def get_config(is_train): class General: log_frequency = 10 name = __name__.rsplit("/")[-1].rsplit(".")[-1] batch_image = 1 if is_train else 1 fp16 = False class Trident: num_branch = 3 if is_train else 1 train_scaleaware = False test_scaleaware = False branch_ids = range(num_branch) if is_train else [1] branch_dilates = [1, 2, 3] if is_train else [2] valid_ranges = [(0, -1), (0, -1), (0, -1)] if is_train else [(0, -1)] valid_ranges_on_origin = True branch_bn_shared = True branch_conv_shared = True branch_deform = False assert num_branch == len(branch_ids) assert num_branch == len(valid_ranges) 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 = 101 num_branch = Trident.num_branch branch_ids = Trident.branch_ids branch_dilates = Trident.branch_dilates branch_bn_shared = Trident.branch_bn_shared branch_conv_shared = Trident.branch_conv_shared branch_deform = Trident.branch_deform class NeckParam: fp16 = General.fp16 normalizer = NormalizeParam.normalizer class RpnParam: fp16 = General.fp16 normalizer = NormalizeParam.normalizer batch_image = General.batch_image * Trident.num_branch class anchor_generate: scale = (2, 4, 8, 16, 32) ratio = (0.5, 1.0, 2.0) stride = 16 image_anchor = 256 class head: conv_channel = 512 mean = (0, 0, 0, 0) std = (1, 1, 1, 1) class proposal: pre_nms_top_n = 12000 if is_train else 6000 post_nms_top_n = 500 if is_train else 300 nms_thr = 0.7 min_bbox_side = 0 class subsample_proposal: proposal_wo_gt = True image_roi = 128 fg_fraction = 0.5 fg_thr = 0.5 bg_thr_hi = 0.5 bg_thr_lo = 0.0 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 + 80 image_roi = 128 batch_image = General.batch_image * Trident.num_branch 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 RoiParam: fp16 = General.fp16 normalizer = NormalizeParam.normalizer out_size = 7 stride = 16 class DatasetParam: if is_train: image_set = ("coco_train2017", ) else: image_set = ("coco_val2017", ) 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, num_branch=Trident.num_branch, scaleaware=Trident.train_scaleaware) rpn_test_sym = None test_sym = None else: train_sym = None rpn_test_sym = detector.get_rpn_test_symbol(backbone, neck, rpn_head, Trident.num_branch) test_sym = detector.get_test_symbol(backbone, neck, rpn_head, roi_extractor, bbox_head, num_branch=Trident.num_branch) 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%s_v1b" % BackboneParam.depth 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 = 5 class schedule: begin_epoch = 0 end_epoch = 12 lr_iter = [ 120000 * 16 // (len(KvstoreParam.gpus) * KvstoreParam.batch_image), 160000 * 16 // (len(KvstoreParam.gpus) * KvstoreParam.batch_image) ] class warmup: type = "gradual" lr = 0.0 iter = 3000 * 16 // (len(KvstoreParam.gpus) * KvstoreParam.batch_image) 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" # 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)) class ResizeParam: short = 800 long = 1200 if is_train else 2000 class PadParam: short = 800 long = 1200 if is_train else 2000 max_num_gt = 100 class ScaleRange: valid_ranges = Trident.valid_ranges cal_on_origin = Trident.valid_ranges_on_origin # True: valid_ranges on origin image scale / valid_ranges on resized image scale class AnchorTarget2DParam: class generate: short = 800 // 16 long = 1200 // 16 stride = 16 scales = (2, 4, 8, 16, 32) 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 trident: invalid_anchor_threshd = 0.3 class RenameParam: mapping = dict(image="data") from core.detection_input import ReadRoiRecord, Resize2DImageBbox, \ ConvertImageFromHwcToChw, Flip2DImageBbox, Pad2DImageBbox, \ RenameRecord, Norm2DImage from models.tridentnet.input import ScaleAwareRange, TridentAnchorTarget2D if is_train: transform = [ ReadRoiRecord(None), Norm2DImage(NormParam), Resize2DImageBbox(ResizeParam), Flip2DImageBbox(), Pad2DImageBbox(PadParam), ConvertImageFromHwcToChw(), ScaleAwareRange(ScaleRange), TridentAnchorTarget2D(AnchorTarget2DParam), RenameRecord(RenameParam.mapping) ] data_name = ["data", "im_info", "gt_bbox"] if Trident.train_scaleaware: data_name.append("valid_ranges") 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 = 20 depth = 101 name = __name__.rsplit("/")[-1].rsplit(".")