Merge pull request #74 from 2017alan/master

Add weight initialization for models.

fastNLP/core/loss.py

@@ -37,5 +37,7 @@ class Loss(object):
         """
         if loss_name == "cross_entropy":
             return torch.nn.CrossEntropyLoss()
+        elif loss_name == 'nll':
+            return torch.nn.NLLLoss()
         else:
             raise NotImplementedError

fastNLP/core/preprocess.py

@@ -297,7 +297,7 @@ class ClassPreprocess(BasePreprocess):
 
         # build vocabulary from scratch if nothing exists
         word2index = DEFAULT_WORD_TO_INDEX.copy()
-        label2index = DEFAULT_WORD_TO_INDEX.copy()
+        label2index = {} # DEFAULT_WORD_TO_INDEX.copy()
 
         # collect every word and label
         for sent, label in data:

fastNLP/modules/aggregation/self_attention.py

@@ -1,8 +1,10 @@
 import torch
 import torch.nn as nn
 from torch.autograd import Variable
+import torch.nn.functional as F
 
 
+from fastNLP.modules.utils import initial_parameter
 class SelfAttention(nn.Module):
     """
     Self Attention Module.
@@ -13,13 +15,18 @@ class SelfAttention(nn.Module):
     num_vec: int, the number of encoded vectors
     """
 
-    def __init__(self, input_size, dim=10, num_vec=10):
+    def __init__(self, input_size, dim=10, num_vec=10 ,drop = 0.5 ,initial_method =None):
         super(SelfAttention, self).__init__()
-        self.W_s1 = nn.Parameter(torch.randn(dim, input_size), requires_grad=True)
-        self.W_s2 = nn.Parameter(torch.randn(num_vec, dim), requires_grad=True)
+        # self.W_s1 = nn.Parameter(torch.randn(dim, input_size), requires_grad=True)
+        # self.W_s2 = nn.Parameter(torch.randn(num_vec, dim), requires_grad=True)
+        self.attention_hops = num_vec
+
+        self.ws1 = nn.Linear(input_size, dim, bias=False)
+        self.ws2 = nn.Linear(dim, num_vec, bias=False)
+        self.drop = nn.Dropout(drop)
         self.softmax = nn.Softmax(dim=2)
         self.tanh = nn.Tanh()
-
+        initial_parameter(self, initial_method)
     def penalization(self, A):
         """
         compute the penalization term for attention module
@@ -32,11 +39,33 @@ class SelfAttention(nn.Module):
         M = M.view(M.size(0), -1)
         return torch.sum(M ** 2, dim=1)
         
-    def forward(self, x):
-        inter = self.tanh(torch.matmul(self.W_s1, torch.transpose(x, 1, 2)))
-        A = self.softmax(torch.matmul(self.W_s2, inter))
-        out = torch.matmul(A, x)
-        out = out.view(out.size(0), -1)
-        penalty = self.penalization(A)
-        return out, penalty
+    def forward(self, outp ,inp):
+        #  the following code can not be use because some word are padding ,these is not such module!
+
+        # inter = self.tanh(torch.matmul(self.W_s1, torch.transpose(x, 1, 2))) # []
+        # A = self.softmax(torch.matmul(self.W_s2, inter))
+        # out = torch.matmul(A, x)
+        # out = out.view(out.size(0), -1)
+        # penalty = self.penalization(A)
+        # return out, penalty
+        outp = outp.contiguous()
+        size = outp.size()  # [bsz, len, nhid]
+
+        compressed_embeddings = outp.view(-1, size[2])  # [bsz*len, nhid*2]
+        transformed_inp = torch.transpose(inp, 0, 1).contiguous()  # [bsz, len]
+        transformed_inp = transformed_inp.view(size[0], 1, size[1])  # [bsz, 1, len]
+        concatenated_inp = [transformed_inp for i in range(self.attention_hops)]
+        concatenated_inp = torch.cat(concatenated_inp, 1)  # [bsz, hop, len]
+
+        hbar = self.tanh(self.ws1(self.drop(compressed_embeddings)))  # [bsz*len, attention-unit]
+        attention = self.ws2(hbar).view(size[0], size[1], -1)  # [bsz, len, hop]
+        attention = torch.transpose(attention, 1, 2).contiguous()  # [bsz, hop, len]
+        penalized_alphas = attention + (
+            -10000 * (concatenated_inp == 0).float())
+        # [bsz, hop, len] + [bsz, hop, len]
+        attention = self.softmax(penalized_alphas.view(-1, size[1]))  # [bsz*hop, len]
+        attention = attention.view(size[0], self.attention_hops, size[1])  # [bsz, hop, len]
+        return torch.bmm(attention, outp), attention  # output --> [baz ,hop ,nhid]
+
+
 

