from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import torch
import torch.nn.quantized.functional
[docs]class LayerNorm(torch.nn.LayerNorm):
r"""This is the quantized version of :class:`~torch.nn.LayerNorm`.
Additional args:
* **scale** - quantization scale of the output, type: double.
* **zero_point** - quantization zero point of the output, type: long.
"""
def __init__(self, normalized_shape, weight, bias, scale, zero_point, eps=1e-5,
elementwise_affine=True):
super(LayerNorm, self).__init__(
normalized_shape, eps=eps, elementwise_affine=elementwise_affine)
self.weight = weight
self.bias = bias
self.scale = scale
self.zero_point = zero_point
def forward(self, input):
return torch.ops.quantized.layer_norm(
input, self.normalized_shape, weight=self.weight, bias=self.bias,
eps=self.eps, output_scale=self.scale, output_zero_point=self.zero_point)
def _get_name(self):
return 'QuantizedLayerNorm'
@classmethod
def from_float(cls, mod):
activation_post_process = mod.activation_post_process
scale, zero_point = mod.activation_post_process.calculate_qparams()
new_mod = cls(
mod.normalized_shape, mod.weight, mod.bias, float(scale),
int(zero_point), mod.eps, mod.elementwise_affine)
return new_mod
[docs]class GroupNorm(torch.nn.GroupNorm):
r"""This is the quantized version of :class:`~torch.nn.GroupNorm`.
Additional args:
* **scale** - quantization scale of the output, type: double.
* **zero_point** - quantization zero point of the output, type: long.
"""
__constants__ = ['num_groups', 'num_channels', 'eps', 'affine']
def __init__(self, num_groups, num_channels, weight, bias, scale, zero_point, eps=1e-5, affine=True):
super(GroupNorm, self).__init__(num_groups, num_channels, eps, affine)
self.weight = weight
self.bias = bias
self.scale = scale
self.zero_point = zero_point
def forward(self, input):
return torch.ops.quantized.group_norm(
input, self.num_groups, self.weight, self.bias, self.eps, self.scale,
self.zero_point)
def _get_name(self):
return 'QuantizedGroupNorm'
@classmethod
def from_float(cls, mod):
activation_post_process = mod.activation_post_process
scale, zero_point = mod.activation_post_process.calculate_qparams()
new_mod = cls(
mod.num_groups, mod.num_channels, mod.weight, mod.bias, float(scale), int(zero_point),
mod.eps, mod.affine)
return new_mod
[docs]class InstanceNorm1d(torch.nn.InstanceNorm1d):
r"""This is the quantized version of :class:`~torch.nn.InstanceNorm1d`.
Additional args:
* **scale** - quantization scale of the output, type: double.
* **zero_point** - quantization zero point of the output, type: long.
"""
def __init__(self, num_features, weight, bias, scale, zero_point,
eps=1e-5, momentum=0.1, affine=False,
track_running_stats=False):
super(InstanceNorm1d, self).__init__(
num_features, eps, momentum, affine, track_running_stats)
self.weight = weight
self.bias = bias
self.scale = scale
self.zero_point = zero_point
def forward(self, input):
return torch.ops.quantized.instance_norm(
input, self.weight, self.bias, self.eps, self.scale,
self.zero_point)
def _get_name(self):
return 'QuantizedInstanceNorm1d'
@classmethod
def from_float(cls, mod):
activation_post_process = mod.activation_post_process
scale, zero_point = mod.activation_post_process.calculate_qparams()
new_mod = cls(
mod.num_features, mod.weight, mod.bias, float(scale), int(zero_point),
mod.eps, mod.affine)
return new_mod
[docs]class InstanceNorm2d(torch.nn.InstanceNorm2d):
r"""This is the quantized version of :class:`~torch.nn.InstanceNorm2d`.
Additional args:
* **scale** - quantization scale of the output, type: double.
* **zero_point** - quantization zero point of the output, type: long.
"""
def __init__(self, num_features, weight, bias, scale, zero_point,
eps=1e-5, momentum=0.1, affine=False,
track_running_stats=False):
super(InstanceNorm2d, self).__init__(
num_features, eps, momentum, affine, track_running_stats)
self.weight = weight
self.bias = bias
self.scale = scale
self.zero_point = zero_point
def forward(self, input):
return torch.ops.quantized.instance_norm(
input, self.weight, self.bias, self.eps, self.scale,
self.zero_point)
def _get_name(self):
return 'QuantizedInstanceNorm2d'
@classmethod
def from_float(cls, mod):
activation_post_process = mod.activation_post_process
scale, zero_point = mod.activation_post_process.calculate_qparams()
new_mod = cls(
mod.num_features, mod.weight, mod.bias, float(scale), int(zero_point),
mod.eps, mod.affine)
return new_mod
[docs]class InstanceNorm3d(torch.nn.InstanceNorm3d):
r"""This is the quantized version of :class:`~torch.nn.InstanceNorm3d`.
Additional args:
* **scale** - quantization scale of the output, type: double.
* **zero_point** - quantization zero point of the output, type: long.
"""
def __init__(self, num_features, weight, bias, scale, zero_point,
eps=1e-5, momentum=0.1, affine=False,
track_running_stats=False):
super(InstanceNorm3d, self).__init__(
num_features, eps, momentum, affine, track_running_stats)
self.weight = weight
self.bias = bias
self.scale = scale
self.zero_point = zero_point
def forward(self, input):
return torch.ops.quantized.instance_norm(
input, self.weight, self.bias, self.eps, self.scale,
self.zero_point)
def _get_name(self):
return 'QuantizedInstanceNorm3d'
@classmethod
def from_float(cls, mod):
activation_post_process = mod.activation_post_process
scale, zero_point = mod.activation_post_process.calculate_qparams()
new_mod = cls(
mod.num_features, mod.weight, mod.bias, float(scale), int(zero_point),
mod.eps, mod.affine)
return new_mod
