oneflow.nn.OFRecordBytesDecoder

class oneflow.nn.OFRecordBytesDecoder(blob_name: str, name: Optional[str] = None)

This operator reads an tensor as bytes. The output might need

further decoding process like cv2.imdecode() for images and decode(“utf-8”)

for characters,depending on the downstream task.

Parameters

• blob_name – The name of the target feature in OFRecord.

• name – The name for this component in the graph.

• input – the Tensor which might be provided by an OFRecordReader.

Returns

The result Tensor encoded with bytes.

For example:

>>> import numpy as np
>>> import oneflow as flow

>>> def example():
...      batch_size = 16
...      record_reader = flow.nn.OFRecordReader(
...         "dataset/",
...         batch_size=batch_size,
...         part_name_suffix_length=5,
...      )
...      val_record = record_reader()

...      bytesdecoder_img = flow.nn.OFRecordBytesDecoder("encoded")

...      image_bytes_batch = bytesdecoder_img(val_record)

...      image_bytes = image_bytes_batch.numpy()[0]
...      return image_bytes
... example()  
array([255 216 255 ...  79 255 217], dtype=uint8)

__init__(blob_name: str, name: Optional[str] = None)

Calls super().__setattr__(‘a’, a) instead of the typical self.a = a to avoid Module.__setattr__ overhead. Module’s __setattr__ has special handling for parameters, submodules, and buffers but simply calls into super().__setattr__ for all other attributes.

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__(blob_name[, name])	Calls super().__setattr__(‘a’, a) instead of the typical self.a = a to avoid Module.__setattr__ overhead.
__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()
_to_memory_format(memory_format)	Casts the parameters and buffers in this module to another memory format.
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)
get_parameter(target)	Return the parameter refenreced by target.
get_submodule(target)	Get submodule accroding to the name of submodule.
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.
make_contiguous_params_group()	Get contiguous parameters group after creating the whole module.
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_empty(*, device)	Moves the parameters and buffers to the specified device without copying storage.
to_global([placement, sbp])	Convert the parameters and buffers to global.
to_local()
to_memory_format(memory_format)
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]