PaddleClas/ppcls/arch/backbone/legendary_models/mobilenet_v1.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, division, print_function

import numpy as np
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 KaimingNormal
import math

from ppcls.arch.backbone.base.theseus_layer import TheseusLayer


__all__ = [
    "MobileNetV1_x0_25", "MobileNetV1_x0_5", "MobileNetV1_x0_75", "MobileNetV1"
]


class ConvBNLayer(TheseusLayer):
    def __init__(self,
                 num_channels,
                 filter_size,
                 num_filters,
                 stride,
                 padding,
                 channels=None,
                 num_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=padding,
            groups=num_groups,
            weight_attr=ParamAttr(
                initializer=KaimingNormal()),
            bias_attr=False)

        self._batch_norm = BatchNorm(
            num_filters,
            act=act)

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


class DepthwiseSeparable(TheseusLayer):
    def __init__(self,
                 num_channels,
                 num_filters1,
                 num_filters2,
                 num_groups,
                 stride,
                 scale):
        super(DepthwiseSeparable, self).__init__()

        self._depthwise_conv = ConvBNLayer(
            num_channels=num_channels,
            num_filters=int(num_filters1 * scale),
            filter_size=3,
            stride=stride,
            padding=1,
            num_groups=int(num_groups * 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, x):
        x = self._depthwise_conv(x)
        x = self._pointwise_conv(x)
        return x


class MobileNet(TheseusLayer):
    def __init__(self, scale=1.0, class_dim=1000):
        super(MobileNet, self).__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 = self.add_sublayer(
            "conv2_1",
            sublayer=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 = self.add_sublayer(
            "conv2_2",
            sublayer=DepthwiseSeparable(
                num_channels=int(64 * scale),
                num_filters1=64,
                num_filters2=128,
                num_groups=64,
                stride=2,
                scale=scale))
        self.block_list.append(conv2_2)

        conv3_1 = self.add_sublayer(
            "conv3_1",
            sublayer=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 = self.add_sublayer(
            "conv3_2",
            sublayer=DepthwiseSeparable(
                num_channels=int(128 * scale),
                num_filters1=128,
                num_filters2=256,
                num_groups=128,
                stride=2,
                scale=scale))
        self.block_list.append(conv3_2)

        conv4_1 = self.add_sublayer(
            "conv4_1",
            sublayer=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 = self.add_sublayer(
            "conv4_2",
            sublayer=DepthwiseSeparable(
                num_channels=int(256 * scale),
                num_filters1=256,
                num_filters2=512,
                num_groups=256,
                stride=2,
                scale=scale))
        self.block_list.append(conv4_2)

        for i in range(5):
            conv5 = self.add_sublayer(
                "conv5_" + str(i + 1),
                sublayer=DepthwiseSeparable(
                    num_channels=int(512 * scale),
                    num_filters1=512,
                    num_filters2=512,
                    num_groups=512,
                    stride=1,
                    scale=scale))
            self.block_list.append(conv5)

        conv5_6 = self.add_sublayer(
            "conv5_6",
            sublayer=DepthwiseSeparable(
                num_channels=int(512 * scale),
                num_filters1=512,
                num_filters2=1024,
                num_groups=512,
                stride=2,
                scale=scale))
        self.block_list.append(conv5_6)

        conv6 = self.add_sublayer(
            "conv6",
            sublayer=DepthwiseSeparable(
                num_channels=int(1024 * scale),
                num_filters1=1024,
                num_filters2=1024,
                num_groups=1024,
                stride=1,
                scale=scale))
        self.block_list.append(conv6)

        self.pool2d_avg = AdaptiveAvgPool2D(1)

        self.out = Linear(
            int(1024 * scale),
            class_dim,
            weight_attr=ParamAttr(initializer=KaimingNormal()))

    def forward(self, x):
        x = self.conv1(x)
        for block in self.block_list:
            x = block(x)
        x = self.pool2d_avg(x)
        x = paddle.flatten(x, start_axis=1, stop_axis=-1)
        x = self.out(x)
        return x


def MobileNetV1_x0_25(**args):
    model = MobileNet(scale=0.25, **args)
    return model


def MobileNetV1_x0_5(**args):
    model = MobileNet(scale=0.5, **args)
    return model


def MobileNetV1_x0_75(**args):
    model = MobileNet(scale=0.75, **args)
    return model


def MobileNetV1(**args):
    model = MobileNet(scale=1.0, **args)
    return model