meddlr.modeling.meta_arch.GeneralizedUNet#

class meddlr.modeling.meta_arch.GeneralizedUNet(dimensions: int, in_channels: int, out_channels: int, channels: Sequence[int], strides: Sequence[int] = 1, kernel_size: Union[Sequence[int], int] = 3, up_kernel_size: Union[Sequence[int], int] = None, dropout: float = 0.0, block_order: Sequence[Union[str, Dict[str, Union[Dict[str, Any], List[Any], Tuple[Any], List[Tuple[str, Any]], Tuple[Tuple[str, Any], ...]]], Tuple[str, Union[Dict[str, Any], List[Any], Tuple[Any], List[Tuple[str, Any]], Tuple[Tuple[str, Any], ...]]], LayerInfo]] = ('conv', 'relu', 'conv', 'relu', 'batchnorm', 'dropout'))[source]#

A general implementation of the U-Net architecture.

The output block does not

Variables

  • down_blocks (nn.ModuleDict) – A dictionary of down-sampling blocks.

  • pool_blocks (nn.ModuleDict) – A dictionary of pooling blocks.

  • up_blocks (nn.ModuleDict) – A dictionary of up-sampling blocks.

  • output_block (nn.Module) – The output block.

Reference:

Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234–241. Springer, 2015.

__init__(dimensions: int, in_channels: int, out_channels: int, channels: Sequence[int], strides: Sequence[int] = 1, kernel_size: Union[Sequence[int], int] = 3, up_kernel_size: Union[Sequence[int], int] = None, dropout: float = 0.0, block_order: Sequence[Union[str, Dict[str, Union[Dict[str, Any], List[Any], Tuple[Any], List[Tuple[str, Any]], Tuple[Tuple[str, Any], ...]]], Tuple[str, Union[Dict[str, Any], List[Any], Tuple[Any], List[Tuple[str, Any]], Tuple[Tuple[str, Any], ...]]], LayerInfo]] = ('conv', 'relu', 'conv', 'relu', 'batchnorm', 'dropout'))[source]#
Parameters

  • dimensions (int) – The number of spatial dimensions.

  • in_channels (int) – The number of input channels.

  • out_channels (int) – The number of output channels.

  • channels (Sequence[int]) – The number of channels in each conv block. The length of this sequence determines the depth of the model.

  • strides (Sequence[int], optional) – The stride of each convolutions in each block.

  • kernel_size (Union[Sequence[int], int], optional) – The kernel size of each convolution. If a sequence is provided, the length must be equal to the depth of the model.

  • up_kernel_size (Union[Sequence[int], int], optional) – The kernel size of each up-sampling convolution. Defaults to kernel_size.

  • dropout (float, optional) – The dropout probability.

  • block_order (Tuple[str, ...], optional) – The order of layers in each convolutional block.

Methods

__init__(dimensions, in_channels, ...[, ...])

param dimensions

The number of spatial dimensions.

add_module(name, module)

Adds a child module to the current module.

apply(fn)

Applies fn recursively to every submodule (as returned by .children()) as well as self.

bfloat16()

Casts all floating point parameters and buffers to bfloat16 datatype.

buffers([recurse])

Returns an iterator over module buffers.

children()

Returns an iterator over immediate children modules.

cpu()

Moves all model parameters and buffers to the CPU.

cuda([device])

Moves all model parameters and buffers to the GPU.

double()

Casts all floating point parameters and buffers to double datatype.

eval()

Sets the module in evaluation mode.

extra_repr()

Set the extra representation of the module

float()

Casts all floating point parameters and buffers to float datatype.

forward(x)

Defines the computation performed at every call.

from_config(cfg, **kwargs)

get_buffer(target)

Returns the buffer given by target if it exists, otherwise throws an error.

get_extra_state()

Returns any extra state to include in the module's state_dict.

get_parameter(target)

Returns the parameter given by target if it exists, otherwise throws an error.

get_submodule(target)

Returns the submodule given by target if it exists, otherwise throws an error.

half()

Casts all floating point parameters and buffers to half datatype.

ipu([device])

Moves all model parameters and buffers to the IPU.

load_state_dict(state_dict[, strict])

Copies parameters and buffers from state_dict into this module and its descendants.

modules()

Returns an iterator over all modules in the network.

named_buffers([prefix, recurse, ...])

Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.

named_children()

Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.

named_modules([memo, prefix, remove_duplicate])

Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself.

named_parameters([prefix, recurse, ...])

Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.

parameters([recurse])

Returns an iterator over module parameters.

register_backward_hook(hook)

Registers a backward hook on the module.

register_buffer(name, tensor[, persistent])

Adds a buffer to the module.

register_forward_hook(hook, *[, prepend, ...])

Registers a forward hook on the module.

register_forward_pre_hook(hook, *[, ...])

Registers a forward pre-hook on the module.

register_full_backward_hook(hook[, prepend])

Registers a backward hook on the module.

register_full_backward_pre_hook(hook[, prepend])

Registers a backward pre-hook on the module.

register_load_state_dict_post_hook(hook)

Registers a post hook to be run after module's load_state_dict is called.

register_module(name, module)

Alias for add_module().

register_parameter(name, param)

Adds a parameter to the module.

register_state_dict_pre_hook(hook)

These hooks will be called with arguments: self, prefix, and keep_vars before calling state_dict on self.

requires_grad_([requires_grad])

Change if autograd should record operations on parameters in this module.

set_extra_state(state)

This function is called from load_state_dict() to handle any extra state found within the state_dict.

share_memory()

See torch.Tensor.share_memory_()

state_dict(*args[, destination, prefix, ...])

Returns a dictionary containing references to the whole state of the module.

to(*args, **kwargs)

Moves and/or casts the parameters and buffers.

to_empty(*, device)

Moves the parameters and buffers to the specified device without copying storage.

train([mode])

Sets the module in training mode.

type(dst_type)

Casts all parameters and buffers to dst_type.

xpu([device])

Moves all model parameters and buffers to the XPU.

zero_grad([set_to_none])

Sets gradients of all model parameters to zero.

Attributes

T_destination

alias of TypeVar('T_destination', bound=Dict[str, Any])

bottleneck

The bottleneck block.

call_super_init

depth

The depth of the model.

dump_patches