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Python cfg.POOLING_SIZE屬性代碼示例

本文整理匯總了Python中model.utils.config.cfg.POOLING_SIZE屬性的典型用法代碼示例。如果您正苦於以下問題：Python cfg.POOLING_SIZE屬性的具體用法？Python cfg.POOLING_SIZE怎麽用？Python cfg.POOLING_SIZE使用的例子？那麽, 這裏精選的屬性代碼示例或許可以為您提供幫助。您也可以進一步了解該屬性所在類model.utils.config.cfg的用法示例。


在下文中一共展示了cfg.POOLING_SIZE屬性的15個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: __init__

# 需要導入模塊: from model.utils.config import cfg [as 別名]
# 或者: from model.utils.config.cfg import POOLING_SIZE [as 別名]
def __init__(self, classes, class_agnostic):
        super(_fasterRCNN, self).__init__()
        self.classes = classes
        self.n_classes = len(classes)
        self.class_agnostic = class_agnostic
        # loss
        self.RCNN_loss_cls = 0
        self.RCNN_loss_bbox = 0

        # define rpn
        self.RCNN_rpn = _RPN(self.dout_base_model)
        self.RCNN_proposal_target = _ProposalTargetLayer(self.n_classes)
        self.RCNN_roi_pool = _RoIPooling(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0/16.0)
        self.RCNN_roi_align = RoIAlignAvg(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0/16.0)

        self.grid_size = cfg.POOLING_SIZE * 2 if cfg.CROP_RESIZE_WITH_MAX_POOL else cfg.POOLING_SIZE
        self.RCNN_roi_crop = _RoICrop()
開發者ID:Feynman27，項目名稱:pytorch-detect-to-track，代碼行數:19，代碼來源:faster_rcnn.py

示例2: __init__

# 需要導入模塊: from model.utils.config import cfg [as 別名]
# 或者: from model.utils.config.cfg import POOLING_SIZE [as 別名]
def __init__(self, classes, class_agnostic):
        super(_fasterRCNN, self).__init__()
        self.classes = classes
        self.n_classes = len(classes)
        self.class_agnostic = class_agnostic
        # loss
        self.RCNN_loss_cls = 0
        self.RCNN_loss_bbox = 0

        # define rpn
        self.RCNN_rpn = _RPN(self.dout_base_model)
        self.RCNN_proposal_target = _ProposalTargetLayer(self.n_classes)
        self.RCNN_roi_pool = _RoIPooling(cfg.POOLING_SIZE, cfg.POOLING_SIZE,
                                         1.0 / 16.0)
        self.RCNN_roi_align = RoIAlignAvg(cfg.POOLING_SIZE, cfg.POOLING_SIZE,
                                          1.0 / 16.0)

        self.grid_size = cfg.POOLING_SIZE * 2 if \
            cfg.CROP_RESIZE_WITH_MAX_POOL else cfg.POOLING_SIZE
        self.RCNN_roi_crop = _RoICrop()
開發者ID:ucbdrive，項目名稱:3d-vehicle-tracking，代碼行數:22，代碼來源:faster_rcnn.py

示例3: __init__

# 需要導入模塊: from model.utils.config import cfg [as 別名]
# 或者: from model.utils.config.cfg import POOLING_SIZE [as 別名]
def __init__(self, classes, class_agnostic, sup=False):
        super(_fasterRCNN, self).__init__()
        self.classes = classes
        self.n_classes = len(classes)
        self.class_agnostic = class_agnostic
        # loss
        self.RCNN_loss_cls = 0
        self.RCNN_loss_bbox = 0

        # define rpn
        self.RCNN_rpn = _RPN(self.dout_base_model)
        self.RCNN_proposal_target = _ProposalTargetLayer(self.n_classes)
        self.RCNN_roi_pool = _RoIPooling(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0/16.0)
        self.RCNN_roi_align = RoIAlignAvg(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0/16.0)

        self.grid_size = cfg.POOLING_SIZE * 2 if cfg.CROP_RESIZE_WITH_MAX_POOL else cfg.POOLING_SIZE
        self.RCNN_roi_crop = _RoICrop()
        self.sup = sup
開發者ID:twangnh，項目名稱:Distilling-Object-Detectors，代碼行數:20，代碼來源:faster_rcnn.py

