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.
in_channels(int): The number of channels in the input image.
out_channels(int): The number of channels produced by the convolution.
kernel_size(int|list|uple): The kernel size. If kernel_size is a tuple,
it must contain two integers, (kernel_size_H, kernel_size_W).
Otherwise, the kernel will be a square.
output_padding(int|list|tuple, optional): Additional size added to one side
of each dimension in the output shape. Default: 0.
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
...
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@@ -312,10 +350,10 @@ class Conv2DTranspose(layers.Layer):
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
stride(int|list|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
dilation(int|list|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
...
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@@ -324,113 +362,98 @@ class Conv2DTranspose(layers.Layer):
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)
weight_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.
bias_attr (ParamAttr|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.
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`
...
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@@ -717,11 +716,9 @@ class Conv3DTranspose(layers.Layer):
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,
