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Task500_Glacier_zonefronts/ensemble/eval_results.json

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Task500_Glacier_zonefronts/fold_0/debug.json

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+    "only_keep_largest_connected_component": "None",
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+}

Task500_Glacier_zonefronts/fold_0/distance_errors.txt

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Task500_Glacier_zonefronts/fold_0/eval_results.json

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Task500_Glacier_zonefronts/fold_0/model_final_checkpoint.model

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+size 420725142

Task500_Glacier_zonefronts/fold_0/model_final_checkpoint.model.pkl

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Task500_Glacier_zonefronts/fold_0/plans.pkl

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Task500_Glacier_zonefronts/fold_0/training_log_2022_4_6_13_42_45.txt

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+Starting... 
+2022-04-06 13:42:45.151882: Creating new 5-fold cross-validation split... 
+2022-04-06 13:42:45.161848: Desired fold for training: 0 
+2022-04-06 13:42:45.162639: This split has 447 training and 112 validation cases. 
+2022-04-06 13:46:30.086602: TRAINING KEYS:
+ odict_keys(['Crane_2002-11-09_ERS_20_2_061', 'Crane_2002-11-18_ERS_20_3_195', 'Crane_2003-01-18_ERS_20_1_061', 'Crane_2006-11-27_ERS_20_1_195', 'Crane_2007-02-05_ERS_20_1_195', 'Crane_2007-03-31_ENVISAT_20_3_467', 'Crane_2007-05-05_ENVISAT_20_1_467', 'Crane_2007-06-09_ENVISAT_20_1_467', 'Crane_2007-07-14_ENVISAT_20_1_467', 'Crane_2007-11-21_PALSAR_17_2_135', 'Crane_2008-01-05_ENVISAT_20_2_467', 'Crane_2008-10-13_TSX_7_3_034', 'Crane_2008-10-24_TSX_7_2_034', 'Crane_2008-11-04_TSX_7_1_034', 'Crane_2008-11-23_PALSAR_17_2_135', 'Crane_2009-09-29_PALSAR_17_1_137', 'Crane_2009-11-02_TSX_7_3_034', 'Crane_2009-11-13_TSX_7_1_034', 'Crane_2010-05-29_ENVISAT_20_3_467', 'Crane_2010-07-03_ENVISAT_20_2_467', 'Crane_2010-09-15_PALSAR_17_4_138', 'Crane_2010-10-14_PALSAR_17_2_135', 'Crane_2010-11-05_TSX_7_2_110', 'Crane_2010-11-16_TSX_7_2_110', 'Crane_2010-12-08_TSX_7_1_110', 'Crane_2010-12-14_TSX_7_1_034', 'Crane_2010-12-19_TSX_7_1_110', 'Crane_2010-12-25_TSX_7_1_034', 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'Jorum_2019-09-22_S1_20_3_009', 'Jorum_2020-01-08_S1_20_3_009', 'Jorum_2020-03-08_S1_20_3_009', 'SI_1995-11-13_ERS_20_3_109', 'SI_1995-11-14_ERS_20_4_118', 'SI_1995-11-15_ERS_20_3_118', 'SI_1995-11-16_ERS_20_4_152', 'SI_1996-01-23_ERS_20_5_109', 'SI_1996-01-24_ERS_20_4_118', 'SI_1996-02-12_ERS_20_2_390', 'SI_1996-02-29_ERS_20_2_152', 'SI_1996-03-18_ERS_20_2_390', 'SI_1997-01-27_ERS_20_4_390', 'SI_2000-09-10_RSAT_20_5_282', 'SI_2000-09-19_RSAT_20_5_063', 'SI_2000-10-04_RSAT_20_5_282', 'SI_2000-10-13_RSAT_20_5_063', 'SI_2002-01-21_ERS_20_2_390', 'SI_2002-02-25_ERS_20_3_390', 'SI_2002-11-12_ERS_20_4_109', 'SI_2002-12-21_ERS_20_3_161', 'SI_2003-12-05_ENVISAT_20_4_152', 'SI_2004-07-02_ENVISAT_20_4_152', 'SI_2004-08-06_ENVISAT_20_4_152', 'SI_2004-09-13_RSAT_12_5_282', 'SI_2004-10-15_ERS_20_5_152', 'SI_2004-11-09_RSAT_12_5_063', 'SI_2004-11-19_ERS_20_4_152', 'SI_2004-11-24_RSAT_12_4_282', 'SI_2004-12-18_RSAT_12_4_282', 'SI_2004-12-24_ENVISAT_20_5_152', 'SI_2004-12-24_ERS_20_5_152', 'SI_2005-01-28_ERS_20_5_152', 'SI_2005-02-28_RSAT_12_5_282', 'SI_2005-03-04_ENVISAT_20_3_152', 'SI_2005-04-08_ENVISAT_20_2_152', 'SI_2005-04-17_RSAT_12_3_282', 'SI_2005-05-11_RSAT_12_2_282', 'SI_2005-07-22_ENVISAT_20_2_152', 'SI_2005-08-26_ENVISAT_20_2_152', 'SI_2006-01-06_RSAT_12_2_282', 'SI_2006-02-23_RSAT_12_1_282', 'SI_2006-03-05_ENVISAT_20_2_381', 'SI_2006-03-19_RSAT_12_2_282', 'SI_2006-04-12_RSAT_12_1_282', 'SI_2006-05-06_RSAT_12_3_282', 'SI_2006-07-03_PALSAR_17_3_135', 'SI_2006-07-23_ENVISAT_20_4_381', 'SI_2006-08-10_RSAT_20_3_282', 'SI_2006-09-03_RSAT_12_2_282', 'SI_2006-09-03_RSAT_20_2_282', 'SI_2006-10-18_ERS_20_4_118', 'SI_2006-11-21_ERS_20_3_109', 'SI_2007-01-01_RSAT_12_3_282', 'SI_2007-01-14_ENVISAT_20_3_381', 'SI_2007-03-14_RSAT_12_2_282', 'SI_2007-03-25_ENVISAT_20_3_381', 'SI_2007-05-01_RSAT_12_2_282', 'SI_2007-06-03_ENVISAT_20_3_381', 'SI_2007-07-08_ENVISAT_20_3_381', 'SI_2007-09-16_ENVISAT_20_3_381', 'SI_2007-10-21_ENVISAT_20_3_381', 'SI_2007-12-03_RSAT_12_4_282', 'SI_2007-12-27_RSAT_12_2_282', 'SI_2008-01-20_RSAT_12_2_282', 