Shortcuts

Source code for torch.nn.quantized.modules.conv

# coding=utf-8
r"""Quantized convolution modules."""

from typing import Optional, List, TypeVar

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.intrinsic as nni
import torch.nn.intrinsic.qat as nniqat

from torch._ops import ops
from torch.nn.common_types import _size_1_t
from torch.nn.modules.utils import _single, _pair, _triple
from torch.nn.quantized.modules.utils import _quantize_weight
from torch.nn.utils import fuse_conv_bn_weights

_SUPPORTED_PADDING = {
    'zeros',
    'reflect'
}


def _reverse_repeat_padding(padding: List[int]) -> List[int]:
    _reversed_padding_repeated_twice: List[int] = []
    N = len(padding)
    for idx in range(N):
        for _ in range(2):
            _reversed_padding_repeated_twice.append(padding[N - idx - 1])
    return _reversed_padding_repeated_twice

class _ConvNd(nn.Module):
    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):
        # All subclasses have this signature - See PR #49702s
        raise NotImplementedError

    def _init(self, in_channels, out_channels, kernel_size, stride,
              padding, dilation,
              transposed, output_padding,
              groups, bias,
              padding_mode='zeros',
              device=None,
              dtype=None) -> None:
        factory_kwargs = {'device': device, 'dtype': dtype}
        super(_ConvNd, self).__init__()

        if in_channels % groups != 0:
            raise ValueError('in_channels must be divisible by groups')
        if out_channels % groups != 0:
            raise ValueError('out_channels must be divisible by groups')
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = kernel_size
        self.stride = stride
        self.padding = padding
        self.dilation = dilation
        self.transposed = transposed
        self.output_padding = output_padding
        self.groups = groups
        if padding_mode not in _SUPPORTED_PADDING:
            raise ValueError("'padding_mode' {} is not supported by quantized convolution".format(padding_mode))
        self.padding_mode = padding_mode
        # Initialize as NCHW. set_weight will internally transpose to NHWC.
        if self.transposed:
            weight_shape = [in_channels, out_channels // self.groups]
        else:
            weight_shape = [out_channels, in_channels // self.groups]
        qweight = torch._empty_affine_quantized(
            weight_shape + list(kernel_size),
            scale=1, zero_point=0, dtype=torch.qint8,
            **{k: v for k, v in factory_kwargs.items() if k != 'dtype'})
        bias_float = (
            torch.zeros(out_channels, dtype=torch.float,
                        **{k: v for k, v in factory_kwargs.items() if k != 'dtype'}) if bias else None)

        self.set_weight_bias(qweight, bias_float)
        self.scale = 1.0
        self.zero_point = 0

    def set_weight_bias(self, qweight, bias_float):
        raise NotImplementedError

    def bias(self):
        raise NotImplementedError

    def _weight_bias(self):
        raise NotImplementedError

    def extra_repr(self):
        s = ('{in_channels}, {out_channels}, kernel_size={kernel_size}'
             ', stride={stride}, scale={scale}, zero_point={zero_point}')
        if self.padding != (0,) * len(self.padding):
            s += ', padding={padding}'
        if self.dilation != (1,) * len(self.dilation):
            s += ', dilation={dilation}'
        if self.output_padding != (0,) * len(self.output_padding):
            s += ', output_padding={output_padding}'
        if self.groups != 1:
            s += ', groups={groups}'
        if self.bias() is None:
            s += ', bias=False'
        return s.format(**self.__dict__)

    # ===== Serialization methods =====
    # The special consideration here is that we have to unpack the weights into
    # their regular QTensor form for serialization. Packed weights should not
    # live outside the process in which they were created, rather they should be
    # derived from the QTensor weight.
    #   self
    #   |--- weight : Tensor
    #   |--- bias : Tensor
    #
    # TODO: maybe change to this when https://github.com/pytorch/pytorch/pull/32958 is landed
    #   self
    #   |--- _packed_params : Conv2dPackedParamsBase or Conv3dPackedParamsBase
    def _save_to_state_dict(self, destination, prefix, keep_vars):
        super(_ConvNd, self)._save_to_state_dict(destination, prefix, keep_vars)
        (w, b) = self._weight_bias()
        destination[prefix + 'weight'] = w
        destination[prefix + 'bias'] = b
        destination[prefix + 'scale'] = torch.tensor(self.scale)
        destination[prefix + 'zero_point'] = torch.tensor(self.zero_point)

