def visualize_top_predicted_bbox(self, pred_sample, coco_image_dir):
"""Visualize the top predicted bounding box."""
assert 'annotation_id' in pred_sample, 'Object annotation id missing!'
assert 'predicted_bounding_boxes' in pred_sample, \
'list of predicted bounding boxes missing!'
if not pred_sample['predicted_bounding_boxes']:
print 'Empty predicted bounding boxes.'
return
bbox_pred_top = pred_sample['predicted_bounding_boxes'][0]
ann_id = pred_sample['annotation_id']
ann = self.refexp_dataset.loadAnns(ids=[ann_id])[0]
image_id = ann['image_id']
img_coco = self.refexp_dataset.loadImgs(ids=[image_id])[0]
iou = cu.iou_bboxes(bbox_pred_top, ann['bbox'])
if 'refexp' in pred_sample or 'refexp_id' in pred_sample:
print 'The Referring expression input to the model is:'
if 'refexp' in pred_sample:
print ' ' + pred_sample['refexp']
else:
refexp_tmp = self.refexp_dataset.loadRefexps(ids=pred_sample['refexp_id'])[0]
print ' ' + refexp_tmp['raw']
I = misc.imread(os.path.join(coco_image_dir, (img_coco['file_name'])))
ax = plt.imshow(I)
ax = plt.axis('off')
ax = plt.title('IoU: %.3f, green bbox: GT, red bbox: predicted' % iou)
cu.draw_bbox(plt.gca(), ann['bbox'], edge_color='green')
cu.draw_bbox(plt.gca(), bbox_pred_top, edge_color='red')
def visualize_top_predicted_bbox(self, pred_sample, coco_image_dir):
"""Visualize the top predicted bounding box."""
assert 'annotation_id' in pred_sample, 'Object annotation id missing!'
assert 'predicted_bounding_boxes' in pred_sample, \
'list of predicted bounding boxes missing!'
if not pred_sample['predicted_bounding_boxes']:
print 'Empty predicted bounding boxes.'
return
bbox_pred_top = pred_sample['predicted_bounding_boxes'][0]
ann_id = pred_sample['annotation_id']
ann = self.refexp_dataset.loadAnns(ids=[ann_id])[0]
image_id = ann['image_id']
img_coco = self.refexp_dataset.loadImgs(ids=[image_id])[0]
iou = cu.iou_bboxes(bbox_pred_top, ann['bbox'])
if 'refexp' in pred_sample or 'refexp_id' in pred_sample:
print 'The Referring expression input to the model is:'
if 'refexp' in pred_sample:
print ' ' + pred_sample['refexp']
else:
refexp_tmp = self.refexp_dataset.loadRefexps(
ids=pred_sample['refexp_id'])[0]
print ' ' + refexp_tmp['raw']
I = misc.imread(os.path.join(coco_image_dir, (img_coco['file_name'])))
ax = plt.imshow(I)
ax = plt.axis('off')
ax = plt.title('IoU: %.3f, green bbox: GT, red bbox: predicted' % iou)
cu.draw_bbox(plt.gca(), ann['bbox'], edge_color='green')
cu.draw_bbox(plt.gca(), bbox_pred_top, edge_color='red')
def get_streams(self):
((image_filename, image_id), object_id_list,
line) = self.image_refexp_pairs[self.index]
if image_id in self.dataset.imgs_with_errors:
line = EOS_IDENTIFIER
stream = get_encoded_line(line, self.vocabulary)
# Assumes stream has EOS word at the end
assert (stream[-1] == self.vocabulary[EOS_IDENTIFIER])
stream = stream[:-1]
filtered_stream = []
for word in stream:
if word != self.vocabulary[UNK_IDENTIFIER]:
filtered_stream.append(word)
stream = filtered_stream
if self.truncate and len(stream) >= self.max_words:
stream = stream[:self.max_words - 1]
self.num_truncates += 1
object_id = object_id_list[1]
object_ann = self.dataset.loadAnns(object_id)[0]
object_category = self.dataset.loadCats(
object_ann['category_id'])[0]['name']
object_bbox = self.dataset.loadAnns(object_id)[0]['bbox']
context_anns_of_same_category = []
context_anns_of_diff_category = []
