forked from 201518018629031/DeepSLRD
/
BU_RvNN_torch.py
337 lines (291 loc) · 13.3 KB
/
BU_RvNN_torch.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
#coding:utf-8
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable as Var
# import sys
# import logging
# #logger
# logger = logging.getLogger()
# logger.setLevel(logging.INFO)
# ch = logging.StreamHandler(sys.stdout)
# ch.setLevel(logging.INFO)
# formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
# ch.setFormatter(formatter)
# logger.addHandler(ch)
class Node_tweet(object):
def __init__(self, idx=None):
self.children = []
#self.index = index
self.idx = idx
self.word = []
self.index = []
#self.height = 1
#self.size = 1
#self.num_leaves = 1
self.parent = None
#self.label = None
################################# generate tree structure ##############################
def gen_nn_inputs(root_node, max_degree=None, only_leaves_have_vals=True, with_labels=False):
"""Given a root node, returns the appropriate inputs to NN.
The NN takes in
x: the values at the leaves (e.g. word indices)
tree: a (n x degree) matrix that provides the computation order.
Namely, a row tree[i] = [a, b, c] in tree signifies that a
and b are children of c, and that the computation
f(a, b) -> c should happen on step i.
"""
_clear_indices(root_node)
#x, leaf_labels = _get_leaf_vals(root_node)
X_word, X_index = _get_leaf_vals(root_node)
tree, internal_word, internal_index = _get_tree_traversal(root_node, len(X_word), max_degree)
#assert all(v is not None for v in x)
#if not only_leaves_have_vals:
# assert all(v is not None for v in internal_x)
X_word.extend(internal_word)
X_index.extend(internal_index)
if max_degree is not None:
assert all(len(t) == max_degree + 1 for t in tree)
'''if with_labels:
labels = leaf_labels + internal_labels
labels_exist = [l is not None for l in labels]
labels = [l or 0 for l in labels]
return (np.array(x, dtype='int32'),
np.array(tree, dtype='int32'),
np.array(labels, dtype=theano.config.floatX),
np.array(labels_exist, dtype=theano.config.floatX))'''
##### debug here #####
'''ls = []
for x in X_word:
l = len(x)
if not l in ls: ls.append(l)
print ls'''
#print type(X_word)
return (np.array(X_word, dtype='float64'),
np.array(X_index, dtype='int32'),
np.array(tree, dtype='int32'))
#return (np.array(X_word),
# np.array(X_index),
# np.array(tree))
def _clear_indices(root_node):
root_node.idx = None
[_clear_indices(child) for child in root_node.children if child]
def _get_leaf_vals(root_node):
"""Get leaf values in deep-to-shallow, left-to-right order."""
all_leaves = []
layer = [root_node]
while layer:
next_layer = []
for node in layer:
if not node.children:
all_leaves.append(node)
else:
next_layer.extend([child for child in node.children[::-1] if child])
layer = next_layer
X_word = []
X_index = []
for idx, leaf in enumerate(reversed(all_leaves)):
leaf.idx = idx
X_word.append(leaf.word)
X_index.append(leaf.index)
#print idx, leaf
#print leaf.word
return X_word, X_index
def _get_tree_traversal(root_node, start_idx=0, max_degree=None):
"""Get computation order of leaves -> root."""
