Fix create_input_pipeline

See davidsandberg#852 (comment)
ppwwyyxx · Dec 1, 2018 · c57e822 · c57e822
1 parent 096ed77
commit c57e822
Showing 1 changed file with 32 additions and 32 deletions.
diff --git a/src/facenet.py b/src/facenet.py
@@ -101,39 +101,39 @@ def random_rotate_image(image):
 FIXED_STANDARDIZATION = 8
 FLIP = 16
 def create_input_pipeline(input_queue, image_size, nrof_preprocess_threads, batch_size_placeholder):
-    images_and_labels_list = []
-    for _ in range(nrof_preprocess_threads):
-        filenames, label, control = input_queue.dequeue()
-        images = []
-        for filename in tf.unstack(filenames):
-            file_contents = tf.read_file(filename)
-            image = tf.image.decode_image(file_contents, 3)
-            image = tf.cond(get_control_flag(control[0], RANDOM_ROTATE),
-                            lambda:tf.py_func(random_rotate_image, [image], tf.uint8), 
-                            lambda:tf.identity(image))
-            image = tf.cond(get_control_flag(control[0], RANDOM_CROP), 
-                            lambda:tf.random_crop(image, image_size + (3,)), 
-                            lambda:tf.image.resize_image_with_crop_or_pad(image, image_size[0], image_size[1]))
-            image = tf.cond(get_control_flag(control[0], RANDOM_FLIP),
-                            lambda:tf.image.random_flip_left_right(image),
-                            lambda:tf.identity(image))
-            image = tf.cond(get_control_flag(control[0], FIXED_STANDARDIZATION),
-                            lambda:(tf.cast(image, tf.float32) - 127.5)/128.0,
-                            lambda:tf.image.per_image_standardization(image))
-            image = tf.cond(get_control_flag(control[0], FLIP),
-                            lambda:tf.image.flip_left_right(image),
-                            lambda:tf.identity(image))
-            #pylint: disable=no-member
-            image.set_shape(image_size + (3,))
-            images.append(image)
-        images_and_labels_list.append([images, label])
+    with tf.name_scope("scope_to_avoid_conflicts"):
+        images_and_labels_list = []
+        for _ in range(nrof_preprocess_threads):
+            filenames, label, control = input_queue.dequeue()
+            images = []
+            for filename in tf.unstack(filenames):
+                file_contents = tf.read_file(filename)
+                image = tf.image.decode_image(file_contents, 3)
+                image = tf.cond(get_control_flag(control[0], RANDOM_ROTATE),
+                                lambda:tf.py_func(random_rotate_image, [image], tf.uint8),
+                                lambda:tf.identity(image))
+                image = tf.cond(get_control_flag(control[0], RANDOM_CROP),
+                                lambda:tf.random_crop(image, image_size + (3,)),
+                                lambda:tf.image.resize_image_with_crop_or_pad(image, image_size[0], image_size[1]))
+                image = tf.cond(get_control_flag(control[0], RANDOM_FLIP),
+                                lambda:tf.image.random_flip_left_right(image),
+                                lambda:tf.identity(image))
+                image = tf.cond(get_control_flag(control[0], FIXED_STANDARDIZATION),
+                                lambda:(tf.cast(image, tf.float32) - 127.5)/128.0,
+                                lambda:tf.image.per_image_standardization(image))
+                image = tf.cond(get_control_flag(control[0], FLIP),
+                                lambda:tf.image.flip_left_right(image),
+                                lambda:tf.identity(image))
+                #pylint: disable=no-member
+                image.set_shape(image_size + (3,))
+                images.append(image)
+            images_and_labels_list.append([images, label])
 
-    image_batch, label_batch = tf.train.batch_join(
-        images_and_labels_list, batch_size=batch_size_placeholder, 
-        shapes=[image_size + (3,), ()], enqueue_many=True,
-        capacity=4 * nrof_preprocess_threads * 100,
-        allow_smaller_final_batch=True)
-
+        image_batch, label_batch = tf.train.batch_join(
+            images_and_labels_list, batch_size=batch_size_placeholder,
+            shapes=[image_size + (3,), ()], enqueue_many=True,
+            capacity=4 * nrof_preprocess_threads * 100,
+            allow_smaller_final_batch=True)
     return image_batch, label_batch
 
 def get_control_flag(control, field):