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How to use is_up method in autotest

Best Python code snippet using autotest_python

d3_fr_unet.py

Source:d3_fr_unet.py

1import torch2import torch.nn as nn3from models.utils import InitWeights4class conv(nn.Module):5    def __init__(self, in_c, out_c, dp=0):6        super(conv, self).__init__()7        self.in_c = in_c8        self.out_c = out_c9        self.conv = nn.Sequential(10            nn.Conv3d(out_c, out_c, kernel_size=3, padding=1, bias=False),11            nn.BatchNorm3d(out_c),12            nn.Dropout3d(dp),13            nn.LeakyReLU(0.1, inplace=True),14            nn.Conv3d(out_c, out_c, kernel_size=3, padding=1, bias=False),15            nn.BatchNorm3d(out_c),16            nn.Dropout3d(dp),17            nn.LeakyReLU(0.1, inplace=True))18        self.relu = nn.LeakyReLU(0.1, inplace=True)19    def forward(self, x):20        res = x21        x = self.conv(x)22        out = x + res23        out = self.relu(out)24        return x25class feature_fuse(nn.Module):26    def __init__(self, in_c, out_c):27        super(feature_fuse, self).__init__()28        self.conv11 = nn.Conv3d(29            in_c, out_c, kernel_size=1, padding=0, bias=False)30        self.conv33 = nn.Conv3d(31            in_c, out_c, kernel_size=3, padding=1, bias=False)32        self.conv33_di = nn.Conv3d(33            in_c, out_c, kernel_size=3, padding=2, bias=False, dilation=2)34        self.norm = nn.BatchNorm3d(out_c)35    def forward(self, x):36        x1 = self.conv11(x)37        x2 = self.conv33(x)38        x3 = self.conv33_di(x)39        out = self.norm(x1+x2+x3)40        return out41class up(nn.Module):42    def __init__(self, in_c, out_c, stride=[1, 2, 2]):43        super(up, self).__init__()44        self.up = nn.Sequential(45            nn.ConvTranspose3d(in_c, out_c, kernel_size=stride,46                               padding=0, stride=stride, bias=False),47            nn.BatchNorm3d(out_c),48            nn.LeakyReLU(0.1, inplace=False))49    def forward(self, x):50        x = self.up(x)51        return x52class down(nn.Module):53    def __init__(self, in_c, out_c, stride=[1, 2, 2]):54        super(down, self).__init__()55        self.down = nn.Sequential(56            nn.Conv3d(in_c, out_c, kernel_size=stride,57                      padding=0, stride=stride, bias=False),58            nn.BatchNorm3d(out_c),59            nn.LeakyReLU(0.1, inplace=True))60    def forward(self, x):61        x = self.down(x)62        return x63class block(nn.Module):64    def __init__(self, in_c, out_c,  dp=0, is_up=False, is_down=False, fuse=False):65        super(block, self).__init__()66        self.in_c = in_c67        self.out_c = out_c68        if fuse == True:69            self.fuse = feature_fuse(in_c, out_c)70        else:71            self.fuse = nn.Conv2d(in_c, out_c, kernel_size=1, stride=1)72        self.is_up = is_up73        self.is_down = is_down74        self.conv = conv(out_c, out_c, dp=dp)75        if self.is_up == True:76            self.up = up(out_c, out_c//2)77        if self.is_down == True:78            self.down = down(out_c, out_c*2)79    def forward(self,  x):80        if self.in_c != self.out_c:81            x = self.fuse(x)82        x = self.conv(x)83        if self.is_up == False and self.is_down == False:84            return x85        elif self.is_up == True and self.is_down == False:86            x_up = self.up(x)87            return x, x_up88        elif self.is_up == False and self.is_down == True:89            x_down = self.down(x)90            return x, x_down91        else:92            x_up = self.up(x)93            x_down = self.down(x)94            return x, x_up, x_down95class final_conv(nn.Module):96    def __init__(self, in_c, out_c) -> None:97        super().__init__()98        self.conv1 = nn.Conv3d(in_c, 1, kernel_size=1)99        self.conv2 = nn.Conv2d(8, out_c, kernel_size=1)100    def forward(self, x):101        x = self.conv1(x)102        x = self.conv2(x.squeeze(1))103        return x104class D3_FR_UNet(nn.Module):105    def __init__(self,  num_classes=1, num_channels=1, feature_scale=2,  dropout=0.2, fuse=True, out_ave=True):106        super(D3_FR_UNet, self).