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# http://www.apache.org/licenses/LICENSE-2.0
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# ============================================================================
"""Combined cells."""
from mindspore import nn
from mindspore.ops.primitive import Primitive
from mindspore._checkparam import Validator
from .normalization import BatchNorm2d, BatchNorm1d
from .activation import get_activation, LeakyReLU
from ..cell import Cell
__all__ = [
'Conv2dBnAct',
'DenseBnAct'
]
[docs]class Conv2dBnAct(Cell):
r"""
A combination of convolution, Batchnorm, and activation layer.
This part is a more detailed overview of Conv2d operation.
Args:
in_channels (int): The number of input channel :math:`C_{in}`.
out_channels (int): The number of output channel :math:`C_{out}`.
kernel_size (Union[int, tuple]): The data type is int or a tuple of 2 integers. Specifies the height
and width of the 2D convolution window. Single int means the value is for both height and width of
the kernel. A tuple of 2 ints means the first value is for the height and the other is for the
width of the kernel.
stride (int): Specifies stride for all spatial dimensions with the same value. The value of stride must be
greater than or equal to 1 and lower than any one of the height and width of the `x`. Default: 1.
pad_mode (str): Specifies padding mode. The optional values are "same", "valid", "pad". Default: "same".
padding (int): Implicit paddings on both sides of the `x`. Default: 0.
dilation (int): Specifies the dilation rate to use for dilated convolution. If set to be :math:`k > 1`,
there will be :math:`k - 1` pixels skipped for each sampling location. Its value must be greater than
or equal to 1 and lower than any one of the height and width of the `x`. Default: 1.
group (int): Splits filter into groups, `in_ channels` and `out_channels` must be
divisible by the number of groups. Default: 1.
has_bias (bool): Specifies whether the layer uses a bias vector. Default: False.
weight_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the convolution kernel.
It can be a Tensor, a string, an Initializer or a 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]): Initializer for the bias vector. Possible
Initializer and string are the same as 'weight_init'. Refer to the values of
Initializer for more details. Default: 'zeros'.
has_bn (bool): Specifies to used batchnorm or not. Default: False.
momentum (float): Momentum for moving average for batchnorm, must be [0, 1]. Default:0.997
eps (float): Term added to the denominator to improve numerical stability for batchnorm, should be greater
than 0. Default: 1e-5.
activation (Union[str, Cell, Primitive]): Specifies activation type. The optional values are as following:
'softmax', 'logsoftmax', 'relu', 'relu6', 'tanh', 'gelu', 'sigmoid',
'prelu', 'leakyrelu', 'hswish', 'hsigmoid'. Default: None.
alpha (float): Slope of the activation function at x < 0 for LeakyReLU. Default: 0.2.
after_fake(bool): Determine whether there must be a fake quantization operation after Cond2dBnAct.
Default: True.
Inputs:
- **x** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`. The data type is float32.
Outputs:
Tensor of shape :math:`(N, C_{out}, H_{out}, W_{out})`. The data type is float32.
Raises:
TypeError: If `in_channels`, `out_channels`, `stride`, `padding` or `dilation` is not an int.
TypeError: If `has_bias` is not a bool.
ValueError: If `in_channels` or `out_channels` `stride`, `padding` or `dilation` is less than 1.
ValueError: If `pad_mode` is not one of 'same', 'valid', 'pad'.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> net = nn.Conv2dBnAct(120, 240, 4, has_bn=True, activation='relu')
>>> x = Tensor(np.ones([1, 120, 1024, 640]), mindspore.float32)
>>> result = net(x)
>>> output = result.shape
>>> print(output)
(1, 240, 1024, 640)
"""
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',
has_bn=False,
momentum=0.997,
eps=1e-5,
activation=None,
alpha=0.2,
after_fake=True):
"""Initialize Conv2dBnAct."""