if not root_node.children:
return [], [], []
layers = []
layer = [root_node]
while layer:
layers.append(layer[:])
next_layer = []
[next_layer.extend([child for child in node.children if child])
for node in layer]
layer = next_layer
tree = []
internal_word = []
internal_index = []
idx = start_idx
for layer in reversed(layers): #reversed()反转函数,如输入"[1,2,3,4,5]",返回"[5,4,3,2,1]"
for node in layer:
if node.idx is not None:
# must be leaf
assert all(child is None for child in node.children)
continue
child_idxs = [(child.idx if child else -1)
for child in node.children] ## idx of child node
if max_degree is not None:
child_idxs.extend([-1] * (max_degree - len(child_idxs)))
assert not any(idx is None for idx in child_idxs)
node.idx = idx
tree.append(child_idxs + [node.idx])
internal_word.append(node.word if node.word is not None else -1)
internal_index.append(node.index if node.index is not None else -1)
idx += 1
return tree, internal_word, internal_index
class RvNN(nn.Module):
def __init__(self, device, word_dim, hidden_dim = 5, Nclass=4, degree=2, irregular_tree=True):
super(RvNN, self).__init__()
assert word_dim > 1 and hidden_dim > 1
self.cudaFlag = device
self.word_dim = word_dim
self.hidden_dim = hidden_dim
self.degree = degree
self.irregular_tree = irregular_tree
self.Nclass = Nclass
self.embedding = nn.Embedding(self.word_dim, self.hidden_dim)
nn.init.normal(self.embedding.weight, mean=0, std=0.1)
self.W_z = self.init_matrix([self.hidden_dim, self.hidden_dim])
self.U_z = self.init_matrix([self.hidden_dim,self.hidden_dim])
self.b_z = self.init_vector(self.hidden_dim)
self.W_r = self.init_matrix([self.hidden_dim, self.hidden_dim])
self.U_r = self.init_matrix([self.hidden_dim, self.hidden_dim])
self.b_r = self.init_vector(self.hidden_dim)
self.W_h = self.init_matrix([self.hidden_dim, self.hidden_dim])
self.U_h = self.init_matrix([self.hidden_dim, self.hidden_dim])
self.b_h = self.init_vector(self.hidden_dim)
self.W_out = self.init_matrix([self.hidden_dim, self.Nclass])
self.b_out = self.init_vector(self.Nclass)
if(self.cudaFlag):
self.W_z = self.W_z.cuda()
self.U_z = self.U_z.cuda()
self.b_z = self.b_z.cuda()
self.W_r = self.W_r.cuda()
self.U_r = self.U_r.cuda()
self.b_r = self.b_r.cuda()
self.W_h = self.W_h.cuda()
self.U_h = self.U_h.cuda()
self.b_h = self.b_h.cuda()
self.W_out = self.W_out.cuda()
self.b_out = self.b_out.cuda()
# self.ix = nn.Linear(self.hidden_dim, self.hidden_dim)
# self.ih = nn.Linear(self.hidden_dim, self.hidden_dim)
# self.fx = nn.Linear(self.hidden_dim, self.hidden_dim)
# self.fh = nn.Linear(self.hidden_dim, self.hidden_dim)
# self.ux = nn.Linear(self.hidden_dim, self.hidden_dim)
# self.uh = nn.Linear(self.hidden_dim, self.hidden_dim)
# self.out = nn.Linear(self.hidden_dim, self.Nclass)
# nn.init.normal(self.ix.weight, mean=0, std=0.1)
# nn.init.constant(self.ix.bias, 0)
# nn.init.normal(self.ih.weight, mean=0, std=0.1)
# nn.init.constant(self.ih.bias, 0)
# nn.init.normal(self.fx.weight, mean=0, std=0.1)
# nn.init.constant(self.fx.bias, 0)
# nn.init.normal(self.fh.weight, mean=0, std=0.1)
# nn.init.constant(self.fh.bias, 0)
# nn.init.normal(self.ux.weight, mean=0, std=0.1)
# nn.init.constant(self.ux.bias, 0)
# nn.init.normal(self.uh.weight, mean=0, std=0.1)
# nn.init.constant(self.uh.bias, 0)
# nn.init.normal(self.out.weight, mean=0, std=0.1)
# nn.init.constant(self.out.bias, 0)
# self.criterion = criterion
def init_matrix(self, shape):
std = 0.1*torch.ones(shape)
return Var(torch.normal(mean=0.0, std=std),requires_grad=True)
def init_vector(self, shape):
return Var(torch.zeros(shape), requires_grad=True)
def getParameters(self):
"""
Get flatParameters
note that getParameters and parameters is not equal in this case
getParameters do not get parameters of output module
:return: 1d tensor
"""
params = []
for m in [self.ix, self.ih, self.ox, self.oh, self.ux, self.uh]:
# we do not get param of output module
l = list(m.parameters())
params.extend(l)
one_dim = [p.view(p.numel()) for p in params]
params = F.torch.cat(one_dim)
if self.cudaFlag:
params = params.cuda()
return params
def hard_sigmoid(self, x):
"""
Computes element-wise hard sigmoid of x.