'SI_2008-04-13_ENVISAT_20_2_381', 'SI_2008-05-18_ENVISAT_20_3_381', 'SI_2008-07-27_ENVISAT_20_4_381', 'SI_2008-08-31_ENVISAT_20_4_381', 'SI_2009-02-22_ENVISAT_20_2_381', 'SI_2009-03-29_ENVISAT_20_4_381', 'SI_2009-07-12_ENVISAT_20_3_381', 'SI_2009-08-16_ENVISAT_20_3_381', 'SI_2010-01-13_PALSAR_17_2_127', 'SI_2010-01-22_ERS_20_3_152', 'SI_2010-02-26_ERS_20_2_152', 'SI_2010-04-02_ERS_20_2_152', 'SI_2010-10-16_PALSAR_17_3_127', 'SI_2010-12-01_PALSAR_17_3_127', 'SI_2011-01-16_PALSAR_17_2_127', 'SI_2011-03-03_PALSAR_17_2_127', 'SI_2011-06-14_TSX_7_1_125', 'SI_2011-07-06_TSX_7_1_125', 'SI_2011-08-19_TSX_7_1_125', 'SI_2011-09-10_TSX_7_1_125', 'SI_2011-09-21_TSX_7_1_125', 'SI_2011-12-29_TSX_7_1_125', 'SI_2012-01-09_TSX_7_1_125', 'SI_2012-03-04_TSX_7_1_125', 'SI_2012-03-15_TSX_7_1_125', 'SI_2012-03-26_TSX_7_1_125', 'SI_2012-05-20_TSX_7_1_125', 'SI_2012-06-22_TSX_7_1_125', 'SI_2012-08-05_TSX_7_1_125', 'SI_2012-08-16_TSX_7_1_125', 'SI_2013-07-12_TSX_7_1_125', 'SI_2013-08-14_TSX_7_1_125', 'SI_2013-09-16_TSX_7_1_125', 'SI_2013-10-19_TSX_7_1_125', 'SI_2013-10-28_TSX_7_1_104', 'SI_2013-11-21_TSX_7_1_125', 'SI_2014-01-04_TSX_7_2_125', 'SI_2014-04-02_TSX_7_1_125', 'SI_2014-12-11_TSX_7_4_125']) 
+2022-04-06 13:46:30.089200: VALIDATION KEYS:
+ odict_keys(['Crane_2007-09-22_ENVISAT_20_1_467', 'Crane_2007-09-24_PALSAR_17_1_137', 'Crane_2007-10-27_ENVISAT_20_2_467', 'Crane_2010-11-27_TSX_7_1_110', 'Crane_2010-11-29_PALSAR_17_1_135', 'Crane_2010-12-30_TSX_7_1_110', 'Crane_2011-06-24_TSX_7_1_110', 'Crane_2012-07-25_TSX_7_2_125', 'Crane_2012-08-10_TSX_7_3_034', 'Crane_2012-11-06_TSX_7_2_034', 'Crane_2013-03-07_TSX_7_2_034', 'Crane_2013-03-29_TSX_7_2_034', 'Crane_2013-06-30_TSX_7_2_110', 'Crane_2014-02-11_TSX_7_3_034', 'DBE_1996-01-25_ERS_20_1_152', 'DBE_2000-09-10_RSAT_20_3_282', 'DBE_2004-04-07_ENVISAT_20_3_424', 'DBE_2004-06-16_ENVISAT_20_3_424', 'DBE_2004-07-21_ENVISAT_20_3_424', 'DBE_2004-11-09_RSAT_12_3_063', 'DBE_2004-12-08_ENVISAT_20_5_424', 'DBE_2004-12-08_ERS_20_5_424', 'DBE_2005-04-27_ENVISAT_20_2_424', 'DBE_2005-08-10_ENVISAT_20_2_424', 'DBE_2006-04-12_ENVISAT_20_1_424', 'DBE_2006-06-21_ENVISAT_20_2_424', 'DBE_2006-06-21_PALSAR_17_2_137', 'DBE_2007-08-15_ENVISAT_20_5_424', 'DBE_2007-09-19_ENVISAT_20_6_424', 'DBE_2008-10-14_TSX_7_3_049', 'DBE_2008-12-29_PALSAR_17_5_129', 'DBE_2009-10-23_TSX_7_2_049', 'DBE_2010-01-22_ERS_20_6_152', 'DBE_2010-04-21_ENVISAT_20_6_424', 'DBE_2010-10-16_PALSAR_17_4_127', 'DBE_2010-11-12_TSX_7_4_049', 'DBE_2010-12-06_PALSAR_17_3_132', 'DBE_2012-03-14_TSX_7_1_110', 'DBE_2012-06-10_TSX_7_1_110', 'DBE_2012-08-15_TSX_7_1_110', 'DBE_2013-01-16_TSX_7_5_110', 'DBE_2013-05-28_TSX_7_1_110', 'DBE_2013-06-19_TSX_7_1_110', 'DBE_2013-11-09_TSX_7_1_110', 'DBE_2013-12-12_TSX_7_3_110', 'JAC_2009-04-27_TSX_6_1_005', 'JAC_2009-06-10_TSX_6_1_005', 'JAC_2009-10-31_TSX_6_1_005', 'JAC_2009-12-03_TSX_6_1_005', 'JAC_2010-09-15_TSX_6_1_005', 'JAC_2010-12-01_TSX_6_1_005', 'JAC_2010-12-23_TSX_6_1_005', 'JAC_2011-05-15_TSX_6_1_005', 'JAC_2011-06-17_TSX_6_1_005', 'JAC_2012-03-07_TSX_6_1_005', 'JAC_2012-04-20_TSX_6_1_005', 'JAC_2012-05-24_TSX_6_1_027', 'JAC_2012-08-20_TSX_6_1_027', 'JAC_2012-09-11_TSX_6_1_027', 'JAC_2013-04-07_TSX_6_1_005', 'JAC_2013-05-22_TSX_6_1_027', 'JAC_2013-06-13_TSX_6_1_027', 'JAC_2013-09-08_TSX_6_1_005', 'JAC_2013-12-17_TSX_6_1_027', 'JAC_2014-01-29_TSX_6_1_005', 'JAC_2014-04-16_TSX_6_1_005', 'JAC_2014-07-03_TSX_6_1_027', 'JAC_2014-07-25_TSX_6_1_027', 'JAC_2014-12-25_TSX_6_1_005', 'JAC_2015-01-05_TSX_6_1_005', 'JAC_2015-05-06_TSX_6_1_005', 'JAC_2015-06-08_TSX_6_1_005', 'JAC_2015-07-11_TSX_6_1_005', 'JAC_2015-11-20_TSX_6_1_005', 'JAC_2015-12-12_TSX_6_1_005', 'Jorum_2003-01-27_ERS_20_1_195', 'Jorum_2007-05-05_ENVISAT_20_2_467', 'Jorum_2007-06-09_ENVISAT_20_2_467', 'Jorum_2008-11-23_PALSAR_17_2_135', 'Jorum_2010-12-08_TSX_7_2_110', 'Jorum_2010-12-19_TSX_7_1_110', 'Jorum_2010-12-25_TSX_7_1_034', 'Jorum_2012-05-14_TSX_7_2_034', 'Jorum_2012-06-16_TSX_7_2_034', 'Jorum_2012-07-30_TSX_7_2_034', 'Jorum_2013-02-24_TSX_7_3_034', 'Jorum_2013-07-22_TSX_7_1_110', 'Jorum_2013-08-02_TSX_7_2_110', 'Jorum_2013-11-26_TSX_7_5_034', 'Jorum_2014-02-11_TSX_7_2_034', 'Jorum_2017-06-16_S1_20_2_009', 'Jorum_2020-06-12_S1_20_3_009', 'SI_1996-01-22_ERS_20_3_109', 'SI_1997-03-03_ERS_20_4_390', 'SI_1999-11-07_ERS_20_2_381', 'SI_2000-02-20_ERS_20_3_381', 'SI_2003-01-25_ERS_20_3_161', 'SI_2004-02-13_ENVISAT_20_4_152', 'SI_2004-12-03_RSAT_12_5_063', 'SI_2006-01-29_ENVISAT_20_3_381', 'SI_2006-05-18_PALSAR_17_2_135', 'SI_2006-08-27_ENVISAT_20_3_381', 'SI_2007-01-25_RSAT_12_1_282', 'SI_2011-07-28_TSX_7_1_125', 'SI_2011-11-26_TSX_7_2_125', 'SI_2012-04-28_TSX_7_1_125', 'SI_2012-07-03_TSX_7_1_125', 'SI_2013-11-08_TSX_7_2_104', 'SI_2013-12-02_TSX_7_1_125', 'SI_2013-12-13_TSX_7_2_125', 'SI_2014-03-22_TSX_7_1_125', 'SI_2014-12-22_TSX_7_2_125']) 
