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 math

import paddle
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
from paddle.fluid.param_attr import ParamAttr
from paddle.utils.download import get_weights_path_from_url

__all__ = []

model_urls = {
    'densenet121':
    ('https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet121_pretrained.pdparams',
     'db1b239ed80a905290fd8b01d3af08e4'),
    'densenet161':
    ('https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet161_pretrained.pdparams',
     '62158869cb315098bd25ddbfd308a853'),
    'densenet169':
    ('https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet169_pretrained.pdparams',
     '82cc7c635c3f19098c748850efb2d796'),
    'densenet201':
    ('https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet201_pretrained.pdparams',
     '16ca29565a7712329cf9e36e02caaf58'),
    'densenet264':
    ('https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet264_pretrained.pdparams',
     '3270ce516b85370bba88cfdd9f60bff4'),
}


class BNACConvLayer(nn.Layer):

    def __init__(self,
                 num_channels,
                 num_filters,
                 filter_size,
                 stride=1,
                 pad=0,
                 groups=1,
                 act="relu"):
        super(BNACConvLayer, self).__init__()
        self._batch_norm = BatchNorm(num_channels, act=act)

        self._conv = Conv2D(in_channels=num_channels,
                            out_channels=num_filters,
                            kernel_size=filter_size,
                            stride=stride,
                            padding=pad,
                            groups=groups,
                            weight_attr=ParamAttr(),
                            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):
        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)

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

        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)))
            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):
        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)

        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"):
        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(),
                            bias_attr=False)
        self._batch_norm = BatchNorm(num_filters, act=act)

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


class DenseNet(nn.Layer):
    """DenseNet model from
    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.

    Args:
        layers (int, optional): Layers of DenseNet. Default: 121.
        bn_size (int, optional): Expansion of growth rate in the middle layer. Default: 4.
        dropout (float, optional): Dropout rate. Default: :math:`0.0`.
        num_classes (int, optional): Output dim of last fc layer. If num_classes <= 0, last fc layer 
                            will not be defined. Default: 1000.
        with_pool (bool, optional): Use pool before the last fc layer or not. Default: True.

    Returns:
        :ref:`api_paddle_nn_Layer`. An instance of DenseNet model.

    Examples:
        .. code-block:: python

            import paddle
            from paddle.vision.models import DenseNet

            # build model
            densenet = DenseNet()

            x = paddle.rand([1, 3, 224, 224])
            out = densenet(x)

            print(out.shape)
            # [1, 1000]
    """

    def __init__(self,
                 layers=121,
                 bn_size=4,
                 dropout=0.,
                 num_classes=1000,
                 with_pool=True):
        super(DenseNet, self).__init__()
        self.num_classes = num_classes
        self.with_pool = with_pool
        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')
        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)))
                pre_num_channels = num_features // 2
                num_features = num_features // 2

        self.batch_norm = BatchNorm(num_features, act="relu")
        if self.with_pool:
            self.pool2d_avg = AdaptiveAvgPool2D(1)

        if self.num_classes > 0:
            stdv = 1.0 / math.sqrt(num_features * 1.0)
            self.out = Linear(
                num_features,
                num_classes,
                weight_attr=ParamAttr(initializer=Uniform(-stdv, stdv)),
                bias_attr=ParamAttr())

    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)

        if self.with_pool:
            y = self.pool2d_avg(conv)

        if self.num_classes > 0:
            y = paddle.flatten(y, start_axis=1, stop_axis=-1)
            y = self.out(y)

        return y


def _densenet(arch, layers, pretrained, **kwargs):
    model = DenseNet(layers=layers, **kwargs)
    if pretrained:
        assert arch in model_urls, "{} model do not have a pretrained model now, you should set pretrained=False".format(
            arch)
        weight_path = get_weights_path_from_url(model_urls[arch][0],
                                                model_urls[arch][1])

        param = paddle.load(weight_path)
        model.set_dict(param)

    return model


def densenet121(pretrained=False, **kwargs):
    """DenseNet 121-layer model from
    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.

    Args:
        pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
                            on ImageNet. Default: False.
        **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`DenseNet <api_paddle_vision_DenseNet>`.

    Returns:
        :ref:`api_paddle_nn_Layer`. An instance of DenseNet 121-layer model.

    Examples:
        .. code-block:: python

            import paddle
            from paddle.vision.models import densenet121

            # build model
            model = densenet121()

            # build model and load imagenet pretrained weight
            # model = densenet121(pretrained=True)

            x = paddle.rand([1, 3, 224, 224])
            out = model(x)

            print(out.shape)
            # [1, 1000]
    """
    return _densenet('densenet121', 121, pretrained, **kwargs)


def densenet161(pretrained=False, **kwargs):
    """DenseNet 161-layer model from
    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.

    Args:
        pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
                            on ImageNet. Default: False.
        **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`DenseNet <api_paddle_vision_DenseNet>`.

    Returns:
        :ref:`api_paddle_nn_Layer`. An instance of DenseNet 161-layer model.

    Examples:
        .. code-block:: python

            from paddle.vision.models import densenet161

            # build model
            model = densenet161()

            # build model and load imagenet pretrained weight
            # model = densenet161(pretrained=True)
    """
    return _densenet('densenet161', 161, pretrained, **kwargs)


def densenet169(pretrained=False, **kwargs):
    """DenseNet 169-layer model from
    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.

    Args:
        pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
                            on ImageNet. Default: False.
        **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`DenseNet <api_paddle_vision_DenseNet>`.

    Returns:
        :ref:`api_paddle_nn_Layer`. An instance of DenseNet 169-layer model.

    Examples:
        .. code-block:: python

            import paddle
            from paddle.vision.models import densenet169

            # build model
            model = densenet169()

            # build model and load imagenet pretrained weight
            # model = densenet169(pretrained=True)

            x = paddle.rand([1, 3, 224, 224])
            out = model(x)

            print(out.shape)
            # [1, 1000]
    """
    return _densenet('densenet169', 169, pretrained, **kwargs)


def densenet201(pretrained=False, **kwargs):
    """DenseNet 201-layer model from
    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.

    Args:
        pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
                            on ImageNet. Default: False.
        **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`DenseNet <api_paddle_vision_DenseNet>`.

    Returns:
        :ref:`api_paddle_nn_Layer`. An instance of DenseNet 201-layer model.

    Examples:
        .. code-block:: python

            import paddle
            from paddle.vision.models import densenet201

            # build model
            model = densenet201()

            # build model and load imagenet pretrained weight
            # model = densenet201(pretrained=True)
            x = paddle.rand([1, 3, 224, 224])
            out = model(x)

            print(out.shape)
            # [1, 1000]
    """
    return _densenet('densenet201', 201, pretrained, **kwargs)


def densenet264(pretrained=False, **kwargs):
    """DenseNet 264-layer model from
    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.

    Args:
        pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
                            on ImageNet. Default: False.
        **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`DenseNet <api_paddle_vision_DenseNet>`.

    Returns:
        :ref:`api_paddle_nn_Layer`. An instance of DenseNet 264-layer model.

    Examples:
        .. code-block:: python

            import paddle
            from paddle.vision.models import densenet264

            # build model
            model = densenet264()

            # build model and load imagenet pretrained weight
            # model = densenet264(pretrained=True)

            x = paddle.rand([1, 3, 224, 224])
            out = model(x)

            print(out.shape)
            # [1, 1000]
    """
    return _densenet('densenet264', 264, pretrained, **kwargs)