PaddleRS/paddlers/models/ppcls/arch/backbone/model_zoo/rednet.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.

# Code was based on https://github.com/d-li14/involution

import paddle
import paddle.nn as nn

from paddle.vision.models import resnet

from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url

MODEL_URLS = {
    "RedNet26":
    "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet26_pretrained.pdparams",
    "RedNet38":
    "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet38_pretrained.pdparams",
    "RedNet50":
    "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet50_pretrained.pdparams",
    "RedNet101":
    "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet101_pretrained.pdparams",
    "RedNet152":
    "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet152_pretrained.pdparams"
}

__all__ = MODEL_URLS.keys()


class Involution(nn.Layer):
    def __init__(self, channels, kernel_size, stride):
        super(Involution, self).__init__()
        self.kernel_size = kernel_size
        self.stride = stride
        self.channels = channels
        reduction_ratio = 4
        self.group_channels = 16
        self.groups = self.channels // self.group_channels
        self.conv1 = nn.Sequential(
            ('conv', nn.Conv2D(
                in_channels=channels,
                out_channels=channels // reduction_ratio,
                kernel_size=1,
                bias_attr=False)),
            ('bn', nn.BatchNorm2D(channels // reduction_ratio)),
            ('activate', nn.ReLU()))
        self.conv2 = nn.Sequential(('conv', nn.Conv2D(
            in_channels=channels // reduction_ratio,
            out_channels=kernel_size**2 * self.groups,
            kernel_size=1,
            stride=1)))
        if stride > 1:
            self.avgpool = nn.AvgPool2D(stride, stride)

    def forward(self, x):
        weight = self.conv2(
            self.conv1(x if self.stride == 1 else self.avgpool(x)))
        b, c, h, w = weight.shape
        weight = weight.reshape(
            (b, self.groups, self.kernel_size**2, h, w)).unsqueeze(2)

        out = nn.functional.unfold(x, self.kernel_size, self.stride,
                                   (self.kernel_size - 1) // 2, 1)
        out = out.reshape(
            (b, self.groups, self.group_channels, self.kernel_size**2, h, w))
        out = (weight * out).sum(axis=3).reshape((b, self.channels, h, w))
        return out


class BottleneckBlock(resnet.BottleneckBlock):
    def __init__(self,
                 inplanes,
                 planes,
                 stride=1,
                 downsample=None,
                 groups=1,
                 base_width=64,
                 dilation=1,
                 norm_layer=None):
        super(BottleneckBlock, self).__init__(inplanes, planes, stride,
                                              downsample, groups, base_width,
                                              dilation, norm_layer)
        width = int(planes * (base_width / 64.)) * groups
        self.conv2 = Involution(width, 7, stride)


class RedNet(resnet.ResNet):
    def __init__(self, block, depth, class_num=1000, with_pool=True):
        super(RedNet, self).__init__(
            block=block, depth=50, num_classes=class_num, with_pool=with_pool)
        layer_cfg = {
            26: [1, 2, 4, 1],
            38: [2, 3, 5, 2],
            50: [3, 4, 6, 3],
            101: [3, 4, 23, 3],
            152: [3, 8, 36, 3]
        }
        layers = layer_cfg[depth]

        self.conv1 = None
        self.bn1 = None
        self.relu = None
        self.inplanes = 64
        self.class_num = class_num
        self.stem = nn.Sequential(
            nn.Sequential(
                ('conv', nn.Conv2D(
                    in_channels=3,
                    out_channels=self.inplanes // 2,
                    kernel_size=3,
                    stride=2,
                    padding=1,
                    bias_attr=False)),
                ('bn', nn.BatchNorm2D(self.inplanes // 2)),
                ('activate', nn.ReLU())),
            Involution(self.inplanes // 2, 3, 1),
            nn.BatchNorm2D(self.inplanes // 2),
            nn.ReLU(),
            nn.Sequential(
                ('conv', nn.Conv2D(
                    in_channels=self.inplanes // 2,
                    out_channels=self.inplanes,
                    kernel_size=3,
                    stride=1,
                    padding=1,
                    bias_attr=False)), ('bn', nn.BatchNorm2D(self.inplanes)),
                ('activate', nn.ReLU())))

        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)

    def forward(self, x):
        x = self.stem(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        if self.with_pool:
            x = self.avgpool(x)

        if self.class_num > 0:
            x = paddle.flatten(x, 1)
            x = self.fc(x)

        return x


def _load_pretrained(pretrained, model, model_url, use_ssld=False):
    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 RedNet26(pretrained=False, **kwargs):
    model = RedNet(BottleneckBlock, 26, **kwargs)
    _load_pretrained(pretrained, model, MODEL_URLS["RedNet26"])
    return model


def RedNet38(pretrained=False, **kwargs):
    model = RedNet(BottleneckBlock, 38, **kwargs)
    _load_pretrained(pretrained, model, MODEL_URLS["RedNet38"])
    return model


def RedNet50(pretrained=False, **kwargs):
    model = RedNet(BottleneckBlock, 50, **kwargs)
    _load_pretrained(pretrained, model, MODEL_URLS["RedNet50"])
    return model


def RedNet101(pretrained=False, **kwargs):
    model = RedNet(BottleneckBlock, 101, **kwargs)
    _load_pretrained(pretrained, model, MODEL_URLS["RedNet101"])
    return model


def RedNet152(pretrained=False, **kwargs):
    model = RedNet(BottleneckBlock, 152, **kwargs)
    _load_pretrained(pretrained, model, MODEL_URLS["RedNet152"])
    return model