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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
import torch.nn.functional as F
_reverse_repeat_padding = nnq.modules.conv._reverse_repeat_padding
class ConvAdd2d(nnq.Conv2d):
r"""
A ConvAdd2d module is a fused module of Conv2d and Add
We adopt the same interface as :class:`torch.ao.nn.quantized.Conv2d`.
Attributes:
Same as torch.ao.nn.quantized.Conv2d
"""
_FLOAT_MODULE = torch.ao.nn.intrinsic.ConvAdd2d # 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().__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, extra_input): # type: ignore[override]
# 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_add(
input, extra_input, self._packed_params, self.scale, self.zero_point
)
def _get_name(self):
return "QuantizedConvAdd2d"
@classmethod
def from_float(cls, mod, use_precomputed_fake_quant=False):
return super().from_float(
mod, use_precomputed_fake_quant=use_precomputed_fake_quant
)
@classmethod
def from_reference(cls, ref_qconv, output_scale, output_zero_point):
return super().from_reference(ref_qconv[0], output_scale, output_zero_point)
class ConvAddReLU2d(nnq.Conv2d):
r"""
A ConvAddReLU2d module is a fused module of Conv2d, Add 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.ao.nn.intrinsic.ConvAddReLU2d # 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().__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, extra_input): # type: ignore[override]
# 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_add_relu(
input, extra_input, self._packed_params, self.scale, self.zero_point
)
def _get_name(self):
return "QuantizedConvAddReLU2d"
@classmethod
def from_float(cls, mod, use_precomputed_fake_quant=False):
return super().from_float(
mod, use_precomputed_fake_quant=use_precomputed_fake_quant
)
@classmethod
def from_reference(cls, ref_qconv, output_scale, output_zero_point):
return super().from_reference(ref_qconv[0], output_scale, output_zero_point)
|
