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Python data.coco方法代碼示例

本文整理匯總了Python中data.coco方法的典型用法代碼示例。如果您正苦於以下問題：Python data.coco方法的具體用法？Python data.coco怎麽用？Python data.coco使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類data的用法示例。


在下文中一共展示了data.coco方法的9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: import data [as 別名]
# 或者: from data import coco [as 別名]
def __init__(self, phase, size, base, extras, head, num_classes):
        super(SSD, self).__init__()
        self.phase = phase
        self.num_classes = num_classes
        if(size==300):
            self.cfg = (coco, voc300)[num_classes == 21]
        else:
            self.cfg = (coco, voc512)[num_classes == 21]
        self.priorbox = PriorBox(self.cfg)
        self.priors = Variable(self.priorbox.forward(), volatile=True)
        self.size = size

        # SSD network
        self.vgg = nn.ModuleList(base)
        # Layer learns to scale the l2 normalized features from conv4_3
        self.L2Norm = L2Norm(512, 20)
        self.extras = nn.ModuleList(extras)

        self.loc = nn.ModuleList(head[0])
        self.conf = nn.ModuleList(head[1])

        if phase == 'test':
            self.softmax = nn.Softmax(dim=-1)
            self.detect = Detect(num_classes, 0, 200, 0.01, 0.45)
開發者ID:soo89，項目名稱:CSD-SSD，代碼行數:26，代碼來源:ssd.py

示例2: __init__

# 需要導入模塊: import data [as 別名]
# 或者: from data import coco [as 別名]
def __init__(self, num_classes, overlap_thresh, prior_for_matching,
                 bkg_label, neg_mining, neg_pos, neg_overlap, encode_target,
                 use_gpu=True, theta=0.01, use_ARM=False):
        super(RefineDetMultiBoxLoss, self).__init__()
        self.use_gpu = use_gpu
        self.num_classes = num_classes
        self.threshold = overlap_thresh
        self.background_label = bkg_label
        self.encode_target = encode_target
        self.use_prior_for_matching = prior_for_matching
        self.do_neg_mining = neg_mining
        self.negpos_ratio = neg_pos
        self.neg_overlap = neg_overlap
        self.variance = cfg['variance']
        self.theta = theta
        self.use_ARM = use_ARM
開發者ID:luuuyi，項目名稱:RefineDet.PyTorch，代碼行數:18，代碼來源:refinedet_multibox_loss.py

示例3: __init__

# 需要導入模塊: import data [as 別名]
# 或者: from data import coco [as 別名]
def __init__(self, phase,model, size, base, extras, head, num_classes):
        super(SSD, self).__init__()
        self.phase = phase
        self.num_classes = num_classes
        self.cfg = (coco, voc)[num_classes == 21]
        self.priorbox = PriorBox(self.cfg)
        self.priors = Variable(self.priorbox.forward(), requires_grad=True)
        self.size = size
        self.model=model
        # SSD network
        self.base = nn.ModuleList(base)
        # Layer learns to scale the l2 normalized features from conv4_3
        self.L2Norm = L2Norm( 512, 20)
        self.extras = nn.ModuleList(extras)

        self.loc = nn.ModuleList(head[0])
        self.conf = nn.ModuleList(head[1])

        if phase == 'test':
            self.softmax = nn.Softmax(dim=-1)
            self.detect = Detect(num_classes, 0, 200, 0.01, 0.45)
開發者ID:yczhang1017，項目名稱:SSD_resnet_pytorch，代碼行數:23，代碼來源:ssd.py

示例4: __init__

# 需要導入模塊: import data [as 別名]
# 或者: from data import coco [as 別名]
def __init__(self, phase, size, base, extras, head, num_classes):
        super(SSD, self).__init__()
        self.phase = phase
        self.num_classes = num_classes
        self.cfg = (coco, voc)[num_classes == 21]
        self.priorbox = PriorBox(self.cfg)
        self.priors = Variable(self.priorbox.forward(), volatile=True)
        self.size = size

        # SSD network
        self.vgg = nn.ModuleList(base)
        # Layer learns to scale the l2 normalized features from conv4_3
        self.L2Norm = L2Norm(512, 20)
        self.extras = nn.ModuleList(extras)

        self.loc = nn.ModuleList(head[0])
        self.conf = nn.ModuleList(head[1])

        if phase == 'test':
            self.softmax = nn.Softmax(dim=-1)
            self.detect = Detect(num_classes, 0, 200, 0.01, 0.45)
開發者ID:bailvwangzi，項目名稱:repulsion_loss_ssd，代碼行數:23，代碼來源:ssd.py

