Added possibility to use fixed image standardization

src/train_softmax.py

@@ -238,7 +238,7 @@ def main(args):
                     learning_rate_placeholder, phase_train_placeholder, batch_size_placeholder, control_placeholder, global_step, 
                     total_loss, train_op, summary_op, summary_writer, regularization_losses, args.learning_rate_schedule_file,
                     stat, cross_entropy_mean, accuracy, learning_rate,
-                    prelogits, prelogits_center_loss, args.random_rotate, args.random_crop, args.random_flip, prelogits_norm, args.prelogits_hist_max)
+                    prelogits, prelogits_center_loss, args.random_rotate, args.random_crop, args.random_flip, prelogits_norm, args.prelogits_hist_max, args.use_fixed_image_standardization)
                 stat['time_train'][epoch-1] = time.time() - t
 
                 if not cont:
@@ -248,7 +248,7 @@ def main(args):
                 if len(val_image_list)>0 and ((epoch-1) % args.validate_every_n_epochs == args.validate_every_n_epochs-1 or epoch==args.max_nrof_epochs):
                     validate(args, sess, epoch, val_image_list, val_label_list, enqueue_op, image_paths_placeholder, labels_placeholder, control_placeholder,
                         phase_train_placeholder, batch_size_placeholder, 
-                        stat, total_loss, regularization_losses, cross_entropy_mean, accuracy, args.validate_every_n_epochs)
+                        stat, total_loss, regularization_losses, cross_entropy_mean, accuracy, args.validate_every_n_epochs, args.use_fixed_image_standardization)
                 stat['time_validate'][epoch-1] = time.time() - t
 
                 # Save variables and the metagraph if it doesn't exist already
@@ -259,7 +259,7 @@ def main(args):
                 if args.lfw_dir:
                     evaluate(sess, enqueue_op, image_paths_placeholder, labels_placeholder, phase_train_placeholder, batch_size_placeholder, control_placeholder, 
                         embeddings, label_batch, lfw_paths, actual_issame, args.lfw_batch_size, args.lfw_nrof_folds, log_dir, step, summary_writer, stat, epoch, 
-                        args.lfw_distance_metric, args.lfw_subtract_mean, args.lfw_use_flipped_images)
+                        args.lfw_distance_metric, args.lfw_subtract_mean, args.lfw_use_flipped_images, args.use_fixed_image_standardization)
                 stat['time_evaluate'][epoch-1] = time.time() - t
 
                 print('Saving statistics')
@@ -304,7 +304,7 @@ def train(args, sess, epoch, image_list, label_list, index_dequeue_op, enqueue_o
       learning_rate_placeholder, phase_train_placeholder, batch_size_placeholder, control_placeholder, step, 
       loss, train_op, summary_op, summary_writer, reg_losses, learning_rate_schedule_file, 
       stat, cross_entropy_mean, accuracy, 
-      learning_rate, prelogits, prelogits_center_loss, random_rotate, random_crop, random_flip, prelogits_norm, prelogits_hist_max):
+      learning_rate, prelogits, prelogits_center_loss, random_rotate, random_crop, random_flip, prelogits_norm, prelogits_hist_max, use_fixed_image_standardization):
     batch_number = 0
 
     if args.learning_rate>0.0:
@@ -322,7 +322,7 @@ def train(args, sess, epoch, image_list, label_list, index_dequeue_op, enqueue_o
     # Enqueue one epoch of image paths and labels
     labels_array = np.expand_dims(np.array(label_epoch),1)
     image_paths_array = np.expand_dims(np.array(image_epoch),1)
-    control_value = facenet.RANDOM_ROTATE * random_rotate + facenet.RANDOM_CROP * random_crop + facenet.RANDOM_FLIP * random_flip
+    control_value = facenet.RANDOM_ROTATE * random_rotate + facenet.RANDOM_CROP * random_crop + facenet.RANDOM_FLIP * random_flip + facenet.FIXED_STANDARDIZATION * use_fixed_image_standardization
     control_array = np.ones_like(labels_array) * control_value
     sess.run(enqueue_op, {image_paths_placeholder: image_paths_array, labels_placeholder: labels_array, control_placeholder: control_array})
 
