PaddleRS/paddlers/custom_models/seg/layers/layers_lib.py

# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# 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.

import paddle
import paddle.nn as nn
import paddle.nn.functional as F


class ConvBNReLU(nn.Layer):
    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 padding='same',
                 **kwargs):
        super().__init__()
        self._conv = nn.Conv2D(
            in_channels, out_channels, kernel_size, padding=padding, **kwargs)
        if 'data_format' in kwargs:
            data_format = kwargs['data_format']
        else:
            data_format = 'NCHW'
        self._batch_norm = nn.BatchNorm2D(out_channels, data_format=data_format)

    def forward(self, x):
        x = self._conv(x)
        x = self._batch_norm(x)
        x = F.relu(x)
        return x


class ConvBN(nn.Layer):
    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 padding='same',
                 **kwargs):
        super().__init__()
        self._conv = nn.Conv2D(
            in_channels, out_channels, kernel_size, padding=padding, **kwargs)
        if 'data_format' in kwargs:
            data_format = kwargs['data_format']
        else:
            data_format = 'NCHW'
        self._batch_norm = nn.BatchNorm2D(out_channels, data_format=data_format)

    def forward(self, x):
        x = self._conv(x)
        x = self._batch_norm(x)
        return x


class ConvReLU(nn.Layer):
    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 padding='same',
                 **kwargs):
        super().__init__()
        self._conv = nn.Conv2D(
            in_channels, out_channels, kernel_size, padding=padding, **kwargs)
        if 'data_format' in kwargs:
            data_format = kwargs['data_format']
        else:
            data_format = 'NCHW'
        self._relu = nn.ReLU()

    def forward(self, x):
        x = self._conv(x)
        x = self._relu(x)
        return x


class Add(nn.Layer):
    def __init__(self):
        super().__init__()

    def forward(self, x, y, name=None):
        return paddle.add(x, y, name)


class Activation(nn.Layer):
    """
    The wrapper of activations.
    Args:
        act (str, optional): The activation name in lowercase. It must be one of ['elu', 'gelu',
            'hardshrink', 'tanh', 'hardtanh', 'prelu', 'relu', 'relu6', 'selu', 'leakyrelu', 'sigmoid',
            'softmax', 'softplus', 'softshrink', 'softsign', 'tanhshrink', 'logsigmoid', 'logsoftmax',
            'hsigmoid']. Default: None, means identical transformation.
    Returns:
        A callable object of Activation.
    Raises:
        KeyError: When parameter `act` is not in the optional range.
    Examples:
        from paddleseg.models.common.activation import Activation
        relu = Activation("relu")
        print(relu)
        # <class 'paddle.nn.layer.activation.ReLU'>
        sigmoid = Activation("sigmoid")
        print(sigmoid)
        # <class 'paddle.nn.layer.activation.Sigmoid'>
        not_exit_one = Activation("not_exit_one")
        # KeyError: "not_exit_one does not exist in the current dict_keys(['elu', 'gelu', 'hardshrink',
        # 'tanh', 'hardtanh', 'prelu', 'relu', 'relu6', 'selu', 'leakyrelu', 'sigmoid', 'softmax',
        # 'softplus', 'softshrink', 'softsign', 'tanhshrink', 'logsigmoid', 'logsoftmax', 'hsigmoid'])"
    """

    def __init__(self, act=None):
        super(Activation, self).__init__()
        self._act = act
        upper_act_names = nn.layer.activation.__dict__.keys()
        lower_act_names = [act.lower() for act in upper_act_names]
        act_dict = dict(zip(lower_act_names, upper_act_names))
        if act is not None:
            if act in act_dict.keys():
                act_name = act_dict[act]
                self.act_func = eval("nn.layer.activation.{}()".format(
                    act_name))
            else:
                raise KeyError("{} does not exist in the current {}".format(
                    act, act_dict.keys()))

    def forward(self, x):
        if self._act is not None:
            return self.act_func(x)
        else:
            return x


class Identity(nn.Layer):
    def __init__(self, *args, **kwargs):
        super(Identity, self).__init__()

    def forward(self, input):
        return input