pp_lcnet_v2.py

# copyright (c) 2022 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 paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddle import ParamAttr
from paddle.nn import AdaptiveAvgPool2D, BatchNorm2D, Conv2D, Dropout, Linear
from paddle.regularizer import L2Decay
from paddle.nn.initializer import KaimingNormal
from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url

MODEL_URLS = {
    "PPLCNetV2_base":
    "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNetV2_base_pretrained.pdparams",
}

__all__ = list(MODEL_URLS.keys())

NET_CONFIG = {
    # in_channels, kernel_size, split_pw, use_rep, use_se, use_shortcut
    "stage1": [64, 3, False, False, False, False],
    "stage2": [128, 3, False, False, False, False],
    "stage3": [256, 5, True, True, True, False],
    "stage4": [512, 5, False, True, False, True],
}


def make_divisible(v, divisor=8, min_value=None):
    if min_value is None:
        min_value = divisor
    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
    if new_v < 0.9 * v:
        new_v += divisor
    return new_v


class ConvBNLayer(TheseusLayer):
    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 stride,
                 groups=1,
                 use_act=True):
        super().__init__()
        self.use_act = use_act
        self.conv = Conv2D(
            in_channels=in_channels,
            out_channels=out_channels,
            kernel_size=kernel_size,
            stride=stride,
            padding=(kernel_size - 1) // 2,
            groups=groups,
            weight_attr=ParamAttr(initializer=KaimingNormal()),
            bias_attr=False)

        self.bn = BatchNorm2D(
            out_channels,
            weight_attr=ParamAttr(regularizer=L2Decay(0.0)),
            bias_attr=ParamAttr(regularizer=L2Decay(0.0)))
        if self.use_act:
            self.act = nn.ReLU()

    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        if self.use_act:
            x = self.act(x)
        return x


class SEModule(TheseusLayer):
    def __init__(self, channel, reduction=4):
        super().__init__()
        self.avg_pool = AdaptiveAvgPool2D(1)
        self.conv1 = Conv2D(
            in_channels=channel,
            out_channels=channel // reduction,
            kernel_size=1,
            stride=1,
            padding=0)
        self.relu = nn.ReLU()
        self.conv2 = Conv2D(
            in_channels=channel // reduction,
            out_channels=channel,
            kernel_size=1,
            stride=1,
            padding=0)
        self.hardsigmoid = nn.Sigmoid()

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


class RepDepthwiseSeparable(TheseusLayer):
    def __init__(self,
                 in_channels,
                 out_channels,
                 stride,
                 dw_size=3,
                 split_pw=False,
                 use_rep=False,
                 use_se=False,
                 use_shortcut=False):
        super().__init__()
        self.is_repped = False

        self.dw_size = dw_size
        self.split_pw = split_pw
        self.use_rep = use_rep
        self.use_se = use_se
        self.use_shortcut = True if use_shortcut and stride == 1 and in_channels == out_channels else False

        if self.use_rep:
            self.dw_conv_list = nn.LayerList()
            for kernel_size in range(self.dw_size, 0, -2):
                if kernel_size == 1 and stride != 1:
                    continue
                dw_conv = ConvBNLayer(
                    in_channels=in_channels,
                    out_channels=in_channels,
                    kernel_size=kernel_size,
                    stride=stride,
                    groups=in_channels,
                    use_act=False)
                self.dw_conv_list.append(dw_conv)
            self.dw_conv = nn.Conv2D(
                in_channels=in_channels,
                out_channels=in_channels,
                kernel_size=dw_size,
                stride=stride,
                padding=(dw_size - 1) // 2,
                groups=in_channels)
        else:
            self.dw_conv = ConvBNLayer(
                in_channels=in_channels,
                out_channels=in_channels,
                kernel_size=dw_size,
                stride=stride,
                groups=in_channels)

        self.act = nn.ReLU()

        if use_se:
            self.se = SEModule(in_channels)

        if self.split_pw:
            pw_ratio = 0.5
            self.pw_conv_1 = ConvBNLayer(
                in_channels=in_channels,
                kernel_size=1,
                out_channels=int(out_channels * pw_ratio),
                stride=1)
            self.pw_conv_2 = ConvBNLayer(
                in_channels=int(out_channels * pw_ratio),
                kernel_size=1,
                out_channels=out_channels,
                stride=1)
        else:
            self.pw_conv = ConvBNLayer(
                in_channels=in_channels,
                kernel_size=1,
                out_channels=out_channels,
                stride=1)

    def forward(self, x):
        if self.use_rep:
            if not self.training and not self.is_repped:
                self.rep()
                self.is_repped = True
            if self.training and self.is_repped:
                self.is_repped = False

            input_x = x
            if not self.training:
                x = self.act(self.dw_conv(x))
            else:
                y = self.dw_conv_list[0](x)
                for dw_conv in self.dw_conv_list[1:]:
                    y += dw_conv(x)
                x = self.act(y)
        else:
            x = self.dw_conv(x)

