Spaces:
Running
on
Zero
Running
on
Zero
| # Copyright (c) Facebook, Inc. and its affiliates. | |
| import glob | |
| import logging | |
| import numpy as np | |
| import os | |
| import tempfile | |
| from collections import OrderedDict | |
| import torch | |
| from PIL import Image | |
| from detectron2.data import MetadataCatalog | |
| from detectron2.utils import comm | |
| from detectron2.utils.file_io import PathManager | |
| from .evaluator import DatasetEvaluator | |
| class CityscapesEvaluator(DatasetEvaluator): | |
| """ | |
| Base class for evaluation using cityscapes API. | |
| """ | |
| def __init__(self, dataset_name): | |
| """ | |
| Args: | |
| dataset_name (str): the name of the dataset. | |
| It must have the following metadata associated with it: | |
| "thing_classes", "gt_dir". | |
| """ | |
| self._metadata = MetadataCatalog.get(dataset_name) | |
| self._cpu_device = torch.device("cpu") | |
| self._logger = logging.getLogger(__name__) | |
| def reset(self): | |
| self._working_dir = tempfile.TemporaryDirectory(prefix="cityscapes_eval_") | |
| self._temp_dir = self._working_dir.name | |
| # All workers will write to the same results directory | |
| # TODO this does not work in distributed training | |
| assert ( | |
| comm.get_local_size() == comm.get_world_size() | |
| ), "CityscapesEvaluator currently do not work with multiple machines." | |
| self._temp_dir = comm.all_gather(self._temp_dir)[0] | |
| if self._temp_dir != self._working_dir.name: | |
| self._working_dir.cleanup() | |
| self._logger.info( | |
| "Writing cityscapes results to temporary directory {} ...".format(self._temp_dir) | |
| ) | |
| class CityscapesInstanceEvaluator(CityscapesEvaluator): | |
| """ | |
| Evaluate instance segmentation results on cityscapes dataset 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 | |
| for input, output in zip(inputs, outputs): | |
| file_name = input["file_name"] | |
| basename = os.path.splitext(os.path.basename(file_name))[0] | |
| pred_txt = os.path.join(self._temp_dir, basename + "_pred.txt") | |
| if "instances" in output: | |
| output = output["instances"].to(self._cpu_device) | |
| num_instances = len(output) | |
| with open(pred_txt, "w") as fout: | |
| for i in range(num_instances): | |
| pred_class = output.pred_classes[i] | |
| classes = self._metadata.thing_classes[pred_class] | |
| class_id = name2label[classes].id | |
| score = output.scores[i] | |
| mask = output.pred_masks[i].numpy().astype("uint8") | |
| png_filename = os.path.join( | |
| self._temp_dir, basename + "_{}_{}.png".format(i, classes) | |
| ) | |
| Image.fromarray(mask * 255).save(png_filename) | |
| fout.write( | |
| "{} {} {}\n".format(os.path.basename(png_filename), class_id, score) | |
| ) | |
| else: | |
| # Cityscapes requires a prediction file for every ground truth image. | |
| with open(pred_txt, "w") as fout: | |
| pass | |
| def evaluate(self): | |
| """ | |
| Returns: | |
| dict: has a key "segm", whose value is a dict of "AP" and "AP50". | |
| """ | |
| comm.synchronize() | |
| if comm.get_rank() > 0: | |
| return | |
| import cityscapesscripts.evaluation.evalInstanceLevelSemanticLabeling 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 | |
| cityscapes_eval.args.gtInstancesFile = os.path.join(self._temp_dir, "gtInstances.json") | |
| # These lines are adopted from | |
| # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalInstanceLevelSemanticLabeling.py # noqa | |
| gt_dir = PathManager.get_local_path(self._metadata.gt_dir) | |
| groundTruthImgList = glob.glob(os.path.join(gt_dir, "*", "*_gtFine_instanceIds.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(gt, cityscapes_eval.args)) | |
| results = cityscapes_eval.evaluateImgLists( | |
| predictionImgList, groundTruthImgList, cityscapes_eval.args | |
| )["averages"] | |
| ret = OrderedDict() | |
| ret["segm"] = {"AP": results["allAp"] * 100, "AP50": results["allAp50%"] * 100} | |
| self._working_dir.cleanup() | |
| return ret | |
| class CityscapesSemSegEvaluator(CityscapesEvaluator): | |
| """ | |
| Evaluate semantic segmentation results on cityscapes dataset 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 | |