Fixed normalization using augment_fn

src/scripts/birdsong_simple_main.py

@@ -18,48 +18,29 @@ BINS = 1025
 N_FRAMES = 216
 N_CHANNELS = 2
 
-def preprocess_input(sample,n_classes,is_training):
+
+def augment_input(sample,n_classes,training):
     """Preprocess a single image of layout [height, width, depth]."""
     
     input_features = sample['input_features']
     labels = sample['labels']
     false_sample = sample['false_sample']
-    batch_size = tf.shape(labels)[0]
-    if is_training:
-        rnd = tf.random.uniform([batch_size])
-        rnd_tiled_feat = tf.tile(tf.reshape(rnd,[batch_size,1,1,1]),[1,BINS,
+    if training:
+        rnd = tf.random.uniform([1])
+        rnd_tiled_feat = tf.tile(tf.reshape(rnd,[1,1,1]),[BINS,
                                                     N_FRAMES,
                                                     N_CHANNELS])
-        rnd_tiled_lbl = tf.tile(tf.reshape(rnd,[batch_size,1]),[1,n_classes+1])
-        
-        false_lbl = tf.cast(tf.one_hot(n_classes+1,n_classes+1),tf.int32)
-        false_lbl = tf.tile(tf.reshape(false_lbl,[1,n_classes+1]),[batch_size,1])
-        
-        input_features = tf.where(rnd_tiled_feat > 1/n_classes,input_features,false_sample)
-        
-        labels = tf.where(rnd_tiled_lbl > 1/n_classes,labels,false_lbl)
 
-    return {'input_features':input_features,'labels':labels,'false_sample':false_sample}
+        rnd_tiled_lbl = tf.tile(tf.reshape(rnd,[1]),[n_classes+1])
 
-def augment_input(sample,n_classes):
-    """Preprocess a single image of layout [height, width, depth]."""
-    
-    input_features = sample['input_features']
-    labels = sample['labels']
-    false_sample = sample['false_sample']
+        false_lbl = tf.cast(tf.one_hot(n_classes+1,n_classes+1),labels.dtype)
 
-    rnd = tf.random.uniform([1])
-    rnd_tiled_feat = tf.tile(tf.reshape(rnd,[1,1,1]),[BINS,
-                                                N_FRAMES,
-                                                N_CHANNELS])
-    
-    rnd_tiled_lbl = tf.tile(tf.reshape(rnd,[1]),[n_classes+1])
-    
-    false_lbl = tf.cast(tf.one_hot(n_classes+1,n_classes+1),labels.dtype)
-    
-    input_features = tf.where(rnd_tiled_feat > 1/n_classes,input_features,false_sample)
-    
-    labels = tf.where(rnd_tiled_lbl > 1/n_classes,labels,false_lbl)
+        input_features = tf.where(rnd_tiled_feat > 1/n_classes,input_features,false_sample)
+
+        labels = tf.where(rnd_tiled_lbl > 1/n_classes,labels,false_lbl)
+
+    input_features = tf.image.per_image_standardization(input_features)
+    false_sample = tf.image.per_image_standardization(false_sample)
 
     return {'input_features':input_features,'labels':labels,'false_sample':false_sample}
 
@@ -148,15 +129,15 @@ def main(argv):
     ds_train = Dataset(data_dir,is_training_set = True)
     n_total = ds_train.n_samples
     
-    def augment_fn(sample):
-        return augment_input(sample,ds_train.n_classes)
+    def augment_fn(sample,training):
+        return augment_input(sample,ds_train.n_classes,training)
     
     dg_train = DataGenerator(ds_train,augment_fn,
                              training_percentage = training_percentage,
                              preload_samples = preload_samples,
                              is_training=True)
     
-    dg_val = DataGenerator(ds_train,None,
+    dg_val = DataGenerator(ds_train,augment_fn,
                            training_percentage = training_percentage,
                            is_validation = True,
                            preload_samples = preload_samples,
@@ -212,7 +193,7 @@ def main(argv):
                     discriminator = generator_model,
                     base_learning_rate_classifier = lr,
                     base_learning_rate_generator = lr*0.001,
-                    base_learning_rate_discriminator = lr*0.0001,
+                    base_learning_rate_discriminator = lr*0.001,
                     learning_rate_fn_classifier = learning_rate_fn,
                     learning_rate_fn_generator = learning_rate_fn,
                     learning_rate_fn_discriminator = learning_rate_fn,

src/utils/data_loader.py

@@ -405,7 +405,7 @@ class DataGenerator(object):
                 'labels':tf.one_hot(sample['bird_id'],self.dataset.n_classes+1),
                 'false_sample':false_spectra}
             if self.augmentation != None:
-                yield self.augmentation(sample)
+                yield self.augmentation(sample,self.is_training)
             else:
                 yield sample
 

src/utils/trainer.py

@@ -186,8 +186,9 @@ class ModelTrainer():
                     axis=-1,
                     ))
 
+        gen_loss = tf.reduce_mean((fake_features - x[1])**2)
         #Wasserstein losses
-        gen_loss = tf.reduce_mean(false)
+        gen_loss += tf.reduce_mean(false)
         discr_loss = tf.reduce_mean(true)-tf.reduce_mean(false)
         
         if len(self.classifier.losses) > 0: