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# mypy: allow-untyped-defs
import torch
import torch.ao.nn.intrinsic
import torch.ao.nn.intrinsic.qat
import torch.ao.nn.quantized as nnq
__all__ = ["BNReLU2d", "BNReLU3d"]
class BNReLU2d(nnq.BatchNorm2d):
r"""
A BNReLU2d module is a fused module of BatchNorm2d and ReLU
We adopt the same interface as :class:`torch.ao.nn.quantized.BatchNorm2d`.
Attributes:
Same as torch.ao.nn.quantized.BatchNorm2d
"""
_FLOAT_MODULE = torch.ao.nn.intrinsic.BNReLU2d
def __init__(self, num_features, eps=1e-5, momentum=0.1, device=None, dtype=None):
super().__init__(
num_features, eps=eps, momentum=momentum, device=device, dtype=dtype
)
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, use_precomputed_fake_quant=False):
# TODO: Add qat support for BNReLU2d
return super().from_float(
mod, use_precomputed_fake_quant=use_precomputed_fake_quant
)
@classmethod
def from_reference(cls, bn_relu, output_scale, output_zero_point):
return super().from_reference(bn_relu[0], output_scale, output_zero_point)
class BNReLU3d(nnq.BatchNorm3d):
r"""
A BNReLU3d module is a fused module of BatchNorm3d and ReLU
We adopt the same interface as :class:`torch.ao.nn.quantized.BatchNorm3d`.
Attributes:
Same as torch.ao.nn.quantized.BatchNorm3d
"""
_FLOAT_MODULE = torch.ao.nn.intrinsic.BNReLU3d
def __init__(self, num_features, eps=1e-5, momentum=0.1, device=None, dtype=None):
super().__init__(
num_features, eps=eps, momentum=momentum, device=device, dtype=dtype
)
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, use_precomputed_fake_quant=False):
# TODO: Add qat support for BNReLU3d
return super().from_float(
mod, use_precomputed_fake_quant=use_precomputed_fake_quant
)
@classmethod
def from_reference(cls, bn_relu, output_scale, output_zero_point):
return super().from_reference(bn_relu[0], output_scale, output_zero_point)
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