Add tensorboard graph logging

.gitignore

@@ -0,0 +1,9 @@
+Data
+results
+cpp_wrappers/cpp_subsampling/build
+*.so
+__pycache__
+.vscode
+tensorboard
+*.pyc
+*.ply

models/network_blocks.py

@@ -1038,19 +1038,21 @@ def assemble_CNN_blocks(inputs, config, dropout_prob):
 
     # Loop over consecutive blocks
     block_in_layer = 0
+    tf_scope_layer = 0
     for block_i, block in enumerate(config.architecture):
 
         # Detect change to next layer
         if np.any([tmp in block for tmp in ['pool', 'strided', 'upsample', 'global']]):
-
             # Save this layer features
             F += [features]
+            block_in_layer = 0
+            tf_scope_layer += 1
 
         # Detect upsampling block to stop
         if 'upsample' in block:
             break
 
-        with tf.variable_scope('layer_{:d}/{:s}_{:d}'.format(layer, block, block_in_layer)):
+        with tf.variable_scope('layer_{:d}/{:s}_{:d}'.format(tf_scope_layer, block, block_in_layer)):
 
             # Get the function for this layer
             block_ops = get_block_ops(block)
@@ -1074,8 +1076,7 @@ def assemble_CNN_blocks(inputs, config, dropout_prob):
             layer += 1
             r *= 2
             fdim *= 2
-            block_in_layer = 0
-
+
         # Save feature vector after global pooling
         if 'global' in block:
             # Save this layer features

utils/trainer.py

@@ -101,7 +101,9 @@ def __init__(self, model, restore_snap=None):
             restorer = tf.train.Saver(restore_vars)
             restorer.restore(self.sess, restore_snap)
             print("Model restored.")
-
+
+        file_writer = tf.summary.FileWriter(join(model.saving_path,"tensorboard"), self.sess.graph)
+        file_writer.close()
     def add_train_ops(self, model):
         """
         Add training ops on top of the model