# Copyright 2020-2021 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""conv"""
import numpy as np
from mindspore import log as logger
from mindspore import context
from mindspore.ops import operations as P
from mindspore.ops.primitive import constexpr
from mindspore.common.parameter import Parameter
from mindspore.common.initializer import initializer
from mindspore.common.tensor import Tensor
from mindspore._checkparam import Validator, Rel, twice, _check_3d_int_or_tuple
from mindspore._extends import cell_attr_register
from ..cell import Cell
__all__ = ['Conv2d', 'Conv2dTranspose', 'Conv1d', 'Conv1dTranspose', 'Conv3d', 'Conv3dTranspose']
class _Conv(Cell):
"""
Applies a N-D convolution over an input signal composed of several input planes.
"""
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride,
pad_mode,
padding,
dilation,
group,
has_bias,
weight_init,
bias_init,
data_format='NCHW',
transposed=False):
"""Initialize _Conv."""
super(_Conv, self).__init__()
self.in_channels = Validator.check_positive_int(in_channels, 'in_channels', self.cls_name)
self.out_channels = Validator.check_positive_int(out_channels, 'out_channels', self.cls_name)
self.kernel_size = kernel_size
self.stride = stride
self.pad_mode = pad_mode
self.weight_init = weight_init
self.bias_init = bias_init
self.format = Validator.check_string(data_format, ['NCHW', 'NHWC', 'NCDHW'], 'format', self.cls_name)
if context.get_context("device_target") != "GPU" and self.format == "NHWC":
raise ValueError(f"For '{self.cls_name}', the \"NHWC\" format only support in GPU target, "
f"but got the 'format' is {self.format} and "
f"the platform is {context.get_context('device_target')}.")
if isinstance(padding, int):
Validator.check_non_negative_int(padding, 'padding', self.cls_name)
self.padding = padding
elif isinstance(padding, tuple):
for pad in padding:
Validator.check_non_negative_int(pad, 'padding item', self.cls_name)
self.padding = padding
else:
raise TypeError(f"For '{self.cls_name}', the type of 'padding' must be int or tuple(int), "
f"but got {type(padding).__name__}.")
self.dilation = dilation
self.group = Validator.check_positive_int(group)
self.has_bias = has_bias
for kernel_size_elem in kernel_size:
Validator.check_positive_int(kernel_size_elem, 'kernel_size item', self.cls_name)
for stride_elem in stride:
Validator.check_positive_int(stride_elem, 'stride item', self.cls_name)
for dilation_elem in dilation:
Validator.check_positive_int(dilation_elem, 'dilation item', self.cls_name)
if in_channels % group != 0:
raise ValueError(f"For '{self.cls_name}', the attr 'in_channels' must be divisible by attr 'group', "
f"but got 'in_channels': {in_channels} and 'group': {group}.")
if out_channels % group != 0:
raise ValueError(f"For '{self.cls_name}', the 'out_channels' must be divisible by attr 'group', "
f"but got 'out_channels': {out_channels} and 'group': {group}.")
if transposed:
shape = [in_channels, out_channels // group, *kernel_size]
else:
shape = [out_channels, *kernel_size, in_channels // group] if self.format == "NHWC" else \
[out_channels, in_channels // group, *kernel_size]
self.weight = Parameter(initializer(self.weight_init, shape), name='weight')
if Validator.check_bool(has_bias, "has_bias", self.cls_name):
self.bias = Parameter(initializer(self.bias_init, [out_channels]), name='bias')
else:
if self.bias_init != 'zeros':
logger.warning("Value of 'has_bias' is False, value of 'bias_init' will be ignored.")
self.bias = None
def construct(self, *inputs):
"""Must be overridden by all subclasses."""
raise NotImplementedError
[文档]class Conv2d(_Conv):
r"""
2D convolution layer.
Calculates the 2D convolution on the input tensor which is typically of shape :math:`(N, C_{in}, H_{in}, W_{in})`,
where :math:`N` is batch size, :math:`C_{in}` is a number of channels,
:math:`H_{in}, W_{in}` are the height and width of the feature layer respectively.
For the tensor of each batch, its shape is :math:`(C_{in}, H_{in}, W_{in})`, the formula is defined as:
.. math::
\text{out}(N_i, C_{\text{out}_j}) = \text{bias}(C_{\text{out}_j}) +
\sum_{k = 0}^{C_{in} - 1} \text{ccor}({\text{weight}(C_{\text{out}_j}, k), \text{X}(N_i, k)})
where :math:`ccor` is the `cross-correlation <https://en.wikipedia.org/wiki/Cross-correlation>`_,
:math:`C_{in}` is the channel number of the input, :math:`out_{j}` corresponds to the jth channel of
the output and :math:`j` is in the range of :math:`[0，C_{out}-1]`. :math:`\text{weight}(C_{\text{out}_j}, k)`
is a convolution kernel slice with shape :math:`(\text{kernel_size[0]}, \text{kernel_size[1]})`,
where :math:`\text{kernel_size[0]}` and :math:`\text{kernel_size[1]}` are the height and width of the convolution
kernel respectively. :math:`\text{bias}` is the bias parameter and :math:`\text{X}` is the input tensor.
The shape of full convolution kernel is
:math:`(C_{out}, C_{in} / \text{group}, \text{kernel_size[0]}, \text{kernel_size[1]})`,
where `group` is the number of groups to split the input `x` in the channel dimension.
For more details, please refers to the paper `Gradient Based Learning Applied to Document
Recognition <http://vision.stanford.edu/cs598_spring07/papers/Lecun98.pdf>`_.
Args:
in_channels (int): The channel number of the input tensor of the Conv2d layer.
out_channels (int): The channel number of the output tensor of the Conv2d layer.
kernel_size (Union[int, tuple[int]]): Specifies the height and width of the 2D convolution kernel.
The data type is an integer or a tuple of two integers. An integer represents the height
and width of the convolution kernel. A tuple of two integers represents the height
and width of the convolution kernel respectively.
stride (Union[int, tuple[int]]): The movement stride of the 2D convolution kernel.
The data type is an integer or a tuple of two integers. An integer represents the movement step size
in both height and width directions. A tuple of two integers represents the movement step size in the height
and width directions respectively. Default: 1.
pad_mode (str): Specifies padding mode. The optional values are
"same", "valid", "pad". Default: "same".
- same: The width of the output is the same as the value of the input divided by `stride`.
If this mode is set, the value of `padding` must be 0.
- valid: Returns a valid calculated output without padding. Excess pixels that do not satisfy the
calculation will be discarded. If this mode is set, the value of `padding` must be 0.
- pad: Pads the input. Padding `padding` size of zero on both sides of the input.
If this mode is set, the value of `padding` must be greater than or equal to 0.
padding (Union[int, tuple[int]]): The number of padding on the height and width directions of the input.
The data type is an integer or a tuple of four integers. If `padding` is an integer,
then the top, bottom, left, and right padding are all equal to `padding`.
If `padding` is a tuple of 4 integers, then the top, bottom, left, and right padding
is equal to `padding[0]`, `padding[1]`, `padding[2]`, and `padding[3]` respectively.
The value should be greater than or equal to 0. Default: 0.
dilation (Union[int, tuple[int]]): Dilation size of 2D convolution kernel.