+2022-04-06 13:48:05.212889: lr: 0.01 
+2022-04-06 13:48:13.483644: Unable to plot network architecture: 
+2022-04-06 13:48:13.507257: No module named 'hiddenlayer' 
+2022-04-06 13:48:13.519965: 
+printing the network instead:
+ 
+2022-04-06 13:48:13.530723: Generic_UNet(
+  (conv_blocks_localization): ModuleList(
+    (0): Sequential(
+      (0): StackedConvLayers(
+        (blocks): Sequential(
+          (0): ConvDropoutNormNonlin(
+            (conv): Conv2d(960, 480, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])
+            (instnorm): InstanceNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
+          )
+        )
+      )
+      (1): StackedConvLayers(
+        (blocks): Sequential(
+          (0): ConvDropoutNormNonlin(
+            (conv): Conv2d(480, 480, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])
+            (instnorm): InstanceNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
+          )
+        )
+      )
+    )
+    (1): Sequential(
+      (0): StackedConvLayers(
+        (blocks): Sequential(
+          (0): ConvDropoutNormNonlin(
+            (conv): Conv2d(960, 480, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])
+            (instnorm): InstanceNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
+          )
+        )
+      )
+      (1): StackedConvLayers(
+        (blocks): Sequential(
+          (0): ConvDropoutNormNonlin(
+            (conv): Conv2d(480, 480, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])
+            (instnorm): InstanceNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
+          )
+        )
+      )
+    )
+    (2): Sequential(
+      (0): StackedConvLayers(
+        (blocks): Sequential(
+          (0): ConvDropoutNormNonlin(
+            (conv): Conv2d(960, 480, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])
+            (instnorm): InstanceNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
+          )
+        )
+      )
+      (1): StackedConvLayers(
+        (blocks): Sequential(
+          (0): ConvDropoutNormNonlin(
+            (conv): Conv2d(480, 480, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])
+            (instnorm): InstanceNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
+          )
+        )
+      )
+    )
+    (3): Sequential(
+      (0): StackedConvLayers(
+        (blocks): Sequential(
+          (0): ConvDropoutNormNonlin(
+            (conv): Conv2d(960, 480, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])
+            (instnorm): InstanceNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
+          )
+        )
+      )
+      (1): StackedConvLayers(
+        (blocks): Sequential(
+          (0): ConvDropoutNormNonlin(
+            (conv): Conv2d(480, 480, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])
+            (instnorm): InstanceNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
+          )
+        )
+      )
+    )
+    (4): Sequential(
+      (0): StackedConvLayers(
+        (blocks): Sequential(
+          (0): ConvDropoutNormNonlin(
+            (conv): Conv2d(512, 256, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])
+            (instnorm): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
+          )
+        )
+      )
+      (1): StackedConvLayers(
+        (blocks): Sequential(
+          (0): ConvDropoutNormNonlin(
+            (conv): Conv2d(256, 256, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])
+            (instnorm): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
+          )
+        )
+      )
+    )
+    (5): Sequential(
+      (0): StackedConvLayers(
+        (blocks): Sequential(
+          (0): ConvDropoutNormNonlin(
+            (conv): Conv2d(256, 128, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])
+            (instnorm): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
+          )
+        )
+      )
+      (1): StackedConvLayers(
+        (blocks): Sequential(
