# Copyright (c) 2020 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. # TODO: define classes of convolutional neural network __all__ = [ 'Conv2D', 'Conv2DTranspose', 'Conv3D', 'Conv3DTranspose', # 'TreeConv', # 'Conv1D' ] import numpy as np from ...fluid.dygraph import layers from ...fluid.initializer import Normal from .. import functional as F from ...fluid.layers import utils from ..functional.conv import _update_padding_nd def _get_default_param_initializer(num_channels, filter_size): filter_elem_num = num_channels * np.prod(filter_size) std = (2.0 / filter_elem_num)**0.5 return Normal(0.0, std, 0) class Conv2D(layers.Layer): """ :alias_main: paddle.nn.Conv2D :alias: paddle.nn.Conv2D,paddle.nn.layer.Conv2D,paddle.nn.layer.conv.Conv2D This interface is used to construct a callable object of the ``Conv2D`` class. For more details, refer to code examples. The convolution2D layer calculates the output based on the input, filter and strides, paddings, dilations, groups parameters. Input and Output are in NCHW format, where N is batch size, C is the number of the feature map, H is the height of the feature map, and W is the width of the feature map. Filter's shape is [MCHW] , where M is the number of output feature map, C is the number of input feature map, H is the height of the filter, and W is the width of the filter. If the groups is greater than 1, C will equal the number of input feature map divided by the groups. Please refer to UFLDL's `convolution `_ for more details. If bias attribution and activation type are provided, bias is added to the output of the convolution, and the corresponding activation function is applied to the final result. For each input :math:`X`, the equation is: .. math:: Out = \\sigma (W \\ast X + b) Where: * :math:`X`: Input value, a ``Tensor`` with NCHW format. * :math:`W`: Filter value, a ``Tensor`` with shape [MCHW] . * :math:`\\ast`: Convolution operation. * :math:`b`: Bias value, a 2-D ``Tensor`` with shape [M, 1]. * :math:`\\sigma`: Activation function. * :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different. Example: - Input: Input shape: :math:`(N, C_{in}, H_{in}, W_{in})` Filter shape: :math:`(C_{out}, C_{in}, H_f, W_f)` - Output: Output shape: :math:`(N, C_{out}, H_{out}, W_{out})` Where .. math:: H_{out}&= \\frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]} + 1 \\\\ W_{out}&= \\frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1 Parameters: num_channels(int): The number of channels in the input image. num_filters(int): The number of filter. It is as same as the output feature map. filter_size (int or tuple): The filter size. If filter_size is a tuple, it must contain two integers, (filter_size_H, filter_size_W). Otherwise, the filter will be a square. padding(int|str|tuple|list, optional): The padding size. Padding coule be in one of the following forms. 1. a string in ['valid', 'same']. 2. an int, which means each spartial dimension(depth, height, width) is zero paded by size of `padding`on both sides 3. a list[int] or tuple[int] whose length is the number of spartial dimensions, which contains the amount of padding on each side for each spartial dimension. It has the form [pad_d1, pad_d2, ...]. 4. a list[int] or tuple[int] whose length is 2 * number of spartial dimensions. It has the form [pad_before, pad_after, pad_before, pad_after, ...] for all spartial 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. stride (int or tuple, optional): The stride size. If stride is a tuple, it must contain two integers, (stride_H, stride_W). Otherwise, the stride_H = stride_W = stride. Default: 1. dilation (int or tuple, optional): The dilation size. If dilation is a tuple, it must contain two integers, (dilation_H, dilation_W). Otherwise, the dilation_H = dilation_W = dilation. Default: 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. Default: 1. param_attr (ParamAttr, optional): The parameter attribute for learnable weights(Parameter) of conv2d. If it is set to None or one attribute of ParamAttr, conv2d will create ParamAttr as param_attr. If the Initializer of the param_attr is not set, the parameter is initialized with :math:`Normal(0.0, std)`, and the :math:`std` is :math:`(\\frac{2.0 }{filter\_elem\_num})^{0.5}`. Default: None. bias_attr (ParamAttr or bool, optional): The 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. Default: None. use_cudnn (bool, optional): Use cudnn kernel or not, it is valid only when the cudnn library is installed. Default: True. act (str, optional): Activation type, if it is set to None, activation is not appended. Default: None. data_format (str, optional): Data