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URLError: <urlopen error [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: certificate has expired (_ssl.c:1131)>
import ssl
ssl._create_default_https_context = ssl._create_unverified_context이걸 코드에 추가하면 됨.
ㅋㅋㅋㅋ
Downloading: "http://data.lip6.fr/cadene/pretrainedmodels/se_resnext50_32x4d-a260b3a4.pth" to /Users/s/.cache/torch/hub/checkpoints/se_resnext50_32x4d-a260b3a4.pth
100%|██████████| 105M/105M [05:10<00:00, 356kB/s]
FPN(
(encoder): SENetEncoder(
(layer0): Sequential(
(conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(pool): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)
)
(layer1): Sequential(
(0): SEResNeXtBottleneck(
(conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(se_module): SEModule(
(avg_pool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(256, 16, kernel_size=(1, 1), stride=(1, 1))
(relu): ReLU(inplace=True)
(fc2): Conv2d(16, 256, kernel_size=(1, 1), stride=(1, 1))
(sigmoid): Sigmoid()
)
(downsample): Sequential(
(0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): SEResNeXtBottleneck(
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(se_module): SEModule(
(avg_pool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(256, 16, kernel_size=(1, 1), stride=(1, 1))
(relu): ReLU(inplace=True)
(fc2): Conv2d(16, 256, kernel_size=(1, 1), stride=(1, 1))
(sigmoid): Sigmoid()
)
)
(2): SEResNeXtBottleneck(
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(se_module): SEModule(
(avg_pool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(256, 16, kernel_size=(1, 1), stride=(1, 1))
(relu): ReLU(inplace=True)
(fc2): Conv2d(16, 256, kernel_size=(1, 1), stride=(1, 1))
(sigmoid): Sigmoid()
)
)
)
(layer2): Sequential(
(0): SEResNeXtBottleneck(
(conv1): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=32, bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(se_module): SEModule(
(avg_pool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(512, 32, kernel_size=(1, 1), stride=(1, 1))
(relu): ReLU(inplace=True)
(fc2): Conv2d(32, 512, kernel_size=(1, 1), stride=(1, 1))
(sigmoid): Sigmoid()
)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): SEResNeXtBottleneck(
(conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(se_module): SEModule(
(avg_pool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(512, 32, kernel_size=(1, 1), stride=(1, 1))
(relu): ReLU(inplace=True)
(fc2): Conv2d(32, 512, kernel_size=(1, 1), stride=(1, 1))
(sigmoid): Sigmoid()
)
)
(2): SEResNeXtBottleneck(
(conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(se_module): SEModule(
(avg_pool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(512, 32, kernel_size=(1, 1), stride=(1, 1))
(relu): ReLU(inplace=True)
(fc2): Conv2d(32, 512, kernel_size=(1, 1), stride=(1, 1))
(sigmoid): Sigmoid()
)
)
(3): SEResNeXtBottleneck(
(conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(se_module): SEModule(
(avg_pool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(512, 32, kernel_size=(1, 1), stride=(1, 1))
(relu): ReLU(inplace=True)
(fc2): Conv2d(32, 512, kernel_size=(1, 1), stride=(1, 1))
(sigmoid): Sigmoid()
)
)
)
(layer3): Sequential(
(0): SEResNeXtBottleneck(
(conv1): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=32, bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(se_module): SEModule(
(avg_pool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(1024, 64, kernel_size=(1, 1), stride=(1, 1))
(relu): ReLU(inplace=True)
(fc2): Conv2d(64, 1024, kernel_size=(1, 1), stride=(1, 1))
(sigmoid): Sigmoid()
)
(downsample): Sequential(
(0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): SEResNeXtBottleneck(
(conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(se_module): SEModule(
(avg_pool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(1024, 64, kernel_size=(1, 1), stride=(1, 1))
(relu): ReLU(inplace=True)
(fc2): Conv2d(64, 1024, kernel_size=(1, 1), stride=(1, 1))
(sigmoid): Sigmoid()
)
)
(2): SEResNeXtBottleneck(
(conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(se_module): SEModule(
(avg_pool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(1024, 64, kernel_size=(1, 1), stride=(1, 1))
(relu): ReLU(inplace=True)
