cellmap_segmentation_challenge.models.vitnet#
Functions
Classes
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Initialize internal Module state, shared by both nn.Module and ScriptModule. |
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Initialize internal Module state, shared by both nn.Module and ScriptModule. |
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Initialize internal Module state, shared by both nn.Module and ScriptModule. |
|
Initialize internal Module state, shared by both nn.Module and ScriptModule. |
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Initialize internal Module state, shared by both nn.Module and ScriptModule. |
|
Initialize internal Module state, shared by both nn.Module and ScriptModule. |
|
Initialize internal Module state, shared by both nn.Module and ScriptModule. |
|
Initialize internal Module state, shared by both nn.Module and ScriptModule. |
|
Initialize internal Module state, shared by both nn.Module and ScriptModule. |
|
Initialize internal Module state, shared by both nn.Module and ScriptModule. |
|
Initialize internal Module state, shared by both nn.Module and ScriptModule. |
|
Initialize internal Module state, shared by both nn.Module and ScriptModule. |
|
Initialize internal Module state, shared by both nn.Module and ScriptModule. |
|
Initialize internal Module state, shared by both nn.Module and ScriptModule. |
- cellmap_segmentation_challenge.models.vitnet.np2th(weights, conv=False)[source]#
Possibly convert HWIO to OIHW.
- class cellmap_segmentation_challenge.models.vitnet.Attention(config, vis)[source]#
Initialize internal Module state, shared by both nn.Module and ScriptModule.
- forward(hidden_states)[source]#
Define the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
- class cellmap_segmentation_challenge.models.vitnet.Mlp(config)[source]#
Initialize internal Module state, shared by both nn.Module and ScriptModule.
- forward(x)[source]#
Define the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
- class cellmap_segmentation_challenge.models.vitnet.Embeddings(config, img_size)[source]#
Initialize internal Module state, shared by both nn.Module and ScriptModule.
- forward(x)[source]#
Define the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
- class cellmap_segmentation_challenge.models.vitnet.Block(config, vis)[source]#
Initialize internal Module state, shared by both nn.Module and ScriptModule.
- forward(x)[source]#
Define the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
- class cellmap_segmentation_challenge.models.vitnet.Encoder(config, vis)[source]#
Initialize internal Module state, shared by both nn.Module and ScriptModule.
- forward(hidden_states)[source]#
Define the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
- class cellmap_segmentation_challenge.models.vitnet.Transformer(config, img_size, vis)[source]#
Initialize internal Module state, shared by both nn.Module and ScriptModule.
- forward(input_ids)[source]#
Define the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
- class cellmap_segmentation_challenge.models.vitnet.Conv3dReLU(in_channels, out_channels, kernel_size, padding=0, stride=1, use_batchnorm=True)[source]#
Initialize internal Module state, shared by both nn.Module and ScriptModule.
- class cellmap_segmentation_challenge.models.vitnet.DecoderBlock(in_channels, out_channels, skip_channels=0, use_batchnorm=True)[source]#
Initialize internal Module state, shared by both nn.Module and ScriptModule.
- forward(x, skip=None)[source]#
Define the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
- class cellmap_segmentation_challenge.models.vitnet.DecoderCup(config, img_size)[source]#
Initialize internal Module state, shared by both nn.Module and ScriptModule.
- forward(hidden_states, features=None)[source]#
Define the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
- class cellmap_segmentation_challenge.models.vitnet.DoubleConv(in_channels, out_channels, mid_channels=None)[source]#
Initialize internal Module state, shared by both nn.Module and ScriptModule.
- forward(x)[source]#
Define the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
- class cellmap_segmentation_challenge.models.vitnet.Down(in_channels, out_channels)[source]#
Initialize internal Module state, shared by both nn.Module and ScriptModule.
- forward(x)[source]#
Define the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
- class cellmap_segmentation_challenge.models.vitnet.CNNEncoder(config, n_channels=2)[source]#
Initialize internal Module state, shared by both nn.Module and ScriptModule.
- forward(x)[source]#
Define the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
- class cellmap_segmentation_challenge.models.vitnet.RegistrationHead(in_channels, out_channels, kernel_size=3, upsampling=1)[source]#
Initialize internal Module state, shared by both nn.Module and ScriptModule.
- class cellmap_segmentation_challenge.models.vitnet.ViTVNet(out_channels, config='ViT-V-Net', img_size=(128, 128, 128), vis=False)[source]#
Initialize internal Module state, shared by both nn.Module and ScriptModule.
- forward(x)[source]#
Define the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.