@@ -362,7 +362,7 @@ def train(args, sess, epoch, image_list, label_list, index_dequeue_op, enqueue_o
 
 def validate(args, sess, epoch, image_list, label_list, enqueue_op, image_paths_placeholder, labels_placeholder, control_placeholder,
              phase_train_placeholder, batch_size_placeholder, 
-             stat, loss, regularization_losses, cross_entropy_mean, accuracy, validate_every_n_epochs):
+             stat, loss, regularization_losses, cross_entropy_mean, accuracy, validate_every_n_epochs, use_fixed_image_standardization):
 
     print('Running forward pass on validation set')
 
@@ -372,7 +372,7 @@ def validate(args, sess, epoch, image_list, label_list, enqueue_op, image_paths_
     # Enqueue one epoch of image paths and labels
     labels_array = np.expand_dims(np.array(label_list[:nrof_images]),1)
     image_paths_array = np.expand_dims(np.array(image_list[:nrof_images]),1)
-    control_array = np.zeros_like(labels_array, np.int32)
+    control_array = np.ones_like(labels_array, np.int32)*facenet.FIXED_STANDARDIZATION * use_fixed_image_standardization
     sess.run(enqueue_op, {image_paths_placeholder: image_paths_array, labels_placeholder: labels_array, control_placeholder: control_array})
 
     loss_array = np.zeros((nrof_batches,), np.float32)
@@ -402,7 +402,7 @@ def validate(args, sess, epoch, image_list, label_list, enqueue_op, image_paths_
 
 
 def evaluate(sess, enqueue_op, image_paths_placeholder, labels_placeholder, phase_train_placeholder, batch_size_placeholder, control_placeholder, 
-        embeddings, labels, image_paths, actual_issame, batch_size, nrof_folds, log_dir, step, summary_writer, stat, epoch, distance_metric, subtract_mean, use_flipped_images):
+        embeddings, labels, image_paths, actual_issame, batch_size, nrof_folds, log_dir, step, summary_writer, stat, epoch, distance_metric, subtract_mean, use_flipped_images, use_fixed_image_standardization):
     start_time = time.time()
     # Run forward pass to calculate embeddings
     print('Runnning forward pass on LFW images')
@@ -413,11 +413,12 @@ def evaluate(sess, enqueue_op, image_paths_placeholder, labels_placeholder, phas
     nrof_images = nrof_embeddings * nrof_flips
     labels_array = np.expand_dims(np.arange(0,nrof_images),1)
     image_paths_array = np.expand_dims(np.repeat(np.array(image_paths),nrof_flips),1)
+    control_array = np.zeros_like(labels_array, np.int32)
+    if use_fixed_image_standardization:
+        control_array += np.ones_like(labels_array)*facenet.FIXED_STANDARDIZATION
     if use_flipped_images:
         # Flip every second image
-        control_array = (labels_array % 2)*16
-    else:
-        control_array = np.zeros_like(labels_array)
+        control_array += (labels_array % 2)*facenet.FLIP
     sess.run(enqueue_op, {image_paths_placeholder: image_paths_array, labels_placeholder: labels_array, control_placeholder: control_array})
 
     embedding_size = int(embeddings.get_shape()[1])
@@ -516,6 +517,8 @@ def parse_arguments(argv):
         help='Performs random horizontal flipping of training images.', action='store_true')
     parser.add_argument('--random_rotate', 
         help='Performs random rotations of training images.', action='store_true')
+    parser.add_argument('--use_fixed_image_standardization', 
+        help='Performs fixed standardization of images.', action='store_true')
     parser.add_argument('--keep_probability', type=float,
         help='Keep probability of dropout for the fully connected layer(s).', default=1.0)
     parser.add_argument('--weight_decay', type=float,