Exemplos de FPN.add_fpn_rpn_outputs em Python

Exemplo n.º 1

def add_generic_rpn_outputs(model, blob_in, dim_in, spatial_scale_in):
    """Add RPN outputs (objectness classification and bounding box regression)
    to an RPN model. Abstracts away the use of FPN.
    是否为目标，对anchor进行回归
    """
    loss_gradients = None
    if cfg.FPN.FPN_ON:
        # Delegate to the FPN module
        # 添加proposals层，输出为(bacth_index, x1, y1, x2, y2)
        # 1. 获得rpn的输出，MxNxAnchors, MxNx4Anchors;
        # 2. 对fpn中的每一层，获取ROIs
        FPN.add_fpn_rpn_outputs(model, blob_in, dim_in, spatial_scale_in)
        if cfg.MODEL.FASTER_RCNN:
            # CollectAndDistributeFpnRpnProposals also labels proposals when in
            # training mode
            # 在该函数中构造候选区域的目标值，回归量以及类别，权重
            model.CollectAndDistributeFpnRpnProposals()
        if model.train:
            loss_gradients = FPN.add_fpn_rpn_losses(model)
    else:
        # Not using FPN, add RPN to a single scale
        # 添加输出
        add_single_scale_rpn_outputs(model, blob_in, dim_in, spatial_scale_in)
        if model.train:
            # 添加loss
            loss_gradients = add_single_scale_rpn_losses(model)
    return loss_gradients

Exemplo n.º 2

Arquivo: rpn_heads.py Projeto: zhonhel/Master_Thesis

def add_generic_rpn_outputs(model, blob_in, dim_in, spatial_scale_in):
    """Add RPN outputs (objectness classification and bounding box regression)
    to an RPN model. Abstracts away the use of FPN.
    """
    loss_gradients = None
    if cfg.FPN.FPN_ON:
        # Delegate to the FPN module
        FPN.add_fpn_rpn_outputs(model, blob_in, dim_in, spatial_scale_in)
        if cfg.MODEL.FASTER_RCNN:
            # CollectAndDistributeFpnRpnProposals also labels proposals when in
            # training mode
            model.CollectAndDistributeFpnRpnProposals()
        if model.train:
            loss_gradients = FPN.add_fpn_rpn_losses(model)

    return loss_gradients