FM / modules

/usr/local/lib64/python3.6/site-packages/torch/nn/intrinsic/quantized/modules/

/usr/local/lib64/python3.6/site-packages/torch/nn/intrinsic/quantized/modules

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
bn_relu.py	2320	0644	edit dl rm
conv_relu.py	5771	0644	edit dl rm
linear_relu.py	1048	0644	edit dl rm
__init__.py	253	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/torch/nn/intrinsic/quantized/modules/bn_relu.py (2320B)

import torch
import torch.nn.intrinsic
import torch.nn.intrinsic.qat
import torch.nn.quantized as nnq

class BNReLU2d(nnq.BatchNorm2d):
    r"""
    A BNReLU2d module is a fused module of BatchNorm2d and ReLU

We adopt the same interface as :class:`torch.nn.quantized.BatchNorm2d`.

Attributes:
        Same as torch.nn.quantized.BatchNorm2d

"""
    _FLOAT_MODULE = torch.nn.intrinsic.BNReLU2d

def __init__(self, num_features, eps=1e-5, momentum=0.1):
        super(BNReLU2d, self).__init__(num_features, eps=eps, momentum=momentum)

def forward(self, input):
        # Temporarily using len(shape) instead of ndim due to JIT issue
        # https://github.com/pytorch/pytorch/issues/23890
        if len(input.shape) != 4:
            raise ValueError("Input shape must be `(N, C, H, W)`!")
        return torch.ops.quantized.batch_norm2d_relu(
            input, self.weight, self.bias, self.running_mean,
            self.running_var, self.eps, self.scale, self.zero_point)

def _get_name(self):
        return 'QuantizedBNReLU2d'

@classmethod
    def from_float(cls, mod):
        # TODO: Add qat support for BNReLU2d
        return super(BNReLU2d, cls).from_float(mod)

class BNReLU3d(nnq.BatchNorm3d):
    r"""
    A BNReLU3d module is a fused module of BatchNorm3d and ReLU

We adopt the same interface as :class:`torch.nn.quantized.BatchNorm3d`.

.. note::
    Attributes: Same as torch.nn.quantized.BatchNorm3d

"""
    _FLOAT_MODULE = torch.nn.intrinsic.BNReLU3d

def __init__(self, num_features, eps=1e-5, momentum=0.1):
        super(BNReLU3d, self).__init__(num_features, eps=eps, momentum=momentum)

def forward(self, input):
        # Temporarily using len(shape) instead of ndim due to JIT issue
        # https://github.com/pytorch/pytorch/issues/23890
        if len(input.shape) != 5:
            raise ValueError("Input shape must be `(N, C, D, H, W)`!")
        return torch.ops.quantized.batch_norm3d_relu(
            input, self.weight, self.bias, self.running_mean,
            self.running_var, self.eps, self.scale, self.zero_point)

def _get_name(self):
        return 'QuantizedBNReLU3d'

@classmethod
    def from_float(cls, mod):
        # TODO: Add qat support for BNReLU3d
        return super(BNReLU3d, cls).from_float(mod)

import torch import torch.nn.intrinsic import torch.nn.intrinsic.qat import torch.nn.quantized as nnq class BNReLU2d(nnq.BatchNorm2d): r""" A BNReLU2d module is a fused module of BatchNorm2d and ReLU We adopt the same interface as :class:`torch.nn.quantized.BatchNorm2d`. Attributes: Same as torch.nn.quantized.BatchNorm2d """ _FLOAT_MODULE = torch.nn.intrinsic.BNReLU2d def __init__(self, num_features, eps=1e-5, momentum=0.1): super(BNReLU2d, self).__init__(num_features, eps=eps, momentum=momentum) def forward(self, input): # Temporarily using len(shape) instead of ndim due to JIT issue # https://github.com/pytorch/pytorch/issues/23890 if len(input.shape) != 4: raise ValueError("Input shape must be `(N, C, H, W)`!") return torch.ops.quantized.batch_norm2d_relu( input, self.weight, self.bias, self.running_mean, self.running_var, self.eps, self.scale, self.zero_point) def _get_name(self): return 'QuantizedBNReLU2d' @classmethod def from_float(cls, mod): # TODO: Add qat support for BNReLU2d return super(BNReLU2d, cls).from_float(mod) class BNReLU3d(nnq.BatchNorm3d): r""" A BNReLU3d module is a fused module of BatchNorm3d and ReLU We adopt the same interface as :class:`torch.nn.quantized.BatchNorm3d`. .. note:: Attributes: Same as torch.nn.quantized.BatchNorm3d """ _FLOAT_MODULE = torch.nn.intrinsic.BNReLU3d def __init__(self, num_features, eps=1e-5, momentum=0.1): super(BNReLU3d, self).__init__(num_features, eps=eps, momentum=momentum) def forward(self, input): # Temporarily using len(shape) instead of ndim due to JIT issue # https://github.com/pytorch/pytorch/issues/23890 if len(input.shape) != 5: raise ValueError("Input shape must be `(N, C, D, H, W)`!") return torch.ops.quantized.batch_norm3d_relu( input, self.weight, self.bias, self.running_mean, self.running_var, self.eps, self.scale, self.zero_point) def _get_name(self): return 'QuantizedBNReLU3d' @classmethod def from_float(cls, mod): # TODO: Add qat support for BNReLU3d return super(BNReLU3d, cls).from_float(mod)