The data type is an integer or a tuple of two integers. If :math:`k > 1`, the kernel is sampled
every `k` elements. The value of `k` on the height and width directions is in range of [1, H]
and [1, W] respectively. Default: 1.
group (int): Splits filter into groups, `in_channels` and `out_channels` must be
divisible by `group`. If the group is equal to `in_channels` and `out_channels`,
this 2D convolution layer also can be called 2D depthwise convolution layer. Default: 1.
has_bias (bool): Whether the Conv2d layer has a bias parameter. Default: False.
weight_init (Union[Tensor, str, Initializer, numbers.Number]): Initialization method of weight parameter.
It can be a Tensor, a string, an Initializer or a numbers.Number. When a string is specified,
values from 'TruncatedNormal', 'Normal', 'Uniform', 'HeUniform' and 'XavierUniform' distributions as well
as constant 'One' and 'Zero' distributions are possible. Alias 'xavier_uniform', 'he_uniform', 'ones'
and 'zeros' are acceptable. Uppercase and lowercase are both acceptable. Refer to the values of
Initializer for more details. Default: 'normal'.
bias_init (Union[Tensor, str, Initializer, numbers.Number]): Initialization method of bias parameter.
Available initialization methods are the same as 'weight_init'. Refer to the values of
Initializer for more details. Default: 'zeros'.
data_format (str): The optional value for data format, is 'NHWC' or 'NCHW'.
Default: 'NCHW'.
Inputs:
- **x** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})` \
or :math:`(N, H_{in}, W_{in}, C_{in})`.
Outputs:
Tensor of shape :math:`(N, C_{out}, H_{out}, W_{out})` or :math:`(N, H_{out}, W_{out}, C_{out})`.
pad_mode is 'same':
.. math::
\begin{array}{ll} \\
H_{out} ＝ \left \lfloor{\frac{H_{in}}{\text{stride[0]}} + 1} \right \rfloor \\
W_{out} ＝ \left \lfloor{\frac{W_{in}}{\text{stride[1]}} + 1} \right \rfloor \\
\end{array}
pad_mode is 'valid':
.. math::
\begin{array}{ll} \\
H_{out} ＝ \left \lfloor{\frac{H_{in} - \text{dilation[0]} \times (\text{kernel_size[0]} - 1) }
{\text{stride[0]}} + 1} \right \rfloor \\
W_{out} ＝ \left \lfloor{\frac{W_{in} - \text{dilation[1]} \times (\text{kernel_size[1]} - 1) }
{\text{stride[1]}} + 1} \right \rfloor \\
\end{array}
pad_mode is 'pad':
.. math::
\begin{array}{ll} \\
H_{out} ＝ \left \lfloor{\frac{H_{in} + padding[0] + padding[1] - (\text{dilation[0]} - 1) \times
\text{kernel_size[0]} - 1 }{\text{stride[0]}} + 1} \right \rfloor \\
W_{out} ＝ \left \lfloor{\frac{W_{in} + padding[2] + padding[3] - (\text{dilation[1]} - 1) \times
\text{kernel_size[1]} - 1 }{\text{stride[1]}} + 1} \right \rfloor \\
\end{array}
Raises:
TypeError: If `in_channels`, `out_channels` or `group` is not an int.
TypeError: If `kernel_size`, `stride`, `padding` or `dilation` is neither an int not a tuple.
ValueError: If `in_channels`, `out_channels`, `kernel_size`, `stride` or `dilation` is less than 1.
ValueError: If `padding` is less than 0.
ValueError: If `pad_mode` is not one of 'same', 'valid', 'pad'.
ValueError: If `padding` is a tuple whose length is not equal to 4.
ValueError: If `pad_mode` is not equal to 'pad' and `padding` is not equal to (0, 0, 0, 0).
ValueError: If `data_format` is neither 'NCHW' not 'NHWC'.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> net = nn.Conv2d(120, 240, 4, has_bias=False, weight_init='normal')
>>> x = Tensor(np.ones([1, 120, 1024, 640]), mindspore.float32)
>>> output = net(x).shape
>>> print(output)
(1, 240, 1024, 640)
"""
@cell_attr_register
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
pad_mode='same',
padding=0,
dilation=1,
group=1,
has_bias=False,
weight_init='normal',
bias_init='zeros',
data_format='NCHW'):
"""Initialize Conv2d."""
kernel_size = twice(kernel_size)
stride = twice(stride)
self._dilation = dilation
dilation = twice(dilation)
super(Conv2d, self).__init__(
in_channels,
out_channels,
kernel_size,
stride,
pad_mode,
padding,
dilation,
group,
has_bias,
weight_init,
bias_init,
data_format)
self.conv2d = P.Conv2D(out_channel=self.out_channels,
kernel_size=self.kernel_size,
mode=1,
pad_mode=self.pad_mode,
pad=self.padding,
stride=self.stride,
dilation=self.dilation,
group=self.group,
data_format=self.format)
self.bias_add = P.BiasAdd(data_format=self.format)
def construct(self, x):
output = self.conv2d(x, self.weight)
if self.has_bias:
output = self.bias_add(output, self.bias)
return output
def extend_repr(self):
s = 'input_channels={}, output_channels={}, kernel_size={}, ' \
'stride={}, pad_mode={}, padding={}, dilation={}, ' \
'group={}, has_bias={}, ' \
'weight_init={}, bias_init={}, format={}'.format(
self.in_channels,
self.out_channels,
self.kernel_size,
self.stride,
self.pad_mode,
self.padding,
self.dilation,
self.group,
self.has_bias,
self.weight_init,
self.bias_init,
self.format)
return s
@constexpr
def _check_input_3d(input_shape, op_name):
if len(input_shape) != 3:
raise ValueError(f"For '{op_name}', the dimension of input should be 3d, but got {len(input_shape)}.")
[文档]class Conv1d(_Conv):
r"""
1D convolution layer.
Calculates the 1D convolution on the input tensor which is typically of shape :math:`(N, C_{in}, L_{in})`,
where :math:`N` is batch size, :math:`C_{in}` is a number of channels and :math:`L_{in}` is a length of sequence.
For the tensor of each batch, its shape is :math:`(C_{in}, L_{in})`, the formula is defined as:
.. math::
\text{out}(N_i, C_{\text{out}_j}) = \text{bias}(C_{\text{out}_j}) +
\sum_{k = 0}^{C_{in} - 1} \text{ccor}({\text{weight}(C_{\text{out}_j}, k), \text{X}(N_i, k)})
where :math:`ccor` is the `cross-correlation <https://en.wikipedia.org/wiki/Cross-correlation>`_,
:math:`C_{in}` is the channel number of the input, :math:`out_{j}` corresponds to the jth channel of
the output and :math:`j` is in the range of :math:`[0，C_{out}-1]`. :math:`\text{weight}(C_{\text{out}_j}, k)`
is a convolution kernel slice with shape :math:`(kernel_size)`, where :math:`\text{kernel_size}` is the width of
the convolution kernel. :math:`\text{bias}` is the bias parameter, and :math:`\text{X}` is the input tensor.
The shape of full convolution kernel is :math:`(C_{out}, C_{in} / \text{group}, \text{kernel_size})`,
where `group` is the number of groups to split the input `x` in the channel dimension.
For more details, please refers to the paper `Gradient Based Learning Applied to Document
Recognition <http://vision.stanford.edu/cs598_spring07/papers/Lecun98.pdf>`_.
Args:
in_channels (int): The channel number of the input tensor of the Conv1d layer.
out_channels (int): The channel number of the output tensor of the Conv1d layer.
kernel_size (int): Specifies the width of the 1D convolution kernel.
stride (int): The movement stride of the 1D convolution kernel. Default: 1.
pad_mode (str): Specifies padding mode. The optional values are
"same", "valid", "pad". Default: "same".
- same: The width of the output is the same as the value of the input divided by `stride`.