See e.g. https://github.com/Theano/Theano/blob/master/theano/tensor/nnet/sigm.py#L279
"""
x = (0.2 * x) + 0.5
x = F.threshold(-x, -1, -1)
x = F.threshold(-x, 0, 0)
return x
def node_forward(self, parent_word, parent_index, child_h, child_exists):
h_tilde = torch.sum(child_h, 0)
parent_embedding = self.embedding(parent_index)
# logger.info('the size of parent_embedding:%s, the size of parent_word: %s'%(parent_embedding.size(), parent_word.size()))
parent_xe = torch.squeeze(torch.mm(parent_embedding.transpose(1, 0), torch.unsqueeze(parent_word.float(), 1)))
z = self.hard_sigmoid(torch.squeeze(self.W_z.mm(torch.unsqueeze(parent_xe,1)) + self.U_z.mm(torch.unsqueeze(h_tilde,1))) + self.b_z)
r = self.hard_sigmoid(torch.squeeze(self.W_r.mm(torch.unsqueeze(parent_xe,1)) + self.U_r.mm(torch.unsqueeze(h_tilde,1))) + self.b_r)
c = F.tanh(torch.squeeze(self.W_h.mm(torch.unsqueeze(parent_xe,1)) + self.U_h.mm(torch.unsqueeze(h_tilde*r,1))) + self.b_h)
h = z*h_tilde + (1 - z)*c
return h
def init_node_child(self, x_word, x_index, num_leaves):
dummy = 0*torch.zeros(self.degree, self.hidden_dim)
if self.cudaFlag:
dummy = dummy.cuda()
# leaf_h = []
for i in range(num_leaves):
if i == 0:
leaf_h = torch.unsqueeze(self.node_forward(x_word[i], x_index[i], dummy, torch.sum(dummy, 1)), 0)
else:
leaf_h = torch.cat((leaf_h, torch.unsqueeze(self.node_forward(x_word[i], x_index[i], dummy, torch.sum(dummy, 1)), 0)), 0)
# print('the shape of leaf_h:%s'%str(leaf_h.size()))
# logger.info('leaf_h:%s'%str(leaf_h))
# leaf_h = torch.Tensor(leaf_h)
# print('the size of leaf_h:%s'%str(leaf_h.size()))
if self.irregular_tree:
init_node_h = torch.cat([leaf_h, leaf_h, leaf_h], 0)
else:
init_node_h = leaf_h
# print('the size of init_node_h:%s'%str(init_node_h.size()))
return leaf_h, init_node_h
def recurrence(self, x_word, x_index, node_info, t, node_h, last_h, num_leaves):
child_exists = (node_info[:-1] > -1).int()
# print('node_info:%s'%str(node_info))
offset = torch.ones_like(child_exists)*2*num_leaves*int(self.irregular_tree) - child_exists * t ### offset???
# offset = child_exists * (-t)
# print('offset:%s'%str(offset))
index = torch.add(node_info[:-1], offset).long()
# index = index[:,:-1]
# index = torch.add(node_info, offset)
# print('the index:%s'%(str(index)))
# child_h = node_h[index] * torch.unsqueeze(child_exists, 1)
child_h = node_h[index]*(child_exists.view(len(child_exists), 1).float())
parent_h = self.node_forward(x_word, x_index, child_h, child_exists)
# print(parent_h.size())
node_h = torch.cat([node_h, parent_h.view(1, self.hidden_dim)], 0)
return node_h[1:], parent_h
def forward(self, x_word, x_index, tree):
self.num_nodes = x_word.shape[0]
num_parents = tree.shape[0]
num_leaves = self.num_nodes - num_parents
leaf_h, init_node_h = self.init_node_child(x_word, x_index, num_leaves)
# print('the tree:%s'%(str(tree)))
# print('num_leaves:%s'%str(num_leaves))
# print('num_parents:%s'%str(num_parents))
# print('the size of init_node_h:%s'%str(init_node_h.size()))
dummy = torch.zeros(self.hidden_dim)
for i in range(num_parents):
init_node_h, dummy = self.recurrence(x_word[num_leaves+i], x_index[num_leaves+i], tree[i], i, init_node_h, dummy, num_leaves)
if(i == 0):
parent_h = torch.unsqueeze(dummy, 0)
else:
parent_h = torch.cat([parent_h, torch.unsqueeze(dummy, 0)], 0)
# output = torch.squeeze((torch.cat([leaf_h, parent_h], 0)).mm(self.W_out)[-1])+self.b_out
# return F.softmax(output)
return torch.squeeze(parent_h[-1:])
class RvNN_Co_GCN(nn.Module):
def __init__(self, input_dim, output_dim):
super(RvNN_Co_GCN, self).__init__()
self.input_dim = input_dim
self.output_dim = output_dim
self.linear = nn.Linear(self.input_dim, self.output_dim)
def forward(self, x):
x = self.linear(x)
# x = F.sigmoid(x)
# return F.softmax(x)
return x