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paddle.nn / functional / conv3d_transpose
conv3d_transpose¶
-
paddle.static.nn.
conv3d_transpose
( x, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1, data_format='NCHW', output_size=None, name=None ) [源代码] ¶
三维转置卷积层(Convlution3d transpose layer)
该层根据输入(input)、卷积核(kernel)和卷积核空洞大小(dilations)、步长(stride)、填充(padding)来计算输出特征层大小或者通过output_size指定输出特征层大小。输入(Input)和输出(Output)为NCDHW或者NDHWC格式。其中N为批尺寸,C为通道数(channel),D为特征深度,H为特征层高度,W为特征层宽度。转置卷积的计算过程相当于卷积的反向计算。转置卷积又被称为反卷积(但其实并不是真正的反卷积)。欲了解卷积转置层细节,请参考下面的说明和 参考文献 。如果参数bias_attr不为False, 转置卷积计算会添加偏置项。
输入 \(X\) 和输出 \(Out\) 函数关系如下:
- 其中:
-
\(X\) : 输入,具有NCDHW或NDHWC格式的5-D Tensor
\(W\) : 卷积核,具有NCDHW格式的5-D Tensor
\(*\) : 卷积操作(注意:转置卷积本质上的计算还是卷积)
\(b\) : 偏置(bias),2-D Tensor,形状为
[M,1]
\(σ\) : 激活函数
\(Out\) : 输出值,NCDHW或NDHWC格式的5-D Tensor,和
X
的形状可能不同
示例
输入:
输入的shape:\((N,C_{in}, D_{in}, H_{in}, W_{in})\)
卷积核的shape:\((C_{in}, C_{out}, D_f, H_f, W_f)\)
输出:
输出的shape:\((N,C_{out}, D_{out}, H_{out}, W_{out})\)
其中:
如果 padding
= "SAME":
如果 padding
= "VALID":
注意:
如果output_size为None,则 \(D_{out}\) = \(D^\prime_{out}\) , \(H_{out}\) = \(H^\prime_{out}\) , \(W_{out}\) = \(W^\prime_{out}\) ;否则,指定的output_size_depth(输出特征层的深度) \(D_{out}\) 应当介于 \(D^\prime_{out}\) 和 \(D^\prime_{out} + strides[0]\) 之间(不包含 \(D^\prime_{out} + strides[0]\) ),指定的output_size_height(输出特征层的高) \(H_{out}\) 应当介于 \(H^\prime_{out}\) 和 \(H^\prime_{out} + strides[1]\) 之间(不包含 \(H^\prime_{out} + strides[1]\) ), 并且指定的output_size_width(输出特征层的宽) \(W_{out}\) 应当介于 \(W^\prime_{out}\) 和 \(W^\prime_{out} + strides[2]\) 之间(不包含 \(W^\prime_{out} + strides[2]\) )。
由于转置卷积可以当成是卷积的反向计算,而根据卷积的输入输出计算公式来说,不同大小的输入特征层可能对应着相同大小的输出特征层,所以对应到转置卷积来说,固定大小的输入特征层对应的输出特征层大小并不唯一。
如果指定了output_size, conv3d_transpose
可以自动计算卷积核的大小。
- 参数:
-
x (Tensor) - 形状为 \([N, C, D, H, W]\) 或 \([N, D, H, W, C]\) 的5-D Tensor,N是批尺寸,C是通道数,D是特征深度,H是特征高度,W是特征宽度,数据类型:float32或float64。
weight (Tensor) - 形状为 \([C, M/g, kD, kH, kW]\) 的卷积核。 M是输出通道数, g是分组的个数,kD是卷积核的深度,kH是卷积核的高度,kW是卷积核的宽度。
bias (int|list|tuple) - 偏置项,形状为: \([M,]\) 。
stride (int|list|tuple,可选) - 步长大小。如果
stride
为元组或列表,则必须包含三个整型数,分别表示深度,垂直和水平滑动步长。否则,表示深度,垂直和水平滑动步长均为stride
。默认值:1。padding (int|list|tuple|str,可选) - 填充padding大小。padding参数在输入特征层每边添加
dilation * (kernel_size - 1) - padding