+          (0): ConvDropoutNormNonlin(
+            (conv): Conv2d(128, 128, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])
+            (instnorm): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
+          )
+        )
+      )
+    )
+    (6): Sequential(
+      (0): StackedConvLayers(
+        (blocks): Sequential(
+          (0): ConvDropoutNormNonlin(
+            (conv): Conv2d(128, 64, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])
+            (instnorm): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
+          )
+        )
+      )
+      (1): StackedConvLayers(
+        (blocks): Sequential(
+          (0): ConvDropoutNormNonlin(
+            (conv): Conv2d(64, 64, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])
+            (instnorm): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
+          )
+        )
+      )
+    )
+    (7): Sequential(
+      (0): StackedConvLayers(
+        (blocks): Sequential(
+          (0): ConvDropoutNormNonlin(
+            (conv): Conv2d(64, 32, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])
+            (instnorm): InstanceNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
+          )
+        )
+      )
+      (1): StackedConvLayers(
+        (blocks): Sequential(
+          (0): ConvDropoutNormNonlin(
+            (conv): Conv2d(32, 32, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])
+            (instnorm): InstanceNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
+          )
+        )
+      )
+    )
+  )
+  (conv_blocks_context): ModuleList(
+    (0): StackedConvLayers(
+      (blocks): Sequential(
+        (0): ConvDropoutNormNonlin(
+          (conv): Conv2d(1, 32, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])
+          (instnorm): InstanceNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
+        )
+        (1): ConvDropoutNormNonlin(
+          (conv): Conv2d(32, 32, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])
+          (instnorm): InstanceNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
+        )
+      )
+    )
+    (1): StackedConvLayers(
+      (blocks): Sequential(
+        (0): ConvDropoutNormNonlin(
+          (conv): Conv2d(32, 64, kernel_size=[3, 3], stride=[2, 2], padding=[1, 1])
+          (instnorm): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
+        )
+        (1): ConvDropoutNormNonlin(
+          (conv): Conv2d(64, 64, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])
+          (instnorm): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
+        )
+      )
+    )
+    (2): StackedConvLayers(
+      (blocks): Sequential(
+        (0): ConvDropoutNormNonlin(
+          (conv): Conv2d(64, 128, kernel_size=[3, 3], stride=[2, 2], padding=[1, 1])
+          (instnorm): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
+        )
+        (1): ConvDropoutNormNonlin(
+          (conv): Conv2d(128, 128, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])
+          (instnorm): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
+        )
+      )
+    )
+    (3): StackedConvLayers(
+      (blocks): Sequential(
+        (0): ConvDropoutNormNonlin(
+          (conv): Conv2d(128, 256, kernel_size=[3, 3], stride=[2, 2], padding=[1, 1])
+          (instnorm): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
+        )
+        (1): ConvDropoutNormNonlin(
+          (conv): Conv2d(256, 256, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])
+          (instnorm): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
+        )
+      )
+    )
+    (4): StackedConvLayers(
+      (blocks): Sequential(
+        (0): ConvDropoutNormNonlin(
+          (conv): Conv2d(256, 480, kernel_size=[3, 3], stride=[2, 2], padding=[1, 1])
+          (instnorm): InstanceNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
+        )
+        (1): ConvDropoutNormNonlin(
+          (conv): Conv2d(480, 480, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])
+          (instnorm): InstanceNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
+        )
+      )
+    )
+    (5): StackedConvLayers(
+      (blocks): Sequential(
+        (0): ConvDropoutNormNonlin(
+          (conv): Conv2d(480, 480, kernel_size=[3, 3], stride=[2, 2], padding=[1, 1])
+          (instnorm): InstanceNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
+        )
