mpr.py

#   Copyright (c) 2020 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.

import numpy as np

import paddle
import paddle.nn as nn
import paddle.nn.functional as F

from ...modules.init import kaiming_normal_, constant_

from .builder import GENERATORS


##########################################################################
def conv(in_channels, out_channels, kernel_size, bias_attr=False, stride=1):
    return nn.Conv2D(in_channels,
                     out_channels,
                     kernel_size,
                     padding=(kernel_size // 2),
                     bias_attr=bias_attr,
                     stride=stride)


##########################################################################
## Channel Attention Layer
class CALayer(nn.Layer):
    def __init__(self, channel, reduction=16, bias_attr=False):
        super(CALayer, self).__init__()
        # global average pooling: feature --> point
        self.avg_pool = nn.AdaptiveAvgPool2D(1)
        # feature channel downscale and upscale --> channel weight
        self.conv_du = nn.Sequential(
            nn.Conv2D(channel,
                      channel // reduction,
                      1,
                      padding=0,
                      bias_attr=bias_attr), nn.ReLU(),
            nn.Conv2D(channel // reduction,
                      channel,
                      1,
                      padding=0,
                      bias_attr=bias_attr), nn.Sigmoid())

    def forward(self, x):
        y = self.avg_pool(x)
        y = self.conv_du(y)
        return x * y


##########################################################################
## Channel Attention Block (CAB)
class CAB(nn.Layer):
    def __init__(self, n_feat, kernel_size, reduction, bias_attr, act):
        super(CAB, self).__init__()
        modules_body = []
        modules_body.append(
            conv(n_feat, n_feat, kernel_size, bias_attr=bias_attr))
        modules_body.append(act)
        modules_body.append(
            conv(n_feat, n_feat, kernel_size, bias_attr=bias_attr))

        self.CA = CALayer(n_feat, reduction, bias_attr=bias_attr)
        self.body = nn.Sequential(*modules_body)

    def forward(self, x):
        res = self.body(x)
        res = self.CA(res)
        res += x
        return res


##########################################################################
##---------- Resizing Modules ----------
class DownSample(nn.Layer):
    def __init__(self, in_channels, s_factor):
        super(DownSample, self).__init__()
        self.down = nn.Sequential(
            nn.Upsample(scale_factor=0.5, mode='bilinear', align_corners=False),
            nn.Conv2D(in_channels,
                      in_channels + s_factor,
                      1,
                      stride=1,
                      padding=0,
                      bias_attr=False))

    def forward(self, x):
        x = self.down(x)
        return x


class UpSample(nn.Layer):
    def __init__(self, in_channels, s_factor):
        super(UpSample, self).__init__()
        self.up = nn.Sequential(
            nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
            nn.Conv2D(in_channels + s_factor,
                      in_channels,
                      1,
                      stride=1,
                      padding=0,
                      bias_attr=False))

    def forward(self, x):
        x = self.up(x)
        return x


class SkipUpSample(nn.Layer):
    def __init__(self, in_channels, s_factor):
        super(SkipUpSample, self).__init__()
        self.up = nn.Sequential(
            nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
            nn.Conv2D(in_channels + s_factor,
                      in_channels,
                      1,
                      stride=1,
                      padding=0,
                      bias_attr=False))

    def forward(self, x, y):
        x = self.up(x)
        x = x + y
        return x


##########################################################################
## U-Net
class Encoder(nn.Layer):
    def __init__(self, n_feat, kernel_size, reduction, act, bias_attr,
                 scale_unetfeats, csff):
        super(Encoder, self).__init__()

        self.encoder_level1 = [
            CAB(n_feat, kernel_size, reduction, bias_attr=bias_attr, act=act)
            for _ in range(2)
        ]
        self.encoder_level2 = [
            CAB(n_feat + scale_unetfeats,
                kernel_size,
                reduction,
                bias_attr=bias_attr,
                act=act) for _ in range(2)
        ]
        self.encoder_level3 = [
            CAB(n_feat + (scale_unetfeats * 2),
                kernel_size,
                reduction,
                bias_attr=bias_attr,
                act=act) for _ in range(2)
        ]

        self.encoder_level1 = nn.Sequential(*self.encoder_level1)
        self.encoder_level2 = nn.Sequential(*self.encoder_level2)
        self.encoder_level3 = nn.Sequential(*self.encoder_level3)

        self.down12 = DownSample(n_feat, scale_unetfeats)
        self.down23 = DownSample(n_feat + scale_unetfeats, scale_unetfeats)