fastNLP/modules/decoder/CRF.py

@@ -1,6 +1,7 @@
 import torch
 from torch import nn
 
+from fastNLP.modules.utils import initial_parameter
 
 def log_sum_exp(x, dim=-1):
     max_value, _ = x.max(dim=dim, keepdim=True)
@@ -19,7 +20,7 @@ def seq_len_to_byte_mask(seq_lens):
 
 
 class ConditionalRandomField(nn.Module):
-    def __init__(self, tag_size, include_start_end_trans=True):
+    def __init__(self, tag_size, include_start_end_trans=True ,initial_method = None):
         """
         :param tag_size: int, num of tags
         :param include_start_end_trans: bool, whether to include start/end tag
@@ -35,8 +36,8 @@ class ConditionalRandomField(nn.Module):
             self.start_scores = nn.Parameter(torch.randn(tag_size))
             self.end_scores = nn.Parameter(torch.randn(tag_size))
 
-        self.reset_parameter()
-
+        # self.reset_parameter()
+        initial_parameter(self, initial_method)
     def reset_parameter(self):
         nn.init.xavier_normal_(self.transition_m)
         if self.include_start_end_trans:

fastNLP/modules/decoder/MLP.py

@@ -1,8 +1,8 @@
 import torch
 import torch.nn as nn
-
+from fastNLP.modules.utils import initial_parameter
 class MLP(nn.Module):
-    def __init__(self, size_layer, num_class=2, activation='relu'):
+    def __init__(self, size_layer, num_class=2, activation='relu' , initial_method = None):
         """Multilayer Perceptrons as a decoder
 
         Args:
@@ -36,7 +36,7 @@ class MLP(nn.Module):
             self.hidden_active = activation
         else:
             raise ValueError("should set activation correctly: {}".format(activation))
-
+        initial_parameter(self, initial_method  )
     def forward(self, x):
         for layer in self.hiddens:
             x = self.hidden_active(layer(x))

fastNLP/modules/encoder/char_embedding.py

@@ -1,11 +1,12 @@
 import torch
 import torch.nn.functional as F
 from torch import nn
+# from torch.nn.init import xavier_uniform
 
-
+from fastNLP.modules.utils import initial_parameter
 class ConvCharEmbedding(nn.Module):
 
-    def __init__(self, char_emb_size=50, feature_maps=(40, 30, 30), kernels=(3, 4, 5)):
+    def __init__(self, char_emb_size=50, feature_maps=(40, 30, 30), kernels=(3, 4, 5),initial_method = None):
         """
         Character Level Word Embedding
         :param char_emb_size: the size of character level embedding. Default: 50
@@ -20,6 +21,8 @@ class ConvCharEmbedding(nn.Module):
             nn.Conv2d(1, feature_maps[i], kernel_size=(char_emb_size, kernels[i]), bias=True, padding=(0, 4))
             for i in range(len(kernels))])
 
