本文整理汇总了Python中maskrcnn_benchmark.modeling.make_layers.make_conv3x3方法的典型用法代码示例。如果您正苦于以下问题：Python make_layers.make_conv3x3方法的具体用法？Python make_layers.make_conv3x3怎么用？Python make_layers.make_conv3x3使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类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个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __init__
# 需要导入模块: from maskrcnn_benchmark.modeling import make_layers [as 别名]
# 或者: from maskrcnn_benchmark.modeling.make_layers import make_conv3x3 [as 别名]
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 示例2: __init__
# 需要导入模块: from maskrcnn_benchmark.modeling import make_layers [as 别名]
# 或者: from maskrcnn_benchmark.modeling.make_layers import make_conv3x3 [as 别名]
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) 示例3: __init__
# 需要导入模块: from maskrcnn_benchmark.modeling import make_layers [as 别名]
# 或者: from maskrcnn_benchmark.modeling.make_layers import make_conv3x3 [as 别名]
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) 示例4: __init__
# 需要导入模块: from maskrcnn_benchmark.modeling import make_layers [as 别名]
# 或者: from maskrcnn_benchmark.modeling.make_layers import make_conv3x3 [as 别名]
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)