# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import annotations from typing import TYPE_CHECKING, Literal import numpy as np from paddle.nn import Layer from paddle.nn.functional.conv import _update_padding_nd from paddle.nn.initializer import Normal from paddle.utils import convert_to_list from .. import functional as F if TYPE_CHECKING: from collections.abc import Sequence from paddle import Tensor from paddle._typing import ( ParamAttrLike, Size2, Size3, Size4, Size6, ) from paddle.nn.functional.common import _PaddingSizeMode __all__ = [] class _Conv3D(Layer): weight: Tensor bias: Tensor def __init__( self, in_channels: int, out_channels: int, kernel_size: Size3, stride: Size3 = 1, padding: _PaddingSizeMode | Size3 | Size6 | Sequence[Size2] = 0, dilation: Size3 = 1, groups: Literal[1] = 1, subm: bool = False, key: str | None = None, padding_mode: Literal['zeros'] = 'zeros', weight_attr: ParamAttrLike | None = None, bias_attr: ParamAttrLike | None = None, data_format: Literal['NDHWC'] = "NDHWC", backend: Literal['igemm'] | None = None, ) -> None: super().__init__() assert weight_attr is not False, ( "weight_attr should not be False in Conv." ) self._param_attr = weight_attr self._bias_attr = bias_attr self._groups = groups self._in_channels = in_channels self._out_channels = out_channels self._data_format = data_format self._subm = subm self._key = key self._backend = backend assert padding_mode == 'zeros', ( "Currently, only support padding_mode='zeros'" ) assert groups == 1, "Currently, only support groups=1" assert backend in [ None, 'igemm', ], "The value of 'backend' in Conv3D should be None or 'igemm'." valid_format = {'NDHWC'} if data_format not in valid_format: raise ValueError( f"data_format must be one of {valid_format}, but got data_format='{data_format}'" ) channel_last = data_format == "NDHWC" dims = 3 self._stride = convert_to_list(stride, dims, 'stride') self._dilation = convert_to_list(dilation, dims, 'dilation') self._kernel_size = convert_to_list(kernel_size, dims, 'kernel_size') self._padding = padding self._padding_mode = padding_mode self._updated_padding, self._padding_algorithm = _update_padding_nd( padding, channel_last, dims ) # the sparse conv restricts the shape is [D, H, W, in_channels, out_channels] filter_shape = [ *self._kernel_size, self._in_channels, self._out_channels, ] def _get_default_param_initializer(): filter_elem_num = np.prod(self._kernel_size) * self._in_channels std = (2.0 / filter_elem_num) ** 0.5 return Normal(0.0, std) self.weight = self.create_parameter( shape=filter_shape, attr=self._param_attr, default_initializer=_get_default_param_initializer(), ) self.bias = self.create_parameter( attr=self._bias_attr, shape=[self._out_channels], is_bias=True ) def forward(self, x: Tensor) -> Tensor: if self._backend is None: out = F.conv._conv3d( x, self.weight, bias=self.bias, stride=self._stride, padding=self._updated_padding, dilation=self._dilation, groups=self._groups, subm=self._subm, key=self._key, data_format=self._data_format, ) elif self._backend == 'igemm': out = F.conv._conv3d_igemm( x, self.weight, bias=self.bias, stride=self._stride, padding=self._updated_padding, dilation=self._dilation, groups=self._groups, subm=self._subm, key=self._key, data_format=self._data_format, ) else: raise ValueError( f"The value of 'backend' in Conv3D should be None or 'igemm', but got {self._backend}." ) return out def extra_repr(self) -> str: main_str = '{_in_channels}, {_out_channels}, kernel_size={_kernel_size}' if self._stride != [1] * len(self._stride): main_str += ', stride={_stride}' if self._padding != 0: main_str += ', padding={_padding}' if self._padding_mode != 'zeros': main_str += ', padding_mode={_padding_mode}' if self._dilation != [1] * len(self._dilation): main_str += ', dilation={_dilation}' if self._groups != 1: main_str += ', groups={_groups}' main_str += ', data_format={_data_format}' return main_str.format(**self.__dict__) class _Conv2D(Layer): weight: Tensor bias: Tensor def __init__( self, in_channels: int, out_channels: int, kernel_size: Size2, stride: Size2 = 1, padding: _PaddingSizeMode | Size2 | Size4 | Sequence[Size2] = 0, dilation: Size2 = 1, groups: Literal[1] = 1, subm: bool = False, key: str | None = None, padding_mode: Literal['zeros'] = 'zeros', weight_attr: ParamAttrLike | None = None, bias_attr: ParamAttrLike | None = None, data_format: Literal["NHWC"] = "NHWC", backend: Literal['igemm'] | None = None, ) -> None: super().