+        initial_parameter(self,initial_method)
+
     def forward(self, x):
         """
         :param x: [batch_size * sent_length, word_length, char_emb_size]
@@ -53,7 +56,7 @@ class LSTMCharEmbedding(nn.Module):
     :param hidden_size: int, the number of hidden units. Default:  equal to char_emb_size.
     """
 
-    def __init__(self, char_emb_size=50, hidden_size=None):
+    def __init__(self, char_emb_size=50, hidden_size=None , initial_method= None):
         super(LSTMCharEmbedding, self).__init__()
         self.hidden_size = char_emb_size if hidden_size is None else hidden_size
 
@@ -62,7 +65,7 @@ class LSTMCharEmbedding(nn.Module):
                             num_layers=1,
                             bias=True,
                             batch_first=True)
-
+        initial_parameter(self, initial_method)
     def forward(self, x):
         """
         :param x:[ n_batch*n_word, word_length, char_emb_size]

fastNLP/modules/encoder/conv.py

@@ -6,6 +6,7 @@ import torch.nn as nn
 from torch.nn.init import xavier_uniform_
 # import torch.nn.functional as F
 
+from fastNLP.modules.utils import initial_parameter
 
 class Conv(nn.Module):
     """
@@ -15,7 +16,7 @@ class Conv(nn.Module):
 
     def __init__(self, in_channels, out_channels, kernel_size,
                  stride=1, padding=0, dilation=1,
-                 groups=1, bias=True, activation='relu'):
+                 groups=1, bias=True, activation='relu',initial_method = None ):
         super(Conv, self).__init__()
         self.conv = nn.Conv1d(
             in_channels=in_channels,
@@ -26,7 +27,7 @@ class Conv(nn.Module):
             dilation=dilation,
             groups=groups,
             bias=bias)
-        xavier_uniform_(self.conv.weight)
+        # xavier_uniform_(self.conv.weight)
 
         activations = {
             'relu': nn.ReLU(),
@@ -37,6 +38,7 @@ class Conv(nn.Module):
             raise Exception(
                 'Should choose activation function from: ' +
                 ', '.join([x for x in activations]))
+        initial_parameter(self, initial_method)
 
     def forward(self, x):
         x = torch.transpose(x, 1, 2)  # [N,L,C] -> [N,C,L]

fastNLP/modules/encoder/conv_maxpool.py

@@ -5,7 +5,7 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from torch.nn.init import xavier_uniform_
-
+from fastNLP.modules.utils import initial_parameter
 
 class ConvMaxpool(nn.Module):
     """
@@ -14,7 +14,7 @@ class ConvMaxpool(nn.Module):
 
     def __init__(self, in_channels, out_channels, kernel_sizes,
                  stride=1, padding=0, dilation=1,
-                 groups=1, bias=True, activation='relu'):
+                 groups=1, bias=True, activation='relu',initial_method = None ):
         super(ConvMaxpool, self).__init__()
 
         # convolution
@@ -47,6 +47,8 @@ class ConvMaxpool(nn.Module):
             raise Exception(
                 "Undefined activation function: choose from: relu")
 
+        initial_parameter(self, initial_method)
+
     def forward(self, x):
         # [N,L,C] -> [N,C,L]
         x = torch.transpose(x, 1, 2)

fastNLP/modules/encoder/linear.py

@@ -1,6 +1,6 @@
 import torch.nn as nn
 
-
+from fastNLP.modules.utils import initial_parameter
 class Linear(nn.Module):
     """
     Linear module
@@ -12,10 +12,10 @@ class Linear(nn.Module):
     bidirectional : If True, becomes a bidirectional RNN
     """
 