+        (1): ConvDropoutNormNonlin(
+          (conv): Conv2d(480, 480, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])
+          (instnorm): InstanceNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
+        )
+      )
+    )
+    (6): StackedConvLayers(
+      (blocks): Sequential(
+        (0): ConvDropoutNormNonlin(
+          (conv): Conv2d(480, 480, kernel_size=[3, 3], stride=[2, 2], padding=[1, 1])
+          (instnorm): InstanceNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
+        )
+        (1): ConvDropoutNormNonlin(
+          (conv): Conv2d(480, 480, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])
+          (instnorm): InstanceNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
+        )
+      )
+    )
+    (7): StackedConvLayers(
+      (blocks): Sequential(
+        (0): ConvDropoutNormNonlin(
+          (conv): Conv2d(480, 480, kernel_size=[3, 3], stride=[2, 2], padding=[1, 1])
+          (instnorm): InstanceNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
+        )
+        (1): ConvDropoutNormNonlin(
+          (conv): Conv2d(480, 480, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])
+          (instnorm): InstanceNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
+        )
+      )
+    )
+    (8): Sequential(
+      (0): StackedConvLayers(
+        (blocks): Sequential(
+          (0): ConvDropoutNormNonlin(
+            (conv): Conv2d(480, 480, kernel_size=[3, 3], stride=[2, 2], padding=[1, 1])
+            (instnorm): InstanceNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
+          )
+        )
+      )
+      (1): StackedConvLayers(
+        (blocks): Sequential(
+          (0): ConvDropoutNormNonlin(
+            (conv): Conv2d(480, 480, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])
+            (instnorm): InstanceNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
+          )
+        )
+      )
+    )
+  )
+  (td): ModuleList()
+  (tu): ModuleList(
+    (0): ConvTranspose2d(480, 480, kernel_size=[2, 2], stride=[2, 2], bias=False)
+    (1): ConvTranspose2d(480, 480, kernel_size=[2, 2], stride=[2, 2], bias=False)
+    (2): ConvTranspose2d(480, 480, kernel_size=[2, 2], stride=[2, 2], bias=False)
+    (3): ConvTranspose2d(480, 480, kernel_size=[2, 2], stride=[2, 2], bias=False)
+    (4): ConvTranspose2d(480, 256, kernel_size=[2, 2], stride=[2, 2], bias=False)
+    (5): ConvTranspose2d(256, 128, kernel_size=[2, 2], stride=[2, 2], bias=False)
+    (6): ConvTranspose2d(128, 64, kernel_size=[2, 2], stride=[2, 2], bias=False)
+    (7): ConvTranspose2d(64, 32, kernel_size=[2, 2], stride=[2, 2], bias=False)
+  )
+  (seg_outputs): ModuleList(
+    (0): Conv2d(480, 5, kernel_size=(1, 1), stride=(1, 1), bias=False)
+    (1): Conv2d(480, 5, kernel_size=(1, 1), stride=(1, 1), bias=False)
+    (2): Conv2d(480, 5, kernel_size=(1, 1), stride=(1, 1), bias=False)
+    (3): Conv2d(480, 5, kernel_size=(1, 1), stride=(1, 1), bias=False)
+    (4): Conv2d(256, 5, kernel_size=(1, 1), stride=(1, 1), bias=False)
+    (5): Conv2d(128, 5, kernel_size=(1, 1), stride=(1, 1), bias=False)
+    (6): Conv2d(64, 5, kernel_size=(1, 1), stride=(1, 1), bias=False)
+    (7): Conv2d(32, 5, kernel_size=(1, 1), stride=(1, 1), bias=False)
+  )
+) 
+2022-04-06 13:48:13.561954: 
+ 
+2022-04-06 13:48:13.574286: svmem(total=99932160000, available=89355911168, percent=10.6, used=7974862848, free=74463080448, active=7446126592, inactive=15933480960, buffers=14180352, cached=17480036352, shared=786059264, slab=754434048) 
+2022-04-06 13:48:13.587833: 
+epoch:  0 
+2022-04-06 13:49:16.764398: train loss : 1.2657 
+2022-04-06 13:49:29.162044: validation loss: 1.0054 
+2022-04-06 13:49:29.184389: Average global foreground Dice: [0.0002, 0.6649, 0.0923, 0.0013] 
+2022-04-06 13:49:29.186826: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2022-04-06 13:49:30.284652: lr: 0.0 
+2022-04-06 13:49:30.286498: saving scheduled checkpoint file... 
+2022-04-06 13:49:30.449905: saving checkpoint... 
+2022-04-06 13:49:35.022912: done, saving took 4.73 seconds 
+2022-04-06 13:49:35.052222: done 
+2022-04-06 13:49:35.057490: This epoch took 81.447241 s
+ 
+2022-04-06 13:49:35.205750: saving checkpoint... 
+2022-04-06 13:49:39.567125: done, saving took 4.51 seconds 
+2022-04-06 13:59:38.934049: finished prediction 
+2022-04-06 13:59:38.935330: evaluation of raw predictions 

