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import os
import numpy as np
import torch
from PIL import Image
from pycocotools import mask as _mask
from utils import REFER, Summary, AverageMeter, intersectionAndUnionGPU, master_only
DATASETS_ATTRIBUTES = {
'refcoco': {'splitBy': "unc", 'dataset_name': 'refcoco'},
'refcoco_plus': {'splitBy': "unc", 'dataset_name': 'refcoco+'},
'refcocog': {'splitBy': "umd", 'dataset_name': 'refcocog'},
}
class RESDataset:
METAINFO: dict = dict(name='Referring Expression Segmentation')
def __init__(self,
image_folder,
dataset_name,
data_path=None,
split='val',
):
self.split = split
self._set_attribute(dataset_name)
json_datas = self.json_file_preprocess(data_path)
self.json_datas = json_datas
self.image_folder = image_folder
def _set_attribute(self, dataset_name):
attr_dict = DATASETS_ATTRIBUTES[dataset_name]
self.splitBy = attr_dict['splitBy']
self.dataset_name = attr_dict['dataset_name']
def __len__(self):
return len(self.json_datas)
def real_len(self):
return len(self.json_datas)
def json_file_preprocess(self, data_path):
splitBy = self.splitBy
dataset_name = self.dataset_name
refer_api = REFER(data_path, dataset_name, splitBy)
ref_ids_train = refer_api.getRefIds(split=self.split)
images_ids_train = refer_api.getImgIds(ref_ids=ref_ids_train)
refs_train = refer_api.loadRefs(ref_ids=ref_ids_train)
self.img2refs = self.create_img_to_refs_mapping(refs_train)
image_infos = []
loaded_images = refer_api.loadImgs(image_ids=images_ids_train)
for item in loaded_images:
item = item.copy()
image_infos.append(item)
self.annotations = refer_api.Anns
refs = [self.img2refs[image_info['id']] for image_info in image_infos]
ret = []
for image_info, ref in zip(image_infos, refs):
if len(ref) == 0:
continue
sents = []
ann_ids = []
for _ref in ref:
for sent in _ref["sentences"]:
text = sent["sent"]
sents.append(text)
ann_ids.append(_ref["ann_id"])
sampled_inds = list(range(len(sents)))
sampled_sents = np.vectorize(sents.__getitem__)(sampled_inds).tolist()
sampled_ann_ids = [ann_ids[ind] for ind in sampled_inds]
selected_labels = sampled_sents
ret.append(
{'image_info': image_info,
'sampled_ann_id': sampled_ann_ids,
'selected_labels': selected_labels,
'image': image_info['file_name']
}
)
return ret
def create_img_to_refs_mapping(self, refs_train):
img2refs = {}
for ref in refs_train:
img2refs[ref["image_id"]] = img2refs.get(ref["image_id"], []) + [ref, ]
return img2refs
def decode_mask(self, annotations_ids, image_info):
flag = False
masks = []
for ann_id in annotations_ids:
if isinstance(ann_id, list):
flag = True
if -1 in ann_id:
assert len(ann_id) == 1
m = np.zeros((image_info["height"], image_info["width"])).astype(
np.uint8
)
else:
m_final = np.zeros(
(image_info["height"], image_info["width"])
).astype(np.uint8)
for ann_id_i in ann_id:
ann = self.annotations[ann_id_i]
if len(ann["segmentation"]) == 0:
m = np.zeros(
(image_info["height"], image_info["width"])
).astype(np.uint8)
else:
if type(ann["segmentation"][0]) == list: # polygon
rle = _mask.frPyObjects(
ann["segmentation"], image_info["height"], image_info["width"], )
else:
rle = ann["segmentation"]
for i in range(len(rle)):
if not isinstance(rle[i]["counts"], bytes):
rle[i]["counts"] = rle[i]["counts"].encode()
m = _mask.decode(rle)
m = np.sum(
m, axis=2
) # sometimes there are multiple binary map (corresponding to multiple segs)
m = m.astype(np.uint8) # convert to np.uint8
m_final = m_final | m
m = m_final
masks.append(m)
continue
ann = self.annotations[ann_id]
if len(ann["segmentation"]) == 0:
m = np.zeros((image_info["height"], image_info["width"])).astype(
np.uint8
)
masks.append(m)
continue
if type(ann["segmentation"][0]) == list: # polygon
rle = _mask.frPyObjects(