if hasattr(self.dataset, 'coco'):
all_anns = self.dataset.coco.imgToAnns[image_id]
else:
all_anns = self.dataset.imgToAnns[image_id]
for ann in all_anns:
if ann['id'] != object_id:
if ann['category_id'] == object_ann['category_id']:
context_anns_of_same_category.append(ann)
else:
context_anns_of_diff_category.append(ann)
neg_anns_of_same_category = []
neg_anns_of_diff_category = []
if self.neg_proposal_source != 'gt':
image_info = self.dataset.loadImgs(image_id)[0]
all_anns = image_info['region_candidates']
for ann in all_anns:
ann['bbox'] = ann['bounding_box']
ann_box = ann['bbox']
iou = iou_bboxes(ann_box, object_bbox)
if iou < 0.5 and ann[
'predicted_object_name'] == object_category:
neg_anns_of_same_category.append(ann)
elif ann['predicted_object_name'] != object_category:
neg_anns_of_diff_category.append(ann)
else:
neg_anns_of_same_category = context_anns_of_same_category
neg_anns_of_diff_category = context_anns_of_diff_category
# subtract one because image is reserved as one context region
if len(context_anns_of_same_category) > self.max_num_context - 1:
rand_sample = sorted(
random.sample(range(len(context_anns_of_same_category)),
self.max_num_context - 1))
context_anns_of_same_category = [
context_anns_of_same_category[idx] for idx in rand_sample
]
elif len(context_anns_of_same_category) < self.max_num_context - 1:
rand_sample = sorted(
random.sample(
range(len(context_anns_of_diff_category)),
min(
self.max_num_context - 1 -
len(context_anns_of_same_category),
len(context_anns_of_diff_category))))
context_anns_of_same_category += [
context_anns_of_diff_category[idx] for idx in rand_sample
]
if len(neg_anns_of_same_category) > self.max_num_negatives:
rand_sample = sorted(
random.sample(range(len(neg_anns_of_same_category)),
self.max_num_negatives))
neg_anns_of_same_category = [
neg_anns_of_same_category[idx] for idx in rand_sample
]
elif len(neg_anns_of_same_category) < self.max_num_negatives:
rand_sample = sorted(
random.sample(
range(len(neg_anns_of_diff_category)),
min(
self.max_num_negatives -
len(neg_anns_of_same_category),
len(neg_anns_of_diff_category))))
neg_anns_of_same_category += [
neg_anns_of_diff_category[idx] for idx in rand_sample
]
# If we are running short of proposal negatives, sample from gt negatives
if len(
neg_anns_of_same_category
) < self.max_num_negatives and self.neg_proposal_source != 'gt':
rand_sample = sorted(
random.sample(
range(len(context_anns_of_diff_category)),
min(
self.max_num_negatives -
len(neg_anns_of_same_category),
len(context_anns_of_diff_category))))
neg_anns_of_same_category += [
context_anns_of_diff_category[idx] for idx in rand_sample
]
pad = self.max_words - (len(stream) + 1) if self.pad else 0
if pad > 0:
self.num_pads += 1
out = {}
timestep_input = np.asarray(
[[self.vocabulary[EOS_IDENTIFIER]] + stream + [-1] * pad],
np.float16)
out['timestep_input'] = np.tile(timestep_input.T,
(1, self.max_num_context))
timestep_cont = np.asarray([[0] + [1] * len(stream) + [0] * pad],
np.float16)
out['timestep_cont'] = np.tile(timestep_cont.T,
(1, self.max_num_context))
timestep_target = np.asarray(
stream + [self.vocabulary[EOS_IDENTIFIER]] + [-1] * pad,
np.float16)
out['timestep_target'] = timestep_target
self.swap_axis_streams.add('timestep_input')
self.swap_axis_streams.add('timestep_target')
self.swap_axis_streams.add('timestep_cont')
