simpler gmm attention implementaiton

config.json

@@ -108,7 +108,7 @@
         [
             {
                 "name": "ljspeech",
-                "path": "/data5/ro/shared/data/keithito/LJSpeech-1.1/",
+                "path": "/root/LJSpeech-1.1/",
                 // "path": "/home/erogol/Data/LJSpeech-1.1",
                 "meta_file_train": "metadata_train.csv",
                 "meta_file_val": "metadata_val.csv"

layers/common_layers.py

@@ -110,6 +110,84 @@ class LocationLayer(nn.Module):
         return processed_attention
 
 
+class GravesAttention(nn.Module):
+    """ Graves attention as described here:
+        - https://arxiv.org/abs/1910.10288
+    """
+    COEF = 0.3989422917366028  # numpy.sqrt(1/(2*numpy.pi))
+
+    def __init__(self, query_dim, K):
+        super(GravesAttention, self).__init__()
+        self._mask_value = 0.0
+        self.K = K
+        # self.attention_alignment = 0.05
+        self.eps = 1e-5
+        self.J = None
+        self.N_a = nn.Sequential(
+            nn.Linear(query_dim, query_dim, bias=True),
+            nn.ReLU(),
+            nn.Linear(query_dim, 3*K, bias=True))
+        self.attention_weights = None
+        self.mu_prev = None
+        self.init_layers()
+
+    def init_layers(self):
+        torch.nn.init.constant_(self.N_a[2].bias[(2*self.K):(3*self.K)], 1.)
+        torch.nn.init.constant_(self.N_a[2].bias[self.K:(2*self.K)], 10)
+
+    def init_states(self, inputs):
+        if self.J is None or inputs.shape[1] > self.J.shape[-1]:
+            self.J = torch.arange(0, inputs.shape[1]+1).to(inputs.device) + 0.5
+        self.attention_weights = torch.zeros(inputs.shape[0], inputs.shape[1]).to(inputs.device)
+        self.mu_prev = torch.zeros(inputs.shape[0], self.K).to(inputs.device)
+
+    # pylint: disable=R0201
+    # pylint: disable=unused-argument
+    def preprocess_inputs(self, inputs):
+        return None
+
+    def forward(self, query, inputs, processed_inputs, mask):
+        """
+        shapes:
+            query: B x D_attention_rnn
+            inputs: B x T_in x D_encoder
+            processed_inputs: place_holder
+            mask: B x T_in
+        """
+        gbk_t = self.N_a(query)
+        gbk_t = gbk_t.view(gbk_t.size(0), -1, self.K)
+
+        # attention model parameters
+        # each B x K
+        g_t = gbk_t[:, 0, :]
+        b_t = gbk_t[:, 1, :]
+        k_t = gbk_t[:, 2, :]
+
+        # attention GMM parameters
+        sig_t = torch.nn.functional.softplus(b_t) + self.eps
+
+        mu_t = self.mu_prev + torch.nn.functional.softplus(k_t)
+        g_t = torch.softmax(g_t, dim=-1) / sig_t + self.eps
+
+        j = self.J[:inputs.size(1)+1]
+
+        # attention weights
+        phi_t = g_t.unsqueeze(-1) * torch.exp(-0.5 * (mu_t.unsqueeze(-1) - j)**2 / (sig_t.unsqueeze(-1)**2))
+
+        # discritize attention weights
+        alpha_t = self.COEF * torch.sum(phi_t, 1)
+        alpha_t = alpha_t[:, 1:] - alpha_t[:, :-1]
+
+        # apply masking
+        if mask is not None:
+            alpha_t.data.masked_fill_(~mask, self._mask_value)
+
+        context = torch.bmm(alpha_t.unsqueeze(1), inputs).squeeze(1)
+        self.attention_weights = alpha_t
+        self.mu_prev = mu_t
+        return context
+
+
 class OriginalAttention(nn.Module):
     """Following the methods proposed here:
         - https://arxiv.org/abs/1712.05884