[-1] batch_image = 3 if is_train else 1 fp16 = True class Trident: num_branch = 3 train_scaleaware = True test_scaleaware = True branch_ids = range(num_branch) branch_dilates = [1, 2, 3] valid_ranges = [(0, 150), (50, 270), (150, -1)] valid_ranges_on_origin = False branch_bn_shared = False branch_conv_shared = True branch_deform = True 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 depth = General.depth normalizer = NormalizeParam.normalizer num_branch = Trident.num_branch branch_ids = Trident.branch_ids branch_dilates = Trident.branch_dilates branch_bn_shared = Trident.branch_bn_shared branch_conv_shared = Trident.branch_conv_shared branch_deform = Trident.branch_deform class NeckParam: fp16 = General.fp16 normalizer = NormalizeParam.normalizer class RpnParam: fp16 = General.fp16 normalizer = NormalizeParam.normalizer batch_image = General.batch_image * Trident.num_branch class anchor_generate: scale = (2, 4, 8, 16, 32) ratio = (0.5, 1.0, 2.0) stride = 16 image_anchor = 256 class head: conv_channel = 512 mean = (0, 0, 0, 0) std = (1, 1, 1, 1) class proposal: pre_nms_top_n = 12000 if is_train else 6000 post_nms_top_n = 500 if is_train else 1000 nms_thr = 0.7 min_bbox_side = 0 class subsample_proposal: proposal_wo_gt = True image_roi = 128 fg_fraction = 0.5 fg_thr = 0.5 bg_thr_hi = 0.5 bg_thr_lo = 0.0 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 + 80 image_roi = 128 batch_image = General.batch_image * Trident.num_branch 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 RoiParam: fp16 = General.fp16 normalizer = NormalizeParam.normalizer out_size = 7 stride = 16 class DatasetParam: if is_train: image_set = ("coco_train2014", "coco_valminusminival2014", "coco_minival2014") else: image_set = ("coco_test-dev2017", ) # 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, num_branch=Trident.num_branch, scaleaware=Trident.train_scaleaware) test_sym = None else: train_sym = None test_sym = detector.get_test_symbol( backbone, neck, rpn_head, roi_extractor, bbox_head, num_branch=Trident.num_branch) class ModelParam: train_symbol = train_sym test_symbol = test_sym from_scratch = False random = True memonger = True memonger_until = "stage3_unit21_plus" class pretrain: prefix = "pretrain_model/resnet-%d" % General.depth epoch = 0 fixed_param = [] def process_weight(sym, arg_params, aux_params): import re import logging logger = logging.getLogger() # for trident non-shared initialization for k in sym.list_arguments(): branch_name = re.sub('_branch\d+', '', k) if k != branch_name and branch_name in arg_params: arg_params[k] = arg_params[branch_name] logger.info('init arg {} with {}'.format(k, branch_name)) for k in sym.list_auxiliary_states(): branch_name = re.sub('_branch\d+', '', k) if k != branch_name and branch_name in aux_params: aux_params[k] = aux_params[branch_name] logger.info('init aux {} with {}'.format(k, branch_name)) class OptimizeParam: class optimizer: type = "sgd" lr = 0.01 / 8 * len(KvstoreParam.gpus) * KvstoreParam.batch_image momentum = 0.9 wd = 0.0001 clip_gradient = 5 class schedule: begin_epoch = 0 end_epoch = 18 lr_iter = [180000 * 16 // (len(KvstoreParam.gpus) * KvstoreParam.batch_image), 240000 * 16 // (len(KvstoreParam.gpus) * KvstoreParam.batch_image)] class warmup: type = "gradual" lr = 0.0 iter = 3000 * 16 // (len(KvstoreParam.gpus) * KvstoreParam.batch_image) class TestScaleParam: short_ranges = [600, 800, 1000, 1200] long_ranges = [2000, 2000, 2000, 2000] @staticmethod def add_resize_info(roidb): ms_roidb = [] for r_ in roidb: for short, long in zip(TestScaleParam.short_ranges, TestScaleParam.