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How to use is_up method in localstack
Best Python code snippet using localstack_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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