bug fix

aaa_helpers.txt

@@ -1,3 +1,5 @@
+nohup python auto_encoder_static.py --name="lhunor_ae_static_r7" > nohup/review/ae_static_r7.out 2>&1 &
+
 one final convolutional layer for smoothing, on the final coordinates
 
 skirts!!!!

auto_encoder_static.py

@@ -37,7 +37,7 @@ from models.cond_encoder import AutoEncoderNew as AutoEncoder
 
 
 def init_wandb():
-    run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/home/workspace/local-data/temp')
+    run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/home/workspace/local-data/temp', tags=config['tags'])
     artifact = wandb.Artifact('code', type='code')
     artifact.add_file('auto_encoder_static.py')
     artifact.add_file('losses.py')
@@ -45,7 +45,7 @@ def init_wandb():
     artifact.add_file('models/cond_encoder.py')
     artifact.add_file('dataset.py')
     artifact.add_file('convnext/models/convnext_tf.py')
-    config_file_names = [key for key in sys.modules.keys()  if key.startswith('configs.')]
+    config_file_names = [key for key in sys.modules.keys()  if key.startswith('configs.review.')]
     for config_file_name in config_file_names:
         artifact.add_file('/'.join(config_file_name.split('.')) + '.py')
     run.log_artifact(artifact)
@@ -138,8 +138,10 @@ def train():
         if step == 0:
             to_log = { **to_log, **{ 'model/summary': wandb.Html(get_summary_html(model)) } }
 
-            
-        wandb.log(to_log, step=step)
+        if step % 100:    
+            wandb.log(to_log, step=step, commit=True)
+        else:
+            wandb.log(to_log, step=step, commit=False)
 
         if step % 10000 == 0:
             manager_enc.save(checkpoint_number=step)

configs/review/config_ae_static.py

@@ -34,12 +34,10 @@ config['cond_ae']['activation'] = 'linear'
 config['gpu'] = '6'
 config['lr'] = 0.0005
 config['name'] = 'static_r7'
-config['notes'] = 'convnext + convnext, | \
- \
-| \
-    deeper model | \
-linear activation| \
-    ' 
+config['notes'] = 'resnet18 based'
+config['tags'] = ['review_7']
+
+    
 
 config['cond_ae']['upscale_output'] = False
 config['pretrained_model'] = ''

models/resnet.py

@@ -175,8 +175,9 @@ class ResNetTypeI(tf.keras.Model):
 
             x = self.pool1(x)
             x = self.bn1(x, training=training)
-            x = tf.nn.relu(x)
+            # x = tf.nn.relu(x)
             x = self.conv1(x)
+            x = tf.nn.tanh(x)
 
         return x