Raise clear error if last training weights are not foundIf using the --weights=last (or --model=last) to resume trainingbut the weights are not found now it raises a clear error message.

Author: waleedka

mrcnn/model.py

@@ -2062,26 +2062,30 @@ def find_last(self):
         """Finds the last checkpoint file of the last trained model in the
         model directory.
         Returns:
-            log_dir: The directory where events and weights are saved
-            checkpoint_path: the path to the last checkpoint file
+            The path of the last checkpoint file
         """
         # Get directory names. Each directory corresponds to a model
         dir_names = next(os.walk(self.model_dir))[1]
         key = self.config.NAME.lower()
         dir_names = filter(lambda f: f.startswith(key), dir_names)
         dir_names = sorted(dir_names)
         if not dir_names:
-            return None, None
+            import errno
+            raise FileNotFoundError(
+                errno.ENOENT,
+                "Could not find model directory under {}".format(self.model_dir))
         # Pick last directory
         dir_name = os.path.join(self.model_dir, dir_names[-1])
         # Find the last checkpoint
         checkpoints = next(os.walk(dir_name))[2]
         checkpoints = filter(lambda f: f.startswith("mask_rcnn"), checkpoints)
         checkpoints = sorted(checkpoints)
         if not checkpoints:
-            return dir_name, None
+            import errno
+            raise FileNotFoundError(
+                errno.ENOENT, "Could not find weight files in {}".format(dir_name))
         checkpoint = os.path.join(dir_name, checkpoints[-1])
-        return dir_name, checkpoint
+        return checkpoint
 
     def load_weights(self, filepath, by_name=False, exclude=None):
         """Modified version of the correspoding Keras function with

samples/balloon/balloon.py

@@ -336,7 +336,7 @@ class InferenceConfig(BalloonConfig):
             utils.download_trained_weights(weights_path)
     elif args.weights.lower() == "last":
         # Find last trained weights
-        weights_path = model.find_last()[1]
+        weights_path = model.find_last()
     elif args.weights.lower() == "imagenet":
         # Start from ImageNet trained weights
         weights_path = model.get_imagenet_weights()

samples/balloon/inspect_balloon_model.ipynb

@@ -265,7 +265,7 @@
     "# weights_path = \"/path/to/mask_rcnn_balloon.h5\"\n",
     "\n",
     "# Or, load the last model you trained\n",
-    "weights_path = model.find_last()[1]\n",
+    "weights_path = model.find_last()\n",
     "\n",
     "# Load weights\n",
     "print(\"Loading weights \", weights_path)\n",

samples/coco/coco.py

@@ -462,7 +462,7 @@ class InferenceConfig(CocoConfig):
         model_path = COCO_MODEL_PATH
     elif args.model.lower() == "last":
         # Find last trained weights
-        model_path = model.find_last()[1]
+        model_path = model.find_last()
     elif args.model.lower() == "imagenet":
         # Start from ImageNet trained weights
         model_path = model.get_imagenet_weights()

samples/coco/inspect_model.ipynb

@@ -270,7 +270,7 @@
     "elif config.NAME == \"coco\":\n",
     "    weights_path = COCO_MODEL_PATH\n",
     "# Or, uncomment to load the last model you trained\n",
-    "# weights_path = model.find_last()[1]\n",
+    "# weights_path = model.find_last()\n",
     "\n",
     "# Load weights\n",
     "print(\"Loading weights \", weights_path)\n",

samples/coco/inspect_weights.ipynb

@@ -150,7 +150,7 @@
     "elif config.NAME == \"coco\":\n",
     "    weights_path = COCO_MODEL_PATH\n",
     "# Or, uncomment to load the last model you trained\n",
-    "# weights_path = model.find_last()[1]\n",
+    "# weights_path = model.find_last()\n",
     "\n",
     "# Load weights\n",
     "print(\"Loading weights \", weights_path)\n",

samples/nucleus/inspect_nucleus_model.ipynb

@@ -258,7 +258,7 @@
     "# weights_path = \"/path/to/mask_rcnn_nucleus.h5\"\n",
     "\n",
     "# Or, load the last model you trained\n",
-    "weights_path = model.find_last()[1]\n",
+    "weights_path = model.find_last()\n",
     "\n",
     "# Load weights\n",
     "print(\"Loading weights \", weights_path)\n",

samples/nucleus/nucleus.py

@@ -464,7 +464,7 @@ def detect(model, dataset_dir, subset):
             utils.download_trained_weights(weights_path)
     elif args.weights.lower() == "last":
         # Find last trained weights
-        weights_path = model.find_last()[1]
+        weights_path = model.find_last()
     elif args.weights.lower() == "imagenet":
         # Start from ImageNet trained weights
         weights_path = model.get_imagenet_weights()

samples/shapes/train_shapes.ipynb

@@ -458,7 +458,7 @@
     "                                \"mrcnn_bbox\", \"mrcnn_mask\"])\n",
     "elif init_with == \"last\":\n",
     "    # Load the last model you trained and continue training\n",
-    "    model.load_weights(model.find_last()[1], by_name=True)"
+    "    model.load_weights(model.find_last(), by_name=True)"
    ]
   },
   {
@@ -875,10 +875,9 @@
     "# Get path to saved weights\n",
     "# Either set a specific path or find last trained weights\n",
     "# model_path = os.path.join(ROOT_DIR, \".h5 file name here\")\n",
-    "model_path = model.find_last()[1]\n",
+    "model_path = model.find_last()\n",
     "\n",
-    "# Load trained weights (fill in path to trained weights here)\n",
-    "assert model_path != \"\", \"Provide path to trained weights\"\n",
+    "# Load trained weights\n",
     "print(\"Loading weights from \", model_path)\n",
     "model.load_weights(model_path, by_name=True)"
    ]