If this mode is set, the value of `padding` must be 0.
- valid: Returns a valid calculated output without padding. Excess pixels that do not satisfy the
calculation will be discarded. If this mode is set, the value of `padding` must be 0.
- pad: Pads the input. Padding `padding` size of zero on both sides of the input.
If this mode is set, the value of `padding` must be greater than or equal to 0.
padding (int): The number of padding on both sides of input.
The value should be greater than or equal to 0. Default: 0.
dilation (int): Dilation size of 1D convolution kernel. If :math:`k > 1`, the kernel is sampled
every `k` elements. The value of `k` is in range of [1, L]. Default: 1.
group (int): Splits filter into groups, `in_channels` and `out_channels` must be
divisible by `group`. Default: 1.
has_bias (bool): Whether the Conv1d layer has a bias parameter. Default: False.
weight_init (Union[Tensor, str, Initializer, numbers.Number]): Initialization method of weight parameter.
It can be a Tensor, a string, an Initializer or a numbers.Number. When a string is specified,
values from 'TruncatedNormal', 'Normal', 'Uniform', 'HeUniform' and 'XavierUniform' distributions as well
as constant 'One' and 'Zero' distributions are possible. Alias 'xavier_uniform', 'he_uniform', 'ones'
and 'zeros' are acceptable. Uppercase and lowercase are both acceptable. Refer to the values of
Initializer for more details. Default: 'normal'.
bias_init (Union[Tensor, str, Initializer, numbers.Number]): Initialization method of bias parameter.
Available initialization methods are the same as 'weight_init'. Refer to the values of
Initializer for more details. Default: 'zeros'.
Inputs:
- **x** (Tensor) - Tensor of shape :math:`(N, C_{in}, L_{in})`.
Outputs:
Tensor of shape :math:`(N, C_{out}, L_{out})`.
pad_mode is 'same':
.. math::
L_{out} ＝ \left \lfloor{\frac{L_{in}}{\text{stride}} + 1} \right \rfloor
pad_mode is 'valid':
.. math::
L_{out} ＝ \left \lfloor{\frac{L_{in} - \text{dilation} \times (\text{kernel_size} - 1) }
{\text{stride}} + 1} \right \rfloor
pad_mode is 'pad':
.. math::
L_{out} ＝ \left \lfloor{\frac{L_{in} + 2 \times padding - (\text{dilation} - 1) \times
\text{kernel_size} - 1 }{\text{stride}} + 1} \right \rfloor
Raises:
TypeError: If `in_channels`, `out_channels`, `kernel_size`, `stride`, `padding` or `dilation` is not an int.
ValueError: If `in_channels`, `out_channels`, `kernel_size`, `stride` or `dilation` is less than 1.
ValueError: If `padding` is less than 0.
ValueError: If `pad_mode` is not one of 'same', 'valid', 'pad'.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> net = nn.Conv1d(120, 240, 4, has_bias=False, weight_init='normal')
>>> x = Tensor(np.ones([1, 120, 640]), mindspore.float32)
>>> output = net(x).shape
>>> print(output)
(1, 240, 640)
"""
@cell_attr_register
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
pad_mode='same',
padding=0,
dilation=1,
group=1,
has_bias=False,
weight_init='normal',
bias_init='zeros'):
"""Initialize Conv1d."""
Validator.check_value_type("kernel_size", kernel_size, [int], self.cls_name)
Validator.check_value_type("stride", stride, [int], self.cls_name)
Validator.check_value_type("padding", padding, [int], self.cls_name)
Validator.check_value_type("dilation", dilation, [int], self.cls_name)
Validator.check_int(kernel_size, 1, Rel.GE, 'kernel_size', self.cls_name)
Validator.check_int(stride, 1, Rel.GE, 'stride', self.cls_name)
Validator.check_non_negative_int(padding, 'padding', self.cls_name)
Validator.check_int(dilation, 1, Rel.GE, 'dilation', self.cls_name)
kernel_size = (1, kernel_size)
stride = (1, stride)
dilation = (1, dilation)
get_shape = P.Shape()
get_dtype = P.DType()
if isinstance(weight_init, Tensor):
weight_init_shape = get_shape(weight_init)
Validator.check_equal_int(len(weight_init_shape), 3, 'weight_init_shape', self.cls_name)
weight_init_dtype = get_dtype(weight_init)
weight_init_value = weight_init.asnumpy()
weight_init_value = np.expand_dims(weight_init_value, 2)
weight_init = Tensor(weight_init_value, weight_init_dtype)
super(Conv1d, self).__init__(
in_channels,
out_channels,
kernel_size,
stride,
pad_mode,
padding,
dilation,
group,
has_bias,
weight_init,
bias_init)
self.padding = (0, 0, padding, padding)
Validator.check_string(pad_mode, ['valid', 'same', 'pad'], 'pad_mode', self.cls_name)
self.conv2d = P.Conv2D(out_channel=self.out_channels,
kernel_size=self.kernel_size,
mode=1,
pad_mode=self.pad_mode,
pad=self.padding,
stride=self.stride,
dilation=self.dilation,
group=self.group)
self.bias_add = P.BiasAdd()
self.expand_dims = P.ExpandDims()
self.squeeze = P.Squeeze(2)
self.shape = P.Shape()
def construct(self, x):
x_shape = self.shape(x)
_check_input_3d(x_shape, self.cls_name)
x = self.expand_dims(x, 2)
output = self.conv2d(x, self.weight)
if self.has_bias:
output = self.bias_add(output, self.bias)
output = self.squeeze(output)
return output
def extend_repr(self):
s = 'input_channels={}, output_channels={}, kernel_size={}, ' \
'stride={}, pad_mode={}, padding={}, dilation={}, ' \
'group={}, has_bias={}, ' \
'weight_init={}, bias_init={}'.format(
self.in_channels,
self.out_channels,
self.kernel_size,
self.stride,
self.pad_mode,
self.padding,
self.dilation,
self.group,
self.has_bias,
self.weight_init,
self.bias_init)
return s
@constexpr
def _check_input_5dims(input_shape, op_name):
if len(input_shape) != 5:
raise ValueError(f"For '{op_name}', the dimension of input should be 5d, but got {len(input_shape)}.")
[文档]class Conv3d(_Conv):
r"""
3D convolution layer.
Calculates the 3D convolution on the input tensor which is typically of shape
:math:`(N, C_{in}, D_{in}, H_{in}, W_{in})`,
where :math:`N` is batch size, :math:`C_{in}` is a number of channels,
:math:`D_{in}, H_{in}, W_{in}` are the depth, height and width of the feature layer respectively.
For the tensor of each batch, its shape is :math:`(C_{in}, D_{in}, H_{in}, W_{in})`, the formula is defined as:
.. math::
\text{out}(N_i, C_{\text{out}_j}) = \text{bias}(C_{\text{out}_j}) +
\sum_{k = 0}^{C_{in} - 1} \text{ccor}({\text{weight}(C_{\text{out}_j}, k), \text{X}(N_i, k)})
where :math:`ccor` is the `cross-correlation <https://en.wikipedia.org/wiki/Cross-correlation>`_,
:math:`C_{in}` is the channel number of the input, :math:`out_{j}` corresponds to the jth channel of
the output and :math:`j` is in the range of :math:`[0，C_{out}-1]`. :math:`\text{weight}(C_{\text{out}_j}, k)`
is a convolution kernel slice with shape
:math:`(\text{kernel_size[0]}, \text{kernel_size[1]}, \text{kernel_size[2]})`,
where :math:`\text{kernel_size[0]}`, :math:`\text{kernel_size[1]}` and :math:`\text{kernel_size[2]}` are
the depth, height and width of the convolution kernel respectively. :math:`\text{bias}` is the bias parameter
and :math:`\text{X}` is the input tensor.