示例4: __init__

# 需要導入模塊: from model.utils.config import cfg [as 別名]
# 或者: from model.utils.config.cfg import POOLING_SIZE [as 別名]
def __init__(self, classes, class_agnostic):
        super(_RFCN, self).__init__()
        self.classes = classes
        self.n_classes = len(classes)
        self.class_agnostic = class_agnostic
        # loss
        self.RCNN_loss_cls = 0
        self.RCNN_loss_bbox = 0

        self.box_num_classes = 1 if class_agnostic else self.n_classes

        # define rpn
        self.RCNN_rpn = _RPN(self.dout_base_model)
        self.RCNN_proposal_target = _ProposalTargetLayer(self.n_classes)
        self.RCNN_psroi_pool_cls = PSRoIPool(cfg.POOLING_SIZE, cfg.POOLING_SIZE,
                                          spatial_scale=1/16.0, group_size=cfg.POOLING_SIZE,
                                          output_dim=self.n_classes)
        self.RCNN_psroi_pool_loc = PSRoIPool(cfg.POOLING_SIZE, cfg.POOLING_SIZE,
                                          spatial_scale=1/16.0, group_size=cfg.POOLING_SIZE,
                                          output_dim=self.box_num_classes * 4)
        self.pooling = nn.AvgPool2d(kernel_size=cfg.POOLING_SIZE, stride=cfg.POOLING_SIZE)
        self.grid_size = cfg.POOLING_SIZE * 2 if cfg.CROP_RESIZE_WITH_MAX_POOL else cfg.POOLING_SIZE
開發者ID:princewang1994，項目名稱:RFCN_CoupleNet.pytorch，代碼行數:24，代碼來源:rfcn.py

示例5: __init__

# 需要導入模塊: from model.utils.config import cfg [as 別名]
# 或者: from model.utils.config.cfg import POOLING_SIZE [as 別名]
def __init__(self, classes, class_agnostic):
        super(_FPN, self).__init__()
        self.classes = classes
        self.n_classes = len(classes)
        self.class_agnostic = class_agnostic
        # loss
        self.RCNN_loss_cls = 0
        self.RCNN_loss_bbox = 0

        self.maxpool2d = nn.MaxPool2d(1, stride=2)
        # define rpn
        self.RCNN_rpn = _RPN_FPN(self.dout_base_model)
        self.RCNN_proposal_target = _ProposalTargetLayer(self.n_classes)

        # NOTE: the original paper used pool_size = 7 for cls branch, and 14 for mask branch, to save the
        # computation time, we first use 14 as the pool_size, and then do stride=2 pooling for cls branch.
        self.RCNN_roi_pool = _RoIPooling(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0/16.0)
        self.RCNN_roi_align = RoIAlignAvg(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0/16.0)
        self.grid_size = cfg.POOLING_SIZE * 2 if cfg.CROP_RESIZE_WITH_MAX_POOL else cfg.POOLING_SIZE
        self.RCNN_roi_crop = _RoICrop()
開發者ID:jwyang，項目名稱:fpn.pytorch，代碼行數:22，代碼來源:fpn.py

示例6: __init__

# 需要導入模塊: from model.utils.config import cfg [as 別名]
# 或者: from model.utils.config.cfg import POOLING_SIZE [as 別名]
def __init__(self, classes, class_agnostic):
        super(_fasterRCNN, self).__init__()
        self.classes = classes
        self.n_classes = len(classes)
        self.class_agnostic = class_agnostic

        
        self.match_net = match_block(self.dout_base_model)


        # loss
        self.RCNN_loss_cls = 0
        self.RCNN_loss_bbox = 0

        # define rpn
        self.RCNN_rpn = _RPN(self.dout_base_model)
        self.RCNN_proposal_target = _ProposalTargetLayer(self.n_classes)

        # self.RCNN_roi_pool = _RoIPooling(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0/16.0)
        # self.RCNN_roi_align = RoIAlignAvg(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0/16.0)

        self.RCNN_roi_pool = ROIPool((cfg.POOLING_SIZE, cfg.POOLING_SIZE), 1.0/16.0)
        self.RCNN_roi_align = ROIAlign((cfg.POOLING_SIZE, cfg.POOLING_SIZE), 1.0/16.0, 0)
        self.triplet_loss = torch.nn.MarginRankingLoss(margin = cfg.TRAIN.MARGIN)
開發者ID:timy90022，項目名稱:One-Shot-Object-Detection，代碼行數:26，代碼來源:faster_rcnn.py