Task500_Glacier_zonefronts/fold_1/debug.json

@@ -0,0 +1,87 @@
+{
+    "all_tr_losses": "[]",
+    "all_val_eval_metrics": "[]",
+    "all_val_losses": "[]",
+    "all_val_losses_tr_mode": "[]",
+    "also_val_in_tr_mode": "False",
+    "amp_grad_scaler": "None",
+    "base_num_features": "32",
+    "basic_generator_patch_size": "[1505 1505]",
+    "batch_dice": "True",
+    "batch_size": "2",
+    "best_MA_tr_loss_for_patience": "None",
+    "best_epoch_based_on_MA_tr_loss": "None",
+    "best_val_eval_criterion_MA": "None",
+    "classes": "[[1, 2, 3, 4]]",
+    "conv_per_stage": "2",
+    "data_aug_params": "{'selected_data_channels': None, 'selected_seg_channels': [0], 'do_elastic': False, 'elastic_deform_alpha': (0.0, 200.0), 'elastic_deform_sigma': (9.0, 13.0), 'p_eldef': 0.2, 'do_scaling': True, 'scale_range': (0.7, 1.4), 'independent_scale_factor_for_each_axis': False, 'p_independent_scale_per_axis': 1, 'p_scale': 0.2, 'do_rotation': True, 'rotation_x': (-3.141592653589793, 3.141592653589793), 'rotation_y': (-0.0, 0.0), 'rotation_z': (-0.0, 0.0), 'rotation_p_per_axis': 1, 'p_rot': 0.2, 'random_crop': False, 'random_crop_dist_to_border': None, 'do_gamma': True, 'gamma_retain_stats': True, 'gamma_range': (0.7, 1.5), 'p_gamma': 0.3, 'do_mirror': True, 'mirror_axes': (0, 1), 'dummy_2D': False, 'mask_was_used_for_normalization': OrderedDict([(0, False)]), 'border_mode_data': 'constant', 'all_segmentation_labels': None, 'move_last_seg_chanel_to_data': False, 'cascade_do_cascade_augmentations': False, 'cascade_random_binary_transform_p': 0.4, 'cascade_random_binary_transform_p_per_label': 1, 'cascade_random_binary_transform_size': (1, 8), 'cascade_remove_conn_comp_p': 0.2, 'cascade_remove_conn_comp_max_size_percent_threshold': 0.15, 'cascade_remove_conn_comp_fill_with_other_class_p': 0.0, 'do_additive_brightness': False, 'additive_brightness_p_per_sample': 0.15, 'additive_brightness_p_per_channel': 0.5, 'additive_brightness_mu': 0.0, 'additive_brightness_sigma': 0.1, 'num_threads': 12, 'num_cached_per_thread': 2, 'patch_size_for_spatialtransform': array([1280, 1024])}",
+    "dataset_directory": "/home/woody/iwi5/iwi5039h/nnUNet_data/nnUNet_preprocessed/Task500_Glacier_zonefronts",
+    "deep_supervision_scales": "[[1, 1, 1], [0.5, 0.5], [0.25, 0.25], [0.125, 0.125], [0.0625, 0.0625], [0.03125, 0.03125], [0.015625, 0.015625], [0.0078125, 0.0078125]]",
+    "deterministic": "False",
+    "dl_tr": "<nnunet.training.dataloading.dataset_loading.DataLoader2D object at 0x7f7ed281c520>",
+    "dl_val": "<nnunet.training.dataloading.dataset_loading.DataLoader2D object at 0x7f7ed2401f10>",
+    "do_dummy_2D_aug": "False",
+    "ds_loss_weights": "[0.50393701 0.2519685  0.12598425 0.06299213 0.03149606 0.01574803\n 0.00787402 0.        ]",
+    "epoch": "0",
+    "experiment_name": "nnUNetTrainerV2",
+    "fold": "1",
+    "folder_with_preprocessed_data": "/home/woody/iwi5/iwi5039h/nnUNet_data/nnUNet_preprocessed/Task500_Glacier_zonefronts/nnUNetData_plans_v2.1_2D_stage0",
+    "fp16": "True",
+    "gt_niftis_folder": "/home/woody/iwi5/iwi5039h/nnUNet_data/nnUNet_preprocessed/Task500_Glacier_zonefronts/gt_segmentations",
+    "inference_pad_border_mode": "constant",