__init__() assert weight_attr is not False, ( "weight_attr should not be False in Conv." ) self._param_attr = weight_attr self._bias_attr = bias_attr self._groups = groups self._in_channels = in_channels self._out_channels = out_channels self._data_format = data_format self._subm = subm self._key = key self._backend = backend assert padding_mode == 'zeros', ( "Currently, only support padding_mode='zeros'" ) assert groups == 1, "Currently, only support groups=1" assert backend in [ None, 'igemm', ], "The value of 'backend' in Conv3D should be None or 'igemm'." valid_format = {'NHWC'} if data_format not in valid_format: raise ValueError( f"data_format must be one of {valid_format}, but got data_format='{data_format}'" ) channel_last = data_format == "NHWC" dims = 2 self._stride = convert_to_list(stride, dims, 'stride') self._dilation = convert_to_list(dilation, dims, 'dilation') self._kernel_size = convert_to_list(kernel_size, dims, 'kernel_size') self._padding = padding self._padding_mode = padding_mode self._updated_padding, self._padding_algorithm = _update_padding_nd( padding, channel_last, dims ) # the sparse conv restricts the shape is [H, W, in_channels, out_channels] filter_shape = [ *self._kernel_size, self._in_channels, self._out_channels, ] def _get_default_param_initializer(): filter_elem_num = np.prod(self._kernel_size) * self._in_channels std = (2.0 / filter_elem_num) ** 0.5 return Normal(0.0, std) self.weight = self.create_parameter( shape=filter_shape, attr=self._param_attr, default_initializer=_get_default_param_initializer(), ) self.bias = self.create_parameter( attr=self._bias_attr, shape=[self._out_channels], is_bias=True ) def forward(self, x: Tensor) -> Tensor: if self._backend is None: out = F.conv._conv2d( x, self.weight, bias=self.bias, stride=self._stride, padding=self._updated_padding, dilation=self._dilation, groups=self._groups, subm=self._subm, key=self._key, data_format=self._data_format, ) elif self._backend == 'igemm': out = F.conv._conv2d_igemm( x, self.weight, bias=self.bias, stride=self._stride, padding=self._updated_padding, dilation=self._dilation, groups=self._groups, subm=self._subm, key=self._key, data_format=self._data_format, ) else: raise ValueError( f"The value of 'backend' in Conv2D should be None or 'igemm', but got {self._backend}." ) return out def extra_repr(self) -> str: main_str = '{_in_channels}, {_out_channels}, kernel_size={_kernel_size}' if self._stride != [1] * len(self._stride): main_str += ', stride={_stride}' if self._padding != 0: main_str += ', padding={_padding}' if self._padding_mode != 'zeros': main_str += ', padding_mode={_padding_mode}' if self._dilation != [1] * len(self._dilation): main_str += ', dilation={_dilation}' if self._groups != 1: main_str += ', groups={_groups}' main_str += ', data_format={_data_format}' return main_str.format(**self.__dict__) class Conv3D(_Conv3D): r""" **Sparse Convolution3d Layer** The Sparse convolution3d layer calculates the output based on the input, filter and strides, paddings, dilations, groups parameters. Input(Input) and Output(Output) are multidimensional SparseCooTensors with a shape of :math:`[N, D, H, W, C]` . Where N is batch size, C is the number of channels, D is the depth of the feature, H is the height of the feature, and W is the width of the feature. If bias attribution is provided, bias is added to the output of the convolution. For each input :math:`X`, the equation is: .. math:: Out = W \ast X + b In the above equation: * :math:`X`: Input value, a tensor with NDHWC format. * :math:`W`: Filter value, a tensor with DHWCM format. * :math:`\\ast`: Convolution operation. * :math:`b`: Bias value, a 1-D tensor with shape [M]. * :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different. Parameters: in_channels(int): The number of input channels in the input image. out_channels(int): The number of output channels produced by the convolution. kernel_size(int|list|tuple): The size of the convolving kernel. stride(int|list|tuple, optional): The stride size. If stride is a list/tuple, it must contain three integers, (stride_D, stride_H, stride_W). Otherwise, the stride_D = stride_H = stride_W = stride. The default value is 1. padding(int|str|tuple|list, optional): The padding size. Padding couple be in one of the following forms. 