support camouflaged-detection

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10834768

data/test/images/image_camouflag_detection.jpg

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4c713215f7fb4da5382c9137347ee52956a7a44d5979c4cffd3c9b6d1d7e878f
+size 19445

modelscope/metainfo.py

@@ -165,6 +165,8 @@ class Pipelines(object):
     easycv_segmentation = 'easycv-segmentation'
     face_2d_keypoints = 'mobilenet_face-2d-keypoints_alignment'
     salient_detection = 'u2net-salient-detection'
+    salient_boudary_detection = 'res2net-salient-detection'
+    camouflaged_detection = 'res2net-camouflaged-detection'
     image_classification = 'image-classification'
     face_detection = 'resnet-face-detection-scrfd10gkps'
     card_detection = 'resnet-card-detection-scrfd34gkps'

modelscope/models/cv/salient_detection/models/__init__.py

@@ -1,3 +1,4 @@
 # The implementation is adopted from U-2-Net, made publicly available under the Apache 2.0 License
 # source code avaiable via https://github.com/xuebinqin/U-2-Net
+from .senet import SENet
 from .u2net import U2NET

modelscope/models/cv/salient_detection/models/backbone/Res2Net_v1b.py

@@ -0,0 +1,187 @@
+# Implementation in this file is modified based on Res2Net-PretrainedModels
+# Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License
+# publicly avaialbe at https://github.com/Res2Net/Res2Net-PretrainedModels/blob/master/res2net_v1b.py
+import math
+
+import torch
+import torch.nn as nn
+
+__all__ = ['Res2Net', 'res2net50_v1b_26w_4s']
+
+
+class Bottle2neck(nn.Module):
+    expansion = 4
+
+    def __init__(self,
+                 inplanes,
+                 planes,
+                 stride=1,
+                 downsample=None,
+                 baseWidth=26,
+                 scale=4,
+                 stype='normal'):
+        """ Constructor
+        Args:
+            inplanes: input channel dimensionality
+            planes: output channel dimensionality
+            stride: conv stride. Replaces pooling layer.
+            downsample: None when stride = 1
+            baseWidth: basic width of conv3x3
+            scale: number of scale.
+            type: 'normal': normal set. 'stage': first block of a new stage.
+        """
+        super(Bottle2neck, self).__init__()
+        width = int(math.floor(planes * (baseWidth / 64.0)))
+        self.conv1 = nn.Conv2d(
+            inplanes, width * scale, kernel_size=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(width * scale)
+        if scale == 1:
+            self.nums = 1
+        else:
+            self.nums = scale - 1
+        if stype == 'stage':
+            self.pool = nn.AvgPool2d(kernel_size=3, stride=stride, padding=1)
+        convs = []
+        bns = []
+        for i in range(self.nums):
+            convs.append(
+                nn.Conv2d(
+                    width,
+                    width,
+                    kernel_size=3,
+                    stride=stride,
+                    padding=1,
+                    bias=False))
+            bns.append(nn.BatchNorm2d(width))
+        self.convs = nn.ModuleList(convs)
+        self.bns = nn.ModuleList(bns)
+        self.conv3 = nn.Conv2d(
+            width * scale, planes * self.expansion, kernel_size=1, bias=False)
+        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stype = stype
+        self.scale = scale
+        self.width = width
+
+    def forward(self, x):
+        residual = x
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+        spx = torch.split(out, self.width, 1)
+        for i in range(self.nums):
+            if i == 0 or self.stype == 'stage':
+                sp = spx[i]
+            else:
+                sp = sp + spx[i]
+            sp = self.convs[i](sp)
+            sp = self.relu(self.bns[i](sp))
+            if i == 0:
+                out = sp
+            else:
+                out = torch.cat((out, sp), 1)
+        if self.scale != 1 and self.stype == 'normal':
+            out = torch.cat((out, spx[self.nums]), 1)
+        elif self.scale != 1 and self.stype == 'stage':
+            out = torch.cat((out, self.pool(spx[self.nums])), 1)
+        out = self.conv3(out)
+        out = self.bn3(out)
+        if self.downsample is not None:
+            residual = self.downsample(x)
+        out += residual
+        out = self.relu(out)
+        return out
+
+
+class Res2Net(nn.Module):
+
+    def __init__(self, block, layers, baseWidth=26, scale=4, num_classes=1000):
+        self.inplanes = 64
+        super(Res2Net, self).__init__()
+        self.baseWidth = baseWidth
+        self.scale = scale
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(3, 32, 3, 2, 1, bias=False), nn.BatchNorm2d(32),
+            nn.ReLU(inplace=True), nn.Conv2d(32, 32, 3, 1, 1, bias=False),
+            nn.BatchNorm2d(32), nn.ReLU(inplace=True),
+            nn.Conv2d(32, 64, 3, 1, 1, bias=False))
+        self.bn1 = nn.BatchNorm2d(64)
+        self.relu = nn.ReLU()
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        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)
+        self.avgpool = nn.AdaptiveAvgPool2d(1)
+        self.fc = nn.Linear(512 * block.expansion, num_classes)
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(
+                    m.weight, mode='fan_out', nonlinearity='relu')
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+    def _make_layer(self, block, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.AvgPool2d(
+                    kernel_size=stride,
+                    stride=stride,
+                    ceil_mode=True,
+                    count_include_pad=False),
+                nn.Conv2d(
+                    self.inplanes,
+                    planes * block.expansion,
+                    kernel_size=1,
+                    stride=1,
+                    bias=False),
+                nn.BatchNorm2d(planes * block.expansion),
+            )
+        layers = []
+        layers.append(
+            block(
+                self.inplanes,
+                planes,
+                stride,
+                downsample=downsample,
+                stype='stage',
+                baseWidth=self.baseWidth,
+                scale=self.scale))
+        self.inplanes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(
+                block(
+                    self.inplanes,
+                    planes,
+                    baseWidth=self.baseWidth,
+                    scale=self.scale))
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+        x = self.avgpool(x)
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+        return x
+
+
+def res2net50_v1b_26w_4s(backbone_path, pretrained=False, **kwargs):
+    """Constructs a Res2Net-50_v1b_26w_4s lib.
+    Args:
+        pretrained (bool): If True, returns a lib pre-trained on ImageNet
+    """
+    model = Res2Net(Bottle2neck, [3, 4, 6, 3], baseWidth=26, scale=4, **kwargs)
+    if pretrained:
+        model_state = torch.load(backbone_path)
+        model.load_state_dict(model_state)
+    return model

