def get_config(is_train): class General: log_frequency = 10 name = __name__.rsplit("/")[-1].rsplit(".")[-1] batch_image = 8 if is_train else 1 fp16 = True loader_worker = 8 class KvstoreParam: kvstore = "nccl" batch_image = General.batch_image gpus = [0, 1, 2, 3, 4, 5, 6, 7] fp16 = General.fp16 class NormalizeParam: normalizer = normalizer_factory(type="localbn", ndev=len(KvstoreParam.gpus)) # normalizer = normalizer_factory(type="gn") class BackboneParam: fp16 = General.fp16 normalizer = NormalizeParam.normalizer class NeckParam: fp16 = General.fp16 normalizer = NormalizeParam.normalizer class RpnParam: fp16 = General.fp16 normalizer = NormalizeParam.normalizer batch_image = General.batch_image nnvm_proposal = True nnvm_rpn_target = False class anchor_generate: scale = (4, ) ratio = (0.5, 1.0, 2.0) stride = (4, 8, 16, 32, 64) image_anchor = 256 max_side = 700 class anchor_assign: allowed_border = 0 pos_thr = 0.7 neg_thr = 0.3 min_pos_thr = 0.0 image_anchor = 256 pos_fraction = 0.5 class head: conv_channel = 256 mean = (0, 0, 0, 0) std = (1, 1, 1, 1) class proposal: pre_nms_top_n = 2000 if is_train else 1000 post_nms_top_n = 2000 if is_train else 1000 nms_thr = 0.7 min_bbox_side = 0 class subsample_proposal: proposal_wo_gt = False image_roi = 512 fg_fraction = 0.25 fg_thr = 0.5 bg_thr_hi = 0.5 bg_thr_lo = 0.0 class bbox_target: num_reg_class = 81 class_agnostic = False weight = (1.0, 1.0, 1.0, 1.0) mean = (0.0, 0.0, 0.0, 0.0) std = (0.1, 0.1, 0.2, 0.2) class BboxParam: fp16 = General.fp16 normalizer = NormalizeParam.normalizer num_class = 1 + 80 image_roi = 512 batch_image = General.batch_image class regress_target: class_agnostic = False mean = (0.0, 0.0, 0.0, 0.0) std = (0.1, 0.1, 0.2, 0.2) class RoiParam: fp16 = General.fp16 normalizer = NormalizeParam.normalizer out_size = 7 stride = (4, 8, 16, 32) roi_canonical_scale = 224 roi_canonical_level = 4 class DatasetParam: if is_train: image_set = ("coco_train2017", ) total_image = 82783 + 35504 else: image_set = ("coco_val2017", ) total_image = 5000 backbone = Backbone(BackboneParam) neck = Neck(NeckParam) rpn_head = RpnHead(RpnParam) roi_extractor = RoiExtractor(RoiParam) bbox_head = BboxHead(BboxParam) detector = Detector() if is_train: train_sym = detector.get_train_symbol(backbone, neck, rpn_head, roi_extractor, bbox_head) rpn_test_sym = None test_sym = None else: train_sym = None rpn_test_sym = detector.get_rpn_test_symbol(backbone, neck, rpn_head) test_sym = detector.get_test_symbol(backbone, neck, rpn_head, roi_extractor, bbox_head) class ModelParam: train_symbol = train_sym test_symbol = test_sym rpn_test_symbol = rpn_test_sym from_scratch = True random = True memonger = False memonger_until = "stage3_unit21_plus" class pretrain: prefix = None epoch = 0 fixed_param = [] def process_weight(sym, arg, aux): for stride in RpnParam.anchor_generate.stride: add_anchor_to_arg(sym, arg, aux, RpnParam.anchor_generate.max_side, stride, RpnParam.anchor_generate.scale, RpnParam.anchor_generate.ratio) class OptimizeParam: class optimizer: type = "sgd" lr = 0.01 / 8 * len(KvstoreParam.gpus) * KvstoreParam.batch_image momentum = 0.9 wd = 1e-4 clip_gradient = None class schedule: mult = 6 begin_epoch = 0 end_epoch = 6 * mult if mult <= 2: lr_iter = [ 60000 * mult * 16 // (len(KvstoreParam.gpus) * KvstoreParam.batch_image), 80000 * mult * 16 // (len(KvstoreParam.gpus) * KvstoreParam.batch_image) ] else: # follow the setting in Rethinking