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_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) 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-v1-50" 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