modelscope/models/cv/salient_detection/models/backbone/__init__.py

@@ -0,0 +1,6 @@
+# Implementation in this file is modified based on Res2Net-PretrainedModels
+# Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License
+# publicly avaialbe at https://github.com/Res2Net/Res2Net-PretrainedModels/blob/master/res2net_v1b.py
+from .Res2Net_v1b import res2net50_v1b_26w_4s
+
+__all__ = ['res2net50_v1b_26w_4s']

modelscope/models/cv/salient_detection/models/modules.py

@@ -0,0 +1,178 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .utils import ConvBNReLU
+
+
+class AreaLayer(nn.Module):
+
+    def __init__(self, in_channel, out_channel):
+        super(AreaLayer, self).__init__()
+        self.lbody = nn.Sequential(
+            nn.Conv2d(out_channel, out_channel, 1),
+            nn.BatchNorm2d(out_channel), nn.ReLU(inplace=True))
+        self.hbody = nn.Sequential(
+            nn.Conv2d(in_channel, out_channel, 1), nn.BatchNorm2d(out_channel),
+            nn.ReLU(inplace=True))
+        self.body = nn.Sequential(
+            nn.Conv2d(2 * out_channel, out_channel, 3, 1, 1),
+            nn.BatchNorm2d(out_channel), nn.ReLU(inplace=True),
+            nn.Conv2d(out_channel, out_channel, 3, 1, 1),
+            nn.BatchNorm2d(out_channel), nn.ReLU(inplace=True),
+            nn.Conv2d(out_channel, 1, 1))
+
+    def forward(self, xl, xh):
+        xl1 = self.lbody(xl)
+        xl1 = F.interpolate(
+            xl1, size=xh.size()[2:], mode='bilinear', align_corners=True)
+        xh1 = self.hbody(xh)
+        x = torch.cat((xl1, xh1), dim=1)
+        x_out = self.body(x)
+        return x_out
+
+
+class EdgeLayer(nn.Module):
+
+    def __init__(self, in_channel, out_channel):
+        super(EdgeLayer, self).__init__()
+        self.lbody = nn.Sequential(
+            nn.Conv2d(out_channel, out_channel, 1),
+            nn.BatchNorm2d(out_channel), nn.ReLU(inplace=True))
+        self.hbody = nn.Sequential(
+            nn.Conv2d(in_channel, out_channel, 1), nn.BatchNorm2d(out_channel),
+            nn.ReLU(inplace=True))
+        self.bodye = nn.Sequential(
+            nn.Conv2d(2 * out_channel, out_channel, 3, 1, 1),
+            nn.BatchNorm2d(out_channel), nn.ReLU(inplace=True),
+            nn.Conv2d(out_channel, out_channel, 3, 1, 1),
+            nn.BatchNorm2d(out_channel), nn.ReLU(inplace=True),
+            nn.Conv2d(out_channel, 1, 1))
+
+    def forward(self, xl, xh):
+        xl1 = self.lbody(xl)
+        xh1 = self.hbody(xh)
+        xh1 = F.interpolate(
+            xh1, size=xl.size()[2:], mode='bilinear', align_corners=True)
+        x = torch.cat((xl1, xh1), dim=1)
+        x_out = self.bodye(x)
+        return x_out
+
+
+class EBlock(nn.Module):
+
+    def __init__(self, inchs, outchs):
+        super(EBlock, self).__init__()
+        self.elayer = nn.Sequential(
+            ConvBNReLU(inchs + 1, outchs, kernel_size=3, padding=1, stride=1),
+            ConvBNReLU(outchs, outchs, 1))
+        self.salayer = nn.Sequential(
+            nn.Conv2d(2, 1, 3, 1, 1, bias=False),
+            nn.BatchNorm2d(1, momentum=0.01), nn.Sigmoid())
+
+    def forward(self, x, edgeAtten):
+        x = torch.cat((x, edgeAtten), dim=1)
+        ex = self.elayer(x)
+        ex_max = torch.max(ex, 1, keepdim=True)[0]
+        ex_mean = torch.mean(ex, dim=1, keepdim=True)
+        xei_compress = torch.cat((ex_max, ex_mean), dim=1)
+
+        scale = self.salayer(xei_compress)
+        x_out = ex * scale
+        return x_out
+
+
+class StructureE(nn.Module):
+
+    def __init__(self, inchs, outchs, EM):