__init__()107        self.out_ave = out_ave108        filters = [64, 128, 256, 512, 1024]109        filters = [int(x / feature_scale) for x in filters]110        self.block1_3 = block(111            num_channels, filters[0],  dp=dropout, is_up=False, is_down=True, fuse=fuse)112        self.block1_2 = block(113            filters[0], filters[0],  dp=dropout, is_up=False, is_down=True, fuse=fuse)114        self.block1_1 = block(115            filters[0]*2, filters[0],  dp=dropout, is_up=False, is_down=True, fuse=fuse)116        self.block10 = block(117            filters[0]*2, filters[0],  dp=dropout, is_up=False, is_down=True, fuse=fuse)118        self.block11 = block(119            filters[0]*2, filters[0],  dp=dropout, is_up=False, is_down=True, fuse=fuse)120        self.block12 = block(121            filters[0]*2, filters[0],  dp=dropout, is_up=False, is_down=False, fuse=fuse)122        self.block13 = block(123            filters[0]*2, filters[0],  dp=dropout, is_up=False, is_down=False, fuse=fuse)124        self.block2_2 = block(125            filters[1], filters[1],  dp=dropout, is_up=True, is_down=True, fuse=fuse)126        self.block2_1 = block(127            filters[1]*2, filters[1],  dp=dropout, is_up=True, is_down=True, fuse=fuse)128        self.block20 = block(129            filters[1]*3, filters[1],  dp=dropout, is_up=True, is_down=True, fuse=fuse)130        self.block21 = block(131            filters[1]*3, filters[1],  dp=dropout, is_up=True, is_down=False, fuse=fuse)132        self.block22 = block(133            filters[1]*3, filters[1],  dp=dropout, is_up=True, is_down=False, fuse=fuse)134        self.block3_1 = block(135            filters[2], filters[2],  dp=dropout, is_up=True, is_down=True, fuse=fuse)136        self.block30 = block(137            filters[2]*2, filters[2],  dp=dropout, is_up=True, is_down=False, fuse=fuse)138        self.block31 = block(139            filters[2]*3, filters[2],  dp=dropout, is_up=True, is_down=False, fuse=fuse)140        self.block40 = block(filters[3], filters[3],141                             dp=dropout, is_up=True, is_down=False, fuse=fuse)142        self.final1 = final_conv(filters[0], num_classes)143        self.final2 = final_conv(filters[0], num_classes)144        self.final3 = final_conv(filters[0], num_classes)145        self.final4 = final_conv(filters[0], num_classes)146        self.final5 = final_conv(filters[0], num_classes)147        self.fuse = nn.Conv3d(5, num_classes, kernel_size=1, padding=0, bias=True)148        self.apply(InitWeights)149    def forward(self, x):150        x1_3, x_down1_3 = self.block1_3(x)151        x1_2, x_down1_2 = self.block1_2(x1_3)152        x2_2, x_up2_2, x_down2_2 = self.block2_2(x_down1_3)153        x1_1, x_down1_1 = self.block1_1(torch.cat([x1_2, x_up2_2], dim=1))154        x2_1, x_up2_1, x_down2_1 = self.block2_1(155            torch.cat([x_down1_2, x2_2], dim=1))156        x3_1, x_up3_1, x_down3_1 = self.block3_1(x_down2_2)157        x10, x_down10 = self.block10(torch.cat([x1_1, x_up2_1], dim=1))158        x20, x_up20, x_down20 = self.block20(159            torch.cat([x_down1_1, x2_1, x_up3_1], dim=1))160        x30, x_up30 = self.block30(torch.cat([x_down2_1, x3_1], dim=1))161        _, x_up40 = self.block40(x_down3_1)162        x11, x_down11 = self.block11(torch.cat([x10, x_up20], dim=1))163        x21, x_up21 = self.block21(torch.cat([x_down10, x20, x_up30], dim=1))164        _, x_up31 = self.block31(torch.cat([x_down20, x30, x_up40], dim=1))165        x12 = self.block12(torch.cat([x11, x_up21], dim=1))166        _, x_up22 = self.block22(torch.cat([x_down11, x21, x_up31], dim=1))167        x13 = self.block13(torch.cat([x12, x_up22], dim=1))168        if self.out_ave == True:169            output = (self.final1(x1_1)+self.final2(x10) +170                      self.final3(x11)+self.final4(x12)+self.final5(x13))/5171        else:172            output = self.final5(x13)...