1. a string in ['valid', 'same']. 2. an int, which means each spatial dimension(depth, height, width) is zero padded by size of `padding` 3. a list[int] or tuple[int] whose length is the number of spatial dimensions, which contains the amount of padding on each side for each spatial dimension. It has the form [pad_d1, pad_d2, ...]. 4. a list[int] or tuple[int] whose length is 2 * number of spatial dimensions. It has the form [pad_before, pad_after, pad_before, pad_after, ...] for all spatial dimensions. 5. a list or tuple of pairs of ints. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension are also included. Each pair of integers correspond to the amount of padding for a dimension of the input. Padding in batch dimension and channel dimension should be [0, 0] or (0, 0). The default value is 0. dilation(int|list|tuple, optional): The dilation size. If dilation is a list/tuple, it must contain three integers, (dilation_D, dilation_H, dilation_W). Otherwise, the dilation_D = dilation_H = dilation_W = dilation. The default value is 1. groups(int, optional): The groups number of the Conv3D Layer. According to grouped convolution in Alex Krizhevsky's Deep CNN paper: when group=2, the first half of the filters is only connected to the first half of the input channels, while the second half of the filters is only connected to the second half of the input channels. The default value is 1, currently, only support groups=1. padding_mode(str, optional): ``'zeros'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. Currently only support ``'zeros'``. weight_attr(ParamAttr, optional): The parameter attribute for learnable parameters/weights of conv3d. If it is set to None or one attribute of ParamAttr, conv3d will create ParamAttr as param_attr. If it is set to None, the parameter is initialized with :math:`Normal(0.0, std)`, and the :math:`std` is :math:`(\frac{2.0 }{filter\_elem\_num})^{0.5}`. The default value is None. bias_attr(ParamAttr|bool, optional): The parameter attribute for the bias of conv3d. If it is set to False, no bias will be added to the output units. If it is set to None or one attribute of ParamAttr, conv3d will create ParamAttr as bias_attr. If the Initializer of the bias_attr is not set, the bias is initialized zero. The default value is None. data_format(str, optional): Data format that specifies the layout of input. It can be "NCDHW" or "NDHWC". Currently, only support "NCDHW". Attribute: **weight** (Parameter): the learnable weights of filters of this layer. **bias** (Parameter): the learnable bias of this layer. Shape: - x: :math:`(N, D_{in}, H_{in}, W_{in}, C_{in})` - weight: :math:`(K_{d}, K_{h}, K_{w}, C_{in}, C_{out})` - bias: :math:`(C_{out})` - output: :math:`(N, D_{out}, H_{out}, W_{out}, C_{out})` Where .. math:: D_{out}&= \frac{(D_{in} + 2 * paddings[0] - (dilations[0] * (kernel\_size[0] - 1) + 1))}{strides[0]} + 1 H_{out}&= \frac{(H_{in} + 2 * paddings[1] - (dilations[1] * (kernel\_size[1] - 1) + 1))}{strides[1]} + 1 W_{out}&= \frac{(W_{in} + 2 * paddings[2] - (dilations[2] * (kernel\_size[2] - 1) + 1))}{strides[2]} + 1 Examples: .. code-block:: python >>> import paddle >>> indices = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 1, 2], [1, 3, 2, 3]] >>> values = [[1], [2], [3], [4]] >>> indices = paddle.to_tensor(indices, dtype='int32') >>> values = paddle.to_tensor(values, dtype='float32') >>> dense_shape = [1, 1, 3, 4, 1] >>> sparse_x = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape, stop_gradient=True) >>> conv = paddle.sparse.nn.Conv3D(1, 1, (1, 3, 3)) >>> y = conv(sparse_x) >>> print(y.shape) [1, 1, 1, 2, 1] """ def __init__( self, in_channels: int, out_channels: int, kernel_size: Size3, stride: Size3 = 1, padding: _PaddingSizeMode | Size3 | Size6 | Sequence[Size2] = 0, dilation: Size3 = 1, groups: Literal[1] = 1, padding_mode: Literal['zeros'] = 'zeros', weight_attr: ParamAttrLike | None = None, bias_attr: ParamAttrLike | None = None, data_format: Literal["NDHWC"] = "NDHWC", ) -> None: super().