ImageNet Pre-training # reduce the lr in the last 60k and 20k iterations lr_iter = [ (DatasetParam.total_image * 2 // 16 * end_epoch - 60000) * 16 // (len(KvstoreParam.gpus) * KvstoreParam.batch_image), (DatasetParam.total_image * 2 // 16 * end_epoch - 20000) * 16 // (len(KvstoreParam.gpus) * KvstoreParam.batch_image) ] class warmup: type = "gradual" lr = 0 iter = 500 class TestParam: min_det_score = 0.05 max_det_per_image = 100 process_roidb = lambda x: x process_output = lambda x, y: x 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" # data processing class NormParam: mean = tuple(i * 255 for i in (0.485, 0.456, 0.406)) # RGB order std = tuple(i * 255 for i in (0.229, 0.224, 0.225)) # data processing class ResizeParam: short = 400 long = 600 class PadParam: short = 400 long = 600 max_num_gt = 100 class AnchorTarget2DParam: def __init__(self): self.generate = self._generate() class _generate: def __init__(self): self.stride = (4, 8, 16, 32, 64) self.short = (100, 50, 25, 13, 7) self.long = (150, 75, 38, 19, 10) scales = (4) aspects = (0.5, 1.0, 2.0) class assign: allowed_border = 0 pos_thr = 0.7 neg_thr = 0.3 min_pos_thr = 0.0 class sample: image_anchor = 256 pos_fraction = 0.5 class RenameParam: mapping = dict(image="data") from core.detection_input import ReadRoiRecord, Resize2DImageBbox, \ ConvertImageFromHwcToChw, Flip2DImageBbox, Pad2DImageBbox, \ RenameRecord, Norm2DImage from models.FPN.input import PyramidAnchorTarget2D if is_train: transform = [ ReadRoiRecord(None), Norm2DImage(NormParam), Resize2DImageBbox(ResizeParam), Flip2DImageBbox(), Pad2DImageBbox(PadParam), ConvertImageFromHwcToChw(), RenameRecord(RenameParam.mapping) ] data_name = ["data"] label_name = ["gt_bbox", "im_info"] if not RpnParam.nnvm_rpn_target: transform.append(PyramidAnchorTarget2D(AnchorTarget2DParam())) label_name += ["rpn_cls_label", "rpn_reg_target", "rpn_reg_weight"] else: transform = [ ReadRoiRecord(None), Norm2DImage(NormParam), Resize2DImageBbox(ResizeParam), Pad2DImageBbox(PadParam), ConvertImageFromHwcToChw(), RenameRecord(RenameParam.mapping) ] data_name = ["data", "im_info", "im_id", "rec_id"] label_name = [] import core.detection_metric as metric rpn_acc_metric = metric.AccWithIgnore( "RpnAcc", ["rpn_cls_loss_output", "rpn_cls_label_blockgrad_output"], []) rpn_l1_metric = metric.L1( "RpnL1", ["rpn_reg_loss_output", "rpn_cls_label_blockgrad_output"], []) # for bbox, the label is generated in network so it is an output box_acc_metric = metric.AccWithIgnore( "RcnnAcc", ["bbox_cls_loss_output", "bbox_label_blockgrad_output"], []) box_l1_metric = metric.L1( "RcnnL1", ["bbox_reg_loss_output", "bbox_label_blockgrad_output"], []) metric_list = [ rpn_acc_metric, rpn_l1_metric, box_acc_metric, box_l1_metric ] return General, KvstoreParam, RpnParam, RoiParam, BboxParam, DatasetParam, \ ModelParam, OptimizeParam, TestParam, \ transform, data_name, label_name, metric_list
def get_config(is_train): class General: log_frequency = 10 name = __name__.rsplit("/")[-1].rsplit(".")