        # Cross Stage Feature Fusion (CSFF)
        if csff:
            self.csff_enc1 = nn.Conv2D(n_feat,
                                       n_feat,
                                       kernel_size=1,
                                       bias_attr=bias_attr)
            self.csff_enc2 = nn.Conv2D(n_feat + scale_unetfeats,
                                       n_feat + scale_unetfeats,
                                       kernel_size=1,
                                       bias_attr=bias_attr)
            self.csff_enc3 = nn.Conv2D(n_feat + (scale_unetfeats * 2),
                                       n_feat + (scale_unetfeats * 2),
                                       kernel_size=1,
                                       bias_attr=bias_attr)

            self.csff_dec1 = nn.Conv2D(n_feat,
                                       n_feat,
                                       kernel_size=1,
                                       bias_attr=bias_attr)
            self.csff_dec2 = nn.Conv2D(n_feat + scale_unetfeats,
                                       n_feat + scale_unetfeats,
                                       kernel_size=1,
                                       bias_attr=bias_attr)
            self.csff_dec3 = nn.Conv2D(n_feat + (scale_unetfeats * 2),
                                       n_feat + (scale_unetfeats * 2),
                                       kernel_size=1,
                                       bias_attr=bias_attr)

    def forward(self, x, encoder_outs=None, decoder_outs=None):
        enc1 = self.encoder_level1(x)
        if (encoder_outs is not None) and (decoder_outs is not None):
            enc1 = enc1 + self.csff_enc1(encoder_outs[0]) + self.csff_dec1(
                decoder_outs[0])

        x = self.down12(enc1)

        enc2 = self.encoder_level2(x)
        if (encoder_outs is not None) and (decoder_outs is not None):
            enc2 = enc2 + self.csff_enc2(encoder_outs[1]) + self.csff_dec2(
                decoder_outs[1])

        x = self.down23(enc2)

        enc3 = self.encoder_level3(x)
        if (encoder_outs is not None) and (decoder_outs is not None):
            enc3 = enc3 + self.csff_enc3(encoder_outs[2]) + self.csff_dec3(
                decoder_outs[2])

        return [enc1, enc2, enc3]


class Decoder(nn.Layer):
    def __init__(self, n_feat, kernel_size, reduction, act, bias_attr,
                 scale_unetfeats):
        super(Decoder, self).__init__()

        self.decoder_level1 = [
            CAB(n_feat, kernel_size, reduction, bias_attr=bias_attr, act=act)
            for _ in range(2)
        ]
        self.decoder_level2 = [
            CAB(n_feat + scale_unetfeats,
                kernel_size,
                reduction,
                bias_attr=bias_attr,
                act=act) for _ in range(2)
        ]
        self.decoder_level3 = [
            CAB(n_feat + (scale_unetfeats * 2),
                kernel_size,
                reduction,
                bias_attr=bias_attr,
                act=act) for _ in range(2)
        ]

        self.decoder_level1 = nn.Sequential(*self.decoder_level1)
        self.decoder_level2 = nn.Sequential(*self.decoder_level2)
        self.decoder_level3 = nn.Sequential(*self.decoder_level3)

        self.skip_attn1 = CAB(n_feat,
                              kernel_size,
                              reduction,
                              bias_attr=bias_attr,
                              act=act)
        self.skip_attn2 = CAB(n_feat + scale_unetfeats,
                              kernel_size,
                              reduction,
                              bias_attr=bias_attr,
                              act=act)

        self.up21 = SkipUpSample(n_feat, scale_unetfeats)
        self.up32 = SkipUpSample(n_feat + scale_unetfeats, scale_unetfeats)

    def forward(self, outs):
        enc1, enc2, enc3 = outs
        dec3 = self.decoder_level3(enc3)

        x = self.up32(dec3, self.skip_attn2(enc2))
        dec2 = self.decoder_level2(x)

        x = self.up21(dec2, self.skip_attn1(enc1))
        dec1 = self.decoder_level1(x)

        return [dec1, dec2, dec3]


##########################################################################
## Original Resolution Block (ORB)
class ORB(nn.Layer):
    def __init__(self, n_feat, kernel_size, reduction, act, bias_attr, num_cab):
        super(ORB, self).__init__()
        modules_body = []
        modules_body = [
            CAB(n_feat, kernel_size, reduction, bias_attr=bias_attr, act=act)
            for _ in range(num_cab)
        ]
        modules_body.append(conv(n_feat, n_feat, kernel_size))
        self.body = nn.Sequential(*modules_body)

    def forward(self, x):
        res = self.body(x)
        res += x
        return res


