class TestParam:
min_det_score = 0.001
max_det_per_image = 100
process_roidb = lambda x: x
if Trident.test_scaleaware:
process_output = lambda x, y: process_branch_outputs(
x, Trident.num_branch, Trident.valid_ranges, Trident.
valid_ranges_on_origin)
else:
process_output = lambda x, y: x
process_rpn_output = lambda x, y: process_branch_rpn_outputs(
x, Trident.num_branch)
class model:
prefix = "experiments/{}/checkpoint".format(General.name)
epoch = OptimizeParam.schedule.end_epoch
class nms:
type = "nms"
thr = 0.5
class coco:
annotation = "data/coco/annotations/instances_minival2014.json"
class TestParam:
min_det_score = 0.001
max_det_per_image = 100
class model:
prefix = "experiments/{}/checkpoint".format(General.name)
epoch = OptimizeParam.schedule.end_epoch
class nms:
type = "nms"
thr = 0.5
class coco:
annotation = "data/coco/annotations/instances_minival2014.json"
if Trident.test_scaleaware:
process_output = lambda x, y: process_branch_outputs(
x, Trident.num_branch, Trident.valid_ranges, Trident.
valid_ranges_on_origin)
else:
process_output = lambda x, y: x
process_rpn_output = lambda x, y: process_branch_rpn_outputs(
x, Trident.num_branch)
def process_roidb(roidb):
ms_roidb = []
for r_ in roidb:
for short, long in zip(ResizeParam.short_ranges,
ResizeParam.long_ranges):
r = r_.copy()
r["resize_long"] = long
r["resize_short"] = short
ms_roidb.append(r)
return ms_roidb