-    def __init__(self, input_size, output_size, bias=True):
+    def __init__(self, input_size, output_size, bias=True,initial_method = None        ):
         super(Linear, self).__init__()
         self.linear = nn.Linear(input_size, output_size, bias)
-
+        initial_parameter(self, initial_method)
     def forward(self, x):
         x = self.linear(x)
         return x

fastNLP/modules/encoder/lstm.py

@@ -1,6 +1,6 @@
 import torch.nn as nn
 
-
+from fastNLP.modules.utils import initial_parameter
 class Lstm(nn.Module):
     """
     LSTM module
@@ -13,11 +13,13 @@ class Lstm(nn.Module):
     bidirectional : If True, becomes a bidirectional RNN. Default: False.
     """
 
-    def __init__(self, input_size, hidden_size=100, num_layers=1, dropout=0, bidirectional=False):
+    def __init__(self, input_size, hidden_size=100, num_layers=1, dropout=0, bidirectional=False , initial_method = None):
         super(Lstm, self).__init__()
         self.lstm = nn.LSTM(input_size, hidden_size, num_layers, bias=True, batch_first=True,
                             dropout=dropout, bidirectional=bidirectional)
-        
+        initial_parameter(self, initial_method)
     def forward(self, x):
         x, _ = self.lstm(x)
         return x
+if __name__ == "__main__":
+    lstm = Lstm(10)

fastNLP/modules/encoder/masked_rnn.py

@@ -4,7 +4,7 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-
+from fastNLP.modules.utils import initial_parameter
 def MaskedRecurrent(reverse=False):
     def forward(input, hidden, cell, mask, train=True, dropout=0):
         """
@@ -192,7 +192,7 @@ def AutogradMaskedStep(num_layers=1, dropout=0, train=True, lstm=False):
 class MaskedRNNBase(nn.Module):
     def __init__(self, Cell, input_size, hidden_size,
                  num_layers=1, bias=True, batch_first=False,
-                 layer_dropout=0, step_dropout=0, bidirectional=False, **kwargs):
+                 layer_dropout=0, step_dropout=0, bidirectional=False, initial_method = None  , **kwargs):
         """
         :param Cell:
         :param input_size:
@@ -226,7 +226,7 @@ class MaskedRNNBase(nn.Module):
                 cell = self.Cell(layer_input_size, hidden_size, self.bias, **kwargs)
                 self.all_cells.append(cell)
                 self.add_module('cell%d' % (layer * num_directions + direction), cell)  # Max的代码写得真好看
-
+        initial_parameter(self, initial_method)
     def reset_parameters(self):
         for cell in self.all_cells:
             cell.reset_parameters()

fastNLP/modules/encoder/variational_rnn.py

@@ -6,6 +6,7 @@ import torch.nn.functional as F
 from torch.nn._functions.thnn import rnnFusedPointwise as fusedBackend
 from torch.nn.parameter import Parameter
 
+from fastNLP.modules.utils import initial_parameter
 
 def default_initializer(hidden_size):
     stdv = 1.0 / math.sqrt(hidden_size)
@@ -172,7 +173,7 @@ def AutogradVarMaskedStep(num_layers=1, lstm=False):
 class VarMaskedRNNBase(nn.Module):
     def __init__(self, Cell, input_size, hidden_size,
                  num_layers=1, bias=True, batch_first=False,
-                 dropout=(0, 0), bidirectional=False, initializer=None, **kwargs):
+                 dropout=(0, 0), bidirectional=False, initializer=None,initial_method = None, **kwargs):
 
         super(VarMaskedRNNBase, self).__init__()
         self.Cell = Cell
@@ -193,7 +194,7 @@ class VarMaskedRNNBase(nn.Module):
                 cell = self.Cell(layer_input_size, hidden_size, self.bias, p=dropout, initializer=initializer, **kwargs)
                 self.all_cells.append(cell)
                 self.add_module('cell%d' % (layer * num_directions + direction), cell)
-
+        initial_parameter(self, initial_method)
     def reset_parameters(self):
         for cell in self.all_cells:
             cell.reset_parameters()
@@ -284,7 +285,7 @@ class VarFastLSTMCell(VarRNNCellBase):
         \end{array}
     """
 