    @torch.jit.export
    def __getstate__(self):
        (w, b) = self._weight_bias()
        return (
            self.in_channels,
            self.out_channels,
            self.kernel_size,
            self.stride,
            self.padding,
            self.dilation,
            self.transposed,
            self.output_padding,
            self.groups,
            self.padding_mode,
            w,
            b,
            self.scale,
            self.zero_point,
            self.training
        )

    # ===== Deserialization methods =====
    # Counterpart to the serialization methods, we must pack the serialized
    # QTensor weight into its packed format for use by the FBGEMM ops.
    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
                              missing_keys, unexpected_keys, error_msgs):
        self.set_weight_bias(
            state_dict[prefix + 'weight'], state_dict[prefix + 'bias'])
        state_dict.pop(prefix + 'weight')
        state_dict.pop(prefix + 'bias')
        self.scale = float(state_dict[prefix + 'scale'])
        state_dict.pop(prefix + 'scale')
        self.zero_point = int(state_dict[prefix + 'zero_point'])
        state_dict.pop(prefix + 'zero_point')
        super(_ConvNd, self)._load_from_state_dict(
            state_dict, prefix, local_metadata, False, missing_keys,
            unexpected_keys, error_msgs)

    @torch.jit.export
    def __setstate__(self, state):
        self.in_channels = state[0]
        self.out_channels = state[1]
        self.kernel_size = state[2]
        self.stride = state[3]
        self.padding = state[4]
        self.dilation = state[5]
        self.transposed = state[6]
        self.output_padding = state[7]
        self.groups = state[8]
        self.padding_mode = state[9]
        self.set_weight_bias(state[10], state[11])
        self.scale = state[12]
        self.zero_point = state[13]
        self.training = state[14]

    def __deepcopy__(self, memo):
        new_instance = type(self).__new__(type(self))
        torch.nn.Module.__init__(new_instance)
        state = self.__getstate__()
        new_instance.__setstate__(state)
        return new_instance

    def __copy__(self):
        return self.__deepcopy__({})

    @classmethod
    def get_qconv(cls, mod, activation_post_process, weight_post_process=None):
        r"""Creates a qconv object and returns it.
        """
        if weight_post_process is None:
            weight_post_process = mod.qconfig.weight()
        weight_post_process(mod.weight)
        act_scale, act_zp = activation_post_process.calculate_qparams()
        assert weight_post_process.dtype == torch.qint8, \
            'Weight observer must have a dtype of qint8'
        qweight = _quantize_weight(mod.weight.float(), weight_post_process)
        # the __init__ call used is the one from derived classes and not the one from _ConvNd
        qconv = cls(mod.in_channels, mod.out_channels, mod.kernel_size,
                    mod.stride, mod.padding, mod.dilation, mod.groups,
                    mod.bias is not None, mod.padding_mode)
        qconv.set_weight_bias(qweight, mod.bias)
        qconv.scale = float(act_scale)
        qconv.zero_point = int(act_zp)
        return qconv