# Write image features to batch
img_info = self.dataset.loadImgs(image_id)[0]
img_wd = float(img_info['width'])
img_ht = float(img_info['height'])
assert (len(object_id_list) <= 2)
fc7_img = self.dataset.image_features[str(
(image_id, [0, 0, int(img_wd - 1),
int(img_ht - 1)]))][0]
out['fc7_img'] = np.tile(fc7_img, (self.max_num_context, 1))
img_bbox_features = np.zeros((self.max_num_context, 5), np.float16)
img_bbox_features[:] = [0, 0, 1, 1, 1]
out['img_bbox_features'] = img_bbox_features
# Write object region features to batch
object_bbox = self.dataset.loadAnns(object_id)[0]['bbox']
fc7_obj = self.dataset.image_features[str((image_id, object_bbox))][0]
out['fc7_obj'] = np.tile(fc7_obj, (self.max_num_context, 1))
bbox_area_ratio = (object_bbox[2] * object_bbox[3]) / (img_wd * img_ht)
bbox_x1y1x2y2 = [
object_bbox[0] / img_wd, object_bbox[1] / img_ht,
(object_bbox[0] + object_bbox[2]) / img_wd,
(object_bbox[1] + object_bbox[3]) / img_ht
]
bbox_features = bbox_x1y1x2y2 + [bbox_area_ratio]
out['bbox_features'] = np.tile(bbox_features,
(self.max_num_context, 1))
# Write context features to batch
context_fc7 = np.tile(fc7_img, (self.max_num_context, 1))
context_bbox_features = np.zeros((self.max_num_context, 5), np.float16)
context_bbox_features[:] = [0, 0, 1, 1, 1]
if len(context_anns_of_same_category) > 0:
other_bboxes = [
ann['bbox'] for ann in context_anns_of_same_category
]
for idx, other_bbox in enumerate(other_bboxes):
other_bbox_area_ratio = (other_bbox[2] *
other_bbox[3]) / (img_wd * img_ht)
other_bbox_x1y1x2y2 = [
other_bbox[0] / img_wd, other_bbox[1] / img_ht,
(other_bbox[0] + other_bbox[2]) / img_wd,
(other_bbox[1] + other_bbox[3]) / img_ht
]
other_bbox_features = other_bbox_x1y1x2y2 + [
other_bbox_area_ratio
]
context_fc7[idx, :] = self.dataset.image_features[str(
(image_id, other_bbox))][0]
context_bbox_features[idx, :] = other_bbox_features
out['context_fc7'] = context_fc7
out['context_bbox_features'] = context_bbox_features
# Write negative features to batch
negative_fc7 = np.zeros(
(self.max_num_negatives, self.dataset.image_feature_length),
np.float16)
negative_bbox_features = np.zeros((self.max_num_negatives, 5),
np.float16)
if len(neg_anns_of_same_category) > 0:
other_bboxes = [ann['bbox'] for ann in neg_anns_of_same_category]
for idx, other_bbox in enumerate(other_bboxes):
other_bbox_area_ratio = (other_bbox[2] *
other_bbox[3]) / (img_wd * img_ht)
other_bbox_x1y1x2y2 = [
other_bbox[0] / img_wd, other_bbox[1] / img_ht,
(other_bbox[0] + other_bbox[2]) / img_wd,
(other_bbox[1] + other_bbox[3]) / img_ht
]
other_bbox_features = other_bbox_x1y1x2y2 + [
other_bbox_area_ratio
]
negative_fc7[idx, :] = self.dataset.image_features[str(
(image_id, other_bbox))][0]
negative_bbox_features[idx, :] = other_bbox_features
out['negative_fc7'] = negative_fc7
out['negative_bbox_features'] = negative_bbox_features
pairwise_similarity = np.asarray([[0] * self.max_num_negatives],
np.float16)
out['pairwise_similarity'] = np.tile(pairwise_similarity,
(self.max_words, 1))
self.swap_axis_streams.add('pairwise_similarity')
self.num_outs += 1
self.next_line()
return out
def evaluate(self, pred_results_path,
thresh_iou=0.5,
thresh_k=1,
flag_ignore_non_existed_object=False,
flag_ignore_non_existed_gt_refexp=False,
flag_missing_objects_verbose=False,
flag_missing_refexps_verbose=False):
"""Evaluate the predicted results for the comprehension task.
