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Source code for torch.nn.quantized.modules.normalization

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

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