__init__( in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, subm=False, key=None, padding_mode=padding_mode, weight_attr=weight_attr, bias_attr=bias_attr, data_format=data_format, ) class Conv2D(_Conv2D): r""" **Sparse Convolution2d Layer** The Sparse convolution2d layer calculates the output based on the input, filter and strides, paddings, dilations, groups parameters. Input(Input) and Output(Output) are multidimensional SparseCooTensors with a shape of :math:`[N, H, W, C]` . Where N is batch size, C is the number of channels, H is the height of the feature, and W is the width of the feature. If bias attribution is provided, bias is added to the output of the convolution. For each input :math:`X`, the equation is: .. math:: Out = W \ast X + b In the above equation: * :math:`X`: Input value, a tensor with NHWC format. * :math:`W`: Filter value, a tensor with HWCM format. * :math:`\\ast`: Convolution operation. * :math:`b`: Bias value, a 1-D tensor with shape [M]. * :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different. Parameters: in_channels(int): The number of input channels in the input image. out_channels(int): The number of output channels produced by the convolution. kernel_size(int|list|tuple): The size of the convolving kernel. stride(int|list|tuple, optional): The stride size. If stride is a list/tuple, it must contain three integers, (stride_H, stride_W). Otherwise, the stride_H = stride_W = stride. The default value is 1. padding(int|str|tuple|list, optional): The padding size. Padding couple be in one of the following forms. 1. a string in ['valid', 'same']. 2. an int, which means each spatial dimension(height, width) is zero padded by size of `padding` 3. a list[int] or tuple[int] whose length is the number of spatial dimensions, which contains the amount of padding on each side for each spatial dimension. It has the form [pad_d1, pad_d2, ...]. 4. a list[int] or tuple[int] whose length is 2 * number of spatial dimensions. It has the form [pad_before, pad_after, pad_before, pad_after, ...] for all spatial dimensions. 5. a list or tuple of pairs of ints. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension are also included. Each pair of integers correspond to the amount of padding for a dimension of the input. Padding in batch dimension and channel dimension should be [0, 0] or (0, 0). The default value is 0. dilation(int|list|tuple, optional): The dilation size. If dilation is a list/tuple, it must contain three integers, (dilation_H, dilation_W). Otherwise, the dilation_H = dilation_W = dilation. The default value is 1. groups(int, optional): The groups number of the Conv2D Layer. According to grouped convolution in Alex Krizhevsky's Deep CNN paper: when group=2, the first half of the filters is only connected to the first half of the input channels, while the second half of the filters is only connected to the second half of the input channels. The default value is 1, currently, only support groups=1. padding_mode(str, optional): ``'zeros'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. Currently only support ``'zeros'``. weight_attr(ParamAttr, optional): The parameter attribute for learnable parameters/weights of conv2d. If it is set to None or one attribute of ParamAttr, conv2d will create ParamAttr as param_attr. If it is set to None, the parameter is initialized with :math:`Normal(0.0, std)`, and the :math:`std` is :math:`(\frac{2.0 }{filter\_elem\_num})^{0.5}`. The default value is None. bias_attr(ParamAttr|bool, optional): The parameter attribute for the bias of conv2d. If it is set to False, no bias will be added to the output units. If it is set to None or one attribute of ParamAttr, conv2d will create ParamAttr as bias_attr. If the Initializer of the bias_attr is not set, the bias is initialized zero. The default value is None. data_format(str, optional): Data format that specifies the layout of input. It can be "NCHW" or "NHWC". Currently, only support "NHWC". Attribute: **weight** (Parameter): the learnable weights of filters of this layer. **bias** (Parameter): the learnable bias of this layer. Shape: - x: :math:`(N, H_{in}, W_{in}, C_{in})` - weight: :math:`(K_{h}, K_{w}, C_{in}, C_{out})` - bias: :math:`(C_{out})` - output: :math:`(N, H_{out}, W_{out}, C_{out})` Where .. math:: H_{out}&= \frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (kernel\_size[0] - 1) + 1))}{strides[0]} + 1 