Args:
pred_results_path: path for the predicted results with the format
described in ./cache_evaluation/format_comprehension_eval.md
thresh_iou: threshold of the IoU ratio of the evaluation
thresh_k: precision@k
flag_ignore_non_existed_object: if set True, the evaluation process
continues with an warning when encountered non existed objects in
self.refexp_dataset. Otherwise stops.
flag_ignore_non_existed_gt_refexp: if set True, the evaluation process
continues when encountered non existed GT referring expressions.
Otherwise stops.
flag_missing_objects_verbose: if set true, will list the ids of all the
missing objects in self.refexp_dataset
flag_missing_refexps_verbose: if set true, will list the ids of all the
missing referring expressions in self.refexp_dataset
Returns:
A two element tuple. The first element is precision@k. The second
element is the predicted results (a dictionary) with an added field
'best_iou' of the best iou for the top k bounding boxes.
"""
# Load predicted results
self.reset_eval_state()
print 'Loading predicted result file for the comprehension task.'
with open(pred_results_path) as fin:
self.pred_results = json.load(fin)
# evaluation
pred_ann_ids_set = set()
pred_refexp_ids_set = set()
score = 0.0
num_valid_pred = 0
for pred_elem in self.pred_results:
# validate the predicted results
assert 'annotation_id' in pred_elem, 'Object annotation id missing!'
assert 'predicted_bounding_boxes' in pred_elem, \
'list of predicted bounding boxes missing!'
ann_id = pred_elem['annotation_id']
gt_bbox = self._get_GT_bbox_with_annotation_id(ann_id) # Need to check - change
if gt_bbox is None:
if flag_ignore_non_existed_object:
print ('Ignore COCO annotation id %d which does not exist in '
'Refexp dataset file for evaluation' % ann_id)
pred_elem['best_iou'] = 0.0
continue
else:
print ('COCO annotation id %d does not exist in Refexp '
'dataset file for evaluation!' % ann_id)
raise
if ('refexp_id' in pred_elem) and not(pred_elem['refexp_id'] in self.gt_refexp_ids_set):
if flag_ignore_non_existed_gt_refexp:
print ('Ignore refexp id %d which does not exist in '
'Refexp dataset file for evaluation' % pred_elem['refexp_id'])
pred_elem['best_iou'] = 0.0
continue
else:
print ('refexp id %d does not exist in Refexp '
'dataset file for evaluation!' % pred_elem['refexp_id'])
raise
pred_ann_ids_set.add(ann_id)
if 'refexp_id' in pred_elem:
pred_refexp_ids_set.add(pred_elem['refexp_id'])
num_valid_pred += 1
# check whether it is a correct prediction
pred_bboxes = pred_elem['predicted_bounding_boxes']
best_iou = 0.0
for k in xrange(min(thresh_k, len(pred_bboxes))):
iou = cu.iou_bboxes(pred_bboxes[k], gt_bbox)
best_iou = max(best_iou, iou)
if best_iou >= thresh_iou:
score += 1.0
pred_elem['best_iou'] = best_iou
score /= num_valid_pred
# warning for missing objects and refexps
gt_ann_ids_left_set = self.gt_ann_ids_set - pred_ann_ids_set
gt_refexp_ids_left_set = self.gt_refexp_ids_set - pred_refexp_ids_set
if gt_ann_ids_left_set:
print ('Missing %d objects in the refexp dataset file in the predicted '
'file' % len(gt_ann_ids_left_set))
if flag_missing_objects_verbose:
print ('The missing object annotation ids are:')
print gt_ann_ids_left_set # TODO pretty print format
if gt_refexp_ids_left_set:
print ('Missing %d refexps in the refexp dataset file in the predicted '
'file' % len(gt_refexp_ids_left_set))
if flag_missing_refexps_verbose:
print ('The missing refexp ids are:')
print gt_refexp_ids_left_set # TODO pretty print format
# summarize the results
print 'The average prec@%d score is %.3f' % (thresh_k, score)
return (score, self.pred_results)
def evaluate(self,
pred_results_path,
thresh_iou=0.5,
thresh_k=1,
flag_ignore_non_existed_object=False,
flag_ignore_non_existed_gt_refexp=False,
flag_missing_objects_verbose=False,
flag_missing_refexps_verbose=False):
"""Evaluate the predicted results for the comprehension task.
