/
model_classify.py
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/
model_classify.py
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# !/usr/bin/env python
# encoding: utf-8
import logging
import torch
import math
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import GATConv
logger = logging.getLogger(__name__)
class ReturnTypeInference(nn.Module):
def __init__(self, args):
super(ReturnTypeInference, self).__init__()
self.args = args
self.dropout_rate = self.args.dropout_rate
self.embedding_dim = self.args.embedding_dim
self.hidden_size = self.args.hidden_size
self.num_gnn_layers = self.args.num_gnn_layers
self.num_classes = self.args.num_classes
self.dropout = nn.Dropout(p=self.dropout_rate)
self.gat = GAT(self.embedding_dim, self.hidden_size, self.hidden_size, heads=1)
self.attn_cfg = GraphLocalAttention(args.hidden_size)
self.linear = nn.Linear(self.hidden_size, self.num_classes)
def forward(self, cfg_nodes, cfg_relations, node_pos):
cfg_nodes = self.gat(cfg_nodes, cfg_relations)
x = self.attn_by_batch(cfg_nodes, node_pos, self.args)
x = self.linear(x)
return x
def mean_by_batch(self, cfg_nodes, node_pos, args):
cfg = torch.zeros((len(node_pos) - 1, self.hidden_size)).to(args.device)
for idx in range(len(node_pos) - 1):
cur = cfg_nodes[node_pos[idx]: node_pos[idx + 1], :]
cfg[idx, :] = torch.mean(cur, dim=0)
return cfg
def attn_by_batch(self, cfg_nodes, node_pos, args):
cfg = torch.zeros((len(node_pos) - 1, self.hidden_size)).to(args.device)
for idx in range(len(node_pos) - 1):
cur = cfg_nodes[node_pos[idx]: node_pos[idx + 1], :]
attn_cur, _ = self.attn_cfg(cur)
cfg[idx, :] = attn_cur
return cfg
class BasicTypeClassifier(nn.Module):
def __init__(self, vocab2vec, word2idx, args):
super(BasicTypeClassifier, self).__init__()
self.vocab2vec = vocab2vec
self.word2idx = word2idx
self.args = args
self.dropout_rate = self.args.dropout_rate
self.embedding_dim = self.args.embedding_dim
self.hidden_size = self.args.hidden_size
self.num_gnn_layers = self.args.num_gnn_layers
self.num_classes = self.args.num_classes
self.const_emb = nn.Embedding(len(word2idx.keys()), self.embedding_dim)
self.const_emb.weight.data.normal_(0, 1 / self.embedding_dim ** 0.5)
self.hierarchical_attn = HierarchicalAttention(self.vocab2vec, self.args)
self.const_map = nn.Linear(self.embedding_dim, self.hidden_size)
self.attn = GlobalAttention(args.hidden_size)
self.attn_const = GlobalAttention(args.hidden_size)
self.linear = nn.Linear(self.hidden_size, self.num_classes)
def forward(self, dfs_seq, const_values):
dfs = self.hierarchical_attn(dfs_seq)
const = self.proc_const(const_values)
x, _ = self.attn(torch.stack([dfs, const], dim=1))
x = self.linear(x)
return x
def proc_const(self, const_values):
c_emb = self.const_emb(const_values)
c_emb = self.const_map(c_emb)
c_emb, _ = self.attn_const(c_emb)
return c_emb
class NumInference(nn.Module):
def __init__(self, vocab2vec, word2idx, args):
super(NumInference, self).__init__()
self.vocab2vec = vocab2vec
self.word2idx = word2idx
self.args = args
self.dropout_rate = self.args.dropout_rate
self.embedding_dim = self.args.embedding_dim
self.hidden_size = self.args.hidden_size
self.num_gnn_layers = self.args.num_gnn_layers
self.num_classes = self.args.num_classes
self.const_emb = nn.Embedding(len(word2idx.keys()), self.embedding_dim)
self.const_emb.weight.data.normal_(0, 1 / self.embedding_dim ** 0.5)
self.hierarchical_attn = HierarchicalAttention(self.vocab2vec, self.args)
self.const_map = nn.Linear(self.embedding_dim, self.hidden_size)
self.attn = GlobalAttention(args.hidden_size)
self.attn_const = GlobalAttention(args.hidden_size)
self.linear = nn.Linear(self.hidden_size, self.num_classes)
def forward(self, dfs_seq, const_values):
dfs = self.hierarchical_attn(dfs_seq)
const = self.proc_const(const_values)
x, _ = self.attn(torch.stack([dfs, const], dim=1))
x = self.linear(x)
return x
def proc_const(self, const_values):
c_emb = self.const_emb(const_values)
c_emb = self.const_map(c_emb)
c_emb, _ = self.attn_const(c_emb)
return c_emb
class GAT(torch.nn.Module):