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" # data processing class ResizeParam: short = 800 long = 1200 if is_train else 2000 class RandResizeParam: short = None # generate on the fly long = None short_ranges = [600, 800, 1000, 1200] long_ranges = [2000, 2000, 2000, 2000] class RandCropParam: mode = "center" # random or center short = 800 long = 1200 class PadParam: short = 800 long = 1200 if is_train else 2000 max_num_gt = 100 class ScaleRange: valid_ranges = Trident.valid_ranges cal_on_origin = Trident.valid_ranges_on_origin # True: valid_ranges on origin image scale / valid_ranges on resized image scale class AnchorTarget2DParam: class generate: short = 800 // 16 long = 1200 // 16 stride = 16 scales = (2, 4, 8, 16, 32) 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 trident: invalid_anchor_threshd = 0.3 class RenameParam: mapping = dict(image="data") from core.detection_input import ReadRoiRecord, RandResize2DImageBbox, RandCrop2DImageBbox, Resize2DImageBboxByRoidb, \ ConvertImageFromHwcToChw, Flip2DImageBbox, Pad2DImageBbox, \ RenameRecord from models.tridentnet.input import ScaleAwareRange, TridentAnchorTarget2D if is_train: transform = [ ReadRoiRecord(None), RandResize2DImageBbox(RandResizeParam), RandCrop2DImageBbox(RandCropParam), Flip2DImageBbox(), Pad2DImageBbox(PadParam), ConvertImageFromHwcToChw(), ScaleAwareRange(ScaleRange), TridentAnchorTarget2D(AnchorTarget2DParam), RenameRecord(RenameParam.mapping) ] data_name = ["data", "im_info", "gt_bbox"] if Trident.train_scaleaware: data_name.append("valid_ranges") label_name = ["rpn_cls_label", "rpn_reg_target", "rpn_reg_weight"] else: transform = [ ReadRoiRecord(None), Resize2DImageBboxByRoidb(), 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 = True 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="syncbn", ndev=len(KvstoreParam.gpus)) 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 = (2, 4, 8, 16, 32) ratio = (0.5, 1.0, 2.0) stride = 16 image_anchor = 256 class head: conv_channel = 512 mean = (0, 0, 0, 0) std = (1, 1, 1, 1) class proposal: pre_nms_top_n = 12000 if is_train else 6000 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 = 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 + 80 image_roi = 512 batch_image = General.batch_image 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 RoiParam: fp16 = General.fp16 normalizer = NormalizeParam.normalizer out_size = 7 stride = 16 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) test_sym = None else: train_sym = None test_sym = detector.get_test_symbol(backbone, neck, rpn_head, roi_extractor, bbox_head) class ModelParam: train_symbol = train_sym test_symbol = 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"] class OptimizeParam: class optimizer: type = "sgd" lr = 0.01 / 8 * len(KvstoreParam.gpus) * KvstoreParam.batch_image momentum = 0.9 wd = 0.0001 clip_gradient = 35 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 = 0.0 iter = 750 * 16 // (len(KvstoreParam.gpus) * KvstoreParam.batch_image) 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) class ResizeParam: short = 800 long = 1200 if is_train else 2000 class PadParam: short = 800 long = 1200 max_num_gt = 100 class AnchorTarget2DParam: class generate: short = 800 // 16 long = 1200 // 16 stride = 16 scales = (2, 4, 8, 16, 32) 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, AnchorTarget2D, Norm2DImage if is_train: transform = [ ReadRoiRecord(None), Norm2DImage(NormParam), Resize2DImageBbox(ResizeParam), Flip2DImageBbox(), Pad2DImageBbox(PadParam), ConvertImageFromHwcToChw(), AnchorTarget2D(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: use_groupsoftmax = True log_frequency = 20 name = __name__.rsplit("/")[-1].rsplit(".")