W_{out}&= \frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (kernel\_size[1] - 1) + 1))}{strides[1]} + 1 Examples: .. code-block:: python >>> import paddle >>> indices = [[0, 0, 0, 0], [0, 0, 1, 2], [1, 3, 2, 3]] >>> values = [[1], [2], [3], [4]] >>> indices = paddle.to_tensor(indices, dtype='int32') >>> values = paddle.to_tensor(values, dtype='float32') >>> dense_shape = [1, 3, 4, 1] >>> sparse_x = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape, stop_gradient=True) >>> conv = paddle.sparse.nn.Conv2D(1, 1, (3, 3)) >>> y = conv(sparse_x) >>> print(y.shape) [1, 1, 2, 1] """ def __init__( self, in_channels: int, out_channels: int, kernel_size: Size2, stride: Size2 = 1, padding: _PaddingSizeMode | Size2 | Size4 | Sequence[Size2] = 0, dilation: Size2 = 1, groups: Literal[1] = 1, padding_mode: Literal['zeros'] = 'zeros', weight_attr: ParamAttrLike | None = None, bias_attr: ParamAttrLike | None = None, data_format: Literal["NHWC"] = "NHWC", ) -> None: super().__init__( in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, subm=False, key=None, padding_mode=padding_mode, weight_attr=weight_attr, bias_attr=bias_attr, data_format=data_format, ) class SubmConv3D(_Conv3D): r""" **Submanifold Sparse Convolution3d Layer** The submanifold sparse convolution3d layer calculates the output based on the input, filter and strides, paddings, dilations, groups parameters. Input(Input) and Output(Output) are multidimensional SparseCooTensors with a shape of :math:`[N, D, H, W, C]` . Where N is batch size, C is the number of channels, D is the depth of the feature, H is the height of the feature, and W is the width of the feature. If bias attribution is provided, bias is added to the output of the convolution. For each input :math:`X`, the equation is: .. math:: Out = W \ast X + b In the above equation: * :math:`X`: Input value, a tensor with NDHWC format. * :math:`W`: Filter value, a tensor with DHWCM format. * :math:`\\ast`: Submanifold Convolution operation, refer to the paper: https://arxiv.org/abs/1706.01307. * :math:`b`: Bias value, a 1-D tensor with shape [M]. * :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different. Parameters: in_channels(int): The number of input channels in the input image. out_channels(int): The number of output channels produced by the convolution. kernel_size(int|list|tuple): The size of the convolving kernel. stride(int|list|tuple, optional): The stride size. If stride is a list/tuple, it must contain three integers, (stride_D, stride_H, stride_W). Otherwise, the stride_D = stride_H = stride_W = stride. The default value is 1. padding(int|str|tuple|list, optional): The padding size. Padding couple be in one of the following forms. 1. a string in ['valid', 'same']. 2. an int, which means each spatial dimension(depth, height, width) is zero padded by size of `padding` 3. a list[int] or tuple[int] whose length is the number of spatial dimensions, which contains the amount of padding on each side for each spatial dimension. It has the form [pad_d1, pad_d2, ...]. 4. a list[int] or tuple[int] whose length is 2 * number of spatial dimensions. It has the form [pad_before, pad_after, pad_before, pad_after, ...] for all spatial dimensions. 5. a list or tuple of pairs of ints. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension are also included. Each pair of integers correspond to the amount of padding for a dimension of the input. Padding in batch dimension and channel dimension should be [0, 0] or (0, 0). The default value is 0. dilation(int|list|tuple, optional): The dilation size. If dilation is a list/tuple, it must contain three integers, (dilation_D, dilation_H, dilation_W). Otherwise, the dilation_D = dilation_H = dilation_W = dilation. The default value is 1. groups(int, optional): The groups number of the Conv3D Layer. According to grouped convolution in Alex Krizhevsky's Deep CNN paper: when group=2, the first half of the filters is only connected to the first half of the input channels, while the second half of the filters is only connected to the second half of the input channels. The default value is 1. padding_mode(str, optional): ``'zeros'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. Currently only support ``'zeros'``. key(str, optional): the key is used to save or use the same rulebook, the definition and role of rulebook