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model.py
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model.py
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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
def hard_pad2d(x, pad):
def pad_side(idx):
pad_len = max(pad - x.size(idx), 0)
return [0, pad_len]
padding = pad_side(3)
padding.extend(pad_side(2))
x = F.pad(x, padding)
return x[:, :, :pad, :pad]
class ResNet(nn.Module):
def __init__(self, config):
super().__init__()
n_layers = config['res_layers']
n_maps = config['res_fmaps']
n_labels = config['n_labels']
self.conv0 = nn.Conv2d(12, n_maps, (3, 3), padding=1)
self.convs = nn.ModuleList([nn.Conv2d(n_maps, n_maps, (3, 3), padding=1) for _ in range(n_layers)])
self.output = nn.Linear(n_maps, n_labels)
self.input_len = None
def forward(self, x):
x = F.relu(self.conv0(x))
old_x = x
for i, conv in enumerate(self.convs):
x = F.relu(conv(x))
if i % 2 == 1:
x += old_x
old_x = x
x = torch.mean(x.view(x.size(0), x.size(1), -1), 2)
return F.log_softmax(self.output(x), 1)
class VDPWIConvNet(nn.Module):
def __init__(self, config):
super().__init__()
def make_conv(n_in, n_out):
conv = nn.Conv2d(n_in, n_out, 3, padding=1)
conv.bias.data.zero_()
nn.init.xavier_normal_(conv.weight)
return conv
self.conv1 = make_conv(12, 128)
self.conv2 = make_conv(128, 164)
self.conv3 = make_conv(164, 192)
self.conv4 = make_conv(192, 192)
self.conv5 = make_conv(192, 128)
self.maxpool2 = nn.MaxPool2d(2, ceil_mode=True)
self.dnn = nn.Linear(128, 128)
self.output = nn.Linear(128, config['n_labels'])
self.input_len = 32
def forward(self, x):
x = hard_pad2d(x, self.input_len)
pool_final = nn.MaxPool2d(2, ceil_mode=True) if x.size(2) == 32 else nn.MaxPool2d(3, 1, ceil_mode=True)
x = self.maxpool2(F.relu(self.conv1(x)))
x = self.maxpool2(F.relu(self.conv2(x)))
x = self.maxpool2(F.relu(self.conv3(x)))
x = self.maxpool2(F.relu(self.conv4(x)))
x = pool_final(F.relu(self.conv5(x)))
x = F.relu(self.dnn(x.view(x.size(0), -1)))
return F.log_softmax(self.output(x), 1)
class VDPWIModel(nn.Module):
def __init__(self, dim, config):
super().__init__()
self.arch = 'vdpwi'
self.hidden_dim = config['rnn_hidden_dim']
self.rnn = nn.LSTM(dim, self.hidden_dim, 1, batch_first=True)
self.device = config['device']
if config['classifier'] == 'vdpwi':
self.classifier_net = VDPWIConvNet(config)
elif config['classifier'] == 'resnet':
self.classifier_net = ResNet(config)
def create_pad_cube(self, sent1, sent2):
pad_cube = []
sent1_lengths = [len(s.split(" ")) for s in sent1]
sent2_lengths = [len(s.split(" ")) for s in sent2]
max_len1 = max(sent1_lengths)
max_len2 = max(sent2_lengths)
for s1_length, s2_length in zip(sent1_lengths, sent2_lengths):
pad_mask = np.ones((max_len1, max_len2))
pad_mask[:s1_length, :s2_length] = 0
pad_cube.append(pad_mask)
pad_cube = np.array(pad_cube)
return torch.from_numpy(pad_cube).float().to(self.device).unsqueeze(0)
def compute_sim_cube(self, seq1, seq2):