format that specifies the layout of input. It can be "NCHW" or "NHWC". Default: "NCHW". dtype (str, optional): Data type, it can be "float32" or "float64". Default: "float32". Attribute: **weight** (Parameter): the learnable weights of filter of this layer. **bias** (Parameter or None): the learnable bias of this layer. Returns: None Raises: ValueError: if ``use_cudnn`` is not a bool value. Examples: .. code-block:: python import numpy as np from paddle import fluid import paddle.fluid.dygraph as dg from paddle import nn x = np.random.uniform(-1, 1, (2, 4, 8, 8)).astype('float32') place = fluid.CPUPlace() with dg.guard(place): x_var = dg.to_variable(x) conv = nn.Conv2D(4, 6, (3, 3)) y_var = conv(x_var) y_np = y_var.numpy() print(y_np.shape) # (2, 6, 6, 6) """ def __init__(self, num_channels, num_filters, filter_size, padding=0, stride=1, dilation=1, groups=1, param_attr=None, bias_attr=None, use_cudnn=True, act=None, data_format="NCHW", dtype='float32'): super(Conv2D, self).__init__() assert param_attr is not False, "param_attr should not be False here." self._num_channels = num_channels self._num_filters = num_filters self._groups = groups if num_channels % groups != 0: raise ValueError("num_channels must be divisible by groups.") self._act = act self._data_format = data_format self._dtype = dtype if not isinstance(use_cudnn, bool): raise ValueError("use_cudnn should be True or False") self._use_cudnn = use_cudnn self._filter_size = utils.convert_to_list(filter_size, 2, 'filter_size') self._stride = utils.convert_to_list(stride, 2, 'stride') self._dilation = utils.convert_to_list(dilation, 2, 'dilation') channel_last = (data_format == "NHWC") self._padding = padding # leave it to F.conv2d self._param_attr = param_attr self._bias_attr = bias_attr num_filter_channels = num_channels // groups filter_shape = [self._num_filters, num_filter_channels ] + self._filter_size self.weight = self.create_parameter( attr=self._param_attr, shape=filter_shape, dtype=self._dtype, default_initializer=_get_default_param_initializer( self._num_channels, filter_shape)) self.bias = self.create_parameter( attr=self._bias_attr, shape=[self._num_filters], dtype=self._dtype, is_bias=True) def forward(self, input): out = F.conv2d( input, self.weight, bias=self.bias, padding=self._padding, stride=self._stride, dilation=self._dilation, groups=self._groups, use_cudnn=self._use_cudnn, act=self._act, data_format=self._data_format) return out class Conv2DTranspose(layers.Layer): """ :alias_main: paddle.nn.Conv2DTranspose :alias: paddle.nn.Conv2DTranspose,paddle.nn.layer.Conv2DTranspose,paddle.nn.layer.conv.Conv2DTranspose This interface is used to construct a callable object of the ``Conv2DTranspose`` class. For more details, refer to code examples. The convolution2D transpose layer calculates the output based on the input, filter, and dilations, strides, paddings. Input and output are in NCHW format. Where N is batch size, C is the number of feature map, H is the height of the feature map, and W is the width of the feature map. Filter's shape is [MCHW] , where M is the number of input feature map, C is the number of output feature map, H is the height of the filter, and W is the width of the filter. If the groups is greater than 1, C will equal the number of input feature map divided by the groups. If bias attribution and activation type are provided, bias is added to the output of the convolution, and the corresponding activation function is applied to the final result. The details of convolution transpose layer, please refer to the following explanation and references `conv2dtranspose `_ . For each input :math:`X`, the equation is: .. math:: Out = \sigma (W \\ast X + b) Where: * :math:`X`: Input value, a ``Tensor`` with NCHW format. * :math:`W`: Filter value, a ``Tensor`` with shape [MCHW] . * :math:`\\ast`: Convolution operation. * :math:`b`: Bias value, a 2-D ``Tensor`` with shape [M, 1]. * :math:`\\sigma`: Activation function. * :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different. Example: - Input: Input shape: :math:`(N, C_{in}, H_{in}, W_{in})` Filter shape: :math:`(C_{in}, C_{out}, H_f, W_f)` - Output: Output shape: :math:`(N, C_{out}, H_{out}, W_{out})` Where .. math:: H^\prime_{out} &= (H_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (H_f - 1) + 1 \\\\ W^\prime_{out} &= (W_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (W_f - 1) + 1 \\\\ H_{out} &\in [ H^\prime_{out}, H^\prime_{out} + strides[0] ) \\\\ W_{out} &\in [ W^\prime_{out}, W^\prime_{out} + strides[1] ) Parameters: num_channels(int): The number of channels in the input image. num_filters(int): The number of the filter. It is as same as the output feature map. filter_size(int or tuple): The filter size. If filter_size is a tuple, it must contain two integers, (filter_size_H, filter_size_W). Otherwise, the filter will be a square. output_size(int or tuple, optional): The output image size. If output size is a tuple, it must contain two integers, (image_H, image_W). None if use filter_size, padding, and stride to calculate output_size. if output_size and filter_size are specified at the same time, They should follow the formula above. Default: None. padding(int|str|tuple|list, optional): The padding size. Padding coule be in one of the following forms. 