refers to https://pdfs.semanticscholar.org/5125/a16039cabc6320c908a4764f32596e018ad3.pdf. The default value is None. weight_attr(ParamAttr, optional): The parameter attribute for learnable parameters/weights of conv3d. If it is set to None or one attribute of ParamAttr, conv3d will create ParamAttr as param_attr. If it is set to None, the parameter is initialized with :math:`Normal(0.0, std)`, and the :math:`std` is :math:`(\frac{2.0 }{filter\_elem\_num})^{0.5}`. The default value is None. bias_attr(ParamAttr|bool, optional): The parameter attribute for the bias of conv3d. If it is set to False, no bias will be added to the output units. If it is set to None or one attribute of ParamAttr, conv3d will create ParamAttr as bias_attr. If the Initializer of the bias_attr is not set, the bias is initialized zero. The default value is None. data_format(str, optional): Data format that specifies the layout of input. It can be "NCDHW" or "NDHWC". Currently, only support "NCDHW". Attribute: **weight** (Parameter): the learnable weights of filters of this layer. **bias** (Parameter): the learnable bias of this layer. Shape: - x: :math:`(N, D_{in}, H_{in}, W_{in}, C_{in})` - weight: :math:`(K_{d}, K_{h}, K_{w}, C_{in}, C_{out})` - bias: :math:`(C_{out})` - output: :math:`(N, D_{out}, H_{out}, W_{out}, C_{out})` Where .. math:: D_{out}&= \frac{(D_{in} + 2 * paddings[0] - (dilations[0] * (kernel\_size[0] - 1) + 1))}{strides[0]} + 1 H_{out}&= \frac{(H_{in} + 2 * paddings[1] - (dilations[1] * (kernel\_size[1] - 1) + 1))}{strides[1]} + 1 W_{out}&= \frac{(W_{in} + 2 * paddings[2] - (dilations[2] * (kernel\_size[2] - 1) + 1))}{strides[2]} + 1 Examples: .. code-block:: python >>> import paddle >>> indices = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 1, 2], [1, 3, 2, 3]] >>> values = [[1], [2], [3], [4]] >>> dense_shape = [1, 1, 3, 4, 1] >>> indices = paddle.to_tensor(indices, dtype='int32') >>> values = paddle.to_tensor(values, dtype='float32') >>> sparse_x = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape, stop_gradient=True) >>> subm_conv = paddle.sparse.nn.SubmConv3D(1, 1, (1, 3, 3)) >>> y = subm_conv(sparse_x) >>> print(y.shape) [1, 1, 3, 4, 1] """ def __init__( self, in_channels: int, out_channels: int, kernel_size: Size3, stride: Size3 = 1, padding: _PaddingSizeMode | Size3 | Size6 | Sequence[Size2] = 0, dilation: Size3 = 1, groups: Literal[1] = 1, padding_mode: Literal['zeros'] = 'zeros', key: str | None = None, weight_attr: ParamAttrLike | None = None, bias_attr: ParamAttrLike | None = None, data_format: Literal["NDHWC"] = "NDHWC", backend: Literal['igemm'] | None = None, ) -> None: super().__init__( in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, subm=True, key=key, padding_mode=padding_mode, weight_attr=weight_attr, bias_attr=bias_attr, data_format=data_format, backend=backend, ) class SubmConv2D(_Conv2D): r""" **Submanifold Sparse Convolution2d Layer** The submanifold sparse convolution2d layer calculates the output based on the input, filter and strides, paddings, dilations, groups parameters. Input(Input) and Output(Output) are multidimensional SparseCooTensors with a shape of :math:`[N, H, W, C]` . Where N is batch size, C is the number of channels, H is the height of the feature, and W is the width of the feature. If bias attribution is provided, bias is added to the output of the convolution. For each input :math:`X`, the equation is: .. math:: Out = W \ast X + b In the above equation: * :math:`X`: Input value, a tensor with NDHWC format. * :math:`W`: Filter value, a tensor with DHWCM format. * :math:`\\ast`: Submanifold Convolution operation, refer to the paper: https://arxiv.org/abs/1706.01307. * :math:`b`: Bias value, a 1-D tensor with shape [M]. * :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different. Parameters: in_channels(int): The number of input channels in the input image. out_channels(int): The number of output channels produced by the convolution. kernel_size(int|list|tuple): The size of the convolving kernel. stride(int|list|tuple, optional): The stride size. If stride is a list/tuple, it must contain two integers, (stride_H, stride_W). Otherwise, the stride_H = stride_W = stride. The default value is 1. padding(int|str|tuple|list, optional): The padding size. Padding couple be in one of the following forms. 