fr_unet.py

Source:fr_unet.py

1import torch2import torch.nn as nn3from models.utils import InitWeights4class conv(nn.Module):5    def __init__(self, in_c, out_c, dp=0):6        super(conv, self).__init__()7        self.in_c = in_c8        self.out_c = out_c9        self.conv = nn.Sequential(10            nn.Conv2d(out_c, out_c, kernel_size=3, padding=1, bias=False),11            nn.BatchNorm2d(out_c),12            nn.Dropout2d(dp),13            nn.LeakyReLU(0.1, inplace=True),14            nn.Conv2d(out_c, out_c, kernel_size=3, padding=1, bias=False),15            nn.BatchNorm2d(out_c),16            nn.Dropout2d(dp),17            nn.LeakyReLU(0.1, inplace=True))18        self.relu = nn.LeakyReLU(0.1, inplace=True)19    def forward(self, x):20        res = x21        x = self.conv(x)22        out = x + res23        out = self.relu(out)24        return x25class feature_fuse(nn.Module):26    def __init__(self, in_c, out_c):27        super(feature_fuse, self).__init__()28        self.conv11 = nn.Conv2d(29            in_c, out_c, kernel_size=1, padding=0, bias=False)30        self.conv33 = nn.Conv2d(31            in_c, out_c, kernel_size=3, padding=1, bias=False)32        self.conv33_di = nn.Conv2d(33            in_c, out_c, kernel_size=3, padding=2, bias=False, dilation=2)34        self.norm = nn.BatchNorm2d(out_c)35    def forward(self, x):36        x1 = self.conv11(x)37        x2 = self.conv33(x)38        x3 = self.conv33_di(x)39        out = self.norm(x1+x2+x3)40        return out41class up(nn.Module):42    def __init__(self, in_c, out_c, dp=0):43        super(up, self).__init__()44        self.up = nn.Sequential(45            nn.ConvTranspose2d(in_c, out_c, kernel_size=2,46                               padding=0, stride=2, bias=False),47            nn.BatchNorm2d(out_c),48            nn.LeakyReLU(0.1, inplace=False))49    def forward(self, x):50        x = self.up(x)51        return x52class down(nn.Module):53    def __init__(self, in_c, out_c, dp=0):54        super(down, self).__init__()55        self.down = nn.Sequential(56            nn.Conv2d(in_c, out_c, kernel_size=2,57                      padding=0, stride=2, bias=False),58            nn.BatchNorm2d(out_c),59            nn.LeakyReLU(0.1, inplace=True))60    def forward(self, x):61        x = self.down(x)62        return x63class block(nn.Module):64    def __init__(self, in_c, out_c,  dp=0, is_up=False, is_down=False, fuse=False):65        super(block, self).__init__()66        self.in_c = in_c67        self.out_c = out_c68        if fuse == True:69            self.fuse = feature_fuse(in_c, out_c)70        else:71            self.fuse = nn.Conv2d(in_c, out_c, kernel_size=1, stride=1)72        self.is_up = is_up73        self.is_down = is_down74        self.conv = conv(out_c, out_c, dp=dp)75        if self.is_up == True:76            self.up = up(out_c, out_c//2)77        if self.is_down == True:78            self.down = down(out_c, out_c*2)79    def forward(self,  x):80        if self.in_c != self.out_c:81            x = self.fuse(x)82        x = self.conv(x)83        if self.is_up == False and self.is_down == False:84            return x85        elif self.is_up == True and self.is_down == False:86            x_up = self.up(x)87            return x, x_up88        elif self.is_up == False and self.is_down == True:89            x_down = self.down(x)90            return x, x_down91        else:92            x_up = self.up(x)93            x_down = self.down(x)94            return x, x_up, x_down95class FR_UNet(nn.Module):96    def __init__(self,  num_classes=1, num_channels=1, feature_scale=2,  dropout=0.1, fuse=True, out_ave=True):97        super(FR_UNet, self).