cls_boxes = cls_boxes[top_inds]
# push new scores onto the minheap
for val in cls_scores:
heapq.heappush(top_scores[cls_ind], val)
# if we've collected more than the max number of detection,
# then pop items off the minheap and update the class threshold
if len(top_scores[cls_ind]) > max_per_set:
while len(top_scores[cls_ind]) > max_per_set:
heapq.heappop(top_scores[cls_ind])
thresh[cls_ind] = top_scores[cls_ind][0]
all_boxes[cls_ind][mb_idx] = np.hstack(
(cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32,
copy=False)
for j in xrange(1, num_classes):
for i in xrange(num_images):
if len(all_boxes[j][i]) > 0:
inds = np.where(all_boxes[j][i][:, -1] > thresh[j])[0]
all_boxes[j][i] = all_boxes[j][i][inds, :]
neon_logger.display('\nApplying NMS to all detections')
all_boxes = valid_set.apply_nms(all_boxes, NMS_THRESH)
neon_logger.display('Evaluating detections')
output_dir = 'frcn_output'
annopath, imagesetfile = valid_set.evaluation(
all_boxes, os.path.join(args.data_dir, output_dir))
run_voc_eval(annopath, imagesetfile, year, image_set, PASCAL_VOC_CLASSES,
os.path.join(args.data_dir, output_dir))
last_strlen = 0
for mb_idx, (x, y) in enumerate(valid_set):
prt_str = "Finished: {} / {}".format(mb_idx, valid_set.nbatches)
sys.stdout.write('\r' + ' ' * last_strlen + '\r')
sys.stdout.write(prt_str.encode('utf-8'))
last_strlen = len(prt_str)
sys.stdout.flush()
# perform forward pass
outputs = model.fprop(x, inference=True)
# retrieve image metadata
im_shape = valid_set.im_shape.get()
im_scale = valid_set.im_scale.get()
# retrieve region proposals generated by the model
(proposals, num_proposals) = proposalLayer.get_proposals()
# convert outputs to bounding boxes
boxes = util.get_bboxes(outputs, proposals, num_proposals,
valid_set.num_classes, im_shape, im_scale,
max_per_image, thresh, nms_thresh)
all_boxes[mb_idx] = boxes
neon_logger.display('Evaluating detections')
annopath, imagesetfile = valid_set.evaluation(all_boxes, args.output_dir)
util.run_voc_eval(annopath, imagesetfile, year, image_set, valid_set.CLASSES,
args.output_dir)
top_inds = np.argsort(-cls_scores)[:max_per_image]
cls_scores = cls_scores[top_inds]
cls_boxes = cls_boxes[top_inds]
# push new scores onto the minheap
for val in cls_scores:
heapq.heappush(top_scores[cls_ind], val)
# if we've collected more than the max number of detection,
# then pop items off the minheap and update the class threshold
if len(top_scores[cls_ind]) > max_per_set:
while len(top_scores[cls_ind]) > max_per_set:
heapq.heappop(top_scores[cls_ind])
thresh[cls_ind] = top_scores[cls_ind][0]
all_boxes[cls_ind][mb_idx] = np.hstack((cls_boxes, cls_scores[:, np.newaxis])).astype(
np.float32, copy=False)
for j in xrange(1, num_classes):
for i in xrange(num_images):
if len(all_boxes[j][i]) > 0:
inds = np.where(all_boxes[j][i][:, -1] > thresh[j])[0]
all_boxes[j][i] = all_boxes[j][i][inds, :]
print '\nApplying NMS to all detections'
all_boxes = valid_set.apply_nms(all_boxes, NMS_THRESH)
print 'Evaluating detections'
output_dir = 'frcn_output'
annopath, imagesetfile = valid_set.evaluation(all_boxes, os.path.join(args.data_dir, output_dir))
run_voc_eval(annopath, imagesetfile, year, image_set, PASCAL_VOC_CLASSES,
os.path.join(args.data_dir, output_dir))
for _ in range(num_images)]
last_strlen = 0
for mb_idx, (x, y) in enumerate(valid_set):
prt_str = "Finished: {} / {}".format(mb_idx, valid_set.nbatches)
sys.stdout.write('\r' + ' '*last_strlen + '\r')
sys.stdout.write(prt_str.encode('utf-8'))
last_strlen = len(prt_str)
sys.stdout.flush()
# perform forward pass
outputs = model.fprop(x, inference=True)
# retrieve image metadata
im_shape = valid_set.im_shape.get()
im_scale = valid_set.im_scale.get()
# retrieve region proposals generated by the model
(proposals, num_proposals) = proposalLayer.get_proposals()
# convert outputs to bounding boxes
boxes = util.get_bboxes(outputs, proposals, num_proposals, valid_set.num_classes,
im_shape, im_scale, max_per_image, thresh, nms_thresh)
all_boxes[mb_idx] = boxes
neon_logger.display('Evaluating detections')
annopath, imagesetfile = valid_set.evaluation(all_boxes, args.output_dir)
util.run_voc_eval(annopath, imagesetfile, year, image_set, valid_set.CLASSES, args.output_dir)
