PaddleX/dygraph/paddlex/cv/nets/ppcls/modeling/architectures/densenet.py

# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
# 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.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import paddle
from paddle import ParamAttr
import paddle.nn as nn
from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform

import math

__all__ = [
    "DenseNet121", "DenseNet161", "DenseNet169", "DenseNet201", "DenseNet264"
]


class BNACConvLayer(nn.Layer):
    def __init__(self,
                 num_channels,
                 num_filters,
                 filter_size,
                 stride=1,
                 pad=0,
                 groups=1,
                 act="relu",
                 name=None):
        super(BNACConvLayer, self).__init__()

        self._batch_norm = BatchNorm(
            num_channels,
            act=act,
            param_attr=ParamAttr(name=name + '_bn_scale'),
            bias_attr=ParamAttr(name + '_bn_offset'),
            moving_mean_name=name + '_bn_mean',
            moving_variance_name=name + '_bn_variance')

        self._conv = Conv2D(
            in_channels=num_channels,
            out_channels=num_filters,
            kernel_size=filter_size,
            stride=stride,
            padding=pad,
            groups=groups,
            weight_attr=ParamAttr(name=name + "_weights"),
            bias_attr=False)

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


class DenseLayer(nn.Layer):
    def __init__(self, num_channels, growth_rate, bn_size, dropout, name=None):
        super(DenseLayer, self).__init__()
        self.dropout = dropout

        self.bn_ac_func1 = BNACConvLayer(
            num_channels=num_channels,
            num_filters=bn_size * growth_rate,
            filter_size=1,
            pad=0,
            stride=1,
            name=name + "_x1")

        self.bn_ac_func2 = BNACConvLayer(
            num_channels=bn_size * growth_rate,
            num_filters=growth_rate,
            filter_size=3,
            pad=1,
            stride=1,
            name=name + "_x2")

        if dropout:
            self.dropout_func = Dropout(p=dropout, mode="downscale_in_infer")

    def forward(self, input):
        conv = self.bn_ac_func1(input)
        conv = self.bn_ac_func2(conv)
        if self.dropout:
            conv = self.dropout_func(conv)
        conv = paddle.concat([input, conv], axis=1)
        return conv


class DenseBlock(nn.Layer):
    def __init__(self,
                 num_channels,
                 num_layers,
                 bn_size,
                 growth_rate,
                 dropout,
                 name=None):
        super(DenseBlock, self).__init__()
        self.dropout = dropout

        self.dense_layer_func = []

        pre_channel = num_channels
        for layer in range(num_layers):
            self.dense_layer_func.append(
                self.add_sublayer(
                    "{}_{}".format(name, layer + 1),
                    DenseLayer(
                        num_channels=pre_channel,
                        growth_rate=growth_rate,
                        bn_size=bn_size,
                        dropout=dropout,
                        name=name + '_' + str(layer + 1))))
            pre_channel = pre_channel + growth_rate

    def forward(self, input):
        conv = input
        for func in self.dense_layer_func:
            conv = func(conv)
        return conv


class TransitionLayer(nn.Layer):
    def __init__(self, num_channels, num_output_features, name=None):
        super(TransitionLayer, self).__init__()

        self.conv_ac_func = BNACConvLayer(
            num_channels=num_channels,
            num_filters=num_output_features,
            filter_size=1,
            pad=0,
            stride=1,
            name=name)

        self.pool2d_avg = AvgPool2D(kernel_size=2, stride=2, padding=0)

    def forward(self, input):
        y = self.conv_ac_func(input)
        y = self.pool2d_avg(y)
        return y


class ConvBNLayer(nn.Layer):
    def __init__(self,
                 num_channels,
                 num_filters,
                 filter_size,
                 stride=1,
                 pad=0,
                 groups=1,
                 act="relu",
                 name=None):
        super(ConvBNLayer, self).__init__()

        self._conv = Conv2D(
            in_channels=num_channels,
            out_channels=num_filters,
            kernel_size=filter_size,
            stride=stride,
            padding=pad,
            groups=groups,
            weight_attr=ParamAttr(name=name + "_weights"),
            bias_attr=False)
        self._batch_norm = BatchNorm(
            num_filters,
            act=act,
            param_attr=ParamAttr(name=name + '_bn_scale'),
            bias_attr=ParamAttr(name + '_bn_offset'),
            moving_mean_name=name + '_bn_mean',
            moving_variance_name=name + '_bn_variance')

