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Python make_layers.make_conv3x3方法代碼示例

本文整理匯總了Python中maskrcnn_benchmark.modeling.make_layers.make_conv3x3方法的典型用法代碼示例。如果您正苦於以下問題：Python make_layers.make_conv3x3方法的具體用法？Python make_layers.make_conv3x3怎麽用？Python make_layers.make_conv3x3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maskrcnn_benchmark.modeling.make_layers的用法示例。


在下文中一共展示了make_layers.make_conv3x3方法的4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 maskrcnn_benchmark.modeling import make_layers [as 別名]
# 或者: from maskrcnn_benchmark.modeling.make_layers import make_conv3x3 [as 別名]
def __init__(self, cfg, in_channels):
        """
        Arguments:
            num_classes (int): number of output classes
            input_size (int): number of channels of the input once it's flattened
            representation_size (int): size of the intermediate representation
        """
        super(MaskRCNNFPNFeatureExtractor, self).__init__()

        resolution = cfg.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION
        scales = cfg.MODEL.ROI_MASK_HEAD.POOLER_SCALES
        sampling_ratio = cfg.MODEL.ROI_MASK_HEAD.POOLER_SAMPLING_RATIO
        pooler = Pooler(
            output_size=(resolution, resolution),
            scales=scales,
            sampling_ratio=sampling_ratio,
        )
        input_size = in_channels
        self.pooler = pooler

        use_gn = cfg.MODEL.ROI_MASK_HEAD.USE_GN
        layers = cfg.MODEL.ROI_MASK_HEAD.CONV_LAYERS
        dilation = cfg.MODEL.ROI_MASK_HEAD.DILATION

        next_feature = input_size
        self.blocks = []
        for layer_idx, layer_features in enumerate(layers, 1):
            layer_name = "mask_fcn{}".format(layer_idx)
            module = make_conv3x3(
                next_feature, layer_features,
                dilation=dilation, stride=1, use_gn=use_gn
            )
            self.add_module(layer_name, module)
            next_feature = layer_features
            self.blocks.append(layer_name)
        self.out_channels = layer_features
開發者ID:Res2Net，項目名稱:Res2Net-maskrcnn，代碼行數:38，代碼來源:roi_mask_feature_extractors.py

示例2: __init__

# 需要導入模塊: from maskrcnn_benchmark.modeling import make_layers [as 別名]
# 或者: from maskrcnn_benchmark.modeling.make_layers import make_conv3x3 [as 別名]
def __init__(self, cfg):
        """
        Arguments:
            num_classes (int): number of output classes
            input_size (int): number of channels of the input once it's flattened
            representation_size (int): size of the intermediate representation
        """
        super(MaskRCNNFPNFeatureExtractor, self).__init__()

        resolution = cfg.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION
        scales = cfg.MODEL.ROI_MASK_HEAD.POOLER_SCALES
        sampling_ratio = cfg.MODEL.ROI_MASK_HEAD.POOLER_SAMPLING_RATIO
        pooler = PyramidRROIAlign(
            output_size=(resolution, resolution),
            scales=scales,
        )
        input_size = cfg.MODEL.BACKBONE.OUT_CHANNELS
        self.pooler = pooler

        use_gn = cfg.MODEL.ROI_MASK_HEAD.USE_GN
        layers = cfg.MODEL.ROI_MASK_HEAD.CONV_LAYERS
        dilation = cfg.MODEL.ROI_MASK_HEAD.DILATION

        self.word_margin = cfg.MODEL.ROI_REC_HEAD.BOXES_MARGIN
        self.det_margin = cfg.MODEL.RRPN.GT_BOX_MARGIN

        self.rescale = self.word_margin / self.det_margin

        next_feature = input_size
        self.blocks = []
        for layer_idx, layer_features in enumerate(layers, 1):
            layer_name = "mask_fcn{}".format(layer_idx)
            module = make_conv3x3(next_feature, layer_features, 
                dilation=dilation, stride=1, use_gn=use_gn
            )
            self.add_module(layer_name, module)
            next_feature = layer_features
            self.blocks.append(layer_name)
開發者ID:clw5180，項目名稱:remote_sensing_object_detection_2019，代碼行數:40，代碼來源:roi_mask_feature_extractors.py

