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
class TestParam: min_det_score = 0.001 max_det_per_image = 100 process_roidb = TestScaleParam.add_resize_info 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 class model: prefix = "experiments/{}/checkpoint".format(General.name) epoch = OptimizeParam.schedule.end_epoch class nms: from operator_py.nms import cython_soft_nms_wrapper type = cython_soft_nms_wrapper thr = 0.5 class coco: annotation = "data/coco/annotations/instances_minival2014.json"