Args:
pred_results_path: path for the predicted results with the format
described in ./cache_evaluation/format_comprehension_eval.md
thresh_iou: threshold of the IoU ratio of the evaluation
thresh_k: precision@k
flag_ignore_non_existed_object: if set True, the evaluation process
continues with an warning when encountered non existed objects in
self.refexp_dataset. Otherwise stops.
flag_ignore_non_existed_gt_refexp: if set True, the evaluation process
continues when encountered non existed GT referring expressions.
Otherwise stops.
flag_missing_objects_verbose: if set true, will list the ids of all the
missing objects in self.refexp_dataset
flag_missing_refexps_verbose: if set true, will list the ids of all the
missing referring expressions in self.refexp_dataset
Returns:
A two element tuple. The first element is precision@k. The second
element is the predicted results (a dictionary) with an added field
'best_iou' of the best iou for the top k bounding boxes.
"""
# Load predicted results
self.reset_eval_state()
print 'Loading predicted result file for the comprehension task.'
with open(pred_results_path) as fin:
self.pred_results = json.load(fin)
# evaluation
pred_ann_ids_set = set()
pred_refexp_ids_set = set()
score = 0.0
num_valid_pred = 0
for pred_elem in self.pred_results:
# validate the predicted results
assert 'annotation_id' in pred_elem, 'Object annotation id missing!'
assert 'predicted_bounding_boxes' in pred_elem, \
'list of predicted bounding boxes missing!'
ann_id = pred_elem['annotation_id']
gt_bbox = self._get_GT_bbox_with_annotation_id(
ann_id) # Need to check - change
if gt_bbox is None:
if flag_ignore_non_existed_object:
print(
'Ignore COCO annotation id %d which does not exist in '
'Refexp dataset file for evaluation' % ann_id)
pred_elem['best_iou'] = 0.0
continue
else:
print(
'COCO annotation id %d does not exist in Refexp '
'dataset file for evaluation!' % ann_id)
raise
if ('refexp_id' in pred_elem) and not (pred_elem['refexp_id']
in self.gt_refexp_ids_set):
if flag_ignore_non_existed_gt_refexp:
print(
'Ignore refexp id %d which does not exist in '
'Refexp dataset file for evaluation' %
pred_elem['refexp_id'])
pred_elem['best_iou'] = 0.0
continue
else:
print(
'refexp id %d does not exist in Refexp '
'dataset file for evaluation!' %
pred_elem['refexp_id'])
raise
pred_ann_ids_set.add(ann_id)
if 'refexp_id' in pred_elem:
pred_refexp_ids_set.add(pred_elem['refexp_id'])
num_valid_pred += 1
# check whether it is a correct prediction
pred_bboxes = pred_elem['predicted_bounding_boxes']
best_iou = 0.0
for k in xrange(min(thresh_k, len(pred_bboxes))):
iou = cu.iou_bboxes(pred_bboxes[k], gt_bbox)
best_iou = max(best_iou, iou)
if best_iou >= thresh_iou:
score += 1.0
pred_elem['best_iou'] = best_iou
score /= num_valid_pred
# warning for missing objects and refexps
gt_ann_ids_left_set = self.gt_ann_ids_set - pred_ann_ids_set
gt_refexp_ids_left_set = self.gt_refexp_ids_set - pred_refexp_ids_set
if gt_ann_ids_left_set:
print(
'Missing %d objects in the refexp dataset file in the predicted '
'file' % len(gt_ann_ids_left_set))
if flag_missing_objects_verbose:
print('The missing object annotation ids are:')
print gt_ann_ids_left_set # TODO pretty print format
if gt_refexp_ids_left_set:
print(
'Missing %d refexps in the refexp dataset file in the predicted '
'file' % len(gt_refexp_ids_left_set))
if flag_missing_refexps_verbose:
print('The missing refexp ids are:')
print gt_refexp_ids_left_set # TODO pretty print format
# summarize the results
print 'The average prec@%d score is %.3f' % (thresh_k, score)
return (score, self.pred_results)
def get_streams(self):
