Add no augmentation sources

Add the possibility to exclude some sources from augmentation by passing a list of sources. This is useful when you want to retrain a model having few images.

mrcnn/model.py

@@ -1636,7 +1636,8 @@ def generate_random_rois(image_shape, count, gt_class_ids, gt_boxes):
 
 
 def data_generator(dataset, config, shuffle=True, augment=False, augmentation=None,
-                   random_rois=0, batch_size=1, detection_targets=False):
+                   random_rois=0, batch_size=1, detection_targets=False,
+                   no_augmentation_sources=[]):
     """A generator that returns images and corresponding target class ids,
     bounding box deltas, and masks.
 
@@ -1673,6 +1674,8 @@ def data_generator(dataset, config, shuffle=True, augment=False, augmentation=No
     outputs list: Usually empty in regular training. But if detection_targets
         is True then the outputs list contains target class_ids, bbox deltas,
         and masks.
+
+    no_augmentation_sources: (list) Optional. List of sources to be skipped for augmentation
     """
     b = 0  # batch item index
     image_index = -1
@@ -1698,10 +1701,18 @@ def data_generator(dataset, config, shuffle=True, augment=False, augmentation=No
 
             # Get GT bounding boxes and masks for image.
             image_id = image_ids[image_index]
-            image, image_meta, gt_class_ids, gt_boxes, gt_masks = \
+
+            # If the image source is not to be augmented pass None as augmentation
+            if dataset.image_info[image_id]['source'] in no_augmentation_sources:
+                image, image_meta, gt_class_ids, gt_boxes, gt_masks = \
                 load_image_gt(dataset, config, image_id, augment=augment,
-                              augmentation=augmentation,
+                              augmentation=None,
                               use_mini_mask=config.USE_MINI_MASK)
+            else:
+                image, image_meta, gt_class_ids, gt_boxes, gt_masks = \
+                    load_image_gt(dataset, config, image_id, augment=augment,
+                                augmentation=augmentation,
+                                use_mini_mask=config.USE_MINI_MASK)
 
             # Skip images that have no instances. This can happen in cases
             # where we train on a subset of classes and the image doesn't
@@ -2272,7 +2283,7 @@ def set_log_dir(self, model_path=None):
             "*epoch*", "{epoch:04d}")
 
     def train(self, train_dataset, val_dataset, learning_rate, epochs, layers,
-              augmentation=None, custom_callbacks=[]):
+              augmentation=None, custom_callbacks=[], no_augmentation_sources=[]):
         """Train the model.
         train_dataset, val_dataset: Training and validation Dataset objects.
         learning_rate: The learning rate to train with
@@ -2299,8 +2310,10 @@ def train(self, train_dataset, val_dataset, learning_rate, epochs, layers,
                     imgaug.augmenters.Fliplr(0.5),
                     imgaug.augmenters.GaussianBlur(sigma=(0.0, 5.0))
                 ])
-	custom_callbacks: (list) Optional. Add custom callbacks to be called
-	    with the keras fit_generator method. Must be list of type keras.callbacks. 
+	    custom_callbacks: (list) Optional. Add custom callbacks to be called
+	        with the keras fit_generator method. Must be list of type keras.callbacks. 
+
+        no_augmentation_sources: (list) Optional. List of sources to be skipped for augmentation
 
                 
         """
@@ -2323,9 +2336,11 @@ def train(self, train_dataset, val_dataset, learning_rate, epochs, layers,
         # Data generators
         train_generator = data_generator(train_dataset, self.config, shuffle=True,
                                          augmentation=augmentation,
-                                         batch_size=self.config.BATCH_SIZE)
+                                         batch_size=self.config.BATCH_SIZE,
+                                         no_augmentation_sources=no_augmentation_sources)
         val_generator = data_generator(val_dataset, self.config, shuffle=True,
-                                       batch_size=self.config.BATCH_SIZE)
+                                       batch_size=self.config.BATCH_SIZE,
+                                       no_augmentation_sources=no_augmentation_sources)
 
         # Callbacks
         callbacks = [