    @staticmethod
    def from_float(cls, mod):
        if hasattr(mod, "weight_fake_quant"):
            # assert type(mod) == cls.__QAT_MODULE, " nnq." + cls.__name__ + \
            # ".from_float only works for " + cls.__QAT_MODULE.__name__
            if type(mod) == cls._NNIQAT_CONV_BN_MODULE:
                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)
            assert hasattr(mod, "activation_post_process"), \
                "Input QAT module must have observer attached"
            weight_post_process = mod.weight_fake_quant
            activation_post_process = mod.activation_post_process
        else:
            assert type(mod) == cls._FLOAT_MODULE, \
                " nnq." + cls.__name__ + ".from_float only works for " + \
                cls._FLOAT_MODULE.__name__ + " but got:" + str(type(mod))
            assert hasattr(mod, "qconfig"), \
                "Input float module must have qconfig defined."
            activation_post_process = mod.activation_post_process
            if type(mod) == cls._NNI_CONV_RELU_MODULE:
                mod = mod[0]
            weight_post_process = mod.qconfig.weight()
        return cls.get_qconv(mod, activation_post_process, weight_post_process)

[docs]class Conv1d(_ConvNd): r"""Applies a 1D convolution over a quantized input signal composed of several quantized input planes. For details on input arguments, parameters, and implementation see :class:`~torch.nn.Conv1d`. .. note:: Only `zeros` is supported for the :attr:`padding_mode` argument. .. note:: Only `torch.quint8` is supported for the input data type. Attributes: weight (Tensor): packed tensor derived from the learnable weight parameter. scale (Tensor): scalar for the output scale zero_point (Tensor): scalar for the output zero point See :class:`~torch.nn.Conv1d` for other attributes. Examples:: >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2) >>> input = torch.randn(20, 16, 100) >>> # quantize input to quint8 >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8) >>> output = m(q_input) """ _FLOAT_MODULE = nn.Conv1d _NNIQAT_CONV_BN_MODULE = nniqat.ConvBn1d _NNI_CONV_RELU_MODULE = nni.ConvReLU1d def __init__(self, in_channels: int, out_channels: int, kernel_size: _size_1_t, stride: _size_1_t = 1, padding: _size_1_t = 0, dilation: _size_1_t = 1, groups: int = 1, bias: bool = True, padding_mode: str = 'zeros', device=None, dtype=None): factory_kwargs = {'device': device, 'dtype': dtype} kernel_size = _single(kernel_size) stride = _single(stride) padding = padding if isinstance(padding, str) else _single(padding) dilation = _single(dilation) # Subclasses of _ConvNd needs to call _init rather than __init__. See # discussion on PR #49702 super(Conv1d, self)._init( in_channels, out_channels, kernel_size, stride, padding, dilation, False, _single(0), groups, bias, padding_mode, **factory_kwargs) def _get_name(self): return 'QuantizedConv1d' def set_weight_bias(self, w: torch.Tensor, b: Optional[torch.Tensor]) -> None: if self.padding_mode == 'zeros': self._packed_params = torch.ops.quantized.conv1d_prepack( w, b, self.stride, self.padding, self.dilation, self.groups) else: self._packed_params = torch.ops.quantized.conv1d_prepack( w, b, self.stride, _pair(0), self.dilation, self.groups) def _weight_bias(self): w, b = torch.ops.quantized.conv1d_unpack(self._packed_params) return w, b def weight(self): return self._weight_bias()[0] def bias(self): return self._weight_bias()[1] 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 ops.quantized.conv1d(input, self._packed_params, self.scale, self.zero_point)
[docs] @classmethod def from_float(cls, mod): r"""Creates a quantized module from a float module or qparams_dict. Args: mod (Module): a float module, either produced by torch.ao.quantization utilities or provided by the user """ return _ConvNd.from_float(cls, mod)
