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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)
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