Python nn.InstanceNorm3d方法代碼示例

本文整理匯總了Python中torch.nn.InstanceNorm3d方法的典型用法代碼示例。如果您正苦於以下問題：Python nn.InstanceNorm3d方法的具體用法？Python nn.InstanceNorm3d怎麽用？Python nn.InstanceNorm3d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torch.nn的用法示例。

在下文中一共展示了nn.InstanceNorm3d方法的11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 torch import nn [as 別名]
# 或者: from torch.nn import InstanceNorm3d [as 別名]
def __init__(self, n_stages, n_filters_in, n_filters_out, normalization='none'):
        super(ConvBlock, self).__init__()

        ops = []
        for i in range(n_stages):
            if i==0:
                input_channel = n_filters_in
            else:
                input_channel = n_filters_out

            ops.append(nn.Conv3d(input_channel, n_filters_out, 3, padding=1))
            if normalization == 'batchnorm':
                ops.append(nn.BatchNorm3d(n_filters_out))
            elif normalization == 'groupnorm':
                ops.append(nn.GroupNorm(num_groups=16, num_channels=n_filters_out))
            elif normalization == 'instancenorm':
                ops.append(nn.InstanceNorm3d(n_filters_out))
            elif normalization != 'none':
                assert False
            ops.append(nn.ReLU(inplace=True))

        self.conv = nn.Sequential(*ops)

開發者ID:JunMa11，項目名稱:SegWithDistMap，代碼行數:24，代碼來源:vnet_multi_task.py

示例2: init

# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import InstanceNorm3d [as 別名]
def __init__(self, n_stages, n_filters_in, n_filters_out, normalization='none'):
        super(ResidualConvBlock, self).__init__()

        ops = []
        for i in range(n_stages):
            if i == 0:
                input_channel = n_filters_in
            else:
                input_channel = n_filters_out

            ops.append(nn.Conv3d(input_channel, n_filters_out, 3, padding=1))
            if normalization == 'batchnorm':
                ops.append(nn.BatchNorm3d(n_filters_out))
            elif normalization == 'groupnorm':
                ops.append(nn.GroupNorm(num_groups=16, num_channels=n_filters_out))
            elif normalization == 'instancenorm':
                ops.append(nn.InstanceNorm3d(n_filters_out))
            elif normalization != 'none':
                assert False

            if i != n_stages-1:
                ops.append(nn.ReLU(inplace=True))

        self.conv = nn.Sequential(*ops)
        self.relu = nn.ReLU(inplace=True)

開發者ID:JunMa11，項目名稱:SegWithDistMap，代碼行數:27，代碼來源:vnet.py

示例3: init

# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import InstanceNorm3d [as 別名]
def __init__(self, num_classes=3, in_channels=1, initial_filter_size=64, kernel_size=3, num_downs=3, norm_layer=nn.InstanceNorm3d):
        # norm_layer=nn.BatchNorm2d, use_dropout=False):
        super(UNet3D, self).__init__()

        # construct unet structure
        unet_block = UnetSkipConnectionBlock(in_channels=initial_filter_size * 2 ** (num_downs-1), out_channels=initial_filter_size * 2 ** num_downs,
                                             num_classes=num_classes, kernel_size=kernel_size, norm_layer=norm_layer, innermost=True)
        for i in range(1, num_downs):
            unet_block = UnetSkipConnectionBlock(in_channels=initial_filter_size * 2 ** (num_downs-(i+1)),
                                                 out_channels=initial_filter_size * 2 ** (num_downs-i),
                                                 num_classes=num_classes, kernel_size=kernel_size, submodule=unet_block, norm_layer=norm_layer)
        unet_block = UnetSkipConnectionBlock(in_channels=in_channels, out_channels=initial_filter_size,
                                             num_classes=num_classes, kernel_size=kernel_size, submodule=unet_block, norm_layer=norm_layer,
                                             outermost=True)

        self.model = unet_block

開發者ID:MIC-DKFZ，項目名稱:basic_unet_example，代碼行數:18，代碼來源:RecursiveUNet3D.py

示例4: initialize_network

# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import InstanceNorm3d [as 別名]
def initialize_network(self):
        if self.threeD:
            conv_op = nn.Conv3d
            dropout_op = nn.Dropout3d
            norm_op = nn.InstanceNorm3d

        else:
            conv_op = nn.Conv2d
            dropout_op = nn.Dropout2d
            norm_op = nn.InstanceNorm2d

        norm_op_kwargs = {'eps': 1e-5, 'affine': True}
        dropout_op_kwargs = {'p': 0, 'inplace': True}
        net_nonlin = nn.LeakyReLU
        net_nonlin_kwargs = {'inplace': True, 'negative_slope': 1e-2}
        self.network = Generic_UNet(self.num_input_channels, self.base_num_features, self.num_classes,
                                    len(self.net_num_pool_op_kernel_sizes),
                                    self.conv_per_stage, 2, conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs,
                                    net_nonlin, net_nonlin_kwargs, True, False, lambda x: x, InitWeights_He(1e-2),
                                    self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, True, True,
                                    basic_block=ConvDropoutNonlinNorm)
        self.network.cuda()
        self.network.inference_apply_nonlin = softmax_helper

