mirror of
https://github.com/modelscope/modelscope.git
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support camouflaged-detection
Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10834768
This commit is contained in:
wendi.hwd
3
data/test/images/image_camouflag_detection.jpg
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3
data/test/images/image_camouflag_detection.jpg
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@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:4c713215f7fb4da5382c9137347ee52956a7a44d5979c4cffd3c9b6d1d7e878f
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size 19445
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@@ -165,6 +165,8 @@ class Pipelines(object):
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easycv_segmentation = 'easycv-segmentation'
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face_2d_keypoints = 'mobilenet_face-2d-keypoints_alignment'
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salient_detection = 'u2net-salient-detection'
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salient_boudary_detection = 'res2net-salient-detection'
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camouflaged_detection = 'res2net-camouflaged-detection'
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image_classification = 'image-classification'
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face_detection = 'resnet-face-detection-scrfd10gkps'
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card_detection = 'resnet-card-detection-scrfd34gkps'
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@@ -1,3 +1,4 @@
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# The implementation is adopted from U-2-Net, made publicly available under the Apache 2.0 License
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# source code avaiable via https://github.com/xuebinqin/U-2-Net
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from .senet import SENet
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from .u2net import U2NET
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@@ -0,0 +1,187 @@
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# Implementation in this file is modified based on Res2Net-PretrainedModels
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# Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License
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# publicly avaialbe at https://github.com/Res2Net/Res2Net-PretrainedModels/blob/master/res2net_v1b.py
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import math
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import torch
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import torch.nn as nn
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__all__ = ['Res2Net', 'res2net50_v1b_26w_4s']
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class Bottle2neck(nn.Module):
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expansion = 4
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def __init__(self,
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inplanes,
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planes,
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stride=1,
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downsample=None,
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baseWidth=26,
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scale=4,
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stype='normal'):
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""" Constructor
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Args:
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inplanes: input channel dimensionality
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planes: output channel dimensionality
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stride: conv stride. Replaces pooling layer.
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downsample: None when stride = 1
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baseWidth: basic width of conv3x3
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scale: number of scale.
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type: 'normal': normal set. 'stage': first block of a new stage.
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"""
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super(Bottle2neck, self).__init__()
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width = int(math.floor(planes * (baseWidth / 64.0)))
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self.conv1 = nn.Conv2d(
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inplanes, width * scale, kernel_size=1, bias=False)
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self.bn1 = nn.BatchNorm2d(width * scale)
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if scale == 1:
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self.nums = 1
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else:
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self.nums = scale - 1
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if stype == 'stage':
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self.pool = nn.AvgPool2d(kernel_size=3, stride=stride, padding=1)
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convs = []
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bns = []
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for i in range(self.nums):
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convs.append(
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nn.Conv2d(
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width,
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width,
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kernel_size=3,
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stride=stride,
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padding=1,
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bias=False))
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bns.append(nn.BatchNorm2d(width))
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self.convs = nn.ModuleList(convs)
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self.bns = nn.ModuleList(bns)
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self.conv3 = nn.Conv2d(
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width * scale, planes * self.expansion, kernel_size=1, bias=False)
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self.bn3 = nn.BatchNorm2d(planes * self.expansion)
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self.relu = nn.ReLU(inplace=True)
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self.downsample = downsample
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self.stype = stype
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self.scale = scale
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self.width = width
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def forward(self, x):
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residual = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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spx = torch.split(out, self.width, 1)
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for i in range(self.nums):
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if i == 0 or self.stype == 'stage':
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sp = spx[i]
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else:
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sp = sp + spx[i]
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sp = self.convs[i](sp)
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sp = self.relu(self.bns[i](sp))
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if i == 0:
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out = sp
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else:
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out = torch.cat((out, sp), 1)
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if self.scale != 1 and self.stype == 'normal':
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out = torch.cat((out, spx[self.nums]), 1)
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elif self.scale != 1 and self.stype == 'stage':