##########################################################################
class ORSNet(nn.Layer):
    def __init__(self, n_feat, scale_orsnetfeats, kernel_size, reduction, act,
                 bias_attr, scale_unetfeats, num_cab):
        super(ORSNet, self).__init__()

        self.orb1 = ORB(n_feat + scale_orsnetfeats, kernel_size, reduction, act,
                        bias_attr, num_cab)
        self.orb2 = ORB(n_feat + scale_orsnetfeats, kernel_size, reduction, act,
                        bias_attr, num_cab)
        self.orb3 = ORB(n_feat + scale_orsnetfeats, kernel_size, reduction, act,
                        bias_attr, num_cab)

        self.up_enc1 = UpSample(n_feat, scale_unetfeats)
        self.up_dec1 = UpSample(n_feat, scale_unetfeats)

        self.up_enc2 = nn.Sequential(
            UpSample(n_feat + scale_unetfeats, scale_unetfeats),
            UpSample(n_feat, scale_unetfeats))
        self.up_dec2 = nn.Sequential(
            UpSample(n_feat + scale_unetfeats, scale_unetfeats),
            UpSample(n_feat, scale_unetfeats))

        self.conv_enc1 = nn.Conv2D(n_feat,
                                   n_feat + scale_orsnetfeats,
                                   kernel_size=1,
                                   bias_attr=bias_attr)
        self.conv_enc2 = nn.Conv2D(n_feat,
                                   n_feat + scale_orsnetfeats,
                                   kernel_size=1,
                                   bias_attr=bias_attr)
        self.conv_enc3 = nn.Conv2D(n_feat,
                                   n_feat + scale_orsnetfeats,
                                   kernel_size=1,
                                   bias_attr=bias_attr)

        self.conv_dec1 = nn.Conv2D(n_feat,
                                   n_feat + scale_orsnetfeats,
                                   kernel_size=1,
                                   bias_attr=bias_attr)
        self.conv_dec2 = nn.Conv2D(n_feat,
                                   n_feat + scale_orsnetfeats,
                                   kernel_size=1,
                                   bias_attr=bias_attr)
        self.conv_dec3 = nn.Conv2D(n_feat,
                                   n_feat + scale_orsnetfeats,
                                   kernel_size=1,
                                   bias_attr=bias_attr)

    def forward(self, x, encoder_outs, decoder_outs):
        x = self.orb1(x)
        x = x + self.conv_enc1(encoder_outs[0]) + self.conv_dec1(
            decoder_outs[0])

        x = self.orb2(x)
        x = x + self.conv_enc2(self.up_enc1(encoder_outs[1])) + self.conv_dec2(
            self.up_dec1(decoder_outs[1]))

        x = self.orb3(x)
        x = x + self.conv_enc3(self.up_enc2(encoder_outs[2])) + self.conv_dec3(
            self.up_dec2(decoder_outs[2]))

        return x


##########################################################################
## Supervised Attention Module
class SAM(nn.Layer):
    def __init__(self, n_feat, kernel_size, bias_attr):
        super(SAM, self).__init__()
        self.conv1 = conv(n_feat, n_feat, kernel_size, bias_attr=bias_attr)
        self.conv2 = conv(n_feat, 3, kernel_size, bias_attr=bias_attr)
        self.conv3 = conv(3, n_feat, kernel_size, bias_attr=bias_attr)

    def forward(self, x, x_img):
        x1 = self.conv1(x)
        img = self.conv2(x) + x_img
        x2 = F.sigmoid(self.conv3(img))
        x1 = x1 * x2
        x1 = x1 + x
        return x1, img


@GENERATORS.register()
class MPRNet(nn.Layer):
    def __init__(self,
                 in_c=3,
                 out_c=3,
                 n_feat=96,
                 scale_unetfeats=48,
                 scale_orsnetfeats=32,
                 num_cab=8,
                 kernel_size=3,
                 reduction=4,
                 bias_attr=False):
        super(MPRNet, self).__init__()
        act = nn.PReLU()
        self.shallow_feat1 = nn.Sequential(
            conv(in_c, n_feat, kernel_size, bias_attr=bias_attr),
            CAB(n_feat, kernel_size, reduction, bias_attr=bias_attr, act=act))
        self.shallow_feat2 = nn.Sequential(
            conv(in_c, n_feat, kernel_size, bias_attr=bias_attr),
            CAB(n_feat, kernel_size, reduction, bias_attr=bias_attr, act=act))
        self.shallow_feat3 = nn.Sequential(
            conv(in_c, n_feat, kernel_size, bias_attr=bias_attr),
            CAB(n_feat, kernel_size, reduction, bias_attr=bias_attr, act=act))

