From 6917c6bd087daa5b3a140bdce0376cb1ea8d2cbc Mon Sep 17 00:00:00 2001
From: Jules Laplace <julescarbon@gmail.com>
Date: Mon, 14 May 2018 23:36:49 +0200
Subject: okayyyyyyy

---
 gen-prime.sh |  4 ++--
 model.py     | 15 +++++++++++++--
 2 files changed, 15 insertions(+), 4 deletions(-)

diff --git a/gen-prime.sh b/gen-prime.sh
index 19ef340..3f1ee11 100755
--- a/gen-prime.sh
+++ b/gen-prime.sh
@@ -54,8 +54,8 @@ function gen_prime_set () {
   # gen_prime $1 6 44100 'zero'
   # gen_prime $1 6 44100 'noise'
   # gen_prime $1 6 44100 'sin' 440
-  gen_prime $1 1 100 'noise' 0 0 True
-  #gen_prime $1 6 100 'sin' 440 0 True
+  gen_prime $1 6 44100 'noise' 0 0 True
+  gen_prime $1 6 44100 'sin' 440 0 True
 }
 
 gen_prime_set jwcglassbeat
diff --git a/model.py b/model.py
index 0a3dc54..12c0e05 100644
--- a/model.py
+++ b/model.py
@@ -329,7 +329,10 @@ class PrimedGenerator(Runner):
         q_max = q_levels
 
         print("_______-___-_---_-____")
+        print("_____________--_-_-_______")
         print("INITTTTTTTT {}".format(primer))
+        if recursive:
+            print("RECURSIVE")
         print(sequences.shape)
         print("__________________--_-__--_________________")
         print("__-__________-_______________")
@@ -403,6 +406,7 @@ class PrimedGenerator(Runner):
                 )
 
             sub_sequence = get_sub_sequence(i, bottom_frame_size)
+            # sub_sequence = sequences[:, i-bottom_frame_size : i]
 
             prev_samples = torch.autograd.Variable(
                 sub_sequence,
@@ -411,14 +415,21 @@ class PrimedGenerator(Runner):
             if self.cuda:
                 prev_samples = prev_samples.cuda()
 
+            print("get upper tier conditioning.. {}".format(i % bottom_frame_size))
             upper_tier_conditioning = \
                 frame_level_outputs[0][:, i % bottom_frame_size, :] \
                                       .unsqueeze(1)
+            print(upper_tier_conditioning.shape)
             sample_dist = self.model.sample_level_mlp(
                 prev_samples, upper_tier_conditioning
             ).squeeze(1).exp_().data
-
-            out_sequences[:, i] = sample_dist.multinomial(1).squeeze(1)
+            print(sample_dist.shape)
+            multi = sample_dist.multinomial(1)
+            print(multi.shape)
+            pred = multi.squeeze(1)
+            print(pred.shape)
+            print(out_sequences.shape)
+            out_sequences[:, i] = pred
 
         torch.backends.cudnn.enabled = True
 
-- 
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