1. a string in ['valid', 'same']. 2. an int, which means each spartial dimension(depth, height, width) is zero paded by size of `padding` on both sides 3. a list[int] or tuple[int] whose length is the number of spartial dimensions, which contains the amount of padding on each side for each spartial dimension. It has the form [pad_d1, pad_d2, ...]. 4. a list[int] or tuple[int] whose length is 2 * number of spartial dimensions. It has the form [pad_before, pad_after, pad_before, pad_after, ...] for all spartial 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. stride(int or tuple, optional): The stride size. If stride is a tuple, it must contain two integers, (stride_H, stride_W). Otherwise, the stride_H = stride_W = stride. Default: 1. dilation(int or tuple, optional): The dilation size. If dilation is a tuple, it must contain two integers, (dilation_H, dilation_W). Otherwise, the dilation_H = dilation_W = dilation. Default: 1. groups(int, optional): The groups number of the Conv2d transpose layer. Inspired by grouped convolution in Alex Krizhevsky's Deep CNN paper, in which 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. Default: 1. param_attr (ParamAttr, optional): The parameter attribute for learnable weights(Parameter) of conv2d_transpose. If it is set to None or one attribute of ParamAttr, conv2d_transpose will create ParamAttr as param_attr. If the Initializer of the param_attr is not set, the parameter is initialized with Xavier. Default: None. bias_attr (ParamAttr or bool, optional): The attribute for the bias of conv2d_transpose. 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_transpose will create ParamAttr as bias_attr. If the Initializer of the bias_attr is not set, the bias is initialized zero. Default: None. use_cudnn(bool, optional): Use cudnn kernel or not, it is valid only when the cudnn library is installed. Default: True. act (str, optional): Activation type, if it is set to None, activation is not appended. Default: None. data_format (str, optional): Data format that specifies the layout of input. It can be "NCHW" or "NHWC". Default: "NCHW". dtype (str, optional): Data type, it can be "float32" or "float64". Default: "float32". Attribute: **weight** (Parameter): the learnable weights of filters of this layer. **bias** (Parameter or None): the learnable bias of this layer. Returns: None Examples: .. code-block:: python import numpy as np from paddle import fluid import paddle.fluid.dygraph as dg from paddle import nn x = np.random.uniform(-1, 1, (2, 4, 8, 8)).astype('float32') place = fluid.CPUPlace() with dg.guard(place): x_var = dg.to_variable(x) conv = nn.Conv2DTranspose(4, 6, (3, 3)) y_var = conv(x_var) y_np = y_var.numpy() print(y_np.shape) # (2, 6, 10, 10) """ def __init__(self, num_channels, num_filters, filter_size, output_size=None, padding=0, stride=1, dilation=1, groups=1, param_attr=None, bias_attr=None, use_cudnn=True, act=None, data_format="NCHW", dtype='float32'): super(Conv2DTranspose, self).__init__() assert param_attr is not False, "param_attr should not be False in conv2d_transpose." self._param_attr = param_attr self._bias_attr = bias_attr self._act = act self._groups = groups self._num_channels = num_channels self._num_filters = num_filters self._use_cudnn = use_cudnn self._data_format = data_format self._dtype = dtype self._stride = utils.convert_to_list(stride, 2, 'stride') self._dilation = utils.convert_to_list(dilation, 2, 'dilation') self._filter_size = utils.convert_to_list(filter_size, 2, 'filter_size') if output_size is None: self._output_size = output_size elif isinstance(output_size, (list, tuple, int)): self._output_size = utils.convert_to_list(output_size, 2, 'output_size') else: raise ValueError( "output_size should be int, ot list[int] or tuple[int]") self._padding = padding filter_shape = [self._num_channels, num_filters // groups ] + self._filter_size self.weight = self.create_parameter( dtype=self._dtype, shape=filter_shape, attr=self._param_attr) self.bias = self.create_parameter( attr=self._bias_attr, shape=[self._num_filters], dtype=self._dtype, is_bias=True) def forward(self, input): out = F.conv2d_transpose( input, self.weight, bias=self.bias, output_size=self._output_size, padding=self._padding, stride=self._stride, dilation=self._dilation, groups=self._groups, use_cudnn=self._use_cudnn, act=self._act, data_format=self._data_format) return out class Conv3D(layers.Layer): """ :alias_main: paddle.nn.Conv3D :alias: paddle.nn.Conv3D,paddle.nn.layer.Conv3D,paddle.nn.layer.conv.Conv3D **Convlution3D Layer** The convolution3D layer calculates the output based on the input, filter and strides, paddings, dilations, groups parameters. Input(Input) and Output(Output) are multidimensional tensors with a shape of :math:`[N, C, D, H, W]` . 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. Convlution3D is similar with Convlution2D but adds one dimension(depth). If bias attribution and activation type are provided, bias is added to the output of the convolution, and the corresponding activation function is applied to the final result. For each input :math:`X`, the equation is: .. math:: Out = \sigma (W \\ast X + b) In the above equation: * :math:`X`: Input value, a tensor with NCDHW or NDHWC format. * :math:`W`: Filter value, a tensor with MCDHW format. * :math:`\\ast`: Convolution operation. * :math:`b`: Bias value, a 2-D tensor with shape [M, 1]. * :math:`\\sigma`: Activation function. * :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different. Example: - Input: Input shape: :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})` Filter shape: :math:`(C_{out}, C_{in}, D_f, H_f, W_f)` - Output: Output shape: :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})` Where .. math:: D_{out}&= \\frac{(D_{in} + 2 * paddings[0] - (dilations[0] * (D_f - 1) + 1))}{strides[0]} + 1 \\\\ H_{out}&= \\frac{(H_{in} + 2 * paddings[1] - (dilations[1] * (H_f - 1) + 1))}{strides[1]} + 1 \\\\ W_{out}&= \\frac{(W_{in} + 2 * paddings[2] - (dilations[2] * (W_f - 1) + 1))}{strides[2]} + 1 Parameters: num_channels(int): The number of channels in the input image. num_filters(int): The number of filter. It is as same as the output image channel. filter_size (int|tuple, optional): The filter size. If filter_size is a tuple, it must contain three integers, (filter_size_D, filter_size_H, filter_size_W). Otherwise, the filter will be a square, filter_size_depth = filter_size_height = filter_size_width = filter_size. stride (int|tuple, optional): The stride size. If stride is a 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 coule be in one of the following forms. 1. a string in ['valid', 'same']. 2. an int, which means each spartial dimension(depth, height, width) is zero paded by size of `padding` 3. a list[int] or tuple[int] whose length is the number of spartial dimensions, which contains the amount of padding on each side for each spartial dimension. It has the form [pad_d1, pad_d2, ...]. 4. a list[int] or tuple[int] whose length is 2 * number of spartial dimensions. It has the form [pad_before, pad_after, pad_before, pad_after, ...] for all spartial 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|tuple, optional): The dilation size. If dilation is a 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. param_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. use_cudnn (bool, optional): Use cudnn kernel or not, it is valid only when the cudnn library is installed. The default value is True. act (str, optional): Activation type, if it is set to None, activation is not appended. The default value is None. data_format (str, optional): Data format that specifies the layout of input. It can be "NCDHW" or "NDHWC". Default: "NCDHW". dtype (str, optional): Data type, it can be "float32" or "float64". Default: "float32". Attribute: **weight** (Parameter): the learnable weights of filters of this layer. **bias** (Parameter): the learnable bias of this layer. Returns: None. Raises: ValueError: If the shapes of input, filter_size, stride, padding and groups mismatch. Examples: .. code-block:: python import numpy as np from paddle import fluid import paddle.fluid.dygraph as dg from paddle import nn x = np.random.uniform(-1, 1, (2, 4, 8, 8, 8)).astype('float32') place = fluid.CPUPlace() with dg.guard(place): x_var = dg.to_variable(x) conv = nn.Conv3D(4, 6, (3, 3, 3)) y_var = conv(x_var) y_np = y_var.numpy() print(y_np.shape) # (2, 6, 6, 6, 6) """ def __init__(self, num_channels, num_filters, filter_size, padding=0, stride=1, dilation=1, groups=1, param_attr=None, bias_attr=None, use_cudnn=True, act=None, data_format="NCDHW", dtype='float32'): super(Conv3D, self).