        # Cross Stage Feature Fusion (CSFF)
        self.stage1_encoder = Encoder(n_feat,
                                      kernel_size,
                                      reduction,
                                      act,
                                      bias_attr,
                                      scale_unetfeats,
                                      csff=False)
        self.stage1_decoder = Decoder(n_feat, kernel_size, reduction, act,
                                      bias_attr, scale_unetfeats)

        self.stage2_encoder = Encoder(n_feat,
                                      kernel_size,
                                      reduction,
                                      act,
                                      bias_attr,
                                      scale_unetfeats,
                                      csff=True)
        self.stage2_decoder = Decoder(n_feat, kernel_size, reduction, act,
                                      bias_attr, scale_unetfeats)

        self.stage3_orsnet = ORSNet(n_feat, scale_orsnetfeats, kernel_size,
                                    reduction, act, bias_attr, scale_unetfeats,
                                    num_cab)

        self.sam12 = SAM(n_feat, kernel_size=1, bias_attr=bias_attr)
        self.sam23 = SAM(n_feat, kernel_size=1, bias_attr=bias_attr)

        self.concat12 = conv(n_feat * 2,
                             n_feat,
                             kernel_size,
                             bias_attr=bias_attr)
        self.concat23 = conv(n_feat * 2,
                             n_feat + scale_orsnetfeats,
                             kernel_size,
                             bias_attr=bias_attr)
        self.tail = conv(n_feat + scale_orsnetfeats,
                         out_c,
                         kernel_size,
                         bias_attr=bias_attr)

    def forward(self, x3_img):
        # Original-resolution Image for Stage 3
        H = x3_img.shape[2]
        W = x3_img.shape[3]

        # Multi-Patch Hierarchy: Split Image into four non-overlapping patches

        # Two Patches for Stage 2
        x2top_img = x3_img[:, :, 0:int(H / 2), :]
        x2bot_img = x3_img[:, :, int(H / 2):H, :]

        # Four Patches for Stage 1
        x1ltop_img = x2top_img[:, :, :, 0:int(W / 2)]
        x1rtop_img = x2top_img[:, :, :, int(W / 2):W]
        x1lbot_img = x2bot_img[:, :, :, 0:int(W / 2)]
        x1rbot_img = x2bot_img[:, :, :, int(W / 2):W]

        ##-------------------------------------------
        ##-------------- Stage 1---------------------
        ##-------------------------------------------
        ## Compute Shallow Features
        x1ltop = self.shallow_feat1(x1ltop_img)
        x1rtop = self.shallow_feat1(x1rtop_img)
        x1lbot = self.shallow_feat1(x1lbot_img)
        x1rbot = self.shallow_feat1(x1rbot_img)

        ## Process features of all 4 patches with Encoder of Stage 1
        feat1_ltop = self.stage1_encoder(x1ltop)
        feat1_rtop = self.stage1_encoder(x1rtop)
        feat1_lbot = self.stage1_encoder(x1lbot)
        feat1_rbot = self.stage1_encoder(x1rbot)

        ## Concat deep features
        feat1_top = [
            paddle.concat((k, v), 3) for k, v in zip(feat1_ltop, feat1_rtop)
        ]
        feat1_bot = [
            paddle.concat((k, v), 3) for k, v in zip(feat1_lbot, feat1_rbot)
        ]

        ## Pass features through Decoder of Stage 1
        res1_top = self.stage1_decoder(feat1_top)
        res1_bot = self.stage1_decoder(feat1_bot)

        ## Apply Supervised Attention Module (SAM)
        x2top_samfeats, stage1_img_top = self.sam12(res1_top[0], x2top_img)
        x2bot_samfeats, stage1_img_bot = self.sam12(res1_bot[0], x2bot_img)

        ## Output image at Stage 1
        stage1_img = paddle.concat([stage1_img_top, stage1_img_bot], 2)
        ##-------------------------------------------
        ##-------------- Stage 2---------------------
        ##-------------------------------------------
        ## Compute Shallow Features
        x2top = self.shallow_feat2(x2top_img)
        x2bot = self.shallow_feat2(x2bot_img)

        ## Concatenate SAM features of Stage 1 with shallow features of Stage 2
        x2top_cat = self.concat12(paddle.concat([x2top, x2top_samfeats], 1))
        x2bot_cat = self.concat12(paddle.concat([x2bot, x2bot_samfeats], 1))

        ## Process features of both patches with Encoder of Stage 2
        feat2_top = self.stage2_encoder(x2top_cat, feat1_top, res1_top)
        feat2_bot = self.stage2_encoder(x2bot_cat, feat1_bot, res1_bot)

        ## Concat deep features
        feat2 = [paddle.concat((k, v), 2) for k, v in zip(feat2_top, feat2_bot)]

        ## Pass features through Decoder of Stage 2
        res2 = self.stage2_decoder(feat2)

        ## Apply SAM
        x3_samfeats, stage2_img = self.sam23(res2[0], x3_img)

        ##-------------------------------------------
        ##-------------- Stage 3---------------------
        ##-------------------------------------------
        ## Compute Shallow Features
        x3 = self.shallow_feat3(x3_img)

        ## Concatenate SAM features of Stage 2 with shallow features of Stage 3
        x3_cat = self.concat23(paddle.concat([x3, x3_samfeats], 1))

        x3_cat = self.stage3_orsnet(x3_cat, feat2, res2)

        stage3_img = self.tail(x3_cat)

        return [stage3_img + x3_img, stage2_img, stage1_img]