__init__()98        self.out_ave = out_ave99        filters = [64, 128, 256, 512, 1024]100        filters = [int(x / feature_scale) for x in filters]101        self.block1_3 = block(102            num_channels, filters[0],  dp=dropout, is_up=False, is_down=True, fuse=fuse)103        self.block1_2 = block(104            filters[0], filters[0],  dp=dropout, is_up=False, is_down=True, fuse=fuse)105        self.block1_1 = block(106            filters[0]*2, filters[0],  dp=dropout, is_up=False, is_down=True, fuse=fuse)107        self.block10 = block(108            filters[0]*2, filters[0],  dp=dropout, is_up=False, is_down=True, fuse=fuse)109        self.block11 = block(110            filters[0]*2, filters[0],  dp=dropout, is_up=False, is_down=True, fuse=fuse)111        self.block12 = block(112            filters[0]*2, filters[0],  dp=dropout, is_up=False, is_down=False, fuse=fuse)113        self.block13 = block(114            filters[0]*2, filters[0],  dp=dropout, is_up=False, is_down=False, fuse=fuse)115        self.block2_2 = block(116            filters[1], filters[1],  dp=dropout, is_up=True, is_down=True, fuse=fuse)117        self.block2_1 = block(118            filters[1]*2, filters[1],  dp=dropout, is_up=True, is_down=True, fuse=fuse)119        self.block20 = block(120            filters[1]*3, filters[1],  dp=dropout, is_up=True, is_down=True, fuse=fuse)121        self.block21 = block(122            filters[1]*3, filters[1],  dp=dropout, is_up=True, is_down=False, fuse=fuse)123        self.block22 = block(124            filters[1]*3, filters[1],  dp=dropout, is_up=True, is_down=False, fuse=fuse)125        self.block3_1 = block(126            filters[2], filters[2],  dp=dropout, is_up=True, is_down=True, fuse=fuse)127        self.block30 = block(128            filters[2]*2, filters[2],  dp=dropout, is_up=True, is_down=False, fuse=fuse)129        self.block31 = block(130            filters[2]*3, filters[2],  dp=dropout, is_up=True, is_down=False, fuse=fuse)131        self.block40 = block(filters[3], filters[3],132                             dp=dropout, is_up=True, is_down=False, fuse=fuse)133        self.final1 = nn.Conv2d(134            filters[0], num_classes, kernel_size=1, padding=0, bias=True)135        self.final2 = nn.Conv2d(136            filters[0], num_classes, kernel_size=1, padding=0, bias=True)137        self.final3 = nn.Conv2d(138            filters[0], num_classes, kernel_size=1, padding=0, bias=True)139        self.final4 = nn.Conv2d(140            filters[0], num_classes, kernel_size=1, padding=0, bias=True)141        self.final5 = nn.Conv2d(142            filters[0], num_classes, kernel_size=1, padding=0, bias=True)143        self.fuse = nn.Conv2d(144            5, num_classes, kernel_size=1, padding=0, bias=True)145        self.apply(InitWeights)146    def forward(self, x):147        x1_3, x_down1_3 = self.block1_3(x)148        x1_2, x_down1_2 = self.block1_2(x1_3)149        x2_2, x_up2_2, x_down2_2 = self.block2_2(x_down1_3)150        x1_1, x_down1_1 = self.block1_1(torch.cat([x1_2, x_up2_2], dim=1))151        x2_1, x_up2_1, x_down2_1 = self.block2_1(152            torch.cat([x_down1_2, x2_2], dim=1))153        x3_1, x_up3_1, x_down3_1 = self.block3_1(x_down2_2)154        x10, x_down10 = self.block10(torch.cat([x1_1, x_up2_1], dim=1))155        x20, x_up20, x_down20 = self.block20(156            torch.cat([x_down1_1, x2_1, x_up3_1], dim=1))157        x30, x_up30 = self.block30(torch.cat([x_down2_1, x3_1], dim=1))158        _, x_up40 = self.block40(x_down3_1)159        x11, x_down11 = self.block11(torch.cat([x10, x_up20], dim=1))160        x21, x_up21 = self.block21(torch.cat([x_down10, x20, x_up30], dim=1))161        _, x_up31 = self.block31(torch.cat([x_down20, x30, x_up40], dim=1))162        x12 = self.block12(torch.cat([x11, x_up21], dim=1))163        _, x_up22 = self.block22(torch.cat([x_down11, x21, x_up31], dim=1))164        x13 = self.block13(torch.cat([x12, x_up22], dim=1))165        if self.out_ave == True:166            output = (self.final1(x1_1)+self.final2(x10) +167                      self.final3(x11)+self.final4(x12)+self.final5(x13))/5168        else:169            output = self.final5(x13)...

