# excerpt from `ppdet/modeling/ops.py`
from ppdet.core.workspace import register, serializable

# ... more operators

@register
@serializable
class GenerateProposals(object):
    # NOTE this class simply wraps a PaddlePaddle operator
    __op__ = fluid.layers.generate_proposals
    # NOTE docstring for args are extracted from PaddlePaddle OP
    __append_doc__ = True

    def __init__(self,
                 pre_nms_top_n=6000,
                 post_nms_top_n=1000,
                 nms_thresh=.5,
                 min_size=.1,
                 eta=1.):
        super(GenerateProposals, self).__init__()
        self.pre_nms_top_n = pre_nms_top_n
        self.post_nms_top_n = post_nms_top_n
        self.nms_thresh = nms_thresh
        self.min_size = min_size
        self.eta = eta

# ... more operators

# excerpt from `ppdet/modeling/anchor_heads/rpn_head.py`
from ppdet.core.workspace import register
from ppdet.modeling.ops import AnchorGenerator, RPNTargetAssign, GenerateProposals

@register
class RPNHead(object):
    """
    RPN Head

    Args:
        anchor_generator (object): `AnchorGenerator` instance
        rpn_target_assign (object): `RPNTargetAssign` instance
        train_proposal (object): `GenerateProposals` instance for training
        test_proposal (object): `GenerateProposals` instance for testing
    """
    __inject__ = [
        'anchor_generator', 'rpn_target_assign', 'train_proposal',
        'test_proposal'
    ]

    def __init__(self,
                 anchor_generator=AnchorGenerator().__dict__,
                 rpn_target_assign=RPNTargetAssign().__dict__,
                 train_proposal=GenerateProposals(12000, 2000).__dict__,
                 test_proposal=GenerateProposals().__dict__):
        super(RPNHead, self).__init__()
        self.anchor_generator = anchor_generator
        self.rpn_target_assign = rpn_target_assign
        self.train_proposal = train_proposal
        self.test_proposal = test_proposal
        if isinstance(anchor_generator, dict):
            self.anchor_generator = AnchorGenerator(**anchor_generator)
        if isinstance(rpn_target_assign, dict):
            self.rpn_target_assign = RPNTargetAssign(**rpn_target_assign)
        if isinstance(train_proposal, dict):
            self.train_proposal = GenerateProposals(**train_proposal)
        if isinstance(test_proposal, dict):
            self.test_proposal = GenerateProposals(**test_proposal)

from ppdet.core.workspace import load_config, merge_config, create

load_config('some_config_file.yml')
merge_config(more_config_options_from_command_line)

rpn_head = create('RPNHead')
# ... code that use the created module!

from ppdet.core.workspace import register

@register
class DarkNet(object):

    __shared__ = ['norm_type', 'weight_prefix_name']

    def __init__(self,
                 depth=53,
                 norm_type='bn',
                 norm_decay=0.,
                 weight_prefix_name=''):
        # 省略内容
        pass

    def __call__(self, input):
        # 省略处理逻辑
        pass

from ppdet.core.workspace import register

@register
class YOLOv3Head(object):

    __inject__ = ['yolo_loss', 'nms']
    __shared__ = ['num_classes', 'weight_prefix_name']

    def __init__(self,
                 num_classes=80,
                 anchors=[[10, 13], [16, 30], [33, 23], [30, 61], [62, 45],
                          [59, 119], [116, 90], [156, 198], [373, 326]],
                 yolo_loss="YOLOv3Loss",
                 nms=MultiClassNMS(
                     score_threshold=0.01,
                     nms_top_k=1000,
                     keep_top_k=100,
                     nms_threshold=0.45,
                     background_label=-1).__dict__):
        # 省略部分内容
        pass

from . import yolo_head
from .yolo_head import *

from ppdet.core.workspace import register

@register
class YOLOv3(object):

    __category__ = 'architecture'
    __inject__ = ['backbone', 'yolo_head']
    __shared__ = ['use_fine_grained_loss']

    def __init__(self,
                 backbone,
                 yolo_head='YOLOv3Head',
                 use_fine_grained_loss=False):
        super(YOLOv3, self).__init__()
        # 省略内容

    def build(self, feed_vars, mode='train'):
        # 省略内容
        pass

    def build_inputs(self, ):
        # 详解见【模型输入设置】章节
        pass

    def train(self, feed_vars):
        return self.build(feed_vars, mode='train')

    def eval(self, feed_vars):
        return self.build(feed_vars, mode='test')

    def test(self, feed_vars):
        return self.build(feed_vars, mode='test')