def forward(self, bottom, top):
self.iterations += 1
try:
printProgress(self.iterations,
config.steps,
prefix='Progress: ',
suffix='Complete',
barLength=50)
except:
pass
item = self.item
for i in range(len(item)):
top[i].data[...] = item[i]
# properly terminate the sub process, once an epoch is finished
if self.iterations == config.steps:
self.process.terminate()
# gen mask
ret = gen_masks(net,
img,
config,
dest_shape=(spider.origin_height, spider.origin_width),
image=args.debug)
if args.useCats:
ret_masks, ret_scores, ret_cats = ret
else:
ret_masks, ret_scores = ret
printProgress(i,
len(ds),
prefix='Progress: ',
suffix='Complete',
barLength=50)
for _ in range(len(ret_masks)):
cat = 1
if args.useCats:
cat = spider.dataset.getCatIds()[int(ret_cats[_].argmax())]
score = float(ret_cats[_].max())
else:
score = float(ret_scores[_])
objn = float(ret_scores[_])
results.append({
'image_id': image_id,
'category_id': cat,
'segmentation': encode(ret_masks[_]),
'score': score,
with open('results/%s.json' % args.model, 'rb') as f:
input_results = cjson.decode(f.read())
results = []
_ = 0
while _ < len(input_results):
sub_results = []
start = _
while _ < len(input_results) and input_results[start][
'image_id'] == input_results[_]['image_id']:
sub_results.append(input_results[_])
_ += 1
printProgress(_,
len(input_results),
'Results preprocess: ',
suffix='Complete',
barLength=50)
sub_results.sort(key=lambda item: item['objn'], reverse=True)
# nms
keep = np.ones(len(sub_results)).astype(np.bool)
if args.nms_threshold < 1:
for i in range(len(sub_results)):
if keep[i]:
for j in range(i + 1, len(sub_results)):
if keep[j] and iou(sub_results[i]['segmentation'],
sub_results[j]['segmentation'],
[False]) > args.nms_threshold:
keep[j] = False