test_servicegroup.py

Source:test_servicegroup.py

...27    def test_service_is_up_forced_down(self):28        kwarg = {'forced_down': True}29        magnum_object = obj_util.get_test_magnum_service_object(30            self.context, **kwarg)31        is_up = self.servicegroup_api.service_is_up(magnum_object)32        self.assertFalse(is_up)33    def test_service_is_up_alive(self):34        kwarg = {'last_seen_up': timeutils.utcnow(True)}35        magnum_object = obj_util.get_test_magnum_service_object(36            self.context, **kwarg)37        is_up = self.servicegroup_api.service_is_up(magnum_object)38        self.assertTrue(is_up)39    def test_service_is_up_alive_with_created(self):40        kwarg = {'created_at': timeutils.utcnow(True)}41        magnum_object = obj_util.get_test_magnum_service_object(42            self.context, **kwarg)43        is_up = self.servicegroup_api.service_is_up(magnum_object)44        self.assertTrue(is_up)45    def test_service_is_up_alive_with_updated(self):46        kwarg = {'updated_at': timeutils.utcnow(True)}47        magnum_object = obj_util.get_test_magnum_service_object(48            self.context, **kwarg)49        is_up = self.servicegroup_api.service_is_up(magnum_object)50        self.assertTrue(is_up)51    def test_service_is_up_alive_with_all_three(self):52        kwarg = {'created_at': timeutils.utcnow(True),53                 'updated_at': timeutils.utcnow(True),54                 'last_seen_up': timeutils.utcnow(True)}55        magnum_object = obj_util.get_test_magnum_service_object(56            self.context, **kwarg)57        is_up = self.servicegroup_api.service_is_up(magnum_object)58        self.assertTrue(is_up)59    def test_service_is_up_alive_with_latest_update(self):60        kwarg = {'created_at': datetime.datetime(1970, 1, 1,61                                                 tzinfo=pytz.UTC),62                 'updated_at': datetime.datetime(1970, 1, 1,63                                                 tzinfo=pytz.UTC),64                 'last_seen_up': timeutils.utcnow(True)}65        magnum_object = obj_util.get_test_magnum_service_object(66            self.context, **kwarg)67        is_up = self.servicegroup_api.service_is_up(magnum_object)68        self.assertTrue(is_up)69    def test_service_is_up_down(self):70        kwarg = {'last_seen_up': datetime.datetime(1970, 1, 1,71                                                   tzinfo=pytz.UTC)}72        magnum_object = obj_util.get_test_magnum_service_object(73            self.context, **kwarg)74        is_up = self.servicegroup_api.service_is_up(magnum_object)75        self.assertFalse(is_up)76    def test_service_is_up_down_with_create(self):77        kwarg = {'created_at': datetime.datetime(1970, 1, 1,78                                                 tzinfo=pytz.UTC)}79        magnum_object = obj_util.get_test_magnum_service_object(80            self.context, **kwarg)81        is_up = self.servicegroup_api.service_is_up(magnum_object)82        self.assertFalse(is_up)83    def test_service_is_up_down_with_update(self):84        kwarg = {'updated_at': datetime.datetime(1970, 1, 1,85                                                 tzinfo=pytz.UTC)}86        magnum_object = obj_util.get_test_magnum_service_object(87            self.context, **kwarg)88        is_up = self.servicegroup_api.service_is_up(magnum_object)89        self.assertFalse(is_up)90    def test_service_is_up_down_with_all_three(self):91        kwarg = {'last_seen_up': datetime.datetime(1970, 1, 1,92                                                   tzinfo=pytz.UTC),93                 'created_at': datetime.datetime(1970, 1, 1,94                                                 tzinfo=pytz.UTC),95                 'updated_at': datetime.datetime(1970, 1, 1,96                                                 tzinfo=pytz.UTC)}97        magnum_object = obj_util.get_test_magnum_service_object(98            self.context, **kwarg)99        is_up = self.servicegroup_api.service_is_up(magnum_object)100        self.assertFalse(is_up)101    def test_service_is_up_down_with_old_update(self):102        kwarg = {'last_seen_up': datetime.datetime(1970, 1, 1,103                                                   tzinfo=pytz.UTC),104                 'created_at': timeutils.utcnow(True),105                 'updated_at': timeutils.utcnow(True)}106        magnum_object = obj_util.get_test_magnum_service_object(107            self.context, **kwarg)108        is_up = self.servicegroup_api.service_is_up(magnum_object)...

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