示例5: __init__

# 需要導入模塊: import data [as 別名]
# 或者: from data import coco [as 別名]
def __init__(self, num_classes, overlap_thresh, prior_for_matching,
                 bkg_label, neg_mining, neg_pos, neg_overlap, encode_target,
                 use_gpu=True, theta=0.01, use_ARM=False):
        super(softRefineDetMultiBoxLoss, self).__init__()
        self.use_gpu = use_gpu
        self.num_classes = num_classes
        self.threshold = overlap_thresh
        self.background_label = bkg_label
        self.encode_target = encode_target
        self.use_prior_for_matching = prior_for_matching
        self.do_neg_mining = neg_mining
        self.negpos_ratio = neg_pos
        self.neg_overlap = neg_overlap
        self.variance = cfg['variance']
        self.theta = theta
        self.use_ARM = use_ARM
開發者ID:wei-yuma，項目名稱:multitrident，代碼行數:18，代碼來源:softrefinedet_multibox_loss.py

示例6: __init__

# 需要導入模塊: import data [as 別名]
# 或者: from data import coco [as 別名]
def __init__(self, num_classes, overlap_thresh, prior_for_matching,
                 bkg_label, neg_mining, neg_pos, neg_overlap, encode_target,
                 use_gpu=True, theta=0.01, use_ARM=False):
        super(RPRefineDetMultiBoxLoss, self).__init__()
        self.use_gpu = use_gpu
        self.num_classes = num_classes
        self.threshold = overlap_thresh
        self.background_label = bkg_label
        self.encode_target = encode_target
        self.use_prior_for_matching = prior_for_matching
        self.do_neg_mining = neg_mining
        self.negpos_ratio = neg_pos
        self.neg_overlap = neg_overlap
        self.variance = cfg['variance']
        self.theta = theta
        self.use_ARM = use_ARM
開發者ID:wei-yuma，項目名稱:multitrident，代碼行數:18，代碼來源:regionpooling_multibox_loss.py

示例7: __init__

# 需要導入模塊: import data [as 別名]
# 或者: from data import coco [as 別名]
def __init__(self, phase, size, base, extras, head, num_classes):
        super(SSD_CON, self).__init__()
        self.phase = phase
        self.num_classes = num_classes
        if(size==300):
            self.cfg = (coco, voc300)[num_classes == 21]
        else:
            self.cfg = (coco, voc512)[num_classes == 21]
        self.priorbox = PriorBox(self.cfg)
        self.priors = Variable(self.priorbox.forward(), volatile=True)
        self.size = size

        # SSD network
        self.vgg = nn.ModuleList(base)
        # Layer learns to scale the l2 normalized features from conv4_3
        self.L2Norm = L2Norm(512, 20)
        self.extras = nn.ModuleList(extras)

        self.loc = nn.ModuleList(head[0])
        self.conf = nn.ModuleList(head[1])

        self.softmax = nn.Softmax(dim=-1)

        if phase == 'test':
            # self.softmax = nn.Softmax(dim=-1)
            self.detect = Detect(num_classes, 0, 200, 0.01, 0.45)
開發者ID:soo89，項目名稱:CSD-SSD，代碼行數:28，代碼來源:csd.py

示例8: __init__

# 需要導入模塊: import data [as 別名]
# 或者: from data import coco [as 別名]
def __init__(self, num_classes, overlap_thresh, prior_for_matching,
                 bkg_label, neg_mining, neg_pos, neg_overlap, encode_target,
                 use_gpu=True):
        super(MultiBoxLoss, self).__init__()
        self.use_gpu = use_gpu
        self.num_classes = num_classes
        self.threshold = overlap_thresh
        self.background_label = bkg_label
        self.encode_target = encode_target
        self.use_prior_for_matching = prior_for_matching
        self.do_neg_mining = neg_mining
        self.negpos_ratio = neg_pos
        self.neg_overlap = neg_overlap
        self.variance = cfg['variance']
開發者ID:soo89，項目名稱:CSD-SSD，代碼行數:16，代碼來源:multibox_loss.py

示例9: __init__

# 需要導入模塊: import data [as 別名]
# 或者: from data import coco [as 別名]
def __init__(self, use_gpu=True, sigma=0.):
        super(RepulsionLoss, self).__init__()
        self.use_gpu = use_gpu
        self.variance = cfg['variance']
        self.sigma = sigma
        
    # TODO
開發者ID:bailvwangzi，項目名稱:repulsion_loss_ssd，代碼行數:9，代碼來源:repulsion_loss.py


注：本文中的data.coco方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。