+    "inference_pad_kwargs": "{'constant_values': 0}",
+    "init_args": "('/home/woody/iwi5/iwi5039h/nnUNet_data/nnUNet_preprocessed/Task500_Glacier_zonefronts/nnUNetPlansv2.1_plans_2D.pkl', 1, '/home/woody/iwi5/iwi5039h/nnUNet_data/RESULTS_FOLDER/nnUNet/2d/Task500_Glacier_zonefronts/nnUNetTrainerV2__nnUNetPlansv2.1', '/home/woody/iwi5/iwi5039h/nnUNet_data/nnUNet_preprocessed/Task500_Glacier_zonefronts', True, 0, True, False, True)",
+    "initial_lr": "0.01",
+    "log_file": "/home/woody/iwi5/iwi5039h/nnUNet_data/RESULTS_FOLDER/nnUNet/2d/Task500_Glacier_zonefronts/nnUNetTrainerV2__nnUNetPlansv2.1/fold_1/training_log_2022_4_11_16_28_44.txt",
+    "lr_scheduler": "None",
+    "lr_scheduler_eps": "0.001",
+    "lr_scheduler_patience": "30",
+    "lr_threshold": "1e-06",
+    "max_num_epochs": "500",
+    "min_region_size_per_class": "None",
+    "min_size_per_class": "None",
+    "net_conv_kernel_sizes": "[[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]]",
+    "net_num_pool_op_kernel_sizes": "[[2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]]",
+    "net_pool_per_axis": "[8, 8]",
+    "normalization_schemes": "OrderedDict([(0, 'nonCT')])",
+    "num_batches_per_epoch": "250",
+    "num_classes": "[5]",
+    "num_input_channels": "1",
+    "num_val_batches_per_epoch": "50",
+    "online_eval_fn": "[]",
+    "online_eval_foreground_dc": "[]",
+    "online_eval_fp": "[]",
+    "online_eval_tp": "[]",
+    "only_keep_largest_connected_component": "None",
+    "optimizer": "SGD (\nParameter Group 0\n    dampening: 0\n    lr: 0.01\n    momentum: 0.99\n    nesterov: True\n    weight_decay: 3e-05\n)",
+    "output_folder": "/home/woody/iwi5/iwi5039h/nnUNet_data/RESULTS_FOLDER/nnUNet/2d/Task500_Glacier_zonefronts/nnUNetTrainerV2__nnUNetPlansv2.1/fold_1",
+    "output_folder_base": "/home/woody/iwi5/iwi5039h/nnUNet_data/RESULTS_FOLDER/nnUNet/2d/Task500_Glacier_zonefronts/nnUNetTrainerV2__nnUNetPlansv2.1",
+    "oversample_foreground_percent": "0.33",
+    "pad_all_sides": "None",
+    "patch_size": "[1280 1024]",
+    "patience": "50",
+    "pin_memory": "True",
+    "plans_file": "/home/woody/iwi5/iwi5039h/nnUNet_data/nnUNet_preprocessed/Task500_Glacier_zonefronts/nnUNetPlansv2.1_plans_2D.pkl",
+    "regions_class_order": "None",
+    "save_best_checkpoint": "True",
+    "save_every": "1",
+    "save_final_checkpoint": "True",
+    "save_intermediate_checkpoints": "True",
+    "save_latest_only": "True",
+    "stage": "0",
+    "threeD": "False",
+    "tr_gen": "<batchgenerators.dataloading.multi_threaded_augmenter.MultiThreadedAugmenter object at 0x7f7ed242aeb0>",
+    "train_loss_MA": "None",
+    "train_loss_MA_alpha": "0.93",
+    "train_loss_MA_eps": "0.0005",
+    "transpose_backward": "[0, 1, 2]",
+    "transpose_forward": "[0, 1, 2]",
+    "unpack_data": "True",
+    "use_mask_for_norm": "OrderedDict([(0, False)])",
+    "use_progress_bar": "True",
+    "val_eval_criterion_MA": "None",
+    "val_eval_criterion_alpha": "0.9",
+    "val_gen": "<batchgenerators.dataloading.multi_threaded_augmenter.MultiThreadedAugmenter object at 0x7f7ed242a6a0>",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}

