oneflow.nn.CropMirrorNormalize

class oneflow.nn.CropMirrorNormalize(color_space='BGR', output_layout='NCHW', crop_h=0, crop_w=0, crop_pos_y=0.5, crop_pos_x=0.5, mean=[0.0], std=[1.0], output_dtype=oneflow.float)

Performs fused cropping, normalization, format conversion (NHWC to NCHW) if desired, and type casting.

Normalization takes the input images and produces the output by using the following formula:

\[output = (input - mean) / std\]

Note

If no cropping arguments are specified, only mirroring and normalization will occur.

This operator allows sequence inputs and supports volumetric data.

The documentation is referenced from: https://docs.nvidia.com/deeplearning/dali/user-guide/docs/supported_ops_legacy.html#nvidia.dali.ops.CropMirrorNormalize.

Parameters

• color_space (str, optional) – The color space of the input image. Default: “BGR”

• output_layout (str, optional) – Tensor data layout for the output. Default: “NCHW”

• crop_h (int, optional) – Cropping the window height (in pixels). Default: 0

• crop_w (int, optional) – Cropping window width (in pixels). Default: 0

• crop_pos_y (float, optional) – Normalized (0.0 - 1.0) vertical position of the start of the cropping window (typically, the upper left corner). Default: 0.5

• crop_pos_x (float, optional) – Normalized (0.0 - 1.0) horizontal position of the cropping window (upper left corner). Default: 0.5

• mean (float or list of float, optional) – Mean pixel values for image normalization. Default: [0.0],

• std (float or list of float, optional) – Standard deviation values for image normalization. Default: [1.0]

• output_dtype (oneflow.dtype, optional) – Output data type. Default: oneflow.float

__init__(color_space: str = 'BGR', output_layout: str = 'NCHW', crop_h: int = 0, crop_w: int = 0, crop_pos_y: float = 0.5, crop_pos_x: float = 0.5, mean: Sequence[float] = [0.0], std: Sequence[float] = [1.0], output_dtype: oneflow._oneflow_internal.dtype = oneflow.float32)

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).
__getstate__()
__gt__(value, /)	Return self>value.
__hash__()	Return hash(self).
__init__([color_space, output_layout, …])	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).
__setstate__(state)
__sizeof__()	Size of object in memory, in bytes.
__str__()	Return str(self).
__subclasshook__	Abstract classes can override this to customize issubclass().
_apply(fn)
_get_backward_hooks()	Returns the backward hooks for use in the call function.
_get_name()
_load_from_state_dict(state_dict, prefix, …)
_maybe_warn_non_full_backward_hook(args, …)
_named_members(get_members_fn[, prefix, recurse])
_register_load_state_dict_pre_hook(hook[, …])	These hooks will be called with arguments: state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs, before loading state_dict into self.
_register_state_dict_hook(hook)	These hooks will be called with arguments: self, state_dict, prefix, local_metadata, after the state_dict of self is set.
_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(input[, mirror])
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_full_backward_hook(hook)	Registers a backward hook on the module.
register_parameter(name, param)	Adds a parameter to the module.
register_state_dict_pre_hook(hook)	These hooks will be called with arguments: self, prefix, and keep_vars before calling state_dict on self.
requires_grad_([requires_grad])	Change if autograd should record operations on parameters in this module.
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.
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.
to_local()
train([mode])	Sets the module in training mode.
zero_grad([set_to_none])	Sets gradients of all model parameters to zero.

Attributes

_grad_t

alias of Union[Tuple[oneflow.Tensor, …], oneflow.Tensor]