Cityscapes semantic segmentation evaluator

Summary: semantic segmentation evaluation from Cityscapes scripts

Reviewed By: ppwwyyxx

Differential Revision: D19453083

fbshipit-source-id: bb51e743e29d5fcc54d845e0c55c6ae8baae60f7

detectron2/data/datasets/builtin.py

@@ -183,15 +183,15 @@ def register_all_cityscapes(root):
             ),
         )
         MetadataCatalog.get(inst_key).set(
-            image_dir=image_dir, gt_dir=gt_dir, evaluator_type="cityscapes", **meta
+            image_dir=image_dir, gt_dir=gt_dir, evaluator_type="cityscapes_instance", **meta
         )
 
         sem_key = key.format(task="sem_seg")
         DatasetCatalog.register(
             sem_key, lambda x=image_dir, y=gt_dir: load_cityscapes_semantic(x, y)
         )
         MetadataCatalog.get(sem_key).set(
-            image_dir=image_dir, gt_dir=gt_dir, evaluator_type="sem_seg", **meta
+            image_dir=image_dir, gt_dir=gt_dir, evaluator_type="cityscapes_sem_seg", **meta
         )
 
 

detectron2/evaluation/__init__.py

@@ -1,5 +1,5 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-from .cityscapes_evaluation import CityscapesEvaluator
+from .cityscapes_evaluation import CityscapesInstanceEvaluator, CityscapesSemSegEvaluator
 from .coco_evaluation import COCOEvaluator
 from .rotated_coco_evaluation import RotatedCOCOEvaluator
 from .evaluator import DatasetEvaluator, DatasetEvaluators, inference_context, inference_on_dataset

detectron2/evaluation/cityscapes_evaluation.py

@@ -1,6 +1,7 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
 import glob
 import logging
+import numpy as np
 import os
 import tempfile
 from collections import OrderedDict
@@ -16,12 +17,7 @@
 
 class CityscapesEvaluator(DatasetEvaluator):
     """
-    Evaluate instance segmentation results using cityscapes API.
-
-    Note:
-        * It does not work in multi-machine distributed training.
-        * It contains a synchronization, therefore has to be used on all ranks.
-        * Only the main process runs evaluation.
+    Base class for evaluation using cityscapes API.
     """
 
     def __init__(self, dataset_name):
@@ -47,6 +43,17 @@ def reset(self):
             "Writing cityscapes results to temporary directory {} ...".format(self._temp_dir)
         )
 
+
+class CityscapesInstanceEvaluator(CityscapesEvaluator):
+    """
+    Evaluate instance segmentation results using cityscapes API.
+
+    Note:
+        * It does not work in multi-machine distributed training.
+        * It contains a synchronization, therefore has to be used on all ranks.
+        * Only the main process runs evaluation.
+    """
+
     def process(self, inputs, outputs):
         from cityscapesscripts.helpers.labels import name2label
 
@@ -110,3 +117,71 @@ def evaluate(self):
         ret["segm"] = {"AP": results["allAp"] * 100, "AP50": results["allAp50%"] * 100}
         self._working_dir.cleanup()
         return ret
+
+
+class CityscapesSemSegEvaluator(CityscapesEvaluator):
+    """
+    Evaluate semantic segmentation results using cityscapes API.
+
+    Note:
+        * It does not work in multi-machine distributed training.
+        * It contains a synchronization, therefore has to be used on all ranks.
+        * Only the main process runs evaluation.
+    """
+
+    def process(self, inputs, outputs):
+        from cityscapesscripts.helpers.labels import trainId2label
+
+        for input, output in zip(inputs, outputs):
+            file_name = input["file_name"]
+            basename = os.path.splitext(os.path.basename(file_name))[0]
+            pred_filename = os.path.join(self._temp_dir, basename + "_pred.png")
+
+            output = output["sem_seg"].argmax(dim=0).to(self._cpu_device).numpy()
+            pred = 255 * np.ones(output.shape, dtype=np.uint8)
+            for train_id, label in trainId2label.items():
+                if label.ignoreInEval:
+                    continue
+                pred[output == train_id] = label.id
+            Image.fromarray(pred).save(pred_filename)
+
+    def evaluate(self):
+        comm.synchronize()
+        if comm.get_rank() > 0:
+            return
+        # Load the Cityscapes eval script *after* setting the required env var,
+        # since the script reads CITYSCAPES_DATASET into global variables at load time.
+        import cityscapesscripts.evaluation.evalPixelLevelSemanticLabeling as cityscapes_eval
+
+        self._logger.info("Evaluating results under {} ...".format(self._temp_dir))
+
+        # set some global states in cityscapes evaluation API, before evaluating
+        cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir)
+        cityscapes_eval.args.predictionWalk = None
+        cityscapes_eval.args.JSONOutput = False
+        cityscapes_eval.args.colorized = False
+
+        # These lines are adopted from
+        # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalPixelLevelSemanticLabeling.py # noqa
+        gt_dir = PathManager.get_local_path(self._metadata.gt_dir)
+        groundTruthImgList = glob.glob(os.path.join(gt_dir, "*", "*_gtFine_labelIds.png"))
+        assert len(
+            groundTruthImgList
+        ), "Cannot find any ground truth images to use for evaluation. Searched for: {}".format(
+            cityscapes_eval.args.groundTruthSearch
+        )
+        predictionImgList = []
+        for gt in groundTruthImgList:
+            predictionImgList.append(cityscapes_eval.getPrediction(cityscapes_eval.args, gt))
+        results = cityscapes_eval.evaluateImgLists(
+            predictionImgList, groundTruthImgList, cityscapes_eval.args
+        )
+        ret = OrderedDict()
+        ret["sem_seg"] = {
+            "IoU": 100.0 * results["averageScoreClasses"],
+            "iIoU": 100.0 * results["averageScoreInstClasses"],
+            "IoU_sup": 100.0 * results["averageScoreCategories"],
+            "iIoU_sup": 100.0 * results["averageScoreInstCategories"],
+        }
+        self._working_dir.cleanup()
+        return ret

