pysegcnn.core.layers.Conv2dSame¶
-
class
pysegcnn.core.layers.
Conv2dSame
(*args, **kwargs)[source]¶ A convolution preserving the shape of its input.
Given the kernel size, the dilation and a stride of 1, the padding is calculated such that the output of the convolution has the same spatial dimensions as the input.
- Attributes
- paddingtuple [int]
The amount of padding, (pad_height, pad_width).
-
__init__
(*args, **kwargs)[source]¶ Initialize.
- Parameters
- *args: `list` [`str`]
- positional arguments passed to
torch.nn.Conv2d
: 'in_channels'
: intNumber of input channels.
'out_channels'
: intNumber of output channels.
'kernel_size'
: int or tuple [int]Size of the convolving kernel.
- positional arguments passed to
- **kwargs: `dict` [`str`]
Additional keyword arguments passed to
torch.nn.Conv2d
.
Methods
__init__
(*args, **kwargs)Initialize.
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
(input)half
()Casts all floating point parameters and buffers to
half
datatype.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])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_parameter
(name, param)Adds a parameter to the module.
requires_grad_
([requires_grad])Change if autograd should record operations on parameters in this module.
reset_parameters
()same_padding
(d, k)Calculate the amount of padding.
share_memory
()state_dict
([destination, prefix, keep_vars])Returns a dictionary containing a whole state of the module.
to
(*args, **kwargs)Moves and/or casts the parameters and buffers.
train
([mode])Sets the module in training mode.
type
(dst_type)Casts all parameters and buffers to
dst_type
.zero_grad
()Sets gradients of all model parameters to zero.
Attributes
T_destination
dump_patches