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 depth = 50 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 KDParam: stage = 'c5' channel = 2048 grad_scale = 5 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) kd_head = KDHead(KDParam) detector = Detector() if is_train: train_sym = detector.get_train_symbol(backbone, neck, rpn_head, roi_extractor, bbox_head, kd_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%s_v1b" % BackboneParam.depth epoch = 0 fixed_param = ["conv0", "stage1", "gamma", "beta"] class teacher_param: prefix = 'teacher_model/faster_r152v1b_fpn_2x/checkpoint' epoch = 12 endpoint = ['stage4_unit3_relu_output'] data_name = ['data'] label_name = ['teacher_label'] 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 = 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 = 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"], []) scalar_metric = metric.ScalarLoss("fitloss", ["fit_loss_output"], []) metric_list = [ rpn_acc_metric, rpn_l1_metric, box_acc_metric, box_l1_metric, scalar_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 depth = 50 class NeckParam: fp16 = General.fp16 normalizer = NormalizeParam.normalizer class RpnParam: num_class = 1 + 80 fp16 = General.fp16 normalizer = NormalizeParam.normalizer batch_image = General.batch_image class anchor_generate: scale = (4 * 2**0, 4 * 2**(1.0 / 3.0), 4 * 2**(2.0 / 3.0)) ratio = (0.5, 1.0, 2.0) stride = (8, 16, 32, 64, 128) image_anchor = None class head: conv_channel = 256 mean = None std = None class proposal: pre_nms_top_n = 1000 post_nms_top_n = None nms_thr = None min_bbox_side = None min_det_score = 0.05 # filter score in network 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 focal_loss: alpha = 0.25 gamma = 2.0 class BboxParam: fp16 = General.fp16 normalizer = NormalizeParam.normalizer num_class = None image_roi = None batch_image = None class regress_target: class_agnostic = None mean = None std = None class RoiParam: fp16 = General.fp16 normalizer = NormalizeParam.normalizer out_size = None stride = None class KDParam: stage = 'c5' channel = 2048 grad_scale = 10 class DatasetParam: if is_train: image_set = ("coco_train2017", ) else: image_set = ("coco_val2017", ) backbone = Backbone(BackboneParam) neck = Neck(NeckParam) rpn_head = RpnHead(RpnParam) kd_head = KDHead(KDParam) detector = Detector() if is_train: train_sym = detector.get_train_symbol(backbone, neck, rpn_head, kd_head) test_sym = None else: train_sym = None test_sym = detector.get_test_symbol(backbone, neck, rpn_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%s_v1b" % BackboneParam.depth epoch = 0 fixed_param = ["conv0", "stage1", "gamma", "beta"] class teacher_param: prefix = 'teacher_model/retina_r152v1b_fpn_1x/checkpoint' epoch = 6 endpoint = ['stage4_unit3_relu_output'] data_name = ['data'] label_name = ['teacher_label'] class OptimizeParam: class optimizer: type = "sgd" lr = 0.005 / 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 = 0.005 / 8 * len( KvstoreParam.gpus) * KvstoreParam.batch_image / 3 iter = 1000 class TestParam: min_det_score = 0 # filter appended boxes 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)) 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.short = (100, 50, 25, 13, 7) self.long = (167, 84, 42, 21, 11) self.stride = (8, 16, 32, 64, 128) scales = (4 * 2**0, 4 * 2**(1.0 / 3.0), 4 * 2**(2.0 / 3.0)) aspects = (0.5, 1.0, 2.0) class assign: allowed_border = 9999 pos_thr = 0.5 neg_thr = 0.4 min_pos_thr = 0.0 class sample: image_anchor = None pos_fraction = None class RenameParam: mapping = dict(image="data") from core.detection_input import ReadRoiRecord, Resize2DImageBbox, \ ConvertImageFromHwcToChw, Flip2DImageBbox, Pad2DImageBbox, \ RenameRecord from models.retinanet.input import PyramidAnchorTarget2D, Norm2DImage if is_train: transform = [ ReadRoiRecord(None), Norm2DImage(NormParam), Resize2DImageBbox(ResizeParam), Flip2DImageBbox(), Pad2DImageBbox(PadParam), ConvertImageFromHwcToChw(), PyramidAnchorTarget2D(AnchorTarget2DParam()), RenameRecord(RenameParam.mapping) ] data_name = ["data"] 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 = [] from models.retinanet import metric from core.detection_metric import ScalarLoss rpn_acc_metric = metric.FGAccMetric("FGAcc", ["cls_loss_output"], ["rpn_cls_label"]) scalar_metric = ScalarLoss("fitloss", ["fit_loss_output"], []) metric_list = [rpn_acc_metric, scalar_metric] return General, KvstoreParam, RpnParam, RoiParam, BboxParam, DatasetParam, \ ModelParam, OptimizeParam, TestParam, \ transform, data_name, label_name, metric_list