update panoptic segmentation path

Reviewed By: rbgirshick

Differential Revision: D20056724

fbshipit-source-id: 4fdb8b560ce64643391cea642b295aac54f65ee9

Author: ppwwyyxx

demo/demo.py

@@ -40,7 +40,12 @@ def get_parser():
     )
     parser.add_argument("--webcam", action="store_true", help="Take inputs from webcam.")
     parser.add_argument("--video-input", help="Path to video file.")
-    parser.add_argument("--input", nargs="+", help="A list of space separated input images")
+    parser.add_argument(
+        "--input",
+        nargs="+",
+        help="A list of space separated input images; "
+        "or a single glob pattern such as 'directory/*.jpg'",
+    )
     parser.add_argument(
         "--output",
         help="A file or directory to save output visualizations. "

detectron2/evaluation/panoptic_evaluation.py

@@ -90,8 +90,9 @@ def evaluate(self):
         if not comm.is_main_process():
             return
 
+        # PanopticApi requires local files
         gt_json = PathManager.get_local_path(self._metadata.panoptic_json)
-        gt_folder = self._metadata.panoptic_root
+        gt_folder = PathManager.get_local_path(self._metadata.panoptic_root)
 
         with tempfile.TemporaryDirectory(prefix="panoptic_eval") as pred_dir:
             logger.info("Writing all panoptic predictions to {} ...".format(pred_dir))

detectron2/modeling/roi_heads/fast_rcnn.py

@@ -126,6 +126,7 @@ def fast_rcnn_inference_single_image(
 class FastRCNNOutputs(object):
     """
     A class that stores information about outputs of a Fast R-CNN head.
+    It provides methods that are used to decode the outputs of a Fast R-CNN head.
     """
 
     def __init__(
@@ -259,19 +260,6 @@ def smooth_l1_loss(self):
         loss_box_reg = loss_box_reg / self.gt_classes.numel()
         return loss_box_reg
 
-    def losses(self):
-        """
-        Compute the default losses for box head in Fast(er) R-CNN,
-        with softmax cross entropy loss and smooth L1 loss.
-
-        Returns:
-            A dict of losses (scalar tensors) containing keys "loss_cls" and "loss_box_reg".
-        """
-        return {
-            "loss_cls": self.softmax_cross_entropy_loss(),
-            "loss_box_reg": self.smooth_l1_loss(),
-        }
-
     def _predict_boxes(self):
         """
         Returns:
@@ -288,6 +276,24 @@ def _predict_boxes(self):
         )
         return boxes.view(num_pred, K * B)
 
+    """
+    A subclass is expected to have the following methods because
+    they are used to query information about the head predictions.0
+    """
+
+    def losses(self):
+        """
+        Compute the default losses for box head in Fast(er) R-CNN,
+        with softmax cross entropy loss and smooth L1 loss.
+
+        Returns:
+            A dict of losses (scalar tensors) containing keys "loss_cls" and "loss_box_reg".
+        """
+        return {
+            "loss_cls": self.softmax_cross_entropy_loss(),
+            "loss_box_reg": self.smooth_l1_loss(),
+        }
+
     def predict_boxes(self):
         """
         Returns:

detectron2/modeling/roi_heads/keypoint_head.py

@@ -1,5 +1,5 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-from typing import Dict, List
+from typing import List
 import torch
 from torch import nn
 from torch.nn import functional as F
@@ -133,10 +133,10 @@ def __init__(self, cfg, input_shape):
             self.num_keypoints * batch_size_per_image * positive_sample_fraction
         )
 
-    def forward(self, x: Dict[str, torch.Tensor], instances: List[Instances]):
+    def forward(self, x, instances: List[Instances]):
         """
         Args:
-            x (dict[str,Tensor]): input region feature(s) provided by :class:`ROIHeads`.
+            x: input region feature(s) provided by :class:`ROIHeads`.
             instances (list[Instances]): contains the boxes & labels corresponding
                 to the input features.
                 Exact format is up to its caller to decide.

detectron2/modeling/roi_heads/mask_head.py

@@ -1,5 +1,5 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-from typing import Dict, List
+from typing import List
 import fvcore.nn.weight_init as weight_init
 import torch
 from torch import nn
@@ -151,10 +151,10 @@ def __init__(self, cfg, input_shape):
         super().__init__()
         self.vis_period = cfg.VIS_PERIOD
 
-    def forward(self, x: Dict[str, torch.Tensor], instances: List[Instances]):
+    def forward(self, x, instances: List[Instances]):
         """
         Args:
-            x (dict[str,Tensor]): input region feature(s) provided by :class:`ROIHeads`.
+            x: input region feature(s) provided by :class:`ROIHeads`.
             instances (list[Instances]): contains the boxes & labels corresponding
                 to the input features.
                 Exact format is up to its caller to decide.