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TD_RvNN_torch.py
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TD_RvNN_torch.py
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#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
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, ini_word, ini_index):
def gen_nn_inputs(root_node, ini_word):
"""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 = [[0, root_node.idx]]
#X_word, X_index = [ini_word], [ini_index]
X_word, X_index = [root_node.word], [root_node.index]
#print X_index
#print X_word
#exit(0)
internal_tree, internal_word, internal_index = _get_tree_path(root_node)
#print internal_tree
#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)
tree.extend(internal_tree)
X_word.extend(internal_word)
X_index.extend(internal_index)
X_word.append(ini_word)
#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 X_word
#print type(X_word)
return (np.array(X_word, dtype='float32'),
np.array(X_index, dtype='int32'),
np.array(tree, dtype='int32'))
#return (np.array(X_word),
# np.array(X_index),
# np.array(tree))
def _get_tree_path(root_node):
"""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
#print 'layer:', layers
tree = []
word = []
index = []
for layer in layers:
for node in layer:
if not node.children:
continue
#child_idxs = [child.idx for child in ] ## idx of child node
for child in node.children:
tree.append([node.idx, child.idx])
word.append(child.word if child.word is not None else -1)
index.append(child.index if child.index is not None else -1)
'''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, word, index
################################ tree rnn class ######################################
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()
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 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, word, index, parent_h):
#h_tilde = torch.sum(child_h, 0)
child_embedding = self.embedding(index)
# logger.info('the size of parent_embedding:%s, the size of parent_word: %s'%(parent_embedding.size(), parent_word.size()))
child_xe = torch.squeeze(torch.mm(child_embedding.transpose(1, 0), torch.unsqueeze(word.float(), 1)))
z = self.hard_sigmoid(torch.squeeze(self.W_z.mm(torch.unsqueeze(child_xe,1)) + self.U_z.mm(torch.unsqueeze(parent_h,1))) + self.b_z)
r = self.hard_sigmoid(torch.squeeze(self.W_r.mm(torch.unsqueeze(child_xe,1)) + self.U_r.mm(torch.unsqueeze(parent_h,1))) + self.b_r)
c = F.tanh(torch.squeeze(self.W_h.mm(torch.unsqueeze(child_xe,1)) + self.U_h.mm(torch.unsqueeze(parent_h*r,1))) + self.b_h)
h = z*parent_h + (1 - z)*c
return h
# def init_node_child(self, x):
# return self.init_vector(self.hidden_dim)
def recurrence(self, x_word, x_index, node_info, node_h, last_h):
parent_h = node_h[node_info[0]]
child_h = self.node_forward(x_word, x_index, parent_h)
node_h = torch.cat([node_h[:node_info[1]], child_h.view(1, self.hidden_dim), node_h[node_info[1]+1:]], 0)
return node_h, child_h
def forward(self, x_word, x_index, num_parent, tree):
for i in range(len(x_word)):
if i == 0:
init_node_h = torch.unsqueeze(self.init_vector(self.hidden_dim), 0)
else:
init_node_h = torch.cat([init_node_h, torch.unsqueeze(self.init_vector(self.hidden_dim), 0)], 0)
dummy = self.init_vector(self.hidden_dim)
if(self.cudaFlag):
init_node_h = init_node_h.cuda()
dummy = dummy.cuda()
for i in range(len(x_word)-1):
init_node_h, dummy = self.recurrence(x_word[i], x_index[i], tree[i], init_node_h, dummy)
if(i==0):
child_hs = torch.unsqueeze(dummy, 0)
else:
child_hs = torch.cat([child_hs, torch.unsqueeze(dummy, 0)], 0)
# print(num_parent)
# output = torch.squeeze(torch.unsqueeze(torch.max(child_hs[num_parent-1:], 0)[0], 0).mm(self.W_out)+self.b_out)
output = torch.squeeze(torch.max(child_hs[num_parent-1:], 0)[0])
# return F.softmax(output)
return output
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