wt_ocr_and_docker/ppocr/modeling/backbones/rec_mv1_enhance.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.

# This code is refer from: https://github.com/PaddlePaddle/PaddleClas/blob/develop/ppcls/arch/backbone/legendary_models/pp_lcnet.py

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

import math
import numpy as np
import paddle
from paddle import ParamAttr, reshape, transpose
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import KaimingNormal
from paddle.regularizer import L2Decay
from paddle.nn.functional import hardswish, hardsigmoid


class ConvBNLayer(nn.Layer):
    def __init__(self,
                 num_channels,
                 filter_size,
                 num_filters,
                 stride,
                 padding,
                 channels=None,
                 num_groups=1,
                 act='hard_swish'):
        super(ConvBNLayer, self).__init__()

        self._conv = Conv2D(
            in_channels=num_channels,
            out_channels=num_filters,
            kernel_size=filter_size,
            stride=stride,
            padding=padding,
            groups=num_groups,
            weight_attr=ParamAttr(initializer=KaimingNormal()),
            bias_attr=False)

        self._batch_norm = BatchNorm(
            num_filters,
            act=act,
            param_attr=ParamAttr(regularizer=L2Decay(0.0)),
            bias_attr=ParamAttr(regularizer=L2Decay(0.0)))

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


class DepthwiseSeparable(nn.Layer):
    def __init__(self,
                 num_channels,
                 num_filters1,
                 num_filters2,
                 num_groups,
                 stride,
                 scale,
                 dw_size=3,
                 padding=1,
                 use_se=False):
        super(DepthwiseSeparable, self).__init__()
        self.use_se = use_se
        self._depthwise_conv = ConvBNLayer(
            num_channels=num_channels,
            num_filters=int(num_filters1 * scale),
            filter_size=dw_size,
            stride=stride,
            padding=padding,
            num_groups=int(num_groups * scale))
        if use_se:
            self._se = SEModule(int(num_filters1 * scale))
        self._pointwise_conv = ConvBNLayer(
            num_channels=int(num_filters1 * scale),
            filter_size=1,
            num_filters=int(num_filters2 * scale),
            stride=1,
            padding=0)

    def forward(self, inputs):
        y = self._depthwise_conv(inputs)
        if self.use_se:
            y = self._se(y)
        y = self._pointwise_conv(y)
        return y


class MobileNetV1Enhance(nn.Layer):
    def __init__(self,
                 in_channels=3,
                 scale=0.5,
                 last_conv_stride=1,
                 last_pool_type='max',
                 **kwargs):
        super().__init__()
        self.scale = scale
        self.block_list = []

        self.conv1 = ConvBNLayer(
            num_channels=3,
            filter_size=3,
            channels=3,
            num_filters=int(32 * scale),
            stride=2,
            padding=1)

        conv2_1 = DepthwiseSeparable(
            num_channels=int(32 * scale),
            num_filters1=32,
            num_filters2=64,
            num_groups=32,
            stride=1,
            scale=scale)
        self.block_list.append(conv2_1)

        conv2_2 = DepthwiseSeparable(
            num_channels=int(64 * scale),
            num_filters1=64,
            num_filters2=128,
            num_groups=64,
            stride=1,
            scale=scale)
        self.block_list.append(conv2_2)

        conv3_1 = DepthwiseSeparable(
            num_channels=int(128 * scale),
            num_filters1=128,
            num_filters2=128,
            num_groups=128,
            stride=1,
            scale=scale)
        self.block_list.append(conv3_1)

        conv3_2 = DepthwiseSeparable(
            num_channels=int(128 * scale),
            num_filters1=128,
            num_filters2=256,
            num_groups=128,
            stride=(2, 1),
            scale=scale)
        self.block_list.append(conv3_2)

        conv4_1 = DepthwiseSeparable(
            num_channels=int(256 * scale),
            num_filters1=256,
            num_filters2=256,
            num_groups=256,
            stride=1,
            scale=scale)
        self.block_list.append(conv4_1)

        conv4_2 = DepthwiseSeparable(
            num_channels=int(256 * scale),
            num_filters1=256,
            num_filters2=512,
            num_groups=256,
            stride=(2, 1),
            scale=scale)
        self.block_list.append(conv4_2)

        for _ in range(5):
            conv5 = DepthwiseSeparable(
                num_channels=int(512 * scale),
                num_filters1=512,
                num_filters2=512,
                num_groups=512,
                stride=1,
                dw_size=5,
                padding=2,
                scale=scale,
                use_se=False)
            self.block_list.append(conv5)

        conv5_6 = DepthwiseSeparable(
            num_channels=int(512 * scale),
            num_filters1=512,
            num_filters2=1024,
            num_groups=512,
            stride=(2, 1),
            dw_size=5,
            padding=2,
            scale=scale,
            use_se=True)
        self.block_list.append(conv5_6)

        conv6 = DepthwiseSeparable(
            num_channels=int(1024 * scale),
            num_filters1=1024,
            num_filters2=1024,
            num_groups=1024,
            stride=last_conv_stride,
            dw_size=5,
            padding=2,
            use_se=True,
            scale=scale)
        self.block_list.append(conv6)

        self.block_list = nn.Sequential(*self.block_list)
        if last_pool_type == 'avg':
            self.pool = nn.AvgPool2D(kernel_size=2, stride=2, padding=0)
        else:
            self.pool = nn.MaxPool2D(kernel_size=2, stride=2, padding=0)
        self.out_channels = int(1024 * scale)

    def forward(self, inputs):
        y = self.conv1(inputs)
        y = self.block_list(y)
        y = self.pool(y)
        return y


class SEModule(nn.Layer):
    def __init__(self, channel, reduction=4):
        super(SEModule, self).__init__()
        self.avg_pool = AdaptiveAvgPool2D(1)
        self.conv1 = Conv2D(
            in_channels=channel,
            out_channels=channel // reduction,
            kernel_size=1,
            stride=1,
            padding=0,
            weight_attr=ParamAttr(),
            bias_attr=ParamAttr())
        self.conv2 = Conv2D(
            in_channels=channel // reduction,
            out_channels=channel,
            kernel_size=1,
            stride=1,
            padding=0,
            weight_attr=ParamAttr(),
            bias_attr=ParamAttr())

    def forward(self, inputs):
        outputs = self.avg_pool(inputs)
        outputs = self.conv1(outputs)
        outputs = F.relu(outputs)
        outputs = self.conv2(outputs)
        outputs = hardsigmoid(outputs)
        return paddle.multiply(x=inputs, y=outputs)