super(Conv2dBnAct, self).__init__()
self.conv = nn.Conv2d(in_channels,
out_channels,
kernel_size=kernel_size,
stride=stride,
pad_mode=pad_mode,
padding=padding,
dilation=dilation,
group=group,
has_bias=has_bias,
weight_init=weight_init,
bias_init=bias_init)
self.has_bn = Validator.check_bool(has_bn, "has_bn")
self.has_act = activation is not None
self.after_fake = Validator.check_bool(after_fake, "after_fake")
if has_bn:
self.batchnorm = BatchNorm2d(out_channels, eps, momentum)
if activation == "leakyrelu":
self.activation = LeakyReLU(alpha)
else:
self.activation = get_activation(activation) if isinstance(activation, str) else activation
if activation is not None and not isinstance(self.activation, (Cell, Primitive)):
raise TypeError("The activation must be str or Cell or Primitive,"" but got {}.".format(activation))
def construct(self, x):
x = self.conv(x)
if self.has_bn:
x = self.batchnorm(x)
if self.has_act:
x = self.activation(x)
return x
[docs]class DenseBnAct(Cell):
r"""
A combination of Dense, Batchnorm, and the activation layer.
This part is a more detailed overview of Dense op.
Args:
in_channels (int): The number of channels in the input space.
out_channels (int): The number of channels in the output space.
weight_init (Union[Tensor, str, Initializer, numbers.Number]): The trainable weight_init parameter. The dtype
is same as `x`. The values of str refer to the function `initializer`. Default: 'normal'.
bias_init (Union[Tensor, str, Initializer, numbers.Number]): The trainable bias_init parameter. The dtype is
same as `x`. The values of str refer to the function `initializer`. Default: 'zeros'.
has_bias (bool): Specifies whether the layer uses a bias vector. Default: True.
has_bn (bool): Specifies to use batchnorm or not. Default: False.
momentum (float): Momentum for moving average for batchnorm, must be [0, 1]. Default:0.9
eps (float): Term added to the denominator to improve numerical stability for batchnorm, should be greater
than 0. Default: 1e-5.
activation (Union[str, Cell, Primitive]): Specifies activation type. The optional values are as following:
'softmax', 'logsoftmax', 'relu', 'relu6', 'tanh', 'gelu', 'sigmoid',
'prelu', 'leakyrelu', 'hswish', 'hsigmoid'. Default: None.
alpha (float): Slope of the activation function at x < 0 for LeakyReLU. Default: 0.2.
after_fake(bool): Determine whether there must be a fake quantization operation after DenseBnAct.
Default: True.
Inputs:
- **x** (Tensor) - Tensor of shape :math:`(N, in\_channels)`. The data type is float32.
Outputs:
Tensor of shape :math:`(N, out\_channels)`. The data type is float32.
Raises:
TypeError: If `in_channels` or `out_channels` is not an int.
TypeError: If `has_bias`, `has_bn` or `after_fake` is not a bool.
TypeError: If `momentum` or `eps` is not a float.
ValueError: If `momentum` is not in range [0, 1.0].
Supported Platforms:
``Ascend`` ``GPU``
Examples:
>>> net = nn.DenseBnAct(3, 4)
>>> x = Tensor(np.random.randint(0, 255, [2, 3]), mindspore.float32)
>>> result = net(x)
>>> output = result.shape
>>> print(output)
(2, 4)
"""
def __init__(self,
in_channels,
out_channels,
weight_init='normal',
bias_init='zeros',
has_bias=True,
has_bn=False,
momentum=0.9,
eps=1e-5,
activation=None,
alpha=0.2,
after_fake=True):
"""Initialize DenseBnAct."""
super(DenseBnAct, self).__init__()
self.dense = nn.Dense(
in_channels,
out_channels,
weight_init,
bias_init,
has_bias)
self.has_bn = Validator.check_bool(has_bn, "has_bn")
self.has_act = activation is not None
self.after_fake = Validator.check_bool(after_fake, "after_fake")
if has_bn:
self.batchnorm = BatchNorm1d(out_channels, eps, momentum)
if activation == "leakyrelu":
self.activation = LeakyReLU(alpha)
else:
self.activation = get_activation(activation) if isinstance(activation, str) else activation
if activation is not None and not isinstance(self.activation, (Cell, Primitive)):
raise TypeError("The activation must be str or Cell or Primitive,"" but got {}.".format(activation))
def construct(self, x):
x = self.dense(x)
if self.has_bn:
x = self.batchnorm(x)
if self.has_act:
x = self.activation(x)
return x