1. a string in ['valid', 'same']. 2. an int, which means each spatial dimension(depth, height, width) is zero padded by size of `padding` 3. a list[int] or tuple[int] whose length is the number of spatial dimensions, which contains the amount of padding on each side for each spatial dimension. It has the form [pad_d1, pad_d2, ...]. 4. a list[int] or tuple[int] whose length is 2 * number of spatial dimensions. It has the form [pad_before, pad_after, pad_before, pad_after, ...] for all spatial dimensions. 5. a list or tuple of pairs of ints. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension are also included. Each pair of integers correspond to the amount of padding for a dimension of the input. Padding in batch dimension and channel dimension should be [0, 0] or (0, 0). The default value is 0. dilation(int|list|tuple, optional): The dilation size. If dilation is a list/tuple, it must contain two integers, (dilation_H, dilation_W). Otherwise, the dilation_H = dilation_W = dilation. The default value is 1. groups(int, optional): The groups number of the Conv2D Layer. According to grouped convolution in Alex Krizhevsky's Deep CNN paper: when group=2, the first half of the filters is only connected to the first half of the input channels, while the second half of the filters is only connected to the second half of the input channels. The default value is 1. padding_mode(str, optional): ``'zeros'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. Currently only support ``'zeros'``. key(str, optional): the key is used to save or use the same rulebook, the definition and role of rulebook refers to https://pdfs.semanticscholar.org/5125/a16039cabc6320c908a4764f32596e018ad3.pdf. The default value is None. weight_attr(ParamAttr, optional): The parameter attribute for learnable parameters/weights of conv2d. If it is set to None or one attribute of ParamAttr, conv2d will create ParamAttr as param_attr. If it is set to None, the parameter is initialized with :math:`Normal(0.0, std)`, and the :math:`std` is :math:`(\frac{2.0 }{filter\_elem\_num})^{0.5}`. The default value is None. bias_attr(ParamAttr|bool, optional): The parameter attribute for the bias of conv2d. If it is set to False, no bias will be added to the output units. If it is set to None or one attribute of ParamAttr, conv2d will create ParamAttr as bias_attr. If the Initializer of the bias_attr is not set, the bias is initialized zero. The default value is None. data_format(str, optional): Data format that specifies the layout of input. It can be "NCHW" or "NHWC". Currently, only support "NHWC". Attribute: **weight** (Parameter): the learnable weights of filters of this layer. **bias** (Parameter): the learnable bias of this layer. Shape: - x: :math:`(N, H_{in}, W_{in}, C_{in})` - weight: :math:`(K_{h}, K_{w}, C_{in}, C_{out})` - bias: :math:`(C_{out})` - output: :math:`(N, H_{out}, W_{out}, C_{out})` Where .. math:: H_{out}&= \frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (kernel\_size[0] - 1) + 1))}{strides[0]} + 1 W_{out}&= \frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (kernel\_size[1] - 1) + 1))}{strides[1]} + 1 Examples: .. code-block:: python >>> import paddle >>> indices = [[0, 0, 0, 0], [0, 0, 1, 2], [1, 3, 2, 3]] >>> values = [[1], [2], [3], [4]] >>> dense_shape = [1, 3, 4, 1] >>> indices = paddle.to_tensor(indices, dtype='int32') >>> values = paddle.to_tensor(values, dtype='float32') >>> sparse_x = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape, stop_gradient=True) >>> subm_conv = paddle.sparse.nn.SubmConv2D(1, 1, (3, 3)) >>> y = subm_conv(sparse_x) >>> print(y.shape) [1, 3, 4, 1] """ def __init__( self, in_channels: int, out_channels: int, kernel_size: Size2, stride: Size2 = 1, padding: _PaddingSizeMode | Size2 | Size4 | Sequence[Size2] = 0, dilation: Size2 = 1, groups: Literal[1] = 1, padding_mode: Literal['zeros'] = 'zeros', key: str | None = None, weight_attr: ParamAttrLike | None = None, bias_attr: ParamAttrLike | None = None, data_format: Literal["NHWC"] = "NHWC", backend: Literal['igemm'] | None = None, ) -> None: super().__init__( in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, subm=True, key=key, padding_mode=padding_mode, weight_attr=weight_attr, bias_attr=bias_attr, data_format=data_format, backend=backend, )