-    def __init__(self, input_size, hidden_size, bias=True, p=(0.5, 0.5), initializer=None):
+    def __init__(self, input_size, hidden_size, bias=True, p=(0.5, 0.5), initializer=None,initial_method =None):
         super(VarFastLSTMCell, self).__init__()
         self.input_size = input_size
         self.hidden_size = hidden_size
@@ -311,7 +312,7 @@ class VarFastLSTMCell(VarRNNCellBase):
         self.p_hidden = p_hidden
         self.noise_in = None
         self.noise_hidden = None
-
+        initial_parameter(self, initial_method)
     def reset_parameters(self):
         for weight in self.parameters():
             if weight.dim() == 1:

fastNLP/modules/utils.py

@@ -2,8 +2,8 @@ from collections import defaultdict
 
 import numpy as np
 import torch
-
-
+import torch.nn.init as init
+import torch.nn as nn
 def mask_softmax(matrix, mask):
     if mask is None:
         result = torch.nn.functional.softmax(matrix, dim=-1)
@@ -11,6 +11,51 @@ def mask_softmax(matrix, mask):
         raise NotImplementedError
     return result
 
+def initial_parameter(net ,initial_method =None):
+
+    if initial_method == 'xavier_uniform':
+        init_method = init.xavier_uniform_
+    elif initial_method=='xavier_normal':
+        init_method = init.xavier_normal_
+    elif initial_method == 'kaiming_normal' or initial_method =='msra':
+        init_method = init.kaiming_normal
+    elif initial_method == 'kaiming_uniform':
+        init_method = init.kaiming_normal
+    elif initial_method == 'orthogonal':
+        init_method = init.orthogonal_
+    elif initial_method == 'sparse':
+        init_method = init.sparse_
+    elif initial_method =='normal':
+        init_method = init.normal_
+    elif initial_method =='uniform':
+        initial_method = init.uniform_
+    else:
+        init_method = init.xavier_normal_
+    def weights_init(m):
+        # classname = m.__class__.__name__
+        if isinstance(m, nn.Conv2d) or isinstance(m,nn.Conv1d) or isinstance(m,nn.Conv3d):  # for all the cnn
+            if initial_method != None:
+                init_method(m.weight.data)
+            else:
+                init.xavier_normal_(m.weight.data)
+            init.normal_(m.bias.data)
+        elif isinstance(m, nn.LSTM):
+            for w in m.parameters():
+                if len(w.data.size())>1:
+                    init_method(w.data)  # weight
+                else:
+                    init.normal_(w.data)  # bias
+        elif hasattr(m, 'weight') and m.weight.requires_grad:
+            init_method(m.weight.data)
+        else:
+            for w in m.parameters() :
+                if  w.requires_grad:
+                    if len(w.data.size())>1:
+                        init_method(w.data)  # weight
+                    else:
+                        init.normal_(w.data)  # bias
+                # print("init else")
+    net.apply(weights_init)
 
 def seq_mask(seq_len, max_len):
     mask = [torch.ge(torch.LongTensor(seq_len), i + 1) for i in range(max_len)]

reproduction/LSTM+self_attention_sentiment_analysis/config.cfg

@@ -0,0 +1,13 @@
+[train]
+epochs = 30
+batch_size = 32
+pickle_path = "./save/"
+validate = true
+save_best_dev = true
+model_saved_path = "./save/"
+rnn_hidden_units = 300
+word_emb_dim = 300
+use_crf = true
+use_cuda = false
+loss_func = "cross_entropy"
+num_classes = 5