[-1] batch_image = 2 if is_train else 1 fp16 = True class KvstoreParam: kvstore = "local" batch_image = General.batch_image gpus = [0, 1, 2, 3, 4, 5, 6, 7] fp16 = General.fp16 class NormalizeParam: if is_train: normalizer = normalizer_factory(type="syncbn", ndev=len(KvstoreParam.gpus)) else: 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 = normalizer_factory( type="fixbn") # old model does not use BN in RPN head batch_image = General.batch_image use_groupsoftmax = General.use_groupsoftmax num_class = (1 + 2) if use_groupsoftmax else 2 class anchor_generate: scale = (2, 4, 8, 16, 32) ratio = (0.5, 1.0, 2.0) stride = 16 image_anchor = 256 class head: conv_channel = 512 mean = (0, 0, 0, 0) std = (1, 1, 1, 1) class proposal: pre_nms_top_n = 12000 if is_train else 6000 post_nms_top_n = 2000 if is_train else 1000 nms_thr = 0.7 min_bbox_side = 0 class subsample_proposal: proposal_wo_gt = True image_roi = 256 fg_fraction = 0.25 fg_thr = 0.5 bg_thr_hi = 0.5 bg_thr_lo = 0.0 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 + 83 image_roi = 256 batch_image = General.batch_image use_groupsoftmax = General.use_groupsoftmax 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 RoiParam: fp16 = General.fp16 normalizer = NormalizeParam.normalizer out_size = 7 stride = 16 class DatasetParam: if is_train: image_set = ("coco_train2014", "coco_valminusminival2014", "cctsdb_train") 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-101" epoch = 0 fixed_param = [] class OptimizeParam: class optimizer: type = "sgd" lr = 0.01 / 8 * len(KvstoreParam.gpus) * KvstoreParam.batch_image momentum = 0.9 wd = 0.0001 clip_gradient = 5 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 = 0.0 iter = 3000 * 16 // (len(KvstoreParam.gpus) * KvstoreParam.batch_image) 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 = "/ws/data/opendata/coco/annotations/instances_minival2014.json" # data processing class GroupParam: # box 83 classes boxv0 = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, \ 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, \ 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83], dtype=np.float32) #COCO benchmark boxv1 = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, \ 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, \ 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 0, 0, 0 ], dtype=np.float32) #CCTSDB benchmark boxv2 = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 81, 82, 83], dtype=np.float32) rpnv0 = np.array([0, 1, 2], dtype=np.float32) # rpn 3 classes rpnv1 = np.array([0, 1, 0], dtype=np.float32) # COCO benchmark rpnv2 = np.array([0, 0, 2], dtype=np.float32) # CCTSDB benchmark rpn_groups = [rpnv0, rpnv1, rpnv2] box_groups = [boxv0, boxv1, boxv2] class ResizeParam: short = 800 long = 1200 if is_train else 2000 class PadParam: short = 800 long = 1200 max_num_gt = 100 class AnchorTarget2DParam: class generate: short = 800 // 16 long = 1200 // 16 stride = 16 scales = (2, 4, 8, 16, 32) aspects = (0.5, 1.0, 2.0) use_groupsoftmax = General.use_groupsoftmax 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 def gtclass2rpn(gtclass): class_gap = 80 gtclass[gtclass > class_gap] = -1 gtclass[gtclass > 0] = 1 gtclass[gtclass < 0] = 2 return gtclass class RenameParam: mapping = dict(image="data") from core.detection_input import ReadRoiRecord, Resize2DImageBbox, \ ConvertImageFromHwcToChw, Flip2DImageBbox, Pad2DImageBbox, \ RenameRecord, AnchorTarget2D, GroupRead if is_train: transform = [ ReadRoiRecord(None), Resize2DImageBbox(ResizeParam), Flip2DImageBbox(), Pad2DImageBbox(PadParam), ConvertImageFromHwcToChw(), AnchorTarget2D(AnchorTarget2DParam), RenameRecord(RenameParam.mapping) ] data_name = ["data", "im_info", "gt_bbox"] label_name = ["rpn_cls_label", "rpn_reg_target", "rpn_reg_weight"] if General.use_groupsoftmax: data_name.append("rpn_group") data_name.append("box_group") transform.append(GroupRead(GroupParam)) else: transform = [ ReadRoiRecord(None), 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