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# Copyright (c) Facebook, Inc. and its affiliates.
# Copyright (c) Meta Platforms, Inc. All Rights Reserved
import itertools
import json
import numpy as np
import os
from collections import OrderedDict
import PIL.Image as Image
import torch
from detectron2.data import DatasetCatalog, MetadataCatalog
from detectron2.utils.comm import all_gather, is_main_process, synchronize
from detectron2.utils.file_io import PathManager
from detectron2.evaluation import SemSegEvaluator
class GeneralizedSemSegEvaluator(SemSegEvaluator):
"""
Evaluate semantic segmentation metrics.
"""
def __init__(
self,
dataset_name,
distributed=True,
output_dir=None,
*,
num_classes=None,
ignore_label=None,
post_process_func=None,
):
super().__init__(
dataset_name,
distributed=distributed,
output_dir=output_dir,
num_classes=num_classes,
ignore_label=ignore_label,
)
meta = MetadataCatalog.get(dataset_name)
try:
self._evaluation_set = meta.evaluation_set
except AttributeError:
self._evaluation_set = None
self.post_process_func = (
post_process_func
if post_process_func is not None
else lambda x, **kwargs: x
)
def process(self, inputs, outputs):
"""
Args:
inputs: the inputs to a model.
It is a list of dicts. Each dict corresponds to an image and
contains keys like "height", "width", "file_name".
outputs: the outputs of a model. It is either list of semantic segmentation predictions
(Tensor [H, W]) or list of dicts with key "sem_seg" that contains semantic
segmentation prediction in the same format.
"""
for input, output in zip(inputs, outputs):
output = self.post_process_func(
output["sem_seg"], image=np.array(Image.open(input["file_name"]))
)
output = output.argmax(dim=0).to(self._cpu_device)
pred = np.array(output, dtype=np.int)
with PathManager.open(
self.input_file_to_gt_file[input["file_name"]], "rb"
) as f:
gt = np.array(Image.open(f), dtype=np.int)
gt[gt == self._ignore_label] = self._num_classes
self._conf_matrix += np.bincount(
(self._num_classes + 1) * pred.reshape(-1) + gt.reshape(-1),
minlength=self._conf_matrix.size,
).reshape(self._conf_matrix.shape)
self._predictions.extend(self.encode_json_sem_seg(pred, input["file_name"]))
def evaluate(self):
"""
Evaluates standard semantic segmentation metrics (http://cocodataset.org/#stuff-eval):
* Mean intersection-over-union averaged across classes (mIoU)
* Frequency Weighted IoU (fwIoU)
* Mean pixel accuracy averaged across classes (mACC)
* Pixel Accuracy (pACC)
"""
if self._distributed:
synchronize()
conf_matrix_list = all_gather(self._conf_matrix)
self._predictions = all_gather(self._predictions)
self._predictions = list(itertools.chain(*self._predictions))
if not is_main_process():
return
self._conf_matrix = np.zeros_like(self._conf_matrix)
for conf_matrix in conf_matrix_list:
self._conf_matrix += conf_matrix
if self._output_dir:
PathManager.mkdirs(self._output_dir)
file_path = os.path.join(self._output_dir, "sem_seg_predictions.json")
with PathManager.open(file_path, "w") as f:
f.write(json.dumps(self._predictions))
acc = np.full(self._num_classes, np.nan, dtype=np.float)
iou = np.full(self._num_classes, np.nan, dtype=np.float)
tp = self._conf_matrix.diagonal()[:-1].astype(np.float)
pos_gt = np.sum(self._conf_matrix[:-1, :-1], axis=0).astype(np.float)
class_weights = pos_gt / np.sum(pos_gt)
pos_pred = np.sum(self._conf_matrix[:-1, :-1], axis=1).astype(np.float)
acc_valid = pos_gt > 0
acc[acc_valid] = tp[acc_valid] / pos_gt[acc_valid]
iou_valid = (pos_gt + pos_pred) > 0
union = pos_gt + pos_pred - tp
iou[acc_valid] = tp[acc_valid] / union[acc_valid]
macc = np.sum(acc[acc_valid]) / np.sum(acc_valid)
miou = np.sum(iou[acc_valid]) / np.sum(iou_valid)
fiou = np.sum(iou[acc_valid] * class_weights[acc_valid])
pacc = np.sum(tp) / np.sum(pos_gt)
res = {}
res["mIoU"] = 100 * miou
res["fwIoU"] = 100 * fiou
for i, name in enumerate(self._class_names):
res["IoU-{}".format(name)] = 100 * iou[i]
res["mACC"] = 100 * macc
res["pACC"] = 100 * pacc
for i, name in enumerate(self._class_names):
res["ACC-{}".format(name)] = 100 * acc[i]
if self._evaluation_set is not None:
for set_name, set_inds in self._evaluation_set.items():
iou_list = []
set_inds = np.array(set_inds, np.int)
mask = np.zeros((len(iou),)).astype(np.bool)
mask[set_inds] = 1
miou = np.sum(iou[mask][acc_valid[mask]]) / np.sum(iou_valid[mask])
pacc = np.sum(tp[mask]) / np.sum(pos_gt[mask])
res["mIoU-{}".format(set_name)] = 100 * miou
res["pAcc-{}".format(set_name)] = 100 * pacc
iou_list.append(miou)
miou = np.sum(iou[~mask][acc_valid[~mask]]) / np.sum(iou_valid[~mask])
pacc = np.sum(tp[~mask]) / np.sum(pos_gt[~mask])
res["mIoU-un{}".format(set_name)] = 100 * miou
res["pAcc-un{}".format(set_name)] = 100 * pacc
iou_list.append(miou)
res["hIoU-{}".format(set_name)] = (
100 * len(iou_list) / sum([1 / iou for iou in iou_list])
)
if self._output_dir:
file_path = os.path.join(self._output_dir, "sem_seg_evaluation.pth")
with PathManager.open(file_path, "wb") as f:
torch.save(res, f)
results = OrderedDict({"sem_seg": res})
self._logger.info(results)
return results
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