示例7: __init__

# 需要導入模塊: from model.utils.config import cfg [as 別名]
# 或者: from model.utils.config.cfg import POOLING_SIZE [as 別名]
def __init__(self, classes, class_agnostic):
        super(_fasterRCNN, self).__init__()
        self.classes = classes
        self.n_classes = len(self.classes)
        self.class_agnostic = class_agnostic
        # loss
        self.RCNN_loss_cls = 0
        self.RCNN_loss_bbox = 0

        # define rpn
        self.RCNN_rpn = _RPN(self.dout_base_model)
        self.RCNN_proposal_target = _ProposalTargetLayer(self.n_classes)
        self.RCNN_roi_pool = _RoIPooling(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0 / 16.0)
        self.RCNN_roi_align = RoIAlignAvg(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0 / 16.0)

        self.grid_size = cfg.POOLING_SIZE * 2 if cfg.CROP_RESIZE_WITH_MAX_POOL else cfg.POOLING_SIZE
        self.RCNN_roi_crop = _RoICrop()
開發者ID:jinyu121，項目名稱:CIOD，代碼行數:19，代碼來源:faster_rcnn.py

示例8: __init__

# 需要導入模塊: from model.utils.config import cfg [as 別名]
# 或者: from model.utils.config.cfg import POOLING_SIZE [as 別名]
def __init__(self, n_classes, class_agnostic):
        super(_fasterRCNN, self).__init__()
        self.n_classes = n_classes
        self.class_agnostic = class_agnostic
        # loss
        self.RCNN_loss_cls = 0
        self.RCNN_loss_bbox = 0

        # define rpn
        self.RCNN_rpn = _RPN(self.dout_base_model)
        self.RCNN_proposal_target = _ProposalTargetLayer(self.n_classes)
        self.RCNN_roi_pool = _RoIPooling(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0/16.0)
        self.RCNN_roi_align = RoIAlignAvg(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0/16.0)

        self.grid_size = cfg.POOLING_SIZE * 2 if cfg.CROP_RESIZE_WITH_MAX_POOL else cfg.POOLING_SIZE
        self.RCNN_roi_crop = _RoICrop()
開發者ID:violetteshev，項目名稱:bottom-up-features，代碼行數:18，代碼來源:faster_rcnn.py

示例9: __init__

# 需要導入模塊: from model.utils.config import cfg [as 別名]
# 或者: from model.utils.config.cfg import POOLING_SIZE [as 別名]
def __init__(self, classes, class_agnostic):
        super(_fasterRCNN, self).__init__()
        self.classes = classes
        self.n_classes = len(classes)
        self.class_agnostic = class_agnostic
               # loss
        self.RCNN_loss_cls = 0
        self.RCNN_loss_bbox = 0

        # define rpn
        self.RCNN_rpn = _RPN(self.dout_base_model)
        self.RCNN_proposal_target = _ProposalTargetLayer(self.n_classes)
        self.RCNN_roi_pool = _RoIPooling(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0/16.0)
        self.RCNN_roi_align = RoIAlignAvg(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0/16.0)

        self.grid_size = cfg.POOLING_SIZE * 2 if cfg.CROP_RESIZE_WITH_MAX_POOL else cfg.POOLING_SIZE
        self.RCNN_roi_crop = _RoICrop()

        self.Dis = Discriminator()
開發者ID:TKKim93，項目名稱:DivMatch，代碼行數:21，代碼來源:Divmatch_faster_rcnn_resnet.py

示例10: __init__

# 需要導入模塊: from model.utils.config import cfg [as 別名]
# 或者: from model.utils.config.cfg import POOLING_SIZE [as 別名]
def __init__(self, classes, class_agnostic):
        super(_da_fasterRCNN, self).__init__()
        self.classes = classes
        self.n_classes = len(classes)
        self.class_agnostic = class_agnostic
        # loss
        self.RCNN_loss_cls = 0
        self.RCNN_loss_bbox = 0