The shape of full convolution kernel is
:math:`(C_{out}, C_{in} / \text{group}, \text{kernel_size[0]}, \text{kernel_size[1]}, \text{kernel_size[2]})`,
where `group` is the number of groups to split the input `x` in the channel dimension.
For more details, please refers to the paper `Gradient Based Learning Applied to Document
Recognition <http://vision.stanford.edu/cs598_spring07/papers/Lecun98.pdf>`_.
Args:
in_channels (int): The channel number of the input tensor of the Conv3d layer.
out_channels (int): The channel number of the output tensor of the Conv3d layer.
kernel_size (Union[int, tuple[int]]): Specifies the depth, height and width of the 3D convolution kernel.
The data type is an integer or a tuple of three integers. An integer represents the depth, height
and width of the convolution kernel. A tuple of three integers represents the depth, height
and width of the convolution kernel respectively.
stride (Union[int, tuple[int]]): The movement stride of the 3D convolution kernel.
The data type is an integer or a tuple of three integers. An integer represents the movement step size
in depth, height and width directions. A tuple of three integers represents the movement step size
in the depth, height and width directions respectively. Default: 1.
pad_mode (str): Specifies padding mode. The optional values are
"same", "valid", "pad". Default: "same".
- same: The width of the output is the same as the value of the input divided by `stride`.
If this mode is set, the value of `padding` must be 0.
- valid: Returns a valid calculated output without padding. Excess pixels that do not satisfy the
calculation will be discarded. If this mode is set, the value of `padding` must be 0.
- pad: Pads the input. Padding `padding` size of zero on both sides of the input.
If this mode is set, the value of `padding` must be greater than or equal to 0.
padding (Union(int, tuple[int])): The number of padding on the depth, height and width directions of the input.
The data type is an integer or a tuple of six integers. If `padding` is an integer,
then the head, tail, top, bottom, left, and right padding are all equal to `padding`.
If `padding` is a tuple of six integers, then the head, tail, top, bottom, left, and right padding
is equal to `padding[0]`, `padding[1]`, `padding[2]`, `padding[3]`, `padding[4]` and `padding[5]`
respectively. The value should be greater than or equal to 0. Default: 0.
dilation (Union[int, tuple[int]]): Dilation size of 3D convolution kernel.
The data type is an integer or a tuple of three integers. If :math:`k > 1`, the kernel is sampled
every `k` elements. The value of `k` on the depth, height and width directions is in range of
[1, D], [1, H] and [1, W] respectively. Default: 1.
group (int): Splits filter into groups, `in_channels` and `out_channels` must be
divisible by `group`. Default: 1. Only 1 is currently supported.
has_bias (bool): Whether the Conv3d layer has a bias parameter. Default: False.
weight_init (Union[Tensor, str, Initializer, numbers.Number]): Initialization method of weight parameter.
It can be a Tensor, a string, an Initializer or a numbers.Number. When a string is specified,
values from 'TruncatedNormal', 'Normal', 'Uniform', 'HeUniform' and 'XavierUniform' distributions as well
as constant 'One' and 'Zero' distributions are possible. Alias 'xavier_uniform', 'he_uniform', 'ones'
and 'zeros' are acceptable. Uppercase and lowercase are both acceptable. Refer to the values of
Initializer for more details. Default: 'normal'.
bias_init (Union[Tensor, str, Initializer, numbers.Number]): Initialization method of bias parameter.
Available initialization methods are the same as 'weight_init'. Refer to the values of
Initializer for more details. Default: 'zeros'.
data_format (str): The optional value for data format. Currently only support "NCDHW".
Inputs:
- **x** (Tensor) - Tensor of shape :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})`.
Currently input data type only support float16 and float32.
Outputs:
Tensor of shape is :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})`.
pad_mode is 'same':
.. math::
\begin{array}{ll} \\
D_{out} ＝ \left \lfloor{\frac{D_{in}}{\text{stride[0]}} + 1} \right \rfloor \\
H_{out} ＝ \left \lfloor{\frac{H_{in}}{\text{stride[1]}} + 1} \right \rfloor \\
W_{out} ＝ \left \lfloor{\frac{W_{in}}{\text{stride[2]}} + 1} \right \rfloor \\
\end{array}
pad_mode is 'valid':
.. math::
\begin{array}{ll} \\
D_{out} ＝ \left \lfloor{\frac{D_{in} - \text{dilation[0]} \times (\text{kernel_size[0]} - 1) }
{\text{stride[0]}} + 1} \right \rfloor \\
H_{out} ＝ \left \lfloor{\frac{H_{in} - \text{dilation[1]} \times (\text{kernel_size[1]} - 1) }
{\text{stride[1]}} + 1} \right \rfloor \\
W_{out} ＝ \left \lfloor{\frac{W_{in} - \text{dilation[2]} \times (\text{kernel_size[2]} - 1) }
{\text{stride[2]}} + 1} \right \rfloor \\
\end{array}
pad_mode is 'pad':
.. math::
\begin{array}{ll} \\
D_{out} ＝ \left \lfloor{\frac{D_{in} + padding[0] + padding[1] - (\text{dilation[0]} - 1) \times
\text{kernel_size[0]} - 1 }{\text{stride[0]}} + 1} \right \rfloor \\
H_{out} ＝ \left \lfloor{\frac{H_{in} + padding[2] + padding[3] - (\text{dilation[1]} - 1) \times
\text{kernel_size[1]} - 1 }{\text{stride[1]}} + 1} \right \rfloor \\
W_{out} ＝ \left \lfloor{\frac{W_{in} + padding[4] + padding[5] - (\text{dilation[2]} - 1) \times
\text{kernel_size[2]} - 1 }{\text{stride[2]}} + 1} \right \rfloor \\
\end{array}
Raises:
TypeError: If `in_channels`, `out_channels` or `group` is not an int.
TypeError: If `kernel_size`, `stride`, `padding` or `dilation` is neither an int nor a tuple.
ValueError: If `out_channels`, `kernel_size`, `stride` or `dilation` is less than 1.
ValueError: If `padding` is less than 0.
ValueError: If `pad_mode` is not one of 'same', 'valid', 'pad'.
ValueError: If `padding` is a tuple whose length is not equal to 6.
ValueError: If `pad_mode` is not equal to 'pad' and `padding` is not equal to (0, 0, 0, 0, 0, 0).