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out = torch.cat((out, self.pool(spx[self.nums])), 1)
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out = self.conv3(out)
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out = self.bn3(out)
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if self.downsample is not None:
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residual = self.downsample(x)
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out += residual
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out = self.relu(out)
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return out
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class Res2Net(nn.Module):
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def __init__(self, block, layers, baseWidth=26, scale=4, num_classes=1000):
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self.inplanes = 64
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super(Res2Net, self).__init__()
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self.baseWidth = baseWidth
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self.scale = scale
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self.conv1 = nn.Sequential(
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nn.Conv2d(3, 32, 3, 2, 1, bias=False), nn.BatchNorm2d(32),
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nn.ReLU(inplace=True), nn.Conv2d(32, 32, 3, 1, 1, bias=False),
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nn.BatchNorm2d(32), nn.ReLU(inplace=True),
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nn.Conv2d(32, 64, 3, 1, 1, bias=False))
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self.bn1 = nn.BatchNorm2d(64)
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self.relu = nn.ReLU()
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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self.layer1 = self._make_layer(block, 64, layers[0])
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self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
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self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
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self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
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self.avgpool = nn.AdaptiveAvgPool2d(1)
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self.fc = nn.Linear(512 * block.expansion, num_classes)
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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nn.init.kaiming_normal_(
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m.weight, mode='fan_out', nonlinearity='relu')
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elif isinstance(m, nn.BatchNorm2d):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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def _make_layer(self, block, planes, blocks, stride=1):
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downsample = None
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if stride != 1 or self.inplanes != planes * block.expansion:
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downsample = nn.Sequential(
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nn.AvgPool2d(
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kernel_size=stride,
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stride=stride,
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ceil_mode=True,
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count_include_pad=False),
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nn.Conv2d(
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self.inplanes,
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planes * block.expansion,
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kernel_size=1,
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stride=1,
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bias=False),
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nn.BatchNorm2d(planes * block.expansion),
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)
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layers = []
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layers.append(
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block(
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self.inplanes,
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planes,
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stride,
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downsample=downsample,
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stype='stage',
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baseWidth=self.baseWidth,
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scale=self.scale))
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self.inplanes = planes * block.expansion
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for i in range(1, blocks):
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layers.append(
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block(
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self.inplanes,
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planes,
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baseWidth=self.baseWidth,
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scale=self.scale))
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return nn.Sequential(*layers)
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def forward(self, x):
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x = self.conv1(x)
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x = self.bn1(x)
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x = self.relu(x)
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x = self.maxpool(x)
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x = self.layer1(x)
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x = self.layer2(x)
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x = self.layer3(x)
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x = self.layer4(x)
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x = self.avgpool(x)
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x = x.view(x.size(0), -1)
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x = self.fc(x)
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return x
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def res2net50_v1b_26w_4s(backbone_path, pretrained=False, **kwargs):
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"""Constructs a Res2Net-50_v1b_26w_4s lib.
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Args:
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pretrained (bool): If True, returns a lib pre-trained on ImageNet
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"""
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model = Res2Net(Bottle2neck, [3, 4, 6, 3], baseWidth=26, scale=4, **kwargs)
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if pretrained:
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model_state = torch.load(backbone_path)
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model.load_state_dict(model_state)
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return model
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@@ -0,0 +1,6 @@
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# Implementation in this file is modified based on Res2Net-PretrainedModels
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# Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License
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# publicly avaialbe at https://github.com/Res2Net/Res2Net-PretrainedModels/blob/master/res2net_v1b.py
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from .Res2Net_v1b import res2net50_v1b_26w_4s
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__all__ = ['res2net50_v1b_26w_4s']
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178
modelscope/models/cv/salient_detection/models/modules.py
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178
modelscope/models/cv/salient_detection/models/modules.py
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@@ -0,0 +1,178 @@
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# Copyright (c) Alibaba, Inc. and its affiliates.
