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Source code for torch.nn.intrinsic.quantized.modules.conv_relu


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

from torch.nn.utils import fuse_conv_bn_weights

class ConvReLU1d(nnq.Conv1d):
    r"""
    A ConvReLU1d module is a fused module of Conv1d and ReLU

    We adopt the same interface as :class:`torch.nn.quantized.Conv1d`.

    Attributes:
        Same as torch.nn.quantized.Conv1d

    """
    _FLOAT_MODULE = torch.nn.intrinsic.ConvReLU1d

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
                 padding=0, dilation=1, groups=1, bias=True,
                 padding_mode='zeros'):
        super(ConvReLU1d, self).__init__(
            in_channels, out_channels, kernel_size, stride=stride,
            padding=padding, dilation=dilation, groups=groups, bias=bias,
            padding_mode=padding_mode)

    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)`!")
        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)

[docs]class ConvReLU2d(nnq.Conv2d): r""" A ConvReLU2d module is a fused module of Conv2d and ReLU We adopt the same interface as :class:`torch.nn.quantized.Conv2d`. Attributes: Same as torch.nn.quantized.Conv2d """ _FLOAT_MODULE = torch.nn.intrinsic.ConvReLU2d def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros'): super(ConvReLU2d, self).__init__( in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias, padding_mode=padding_mode) 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.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)
[docs]class ConvReLU3d(nnq.Conv3d): r""" A ConvReLU3d module is a fused module of Conv3d and ReLU We adopt the same interface as :class:`torch.nn.quantized.Conv3d`. Attributes: Same as torch.nn.quantized.Conv3d """ _FLOAT_MODULE = torch.nn.intrinsic.ConvReLU3d def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros'): super(ConvReLU3d, self).__init__( in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias, padding_mode=padding_mode) 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.conv3d_relu( input, self._packed_params, self.scale, self.zero_point) def _get_name(self): return 'QuantizedConvReLU3d' @classmethod def from_float(cls, mod): # TODO: Add qat support for ConvReLU3d and ConvBnReLU3d return super(ConvReLU3d, cls).from_float(mod)

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