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


class DenseNet(nn.Layer):
    def __init__(self, layers=60, bn_size=4, dropout=0, class_dim=1000):
        super(DenseNet, self).__init__()

        supported_layers = [121, 161, 169, 201, 264]
        assert layers in supported_layers, \
            "supported layers are {} but input layer is {}".format(
                supported_layers, layers)
        densenet_spec = {
            121: (64, 32, [6, 12, 24, 16]),
            161: (96, 48, [6, 12, 36, 24]),
            169: (64, 32, [6, 12, 32, 32]),
            201: (64, 32, [6, 12, 48, 32]),
            264: (64, 32, [6, 12, 64, 48])
        }
        num_init_features, growth_rate, block_config = densenet_spec[layers]

        self.conv1_func = ConvBNLayer(
            num_channels=3,
            num_filters=num_init_features,
            filter_size=7,
            stride=2,
            pad=3,
            act='relu',
            name="conv1")

        self.pool2d_max = MaxPool2D(kernel_size=3, stride=2, padding=1)

        self.block_config = block_config

        self.dense_block_func_list = []
        self.transition_func_list = []
        pre_num_channels = num_init_features
        num_features = num_init_features
        for i, num_layers in enumerate(block_config):
            self.dense_block_func_list.append(
                self.add_sublayer(
                    "db_conv_{}".format(i + 2),
                    DenseBlock(
                        num_channels=pre_num_channels,
                        num_layers=num_layers,
                        bn_size=bn_size,
                        growth_rate=growth_rate,
                        dropout=dropout,
                        name='conv' + str(i + 2))))

            num_features = num_features + num_layers * growth_rate
            pre_num_channels = num_features

            if i != len(block_config) - 1:
                self.transition_func_list.append(
                    self.add_sublayer(
                        "tr_conv{}_blk".format(i + 2),
                        TransitionLayer(
                            num_channels=pre_num_channels,
                            num_output_features=num_features // 2,
                            name='conv' + str(i + 2) + "_blk")))
                pre_num_channels = num_features // 2
                num_features = num_features // 2

        self.batch_norm = BatchNorm(
            num_features,
            act="relu",
            param_attr=ParamAttr(name='conv5_blk_bn_scale'),
            bias_attr=ParamAttr(name='conv5_blk_bn_offset'),
            moving_mean_name='conv5_blk_bn_mean',
            moving_variance_name='conv5_blk_bn_variance')

        self.pool2d_avg = AdaptiveAvgPool2D(1)

        stdv = 1.0 / math.sqrt(num_features * 1.0)

        self.out = Linear(
            num_features,
            class_dim,
            weight_attr=ParamAttr(
                initializer=Uniform(-stdv, stdv), name="fc_weights"),
            bias_attr=ParamAttr(name="fc_offset"))

    def forward(self, input):
        conv = self.conv1_func(input)
        conv = self.pool2d_max(conv)

        for i, num_layers in enumerate(self.block_config):
            conv = self.dense_block_func_list[i](conv)
            if i != len(self.block_config) - 1:
                conv = self.transition_func_list[i](conv)

        conv = self.batch_norm(conv)
        y = self.pool2d_avg(conv)
        y = paddle.flatten(y, start_axis=1, stop_axis=-1)
        y = self.out(y)
        return y


def DenseNet121(**args):
    model = DenseNet(layers=121, **args)
    return model


def DenseNet161(**args):
    model = DenseNet(layers=161, **args)
    return model


def DenseNet169(**args):
    model = DenseNet(layers=169, **args)
    return model


def DenseNet201(**args):
    model = DenseNet(layers=201, **args)
    return model


def DenseNet264(**args):
    model = DenseNet(layers=264, **args)
    return model