        if self.use_se:
            x = self.se(x)
        if self.split_pw:
            x = self.pw_conv_1(x)
            x = self.pw_conv_2(x)
        else:
            x = self.pw_conv(x)
        if self.use_shortcut:
            x = x + input_x
        return x

    def rep(self):
        kernel, bias = self._get_equivalent_kernel_bias()
        self.dw_conv.weight.set_value(kernel)
        self.dw_conv.bias.set_value(bias)

    def _get_equivalent_kernel_bias(self):
        kernel_sum = 0
        bias_sum = 0
        for dw_conv in self.dw_conv_list:
            kernel, bias = self._fuse_bn_tensor(dw_conv)
            kernel = self._pad_tensor(kernel, to_size=self.dw_size)
            kernel_sum += kernel
            bias_sum += bias
        return kernel_sum, bias_sum

    def _fuse_bn_tensor(self, branch):
        kernel = branch.conv.weight
        running_mean = branch.bn._mean
        running_var = branch.bn._variance
        gamma = branch.bn.weight
        beta = branch.bn.bias
        eps = branch.bn._epsilon
        std = (running_var + eps).sqrt()
        t = (gamma / std).reshape((-1, 1, 1, 1))
        return kernel * t, beta - running_mean * gamma / std

    def _pad_tensor(self, tensor, to_size):
        from_size = tensor.shape[-1]
        if from_size == to_size:
            return tensor
        pad = (to_size - from_size) // 2
        return F.pad(tensor, [pad, pad, pad, pad])


class PPLCNetV2(TheseusLayer):
    def __init__(self,
                 scale,
                 depths,
                 class_num=1000,
                 dropout_prob=0,
                 use_last_conv=True,
                 class_expand=1280):
        super().__init__()
        self.scale = scale
        self.use_last_conv = use_last_conv
        self.class_expand = class_expand

        self.stem = nn.Sequential(* [
            ConvBNLayer(
                in_channels=3,
                kernel_size=3,
                out_channels=make_divisible(32 * scale),
                stride=2), RepDepthwiseSeparable(
                    in_channels=make_divisible(32 * scale),
                    out_channels=make_divisible(64 * scale),
                    stride=1,
                    dw_size=3)
        ])

        # stages
        self.stages = nn.LayerList()
        for depth_idx, k in enumerate(NET_CONFIG):
            in_channels, kernel_size, split_pw, use_rep, use_se, use_shortcut = NET_CONFIG[
                k]
            self.stages.append(
                nn.Sequential(* [
                    RepDepthwiseSeparable(
                        in_channels=make_divisible((in_channels if i == 0 else
                                                    in_channels * 2) * scale),
                        out_channels=make_divisible(in_channels * 2 * scale),
                        stride=2 if i == 0 else 1,
                        dw_size=kernel_size,
                        split_pw=split_pw,
                        use_rep=use_rep,
                        use_se=use_se,
                        use_shortcut=use_shortcut)
                    for i in range(depths[depth_idx])
                ]))

        self.avg_pool = AdaptiveAvgPool2D(1)

        if self.use_last_conv:
            self.last_conv = Conv2D(
                in_channels=make_divisible(NET_CONFIG["stage4"][0] * 2 *
                                           scale),
                out_channels=self.class_expand,
                kernel_size=1,
                stride=1,
                padding=0,
                bias_attr=False)
            self.act = nn.ReLU()
            self.dropout = Dropout(p=dropout_prob, mode="downscale_in_infer")

        self.flatten = nn.Flatten(start_axis=1, stop_axis=-1)
        in_features = self.class_expand if self.use_last_conv else NET_CONFIG[
            "stage4"][0] * 2 * scale
        self.fc = Linear(in_features, class_num)

    def forward(self, x):
        x = self.stem(x)
        for stage in self.stages:
            x = stage(x)
        x = self.avg_pool(x)
        if self.use_last_conv:
            x = self.last_conv(x)
            x = self.act(x)
            x = self.dropout(x)
        x = self.flatten(x)
        x = self.fc(x)
        return x


def _load_pretrained(pretrained, model, model_url, use_ssld):
    if pretrained is False:
        pass
    elif pretrained is True:
        load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
    elif isinstance(pretrained, str):
        load_dygraph_pretrain(model, pretrained)
    else:
        raise RuntimeError(
            "pretrained type is not available. Please use `string` or `boolean` type."
        )


def PPLCNetV2_base(pretrained=False, use_ssld=False, **kwargs):
    """
    PPLCNetV2_base
    Args:
        pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
                    If str, means the path of the pretrained model.
        use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
    Returns:
        model: nn.Layer. Specific `PPLCNetV2_base` model depends on args.
    """
    model = PPLCNetV2(
        scale=1.0, depths=[2, 2, 6, 2], dropout_prob=0.2, **kwargs)
    _load_pretrained(pretrained, model, MODEL_URLS["PPLCNetV2_base"], use_ssld)
    return model