model.py: renaming variables, removing dropout from lstm cell state, removing conversions now handled by amp

model.py

@@ -5,7 +5,6 @@ from torch import nn
 from torch.nn import functional as F
 from layers import ConvNorm, LinearNorm
 from utils import to_gpu, get_mask_from_lengths
-from fp16_optimizer import fp32_to_fp16, fp16_to_fp32
 
 
 class LocationLayer(nn.Module):
@@ -355,8 +354,6 @@ class Decoder(nn.Module):
             cell_input, (self.attention_hidden, self.attention_cell))
         self.attention_hidden = F.dropout(
             self.attention_hidden, self.p_attention_dropout, self.training)
-        self.attention_cell = F.dropout(
-            self.attention_cell, self.p_attention_dropout, self.training)
 
         attention_weights_cat = torch.cat(
             (self.attention_weights.unsqueeze(1),
@@ -372,8 +369,6 @@ class Decoder(nn.Module):
             decoder_input, (self.decoder_hidden, self.decoder_cell))
         self.decoder_hidden = F.dropout(
             self.decoder_hidden, self.p_decoder_dropout, self.training)
-        self.decoder_cell = F.dropout(
-            self.decoder_cell, self.p_decoder_dropout, self.training)
 
         decoder_hidden_attention_context = torch.cat(
             (self.decoder_hidden, self.attention_context), dim=1)
@@ -489,10 +484,6 @@ class Tacotron2(nn.Module):
             (text_padded, input_lengths, mel_padded, max_len, output_lengths),
             (mel_padded, gate_padded))
 
-    def parse_input(self, inputs):
-        inputs = fp32_to_fp16(inputs) if self.fp16_run else inputs
-        return inputs
-
     def parse_output(self, outputs, output_lengths=None):
         if self.mask_padding and output_lengths is not None:
             mask = ~get_mask_from_lengths(output_lengths)
@@ -503,20 +494,18 @@ class Tacotron2(nn.Module):
             outputs[1].data.masked_fill_(mask, 0.0)
             outputs[2].data.masked_fill_(mask[:, 0, :], 1e3)  # gate energies
 
-        outputs = fp16_to_fp32(outputs) if self.fp16_run else outputs
         return outputs
 
     def forward(self, inputs):
-        inputs, input_lengths, targets, max_len, \
-            output_lengths = self.parse_input(inputs)
-        input_lengths, output_lengths = input_lengths.data, output_lengths.data
+        text_inputs, text_lengths, mels, max_len, output_lengths = inputs
+        text_lengths, output_lengths = text_lengths.data, output_lengths.data
 
-        embedded_inputs = self.embedding(inputs).transpose(1, 2)
+        embedded_inputs = self.embedding(text_inputs).transpose(1, 2)
 
-        encoder_outputs = self.encoder(embedded_inputs, input_lengths)
+        encoder_outputs = self.encoder(embedded_inputs, text_lengths)
 
         mel_outputs, gate_outputs, alignments = self.decoder(
-            encoder_outputs, targets, memory_lengths=input_lengths)
+            encoder_outputs, mels, memory_lengths=text_lengths)
 
         mel_outputs_postnet = self.postnet(mel_outputs)
         mel_outputs_postnet = mel_outputs + mel_outputs_postnet
@@ -526,7 +515,6 @@ class Tacotron2(nn.Module):
             output_lengths)
 
     def inference(self, inputs):
-        inputs = self.parse_input(inputs)
         embedded_inputs = self.embedding(inputs).transpose(1, 2)
         encoder_outputs = self.encoder.inference(embedded_inputs)
         mel_outputs, gate_outputs, alignments = self.decoder.inference(