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from .utils import ConvBNReLU
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class AreaLayer(nn.Module):
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def __init__(self, in_channel, out_channel):
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super(AreaLayer, self).__init__()
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self.lbody = nn.Sequential(
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nn.Conv2d(out_channel, out_channel, 1),
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nn.BatchNorm2d(out_channel), nn.ReLU(inplace=True))
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self.hbody = nn.Sequential(
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nn.Conv2d(in_channel, out_channel, 1), nn.BatchNorm2d(out_channel),
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nn.ReLU(inplace=True))
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self.body = nn.Sequential(
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nn.Conv2d(2 * out_channel, out_channel, 3, 1, 1),
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nn.BatchNorm2d(out_channel), nn.ReLU(inplace=True),
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nn.Conv2d(out_channel, out_channel, 3, 1, 1),
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nn.BatchNorm2d(out_channel), nn.ReLU(inplace=True),
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nn.Conv2d(out_channel, 1, 1))
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def forward(self, xl, xh):
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xl1 = self.lbody(xl)
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xl1 = F.interpolate(
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xl1, size=xh.size()[2:], mode='bilinear', align_corners=True)
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xh1 = self.hbody(xh)
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x = torch.cat((xl1, xh1), dim=1)
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x_out = self.body(x)
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return x_out
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class EdgeLayer(nn.Module):
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def __init__(self, in_channel, out_channel):
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super(EdgeLayer, self).__init__()
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self.lbody = nn.Sequential(
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nn.Conv2d(out_channel, out_channel, 1),
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nn.BatchNorm2d(out_channel), nn.ReLU(inplace=True))
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self.hbody = nn.Sequential(
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nn.Conv2d(in_channel, out_channel, 1), nn.BatchNorm2d(out_channel),
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nn.ReLU(inplace=True))
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self.bodye = nn.Sequential(
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nn.Conv2d(2 * out_channel, out_channel, 3, 1, 1),
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nn.BatchNorm2d(out_channel), nn.ReLU(inplace=True),
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nn.Conv2d(out_channel, out_channel, 3, 1, 1),
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nn.BatchNorm2d(out_channel), nn.ReLU(inplace=True),
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nn.Conv2d(out_channel, 1, 1))
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def forward(self, xl, xh):
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xl1 = self.lbody(xl)
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xh1 = self.hbody(xh)
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xh1 = F.interpolate(
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xh1, size=xl.size()[2:], mode='bilinear', align_corners=True)
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x = torch.cat((xl1, xh1), dim=1)
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x_out = self.bodye(x)
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return x_out
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class EBlock(nn.Module):
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def __init__(self, inchs, outchs):
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super(EBlock, self).__init__()
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self.elayer = nn.Sequential(
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ConvBNReLU(inchs + 1, outchs, kernel_size=3, padding=1, stride=1),
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ConvBNReLU(outchs, outchs, 1))
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self.salayer = nn.Sequential(
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nn.Conv2d(2, 1, 3, 1, 1, bias=False),
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nn.BatchNorm2d(1, momentum=0.01), nn.Sigmoid())
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def forward(self, x, edgeAtten):
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x = torch.cat((x, edgeAtten), dim=1)
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ex = self.elayer(x)
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ex_max = torch.max(ex, 1, keepdim=True)[0]
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ex_mean = torch.mean(ex, dim=1, keepdim=True)
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xei_compress = torch.cat((ex_max, ex_mean), dim=1)
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scale = self.salayer(xei_compress)
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x_out = ex * scale
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return x_out
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class StructureE(nn.Module):
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def __init__(self, inchs, outchs, EM):
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super(StructureE, self).__init__()
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self.ne_modules = int(inchs / EM)
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NM = int(outchs / self.ne_modules)
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elayes = []
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for i in range(self.ne_modules):
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emblock = EBlock(EM, NM)