ValueError: If `data_format` is not 'NCDHW'.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> x = Tensor(np.ones([16, 3, 10, 32, 32]), mindspore.float32)
>>> conv3d = nn.Conv3d(in_channels=3, out_channels=32, kernel_size=(4, 3, 3))
>>> output = conv3d(x)
>>> print(output.shape)
(16, 32, 10, 32, 32)
"""
@cell_attr_register
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
pad_mode='same',
padding=0,
dilation=1,
group=1,
has_bias=False,
weight_init='normal',
bias_init='zeros',
data_format='NCDHW'):
"""Initialize Conv3d."""
kernel_size = _check_3d_int_or_tuple("kernel_size", kernel_size, self.cls_name)
stride = _check_3d_int_or_tuple("stride", stride, self.cls_name)
dilation = _check_3d_int_or_tuple("dilation", dilation, self.cls_name)
Validator.check_value_type('padding', padding, (int, tuple), self.cls_name)
if isinstance(padding, tuple):
Validator.check_equal_int(len(padding), 6, 'padding size', self.cls_name)
super(Conv3d, self).__init__(
in_channels,
out_channels,
kernel_size,
stride,
pad_mode,
padding,
dilation,
group,
has_bias,
weight_init,
bias_init,
data_format)
self.conv3d = P.Conv3D(out_channel=self.out_channels,
kernel_size=self.kernel_size,
mode=1,
pad_mode=self.pad_mode,
pad=self.padding,
stride=self.stride,
dilation=self.dilation,
group=self.group,
data_format=self.format)
self.bias_add = P.BiasAdd(data_format=self.format)
self.shape = P.Shape()
def construct(self, x):
x_shape = self.shape(x)
_check_input_5dims(x_shape, self.cls_name)
output = self.conv3d(x, self.weight)
if self.has_bias:
output = self.bias_add(output, self.bias)
return output
def extend_repr(self):
s = 'input_channels={}, output_channels={}, kernel_size={}, ' \
'stride={}, pad_mode={}, padding={}, dilation={}, ' \
'group={}, has_bias={}, ' \
'weight_init={}, bias_init={}, format={}'.format(
self.in_channels,
self.out_channels,
self.kernel_size,
self.stride,
self.pad_mode,
self.padding,
self.dilation,
self.group,
self.has_bias,
self.weight_init,
self.bias_init,
self.format)
return s
[文档]class Conv3dTranspose(_Conv):
r"""
3D transposed convolution layer.
Calculates a 3D transposed convolution, which can be regarded as Conv3d for the gradient of the input.
It also called deconvolution (although it is not an actual deconvolution).
The input is typically of shape :math:`(N, C, D, H, W)`, where :math:`N` is batch size, :math:`C` is a number of
channels, :math:`D_{in}, H_{in}, W_{in}` are the depth, height and width of the feature layer respectively.
When Conv3d and Conv3dTranspose are initialized with the same parameters, and `pad_mode` is set to 'pad',
:math:`dilation * (kernel\_size - 1) - padding` amount of zero will be paded to the depth, height and width
directions of the input, they are inverses of each other in regard to the input and output shapes in this case.
However, when `stride` > 1, Conv2d maps multiple input shapes to the same output shape. Deconvolutional network
can refer to `Deconvolutional Networks <https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf>`_.
Args:
in_channels (int): The channel number of the input tensor of the Conv3dTranspose layer.
out_channels (int): The channel number of the output tensor of the Conv3dTranspose layer.
kernel_size (Union[int, tuple[int]]): Specifies the depth, height and width of the 3D convolution kernel.
The data type is an integer or a tuple of three integers. An integer represents the depth, height
and width of the convolution kernel. A tuple of three integers represents the depth, height
and width of the convolution kernel respectively.
stride (Union[int, tuple[int]]): The movement stride of the 3D convolution kernel.
The data type is an integer or a tuple of three integers. An integer represents the movement step size
in depth, height and width directions. A tuple of three integers represents the movement step size
in the depth, height and width directions respectively. Default: 1.
pad_mode (str): Specifies padding mode. The optional values are
"same", "valid", "pad". Default: "same".
- same: The width of the output is the same as the value of the input divided by `stride`.
If this mode is set, the value of `padding` must be 0.
- valid: Returns a valid calculated output without padding. Excess pixels that do not satisfy the
calculation will be discarded. If this mode is set, the value of `padding` must be 0.
- pad: Pads the input. Padding `padding` size of zero on both sides of the input.
If this mode is set, the value of `padding` must be greater than or equal to 0.
padding (Union(int, tuple[int])): The number of padding on the depth, height and width directions of the input.
The data type is an integer or a tuple of six integers. If `padding` is an integer,
then the head, tail, top, bottom, left, and right padding are all equal to `padding`.
If `padding` is a tuple of six integers, then the head, tail, top, bottom, left, and right padding
is equal to `padding[0]`, `padding[1]`, `padding[2]`, `padding[3]`, `padding[4]` and `padding[5]`
respectively. The value should be greater than or equal to 0. Default: 0.
dilation (Union(int, tuple[int])): Dilation size of 3D convolution kernel.
The data type is an integer or a tuple of three integers. If :math:`k > 1`, the kernel is sampled
every `k` elements. The value of `k` on the depth, height and width directions is in range of
[1, D], [1, H] and [1, W] respectively. Default: 1.
group (int): Splits filter into groups, `in_channels` and `out_channels` must be
divisible by `group`. Default: 1. Only 1 is currently supported.
output_padding (Union(int, tuple[int])): The number of padding on the depth, height and width directions of
the output. The data type is an integer or a tuple of six integers. If `output_padding` is an integer,
then the head, tail, top, bottom, left, and right padding are all equal to `output_padding`.
If `output_padding` is a tuple of six integers, then the head, tail, top, bottom, left, and right padding
is equal to `output_padding[0]`, `output_padding[1]`, `output_padding[2]`, `output_padding[3]`,
`output_padding[4]` and `output_padding[5]` respectively. The value should be greater than or equal to 0.
Default: 0.
has_bias (bool): Whether the Conv3dTranspose layer has a bias parameter. Default: False.
weight_init (Union[Tensor, str, Initializer, numbers.Number]): Initialization method of weight parameter.
It can be a Tensor, a string, an Initializer or a numbers.Number. When a string is specified,
values from 'TruncatedNormal', 'Normal', 'Uniform', 'HeUniform' and 'XavierUniform' distributions as well
as constant 'One' and 'Zero' distributions are possible. Alias 'xavier_uniform', 'he_uniform', 'ones'
and 'zeros' are acceptable. Uppercase and lowercase are both acceptable. Refer to the values of
Initializer for more details. Default: 'normal'.
bias_init (Union[Tensor, str, Initializer, numbers.Number]): Initialization method of bias parameter.
Available initialization methods are the same as 'weight_init'. Refer to the values of
Initializer for more details. Default: 'zeros'.
data_format (str): The optional value for data format. Currently only support 'NCDHW'.
Inputs:
- **x** (Tensor) - Tensor of shape :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})`.
Currently input data type only support float16 and float32.
Outputs:
Tensor, the shape is :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})`.
pad_mode is 'same':
.. math::
\begin{array}{ll} \\
D_{out} ＝ \left \lfloor{\frac{D_{in}}{\text{stride[0]}} + 1} \right \rfloor \\
H_{out} ＝ \left \lfloor{\frac{H_{in}}{\text{stride[1]}} + 1} \right \rfloor \\
W_{out} ＝ \left \lfloor{\frac{W_{in}}{\text{stride[2]}} + 1} \right \rfloor \\
\end{array}
pad_mode is 'valid':
.. math::
\begin{array}{ll} \\
D_{out} ＝ \left \lfloor{\frac{D_{in} - \text{dilation[0]} \times (\text{kernel_size[0]} - 1) }
{\text{stride[0]}} + 1} \right \rfloor \\
H_{out} ＝ \left \lfloor{\frac{H_{in} - \text{dilation[1]} \times (\text{kernel_size[1]} - 1) }
{\text{stride[1]}} + 1} \right \rfloor \\
W_{out} ＝ \left \lfloor{\frac{W_{in} - \text{dilation[2]} \times (\text{kernel_size[2]} - 1) }
{\text{stride[2]}} + 1} \right \rfloor \\
\end{array}
pad_mode is 'pad':
.. math::
\begin{array}{ll} \\
D_{out} ＝ \left \lfloor{\frac{D_{in} + padding[0] + padding[1] - (\text{dilation[0]} - 1) \times
\text{kernel_size[0]} - 1 }{\text{stride[0]}} + 1} \right \rfloor \\
H_{out} ＝ \left \lfloor{\frac{H_{in} + padding[2] + padding[3] - (\text{dilation[1]} - 1) \times
\text{kernel_size[1]} - 1 }{\text{stride[1]}} + 1} \right \rfloor \\
W_{out} ＝ \left \lfloor{\frac{W_{in} + padding[4] + padding[5] - (\text{dilation[2]} - 1) \times
\text{kernel_size[2]} - 1 }{\text{stride[2]}} + 1} \right \rfloor \\
\end{array}
Supported Platforms:
``Ascend`` ``GPU``
Raises:
TypeError: If `in_channels`, `out_channels` or `group` is not an int.
