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)

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__(blob_name[, name])	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)
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]