def compute_sim(prism1, prism2):
prism1_len = prism1.norm(dim=3)
prism2_len = prism2.norm(dim=3)
dot_prod = torch.matmul(prism1.unsqueeze(3), prism2.unsqueeze(4))
dot_prod = dot_prod.squeeze(3).squeeze(3)
cos_dist = dot_prod / (prism1_len * prism2_len + 1E-8)
l2_dist = ((prism1 - prism2).norm(dim=3))
return torch.stack([dot_prod, cos_dist, l2_dist], 1)
def compute_prism(seq1, seq2):
prism1 = seq1.repeat(seq2.size(1), 1, 1, 1)
prism2 = seq2.repeat(seq1.size(1), 1, 1, 1)
prism1 = prism1.permute(1, 2, 0, 3).contiguous()
prism2 = prism2.permute(1, 0, 2, 3).contiguous()
return compute_sim(prism1, prism2)
sim_cube = torch.Tensor(seq1.size(0), 12, seq1.size(1), seq2.size(1))
sim_cube = sim_cube.to(self.device)
seq1_f = seq1[:, :, :self.hidden_dim]
seq1_b = seq1[:, :, self.hidden_dim:]
seq2_f = seq2[:, :, :self.hidden_dim]
seq2_b = seq2[:, :, self.hidden_dim:]
sim_cube[:, 0:3] = compute_prism(seq1, seq2)
sim_cube[:, 3:6] = compute_prism(seq1_f, seq2_f)
sim_cube[:, 6:9] = compute_prism(seq1_b, seq2_b)
sim_cube[:, 9:12] = compute_prism(seq1_f + seq1_b, seq2_f + seq2_b)
return sim_cube
def compute_focus_cube(self, sim_cube, pad_cube):
neg_magic = -10000
pad_cube = pad_cube.repeat(12, 1, 1, 1)
pad_cube = pad_cube.permute(1, 0, 2, 3).contiguous()
sim_cube = neg_magic * pad_cube + sim_cube
mask = torch.Tensor(*sim_cube.size()).to(self.device)
mask[:, :, :, :] = 0.1
def build_mask(index):
max_mask = sim_cube[:, index].clone()
for _ in range(min(sim_cube.size(2), sim_cube.size(3))):
values, indices = torch.max(max_mask.view(sim_cube.size(0), -1), 1)
row_indices = indices / sim_cube.size(3)
col_indices = indices % sim_cube.size(3)
row_indices = row_indices.unsqueeze(1)
col_indices = col_indices.unsqueeze(1).unsqueeze(1)
for i, (row_i, col_i, val) in enumerate(zip(row_indices, col_indices, values)):
if val < neg_magic / 2:
continue
mask[i, :, row_i, col_i] = 1
max_mask[i, row_i, :] = neg_magic
max_mask[i, :, col_i] = neg_magic
build_mask(9)
build_mask(10)
focus_cube = mask * sim_cube * (1 - pad_cube)
return focus_cube
def forward(self, sent1, sent2, ext_feats=None, word_to_doc_count=None, raw_sent1=None, raw_sent2=None):
pad_cube = self.create_pad_cube(raw_sent1, raw_sent2)
sent1 = sent1.permute(0, 2, 1).contiguous()
sent2 = sent2.permute(0, 2, 1).contiguous()
seq1f, _ = self.rnn(sent1)
seq2f, _ = self.rnn(sent2)
seq1b, _ = self.rnn(torch.cat(sent1.split(1, 1)[::-1], 1))
seq2b, _ = self.rnn(torch.cat(sent2.split(1, 1)[::-1], 1))
seq1 = torch.cat([seq1f, seq1b], 2)
seq2 = torch.cat([seq2f, seq2b], 2)
sim_cube = self.compute_sim_cube(seq1, seq2)
truncate = self.classifier_net.input_len
sim_cube = sim_cube[:, :, :pad_cube.size(2), :pad_cube.size(3)].contiguous()
if truncate is not None:
sim_cube = sim_cube[:, :, :truncate, :truncate].contiguous()
pad_cube = pad_cube[:, :, :sim_cube.size(2), :sim_cube.size(3)].contiguous()
focus_cube = self.compute_focus_cube(sim_cube, pad_cube)
log_prob = self.classifier_net(focus_cube)
return log_prob