TypeError: If `kernel_size`, `stride`, `padding` , `dilation` or `output_padding`
is neither an int not a tuple of three.
TypeError: If input data type is not float16 or float32.
ValueError: If `in_channels`, `out_channels`, `kernel_size`, `stride` or `dilation` is less than 1.
ValueError: If `padding` is less than 0.
ValueError: If `pad_mode` is not one of 'same', 'valid', 'pad'.
ValueError: If `padding` is a tuple whose length is not equal to 6.
ValueError: If `pad_mode` is not equal to 'pad' and `padding` is not equal to (0, 0, 0, 0, 0, 0).
ValueError: If `data_format` is not 'NCDHW'.
Examples:
>>> x = Tensor(np.ones([32, 16, 10, 32, 32]), mindspore.float32)
>>> conv3d_transpose = nn.Conv3dTranspose(in_channels=16, out_channels=3, kernel_size=(4, 6, 2),
... pad_mode='pad')
>>> output = conv3d_transpose(x)
>>> print(output.shape)
(32, 3, 13, 37, 33)
"""
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
pad_mode='same',
padding=0,
dilation=1,
group=1,
output_padding=0,
has_bias=False,
weight_init='normal',
bias_init='zeros',
data_format='NCDHW'):
"""Initialize Conv3dTranspose."""
kernel_size = _check_3d_int_or_tuple("kernel_size", kernel_size, self.cls_name)
stride = _check_3d_int_or_tuple("stride", stride, self.cls_name)
dilation = _check_3d_int_or_tuple("dilation", dilation, self.cls_name)
Validator.check_value_type('padding', padding, (int, tuple), self.cls_name)
if isinstance(padding, tuple):
Validator.check_equal_int(len(padding), 6, 'padding size', self.cls_name)
self.output_padding = _check_3d_int_or_tuple("output_padding", output_padding, self.cls_name,
greater_zero=False)
super(Conv3dTranspose, self).__init__(
in_channels,
out_channels,
kernel_size,
stride,
pad_mode,
padding,
dilation,
group,
has_bias,
weight_init,
bias_init,
data_format,
transposed=True)
self.conv3d_transpose = P.Conv3DTranspose(in_channel=self.in_channels,
out_channel=self.out_channels,
kernel_size=self.kernel_size,
mode=1,
pad_mode=self.pad_mode,
pad=self.padding,
stride=self.stride,
dilation=self.dilation,
group=self.group,
output_padding=self.output_padding,
data_format=self.format)
self.bias_add = P.BiasAdd(data_format=self.format)
self.shape = P.Shape()
def construct(self, x):
x_shape = self.shape(x)
_check_input_5dims(x_shape, self.cls_name)
output = self.conv3d_transpose(x, self.weight)
if self.has_bias:
output = self.bias_add(output, self.bias)
return output
def extend_repr(self):
s = 'input_channels={}, output_channels={}, kernel_size={}, ' \
'stride={}, pad_mode={}, padding={}, dilation={}, ' \
'group={}, has_bias={}, ' \
'weight_init={}, bias_init={}'.format(self.in_channels,
self.out_channels,
self.kernel_size,
self.stride,
self.pad_mode,
self.padding,
self.dilation,
self.group,
self.has_bias,
self.weight_init,
self.bias_init)
return s
def _deconv_output_length(is_valid, is_same, is_pad, input_length, filter_size, stride_size, dilation_size, padding):
"""Calculate the width and height of output."""
length = 0
filter_size = filter_size + (filter_size - 1) * (dilation_size - 1)
if is_valid:
if filter_size - stride_size > 0:
length = input_length * stride_size + filter_size - stride_size
else:
length = input_length * stride_size
elif is_same:
length = input_length * stride_size
elif is_pad:
length = input_length * stride_size - padding + filter_size - stride_size
return length
[文档]class Conv2dTranspose(_Conv):
r"""
2D transposed convolution layer.
Calculates a 2D transposed convolution, which can be regarded as Conv2d for the gradient of the input.
It also called deconvolution (although it is not an actual deconvolution).
The input is typically of shape :math:`(N, C, H, W)`, where :math:`N` is batch size, :math:`C` is a number of
channels, :math:`H_{in}, W_{in}` are the height and width of the feature layer respectively.
When Conv2d and Conv2dTranspose are initialized with the same parameters, and `pad_mode` is set to 'pad',
:math:`dilation * (kernel\_size - 1) - padding` amount of zero will be paded to the height and width
directions of the input, they are inverses of each other in regard to the input and output shapes in this case.
However, when `stride` > 1, Conv2d maps multiple input shapes to the same output shape. Deconvolutional network
can refer to `Deconvolutional Networks <https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf>`_.
Args:
in_channels (int): The channel number of the input tensor of the Conv2dTranspose layer.
out_channels (int): The channel number of the output tensor of the Conv2dTranspose layer.
kernel_size (Union[int, tuple]): Specifies the height and width of the 2D convolution kernel.
The data type is an integer or a tuple of two integers. An integer represents the height
and width of the convolution kernel. A tuple of two integers represents the height
and width of the convolution kernel respectively.
stride (Union[int, tuple[int]]): The movement stride of the 2D convolution kernel.
The data type is an integer or a tuple of two integers. An integer represents the movement step size
in both height and width directions. A tuple of two integers represents the movement step size in the height
and width directions respectively. Default: 1.
pad_mode (str): Specifies padding mode. The optional values are
"same", "valid", "pad". Default: "same".
- same: The width of the output is the same as the value of the input divided by `stride`.
If this mode is set, the value of `padding` must be 0.
- valid: Returns a valid calculated output without padding. Excess pixels that do not satisfy the
calculation will be discarded. If this mode is set, the value of `padding` must be 0.
- pad: Pads the input. Padding `padding` size of zero on both sides of the input.
If this mode is set, the value of `padding` must be greater than or equal to 0.
padding (Union[int, tuple[int]]): The number of padding on the height and width directions of the input.
The data type is an integer or a tuple of four integers. If `padding` is an integer,
then the top, bottom, left, and right padding are all equal to `padding`.
If `padding` is a tuple of 4 integers, then the top, bottom, left, and right padding
is equal to `padding[0]`, `padding[1]`, `padding[2]`, and `padding[3]` respectively.
The value should be greater than or equal to 0. Default: 0.
dilation (Union[int, tuple[int]]): Dilation size of 2D convolution kernel.
The data type is an integer or a tuple of two integers. If :math:`k > 1`, the kernel is sampled
every `k` elements. The value of `k` on the height and width directions is in range of [1, H]
and [1, W] respectively. Default: 1.
group (int): Splits filter into groups, `in_channels` and `out_channels` must be divisible by `group`.
Default: 1.
has_bias (bool): Whether the Conv2dTranspose layer has a bias parameter. Default: False.
weight_init (Union[Tensor, str, Initializer, numbers.Number]): Initialization method of weight parameter.