个0。如果它是一个字符串,可以是"VALID"或者"SAME",表示填充算法,计算细节可参考上述padding
= "SAME"或padding
= "VALID" 时的计算公式。如果它是一个元组或列表,它可以有3种格式:(1)包含5个二元组:当data_format
为"NCDHW"时为 [[0,0], [0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]],当data_format
为"NDHWC"时为[[0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]];(2)包含6个整数值:[pad_depth_front, pad_depth_back, pad_height_top, pad_height_bottom, pad_width_left, pad_width_right];(3)包含3个整数值:[pad_depth, pad_height, pad_width],此时 pad_depth_front = pad_depth_back = pad_depth, pad_height_top = pad_height_bottom = pad_height, pad_width_left = pad_width_right = pad_width。若为一个整数,pad_depth = pad_height = pad_width = padding。默认值:0。output_padding (int|list|tuple, optional): 输出形状上一侧额外添加的大小. 默认值: 0.
dilation (int|list|tuple,可选) - 空洞大小。空洞卷积时会使用该参数,卷积核对输入进行卷积时,感受野里每相邻两个特征点之间的空洞信息。如果空洞大小为列表或元组,则必须包含两个整型数:(dilation_height,dilation_width)。若为一个整数,dilation_height = dilation_width = dilation。默认值:1。
groups (int,可选) - 三维转置卷积层的组数。从Alex Krizhevsky的CNN Deep论文中的群卷积中受到启发,当group=2时,输入和卷积核分别根据通道数量平均分为两组,第一组卷积核和第一组输入进行卷积计算,第二组卷积核和第二组输入进行卷积计算。默认:group = 1。
weight_attr (ParamAttr,可选) - 指定权重参数属性的对象。默认值为None,表示使用默认的权重参数属性。具体用法请参见 ParamAttr 。
bias_attr (ParamAttr|bool,可选)- 指定偏置参数属性的对象。若
bias_attr
为bool类型,只支持为False,表示没有偏置参数。默认值为None,表示使用默认的偏置参数属性。具体用法请参见 ParamAttr 。data_format (str,可选) - 指定输入的数据格式,输出的数据格式将与输入保持一致,可以是"NCHW"和"NHWC"。N是批尺寸,C是通道数,H是特征高度,W是特征宽度。默认值:"NCHW"。
name (str,可选) – 具体用法请参见 cn_api_guide_Name ,一般无需设置,默认值:None。
返回:5-D Tensor,数据类型与 input
一致。如果未指定激活层,则返回转置卷积计算的结果,如果指定激活层,则返回转置卷积和激活计算之后的最终结果。
返回类型:Tensor
- 抛出异常:
-
ValueError
- 如果输入的shape、kernel_size、stride、padding和groups不匹配。ValueError
- 如果data_format
既不是"NCDHW"也不是"NDHWC"。ValueError
- 如果padding
是字符串,既不是"SAME"也不是"VALID"。ValueError
- 如果padding
含有5个二元组,与批尺寸对应维度的值不为0或者与通道对应维度的值不为0。ValueError
- 如果output_size
和filter_size
同时为None。ShapeError
- 如果输入不是5-D Tensor。ShapeError
- 如果输入和卷积核的维度大小不相同。ShapeError
- 如果输入的维度大小与stride
之差不是2。
代码示例
import paddle
import paddle.nn.functional as F
x_var = paddle.randn((2, 3, 8, 8, 8), dtype='float32')
w_var = paddle.randn((3, 6, 3, 3, 3), dtype='float32')
y_var = F.conv3d_transpose(x_var, w_var)
y_np = y_var.numpy()
print(y_np.shape)
# (2, 6, 10, 10, 10)
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