Task500_Glacier_zonefronts/fold_1/eval_results.json

@@ -0,0 +1 @@
+{"Zone_Segmentation": {"Zone_Precision": {"Average_Precision": 0.8699089860272374, "Average_Precision_NA_Area": 0.9898915319462672, "Average_Precision_Stone": 0.8194329524324107, "Average_Precision_Glacier": 0.7387079917131683, "Average_Precision_Ocean_and_Ice_Melange": 0.9316034680171035}, "Zone_Recall": {"Average_Recall": 0.794893555935646, "Average_Recall_NA_Area": 0.8302311418132979, "Average_Recall_Stone": 0.5766870945716255, "Average_Recall_Glacier": 0.9149633884152245, "Average_Recall_Ocean_and_Ice_Melange": 0.857692598942436}, "Zone_F1": {"Average_F1 Score": 0.8072375534066539, "Average_F1 Score_NA_Area": 0.8953234616091149, "Average_F1 Score_Stone": 0.6581139098738448, "Average_F1 Score_Glacier": 0.8136732978345352, "Average_F1 Score_Ocean_and_Ice_Melange": 0.8618395443091198}, "Zone_IoU": {"Average_IoU": 0.7119578319540647, "Average_IoU_NA_Area": 0.8230900449268368, "Average_IoU_Stone": 0.514943299403505, "Average_IoU_Glacier": 0.6946805004515082, 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+    "train_loss_MA_eps": "0.0005",
+    "transpose_backward": "[0, 1, 2]",
+    "transpose_forward": "[0, 1, 2]",
+    "unpack_data": "True",
+    "use_mask_for_norm": "OrderedDict([(0, False)])",
+    "use_progress_bar": "True",
+    "val_eval_criterion_MA": "None",
+    "val_eval_criterion_alpha": "0.9",
+    "val_gen": "<batchgenerators.dataloading.multi_threaded_augmenter.MultiThreadedAugmenter object at 0x7fd53ab19be0>",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}

Task500_Glacier_zonefronts/fold_4/eval_results.json

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