+        super(StructureE, self).__init__()
+        self.ne_modules = int(inchs / EM)
+        NM = int(outchs / self.ne_modules)
+        elayes = []
+        for i in range(self.ne_modules):
+            emblock = EBlock(EM, NM)
+            elayes.append(emblock)
+        self.emlayes = nn.ModuleList(elayes)
+        self.body = nn.Sequential(
+            ConvBNReLU(outchs, outchs, 3, 1, 1), ConvBNReLU(outchs, outchs, 1))
+
+    def forward(self, x, edgeAtten):
+        if edgeAtten.size() != x.size():
+            edgeAtten = F.interpolate(
+                edgeAtten, x.size()[2:], mode='bilinear', align_corners=False)
+        xx = torch.chunk(x, self.ne_modules, dim=1)
+        efeas = []
+        for i in range(self.ne_modules):
+            xei = self.emlayes[i](xx[i], edgeAtten)
+            efeas.append(xei)
+        efeas = torch.cat(efeas, dim=1)
+        x_out = self.body(efeas)
+        return x_out
+
+
+class ABlock(nn.Module):
+
+    def __init__(self, inchs, outchs, k):
+        super(ABlock, self).__init__()
+        self.alayer = nn.Sequential(
+            ConvBNReLU(inchs, outchs, k, 1, k // 2),
+            ConvBNReLU(outchs, outchs, 1))
+        self.arlayer = nn.Sequential(
+            ConvBNReLU(inchs, outchs, k, 1, k // 2),
+            ConvBNReLU(outchs, outchs, 1))
+        self.fusion = ConvBNReLU(2 * outchs, outchs, 1)
+
+    def forward(self, x, areaAtten):
+        xa = x * areaAtten
+        xra = x * (1 - areaAtten)
+        xout = self.fusion(torch.cat((xa, xra), dim=1))
+        return xout
+
+
+class AMFusion(nn.Module):
+
+    def __init__(self, inchs, outchs, AM):
+        super(AMFusion, self).__init__()
+        self.k = [3, 3, 5, 5]
+        self.conv_up = ConvBNReLU(inchs, outchs, 3, 1, 1)
+        self.up = nn.Upsample(
+            scale_factor=2, mode='bilinear', align_corners=True)
+        self.na_modules = int(outchs / AM)
+        alayers = []
+        for i in range(self.na_modules):
+            layer = ABlock(AM, AM, self.k[i])
+            alayers.append(layer)
+        self.alayers = nn.ModuleList(alayers)
+        self.fusion_0 = ConvBNReLU(outchs, outchs, 3, 1, 1)
+        self.fusion_e = nn.Sequential(
+            nn.Conv2d(
+                outchs, outchs, kernel_size=(3, 1), padding=(1, 0),
+                bias=False), nn.BatchNorm2d(outchs), nn.ReLU(inplace=True),
+            nn.Conv2d(
+                outchs, outchs, kernel_size=(1, 3), padding=(0, 1),
+                bias=False), nn.BatchNorm2d(outchs), nn.ReLU(inplace=True))
+        self.fusion_e1 = nn.Sequential(
+            nn.Conv2d(
+                outchs, outchs, kernel_size=(5, 1), padding=(2, 0),
+                bias=False), nn.BatchNorm2d(outchs), nn.ReLU(inplace=True),
+            nn.Conv2d(
+                outchs, outchs, kernel_size=(1, 5), padding=(0, 2),
+                bias=False), nn.BatchNorm2d(outchs), nn.ReLU(inplace=True))
+        self.fusion = ConvBNReLU(3 * outchs, outchs, 1)
+
+    def forward(self, xl, xh, xhm):
+        xh1 = self.up(self.conv_up(xh))
+        x = xh1 + xl
+        xm = self.up(torch.sigmoid(xhm))
+        xx = torch.chunk(x, self.na_modules, dim=1)
+        xxmids = []
+        for i in range(self.na_modules):
+            xi = self.alayers[i](xx[i], xm)
+            xxmids.append(xi)
+        xfea = torch.cat(xxmids, dim=1)
+        x0 = self.fusion_0(xfea)
+        x1 = self.fusion_e(xfea)
+        x2 = self.fusion_e1(xfea)
+        x_out = self.fusion(torch.cat((x0, x1, x2), dim=1))
+        return x_out

