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import torch
import torch.ao.nn.intrinsic
import torch.nn.intrinsic.qat
import torch.nn.functional as F
import torch.ao.nn.quantized as nnq
from torch.nn.utils import fuse_conv_bn_weights
_reverse_repeat_padding = nnq.modules.conv._reverse_repeat_padding
# TODO: factor out the common parts to ConvNd
class ConvReLU1d(nnq.Conv1d):
r"""
A ConvReLU1d module is a fused module of Conv1d and ReLU
We adopt the same interface as :class:`torch.ao.nn.quantized.Conv1d`.
Attributes:
Same as torch.ao.nn.quantized.Conv1d
"""
_FLOAT_MODULE = torch.nn.intrinsic.ConvReLU1d # type: ignore[assignment]
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True,
padding_mode='zeros', device=None, dtype=None):
super(ConvReLU1d, self).__init__(
in_channels, out_channels, kernel_size, stride=stride,
padding=padding, dilation=dilation, groups=groups, bias=bias,
padding_mode=padding_mode, 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) != 3:
raise ValueError("Input shape must be `(N, C, L)`!")
if self.padding_mode != 'zeros':
# Padding in Conv1d is stored as (p, p), need to get (p,)
_reversed_padding_repeated_twice = _reverse_repeat_padding(self.padding[:1])
input = F.pad(input, _reversed_padding_repeated_twice,
mode=self.padding_mode)
return torch.ops.quantized.conv1d_relu(
input, self._packed_params, self.scale, self.zero_point)
def _get_name(self):
return 'QuantizedConvReLU1d'
@classmethod
def from_float(cls, mod):
if type(mod) == torch.nn.intrinsic.qat.ConvBnReLU1d:
mod.weight, mod.bias = fuse_conv_bn_weights(
mod.weight, mod.bias, mod.bn.running_mean, mod.bn.running_var,
mod.bn.eps, mod.bn.weight, mod.bn.bias)
return super(ConvReLU1d, cls).from_float(mod)
@classmethod
def from_reference(cls, ref_qconv, output_scale, output_zero_point):
assert type(ref_qconv) != torch.nn.intrinsic.ConvBnReLU1d, \
"BatchNorm1d should be fused into Conv1d before converting to reference module"
return super().from_reference(ref_qconv[0], output_scale, output_zero_point)
class ConvReLU2d(nnq.Conv2d):
r"""
A ConvReLU2d module is a fused module of Conv2d and ReLU
We adopt the same interface as :class:`torch.ao.nn.quantized.Conv2d`.
Attributes:
Same as torch.ao.nn.quantized.Conv2d
"""
_FLOAT_MODULE = torch.nn.intrinsic.ConvReLU2d # type: ignore[assignment]
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True,
padding_mode='zeros', device=None, dtype=None):
super(ConvReLU2d, self).__init__(
in_channels, out_channels, kernel_size, stride=stride,
padding=padding, dilation=dilation, groups=groups, bias=bias,
padding_mode=padding_mode, 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)`!")
if self.padding_mode != 'zeros':
_reversed_padding_repeated_twice = _reverse_repeat_padding(self.padding)
input = F.pad(input, _reversed_padding_repeated_twice,
mode=self.padding_mode)
return torch.ops.quantized.conv2d_relu(
input, self._packed_params, self.scale, self.zero_point)
def _get_name(self):
return 'QuantizedConvReLU2d'
@classmethod
def from_float(cls, mod):
if type(mod) == torch.nn.intrinsic.qat.ConvBnReLU2d:
mod.weight, mod.bias = fuse_conv_bn_weights(
mod.weight, mod.bias, mod.bn.running_mean, mod.bn.running_var,
mod.bn.eps, mod.bn.weight, mod.bn.bias)
return super(ConvReLU2d, cls).from_float(mod)
@classmethod
def from_reference(cls, ref_qconv, output_scale, output_zero_point):
assert type(ref_qconv) != torch.nn.intrinsic.ConvBnReLU2d, \
"BatchNorm2d should be fused into Conv2d before converting to reference module"
return super().from_reference(ref_qconv[0], output_scale, output_zero_point)
class ConvReLU3d(nnq.Conv3d):
r"""
A ConvReLU3d module is a fused module of Conv3d and ReLU
We adopt the same interface as :class:`torch.ao.nn.quantized.Conv3d`.
Attributes: Same as torch.ao.nn.quantized.Conv3d
"""
_FLOAT_MODULE = torch.nn.intrinsic.ConvReLU3d # type: ignore[assignment]
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True,
padding_mode='zeros', device=None, dtype=None):
assert padding_mode != 'reflect', "Conv3d does not support reflection padding"
super(ConvReLU3d, self).__init__(
in_channels, out_channels, kernel_size, stride=stride,
padding=padding, dilation=dilation, groups=groups, bias=bias,
padding_mode=padding_mode, 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)`!")
if self.padding_mode != 'zeros':
_reversed_padding_repeated_twice = _reverse_repeat_padding(self.padding)
input = F.pad(input, _reversed_padding_repeated_twice,
mode=self.padding_mode)
return torch.ops.quantized.conv3d_relu(
input, self._packed_params, self.scale, self.zero_point)
def _get_name(self):
return 'QuantizedConvReLU3d'
@classmethod
def from_float(cls, mod):
if type(mod) == torch.nn.intrinsic.qat.ConvBnReLU3d:
mod.weight, mod.bias = fuse_conv_bn_weights(
mod.weight,
mod.bias,
mod.bn.running_mean,
mod.bn.running_var,
mod.bn.eps,
mod.bn.weight,
mod.bn.bias,
)
return super(ConvReLU3d, cls).from_float(mod)
@classmethod
def from_reference(cls, ref_qconv, output_scale, output_zero_point):
assert type(ref_qconv) != torch.nn.intrinsic.ConvBnReLU3d, \
"BatchNorm3d should be fused into Conv3d before converting to reference module"
return super().from_reference(ref_qconv[0], output_scale, output_zero_point)
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