From bb9d6b928e8ea2ead95cdad14e1b68dd870d16c1 Mon Sep 17 00:00:00 2001
From: Jules Laplace <julescarbon@gmail.com>
Date: Mon, 14 May 2018 23:34:27 +0200
Subject: okayyyyyyy

---
 model.py | 32 +++-----------------------------
 1 file changed, 3 insertions(+), 29 deletions(-)

(limited to 'model.py')

diff --git a/model.py b/model.py
index 8592ddc..0a3dc54 100644
--- a/model.py
+++ b/model.py
@@ -329,25 +329,18 @@ class PrimedGenerator(Runner):
         q_max = q_levels
 
         print("_______-___-_---_-____")
-        print("_____________--_-_-_______")
         print("INITTTTTTTT {}".format(primer))
-        if recursive:
-            print("RECURSIVE")
         print(sequences.shape)
         print("__________________--_-__--_________________")
         print("__-__________-_______________")
 
         def _noise(x):
             for i in range(n_samples):
-                if (i % 1000) == 0:
-                    print("{}...".format(i))
                 x[:, i] = int(random.triangular(q_min, q_max))
             return x
         def _sin(x):
             primer_freq = float(prime_param_a)
             for i in range(n_samples):
-                if (i % 1000) == 0:
-                    print("{}...".format(i))
                 x[:, i] = int((math.sin(i/44100 * primer_freq) + 1) / 2 * (q_max - q_min) + q_min)
             return x
 
@@ -357,19 +350,14 @@ class PrimedGenerator(Runner):
           'sin': _sin,
         }
 
-        print("BUILDING SEQUENCES")
-
         sequences = sequence_lookup.get(primer, 'zero')(sequences)
         # out_sequences = sequence_lookup.get(primer, 'zero')(out_sequences)
         out_sequences[:, :self.model.lookback] = sequences[:, :self.model.lookback]
 
-        print("BUILT....")
-
         # here we are generating the subsequence each time,
         # but maybe we want to generate the subsequence bottom_frame_size length
         # and then draw from that, although this will really emphasize the recursion
         def get_sub_sequence(i, n):
-            print("get subsequence {} {}".format(i, n))
             sub_sequence_a = sequences[:, i-n : i]        # generated
             sub_sequence_b = out_sequences[:, i-n : i]    # recursive
             tmp_sub_sequence = tmp_sequences[:, i-n : i]  # tmp/output
@@ -382,11 +370,7 @@ class PrimedGenerator(Runner):
 
             return tmp_sub_sequence
 
-        print("ENTERING LOOP....")
-
         for i in range(self.model.lookback, self.model.lookback + seq_len):
-            # if (i % 100) == 0:
-            print("{}...".format(i))
 
             for (tier_index, rnn) in \
                     reversed(list(enumerate(self.model.frame_level_rnns))):
@@ -417,11 +401,8 @@ class PrimedGenerator(Runner):
                 frame_level_outputs[tier_index] = self.run_rnn(
                     rnn, prev_samples, upper_tier_conditioning
                 )
-                print("ran rnn")
 
-            print("at bottom frame")
-            # sub_sequence = get_sub_sequence(i, bottom_frame_size)
-            sub_sequence = sequences[:, i-bottom_frame_size : i]
+            sub_sequence = get_sub_sequence(i, bottom_frame_size)
 
             prev_samples = torch.autograd.Variable(
                 sub_sequence,
@@ -430,21 +411,14 @@ 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
-            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
+
+            out_sequences[:, i] = sample_dist.multinomial(1).squeeze(1)
 
         torch.backends.cudnn.enabled = True
 
-- 
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