__init__() assert param_attr is not False, "param_attr should not be False here." self._num_channels = num_channels self._num_filters = num_filters self._groups = groups self._act = act self._use_cudnn = use_cudnn self._dtype = dtype self._data_format = data_format self._stride = utils.convert_to_list(stride, 3, 'stride') self._dilation = utils.convert_to_list(dilation, 3, 'dilation') self._filter_size = utils.convert_to_list(filter_size, 3, 'filter_size') channel_last = (data_format == "NDHWC") self._padding = padding self._param_attr = param_attr self._bias_attr = bias_attr if num_channels % groups != 0: raise ValueError("num_channels must be divisible by groups.") num_filter_channels = num_channels // groups filter_shape = [num_filters, num_filter_channels] + self._filter_size self.weight = self.create_parameter( attr=self._param_attr, shape=filter_shape, dtype=self._dtype, default_initializer=_get_default_param_initializer( self._num_channels, self._filter_size)) self.bias = self.create_parameter( attr=self._bias_attr, shape=[self._num_filters], dtype=self._dtype, is_bias=True) def forward(self, input): out = F.conv3d( input, self.weight, bias=self.bias, padding=self._padding, stride=self._stride, dilation=self._dilation, groups=self._groups, use_cudnn=self._use_cudnn, act=self._act, data_format=self._data_format) return out class Conv3DTranspose(layers.Layer): """ :alias_main: paddle.nn.Conv3DTranspose :alias: paddle.nn.Conv3DTranspose,paddle.nn.layer.Conv3DTranspose,paddle.nn.layer.conv.Conv3DTranspose **Convlution3D transpose layer** The convolution3D transpose layer calculates the output based on the input, filter, and dilations, strides, paddings. Input(Input) and output(Output) are in NCDHW format. 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. Parameters(dilations, strides, paddings) are two elements. These two elements represent height and width, respectively. The details of convolution transpose layer, please refer to the following explanation and references `therein `_. If bias attribution and activation type are provided, bias is added to the output of the convolution, and the corresponding activation function is applied to the final result. For each input :math:`X`, the equation is: .. math:: Out = \sigma (W \\ast X + b) In the above equation: * :math:`X`: Input value, a tensor with NCDHW format. * :math:`W`: Filter value, a tensor with MCDHW format. * :math:`\\ast`: Convolution operation. * :math:`b`: Bias value, a 2-D tensor with shape [M, 1]. * :math:`\\sigma`: Activation function. * :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different. Example: - Input: Input shape: :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})` Filter shape: :math:`(C_{in}, C_{out}, D_f, H_f, W_f)` - Output: Output shape: :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})` Where .. math:: D^\prime_{out} &= (D_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (D_f - 1) + 1 \\\\ H^\prime_{out} &= (H_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (H_f - 1) + 1 \\\\ W^\prime_{out} &= (W_{in} - 1) * strides[2] - 2 * paddings[2] + dilations[2] * (W_f - 1) + 1 \\\\ D_{out} &\in [ D^\prime_{out}, D^\prime_{out} + strides[0] ] \\\\ H_{out} &\in [ H^\prime_{out}, H^\prime_{out} + strides[1] ] \\\\ **Note**: The conv3d_transpose can be seen as the backward of the conv3d. For conv3d, when stride > 1, conv3d maps multiple input shape to the same output shape, so for conv3d_transpose, when stride > 1, input shape maps multiple output shape. If output_size is None, :math:`H_{out} = H^\prime_{out}, :math:`H_{out} = \ H^\prime_{out}, W_{out} = W^\prime_{out}`; else, the :math:`D_{out}` of the output size must between :math:`D^\prime_{out}` and :math:`D^\prime_{out} + strides[0]`, the :math:`H_{out}` of the output size must between :math:`H^\prime_{out}` and :math:`H^\prime_{out} + strides[1]`, and the :math:`W_{out}` of the output size must between :math:`W^\prime_{out}` and :math:`W^\prime_{out} + strides[2]`, conv3d_transpose can compute the kernel size automatically. Parameters: num_channels(int): The number of channels in the input image. num_filters(int): The number of the filter. It is as same as the output image channel. filter_size(int|tuple): The filter size. If filter_size is a tuple, it must contain three integers, (filter_size_D, filter_size_H, filter_size_W). Otherwise, the filter will be a square. output_size(int or tuple, optional): The output image size. If output size is a tuple, it must contain two integers, (image_H, image_W). None if use filter_size, padding, and stride to calculate output_size. if output_size and filter_size are specified at the same time, They should follow the formula above. Default: None. padding(int|str|tuple|list, optional): The padding size. Padding coule be in one of the following forms. 