(fc2): Conv2d(64, 1024, kernel_size=(1, 1), stride=(1, 1))
(sigmoid): Sigmoid()
)
)
(3): SEResNeXtBottleneck(
(conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(se_module): SEModule(
(avg_pool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(1024, 64, kernel_size=(1, 1), stride=(1, 1))
(relu): ReLU(inplace=True)
(fc2): Conv2d(64, 1024, kernel_size=(1, 1), stride=(1, 1))
(sigmoid): Sigmoid()
)
)
(4): SEResNeXtBottleneck(
(conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(se_module): SEModule(
(avg_pool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(1024, 64, kernel_size=(1, 1), stride=(1, 1))
(relu): ReLU(inplace=True)
(fc2): Conv2d(64, 1024, kernel_size=(1, 1), stride=(1, 1))
(sigmoid): Sigmoid()
)
)
(5): SEResNeXtBottleneck(
(conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(se_module): SEModule(
(avg_pool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(1024, 64, kernel_size=(1, 1), stride=(1, 1))
(relu): ReLU(inplace=True)
(fc2): Conv2d(64, 1024, kernel_size=(1, 1), stride=(1, 1))
(sigmoid): Sigmoid()
)
)
)
(layer4): Sequential(
(0): SEResNeXtBottleneck(
(conv1): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=32, bias=False)
(bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(se_module): SEModule(
(avg_pool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(2048, 128, kernel_size=(1, 1), stride=(1, 1))
(relu): ReLU(inplace=True)
(fc2): Conv2d(128, 2048, kernel_size=(1, 1), stride=(1, 1))
(sigmoid): Sigmoid()
)
(downsample): Sequential(
(0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): SEResNeXtBottleneck(
(conv1): Conv2d(2048, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
(bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(se_module): SEModule(
(avg_pool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(2048, 128, kernel_size=(1, 1), stride=(1, 1))
(relu): ReLU(inplace=True)
(fc2): Conv2d(128, 2048, kernel_size=(1, 1), stride=(1, 1))
(sigmoid): Sigmoid()
)
)
(2): SEResNeXtBottleneck(
(conv1): Conv2d(2048, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
(bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(se_module): SEModule(
(avg_pool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(2048, 128, kernel_size=(1, 1), stride=(1, 1))
(relu): ReLU(inplace=True)
(fc2): Conv2d(128, 2048, kernel_size=(1, 1), stride=(1, 1))
(sigmoid): Sigmoid()
)
)
)
)
(decoder): FPNDecoder(
(p5): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))
(p4): FPNBlock(
(skip_conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))
)
(p3): FPNBlock(
(skip_conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
)
(p2): FPNBlock(
(skip_conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
)
(seg_blocks): ModuleList(
(0): SegmentationBlock(
(block): Sequential(
(0): Conv3x3GNReLU(
(block): Sequential(
(0): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): GroupNorm(32, 128, eps=1e-05, affine=True)
(2): ReLU(inplace=True)
)
)
(1): Conv3x3GNReLU(
(block): Sequential(
(0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): GroupNorm(32, 128, eps=1e-05, affine=True)
(2): ReLU(inplace=True)
)
)
(2): Conv3x3GNReLU(
(block): Sequential(
(0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): GroupNorm(32, 128, eps=1e-05, affine=True)
(2): ReLU(inplace=True)
)
)
)
)
(1): SegmentationBlock(
(block): Sequential(
(0): Conv3x3GNReLU(
(block): Sequential(
(0): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): GroupNorm(32, 128, eps=1e-05, affine=True)
(2): ReLU(inplace=True)
)
)
(1): Conv3x3GNReLU(
(block): Sequential(
(0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): GroupNorm(32, 128, eps=1e-05, affine=True)
(2): ReLU(inplace=True)
)
)
)
)
(2-3): 2 x SegmentationBlock(
(block): Sequential(
(0): Conv3x3GNReLU(
(block): Sequential(
(0): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): GroupNorm(32, 128, eps=1e-05, affine=True)
(2): ReLU(inplace=True)
)
)
)
)
)
(merge): MergeBlock()
(dropout): Dropout2d(p=0.2, inplace=True)
)
(segmentation_head): SegmentationHead(
(0): Conv2d(128, 6, kernel_size=(1, 1), stride=(1, 1))
(1): UpsamplingBilinear2d(scale_factor=4.0, mode='bilinear')
(2): Activation(
(activation): Sigmoid()
)
)
)
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