ann["segmentation"], image_info["height"], image_info["width"]
)
else:
rle = ann["segmentation"]
for i in range(len(rle)):
if not isinstance(rle[i]["counts"], bytes):
rle[i]["counts"] = rle[i]["counts"].encode()
m = _mask.decode(rle)
m = np.sum(m, axis=2) # sometimes there are multiple binary map (corresponding to multiple segs)
m = m.astype(np.uint8) # convert to np.uint8
masks.append(m)
masks = np.stack(masks, axis=0)
# if self.pad_image_to_square:
masks = torch.from_numpy(masks)
return masks
def only_get_text_infos(self, json_data):
return {'sampled_sents': json_data['selected_labels']}
def get_questions(self, text_require_infos):
sampled_sents = text_require_infos['sampled_sents']
ret = []
for sent in sampled_sents:
ret.append("<image>\n Please segment {} in this image.".format(sent))
return ret
def filter_data_dict(self, data_dict):
names = ['image', 'text', 'gt_masks', 'img_id']
ret = {name: data_dict[name] for name in names}
return ret
def __getitem__(self, index):
index = index % self.real_len()
data_dict = self.json_datas[index]
text_require_infos = self.only_get_text_infos(data_dict)
questions = self.get_questions(text_require_infos)
assert data_dict.get('image', None) is not None
if data_dict.get('image', None) is not None:
image_file = data_dict['image']
image_file = os.path.join(self.image_folder, image_file)
image = Image.open(image_file).convert('RGB')
# process and get masks for evaluation
masks = self.decode_mask(data_dict['sampled_ann_id'], data_dict['image_info'])
data_dict['gt_masks'] = masks
data_dict['image'] = image
data_dict['text'] = questions
data_dict['img_id'] = str(index)
return self.filter_data_dict(data_dict)
@master_only
def evaluate(self, result, work_dir):
trackers = {
"intersection": AverageMeter("Intersec", ":6.3f", Summary.SUM),
"union": AverageMeter("Union", ":6.3f", Summary.SUM),
"gIoU": AverageMeter("gIoU", ":6.3f", Summary.SUM)
}
for pred_dict in result:
intersection, union, accuracy_iou = 0.0, 0.0, 0.0
masks = pred_dict['prediction_masks']
_masks = []
for mask in masks:
if mask is not None:
mask = rle_to_mask(mask)
_masks.append(mask)
targets = pred_dict['gt_masks']
_targets = rle_to_mask(targets)
for i_item, _mask in enumerate(_masks):
if _mask is None:
continue
_target = _targets[i_item: i_item+1]
for prediction, target in zip(_mask, _target):
prediction = torch.from_numpy(prediction).int().cuda()
target = torch.from_numpy(target).int().cuda()
intersect, union_, _ = intersectionAndUnionGPU(
prediction.contiguous().clone(), target.contiguous(), 2, ignore_index=255
)
intersection += intersect
union += union_
accuracy_iou += intersect / (union_ + 1e-5)
accuracy_iou[union_ == 0] += 1.0
intersection, union = intersection.cpu().numpy(), union.cpu().numpy()
accuracy_iou = accuracy_iou.cpu().numpy() / _targets.shape[0]
trackers["intersection"].update(intersection)
trackers["union"].update(union)
trackers["gIoU"].update(accuracy_iou, n=_targets.shape[0])
cur_results = {'pixel_intersection': trackers["intersection"].sum[1],
'pixel_union': trackers["union"].sum[1],
'gIoU': trackers["gIoU"].avg[1],
'mask_counts': trackers["gIoU"].count,
}
class_iou = cur_results['pixel_intersection'] / (cur_results['pixel_union'] + 1e-10)
global_iou = cur_results['gIoU']
print('============================================', 'current')
print('CIoU: {}, GIoU: {}'.format(class_iou, global_iou), 'current')
print('============================================', 'current')
print('RES_{}_{} successfully finished evaluating'.format(self.dataset_name, self.split),
'current')
return {'Acc': class_iou}
def rle_to_mask(rle):
mask = []
for r in rle:
m = _mask.decode(r)
m = np.uint8(m)
mask.append(m)
mask = np.stack(mask, axis=0)
return mask |