開發者ID:MIC-DKFZ，項目名稱:nnUNet，代碼行數:25，代碼來源:nnUNetTrainerV2_lReLU_convlReLUIN.py

示例5: initialize_network

# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import InstanceNorm3d [as 別名]
def initialize_network(self):
        if self.threeD:
            conv_op = nn.Conv3d
            dropout_op = nn.Dropout3d
            norm_op = nn.InstanceNorm3d

        else:
            conv_op = nn.Conv2d
            dropout_op = nn.Dropout2d
            norm_op = nn.InstanceNorm2d

        norm_op_kwargs = {'eps': 1e-5, 'affine': True}
        dropout_op_kwargs = {'p': 0, 'inplace': True}
        net_nonlin = nn.ReLU
        net_nonlin_kwargs = {'inplace': True}
        self.network = Generic_UNet(self.num_input_channels, self.base_num_features, self.num_classes,
                                    len(self.net_num_pool_op_kernel_sizes),
                                    self.conv_per_stage, 2, conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs,
                                    net_nonlin, net_nonlin_kwargs, True, False, lambda x: x, InitWeights_He(0),
                                    self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, True, True)
        if torch.cuda.is_available():
            self.network.cuda()
        self.network.inference_apply_nonlin = softmax_helper

開發者ID:MIC-DKFZ，項目名稱:nnUNet，代碼行數:25，代碼來源:nnUNetTrainerV2_ReLU.py

示例6: initialize_network

# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import InstanceNorm3d [as 別名]
def initialize_network(self):
        if self.threeD:
            conv_op = nn.Conv3d
            dropout_op = nn.Dropout3d
            norm_op = nn.InstanceNorm3d

        else:
            conv_op = nn.Conv2d
            dropout_op = nn.Dropout2d
            norm_op = nn.InstanceNorm2d

        norm_op_kwargs = {'eps': 1e-5, 'affine': True}
        dropout_op_kwargs = {'p': 0, 'inplace': True}
        net_nonlin = nn.LeakyReLU
        net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True}
        self.network = Generic_UNet(self.num_input_channels, self.base_num_features, self.num_classes,
                                    len(self.net_num_pool_op_kernel_sizes),
                                    self.conv_per_stage, 2, conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs,
                                    net_nonlin, net_nonlin_kwargs, True, False, lambda x: x, InitWeights_He(0),
                                    self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, True, True,
                                    seg_output_use_bias=True)
        if torch.cuda.is_available():
            self.network.cuda()
        self.network.inference_apply_nonlin = softmax_helper

開發者ID:MIC-DKFZ，項目名稱:nnUNet，代碼行數:26，代碼來源:nnUNetTrainerV2_lReLU_biasInSegOutput.py

示例7: initialize_network

# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import InstanceNorm3d [as 別名]
def initialize_network(self):
        if self.threeD:
            conv_op = nn.Conv3d
            dropout_op = nn.Dropout3d
            norm_op = nn.InstanceNorm3d

        else:
            conv_op = nn.Conv2d
            dropout_op = nn.Dropout2d
            norm_op = nn.InstanceNorm2d

        norm_op_kwargs = {'eps': 1e-5, 'affine': True}
        dropout_op_kwargs = {'p': 0, 'inplace': True}
        net_nonlin = Mish
        net_nonlin_kwargs = {}
        self.network = Generic_UNet(self.num_input_channels, self.base_num_features, self.num_classes,
                                    len(self.net_num_pool_op_kernel_sizes),
                                    self.conv_per_stage, 2, conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs,
                                    net_nonlin, net_nonlin_kwargs, True, False, lambda x: x, InitWeights_He(0),
                                    self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, True, True)
        if torch.cuda.is_available():
            self.network.cuda()
        self.network.inference_apply_nonlin = softmax_helper

開發者ID:MIC-DKFZ，項目名稱:nnUNet，代碼行數:25，代碼來源:nnUNetTrainerV2_Mish.py

示例8: initialize_network

# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import InstanceNorm3d [as 別名]
def initialize_network(self):
        self.base_num_features = 24  # otherwise we run out of VRAM
        if self.threeD:
            conv_op = nn.Conv3d
            dropout_op = nn.Dropout3d
            norm_op = nn.InstanceNorm3d

        else:
            conv_op = nn.Conv2d
            dropout_op = nn.Dropout2d
            norm_op = nn.InstanceNorm2d

        norm_op_kwargs = {'eps': 1e-5, 'affine': True}
        dropout_op_kwargs = {'p': 0, 'inplace': True}
        net_nonlin = nn.LeakyReLU
        net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True}
        self.network = Generic_UNet(self.num_input_channels, self.base_num_features, self.num_classes,
                                    len(self.net_num_pool_op_kernel_sizes),
                                    3, 2, conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs,
                                    net_nonlin, net_nonlin_kwargs, True, False, lambda x: x, InitWeights_He(1e-2),
                                    self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, True, True)
        if torch.cuda.is_available():
            self.network.cuda()
        self.network.inference_apply_nonlin = softmax_helper