projects/PointRend/train_net.py

@@ -16,7 +16,8 @@
 from detectron2.data import MetadataCatalog
 from detectron2.engine import DefaultTrainer, default_argument_parser, default_setup, launch
 from detectron2.evaluation import (
-    CityscapesEvaluator,
+    CityscapesInstanceEvaluator,
+    CityscapesSemSegEvaluator,
     COCOEvaluator,
     DatasetEvaluators,
     LVISEvaluator,
@@ -50,11 +51,16 @@ def build_evaluator(cls, cfg, dataset_name, output_folder=None):
             return LVISEvaluator(dataset_name, cfg, True, output_folder)
         if evaluator_type == "coco":
             return COCOEvaluator(dataset_name, cfg, True, output_folder)
-        if evaluator_type == "cityscapes":
+        if evaluator_type == "cityscapes_instance":
             assert (
                 torch.cuda.device_count() >= comm.get_rank()
             ), "CityscapesEvaluator currently do not work with multiple machines."
-            return CityscapesEvaluator(dataset_name)
+            return CityscapesInstanceEvaluator(dataset_name)
+        if evaluator_type == "cityscapes_sem_seg":
+            assert (
+                torch.cuda.device_count() >= comm.get_rank()
+            ), "CityscapesEvaluator currently do not work with multiple machines."
+            return CityscapesSemSegEvaluator(dataset_name)
         if len(evaluator_list) == 0:
             raise NotImplementedError(
                 "no Evaluator for the dataset {} with the type {}".format(

tools/plain_train_net.py

@@ -35,7 +35,8 @@
 )
 from detectron2.engine import default_argument_parser, default_setup, launch
 from detectron2.evaluation import (
-    CityscapesEvaluator,
+    CityscapesInstanceEvaluator,
+    CityscapesSemSegEvaluator,
     COCOEvaluator,
     COCOPanopticEvaluator,
     DatasetEvaluators,
@@ -82,11 +83,16 @@ def get_evaluator(cfg, dataset_name, output_folder=None):
         evaluator_list.append(COCOEvaluator(dataset_name, cfg, True, output_folder))
     if evaluator_type == "coco_panoptic_seg":
         evaluator_list.append(COCOPanopticEvaluator(dataset_name, output_folder))
-    if evaluator_type == "cityscapes":
+    if evaluator_type == "cityscapes_instance":
         assert (
             torch.cuda.device_count() >= comm.get_rank()
         ), "CityscapesEvaluator currently do not work with multiple machines."
-        return CityscapesEvaluator(dataset_name)
+        return CityscapesInstanceEvaluator(dataset_name)
+    if evaluator_type == "cityscapes_sem_seg":
+        assert (
+            torch.cuda.device_count() >= comm.get_rank()
+        ), "CityscapesEvaluator currently do not work with multiple machines."
+        return CityscapesSemSegEvaluator(dataset_name)
     if evaluator_type == "pascal_voc":
         return PascalVOCDetectionEvaluator(dataset_name)
     if evaluator_type == "lvis":

tools/train_net.py

@@ -27,7 +27,8 @@
 from detectron2.data import MetadataCatalog
 from detectron2.engine import DefaultTrainer, default_argument_parser, default_setup, hooks, launch
 from detectron2.evaluation import (
-    CityscapesEvaluator,
+    CityscapesInstanceEvaluator,
+    CityscapesSemSegEvaluator,
     COCOEvaluator,
     COCOPanopticEvaluator,
     DatasetEvaluators,
@@ -74,11 +75,16 @@ def build_evaluator(cls, cfg, dataset_name, output_folder=None):
             evaluator_list.append(COCOEvaluator(dataset_name, cfg, True, output_folder))
         if evaluator_type == "coco_panoptic_seg":
             evaluator_list.append(COCOPanopticEvaluator(dataset_name, output_folder))
-        elif evaluator_type == "cityscapes":
+        if evaluator_type == "cityscapes_instance":
             assert (
                 torch.cuda.device_count() >= comm.get_rank()
             ), "CityscapesEvaluator currently do not work with multiple machines."
-            return CityscapesEvaluator(dataset_name)
+            return CityscapesInstanceEvaluator(dataset_name)
+        if evaluator_type == "cityscapes_sem_seg":
+            assert (
+                torch.cuda.device_count() >= comm.get_rank()
+            ), "CityscapesEvaluator currently do not work with multiple machines."
+            return CityscapesSemSegEvaluator(dataset_name)
         elif evaluator_type == "pascal_voc":
             return PascalVOCDetectionEvaluator(dataset_name)
         elif evaluator_type == "lvis":