def __init__(self, in_channels, hidden_channels, out_channels, heads):
super(GAT, self).__init__()
self.conv1 = GATConv(in_channels, hidden_channels, heads, dropout=0.6)
self.conv2 = GATConv(hidden_channels * heads, out_channels, heads=1,
concat=False, dropout=0.6)
def forward(self, x, edge_index):
x = F.dropout(x, p=0.6, training=self.training)
x = F.elu(self.conv1(x, edge_index))
x = F.dropout(x, p=0.6, training=self.training)
x = self.conv2(x, edge_index)
return x
class GraphLocalAttention(nn.Module):
def __init__(self, hidden_dim):
super().__init__()
self.hidden_dim = hidden_dim
self.projection = nn.Sequential(
nn.Linear(hidden_dim, hidden_dim // 5),
nn.ReLU(True),
nn.Linear(hidden_dim // 5, 1))
def forward(self, encoder_outputs):
energy = self.projection(encoder_outputs)
energy = energy.squeeze(-1)
weights = F.softmax(energy, dim=0)
outputs = (encoder_outputs * weights.unsqueeze(-1)).sum(dim=0)
return outputs, weights
class GlobalAttention(nn.Module):
def __init__(self, hidden_dim):
super().__init__()
self.hidden_dim = hidden_dim
self.projection = nn.Sequential(
nn.Linear(hidden_dim, hidden_dim // 5),
nn.ReLU(True),
nn.Linear(hidden_dim // 5, 1))
def forward(self, encoder_outputs):
energy = self.projection(encoder_outputs)
energy = energy.squeeze(-1)
weights = F.softmax(energy, dim=1)
outputs = (encoder_outputs * weights.unsqueeze(-1)).sum(dim=1)
return outputs, weights
class HierarchicalAttention(nn.Module):
def __init__(self, vocab2vec, args):
super(HierarchicalAttention, self).__init__()
self.vocab2vec = vocab2vec
self.args = args
self.embedding_dim = self.args.embedding_dim
self.hidden_size = self.args.hidden_size
self.rnn_layers = self.args.rnn_layers
self.dropout_rate = self.args.dropout_rate
self.dfs_attn = DFSAttention(self.vocab2vec, self.embedding_dim,
self.hidden_size, self.rnn_layers,
self.dropout_rate, self.args)
self.rnn = nn.LSTM(self.hidden_size, self.hidden_size, num_layers=self.rnn_layers,
bidirectional=True, batch_first=True, dropout=self.dropout_rate)
self.global_attn = GlobalAttention(args.hidden_size)
self.fc = nn.Linear(2 * self.hidden_size, self.hidden_size)
self.dropout = nn.Dropout(self.dropout_rate)
def forward(self, dfs_seq_list):
output_list = []
dfs_seq_list = dfs_seq_list.permute(1, 0, 2)
for dfs in dfs_seq_list:
output = self.dfs_attn(dfs)
output_list.append(output)
output = torch.stack(output_list, dim=1)
output, _ = self.rnn(output)
query = self.dropout(output)
attn_output, attn = self.attn_net(output, query)
logit = self.fc(attn_output)
logit = self.dropout(logit)
return logit
def attn_net(self, x, query, mask=None):
d_k = query.size(-1)
scores = torch.matmul(query, x.transpose(1, 2)) / math.sqrt(d_k)
p_attn = F.softmax(scores, dim=-1)
context = torch.matmul(p_attn, x).sum(1)
return context, p_attn
class DFSAttention(nn.Module):
def __init__(self, vocab2vec, embedding_dim, hidden_size, rnn_layers, dropout_rate, args):
super(DFSAttention, self).__init__()
self.vocab2vec = vocab2vec
self.args = args
self.embedding_dim = embedding_dim
self.hidden_size = hidden_size
self.rnn_layers = rnn_layers
self.dropout_rate = dropout_rate
num_embed = len(self.vocab2vec)
pretrained_dict = self.vocab2vec
pretrained_dict[1, :] = np.zeros(self.embedding_dim)
pretrained_dict = torch.from_numpy(pretrained_dict)
self.emb = nn.Embedding(num_embeddings=num_embed,
embedding_dim=self.embedding_dim,
dtype=torch.float32).from_pretrained(pretrained_dict).float()
self.rnn = nn.LSTM(self.embedding_dim, self.hidden_size, num_layers=self.rnn_layers,
bidirectional=True, batch_first=True, dropout=self.dropout_rate)
self.fc = nn.Linear(2 * self.hidden_size, self.hidden_size)
self.dropout = nn.Dropout(self.dropout_rate)
def forward(self, inputs):
inputs = self.emb(inputs)
output, _ = self.rnn(inputs)
query = self.dropout(output)
attn_output, attn = self.attn_net(output, query) #
logit = self.fc(attn_output)
return logit
def attn_net(self, x, query, mask=None):
d_k = query.size(-1)
scores = torch.matmul(query, x.transpose(1, 2)) / math.sqrt(d_k)
p_attn = F.softmax(scores, dim=-1)
context = torch.matmul(p_attn, x).sum(1)
return context, p_attn