lol typo

1 files changed, 7 insertions, 8 deletions

diff --git a/model.py b/model.py
index c84a3e5..1a6c14c 100644
--- a/model.py
+++ b/model.py
@@ -321,9 +321,6 @@ class PrimedGenerator(Runner):
         sequences = torch.LongTensor(n_seqs, n_samples) # 64-bit int
         frame_level_outputs = [None for _ in self.model.frame_level_rnns]
 
-        out_sequences = torch.LongTensor(n_seqs, n_samples).fill_(utils.q_zero(self.model.q_levels))
-        tmp_sequences = torch.LongTensor(n_seqs, n_samples).fill_(utils.q_zero(self.model.q_levels))
-
         q_levels = self.model.q_levels
         q_width = 64
         q_min = int(q_levels / 2 - q_width / 2)
@@ -369,6 +366,8 @@ class PrimedGenerator(Runner):
         }
 
         sequences = sequence_lookup.get(primer, 'zero')(sequences)
+        out_sequences = torch.LongTensor(n_seqs, n_samples).fill_(utils.q_zero(self.model.q_levels))
+        tmp_sequences = torch.LongTensor(n_seqs, n_samples).fill_(utils.q_zero(self.model.q_levels))
         # out_sequences = sequence_lookup.get(primer, 'zero')(out_sequences)
         out_sequences[:, :self.model.lookback] = sequences[:, :self.model.lookback]
 
@@ -383,7 +382,7 @@ class PrimedGenerator(Runner):
             pj = bottom_frame_size - n
             for j in range(n):
                 ratio = (pj + j) / (bottom_frame_size-1)
-                ratio *= ratio
+                # ratio *= ratio
                 a = sub_sequence_a[:, j].float() * (1-ratio)
                 b = sub_sequence_b[:, j].float() * ratio
                 tmp_sub_sequence[:, j] = torch.clamp(a + b, 0, q_levels).long()
@@ -399,8 +398,8 @@ class PrimedGenerator(Runner):
                 if i % rnn.n_frame_samples != 0:
                     continue
 
-                sub_sequence = get_sub_sequence(i, rnn.n_frame_samples)
-                # sub_sequence = out_sequences[:, i - rnn.n_frame_samples : i],
+                # sub_sequence = get_sub_sequence(i, rnn.n_frame_samples)
+                sub_sequence = out_sequences[:, i - rnn.n_frame_samples : i],
 
                 prev_samples = torch.autograd.Variable(
                     2 * utils.linear_dequantize(
@@ -425,8 +424,8 @@ class PrimedGenerator(Runner):
                     rnn, prev_samples, upper_tier_conditioning
                 )
 
-            # sub_sequence = get_sub_sequence(i, bottom_frame_size)
-            sub_sequence = out_sequences[:, i-bottom_frame_size : i]
+            sub_sequence = get_sub_sequence(i, bottom_frame_size)
+            # sub_sequence = out_sequences[:, i-bottom_frame_size : i]
 
             prev_samples = torch.autograd.Variable(
                 sub_sequence,