oneflow.nn.ReflectionPad2d¶
-
class
oneflow.nn.
ReflectionPad2d
(padding: Union[int, Tuple[int, int, int, int]])¶ This operator pads the input tensor using the reflection of the input boundary.
The interface is consistent with PyTorch. The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.ReflectionPad2d.html.
- Parameters
padding (Union[int,tuple]) – The size or bundary of padding, if is int uses the same padding in all dimension; if 4-dims tuple, uses \((\text{padding}_{\text{left}}, \text{padding}_{\text{right}}, \text{padding}_{\text{top}}, \text{padding}_{\text{bottom}} )\)
- Returns
Returns a new tensor which is result of the reflection padding of the input tensor.
- Return type
- Shape:
Input: \((N, C, H_{\text{in}}, W_{\text{in}})\)
Output: \((N, C, H_{\text{out}}, W_{\text{out}})\) where
\(H_{\text{out}} = H_{\text{in}} + \text{padding}_{\text{top}} + \text{padding}_{\text{bottom}}\)
\(W_{\text{out}} = W_{\text{in}} + \text{padding}_{\text{left}} + \text{padding}_{\text{right}}\)
For example:
>>> import oneflow as flow >>> import numpy as np >>> input = flow.tensor(np.arange(18).reshape((1, 2, 3, 3)).astype(np.float32)) >>> m = flow.nn.ReflectionPad2d((2, 2, 1, 1)) >>> out = m(input) >>> out tensor([[[[ 5., 4., 3., 4., 5., 4., 3.], [ 2., 1., 0., 1., 2., 1., 0.], [ 5., 4., 3., 4., 5., 4., 3.], [ 8., 7., 6., 7., 8., 7., 6.], [ 5., 4., 3., 4., 5., 4., 3.]], [[14., 13., 12., 13., 14., 13., 12.], [11., 10., 9., 10., 11., 10., 9.], [14., 13., 12., 13., 14., 13., 12.], [17., 16., 15., 16., 17., 16., 15.], [14., 13., 12., 13., 14., 13., 12.]]]], dtype=oneflow.float32)
-
__init__
(padding: Union[int, Tuple[int, int, int, int]]) → None¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__call__
(*args, **kwargs)Call self as a function.
__delattr__
(name, /)Implement delattr(self, name).
__dir__
()Default dir() implementation.
__eq__
(value, /)Return self==value.
__format__
(format_spec, /)Default object formatter.
__ge__
(value, /)Return self>=value.
__getattr__
(name)__getattribute__
(name, /)Return getattr(self, name).
__gt__
(value, /)Return self>value.
__hash__
()Return hash(self).
__init__
(padding)Initialize self.
__init_subclass__
This method is called when a class is subclassed.
__le__
(value, /)Return self<=value.
__lt__
(value, /)Return self<value.
__ne__
(value, /)Return self!=value.
__new__
(**kwargs)Create and return a new object.
__reduce__
()Helper for pickle.
__reduce_ex__
(protocol, /)Helper for pickle.
__repr__
()Return repr(self).
__setattr__
(name, value)Implement setattr(self, name, value).
__sizeof__
()Size of object in memory, in bytes.
__str__
()Return str(self).
__subclasshook__
Abstract classes can override this to customize issubclass().
_apply
(fn[, applied_dict])_get_name
()_load_from_state_dict
(state_dict, prefix, …)_named_members
(get_members_fn[, prefix, recurse])_save_to_state_dict
(destination, prefix, …)_shallow_repr
()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.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)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_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.
state_dict
([destination, prefix, keep_vars])Returns a dictionary containing a whole state of the module.
to
([device])Moves the parameters and buffers.
to_consistent
(*args, **kwargs)This interface is no longer available, please use
oneflow.nn.Module.to_global()
instead.to_global
([placement, sbp])Convert the parameters and buffers to global.
train
([mode])Sets the module in training mode.
zero_grad
([set_to_none])Sets gradients of all model parameters to zero.