modelscope/models/cv/salient_detection/models/senet.py

@@ -0,0 +1,74 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .backbone import res2net50_v1b_26w_4s as res2net
+from .modules import AMFusion, AreaLayer, EdgeLayer, StructureE
+from .utils import ASPP, CBAM, ConvBNReLU
+
+
+class SENet(nn.Module):
+
+    def __init__(self, backbone_path=None, pretrained=False):
+        super(SENet, self).__init__()
+        resnet50 = res2net(backbone_path, pretrained)
+        self.layer0_1 = nn.Sequential(resnet50.conv1, resnet50.bn1,
+                                      resnet50.relu)
+        self.maxpool = resnet50.maxpool
+        self.layer1 = resnet50.layer1
+        self.layer2 = resnet50.layer2
+        self.layer3 = resnet50.layer3
+        self.layer4 = resnet50.layer4
+        self.aspp3 = ASPP(1024, 256)
+        self.aspp4 = ASPP(2048, 256)
+        self.cbblock3 = CBAM(inchs=256, kernel_size=5)
+        self.cbblock4 = CBAM(inchs=256, kernel_size=5)
+        self.up = nn.Upsample(
+            mode='bilinear', scale_factor=2, align_corners=False)
+        self.conv_up = ConvBNReLU(512, 512, 1)
+        self.aux_edge = EdgeLayer(512, 256)
+        self.aux_area = AreaLayer(512, 256)
+        self.layer1_enhance = StructureE(256, 128, 128)
+        self.layer2_enhance = StructureE(512, 256, 128)
+        self.layer3_decoder = AMFusion(512, 256, 128)
+        self.layer2_decoder = AMFusion(256, 128, 128)
+        self.out_conv_8 = nn.Conv2d(256, 1, 1)
+        self.out_conv_4 = nn.Conv2d(128, 1, 1)
+
+    def forward(self, x):
+        layer0 = self.layer0_1(x)
+        layer0s = self.maxpool(layer0)
+        layer1 = self.layer1(layer0s)
+        layer2 = self.layer2(layer1)
+        layer3 = self.layer3(layer2)
+        layer4 = self.layer4(layer3)
+        layer3_eh = self.cbblock3(self.aspp3(layer3))
+        layer4_eh = self.cbblock4(self.aspp4(layer4))
+        layer34 = self.conv_up(
+            torch.cat((self.up(layer4_eh), layer3_eh), dim=1))
+        edge_atten = self.aux_edge(layer1, layer34)
+        area_atten = self.aux_area(layer1, layer34)
+        edge_atten_ = torch.sigmoid(edge_atten)
+        layer1_eh = self.layer1_enhance(layer1, edge_atten_)
+        layer2_eh = self.layer2_enhance(layer2, edge_atten_)
+        layer2_fu = self.layer3_decoder(layer2_eh, layer34, area_atten)
+        out_8 = self.out_conv_8(layer2_fu)
+        layer1_fu = self.layer2_decoder(layer1_eh, layer2_fu, out_8)
+        out_4 = self.out_conv_4(layer1_fu)
+        out_16 = F.interpolate(
+            area_atten,
+            size=x.size()[2:],
+            mode='bilinear',
+            align_corners=False)
+        out_8 = F.interpolate(
+            out_8, size=x.size()[2:], mode='bilinear', align_corners=False)
+        out_4 = F.interpolate(
+            out_4, size=x.size()[2:], mode='bilinear', align_corners=False)
+        edge_out = F.interpolate(
+            edge_atten_,
+            size=x.size()[2:],
+            mode='bilinear',
+            align_corners=False)
+
+        return out_4.sigmoid(), out_8.sigmoid(), out_16.sigmoid(), edge_out

