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

import torch
import torch.nn.quantized.functional

[docs]class ReLU6(torch.nn.ReLU): r"""Applies the element-wise function: :math:`\text{ReLU6}(x) = \min(\max(x_0, x), q(6))`, where :math:`x_0` is the zero_point, and :math:`q(6)` is the quantized representation of number 6. Args: inplace: can optionally do the operation in-place. Default: ``False`` Shape: - Input: :math:`(N, *)` where `*` means, any number of additional dimensions - Output: :math:`(N, *)`, same shape as the input .. image:: ../scripts/activation_images/ReLU6.png Examples:: >>> m = nn.quantized.ReLU6() >>> input = torch.randn(2) >>> input = torch.quantize_per_tensor(input, 1.0, 0, dtype=torch.qint32) >>> output = m(input) """ def __init__(self, inplace=False): super(ReLU6, self).__init__(inplace) self.inplace = inplace def forward(self, input): return torch.ops.quantized.relu6(input, self.inplace) def _get_name(self): return 'QuantizedReLU6' @staticmethod def from_float(mod): return ReLU6(mod.inplace)
[docs]class Hardswish(torch.nn.Hardswish): r"""This is the quantized version of :class:`~torch.nn.Hardswish`. Args: scale: quantization scale of the output tensor zero_point: quantization zero point of the output tensor """ def __init__(self, scale, zero_point): super(Hardswish, self).__init__() self.scale = scale self.zero_point = zero_point def forward(self, input): return torch.nn.quantized.functional.hardswish( input, scale=self.scale, zero_point=self.zero_point) def _get_name(self): return 'QuantizedHardswish' @staticmethod def from_float(mod): scale, zero_point = mod.activation_post_process.calculate_qparams() return Hardswish(float(scale), int(zero_point))
[docs]class ELU(torch.nn.ELU): r"""This is the quantized equivalent of :class:`~torch.nn.ELU`. Args: scale: quantization scale of the output tensor zero_point: quantization zero point of the output tensor alpha: the alpha constant """ def __init__(self, scale, zero_point, alpha=1.): super(ELU, self).__init__(alpha) self.scale = scale self.zero_point = zero_point def forward(self, input): return torch.nn.quantized.functional.elu( input, self.scale, self.zero_point, self.alpha) def _get_name(self): return 'QuantizedELU' @staticmethod def from_float(mod): scale, zero_point = mod.activation_post_process.calculate_qparams() return ELU(float(scale), int(zero_point), mod.alpha)
[docs]class LeakyReLU(torch.nn.LeakyReLU): r"""This is the quantized equivalent of :class:`~torch.nn.LeakyReLU`. Args: scale: quantization scale of the output tensor zero_point: quantization zero point of the output tensor negative_slope: Controls the angle of the negative slope. Default: 1e-2 """ def __init__(self, scale: float, zero_point: int, negative_slope: float = 1e-2, inplace: bool = False, device=None, dtype=None) -> None: factory_kwargs = {'device': device, 'dtype': dtype} super().__init__(negative_slope, inplace) self.register_buffer('scale', torch.tensor(scale, **factory_kwargs)) self.register_buffer('zero_point', torch.tensor(zero_point, **factory_kwargs)) def forward(self, input): return torch.ops.quantized.leaky_relu( input, self.negative_slope, self.inplace, self.scale, self.zero_point) def _get_name(self): return 'QuantizedLeakyReLU' @classmethod def from_float(cls, mod): scale, zero_point = mod.activation_post_process.calculate_qparams() return cls(float(scale), int(zero_point), mod.negative_slope, mod.inplace)
[docs]class Sigmoid(torch.nn.Sigmoid): r"""This is the quantized equivalent of :class:`~torch.nn.Sigmoid`. Args: scale: quantization scale of the output tensor zero_point: quantization zero point of the output tensor """ def __init__(self, output_scale: float, output_zero_point: int): super().__init__() self.output_scale = output_scale self.output_zero_point = output_zero_point def forward(self, input): return torch.ops.quantized.sigmoid(input, self.output_scale, self.output_zero_point) @classmethod def from_float(cls, mod): output_scale, output_zero_point = mod.activation_post_process.calculate_qparams() return cls(float(output_scale), int(output_zero_point))

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