開發者ID:MIC-DKFZ，項目名稱:nnUNet，代碼行數:26，代碼來源:nnUNetTrainerV2_3ConvPerStage.py

示例9: initialize_network

# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import InstanceNorm3d [as 別名]
def initialize_network(self):
        if self.threeD:
            conv_op = nn.Conv3d
            dropout_op = nn.Dropout3d
            norm_op = nn.InstanceNorm3d

        else:
            conv_op = nn.Conv2d
            dropout_op = nn.Dropout2d
            norm_op = nn.InstanceNorm2d

        norm_op_kwargs = {'eps': 1e-5, 'affine': True}
        dropout_op_kwargs = {'p': 0, 'inplace': True}
        net_nonlin = nn.ReLU
        net_nonlin_kwargs = {'inplace': True}
        self.network = Generic_UNet(self.num_input_channels, self.base_num_features, self.num_classes,
                                    len(self.net_num_pool_op_kernel_sizes),
                                    self.conv_per_stage, 2, conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs,
                                    net_nonlin, net_nonlin_kwargs, True, False, lambda x: x, InitWeights_He(0),
                                    self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, True, True,
                                    basic_block=ConvDropoutNonlinNorm)
        if torch.cuda.is_available():
            self.network.cuda()
        self.network.inference_apply_nonlin = softmax_helper

開發者ID:MIC-DKFZ，項目名稱:nnUNet，代碼行數:26，代碼來源:nnUNetTrainerV2_ReLU_convReLUIN.py

示例10: initialize_network

# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import InstanceNorm3d [as 別名]
def initialize_network(self):
        if self.threeD:
            conv_op = nn.Conv3d
            dropout_op = nn.Dropout3d
            norm_op = nn.InstanceNorm3d

        else:
            conv_op = nn.Conv2d
            dropout_op = nn.Dropout2d
            norm_op = nn.InstanceNorm2d

        norm_op_kwargs = {'eps': 1e-5, 'affine': True}
        dropout_op_kwargs = {'p': 0, 'inplace': True}
        net_nonlin = nn.LeakyReLU
        net_nonlin_kwargs = {'inplace': True, 'negative_slope': 2e-1}
        self.network = Generic_UNet(self.num_input_channels, self.base_num_features, self.num_classes,
                                    len(self.net_num_pool_op_kernel_sizes),
                                    self.conv_per_stage, 2, conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs,
                                    net_nonlin, net_nonlin_kwargs, True, False, lambda x: x, InitWeights_He(0),
                                    self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, True, True)
        if torch.cuda.is_available():
            self.network.cuda()
        self.network.inference_apply_nonlin = softmax_helper

開發者ID:MIC-DKFZ，項目名稱:nnUNet，代碼行數:25，代碼來源:nnUNetTrainerV2_LReLU_slope_2en1.py

示例11: initialize_network

# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import InstanceNorm3d [as 別名]
def initialize_network(self):
        if self.threeD:
            conv_op = nn.Conv3d
            dropout_op = nn.Dropout3d
            norm_op = nn.InstanceNorm3d

        else:
            conv_op = nn.Conv2d
            dropout_op = nn.Dropout2d
            norm_op = nn.InstanceNorm2d

        norm_op_kwargs = {'eps': 1e-5, 'affine': True}
        dropout_op_kwargs = {'p': 0, 'inplace': True}
        net_nonlin = nn.ReLU
        net_nonlin_kwargs = {'inplace': True}
        self.network = Generic_UNet(self.num_input_channels, self.base_num_features, self.num_classes,
                                    len(self.net_num_pool_op_kernel_sizes),
                                    self.conv_per_stage, 2, conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs,
                                    net_nonlin, net_nonlin_kwargs, True, False, lambda x: x, InitWeights_He(0),
                                    self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, True, True,
                                    seg_output_use_bias=True)
        self.network.cuda()
        self.network.inference_apply_nonlin = softmax_helper

開發者ID:MIC-DKFZ，項目名稱:nnUNet，代碼行數:25，代碼來源:nnUNetTrainerV2_ReLU_biasInSegOutput.py

注：本文中的torch.nn.InstanceNorm3d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。

示例1: __init__

示例2: __init__

示例3: __init__