surveillance_monitoring/dependence/PaddleDetection/static/ppdet/modeling/backbones/bifpn.py

# Copyright (c) 2020 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.

from __future__ import absolute_import
from __future__ import division

from paddle import fluid
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.regularizer import L2Decay
from paddle.fluid.initializer import Constant, Xavier

from ppdet.core.workspace import register

__all__ = ['BiFPN']


class FusionConv(object):
    def __init__(self, num_chan):
        super(FusionConv, self).__init__()
        self.num_chan = num_chan

    def __call__(self, inputs, name=''):
        x = fluid.layers.swish(inputs)
        # depthwise
        x = fluid.layers.conv2d(
            x,
            self.num_chan,
            filter_size=3,
            padding='SAME',
            groups=self.num_chan,
            param_attr=ParamAttr(
                initializer=Xavier(), name=name + '_dw_w'),
            bias_attr=False)
        # pointwise
        x = fluid.layers.conv2d(
            x,
            self.num_chan,
            filter_size=1,
            param_attr=ParamAttr(
                initializer=Xavier(), name=name + '_pw_w'),
            bias_attr=ParamAttr(
                regularizer=L2Decay(0.), name=name + '_pw_b'))
        # bn + act
        x = fluid.layers.batch_norm(
            x,
            momentum=0.997,
            epsilon=1e-04,
            param_attr=ParamAttr(
                initializer=Constant(1.0),
                regularizer=L2Decay(0.),
                name=name + '_bn_w'),
            bias_attr=ParamAttr(
                regularizer=L2Decay(0.), name=name + '_bn_b'))
        return x


class BiFPNCell(object):
    def __init__(self, num_chan, levels=5):
        super(BiFPNCell, self).__init__()
        self.levels = levels
        self.num_chan = num_chan
        num_trigates = levels - 2
        num_bigates = levels
        self.trigates = fluid.layers.create_parameter(
            shape=[num_trigates, 3],
            dtype='float32',
            default_initializer=fluid.initializer.Constant(1.))
        self.bigates = fluid.layers.create_parameter(
            shape=[num_bigates, 2],
            dtype='float32',
            default_initializer=fluid.initializer.Constant(1.))
        self.eps = 1e-4

    def __call__(self, inputs, cell_name=''):
        assert len(inputs) == self.levels

        def upsample(feat):
            return fluid.layers.resize_nearest(feat, scale=2.)

        def downsample(feat):
            return fluid.layers.pool2d(
                feat,
                pool_type='max',
                pool_size=3,
                pool_stride=2,
                pool_padding='SAME')

        fuse_conv = FusionConv(self.num_chan)

        # normalize weight
        trigates = fluid.layers.relu(self.trigates)
        bigates = fluid.layers.relu(self.bigates)
        trigates /= fluid.layers.reduce_sum(
            trigates, dim=1, keep_dim=True) + self.eps
        bigates /= fluid.layers.reduce_sum(
            bigates, dim=1, keep_dim=True) + self.eps

        feature_maps = list(inputs)  # make a copy
        # top down path
        for l in range(self.levels - 1):
            p = self.levels - l - 2
            w1 = fluid.layers.slice(
                bigates, axes=[0, 1], starts=[l, 0], ends=[l + 1, 1])
            w2 = fluid.layers.slice(
                bigates, axes=[0, 1], starts=[l, 1], ends=[l + 1, 2])
            above = upsample(feature_maps[p + 1])
            feature_maps[p] = fuse_conv(
                w1 * above + w2 * inputs[p],
                name='{}_tb_{}'.format(cell_name, l))
        # bottom up path
        for l in range(1, self.levels):
            p = l
            name = '{}_bt_{}'.format(cell_name, l)
            below = downsample(feature_maps[p - 1])
            if p == self.levels - 1:
                # handle P7
                w1 = fluid.layers.slice(
                    bigates, axes=[0, 1], starts=[p, 0], ends=[p + 1, 1])
                w2 = fluid.layers.slice(
                    bigates, axes=[0, 1], starts=[p, 1], ends=[p + 1, 2])
                feature_maps[p] = fuse_conv(
                    w1 * below + w2 * inputs[p], name=name)
            else:
                w1 = fluid.layers.slice(
                    trigates, axes=[0, 1], starts=[p - 1, 0], ends=[p, 1])
                w2 = fluid.layers.slice(
                    trigates, axes=[0, 1], starts=[p - 1, 1], ends=[p, 2])
                w3 = fluid.layers.slice(
                    trigates, axes=[0, 1], starts=[p - 1, 2], ends=[p, 3])
                feature_maps[p] = fuse_conv(
                    w1 * feature_maps[p] + w2 * below + w3 * inputs[p],
                    name=name)
        return feature_maps


@register
class BiFPN(object):
    """
    Bidirectional Feature Pyramid Network, see https://arxiv.org/abs/1911.09070

    Args:
        num_chan (int): number of feature channels
        repeat (int): number of repeats of the BiFPN module
        level (int): number of FPN levels, default: 5
    """

    def __init__(self, num_chan, repeat=3, levels=5):
        super(BiFPN, self).__init__()
        self.num_chan = num_chan
        self.repeat = repeat
        self.levels = levels

    def __call__(self, inputs):
        feats = []
        # NOTE add two extra levels
        for idx in range(self.levels):
            if idx <= len(inputs):
                if idx == len(inputs):
                    feat = inputs[-1]
                else:
                    feat = inputs[idx]

                if feat.shape[1] != self.num_chan:
                    feat = fluid.layers.conv2d(
                        feat,
                        self.num_chan,
                        filter_size=1,
                        padding='SAME',
                        param_attr=ParamAttr(initializer=Xavier()),
                        bias_attr=ParamAttr(regularizer=L2Decay(0.)))
                    feat = fluid.layers.batch_norm(
                        feat,
                        momentum=0.997,
                        epsilon=1e-04,
                        param_attr=ParamAttr(
                            initializer=Constant(1.0), regularizer=L2Decay(0.)),
                        bias_attr=ParamAttr(regularizer=L2Decay(0.)))

            if idx >= len(inputs):
                feat = fluid.layers.pool2d(
                    feat,
                    pool_type='max',
                    pool_size=3,
                    pool_stride=2,
                    pool_padding='SAME')
            feats.append(feat)

        biFPN = BiFPNCell(self.num_chan, self.levels)
        for r in range(self.repeat):
            feats = biFPN(feats, 'bifpn_{}'.format(r))
        return feats