mindspore/mindspore/nn/layer/combined.py

# Copyright 2020 Huawei Technologies Co., Ltd
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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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'
]


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 input. 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 input. 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 input. 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:0.9
        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 Cond2dBnAct.

    Inputs:
        - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.

    Outputs:
        Tensor of shape :math:`(N, C_{out}, H_{out}, W_{out})`.

    Supported Platforms:
        ``Ascend`` ``GPU``

    Examples:
        >>> net = nn.Conv2dBnAct(120, 240, 4, has_bn=True, activation='relu')
        >>> input = Tensor(np.ones([1, 120, 1024, 640]), mindspore.float32)
        >>> result = net(input)
        >>> 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):
        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


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 input. 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 input. 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.

    Inputs:
        - **input** (Tensor) - Tensor of shape :math:`(N, in\_channels)`.

    Outputs:
        Tensor of shape :math:`(N, out\_channels)`.

    Supported Platforms:
        ``Ascend``

    Examples:
        >>> net = nn.DenseBnAct(3, 4)
        >>> input = Tensor(np.random.randint(0, 255, [2, 3]), mindspore.float32)
        >>> result = net(input)
        >>> 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):
        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