modelscope/models/cv/salient_detection/models/utils.py

@@ -0,0 +1,105 @@
+# Implementation in this file is modified based on deeplabv3
+# Originally MIT license,publicly avaialbe at https://github.com/fregu856/deeplabv3/blob/master/model/aspp.py
+# Implementation in this file is modified based on attention-module
+# Originally MIT license,publicly avaialbe at https://github.com/Jongchan/attention-module/blob/master/MODELS/cbam.py
+import torch
+import torch.nn as nn
+
+
+class ConvBNReLU(nn.Module):
+
+    def __init__(self,
+                 inplanes,
+                 planes,
+                 kernel_size=3,
+                 stride=1,
+                 padding=0,
+                 dilation=1,
+                 bias=False):
+        super(ConvBNReLU, self).__init__()
+        self.block = nn.Sequential(
+            nn.Conv2d(
+                inplanes,
+                planes,
+                kernel_size,
+                stride=stride,
+                padding=padding,
+                dilation=dilation,
+                bias=bias), nn.BatchNorm2d(planes), nn.ReLU(inplace=True))
+
+    def forward(self, x):
+        return self.block(x)
+
+
+class ASPP(nn.Module):
+
+    def __init__(self, in_dim, out_dim):
+        super(ASPP, self).__init__()
+        mid_dim = 128
+        self.conv1 = ConvBNReLU(in_dim, mid_dim, kernel_size=1)
+        self.conv2 = ConvBNReLU(
+            in_dim, mid_dim, kernel_size=3, padding=2, dilation=2)
+        self.conv3 = ConvBNReLU(
+            in_dim, mid_dim, kernel_size=3, padding=5, dilation=5)
+        self.conv4 = ConvBNReLU(
+            in_dim, mid_dim, kernel_size=3, padding=7, dilation=7)
+        self.conv5 = ConvBNReLU(in_dim, mid_dim, kernel_size=1, padding=0)
+        self.fuse = ConvBNReLU(5 * mid_dim, out_dim, 3, 1, 1)
+        self.global_pooling = nn.AdaptiveAvgPool2d(1)
+
+    def forward(self, x):
+        conv1 = self.conv1(x)
+        conv2 = self.conv2(x)
+        conv3 = self.conv3(x)
+        conv4 = self.conv4(x)
+        xg = self.conv5(self.global_pooling(x))
+        conv5 = nn.Upsample((x.shape[2], x.shape[3]), mode='nearest')(xg)
+        return self.fuse(torch.cat((conv1, conv2, conv3, conv4, conv5), 1))
+
+
+class ChannelAttention(nn.Module):
+
+    def __init__(self, inchs, ratio=16):
+        super(ChannelAttention, self).__init__()
+        self.avg_pool = nn.AdaptiveAvgPool2d(1)
+        self.max_pool = nn.AdaptiveMaxPool2d(1)
+        self.fc = nn.Sequential(
+            nn.Conv2d(inchs, inchs // 16, 1, bias=False), nn.ReLU(),
+            nn.Conv2d(inchs // 16, inchs, 1, bias=False))
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, x):
+        avg_out = self.fc(self.avg_pool(x))
+        max_out = self.fc(self.max_pool(x))
+        out = avg_out + max_out
+        return self.sigmoid(out)
+
+
+class SpatialAttention(nn.Module):
+
+    def __init__(self, kernel_size=7):
+        super(SpatialAttention, self).__init__()
+
+        self.conv1 = nn.Conv2d(
+            2, 1, kernel_size, padding=kernel_size // 2, bias=False)
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, x):
+        avg_out = torch.mean(x, dim=1, keepdim=True)
+        max_out, _ = torch.max(x, dim=1, keepdim=True)
+        x = torch.cat([avg_out, max_out], dim=1)
+        x = self.conv1(x)
+        return self.sigmoid(x)
+
+
+class CBAM(nn.Module):
+
+    def __init__(self, inchs, kernel_size=7):
+        super().__init__()
+        self.calayer = ChannelAttention(inchs=inchs)
+        self.saLayer = SpatialAttention(kernel_size=kernel_size)
+
+    def forward(self, x):
+        xca = self.calayer(x) * x
+        xsa = self.saLayer(xca) * xca
+        return xsa