It can be a Tensor, a string, an Initializer or a numbers.Number. When a string is specified,
values from 'TruncatedNormal', 'Normal', 'Uniform', 'HeUniform' and 'XavierUniform' distributions as well
as constant 'One' and 'Zero' distributions are possible. Alias 'xavier_uniform', 'he_uniform', 'ones'
and 'zeros' are acceptable. Uppercase and lowercase are both acceptable. Refer to the values of
Initializer for more details. Default: 'normal'.
bias_init (Union[Tensor, str, Initializer, numbers.Number]): Initialization method of bias parameter.
Available initialization methods are the same as 'weight_init'. Refer to the values of
Initializer for more details. Default: 'zeros'.
Inputs:
- **x** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
Outputs:
Tensor of shape :math:`(N, C_{out}, H_{out}, W_{out})`.
pad_mode is 'same':
.. math::
\begin{array}{ll} \\
H_{out} ＝ \left \lfloor{\frac{H_{in}}{\text{stride[0]}} + 1} \right \rfloor \\
W_{out} ＝ \left \lfloor{\frac{W_{in}}{\text{stride[1]}} + 1} \right \rfloor \\
\end{array}
pad_mode is 'valid':
.. math::
\begin{array}{ll} \\
H_{out} ＝ \left \lfloor{\frac{H_{in} - \text{dilation[0]} \times (\text{kernel_size[0]} - 1) }
{\text{stride[0]}} + 1} \right \rfloor \\
W_{out} ＝ \left \lfloor{\frac{W_{in} - \text{dilation[1]} \times (\text{kernel_size[1]} - 1) }
{\text{stride[1]}} + 1} \right \rfloor \\
\end{array}
pad_mode is 'pad':
.. math::
\begin{array}{ll} \\
H_{out} ＝ \left \lfloor{\frac{H_{in} + padding[0] + padding[1] - (\text{dilation[0]} - 1) \times
\text{kernel_size[0]} - 1 }{\text{stride[0]}} + 1} \right \rfloor \\
W_{out} ＝ \left \lfloor{\frac{W_{in} + padding[2] + padding[3] - (\text{dilation[1]} - 1) \times
\text{kernel_size[1]} - 1 }{\text{stride[1]}} + 1} \right \rfloor \\
\end{array}
Raises:
TypeError: If `in_channels`, `out_channels` or `group` is not an int.
TypeError: If `kernel_size`, `stride`, `padding` or `dilation` is neither an int not a tuple.
ValueError: If `in_channels`, `out_channels`, `kernel_size`, `stride` or `dilation` is less than 1.
ValueError: If `padding` is less than 0.
ValueError: If `pad_mode` is not one of 'same', 'valid', 'pad'.
ValueError: If `padding` is a tuple whose length is not equal to 4.
ValueError: If `pad_mode` is not equal to 'pad' and `padding` is not equal to (0, 0, 0, 0).
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> net = nn.Conv2dTranspose(3, 64, 4, has_bias=False, weight_init='normal', pad_mode='pad')
>>> x = Tensor(np.ones([1, 3, 16, 50]), mindspore.float32)
>>> output = net(x).shape
>>> print(output)
(1, 64, 19, 53)
"""
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
pad_mode='same',
padding=0,
dilation=1,
group=1,
has_bias=False,
weight_init='normal',
bias_init='zeros'):
"""Initialize Conv2dTranspose."""
kernel_size = twice(kernel_size)
stride = twice(stride)
dilation = twice(dilation)
Validator.check_value_type('padding', padding, (int, tuple), self.cls_name)
if isinstance(padding, tuple):
Validator.check_equal_int(len(padding), 4, 'padding size', self.cls_name)
# out_channels and in_channels swap.
# cause Conv2DBackpropInput's out_channel refers to Conv2D's out_channel,
# then Conv2dTranspose's out_channel refers to Conv2DBackpropInput's in_channel.
super(Conv2dTranspose, self).__init__(
in_channels,
out_channels,
kernel_size,
stride,
pad_mode,
padding,
dilation,
group,
has_bias,
weight_init,
bias_init,
transposed=True)
self.in_channels = in_channels
self.out_channels = out_channels
self.shape = P.Shape()
Validator.check_string(pad_mode, ['valid', 'same', 'pad'], 'pad_mode', self.cls_name)
self.is_valid = self.pad_mode == 'valid'
self.is_same = self.pad_mode == 'same'
self.is_pad = self.pad_mode == 'pad'
if Validator.check_bool(has_bias, "has_bias", self.cls_name):
self.bias = Parameter(initializer(bias_init, [out_channels]), name='bias')
# cause Conv2DTranspose's out_channel refers to Conv2D's out_channel.
self.conv2d_transpose = P.Conv2DTranspose(out_channel=in_channels,
kernel_size=kernel_size,
mode=1,
pad_mode=pad_mode,
pad=padding,
stride=stride,
dilation=dilation,
group=group)
self.bias_add = P.BiasAdd()
if isinstance(self.padding, int):
self.padding_top, self.padding_bottom, self.padding_left, self.padding_right = (self.padding,) * 4
else:
self.padding_top, self.padding_bottom, self.padding_left, self.padding_right = self.padding
def shard(self, strategy):
self.conv2d_transpose.shard(strategy)
return self
def construct(self, x):
n, _, h, w = self.shape(x)
h_out = _deconv_output_length(self.is_valid, self.is_same, self.is_pad, h, self.kernel_size[0],
self.stride[0], self.dilation[0], self.padding_top + self.padding_bottom)
w_out = _deconv_output_length(self.is_valid, self.is_same, self.is_pad, w, self.kernel_size[1],
self.stride[1], self.dilation[1], self.padding_left + self.padding_right)
if self.has_bias:
return self.bias_add(self.conv2d_transpose(x, self.weight, (n, self.out_channels, h_out, w_out)),
self.bias)
return self.conv2d_transpose(x, self.weight, (n, self.out_channels, h_out, w_out))
def extend_repr(self):
s = 'input_channels={}, output_channels={}, kernel_size={}, ' \
'stride={}, pad_mode={}, padding={}, dilation={}, ' \
'group={}, has_bias={}, ' \
'weight_init={}, bias_init={}'.format(self.in_channels,
self.out_channels,
self.kernel_size,
self.stride,
self.pad_mode,
self.padding,
self.dilation,
self.group,
self.has_bias,
self.weight_init,
self.bias_init)
return s
[文档]class Conv1dTranspose(_Conv):
r"""
1D transposed convolution layer.
Calculates a 1D transposed convolution, which can be regarded as Conv1d for the gradient of the input.
It also called deconvolution (although it is not an actual deconvolution).
The input is typically of shape :math:`(N, C, L)`, where :math:`N` is batch size, :math:`C` is a number of channels
and :math:`L_{in}` is a length of sequence.
When Conv1d and ConvTranspose1d are initialized with the same parameters, and `pad_mode` is set to 'pad',
:math:`dilation * (kernel\_size - 1) - padding` amount of zero will be paded to both sizes of input,
they are inverses of each other in regard to the input and output shapes in this case.
However, when `stride` > 1, Conv1d maps multiple input shapes to the same output shape. Deconvolutional network
can refer to `Deconvolutional Networks <https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf>`_.
Args:
in_channels (int): The channel number of the input tensor of the Conv1dTranspose layer.
out_channels (int): The channel number of the output tensor of the Conv1dTranspose layer.
kernel_size (int): Specifies the width of the 1D convolution kernel.
stride (int): The movement stride of the 1D convolution kernel. Default: 1.
pad_mode (str): Specifies padding mode. The optional values are
"same", "valid", "pad". Default: "same".