[docs]class Conv2d(_ConvNd): r"""Applies a 2D convolution over a quantized input signal composed of several quantized input planes. For details on input arguments, parameters, and implementation see :class:`~torch.nn.Conv2d`. .. note:: Only `zeros` is supported for the :attr:`padding_mode` argument. .. note:: Only `torch.quint8` is supported for the input data type. Attributes: weight (Tensor): packed tensor derived from the learnable weight parameter. scale (Tensor): scalar for the output scale zero_point (Tensor): scalar for the output zero point See :class:`~torch.nn.Conv2d` for other attributes. Examples:: >>> # With square kernels and equal stride >>> m = nn.quantized.Conv2d(16, 33, 3, stride=2) >>> # non-square kernels and unequal stride and with padding >>> m = nn.quantized.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2)) >>> # non-square kernels and unequal stride and with padding and dilation >>> m = nn.quantized.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2), dilation=(3, 1)) >>> input = torch.randn(20, 16, 50, 100) >>> # quantize input to quint8 >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8) >>> output = m(q_input) """ _FLOAT_MODULE = nn.Conv2d _NNIQAT_CONV_BN_MODULE = nniqat.ConvBn2d _NNI_CONV_RELU_MODULE = nni.ConvReLU2d 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): factory_kwargs = {'device': device, 'dtype': dtype} kernel_size = _pair(kernel_size) stride = _pair(stride) padding = _pair(padding) dilation = _pair(dilation) # Subclasses of _ConvNd need to call _init rather than __init__. See # discussion on PR #49702 super(Conv2d, self)._init( in_channels, out_channels, kernel_size, stride, padding, dilation, False, _pair(0), groups, bias, padding_mode, **factory_kwargs) def _get_name(self): return 'QuantizedConv2d' def set_weight_bias(self, w: torch.Tensor, b: Optional[torch.Tensor]) -> None: if self.padding_mode == 'zeros': self._packed_params = torch.ops.quantized.conv2d_prepack( w, b, self.stride, self.padding, self.dilation, self.groups) else: self._packed_params = torch.ops.quantized.conv2d_prepack( w, b, self.stride, _pair(0), self.dilation, self.groups) def _weight_bias(self): return self._packed_params.unpack() def weight(self): return self._weight_bias()[0] def bias(self): return self._weight_bias()[1] 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 ops.quantized.conv2d( input, self._packed_params, self.scale, self.zero_point)
[docs] @classmethod def from_float(cls, mod): r"""Creates a quantized module from a float module or qparams_dict. Args: mod (Module): a float module, either produced by torch.ao.quantization utilities or provided by the user """ return _ConvNd.from_float(cls, mod)
[docs]class Conv3d(_ConvNd): r"""Applies a 3D convolution over a quantized input signal composed of several quantized input planes. For details on input arguments, parameters, and implementation see :class:`~torch.nn.Conv3d`. .. note:: Only `zeros` is supported for the :attr:`padding_mode` argument. .. note:: Only `torch.quint8` is supported for the input data type. Attributes: weight (Tensor): packed tensor derived from the learnable weight parameter. scale (Tensor): scalar for the output scale zero_point (Tensor): scalar for the output zero point See :class:`~torch.nn.Conv3d` for other attributes. Examples:: >>> # With square kernels and equal stride >>> m = nn.quantized.Conv3d(16, 33, 3, stride=2) >>> # non-square kernels and unequal stride and with padding >>> m = nn.quantized.Conv3d(16, 33, (3, 5, 5), stride=(1, 2, 2), padding=(1, 2, 2)) >>> # non-square kernels and unequal stride and with padding and dilation >>> m = nn.quantized.Conv3d(16, 33, (3, 5, 5), stride=(1, 2, 2), padding=(1, 2, 2), dilation=(1, 2, 2)) >>> input = torch.randn(20, 16, 56, 56, 56) >>> # quantize input to quint8 >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8) >>> output = m(q_input) """ _FLOAT_MODULE = nn.Conv3d _NNIQAT_CONV_BN_MODULE = nniqat.ConvBn3d _NNI_CONV_RELU_MODULE = nni.ConvReLU3d 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" factory_kwargs = {'device': device, 'dtype': dtype} kernel_size = _triple(kernel_size) stride = _triple(stride) padding = _triple(padding) dilation = _triple(dilation) # Subclasses of _ConvNd need to call _init rather than __init__. See # discussion on PR #49702 super(Conv3d, self)._init( in_channels, out_channels, kernel_size, stride, padding, dilation, False, _triple(0), groups, bias, padding_mode, **factory_kwargs) def _get_name(self): return 'QuantizedConv3d' def set_weight_bias(self, w: torch.Tensor, b: Optional[torch.Tensor]) -> None: if self.padding_mode == 'zeros': self._packed_params = torch.ops.quantized.conv3d_prepack( w, b, self.stride, self.padding, self.dilation, self.groups) else: self._packed_params = torch.ops.quantized.conv3d_prepack( w, b, self.stride, _triple(0), self.dilation, self.groups) def _weight_bias(self): return self._packed_params.unpack() def weight(self): return self._weight_bias()[0] def bias(self): return self._weight_bias()[1] 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 ops.quantized.conv3d( input, self._packed_params, self.scale, self.zero_point)
[docs] @classmethod def from_float(cls, mod): r"""Creates a quantized module from a float module or qparams_dict. Args: mod (Module): a float module, either produced by torch.ao.quantization utilities or provided by the user """ return _ConvNd.from_float(cls, mod)
# === Transposed Convolutions === MOD = TypeVar('MOD', bound=nn.modules.conv._ConvNd) class _ConvTransposeNd(_ConvNd): _FLOAT_MODULE = MOD def __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation, transposed, output_padding, groups, bias, padding_mode, device=None, dtype=None): if padding_mode != 'zeros': raise ValueError('Only "zeros" padding mode is supported for {}'.format(self.__class__.__name__)) factory_kwargs = {'device': device, 'dtype': dtype} # Subclasses of _ConvNd need to call _init rather than __init__. See # discussion on PR #49702 super(_ConvTransposeNd, self)._init( in_channels, out_channels, kernel_size, stride, padding, dilation, transposed, output_padding, groups, bias, padding_mode, **factory_kwargs) def _input_padding(self, kernel_size: List[int], dilation: List[int], padding: List[int]) -> List[int]: res = torch.jit.annotate(List[int], []) for kdx in range(len(kernel_size)): pad = (dilation[kdx] * (kernel_size[kdx] - 1) - padding[kdx]) res.append(pad) return res @classmethod def from_float(cls, mod): r"""Creates a quantized module from a float module or qparams_dict. Args: mod (Module): a float module, either produced by torch.ao.quantization utilities or provided by the user """ # derived classes override cls._FLOAT_MODULE attribute msg = ' nnq.' + cls.__name__ + '.from_float only works for ' + \ cls._FLOAT_MODULE.__name__ # type: ignore[attr-defined] assert type(mod) == cls._FLOAT_MODULE, msg assert hasattr(mod, 'qconfig'), \ 'Input float module must have qconfig defined.' weight_post_process = mod.qconfig.weight() weight_post_process(mod.weight) act_scale, act_zp = mod.activation_post_process.calculate_qparams() assert weight_post_process.dtype == torch.qint8, \ 'Weight observer must have a dtype of qint8' qweight = _quantize_weight(mod.weight.float(), weight_post_process) # the __init__ call used is the one from derived classes and not the one from _ConvTransposeNd qconv = cls(mod.in_channels, mod.out_channels, mod.kernel_size, # type: ignore[call-arg] mod.stride, mod.padding, mod.output_padding, mod.groups, mod.bias is not None, mod.dilation, mod.padding_mode) qconv.set_weight_bias(qweight, mod.bias) qconv.scale = float(act_scale) qconv.zero_point = int(act_zp) return qconv
[docs]class ConvTranspose1d(_ConvTransposeNd): r"""Applies a 1D transposed convolution operator over an input image composed of several input planes. For details on input arguments, parameters, and implementation see :class:`~torch.nn.ConvTranspose1d`. .. note:: Currently only the QNNPACK engine is implemented. Please, set the `torch.backends.quantized.engine = 'qnnpack'` For special notes, please, see :class:`~torch.nn.quantized.Conv1d` Attributes: weight (Tensor): packed tensor derived from the learnable weight parameter. scale (Tensor): scalar for the output scale zero_point (Tensor): scalar for the output zero point See :class:`~torch.nn.ConvTranspose2d` for other attributes. Examples:: >>> torch.backends.quantized.engine = 'qnnpack' >>> # With square kernels and equal stride >>> m = nnq.ConvTranspose1d(16, 33, 3, stride=2) >>> # non-square kernels and unequal stride and with padding >>> m = nnq.ConvTranspose1d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2)) >>> input = torch.randn(20, 16, 50) >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8) >>> output = m(q_input) >>> # exact output size can be also specified as an argument >>> input = torch.randn(1, 16, 12) >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8) >>> downsample = nnq.Conv1d(16, 16, 3, stride=2, padding=1) >>> upsample = nnq.ConvTranspose1d(16, 16, 3, stride=2, padding=1) >>> h = downsample(q_input) >>> h.size() torch.Size([1, 16, 6]) >>> output = upsample(h, output_size=input.size()) >>> output.size() torch.Size([1, 16, 12]) """ _FLOAT_MODULE = nn.ConvTranspose1d def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, output_padding=0, groups=1, bias=True, dilation=1, padding_mode='zeros', device=None, dtype=None): factory_kwargs = {'device': device, 'dtype': dtype} kernel_size = _pair(kernel_size) stride = _pair(stride) padding = _pair(padding) dilation = _pair(dilation) output_padding = _pair(output_padding) super(ConvTranspose1d, self).__init__( in_channels, out_channels, kernel_size, stride, padding, dilation, True, output_padding, groups, bias, padding_mode, **factory_kwargs) def _get_name(self): return 'QuantizedConvTranpose1d' def set_weight_bias(self, w: torch.Tensor, b: Optional[torch.Tensor]) -> None: self._packed_params = torch.ops.quantized.conv_transpose1d_prepack( w, b, self.stride, self.padding, self.output_padding, self.dilation, self.groups) def _weight_bias(self): w, b = torch.ops.quantized.conv_transpose1d_unpack(self._packed_params) return w, b def weight(self): (w, _) = self._weight_bias() return w def bias(self): (_, b) = self._weight_bias() return b 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.conv_transpose1d( input, self._packed_params, self.scale, self.zero_point)
[docs]class ConvTranspose2d(_ConvTransposeNd): r"""Applies a 2D transposed convolution operator over an input image composed of several input planes. For details on input arguments, parameters, and implementation see :class:`~torch.nn.ConvTranspose2d`. For special notes, please, see :class:`~torch.nn.quantized.Conv2d` Attributes: weight (Tensor): packed tensor derived from the learnable weight parameter. scale (Tensor): scalar for the output scale zero_point (Tensor): scalar for the output zero point See :class:`~torch.nn.ConvTranspose2d` for other attributes. Examples:: >>> # QNNPACK or FBGEMM as backend >>> torch.backends.quantized.engine = 'qnnpack' >>> # With square kernels and equal stride >>> m = nnq.ConvTranspose2d(16, 33, 3, stride=2) >>> # non-square kernels and unequal stride and with padding >>> m = nnq.ConvTranspose2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2)) >>> input = torch.randn(20, 16, 50, 100) >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8) >>> output = m(q_input) >>> # exact output size can be also specified as an argument >>> input = torch.randn(1, 16, 12, 12) >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8) >>> downsample = nnq.Conv2d(16, 16, 3, stride=2, padding=1) >>> upsample = nnq.ConvTranspose2d(16, 16, 3, stride=2, padding=1) >>> h = downsample(q_input) >>> h.size() torch.Size([1, 16, 6, 6]) >>> output = upsample(h, output_size=input.size()) >>> output.size() torch.Size([1, 16, 12, 12]) """ _FLOAT_MODULE = nn.ConvTranspose2d def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, output_padding=0, groups=1, bias=True, dilation=1, padding_mode='zeros', device=None, dtype=None): factory_kwargs = {'device': device, 'dtype': dtype} kernel_size = _pair(kernel_size) stride = _pair(stride) padding = _pair(padding) dilation = _pair(dilation) output_padding = _pair(output_padding) super(ConvTranspose2d, self).