From 7ba3316d070965d2b916019e6cf667428c949dee Mon Sep 17 00:00:00 2001
From: Garnet <38901503+GreenGarnets@users.noreply.github.com>
Date: Sun, 12 Jan 2020 02:18:24 +0900
Subject: [PATCH] add cnn layer

---
 train.py | 15 ++++++++++-----
 1 file changed, 10 insertions(+), 5 deletions(-)

diff --git a/train.py b/train.py
index 2f54407..af9072f 100644
--- a/train.py
+++ b/train.py
@@ -68,8 +68,8 @@ def infer(model, filename) :
     song.synthesize(diff='ka')
     song.save(filename = "infer.wav")
 
-if __name__ == "__main__":
-
+def main():
+    
     model = voltexNet()
     #model.load_state_dict(torch.load("./model/model_bestAcc_.pth"))
     #print ("load model")
@@ -81,7 +81,7 @@ def infer(model, filename) :
     #input = torch.rand(128,3,80,15)
 
     criterion = nn.CrossEntropyLoss()
-    optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
+    optimizer = optim.SGD(model.parameters(), lr=0.2, momentum=0.9)
     scheduler = lr_scheduler.StepLR(optimizer, step_size=200, gamma=0.1)
 
     # kshDataset에서는 (곡의 길이 * 25 = batch) * (sample rate * 0.04)의 tensor가 넘어옴
@@ -97,7 +97,7 @@ def infer(model, filename) :
     filenames = os.listdir(dirname)
     valnames = os.listdir(valname)
 
-    batch = 256
+    batch = 512
     song_index = 0
     best_Acc = 0
 
@@ -115,6 +115,7 @@ def infer(model, filename) :
             #print(full_filename)
 
             input = KshDataset.music_load(full_filename + '/nofx.ogg')
+            #print(input.shape)
             input = input.reshape(input.shape[0], 1, -1)       
             target = KshDataset.timeStamp(full_filename + '/exh.ksh', input.shape[0]) 
             try : 
@@ -136,6 +137,7 @@ def infer(model, filename) :
 
                     pred = model(input[i:i+batch],batch)
                     
+                    
                     loss = criterion(pred.squeeze(), target[i:i+batch].squeeze())
                     loss.backward()
                     optimizer.step()
@@ -200,7 +202,7 @@ def infer(model, filename) :
         if acc != 0 :
             acc = acc / (len(valnames) - noneScore)
                 
-        print("epoch : " + str(epoch) + "\ttrain_loss : " + str(epoch_loss/index) + "\tacc : " + str(acc) + "\n")
+        print("epoch : " + str(epoch) + "\ttrain_loss : " + str(epoch_loss/index) + "\ttest_acc : " + str(acc) + "\n")
         
         if acc > best_Acc :
             best_Acc = acc
@@ -215,4 +217,7 @@ def infer(model, filename) :
     infer(mode, "./data_test/songs/badapple_nomico_alreco")
     
 
+
+if __name__ == "__main__":
+    main()
     
\ No newline at end of file