        # define rpn
        self.RCNN_rpn = _RPN(self.dout_base_model)
        self.RCNN_proposal_target = _ProposalTargetLayer(self.n_classes)
        self.RCNN_roi_pool = _RoIPooling(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0/16.0)
        self.RCNN_roi_align = RoIAlignAvg(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0/16.0)

        self.grid_size = cfg.POOLING_SIZE * 2 if cfg.CROP_RESIZE_WITH_MAX_POOL else cfg.POOLING_SIZE
        self.RCNN_roi_crop = _RoICrop()

        self.Dis = Discriminator()
開發者ID:TKKim93，項目名稱:DivMatch，代碼行數:21，代碼來源:DivMatch_faster_rcnn_vgg16.py

示例11: __init__

# 需要導入模塊: from model.utils.config import cfg [as 別名]
# 或者: from model.utils.config.cfg import POOLING_SIZE [as 別名]
def __init__(self, classes):
        super(_StereoRCNN, self).__init__()
        self.classes = classes
        self.n_classes = len(classes)

        # loss
        self.RCNN_loss_cls = 0
        self.RCNN_loss_bbox_left_right = 0
        self.RCNN_loss_dis = 0
        self.RCNN_loss_dim = 0
        self.RCNN_loss_dim_orien = 0
        self.RCNN_loss_kpts = 0

        self.maxpool2d = nn.MaxPool2d(1, stride=2)
        # define rpn
        self.RCNN_rpn = _Stereo_RPN(self.dout_base_model)
        self.RCNN_proposal_target = _ProposalTargetLayer(self.n_classes)

        self.RCNN_roi_align = RoIAlignAvg(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0/16.0)
        self.RCNN_roi_kpts_align = RoIAlignAvg(cfg.POOLING_SIZE*2, cfg.POOLING_SIZE*2, 1.0/16.0)
開發者ID:HKUST-Aerial-Robotics，項目名稱:Stereo-RCNN，代碼行數:22，代碼來源:stereo_rcnn.py

示例12: __init__

# 需要導入模塊: from model.utils.config import cfg [as 別名]
# 或者: from model.utils.config.cfg import POOLING_SIZE [as 別名]
def __init__(self, classes, class_agnostic,context):
        super(_fasterRCNN, self).__init__()
        self.classes = classes
        self.n_classes = len(classes)
        self.class_agnostic = class_agnostic
        # loss
        self.RCNN_loss_cls = 0
        self.RCNN_loss_bbox = 0
        self.context = context
        # define rpn
        self.RCNN_rpn = _RPN(self.dout_base_model)
        self.RCNN_proposal_target = _ProposalTargetLayer(self.n_classes)
        self.RCNN_roi_pool = _RoIPooling(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0/16.0)
        self.RCNN_roi_align = RoIAlignAvg(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0/16.0)

        self.grid_size = cfg.POOLING_SIZE * 2 if cfg.CROP_RESIZE_WITH_MAX_POOL else cfg.POOLING_SIZE
        self.RCNN_roi_crop = _RoICrop()
開發者ID:VisionLearningGroup，項目名稱:DA_Detection，代碼行數:19，代碼來源:faster_rcnn_global.py

示例13: __init__

# 需要導入模塊: from model.utils.config import cfg [as 別名]
# 或者: from model.utils.config.cfg import POOLING_SIZE [as 別名]
def __init__(self, classes, class_agnostic,lc):
        super(_fasterRCNN, self).__init__()
        self.classes = classes
        self.n_classes = len(classes)
        self.class_agnostic = class_agnostic
        # loss
        self.RCNN_loss_cls = 0
        self.RCNN_loss_bbox = 0
        self.lc = lc
        # define rpn
        self.RCNN_rpn = _RPN(self.dout_base_model)
        self.RCNN_proposal_target = _ProposalTargetLayer(self.n_classes)
        self.RCNN_roi_pool = _RoIPooling(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0/16.0)
        self.RCNN_roi_align = RoIAlignAvg(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0/16.0)

        self.grid_size = cfg.POOLING_SIZE * 2 if cfg.CROP_RESIZE_WITH_MAX_POOL else cfg.POOLING_SIZE
        self.RCNN_roi_crop = _RoICrop()
開發者ID:VisionLearningGroup，項目名稱:DA_Detection，代碼行數:19，代碼來源:faster_rcnn_local.py

