Bering.models.ImageEncoder

class Bering.models.ImageEncoder(image_dims, cnn_layer_dims=[6, 16, 32], mlp_layer_dims=[32, 32], spp_output_size=[4, 2, 1])[source]

Convolutional neural network to learn representation from staining images of different sizes.

Parameters:

image_dims (Sequence[int]) – dimensions of the input image (n_samples x n_channels x W x H)
cnn_layer_dims (Sequence[int]) – dimensions of CNN layers
mlp_layer_dim – dimensions of FC layers in the end
spp_output_size (Sequence[int]) – size of spatial pyramid pooling. the total size of the spp layer in the dimension of input layer in FCN. Refer to spatial_pyramid_pool().

Methods

`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.
`compile`(args, *kwargs)	Compile this Module's forward using `torch.compile()`.
`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`(images)	Run SPP layer and FC layers for edges with convolutional embedding features
`get_buffer`(target)	Returns the buffer given by `target` if it exists, otherwise throws an error.
`get_conv2d_embedding`(x)	Get the shared convolution embedding (as the input of SPP) of a large image for edges that derived from this image.
`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, assign])	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[, recurse])	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])	Resets gradients of all model parameters.
`__call__`(args, *kwargs)	Call self as a function.

Attributes

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