reproduction/LSTM+self_attention_sentiment_analysis/main.py

@@ -0,0 +1,80 @@
+
+import os
+
+import torch.nn.functional as F
+
+from fastNLP.loader.dataset_loader import ClassDatasetLoader as Dataset_loader
+from fastNLP.loader.embed_loader import EmbedLoader as EmbedLoader
+from fastNLP.loader.config_loader import ConfigSection
+from fastNLP.loader.config_loader import ConfigLoader
+
+from fastNLP.models.base_model import BaseModel
+
+from fastNLP.core.preprocess import ClassPreprocess as Preprocess
+from fastNLP.core.trainer   import ClassificationTrainer
+
+from fastNLP.modules.encoder.embedding import Embedding as Embedding
+from fastNLP.modules.encoder.lstm import Lstm
+from fastNLP.modules.aggregation.self_attention import SelfAttention
+from fastNLP.modules.decoder.MLP import MLP
+
+
+train_data_path =  'small_train_data.txt'
+dev_data_path = 'small_dev_data.txt'
+# emb_path = 'glove.txt'
+
+lstm_hidden_size = 300
+embeding_size = 300
+attention_unit = 350
+attention_hops = 10
+class_num = 5
+nfc = 3000
+### data load  ###
+train_dataset = Dataset_loader(train_data_path)
+train_data = train_dataset.load()
+
+dev_args = Dataset_loader(dev_data_path)
+dev_data = dev_args.load()
+
+######  preprocess ####
+preprocess = Preprocess()
+word2index, label2index = preprocess.build_dict(train_data)
+train_data, dev_data = preprocess.run(train_data, dev_data)
+
+
+
+# emb = EmbedLoader(emb_path)
+# embedding = emb.load_embedding(emb_dim= embeding_size , emb_file= emb_path ,word_dict= word2index)
+### construct vocab ###
+
+class SELF_ATTENTION_YELP_CLASSIFICATION(BaseModel):
+    def __init__(self, args=None):
+        super(SELF_ATTENTION_YELP_CLASSIFICATION,self).__init__()
+        self.embedding = Embedding(len(word2index) ,embeding_size , init_emb= None )
+        self.lstm = Lstm(input_size = embeding_size,hidden_size = lstm_hidden_size ,bidirectional = True)
+        self.attention = SelfAttention(lstm_hidden_size * 2 ,dim =attention_unit ,num_vec=attention_hops)
+        self.mlp = MLP(size_layer=[lstm_hidden_size * 2*attention_hops ,nfc ,class_num ] ,num_class=class_num  ,)
+    def forward(self,x):
+        x_emb = self.embedding(x)
+        output = self.lstm(x_emb)
+        after_attention, penalty = self.attention(output,x)
+        after_attention =after_attention.view(after_attention.size(0),-1)
+        output = self.mlp(after_attention)
+        return output
+
+    def loss(self, predict, ground_truth):
+        print("predict:%s; g:%s" % (str(predict.size()), str(ground_truth.size())))
+        print(ground_truth)
+        return F.cross_entropy(predict, ground_truth)
+
+train_args = ConfigSection()
+ConfigLoader("good path").load_config('config.cfg',{"train": train_args})
+train_args['vocab'] = len(word2index)
+
+
+trainer = ClassificationTrainer(**train_args.data)
+
+# for k in train_args.__dict__.keys():
+#     print(k, train_args[k])
+model = SELF_ATTENTION_YELP_CLASSIFICATION(train_args)
+trainer.train(model,train_data , dev_data)

setup.py

@@ -2,18 +2,18 @@
 # coding=utf-8
 from setuptools import setup, find_packages
 
-with open('README.md') as f:
+with open('README.md', encoding='utf-8') as f:
     readme = f.read()
 
-with open('LICENSE') as f:
+with open('LICENSE', encoding='utf-8') as f:
     license = f.read()
 
-with open('requirements.txt') as f:
+with open('requirements.txt', encoding='utf-8') as f:
     reqs = f.read()
 
 setup(
     name='fastNLP',
-    version='0.0.1',
+    version='0.0.3',
     description='fastNLP: Deep Learning Toolkit for NLP, developed by Fudan FastNLP Team',
     long_description=readme,
     license=license,