faster_r152v1bc4_c5_512roi_2x.py

from symbol.builder import FasterRcnn as Detector
from symbol.builder import ResNetV1bC4 as Backbone
from symbol.builder import Neck
from symbol.builder import RpnHead
from symbol.builder import RoiAlign as RoiExtractor
from symbol.builder import BboxC5V1Head as BboxHead
from mxnext.complicate import normalizer_factory


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     = "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 = 152


    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 = True
        memonger_until = "stage3_unit1_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 = 35

        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 = 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 = 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
        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