本文整理汇总了Python中maskrcnn_benchmark.modeling.make_layers.group_norm方法的典型用法代码示例。如果您正苦于以下问题：Python make_layers.group_norm方法的具体用法？Python make_layers.group_norm怎么用？Python make_layers.group_norm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.group_norm方法的5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 group_norm [as 别名]
def __init__(
self,
in_channels,
bottleneck_channels,
out_channels,
num_groups=1,
stride_in_1x1=True,
stride=1,
dilation=1
):
super(BottleneckWithGN, self).__init__(
in_channels=in_channels,
bottleneck_channels=bottleneck_channels,
out_channels=out_channels,
num_groups=num_groups,
stride_in_1x1=stride_in_1x1,
stride=stride,
dilation=dilation,
norm_func=group_norm
) 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 group_norm [as 别名]
def __init__(
self,
in_channels,
bottleneck_channels,
out_channels,
num_groups=1,
stride_in_1x1=True,
stride=1,
dilation=1,
scale=4
):
super(Bottle2neckWithGN, self).__init__(
in_channels=in_channels,
bottleneck_channels=bottleneck_channels,
out_channels=out_channels,
num_groups=num_groups,
stride_in_1x1=stride_in_1x1,
stride=stride,
dilation=dilation,
scale=4,
norm_func=group_norm
) 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 group_norm [as 别名]
def __init__(
self,
in_channels,
bottleneck_channels,
out_channels,
num_groups=1,
stride_in_1x1=True,
stride=1,
dilation=1,
dcn_config={}
):
super(BottleneckWithGN, self).__init__(
in_channels=in_channels,
bottleneck_channels=bottleneck_channels,
out_channels=out_channels,
num_groups=num_groups,
stride_in_1x1=stride_in_1x1,
stride=stride,
dilation=dilation,
norm_func=group_norm,
dcn_config=dcn_config
) 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 group_norm [as 别名]
def __init__(self, in_channels, out_channels, kernel_size, stride=1, relu=True, same_padding=False, gn=False):
super(Conv2dGroup, self).__init__()
padding = int((kernel_size - 1) / 2) if same_padding else 0
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding=padding)
self.gn = GN(out_channels) if gn else None # nn.BatchNorm2d(out_channels, eps=0.001, momentum=0, affine=True) if gn else None #
self.relu = nn.ReLU(inplace=True) if relu else None 示例5: __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 group_norm [as 别名]
def __init__(self, cfg, in_channels):
super(FPNXconv1fcFeatureExtractor, self).__init__()
resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION
scales = cfg.MODEL.ROI_BOX_HEAD.POOLER_SCALES
sampling_ratio = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO
pooler = Pooler(
output_size=(resolution, resolution),
scales=scales,
sampling_ratio=sampling_ratio,
)
self.pooler = pooler
use_gn = cfg.MODEL.ROI_BOX_HEAD.USE_GN
conv_head_dim = cfg.MODEL.ROI_BOX_HEAD.CONV_HEAD_DIM
num_stacked_convs = cfg.MODEL.ROI_BOX_HEAD.NUM_STACKED_CONVS
dilation = cfg.MODEL.ROI_BOX_HEAD.DILATION
xconvs = []
for ix in range(num_stacked_convs):
xconvs.append(
nn.Conv2d(
in_channels,
conv_head_dim,
kernel_size=3,
stride=1,
padding=dilation,
dilation=dilation,
bias=False if use_gn else True
)
)
in_channels = conv_head_dim
if use_gn:
xconvs.append(group_norm(in_channels))
xconvs.append(nn.ReLU(inplace=True))
self.add_module("xconvs", nn.Sequential(*xconvs))
for modules in [self.xconvs,]:
for l in modules.modules():
if isinstance(l, nn.Conv2d):
torch.nn.init.normal_(l.weight, std=0.01)
if not use_gn:
torch.nn.init.constant_(l.bias, 0)
input_size = conv_head_dim * resolution ** 2
representation_size = cfg.MODEL.ROI_BOX_HEAD.MLP_HEAD_DIM
self.fc6 = make_fc(input_size, representation_size, use_gn=False)
self.out_channels = representation_size