pysegcnn.core.models.UNet¶
-
class
pysegcnn.core.models.
UNet
(in_channels, nclasses, filters, skip, **kwargs)[source]¶ A PyTorch implementation of U-Net.
- Slightly modified version of U-Net:
each convolution is followed by a batch normalization layer
the upsampling is implemented by a 2x2 max unpooling operation
- Parameters
in_channels (int) – Number of channels of the input images.
nclasses (int) – Number of classes.
filters (list [int]) – List of input channels to each convolutional block.
skip (bool) – Whether to apply skip connections from the encoder to the decoder.
**kwargs (‘dict’ [str]) – Additional keyword arguments passed to pysegcnn.core.layers.Conv2dSame.
- Returns
- Return type
None.
- Attributes
state
Return the model state file.
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.
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)Forward propagation of U-Net.
freeze
()Freeze the weights of a model.
half
()Casts all floating point parameters and buffers to
half
datatype.load
(state_file)Load a model state.
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.
save
(state_file, optimizer[, bands])Save the model state.
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
.unfreeze
()Unfreeze the weights of a model.
zero_grad
()Sets gradients of all model parameters to zero.
__call__
share_memory
-
__init__
(in_channels, nclasses, filters, skip, **kwargs)[source]¶ Initializes internal Module state, shared by both nn.Module and ScriptModule.
Methods
__init__
(in_channels, nclasses, filters, …)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)Forward propagation of U-Net.
freeze
()Freeze the weights of a model.
half
()Casts all floating point parameters and buffers to
half
datatype.load
(state_file)Load a model state.
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.
save
(state_file, optimizer[, bands])Save the model state.
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
.unfreeze
()Unfreeze the weights of a model.
zero_grad
()Sets gradients of all model parameters to zero.
Attributes
T_destination
dump_patches
state
Return the model state file.