eugene.models.Conv1DTower

class eugene.models.Conv1DTower(input_len, input_channels, conv_channels, conv_kernels, conv_strides=None, conv_dilations=None, conv_padding='valid', conv_biases=True, activations='relu', pool_types='max', pool_kernels=None, pool_strides=None, pool_dilations=None, pool_padding=None, dropout_rates=0.0, batchnorm=False, batchnorm_first=False)

Generates a PyTorch module for multiple convolutional layers

Meant to allow mulitple convolutional blocks to be stacked together. Currently the basis of convolutional architectures in the model zoo. Soon to be deprecated in favor of Conv1D blocks wrapped by a Tower

Parameters:

  • input_len (int) – Length of the input sequence

  • input_channels (int) – Number of channels in the input sequence

  • conv_channels (list) – Number of channels in each convolutional layer

  • conv_kernels (list) – Size of the kernel in each convolutional layer

  • conv_strides (list) – Stride of the convolutional layers. Applies the same stride to all layers

  • conv_dilations (list) – Dilation of the convolutional layers. Applies the same dilation to all layers

  • conv_padding (list) – Padding of the convolutional layers. Applies the same padding to all layers

  • conv_biases (list) – Whether to use biases in the convolutional layers. Applies the same setting to all layers

  • activations (list) – Activation function of the convolutional layers. Applies the same activation to all layers

  • pool_types (list) – Pooling function of the convolutional layers. Applies the same pooling to all layers

  • pool_kernels (list) – Size of the kernel in each pooling layer

  • pool_strides (list) – Stride of the pooling layers. Applies the same stride to all layers

  • pool_dilations (list) – Dilation of the pooling layers. Applies the same dilation to all layers

  • pool_padding (list) – Padding of the pooling layers. Applies the same padding to all layers

  • dropout_rates (list) – Dropout rate of the convolutional layers. Applies the same rate to all layers

  • batchnorm (bool) – Whether to use batchnorm in the convolutional layers

  • batchnorm_first (bool) – Whether to use batchnorm before the activation function

__init__(input_len, input_channels, conv_channels, conv_kernels, conv_strides=None, conv_dilations=None, conv_padding='valid', conv_biases=True, activations='relu', pool_types='max', pool_kernels=None, pool_strides=None, pool_dilations=None, pool_padding=None, dropout_rates=0.0, batchnorm=False, batchnorm_first=False)

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