Add VDPWI core models

vdpwi/data.py

@@ -12,6 +12,7 @@ class Configs(object):
         parser.add_argument("--input_model", type=str, default="local_saves/model.pt")
         parser.add_argument("--lr", type=float, default=1E-4)
         parser.add_argument("--mbatch_size", type=int, default=40)
+        parser.add_argument("--n_labels", type=int, default=5)
         parser.add_argument("--output_model", type=str, default="local_saves/model.pt")
         parser.add_argument("--restore", action="store_true", default=False)
         parser.add_argument("--wordvecs_file", type=str, default="local_data/glove/glove.840B.300d.txt")

vdpwi/model.py

@@ -0,0 +1,96 @@
+from torch.autograd import Variable
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+
+class SerializableModule(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def save(self, filename):
+        torch.save(self.state_dict(), filename)
+
+    def load(self, filename):
+        self.load_state_dict(torch.load(filename, map_location=lambda storage, loc: storage))
+
+class VDPWIConvNet(SerializableModule):
+    def __init__(self, n_labels):
+        self.conv1 = nn.Conv2d(13, 128, 3, padding=1)
+        self.conv2 = nn.Conv2d(128, 164, 3, padding=1)
+        self.conv3 = nn.Conv2d(164, 192, 3, padding=1)
+        self.conv4 = nn.Conv2d(192, 192, 3, padding=1)
+        self.conv5 = nn.Conv2d(192, 128, 3, padding=1)
+        self.maxpool2 = nn.MaxPool2d(2, ceil_mode=True)
+        self.dnn = nn.Linear(128, 128)
+        self.output = nn.Linear(128, n_labels)
+
+    def forward(self, x):
+        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 self.output(x)
+
+class VDPWIModel(SerializableModule):
+    def __init__(self, embedding, config, classifier_net=None):
+        super().__init__()
+        self.rnn = nn.LSTM(300, config.rnn_hidden_dim, 1, bidirectional=True)
+        self.embedding = embedding
+        self.classifier_net = VDPWIConvNet(config.n_labels) if classifier is None else classifier_net
+
+    def compute_sim_cube(self, seq1, seq2):
+        def compute_sim(h1, h2):
+            h1_len = torch.sqrt(torch.sum(h1**2))
+            h2_len = torch.sqrt(torch.sum(h2**2))
+
+            dot_prod = torch.dot(h1, h2)
+            cos_dist = dot_prod / (h1_len * h2_len + 1E-8)
+            l2_dist = torch.sqrt(torch.sum((h1 - h2)**2))
+            return dot_prod, cos_dist, l2_dist
+
+        sim_cube = Variable(torch.Tensor(13, seq1.size(0), seq2.size(0)).cuda())
+        seq1_f = seq1[:, 0]
+        seq1_b = seq1[:, 1]
+        seq2_f = seq2[:, 0]
+        seq2_b = seq2[:, 1]
+        for t, (h1f, h1b) in enumerate(zip(seq1_f, seq1_b)):
+            for s, (h2f, h2b) in enumerate(zip(seq2_f, seq2_b)):
+                sim_cube[0:3, t, s] = compute_sim(torch.cat([h1f, h1b]), torch.cat([h2f, h2b]))
+                sim_cube[3:6, t, s] = compute_sim(h1f, h2f)
+                sim_cube[6:9, t, s] = compute_sim(h1b, h2b)
+                sim_cube[9:12, t, s] = compute_sim(h1f + h1b, h2f + h2b)
+        return sim_cube
+
+    def compute_focus_cube(self, sim_cube):
+        mask = Variable(torch.Tensor(*sim_cube.size()).cuda())
+        def build_mask(index):
+            s1tag = np.zeros(sim_cube.size(1))
+            s2tag = np.zeros(sim_cube.size(2))
+            _, indices = torch.sort(sim_cube[index].view(-1), descending=True)
+            for i, index in enumerate(indices):
+                if i >= len(s1tag) + len(s2tag):
+                    break
+                pos1, pos2 = index // len(s1tag), index % len(s2tag)
+                if s1tag[pos1] + s2tag[pos2] == 0:
+                    s1tag[pos1] = s2tag[pos2] = 1
+                    mask[:, pos1, pos2] = 1
+        build_mask(10)
+        build_mask(11)
+        mask[12, :, :] = 1
+        return mask * sim_cube
+
+    def forward(self, x1, x2):
+        x1 = self.embedding(x1)
+        x2 = self.embedding(x2)
+        seq1, _ = self.rnn(x1, batch_first=True)
+        seq2, _ = self.rnn(x2, batch_first=True)
+        seq1 = seq1.squeeze(1) # batch size assumed to be 1
+        seq2 = seq2.squeeze(1)
+        sim_cube = self.compute_sim_cube(seq1, seq2)
+        focus_cube = self.compute_focus_cube(sim_cube)
+        logits = self.classifier_net(focus_cube.unsqueeze(0))
+        return torch.log(F.softmax(logits))