IMDB_test/" | |||||
opt=Config | |||||
# load data | |||||
dataloaders = { | |||||
"IMDB":IMDBLoader(), | |||||
"YELP":yelpLoader(), | |||||
"SST-5":SSTLoader(subtree=True,fine_grained=True), | |||||
"SST-3":SSTLoader(subtree=True,fine_grained=False) | |||||
} | |||||
if opt.task_name not in ["IMDB", "YELP", "SST-5", "SST-3"]: | |||||
raise ValueError("task name must in ['IMDB', 'YELP, 'SST-5', 'SST-3']") | |||||
dataloader = dataloaders[opt.task_name] | |||||
datainfo=dataloader.process(opt.datapath) | |||||
# print(datainfo.datasets["train"]) | |||||
# print(datainfo) | |||||
# define model | |||||
vocab=datainfo.vocabs['words'] | |||||
embed = StaticEmbedding(vocab, model_dir_or_name='en-glove-840b-300', requires_grad=True) | |||||
model=BiLSTM_SELF_ATTENTION(init_embed=embed, num_classes=opt.num_classes, hidden_dim=opt.hidden_dim, num_layers=opt.num_layers, attention_unit=opt.attention_unit, attention_hops=opt.attention_hops, nfc=opt.nfc) | |||||
# define loss_function and metrics | |||||
loss=CrossEntropyLoss() | |||||
metrics=AccuracyMetric() | |||||
optimizer= Adam([param for param in model.parameters() if param.requires_grad==True], lr=opt.lr) | |||||
def train(datainfo, model, optimizer, loss, metrics, opt): | |||||
trainer = Trainer(datainfo.datasets['train'], model, optimizer=optimizer, loss=loss, | |||||
metrics=metrics, dev_data=datainfo.datasets['dev'], device=0, check_code_level=-1, | |||||
n_epochs=opt.train_epoch, save_path=opt.save_model_path) | |||||
trainer.train() | |||||
def test(datainfo, metrics, opt): | |||||
# load model | |||||
model = ModelLoader.load_pytorch_model(opt.load_model_path) | |||||
print("model loaded!") | |||||
# Tester | |||||
tester = Tester(datainfo.datasets['test'], model, metrics, batch_size=4, device=0) | |||||
acc = tester.test() | |||||
print("acc=",acc) | |||||
parser = argparse.ArgumentParser() | |||||
parser.add_argument('--mode', required=True, dest="mode",help='set the model\'s model') | |||||
args = parser.parse_args() | |||||
if args.mode == 'train': | |||||
train(datainfo, model, optimizer, loss, metrics, opt) | |||||
elif args.mode == 'test': | |||||
test(datainfo, metrics, opt) | |||||
else: | |||||
print('no mode specified for model!') | |||||
parser.print_help() |