示例3: __init__

# 需要導入模塊: from maskrcnn_benchmark.modeling import make_layers [as 別名]
# 或者: from maskrcnn_benchmark.modeling.make_layers import make_conv3x3 [as 別名]
def __init__(self, cfg):
        """
        Arguments:
            num_classes (int): number of output classes
            input_size (int): number of channels of the input once it's flattened
            representation_size (int): size of the intermediate representation
        """
        super(MaskRCNNFPNFeatureExtractor, self).__init__()

        resolution = cfg.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION
        scales = cfg.MODEL.ROI_MASK_HEAD.POOLER_SCALES
        sampling_ratio = cfg.MODEL.ROI_MASK_HEAD.POOLER_SAMPLING_RATIO
        pooler = Pooler(
            output_size=(resolution, resolution),
            scales=scales,
            sampling_ratio=sampling_ratio
        )
        input_size = cfg.MODEL.BACKBONE.OUT_CHANNELS
        self.pooler = pooler

        use_gn = cfg.MODEL.ROI_MASK_HEAD.USE_GN
        layers = cfg.MODEL.ROI_MASK_HEAD.CONV_LAYERS
        dilation = cfg.MODEL.ROI_MASK_HEAD.DILATION

        next_feature = input_size
        self.blocks = []
        for layer_idx, layer_features in enumerate(layers, 1):
            layer_name = "mask_fcn{}".format(layer_idx)
            module = make_conv3x3(next_feature, layer_features, 
                dilation=dilation, stride=1, use_gn=use_gn
            )
            self.add_module(layer_name, module)
            next_feature = layer_features
            self.blocks.append(layer_name)
開發者ID:clw5180，項目名稱:remote_sensing_object_detection_2019，代碼行數:36，代碼來源:roi_rec_feature_extractors.py

示例4: __init__

# 需要導入模塊: from maskrcnn_benchmark.modeling import make_layers [as 別名]
# 或者: from maskrcnn_benchmark.modeling.make_layers import make_conv3x3 [as 別名]
def __init__(self, cfg):
        """
        Arguments:
            num_classes (int): number of output classes
            input_size (int): number of channels of the input once it's flattened
            representation_size (int): size of the intermediate representation
        """
        super(MaskRCNNFPNFeatureExtractor, self).__init__()

        resolution = cfg.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION
        scales = cfg.MODEL.ROI_MASK_HEAD.POOLER_SCALES
        sampling_ratio = cfg.MODEL.ROI_MASK_HEAD.POOLER_SAMPLING_RATIO
        pooler = Pooler(
            output_size=(resolution, resolution),
            scales=scales,
            sampling_ratio=sampling_ratio,
        )
        input_size = cfg.MODEL.BACKBONE.OUT_CHANNELS
        self.pooler = pooler

        use_gn = cfg.MODEL.ROI_MASK_HEAD.USE_GN
        layers = cfg.MODEL.ROI_MASK_HEAD.CONV_LAYERS
        dilation = cfg.MODEL.ROI_MASK_HEAD.DILATION

        next_feature = input_size
        self.blocks = []
        for layer_idx, layer_features in enumerate(layers, 1):
            layer_name = "mask_fcn{}".format(layer_idx)
            module = make_conv3x3(next_feature, layer_features, 
                dilation=dilation, stride=1, use_gn=use_gn
            )
            self.add_module(layer_name, module)
            next_feature = layer_features
            self.blocks.append(layer_name)
開發者ID:clw5180，項目名稱:remote_sensing_object_detection_2019，代碼行數:36，代碼來源:roi_mask_feature_extractors.py


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