pytorch smp 에서 SSL오류 내면서 모델 다운 못 받을 때

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_context

이걸 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()
    )
  )
)