oneflow.nn.QatConv1d¶
-
class
oneflow.nn.
QatConv1d
(in_channels: int, out_channels: int, kernel_size: Union[int, Tuple[int]], stride: Union[int, Tuple[int]] = 1, padding: Union[str, int, Tuple[int]] = 0, dilation: Union[int, Tuple[int]] = 1, groups: int = 1, bias: bool = True, padding_mode: str = 'zeros', quantization_formula: str = 'google', quantization_bit: int = 8, quantization_scheme: str = 'symmetric', weight_quant_per_layer: bool = True, input_quant_momentum: float = 0.95)¶ A Conv1d module attached with nn.MinMaxObserver, nn.MovingAverageMinMaxObserver and nn.FakeQuantization modules for weight and input, used for quantization aware training.
The parameters of QatConv1d are the same as
Conv1d
with some extra parameters for fake quantization, seeMinMaxObserver
,MovingAverageMinMaxObserver
andFakeQuantization
for more details.- Parameters
in_channels (int) – Number of channels in the input image
out_channels (int) – Number of channels produced by the convolution
kernel_size (int or tuple) – Size of the convolving kernel
stride (int or tuple, optional) – Stride of the convolution. Default: 1
padding (int, tuple or str, optional) – Padding added to both sides of the input. Default: 0
dilation (int or tuple, optional) – Spacing between kernel elements. Default: 1
groups (int, optional) – Number of blocked connections from input channels to output channels. Default: 1
bias (bool, optional) – If
True
, adds a learnable bias to the output. Default:True
padding_mode (string, optional) –
'zeros'
. Default:'zeros'
quantization_formula (str) – Support “google” or “cambricon”.
quantization_bit (int) – Quantize input to uintX / intX, X can be in range [2, 8]. Defaults to 8.
quantization_scheme (str) – “symmetric” or “affine”, quantize to signed / unsigned integer. Defaults to “symmetric”.
weight_quant_per_layer (bool) – True or False, means per-layer / per-channel for weight quantization. Defaults to True.
input_quant_momentum (float) – Smoothing parameter for exponential moving average operation for input quantization. Defaults to 0.95.
- Shape:
Input: \((N, C_{in}, L_{in})\)
Output: \((N, C_{out}, L_{out})\) where
\[\begin{split}L_{out} = \\left\\lfloor\\frac{L_{in} + 2 \\times \\text{padding} - \\text{dilation} \\times (\\text{kernel\\_size} - 1) - 1}{\\text{stride}} + 1\\right\\rfloor\end{split}\]
-
weight
¶ the learnable weights of the module of shape \((\\text{out\\_channels}, \\frac{\\text{in\\_channels}}{\\text{groups}}, \\text{kernel\\_size})\). The values of these weights are sampled from \(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\) where \(k = \\frac{groups}{C_\\text{in} * \\text{kernel\\_size}}\)
- Type
-
bias
¶ the learnable bias of the module of shape (out_channels). If
bias
isTrue
, then the values of these weights are sampled from \(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\) where \(k = \\frac{groups}{C_\\text{in} * \\text{kernel\\_size}}\)- Type
For example:
>>> import numpy as np >>> import oneflow as flow >>> import oneflow.nn as nn >>> arr = np.random.randn(20, 16, 50) >>> input = flow.Tensor(arr) >>> m = nn.QatConv1d(16, 33, 3, stride=2, quantization_formula="google", quantization_bit=8, quantization_scheme="symmetric") >>> output = m(input)
-
__init__
(in_channels: int, out_channels: int, kernel_size: Union[int, Tuple[int]], stride: Union[int, Tuple[int]] = 1, padding: Union[str, int, Tuple[int]] = 0, dilation: Union[int, Tuple[int]] = 1, groups: int = 1, bias: bool = True, padding_mode: str = 'zeros', quantization_formula: str = 'google', quantization_bit: int = 8, quantization_scheme: str = 'symmetric', weight_quant_per_layer: bool = True, input_quant_momentum: float = 0.95)¶ 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__
(in_channels, out_channels, kernel_size)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)_conv_forward
(x, weight, bias)_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
(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_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
, andkeep_vars
before callingstate_dict
onself
.requires_grad_
([requires_grad])Change if autograd should record operations on parameters in this module.
reset_parameters
()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]