((image_filename, image_id), object_id_list, line) = self.image_refexp_pairs[self.index]
if image_id in self.dataset.imgs_with_errors:
line = EOS_IDENTIFIER
stream = get_encoded_line(line, self.vocabulary)
# Assumes stream has EOS word at the end
assert (stream[-1] == self.vocabulary[EOS_IDENTIFIER])
stream = stream[:-1]
filtered_stream = []
for word in stream:
if word != self.vocabulary[UNK_IDENTIFIER]:
filtered_stream.append(word)
stream = filtered_stream
if self.truncate and len(stream) >= self.max_words:
stream = stream[:self.max_words-1]
self.num_truncates += 1
object_id = object_id_list[1]
object_ann = self.dataset.loadAnns(object_id)[0]
object_category = self.dataset.loadCats(object_ann['category_id'])[0]['name']
object_bbox = self.dataset.loadAnns(object_id)[0]['bbox']
context_anns_of_same_category = []
context_anns_of_diff_category = []
if hasattr(self.dataset, 'coco'):
all_anns = self.dataset.coco.imgToAnns[image_id]
else:
all_anns = self.dataset.imgToAnns[image_id]
for ann in all_anns:
if ann['id'] != object_id:
if ann['category_id'] == object_ann['category_id']:
context_anns_of_same_category.append(ann)
else:
context_anns_of_diff_category.append(ann)
neg_anns_of_same_category = []
neg_anns_of_diff_category = []
if self.neg_proposal_source != 'gt':
image_info = self.dataset.loadImgs(image_id)[0]
all_anns = image_info['region_candidates']
for ann in all_anns:
ann['bbox'] = ann['bounding_box']
ann_box = ann['bbox']
iou = iou_bboxes(ann_box, object_bbox)
if iou < 0.5 and ann['predicted_object_name'] == object_category:
neg_anns_of_same_category.append(ann)
elif ann['predicted_object_name'] != object_category:
neg_anns_of_diff_category.append(ann)
else:
neg_anns_of_same_category = context_anns_of_same_category
neg_anns_of_diff_category = context_anns_of_diff_category
# subtract one because image is reserved as one context region
if len(context_anns_of_same_category) > self.max_num_context-1:
rand_sample = sorted(random.sample(range(len(context_anns_of_same_category)), self.max_num_context - 1))
context_anns_of_same_category = [context_anns_of_same_category[idx] for idx in rand_sample]
elif len(context_anns_of_same_category) < self.max_num_context-1:
rand_sample = sorted(random.sample(range(len(context_anns_of_diff_category)),
min(self.max_num_context - 1 - len(context_anns_of_same_category),
len(context_anns_of_diff_category))))
context_anns_of_same_category += [context_anns_of_diff_category[idx] for idx in rand_sample]
if len(neg_anns_of_same_category) > self.max_num_negatives:
rand_sample = sorted(random.sample(range(len(neg_anns_of_same_category)),self.max_num_negatives))
neg_anns_of_same_category = [neg_anns_of_same_category[idx] for idx in rand_sample]
elif len(neg_anns_of_same_category) < self.max_num_negatives:
rand_sample = sorted(random.sample(range(len(neg_anns_of_diff_category)),
min(self.max_num_negatives-len(neg_anns_of_same_category),
len(neg_anns_of_diff_category))))
neg_anns_of_same_category += [neg_anns_of_diff_category[idx] for idx in rand_sample]
# If we are running short of proposal negatives, sample from gt negatives
if len(neg_anns_of_same_category) < self.max_num_negatives and self.neg_proposal_source != 'gt':
rand_sample = sorted(random.sample(range(len(context_anns_of_diff_category)),
min(self.max_num_negatives-len(neg_anns_of_same_category),
len(context_anns_of_diff_category))))
neg_anns_of_same_category += [context_anns_of_diff_category[idx] for idx in rand_sample]
pad = self.max_words - (len(stream) + 1) if self.pad else 0
if pad > 0:
self.num_pads += 1
out = {}
timestep_input = np.asarray([[self.vocabulary[EOS_IDENTIFIER]] + stream + [-1] * pad], np.float16)
