def get_infer_results(outs_res, eval_type, catid):
"""
Get result at the stage of inference.
The output format is dictionary containing bbox or mask result.
For example, bbox result is a list and each element contains
image_id, category_id, bbox and score.
"""
if outs_res is None or len(outs_res) == 0:
raise ValueError(
'The number of valid detection result if zero. Please use reasonable model and check input data.'
)
infer_res = {}
if 'bbox' in eval_type:
box_res = []
for outs in outs_res:
box_res += get_det_res(outs['bbox'], outs['bbox_num'],
outs['im_id'], catid)
infer_res['bbox'] = box_res
if 'mask' in eval_type:
seg_res = []
for outs in outs_res:
seg_res += get_seg_res(outs['mask'], outs['bbox_num'],
outs['im_id'], catid)
infer_res['mask'] = seg_res
return infer_res
def get_infer_results(outs, catid):
"""
Get result at the stage of inference.
The output format is dictionary containing bbox or mask result.
For example, bbox result is a list and each element contains
image_id, category_id, bbox and score.
"""
if outs is None or len(outs) == 0:
raise ValueError(
'The number of valid detection result if zero. Please use reasonable model and check input data.'
)
im_id = outs['im_id']
infer_res = {}
if 'bbox' in outs:
infer_res['bbox'] = get_det_res(outs['bbox'], outs['score'],
outs['label'], outs['bbox_num'], im_id,
catid)
if 'mask' in outs:
# mask post process
infer_res['mask'] = get_seg_res(outs['mask'], outs['score'],
outs['label'], outs['bbox_num'], im_id,
catid)
if 'segm' in outs:
infer_res['segm'] = get_solov2_segm_res(outs, im_id, catid)
return infer_res
def get_infer_results(outs_res, eval_type, catid):
"""
Get result at the stage of inference.
The output format is dictionary containing bbox or mask result.
For example, bbox result is a list and each element contains
image_id, category_id, bbox and score.
"""
if outs_res is None or len(outs_res) == 0:
raise ValueError(
'The number of valid detection result if zero. Please use reasonable model and check input data.'
)
infer_res = {k: [] for k in eval_type}
for i, outs in enumerate(outs_res):
im_id = outs['im_id']
im_shape = outs['im_shape']
scale_factor = outs['scale_factor']
if 'bbox' in eval_type:
infer_res['bbox'] += get_det_res(outs['bbox'], outs['bbox_num'],
im_id, catid)
if 'mask' in eval_type:
# mask post process
infer_res['mask'] += get_seg_res(outs['mask'], outs['bbox_num'],
im_id, catid)
return infer_res