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elayes.append(emblock)
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self.emlayes = nn.ModuleList(elayes)
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self.body = nn.Sequential(
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ConvBNReLU(outchs, outchs, 3, 1, 1), ConvBNReLU(outchs, outchs, 1))
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def forward(self, x, edgeAtten):
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if edgeAtten.size() != x.size():
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edgeAtten = F.interpolate(
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edgeAtten, x.size()[2:], mode='bilinear', align_corners=False)
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xx = torch.chunk(x, self.ne_modules, dim=1)
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efeas = []
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for i in range(self.ne_modules):
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xei = self.emlayes[i](xx[i], edgeAtten)
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efeas.append(xei)
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efeas = torch.cat(efeas, dim=1)
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x_out = self.body(efeas)
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return x_out
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class ABlock(nn.Module):
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def __init__(self, inchs, outchs, k):
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super(ABlock, self).__init__()
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self.alayer = nn.Sequential(
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ConvBNReLU(inchs, outchs, k, 1, k // 2),
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ConvBNReLU(outchs, outchs, 1))
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self.arlayer = nn.Sequential(
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ConvBNReLU(inchs, outchs, k, 1, k // 2),
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ConvBNReLU(outchs, outchs, 1))
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self.fusion = ConvBNReLU(2 * outchs, outchs, 1)
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def forward(self, x, areaAtten):
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xa = x * areaAtten
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xra = x * (1 - areaAtten)
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xout = self.fusion(torch.cat((xa, xra), dim=1))
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return xout
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class AMFusion(nn.Module):
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def __init__(self, inchs, outchs, AM):
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super(AMFusion, self).__init__()
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self.k = [3, 3, 5, 5]
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self.conv_up = ConvBNReLU(inchs, outchs, 3, 1, 1)
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self.up = nn.Upsample(
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scale_factor=2, mode='bilinear', align_corners=True)
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self.na_modules = int(outchs / AM)
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alayers = []
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for i in range(self.na_modules):
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layer = ABlock(AM, AM, self.k[i])
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alayers.append(layer)
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self.alayers = nn.ModuleList(alayers)
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self.fusion_0 = ConvBNReLU(outchs, outchs, 3, 1, 1)
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self.fusion_e = nn.Sequential(
|
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nn.Conv2d(
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outchs, outchs, kernel_size=(3, 1), padding=(1, 0),
|
||||
bias=False), nn.BatchNorm2d(outchs), nn.ReLU(inplace=True),
|
||||
nn.Conv2d(
|
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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(
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outchs, outchs, kernel_size=(5, 1), padding=(2, 0),
|
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bias=False), nn.BatchNorm2d(outchs), nn.ReLU(inplace=True),
|
||||
nn.Conv2d(
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outchs, outchs, kernel_size=(1, 5), padding=(0, 2),
|
||||
bias=False), nn.BatchNorm2d(outchs), nn.ReLU(inplace=True))
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self.fusion = ConvBNReLU(3 * outchs, outchs, 1)
|
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|
||||
def forward(self, xl, xh, xhm):
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xh1 = self.up(self.conv_up(xh))
|
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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):
|
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xi = self.alayers[i](xx[i], xm)
|
||||
xxmids.append(xi)
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xfea = torch.cat(xxmids, dim=1)
|
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x0 = self.fusion_0(xfea)
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||||
x1 = self.fusion_e(xfea)
|
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x2 = self.fusion_e1(xfea)
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x_out = self.fusion(torch.cat((x0, x1, x2), dim=1))
|
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return x_out
|
||||
74
modelscope/models/cv/salient_detection/models/senet.py
Normal file
74
modelscope/models/cv/salient_detection/models/senet.py
Normal file
@@ -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
|
||||
105
modelscope/models/cv/salient_detection/models/utils.py
Normal file
105
modelscope/models/cv/salient_detection/models/utils.py
Normal file
@@ -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
|
||||
@@ -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()
|
||||
|
||||
@@ -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):
|
||||
|
||||
@@ -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()
|
||||
|
||||
Reference in New Issue
Block a user