Update src/music/pipeline/encoded2rep.py

src/music/pipeline/encoded2rep.py

@@ -45,44 +45,45 @@ def encoded2rep(encoded_path, rep_path=None, return_rep=False, verbose=False, le
 
     error_msg = 'Error in music transformer mapping.'
     if verbose: print(' ' * level + 'Mapping to final music representations')
-    try:
-        error_msg += ' Error in encoded file loading?'
-        with open(encoded_path, 'rb') as f:
-            data = pickle.load(f)
-        performance = [str(w) for w in data['main'] if w != 1]
-        assert len(performance) % 5 == 0
-        if(len(performance) == 0):
-            error_msg += " Error: No midi messages in primer file"
-            assert False
-        error_msg += ' Nope, error in tokenization?'
-        perf = ' '.join(performance)
-        # tokenized = torch.IntTensor(TOKENIZER.encode(perf)).unsqueeze(dim=0)
-        error_msg += ' Nope. Maybe in performance encoding?'
-        # reps = []
-        # for i_chunk in range(min(tokenized.shape[1] // 510 - 1, 8)):
-        #     chunk_tokenized = tokenized[:, i_chunk * 510: (i_chunk + 1) * 510 + 2]
-        #     rep = MODEL(chunk_tokenized)
-        #     reps.append(rep.detach().numpy())
-        # representation = np.mean(reps, axis=0)
-        p = [int(p) for p in perf.split(' ')]
-        # print('PERF:', np.sum(p), perf)
-        representation = MODEL.encode(perf)
-        # print('model weights sum: ', np.sum([param.detach().data.numpy().sum() for param in list(MODEL.parameters())]))
-        # print('reprep', representation)
-        error_msg += ' Nope. Saving performance?'
-        np.savetxt(rep_path, representation)
-        error_msg += ' Nope.'
-        if verbose: print(' ' * (level + 2) + 'Success.')
-        if return_rep:
-            return rep_path, representation, ''
-        else:
-            return rep_path, ''
-    except:
-        if verbose: print(' ' * (level + 2) + f'Failed with error: {error_msg}')
-        if return_rep:
-            return None, None, error_msg
-        else:
-            return None, error_msg
+   # try:
+    error_msg += ' Error in encoded file loading?'
+    with open(encoded_path, 'rb') as f:
+        data = pickle.load(f)
+    performance = [str(w) for w in data['main'] if w != 1]
+    assert len(performance) % 5 == 0
+    if(len(performance) == 0):
+        error_msg += " Error: No midi messages in primer file"
+        assert False
+    error_msg += ' Nope, error in tokenization?'
+    perf = ' '.join(performance)
+    # tokenized = torch.IntTensor(TOKENIZER.encode(perf)).unsqueeze(dim=0)
+    error_msg += ' Nope. Maybe in performance encoding?'
+    # reps = []
+    # for i_chunk in range(min(tokenized.shape[1] // 510 - 1, 8)):
+    #     chunk_tokenized = tokenized[:, i_chunk * 510: (i_chunk + 1) * 510 + 2]
+    #     rep = MODEL(chunk_tokenized)
+    #     reps.append(rep.detach().numpy())
+    # representation = np.mean(reps, axis=0)
+    p = [int(p) for p in perf.split(' ')]
+    # print('PERF:', np.sum(p), perf)
+    representation = MODEL.encode(perf)
+    # print('model weights sum: ', np.sum([param.detach().data.numpy().sum() for param in list(MODEL.parameters())]))
+    # print('reprep', representation)
+    error_msg += ' Nope. Saving performance?'
+    np.savetxt(rep_path, representation)
+    error_msg += ' Nope.'
+    if verbose: print(' ' * (level + 2) + 'Success.')
+    if return_rep:
+        return rep_path, representation, ''
+    else:
+        return rep_path, ''
+  
+    #except:
+     #   if verbose: print(' ' * (level + 2) + f'Failed with error: {error_msg}')
+      #  if return_rep:
+       #     return None, None, error_msg
+        #else:
+         #   return None, error_msg
 
 if __name__ == "__main__":
     representation = encoded2rep("/home/cedric/Documents/pianocktail/data/music/encoded/single_videos_midi_processed_encoded/chris_dawson_all_of_me_.pickle")