1. a string in ['valid', 'same']. 2. an int, which means each spartial dimension(depth, height, width) is zero paded by size of `padding` 3. a list[int] or tuple[int] whose length is the number of spartial dimensions, which contains the amount of padding on each side for each spartial dimension. It has the form [pad_d1, pad_d2, ...]. 4. a list[int] or tuple[int] whose length is 2 * number of spartial dimensions. It has the form [pad_before, pad_after, pad_before, pad_after, ...] for all spartial 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. stride(int|tuple, optional): The stride size. It means the stride in transposed convolution. If stride is a tuple, it must contain three integers, (stride_depth, stride_height, stride_width). Otherwise, stride_depth = stride_height = stride_width = stride. The default value is 1. dilation(int|tuple, optional): The dilation size. If dilation is a 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 transpose layer. Inspired by grouped convolution in Alex Krizhevsky's Deep CNN paper, in which 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. param_attr (ParamAttr, optional): The parameter attribute for learnable parameters/weights of conv3d_transpose. If it is set to None or one attribute of ParamAttr, conv3d_transpose will create ParamAttr as param_attr. If the Initializer of the param_attr is not set, the parameter is initialized with Xavier. The default value is None. bias_attr (ParamAttr|bool, optional): The parameter attribute for the bias of conv3d_transpose. 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_transpose 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. use_cudnn(bool, optional): Use cudnn kernel or not, it is valid only when the cudnn library is installed. The default value is True. act (str, optional): Activation type, if it is set to None, activation is not appended. The default value is None. data_format (str, optional): Data format that specifies the layout of input. It can be "NCDHW" or "NDHWC". Default: "NCDHW". Attribute: **weight** (Parameter): the learnable weights of filters of this layer. **bias** (Parameter): the learnable bias of this layer. Returns: None. Raises: ValueError: If the shapes of input, filter_size, stride, padding and groups mismatch. Examples: .. code-block:: python import numpy as np from paddle import fluid import paddle.fluid.dygraph as dg from paddle import nn x = np.random.uniform(-1, 1, (2, 4, 8, 8, 8)).astype('float32') place = fluid.CPUPlace() with dg.guard(place): x_var = dg.to_variable(x) conv = nn.Conv3DTranspose(4, 6, (3, 3, 3)) y_var = conv(x_var) y_np = y_var.numpy() print(y_np.shape) # (2, 6, 10, 10, 10) """ def __init__(self, num_channels, num_filters, filter_size, output_size=None, padding=0, stride=1, dilation=1, groups=1, param_attr=None, bias_attr=None, use_cudnn=True, act=None, data_format="NCDHW", dtype='float32'): super(Conv3DTranspose, self).__init__() if not isinstance(use_cudnn, bool): raise ValueError("use_cudnn should be True or False") assert param_attr is not False, "param_attr should not be False in conv3d_transpose." self._num_channels = num_channels self._num_filters = num_filters self._groups = groups self._use_cudnn = use_cudnn self._act = act self._dtype = dtype self._data_format = data_format self._stride = utils.convert_to_list(stride, 3, 'stride') self._dilation = utils.convert_to_list(dilation, 3, 'dilation') self._filter_size = utils.convert_to_list(filter_size, 3, 'filter_size') channel_last = (data_format == "NDHWC") self._padding = padding if output_size is None: self._output_size = output_size elif isinstance(output_size, (list, tuple, int)): self._output_size = utils.convert_to_list(output_size, 3, 'output_size') else: raise ValueError( "output_size should be int, ot list[int] or tuple[int]") self._param_attr = param_attr self._bias_attr = bias_attr filter_shape = [num_channels, num_filters // groups] + self._filter_size self.weight = self.create_parameter( dtype=self._dtype, shape=filter_shape, attr=self._param_attr) self.bias = self.create_parameter( attr=self._bias_attr, shape=[self._num_filters], dtype=self._dtype, is_bias=True) def forward(self, input): out = F.conv3d_transpose( input, self.weight, bias=self.bias, output_size=self._output_size, padding=self._padding, stride=self._stride, dilation=self._dilation, groups=self._groups, use_cudnn=self._use_cudnn, act=self._act, data_format=self._data_format) return out