eugene.models.Conv1DBlock

class eugene.models.Conv1DBlock(input_len, input_channels, output_channels, conv_kernel, conv_type='conv1d', conv_stride=1, conv_padding='valid', conv_dilation=1, conv_bias=True, activation='relu', pool_type='max', pool_kernel=1, pool_stride=None, pool_padding=0, norm_type='batchnorm', norm_dim=None, dropout_rate=0.0, order='conv-norm-act-pool-dropout')

Flexible block for convolutional models

Allows for flexible specification of convolutional layers, pooling layers, normalization layers, activation layers, and dropout layers.

Parameters:

  • input_len (int) – The length of the input. The last dimension of the input tensor.

  • input_channels (int) – The number of input channels. The second to last dimension of the input tensor.

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

  • conv_kernel (int) – The size of the convolutional kernel.

  • conv_type (str or callable) – The type of convolutional layer to use. If a string, must be a key in eugene.models.base._layers.CONVOLUTION_REGISTRY. If a callable, must be a subclass of torch.nn.Module.

  • conv_stride (int) – The stride of the convolutional kernel.

  • conv_padding (str or int) – The padding of the convolutional kernel. See torch.nn.Conv1d for more details.

  • conv_dilation (int) – The dilation of the convolutional kernel.

  • conv_bias (bool) – Whether or not to include a bias term in the convolutional layer.

  • activation (str or callable) – The type of activation to use. If a string, must be a key in eugene.models.base._layers.ACTIVATION_REGISTRY. If a callable, must be a subclass of torch.nn.Module.

  • pool_type (str or callable) – The type of pooling layer to use. If a string, must be a key in eugene.models.base._layers.POOLING_REGISTRY. If a callable, must be a subclass of torch.nn.Module.

  • pool_kernel (int) – The size of the pooling kernel.

  • pool_stride (int) – The stride of the pooling kernel.

  • pool_padding (int) – The padding of the pooling kernel.

  • norm_type (str or callable) – The type of normalization layer to use. If a string, must be a key in eugene.models.base._layers.NORMALIZER_REGISTRY. If a callable, must be a subclass of torch.nn.Module.

  • norm_dim (int) – The dimension to normalize over. If None, defaults to the number of output channels.

  • dropout_rate (float) – The dropout rate to use. If None, no dropout is used.

  • order (str) – The order of the layers in the block. Must be a string of the following characters: conv, norm, act, pool, dropout. For example, the string conv-norm-act-pool-dropout would result in a block with a convolutional layer, a normalization layer, an activation layer, a pooling layer, and a dropout layer in that order. If None, defaults to conv-norm-act-pool-dropout.

__init__(input_len, input_channels, output_channels, conv_kernel, conv_type='conv1d', conv_stride=1, conv_padding='valid', conv_dilation=1, conv_bias=True, activation='relu', pool_type='max', pool_kernel=1, pool_stride=None, pool_padding=0, norm_type='batchnorm', norm_dim=None, dropout_rate=0.0, order='conv-norm-act-pool-dropout')

Initializes internal Module state, shared by both nn.Module and ScriptModule.

Methods

__init__(input_len, input_channels, ...[, ...])

Initializes internal Module state, shared by both nn.Module and ScriptModule.

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.

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)

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)

Registers a backward 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.

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

dump_patches

training