[-1] batch_image = 2 if is_train else 1 fp16 = False class KvstoreParam: kvstore = "nccl" batch_image = General.batch_image gpus = [0, 1, 2, 3, 4, 5, 6, 7] fp16 = General.fp16 class NormalizeParam: normalizer = normalizer_factory(type="fixbn") class BackboneParam: fp16 = General.fp16 normalizer = NormalizeParam.normalizer class NeckParam: fp16 = General.fp16 normalizer = NormalizeParam.normalizer class RpnParam: fp16 = General.fp16 normalizer = NormalizeParam.normalizer batch_image = General.batch_image class anchor_generate: scale = (8, ) ratio = (0.5, 1.0, 2.0) stride = (4, 8, 16, 32, 64) image_anchor = 256 class head: conv_channel = 256 mean = (0, 0, 0, 0) std = (1, 1, 1, 1) class proposal: pre_nms_top_n = 2000 if is_train else 1000 post_nms_top_n = 2000 if is_train else 1000 nms_thr = 0.7 min_bbox_side = 0 class subsample_proposal: proposal_wo_gt = False image_roi = 512 fg_fraction = 0.25 fg_thr = 0.5 bg_thr_hi = 0.5 bg_thr_lo = 0.0 class bbox_target: num_reg_class = 81 class_agnostic = False weight = (1.0, 1.0, 1.0, 1.0) mean = (0.0, 0.0, 0.0, 0.0) std = (0.1, 0.1, 0.2, 0.2) class BboxParam: fp16 = General.fp16 normalizer = NormalizeParam.normalizer num_class = 1 + 80 image_roi = 512 batch_image = General.batch_image class regress_target: class_agnostic = False mean = (0.0, 0.0, 0.0, 0.0) std = (0.1, 0.1, 0.2, 0.2) class RoiParam: fp16 = General.fp16 normalizer = NormalizeParam.normalizer out_size = 7 stride = (4, 8, 16, 32) roi_canonical_scale = 224 roi_canonical_level = 4 class DatasetParam: if is_train: image_set = ("coco_train2014", "coco_valminusminival2014") else: image_set = ("coco_minival2014", ) backbone = Backbone(BackboneParam) neck = Neck(NeckParam) rpn_head = RpnHead(RpnParam) roi_extractor = RoiExtractor(RoiParam) bbox_head = BboxHead(BboxParam) detector = Detector() if is_train: train_sym = detector.get_train_symbol(backbone, neck, rpn_head, roi_extractor, bbox_head) rpn_test_sym = None test_sym = None else: train_sym = None rpn_test_sym = detector.get_rpn_test_symbol(backbone, neck, rpn_head) test_sym = detector.get_test_symbol(backbone, neck, rpn_head, roi_extractor, bbox_head) class ModelParam: train_symbol = train_sym test_symbol = test_sym rpn_test_symbol = rpn_test_sym from_scratch = False random = True memonger = False memonger_until = "stage3_unit21_plus" class pretrain: prefix = "pretrain_model/resnet-v1-101" epoch = 0 fixed_param = ["conv0", "stage1", "gamma", "beta"] class OptimizeParam: class optimizer: type = "sgd" lr = 0.01 / 8 * len(KvstoreParam.gpus) * KvstoreParam.batch_image momentum = 0.9 wd = 0.0001 clip_gradient = None class schedule: begin_epoch = 0 end_epoch = 6 lr_iter = [ 60000 * 16 // (len(KvstoreParam.gpus) * KvstoreParam.batch_image), 80000 * 16 // (len(KvstoreParam.gpus) * KvstoreParam.batch_image) ] class warmup: type = "gradual" lr = 0.01 / 8 * len( KvstoreParam.gpus) * KvstoreParam.batch_image / 3.0 iter = 500 class TestParam: min_det_score = 0.05 max_det_per_image = 100 process_roidb = lambda x: x process_output = lambda x, y: x 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" # data processing class NormParam: mean = (122.7717, 115.9465, 102.9801) # RGB order std = (1.0, 1.0, 1.0) # data processing class ResizeParam: short = 800 long = 1333 class PadParam: short = 800 long = 1333 max_num_gt = 100 class AnchorTarget2DParam: def __init__(self): self.generate = self._generate() class _generate: def __init__(self): self.stride = (4, 8, 16, 32, 64) self.short = (200, 100, 50, 25, 13) self.long = (334, 167, 84, 42, 21) scales = (8) aspects = (0.5, 1.0, 2.0) class assign: allowed_border = 0 pos_thr = 0.7 neg_thr = 0.3 min_pos_thr = 0.0 class sample: image_anchor = 256 pos_fraction = 0.5 class RenameParam: mapping = dict(image="data") from core.detection_input import ReadRoiRecord, Resize2DImageBbox, \ ConvertImageFromHwcToChw, Flip2DImageBbox, Pad2DImageBbox, \ RenameRecord, Norm2DImage from models.FPN.input import PyramidAnchorTarget2D if is_train: transform = [ ReadRoiRecord(None), Norm2DImage(NormParam), Resize2DImageBbox(ResizeParam), Flip2DImageBbox(), Pad2DImageBbox(PadParam), ConvertImageFromHwcToChw(), PyramidAnchorTarget2D(AnchorTarget2DParam()), RenameRecord(RenameParam.mapping) ] data_name = ["data", "im_info", "gt_bbox"] label_name = ["rpn_cls_label", "rpn_reg_target", "rpn_reg_weight"] else: transform = [ ReadRoiRecord(None), Norm2DImage(NormParam), Resize2DImageBbox(ResizeParam), ConvertImageFromHwcToChw(), RenameRecord(RenameParam.mapping) ] data_name = ["data", "im_info", "im_id", "rec_id"] label_name = [] import core.detection_metric as metric rpn_acc_metric = metric.AccWithIgnore("RpnAcc", ["rpn_cls_loss_output"], ["rpn_cls_label"]) rpn_l1_metric = metric.L1("RpnL1", ["rpn_reg_loss_output"], ["rpn_cls_label"]) # for bbox, the label is generated in network so it is an output box_acc_metric = metric.AccWithIgnore( "RcnnAcc", ["bbox_cls_loss_output", "bbox_label_blockgrad_output"], []) box_l1_metric = metric.L1( "RcnnL1", ["bbox_reg_loss_output", "bbox_label_blockgrad_output"], []) metric_list = [ rpn_acc_metric, rpn_l1_metric, box_acc_metric, box_l1_metric ] return General, KvstoreParam, RpnParam, RoiParam, BboxParam, DatasetParam, \ ModelParam, OptimizeParam, TestParam, \ transform, data_name, label_name, metric_list
def get_config(is_train): class General: log_frequency = 10 name = __name__.rsplit("/")[-1].rsplit(".")[-1] batch_image = 2 if is_train else 1 fp16 = False long_side = 1280 short_side = 960 class KvstoreParam: kvstore = "local" batch_image = General.batch_image gpus = [0, 1, 2, 3, 4, 5, 6, 7] fp16 = General.fp16 class NormalizeParam: # normalizer = normalizer_factory(type="syncbn", ndev=len(KvstoreParam.gpus)) normalizer = normalizer_factory(type="fixbn") class BackboneParam: fp16 = General.fp16 normalizer = NormalizeParam.normalizer depth = 50 class NeckParam: fp16 = General.fp16 normalizer = NormalizeParam.normalizer class RpnParam: fp16 = General.fp16 normalizer = NormalizeParam.normalizer batch_image = General.batch_image nnvm_proposal = True nnvm_rpn_target = False use_symbolic_proposal = None class anchor_generate: scale = (8, ) ratio = (0.5, 1.0, 2.0) stride = (4, 8, 16, 32, 64) image_anchor = 256 max_side = 1280 class anchor_assign: allowed_border = 0 pos_thr = 0.7 neg_thr = 0.3 min_pos_thr = 0.3 image_anchor = 256 pos_fraction = 0.5 class head: conv_channel = 256 mean = (0, 0, 0, 0) std = (1, 1, 1, 1) class proposal: pre_nms_top_n = 2000 if is_train else 1000 post_nms_top_n = 2000 if is_train else 1000 nms_thr = 0.7 min_bbox_side = 0 class subsample_proposal: proposal_wo_gt = False image_roi = 512 fg_fraction = 0.25 fg_thr = 0.5 bg_thr_hi = 0.5 bg_thr_lo = 0.5 class bbox_target: num_reg_class = 2 class_agnostic = True weight = (1.0, 1.0, 1.0, 1.0) mean = (0.0, 0.0, 0.0, 0.0) std = (0.1, 0.1, 0.2, 0.2) class BboxParam: fp16 = General.fp16 normalizer = NormalizeParam.normalizer num_class = 1 + 54 image_roi = 512 batch_image = General.batch_image stage = "1st" loss_weight = 1.0 class regress_target: class_agnostic = True mean = (0.0, 0.0, 0.0, 0.0) std = (0.1, 0.1, 0.2, 0.2) class subsample_proposal: proposal_wo_gt = False image_roi = 512 fg_fraction = 0.25 fg_thr = 0.6 bg_thr_hi = 0.6 bg_thr_lo = 0.6 class bbox_target: num_reg_class = 2 class_agnostic = True weight = (1.0, 1.0, 1.0, 1.0) mean = (0.0, 0.0, 0.0, 0.0) std = (0.1, 0.1, 0.2, 0.2) class BboxParam2nd: fp16 = General.fp16 normalizer = NormalizeParam.normalizer num_class = 1 + 54 image_roi = 512 batch_image = General.batch_image stage = "2nd" loss_weight = 0.5 class regress_target: class_agnostic = True mean = (0.0, 0.0, 0.0, 0.0) std = (0.05, 0.05, 0.1, 0.1) class subsample_proposal: proposal_wo_gt = False image_roi = 512 fg_fraction = 0.25 fg_thr = 0.7 bg_thr_hi = 0.7 bg_thr_lo = 0.7 class bbox_target: num_reg_class = 2 class_agnostic = True weight = (1.0, 1.0, 1.0, 1.0) mean = (0.0, 0.0, 0.0, 0.0) std = (0.05, 0.05, 0.1, 0.1) class BboxParam3rd: fp16 = General.fp16 normalizer = NormalizeParam.normalizer num_class = 1 + 54 image_roi = 512 batch_image = General.batch_image stage = "3rd" loss_weight = 0.25 class regress_target: class_agnostic = True mean = (0.0, 0.0, 0.0, 0.0) std = (0.033, 0.033, 0.067, 0.067) class subsample_proposal: proposal_wo_gt = None image_roi = None fg_fraction = None fg_thr = None bg_thr_hi = None bg_thr_lo = None class bbox_target: num_reg_class = None class_agnostic = None weight = None mean = None std = None class RoiParam: fp16 = General.fp16 normalizer = NormalizeParam.normalizer out_size = 7 stride = (4, 8, 16, 32) roi_canonical_scale = 224 roi_canonical_level = 4 class DatasetParam: if is_train: image_set = ("vending_train", ) else: image_set = ("coco_val2017", ) backbone = Backbone(BackboneParam) neck = Neck(NeckParam) rpn_head = RpnHead(RpnParam) roi_extractor = RoiExtractor(RoiParam) bbox_head = BboxHead(BboxParam) bbox_head_2nd = BboxHead(BboxParam2nd) bbox_head_3rd = BboxHead(BboxParam3rd) detector = Detector() if is_train: train_sym = detector.get_train_symbol(backbone, neck, rpn_head, roi_extractor, bbox_head, bbox_head_2nd, bbox_head_3rd) rpn_test_sym = None test_sym = None else: train_sym = None rpn_test_sym = detector.get_rpn_test_symbol(backbone, neck, rpn_head) test_sym = detector.get_test_symbol(backbone, neck, rpn_head, roi_extractor, bbox_head, bbox_head_2nd, bbox_head_3rd) class ModelParam: train_symbol = train_sym test_symbol = test_sym rpn_test_symbol = rpn_test_sym from_scratch = False random = True memonger = False memonger_until = "stage3_unit21_plus" class pretrain: prefix = "pretrain_model/resnet-v1-50" epoch = 0 fixed_param = ["conv0", "stage1", "gamma", "beta"] def process_weight(sym, arg, aux): for stride in RpnParam.anchor_generate.stride: add_anchor_to_arg(sym, arg, aux, RpnParam.anchor_generate.max_side, stride, RpnParam.anchor_generate.scale, RpnParam.anchor_generate.ratio) class QuantizeTrainingParam: quantize_flag = False # quantized_op = ("Convolution", "FullyConnected", "Deconvolution","Concat", "Pooling", "add_n", "elemwise_add") quantized_op = ("Convolution", "FullyConnected", "Deconvolution") class WeightQuantizeParam: delay_quant = 0 ema_decay = 0.99 grad_mode = "ste" is_weight = True is_weight_perchannel = False quant_mode = "minmax" class ActQuantizeParam: delay_quant = 0 ema_decay = 0.99 grad_mode = "ste" is_weight = False is_weight_perchannel = False quant_mode = "minmax" class OptimizeParam: class optimizer: type = "sgd" lr = 0.01 / 8 * len(KvstoreParam.gpus) * KvstoreParam.batch_image momentum = 0.9 wd = 0.0001 clip_gradient = None class schedule: begin_epoch = 0 end_epoch = 6 lr_iter = [ 60000 * 16 // (len(KvstoreParam.gpus) * KvstoreParam.batch_image), 80000 * 16 // (len(KvstoreParam.gpus) * KvstoreParam.batch_image) ] class warmup: type = "gradual" lr = 0.01 / 8 * len( KvstoreParam.gpus) * KvstoreParam.batch_image / 3.0 iter = 500 class TestParam: min_det_score = 0.05 max_det_per_image = 100 process_roidb = lambda x: x process_output = lambda x, y: x class model: prefix = "experiments/{}/checkpoint".format(General.name) epoch = OptimizeParam.schedule.end_epoch class nms: type = "nms" thr = 0.6 class coco: annotation = "data/coco/annotations/instances_val2017.json" # data processing class NormParam: mean = (123.675, 116.28, 103.53) # RGB order std = (58.395, 57.12, 57.375) # data processing class ResizeParam: short = 960 long = 1280 class PadParam: short = 960 long = 1280 max_num_gt = 100 class AnchorTarget2DParam: def __init__(self): self.generate = self._generate() self.mean = (0, 0, 0, 0) self.std = (1, 1, 1, 1) class _generate: def __init__(self): self.stride = (4, 8, 16, 32, 64) self.short = (240, 120, 60, 30, 15) self.long = (320, 160, 80, 40, 20) scales = (8, ) aspects = (0.5, 1.0, 2.0) class assign: allowed_border = 0 pos_thr = 0.7 neg_thr = 0.3 min_pos_thr = 0.3 class sample: image_anchor = 256 pos_fraction = 0.5 class RenameParam: mapping = dict(image="data") from core.detection_input import ReadRoiRecord, Resize2DImageBbox, \ ConvertImageFromHwcToChw, Flip2DImageBbox, Pad2DImageBbox, \ RenameRecord, Norm2DImage from models.FPN.input import PyramidAnchorTarget2D if is_train: transform = [ ReadRoiRecord(None), Norm2DImage(NormParam), Resize2DImageBbox(ResizeParam), Flip2DImageBbox(), Pad2DImageBbox(PadParam), ConvertImageFromHwcToChw(), RenameRecord(RenameParam.mapping) ] data_name = ["data"] label_name = ["gt_bbox", "im_info"] if not RpnParam.nnvm_rpn_target: transform.append(PyramidAnchorTarget2D(AnchorTarget2DParam())) label_name += ["rpn_cls_label", "rpn_reg_target", "rpn_reg_weight"] else: transform = [ ReadRoiRecord(None), Norm2DImage(NormParam), Resize2DImageBbox(ResizeParam), ConvertImageFromHwcToChw(), RenameRecord(RenameParam.mapping) ] data_name = ["data", "im_info", "im_id", "rec_id"] label_name = [] import core.detection_metric as metric rpn_acc_metric = metric.AccWithIgnore( "RpnAcc", ["rpn_cls_loss_output", "rpn_cls_label_blockgrad_output"], []) rpn_l1_metric = metric.L1( "RpnL1", ["rpn_reg_loss_output", "rpn_cls_label_blockgrad_output"], []) # for bbox, the label is generated in network so it is an output # stage1 metric box_acc_metric_1st = metric.AccWithIgnore( "RcnnAcc_1st", ["bbox_cls_loss_1st_output", "bbox_label_blockgrad_1st_output"], []) box_l1_metric_1st = metric.L1( "RcnnL1_1st", ["bbox_reg_loss_1st_output", "bbox_label_blockgrad_1st_output"], []) # stage2 metric box_acc_metric_2nd = metric.AccWithIgnore( "RcnnAcc_2nd", ["bbox_cls_loss_2nd_output", "bbox_label_blockgrad_2nd_output"], []) box_l1_metric_2nd = metric.L1( "RcnnL1_2nd", ["bbox_reg_loss_2nd_output", "bbox_label_blockgrad_2nd_output"], []) # stage3 metric box_acc_metric_3rd = metric.AccWithIgnore( "RcnnAcc_3rd", ["bbox_cls_loss_3rd_output", "bbox_label_blockgrad_3rd_output"], []) box_l1_metric_3rd = metric.L1( "RcnnL1_3rd", ["bbox_reg_loss_3rd_output", "bbox_label_blockgrad_3rd_output"], []) metric_list = [ rpn_acc_metric, rpn_l1_metric, box_acc_metric_1st, box_l1_metric_1st, box_acc_metric_2nd, box_l1_metric_2nd, box_acc_metric_3rd, box_l1_metric_3rd ] return General, KvstoreParam, RpnParam, RoiParam, BboxParam, DatasetParam, \ ModelParam, OptimizeParam, TestParam, \ transform, data_name, label_name, metric_list