x (Tensor): The input is 3-D Tensor with shape [N, C, L], the data type
of input is float16 or float32 or float64.
weight (Tensor): The convolution kernel with shape [M, C/g, K], where M is
the number of output channels, g is the number of groups, K is the kernel's size.
bias (Tensor, optional): The bias with shape [M,]. Default: None.
stride (int or tuple, optional): The stride size. If stride is a tuple, it must
contain one integers, (stride_size). Default: 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 the feature map is zero paded by size of `padding` on both sides.
3. a list[int] or tuple[int] whose length is 1, which means the feature map is zero paded by size of `padding[0]` on both sides.
4. a list[int] or tuple[int] whose length is 2. It has the form [pad_before, pad_after].
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 or tuple, optional): The dilation size. If dilation is a tuple, it must
contain one integer, (dilation_size). Default: 1.
groups (int, optional): The groups number of the conv1d function. 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.
data_format (str, optional): Specify the data format of the input, and the data format of the output
will be consistent with that of the input. An optional string from: `"NCL"`, `"NLC"`.
The default is `"NCL"`. When it is `"NCL"`, the data is stored in the order of:
`[batch_size, input_channels, feature_length]`.
name(str, optional): For detailed information, please refer
to :ref:`api_guide_Name`. Usually name is no need to set and
None by default.
Returns:
A tensor representing the conv1d, whose data type is the
same with input.
Raises:
ValueError: If the channel dimmention of the input is less than or equal to zero.
ValueError: If `data_format` is not "NCL" or "NLC".
ValueError: If `padding` is a string, but not "SAME" or "VALID".
ValueError: If `padding` is a tuple, but the element corresponding to the input's batch size is not 0
or the element corresponding to the input's channel is not 0.
ShapeError: If the input is not 3-D Tensor.
ShapeError: If the input's dimension size and filter's dimension size not equal.
ShapeError: If the dimension size of input minus the size of `stride` is not 1.
ShapeError: If the number of input channels is not equal to filter's channels * groups.
ShapeError: If the number of output channels is not be divided by groups.
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
import numpy as np
x = np.array([[[4, 8, 1, 9],
[7, 2, 0, 9],
[6, 9, 2, 6]]]).astype(np.float32)
w=np.array(
[[[9, 3, 4],
[0, 0, 7],
[2, 5, 6]],
[[0, 3, 4],
[2, 9, 7],
[5, 6, 8]]]).astype(np.float32)
paddle.disable_static()
x_var = paddle.to_tensor(x)
w_var = paddle.to_tensor(w)
y_var = F.conv1d(x_var, w_var)
y_np = y_var.numpy()
print(y_np)
# [[[133. 238.]
# [160. 211.]]]
"""
cudnn_version=get_cudnn_version()
ifcudnn_versionisnotNone:
use_cudnn=True
else:
use_cudnn=False
ifdata_formatnotin["NCL","NLC"]:
raiseValueError("Attr(data_format) should be 'NCL' or 'NLC'. "