modelscope/models/cv/salient_detection/salient_model.py

@@ -2,7 +2,6 @@
 import os.path as osp
 
 import cv2
-import numpy as np
 import torch
 from PIL import Image
 from torchvision import transforms
@@ -10,8 +9,9 @@ from torchvision import transforms
 from modelscope.metainfo import Models
 from modelscope.models.base.base_torch_model import TorchModel
 from modelscope.models.builder import MODELS
+from modelscope.utils.config import Config
 from modelscope.utils.constant import ModelFile, Tasks
-from .models import U2NET
+from .models import U2NET, SENet
 
 
 @MODELS.register_module(
@@ -22,13 +22,25 @@ class SalientDetection(TorchModel):
         """str -- model file root."""
         super().__init__(model_dir, *args, **kwargs)
         model_path = osp.join(model_dir, ModelFile.TORCH_MODEL_FILE)
-        self.model = U2NET(3, 1)
+
+        self.norm_mean = [0.485, 0.456, 0.406]
+        self.norm_std = [0.229, 0.224, 0.225]
+        self.norm_size = (320, 320)
+
+        config_path = osp.join(model_dir, 'config.py')
+        if osp.exists(config_path) is False:
+            self.model = U2NET(3, 1)
+        else:
+            self.model = SENet(backbone_path=None, pretrained=False)
+            config = Config.from_file(config_path)
+            self.norm_mean = config.norm_mean
+            self.norm_std = config.norm_std
+            self.norm_size = config.norm_size
         checkpoint = torch.load(model_path, map_location='cpu')
         self.transform_input = transforms.Compose([
-            transforms.Resize((320, 320)),
+            transforms.Resize(self.norm_size),
             transforms.ToTensor(),
-            transforms.Normalize(
-                mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+            transforms.Normalize(mean=self.norm_mean, std=self.norm_std)
         ])
         self.model.load_state_dict(checkpoint)
         self.model.eval()

modelscope/pipelines/cv/image_salient_detection_pipeline.py

@@ -12,6 +12,11 @@ from modelscope.utils.constant import Tasks
 
 @PIPELINES.register_module(
     Tasks.semantic_segmentation, module_name=Pipelines.salient_detection)
+@PIPELINES.register_module(
+    Tasks.semantic_segmentation,
+    module_name=Pipelines.salient_boudary_detection)
+@PIPELINES.register_module(
+    Tasks.semantic_segmentation, module_name=Pipelines.camouflaged_detection)
 class ImageSalientDetectionPipeline(Pipeline):
 
     def __init__(self, model: str, **kwargs):

tests/pipelines/test_salient_detection.py

@@ -23,6 +23,27 @@ class SalientDetectionTest(unittest.TestCase, DemoCompatibilityCheck):
         import cv2
         cv2.imwrite(input_location + '_salient.jpg', result[OutputKeys.MASKS])
 
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_salient_boudary_detection(self):
+        input_location = 'data/test/images/image_salient_detection.jpg'
+        model_id = 'damo/cv_res2net_salient-detection'
+        salient_detect = pipeline(Tasks.semantic_segmentation, model=model_id)
+        result = salient_detect(input_location)
+        import cv2
+        cv2.imwrite(input_location + '_boudary_salient.jpg',
+                    result[OutputKeys.MASKS])
+
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_camouflag_detection(self):
+        input_location = 'data/test/images/image_camouflag_detection.jpg'
+        model_id = 'damo/cv_res2net_camouflaged-detection'
+        camouflag_detect = pipeline(
+            Tasks.semantic_segmentation, model=model_id)
+        result = camouflag_detect(input_location)
+        import cv2
+        cv2.imwrite(input_location + '_camouflag.jpg',
+                    result[OutputKeys.MASKS])
+
     @unittest.skip('demo compatibility test is only enabled on a needed-basis')
     def test_demo_compatibility(self):
         self.compatibility_check()