out['timestep_input'] = np.tile(timestep_input.T, (1,self.max_num_context))
timestep_cont = np.asarray([[0] + [1] * len(stream) + [0] * pad], np.float16)
out['timestep_cont'] = np.tile(timestep_cont.T, (1,self.max_num_context))
timestep_target = np.asarray(stream + [self.vocabulary[EOS_IDENTIFIER]] + [-1] * pad, np.float16)
out['timestep_target'] = timestep_target
self.swap_axis_streams.add('timestep_input')
self.swap_axis_streams.add('timestep_target')
self.swap_axis_streams.add('timestep_cont')
# Write image features to batch
img_info = self.dataset.loadImgs(image_id)[0]
img_wd = float(img_info['width'])
img_ht = float(img_info['height'])
assert(len(object_id_list) <= 2)
fc7_img = self.dataset.image_features[str((image_id, [0, 0, int(img_wd - 1), int(img_ht - 1)]))][0]
out['fc7_img'] = np.tile(fc7_img, (self.max_num_context, 1))
img_bbox_features = np.zeros((self.max_num_context, 5), np.float16)
img_bbox_features[:] = [0,0,1,1,1]
out['img_bbox_features'] = img_bbox_features
# Write object region features to batch
object_bbox = self.dataset.loadAnns(object_id)[0]['bbox']
fc7_obj = self.dataset.image_features[str((image_id, object_bbox))][0]
out['fc7_obj'] = np.tile(fc7_obj, (self.max_num_context, 1))
bbox_area_ratio = (object_bbox[2] * object_bbox[3]) / (img_wd * img_ht)
bbox_x1y1x2y2 = [object_bbox[0] / img_wd, object_bbox[1] / img_ht,
(object_bbox[0] + object_bbox[2]) / img_wd, (object_bbox[1] + object_bbox[3]) / img_ht]
bbox_features = bbox_x1y1x2y2 + [bbox_area_ratio]
out['bbox_features'] = np.tile(bbox_features, (self.max_num_context, 1))
# Write context features to batch
context_fc7 = np.tile(fc7_img, (self.max_num_context, 1))
context_bbox_features = np.zeros((self.max_num_context, 5), np.float16)
context_bbox_features[:] = [0,0,1,1,1]
if len(context_anns_of_same_category) > 0:
other_bboxes = [ann['bbox'] for ann in context_anns_of_same_category]
for idx, other_bbox in enumerate(other_bboxes):
other_bbox_area_ratio = (other_bbox[2] * other_bbox[3]) / (img_wd * img_ht)
other_bbox_x1y1x2y2 = [other_bbox[0] / img_wd, other_bbox[1] / img_ht,
(other_bbox[0] + other_bbox[2]) / img_wd, (other_bbox[1] + other_bbox[3]) / img_ht]
other_bbox_features = other_bbox_x1y1x2y2 + [other_bbox_area_ratio]
context_fc7[idx,:] = self.dataset.image_features[str((image_id, other_bbox))][0]
context_bbox_features[idx,:] = other_bbox_features
out['context_fc7'] = context_fc7
out['context_bbox_features'] = context_bbox_features
# Write negative features to batch
negative_fc7 = np.zeros((self.max_num_negatives, self.dataset.image_feature_length),np.float16)
negative_bbox_features = np.zeros((self.max_num_negatives, 5),np.float16)
if len(neg_anns_of_same_category) > 0:
other_bboxes = [ann['bbox'] for ann in neg_anns_of_same_category]
for idx, other_bbox in enumerate(other_bboxes):
other_bbox_area_ratio = (other_bbox[2] * other_bbox[3]) / (img_wd * img_ht)
other_bbox_x1y1x2y2 = [other_bbox[0] / img_wd, other_bbox[1] / img_ht,
(other_bbox[0] + other_bbox[2]) / img_wd, (other_bbox[1] + other_bbox[3]) / img_ht]
other_bbox_features = other_bbox_x1y1x2y2 + [other_bbox_area_ratio]
negative_fc7[idx,:] = self.dataset.image_features[str((image_id, other_bbox))][0]
negative_bbox_features[idx,:] = other_bbox_features
out['negative_fc7'] = negative_fc7
out['negative_bbox_features'] = negative_bbox_features
pairwise_similarity = np.asarray([[0] * self.max_num_negatives], np.float16)
out['pairwise_similarity'] = np.tile(pairwise_similarity, (self.max_words,1))
self.swap_axis_streams.add('pairwise_similarity')
self.num_outs += 1
self.next_line()
return out