__init__( in_channels, out_channels, kernel_size, stride, padding, dilation, True, output_padding, groups, bias, padding_mode, **factory_kwargs) def _get_name(self): return 'QuantizedConvTranpose2d' def set_weight_bias(self, w: torch.Tensor, b: Optional[torch.Tensor]) -> None: self._packed_params = torch.ops.quantized.conv_transpose2d_prepack( w, b, self.stride, self.padding, self.output_padding, self.dilation, self.groups) def _weight_bias(self): w, b = torch.ops.quantized.conv2d_unpack(self._packed_params) return w, b def weight(self): (w, _) = self._weight_bias() return w def bias(self): (_, b) = self._weight_bias() return b 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 ops.quantized.conv_transpose2d( input, self._packed_params, self.scale, self.zero_point)
[docs]class ConvTranspose3d(_ConvTransposeNd): r"""Applies a 3D transposed convolution operator over an input image composed of several input planes. For details on input arguments, parameters, and implementation see :class:`~torch.nn.ConvTranspose3d`. .. note:: Currently only the FBGEMM engine is implemented. Please, set the `torch.backends.quantized.engine = 'fbgemm'` For special notes, please, see :class:`~torch.nn.quantized.Conv3d` Attributes: weight (Tensor): packed tensor derived from the learnable weight parameter. scale (Tensor): scalar for the output scale zero_point (Tensor): scalar for the output zero point See :class:`~torch.nn.ConvTranspose3d` for other attributes. Examples:: >>> torch.backends.quantized.engine = 'fbgemm' >>> # With cubic kernels and equal stride >>> m = nnq.ConvTranspose3d(16, 33, 3, stride=2) >>> # non-cubic kernels and unequal stride and with padding >>> m = nnq.ConvTranspose3d(16, 33, (3, 3, 5), stride=(2, 1, 1), padding=(4, 2, 2)) >>> input = torch.randn(20, 16, 50, 100, 100) >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8) >>> output = m(q_input) >>> # exact output size can be also specified as an argument >>> input = torch.randn(1, 16, 12, 12, 12) >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8) >>> downsample = nnq.Conv3d(16, 16, 3, stride=2, padding=1) >>> upsample = nnq.ConvTranspose3d(16, 16, 3, stride=2, padding=1) >>> h = downsample(q_input) >>> h.size() torch.Size([1, 16, 6, 6, 6]) >>> output = upsample(h, output_size=input.size()) >>> output.size() torch.Size([1, 16, 12, 12, 12]) """ _FLOAT_MODULE = nn.ConvTranspose3d def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, output_padding=0, groups=1, bias=True, dilation=1, padding_mode='zeros', device=None, dtype=None): factory_kwargs = {'device': device, 'dtype': dtype} kernel_size = _pair(kernel_size) stride = _pair(stride) padding = _pair(padding) dilation = _pair(dilation) output_padding = _pair(output_padding) super(ConvTranspose3d, self).__init__( in_channels, out_channels, kernel_size, stride, padding, dilation, True, output_padding, groups, bias, padding_mode, **factory_kwargs) def _get_name(self): return 'QuantizedConvTranpose3d' def set_weight_bias(self, w: torch.Tensor, b: Optional[torch.Tensor]) -> None: self._packed_params = torch.ops.quantized.conv_transpose3d_prepack( w, b, self.stride, self.padding, self.output_padding, self.dilation, self.groups) def _weight_bias(self): w, b = torch.ops.quantized.conv3d_unpack(self._packed_params) return w, b def weight(self): (w, _) = self._weight_bias() return w def bias(self): (_, b) = self._weight_bias() return b 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, T, H, W)`!") return ops.quantized.conv_transpose3d( input, self._packed_params, self.scale, self.zero_point)

Docs

Access comprehensive developer documentation for PyTorch

View Docs

Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

Resources

Find development resources and get your questions answered

View Resources