示例14: __init__

# 需要導入模塊: from model.utils.config import cfg [as 別名]
# 或者: from model.utils.config.cfg import POOLING_SIZE [as 別名]
def __init__(self, classes, class_agnostic,lc,gc):
        super(_fasterRCNN, self).__init__()
        self.classes = classes
        self.n_classes = len(classes)
        self.class_agnostic = class_agnostic
        # loss
        self.RCNN_loss_cls = 0
        self.RCNN_loss_bbox = 0
        self.lc = lc
        self.gc = gc
        # define rpn
        self.RCNN_rpn = _RPN(self.dout_base_model)
        self.RCNN_proposal_target = _ProposalTargetLayer(self.n_classes)
        self.RCNN_roi_pool = _RoIPooling(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0/16.0)
        self.RCNN_roi_align = RoIAlignAvg(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0/16.0)

        self.grid_size = cfg.POOLING_SIZE * 2 if cfg.CROP_RESIZE_WITH_MAX_POOL else cfg.POOLING_SIZE
        self.RCNN_roi_crop = _RoICrop()
開發者ID:VisionLearningGroup，項目名稱:DA_Detection，代碼行數:20，代碼來源:faster_rcnn_global_local.py

示例15: __init__

# 需要導入模塊: from model.utils.config import cfg [as 別名]
# 或者: from model.utils.config.cfg import POOLING_SIZE [as 別名]
def __init__(self, classes, class_agnostic):
        super(_RFCN, self).__init__()
        self.classes = classes
        self.n_classes = len(classes)
        self.n_reg_classes = (1 if class_agnostic else len(classes))
        self.class_agnostic = class_agnostic
        self.n_bbox_reg = (4 if class_agnostic else len(classes))
        # loss
        self.RFCN_loss_cls = 0
        self.RFCN_loss_bbox = 0

        # define rpn
        self.RFCN_rpn = _RPN(self.dout_base_model)
        self.RFCN_proposal_target = _ProposalTargetLayer(self.n_classes)
        self.RFCN_tracking_proposal_target = _TrackingProposalTargetLayer(self.n_classes)
        #self.RFCN_roi_pool = _RoIPooling(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0/16.0)
        self.RFCN_psroi_cls_pool = _PSRoIPooling(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 
                                spatial_scale=1.0/16.0, group_size=7, output_dim=self.n_classes)
        self.RFCN_psroi_loc_pool = _PSRoIPooling(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 
                                spatial_scale=1.0/16.0, group_size=7, output_dim=4*self.n_reg_classes)
        #self.RFCN_roi_align = RoIAlignAvg(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0/16.0)

        self.grid_size = cfg.POOLING_SIZE * 2 if cfg.CROP_RESIZE_WITH_MAX_POOL else cfg.POOLING_SIZE
        #self.RFCN_roi_crop = _RoICrop()

	self.RFCN_cls_net = nn.Conv2d(512,self.n_classes*7*7, [1,1], padding=0, stride=1)
        nn.init.normal(self.RFCN_cls_net.weight.data, 0.0, 0.01)
        
	self.RFCN_bbox_net = nn.Conv2d(512, 4*self.n_reg_classes*7*7, [1,1], padding=0, stride=1)
	nn.init.normal(self.RFCN_bbox_net.weight.data, 0.0, 0.01)

	#self.corr_bbox_net = nn.Conv2d(1051, 4*self.n_reg_classes*7*7, [1,1], padding=0, stride=1)
	#nn.init.normal(self.corr_bbox_net.weight.data, 0.0, 0.01)

	self.conv3_corr_layer = Correlation(pad_size=8, kernel_size=1, max_displacement=8, stride1=2, stride2=2)
	self.conv4_corr_layer = Correlation(pad_size=8, kernel_size=1, max_displacement=8, stride1=1, stride2=1)
	self.conv5_corr_layer = Correlation(pad_size=8, kernel_size=1, max_displacement=8, stride1=1, stride2=1) 

        self.RFCN_cls_score = nn.AvgPool2d((7,7), stride=(7,7))
        self.RFCN_bbox_pred = nn.AvgPool2d((7,7), stride=(7,7))
        self.RFCN_tracking_pred = nn.AvgPool2d((7,7), stride=(7,7))
開發者ID:Feynman27，項目名稱:pytorch-detect-to-track，代碼行數:43，代碼來源:rfcn.py


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