- same: The width of the output is the same as the value of the input divided by `stride`.
If this mode is set, the value of `padding` must be 0.
- valid: Returns a valid calculated output without padding. Excess pixels that do not satisfy the
calculation will be discarded. If this mode is set, the value of `padding` must be 0.
- pad: Pads the input. Padding `padding` size of zero on both sides of the input.
If this mode is set, the value of `padding` must be greater than or equal to 0.
padding (int): The number of padding on both sides of input.
The value should be greater than or equal to 0. Default: 0.
dilation (int): Dilation size of 1D convolution kernel. If :math:`k > 1`, the kernel is sampled
every `k` elements. The value of `k` is in range of [1, L]. Default: 1.
group (int): Splits filter into groups, `in_channels` and `out_channels` must be
divisible by `group`. When `group` > 1, the Ascend platform is not supported yet. Default: 1.
has_bias (bool): Whether the Conv1dTranspose layer has a bias parameter. Default: False.
weight_init (Union[Tensor, str, Initializer, numbers.Number]): Initialization method of weight parameter.
It can be a Tensor, a string, an Initializer or a numbers.Number. When a string is specified,
values from 'TruncatedNormal', 'Normal', 'Uniform', 'HeUniform' and 'XavierUniform' distributions as well
as constant 'One' and 'Zero' distributions are possible. Alias 'xavier_uniform', 'he_uniform', 'ones'
and 'zeros' are acceptable. Uppercase and lowercase are both acceptable. Refer to the values of
Initializer for more details. Default: 'normal'.
bias_init (Union[Tensor, str, Initializer, numbers.Number]): Initialization method of bias parameter.
Available initialization methods are the same as 'weight_init'. Refer to the values of
Initializer for more details. Default: 'zeros'.
Inputs:
- **x** (Tensor) - Tensor of shape :math:`(N, C_{in}, L_{in})`.
Outputs:
Tensor of shape :math:`(N, C_{out}, L_{out})`.
pad_mode is 'same':
.. math::
L_{out} ＝ \left \lfloor{\frac{L_{in}}{\text{stride}} + 1} \right \rfloor
pad_mode is 'valid':
.. math::
L_{out} ＝ \left \lfloor{\frac{L_{in} - \text{dilation} \times (\text{kernel_size} - 1) }
{\text{stride}} + 1} \right \rfloor
pad_mode is 'pad':
.. math::
L_{out} ＝ \left \lfloor{\frac{L_{in} + 2 \times padding - (\text{dilation} - 1) \times
\text{kernel_size} - 1 }{\text{stride}} + 1} \right \rfloor
Raises:
TypeError: If `in_channels`, `out_channels`, `kernel_size`, `stride`, `padding` or `dilation` is not an int.
ValueError: If `in_channels`, `out_channels`, `kernel_size`, `stride` or `dilation` is less than 1.
ValueError: If `padding` is less than 0.
ValueError: If `pad_mode` is not one of 'same', 'valid', 'pad'.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> net = nn.Conv1dTranspose(3, 64, 4, has_bias=False, weight_init='normal', pad_mode='pad')
>>> x = Tensor(np.ones([1, 3, 50]), mindspore.float32)
>>> output = net(x).shape
>>> print(output)
(1, 64, 53)
"""
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
pad_mode='same',
padding=0,
dilation=1,
group=1,
has_bias=False,
weight_init='normal',
bias_init='zeros'):
"""Initialize Conv1dTranspose."""
Validator.check_value_type("kernel_size", kernel_size, [int], self.cls_name)
Validator.check_value_type("stride", stride, [int], self.cls_name)
Validator.check_value_type("padding", padding, [int], self.cls_name)
Validator.check_value_type("dilation", dilation, [int], self.cls_name)
Validator.check_int(kernel_size, 1, Rel.GE, 'kernel_size', self.cls_name)
Validator.check_int(stride, 1, Rel.GE, 'stride', self.cls_name)
Validator.check_non_negative_int(padding, 'padding', self.cls_name)
Validator.check_int(dilation, 1, Rel.GE, 'dilation', self.cls_name)
kernel_size = (1, kernel_size)
stride = (1, stride)
dilation = (1, dilation)
get_shape = P.Shape()
get_dtype = P.DType()
if isinstance(weight_init, Tensor):
weight_init_shape = get_shape(weight_init)
Validator.check_equal_int(len(weight_init_shape), 3, 'weight_init_shape', self.cls_name)
weight_init_dtype = get_dtype(weight_init)
weight_init_value = weight_init.asnumpy()
weight_init_value = np.expand_dims(weight_init_value, 2)
weight_init = Tensor(weight_init_value, weight_init_dtype)
# out_channels and in_channels swap.
# cause Conv2DBackpropInput's out_channel refers to Conv2D's out_channel,
# then Conv1dTranspose's out_channel refers to Conv2DBackpropInput's in_channel.
super(Conv1dTranspose, self).__init__(
in_channels,
out_channels,
kernel_size,
stride,
pad_mode,
padding,
dilation,
group,
has_bias,
weight_init,
bias_init,
transposed=True)
self.padding = (0, 0, padding, padding)
self.in_channels = in_channels
self.out_channels = out_channels
self.shape = P.Shape()
Validator.check_string(pad_mode, ['valid', 'same', 'pad'], 'pad_mode', self.cls_name)
self.is_valid = self.pad_mode == 'valid'
self.is_same = self.pad_mode == 'same'
self.is_pad = self.pad_mode == 'pad'
if Validator.check_bool(has_bias, "has_bias", self.cls_name):
self.bias = Parameter(initializer(bias_init, [out_channels]), name='bias')
# cause Conv2DBackpropInput's out_channel refers to Conv2D's out_channel.
self.conv2d_transpose = P.Conv2DBackpropInput(out_channel=in_channels,
kernel_size=kernel_size,
mode=1,
pad_mode=pad_mode,
pad=self.padding,
stride=stride,
dilation=dilation,
group=group)
self.bias_add = P.BiasAdd()
self.expand_dims = P.ExpandDims()
self.squeeze = P.Squeeze(2)
def shard(self, strategy):
self.conv2d_transpose.shard(strategy)
return self
def construct(self, x):
x_shape = self.shape(x)
_check_input_3d(x_shape, self.cls_name)
x = self.expand_dims(x, 2)
n, _, h, w = self.shape(x)
h_out = _deconv_output_length(self.is_valid, self.is_same, self.is_pad, h, self.kernel_size[0],
self.stride[0], self.dilation[0], self.padding[0] + self.padding[1])
w_out = _deconv_output_length(self.is_valid, self.is_same, self.is_pad, w, self.kernel_size[1],
self.stride[1], self.dilation[1], self.padding[2] + self.padding[3])
output = self.conv2d_transpose(x, self.weight, (n, self.out_channels, h_out, w_out))
if self.has_bias:
output = self.bias_add(output, self.bias)
output = self.squeeze(output)
return output
def extend_repr(self):
s = 'input_channels={}, output_channels={}, kernel_size={}, ' \
'stride={}, pad_mode={}, padding={}, dilation={}, ' \
'group={}, has_bias={}, ' \
'weight_init={}, bias_init={}'.format(self.in_channels,
self.out_channels,
self.kernel_size,
self.stride,
self.pad_mode,
self.padding,
self.dilation,
self.group,
self.has_bias,
self.weight_init,
self.bias_init)
return s