Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_0/progress.png filter=lfs diff=lfs merge=lfs -text
+Dataset542/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_0/progress.png filter=lfs diff=lfs merge=lfs -text
+Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_0/progress.png filter=lfs diff=lfs merge=lfs -text

Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/dataset.json

@@ -0,0 +1,14 @@
+{
+    "labels": {
+        "label_001": "1",
+        "background": 0
+    },
+    "channel_names": {
+        "0": "CT_zscore_synthrad"
+    },
+    "numTraining": 309,
+    "file_ending": ".nii.gz",
+    "test_set": [
+        "case_0000"
+    ]
+}

Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/dataset_fingerprint.json

@@ -0,0 +1,3108 @@
+{
+    "foreground_intensity_properties_per_channel": {
+        "0": {
+            "max": 3071.0,
+            "mean": -239.3903045654297,
+            "median": -185.0,
+            "min": -1024.0,
+            "percentile_00_5": -1024.0,
+            "percentile_99_5": 479.0,
+            "std": 328.2787170410156
+        }
+    },
+    "median_relative_size_after_cropping": 1.0,
+    "shapes_after_crop": [
+        [
+            88,
+            309,
+            310
+        ],
+        [
+            88,
+            309,
+            289
+        ],
+        [
+            87,
+            310,
+            312
+        ],
+        [
+            86,
+            308,
+            315
+        ],
+        [
+            87,
+            313,
+            311
+        ],
+        [
+            82,
+            309,
+            308
+        ],
+        [
+            87,
+            310,
+            311
+        ],
+        [
+            88,
+            308,
+            309
+        ],
+        [
+            82,
+            310,
+            308
+        ],
+        [
+            81,
+            448,
+            449
+        ],
+        [
+            85,
+            317,
+            309
+        ],
+        [
+            86,
+            311,
+            314
+        ],
+        [
+            76,
+            459,
+            455
+        ],
+        [
+            88,
+            308,
+            310
+        ],
+        [
+            50,
+            510,
+            510
+        ],
+        [
+            86,
+            310,
+            311
+        ],
+        [
+            78,
+            311,
+            309
+        ],
+        [
+            87,
+            290,
+            291
+        ],
+        [
+            87,
+            309,
+            308
+        ],
+        [
+            94,
+            393,
+            393
+        ],
+        [
+            87,
+            311,
+            308
+        ],
+        [
+            87,
+            314,
+            309
+        ],
+        [
+            87,
+            309,
+            309
+        ],
+        [
+            100,
+            368,
+            370
+        ],
+        [
+            88,
+            308,
+            307
+        ],
+        [
+            100,
+            498,
+            499
+        ],
+        [
+            87,
+            308,
+            313
+        ],
+        [
+            87,
+            313,
+            310
+        ],
+        [
+            86,
+            314,
+            311
+        ],
+        [
+            88,
+            309,
+            309
+        ],
+        [
+            87,
+            312,
+            309
+        ],
+        [
+            80,
+            308,
+            313
+        ],
+        [
+            87,
+            309,
+            310
+        ],
+        [
+            86,
+            309,
+            308
+        ],
+        [
+            88,
+            308,
+            309
+        ],
+        [
+            88,
+            307,
+            308
+        ],
+        [
+            79,
+            311,
+            310
+        ],
+        [
+            115,
+            560,
+            560
+        ],
+        [
+            79,
+            450,
+            451
+        ],
+        [
+            85,
+            308,
+            309
+        ],
+        [
+            87,
+            308,
+            307
+        ],
+        [
+            88,
+            310,
+            305
+        ],
+        [
+            86,
+            313,
+            311
+        ],
+        [
+            87,
+            310,
+            308
+        ],
+        [
+            85,
+            316,
+            308
+        ],
+        [
+            73,
+            314,
+            313
+        ],
+        [
+            86,
+            312,
+            311
+        ],
+        [
+            87,
+            309,
+            310
+        ],
+        [
+            86,
+            311,
+            308
+        ],
+        [
+            87,
+            306,
+            307
+        ],
+        [
+            82,
+            308,
+            307
+        ],
+        [
+            87,
+            310,
+            308
+        ],
+        [
+            80,
+            288,
+            303
+        ],
+        [
+            81,
+            450,
+            428
+        ],
+        [
+            86,
+            308,
+            311
+        ],
+        [
+            87,
+            311,
+            310
+        ],
+        [
+            86,
+            310,
+            317
+        ],
+        [
+            87,
+            311,
+            310
+        ],
+        [
+            87,
+            309,
+            307
+        ],
+        [
+            77,
+            453,
+            456
+        ],
+        [
+            100,
+            374,
+            405
+        ],
+        [
+            80,
+            310,
+            312
+        ],
+        [
+            87,
+            308,
+            308
+        ],
+        [
+            81,
+            430,
+            449
+        ],
+        [
+            78,
+            452,
+            433
+        ],
+        [
+            80,
+            454,
+            450
+        ],
+        [
+            82,
+            450,
+            451
+        ],
+        [
+            80,
+            432,
+            450
+        ],
+        [
+            80,
+            448,
+            452
+        ],
+        [
+            81,
+            451,
+            450
+        ],
+        [
+            80,
+            451,
+            449
+        ],
+        [
+            80,
+            449,
+            453
+        ],
+        [
+            81,
+            448,
+            412
+        ],
+        [
+            80,
+            449,
+            433
+        ],
+        [
+            81,
+            449,
+            449
+        ],
+        [
+            81,
+            452,
+            421
+        ],
+        [
+            77,
+            458,
+            450
+        ],
+        [
+            81,
+            450,
+            451
+        ],
+        [
+            73,
+            453,
+            412
+        ],
+        [
+            77,
+            452,
+            417
+        ],
+        [
+            80,
+            449,
+            431
+        ],
+        [
+            81,
+            451,
+            442
+        ],
+        [
+            81,
+            450,
+            413
+        ],
+        [
+            81,
+            450,
+            450
+        ],
+        [
+            79,
+            451,
+            454
+        ],
+        [
+            80,
+            449,
+            453
+        ],
+        [
+            80,
+            452,
+            431
+        ],
+        [
+            80,
+            455,
+            451
+        ],
+        [
+            56,
+            451,
+            450
+        ],
+        [
+            81,
+            452,
+            448
+        ],
+        [
+            80,
+            452,
+            450
+        ],
+        [
+            74,
+            452,
+            418
+        ],
+        [
+            68,
+            454,
+            449
+        ],
+        [
+            51,
+            449,
+            427
+        ],
+        [
+            70,
+            456,
+            393
+        ],
+        [
+            80,
+            454,
+            450
+        ],
+        [
+            78,
+            439,
+            450
+        ],
+        [
+            82,
+            449,
+            431
+        ],
+        [
+            79,
+            451,
+            445
+        ],
+        [
+            80,
+            430,
+            455
+        ],
+        [
+            82,
+            449,
+            447
+        ],
+        [
+            82,
+            400,
+            449
+        ],
+        [
+            81,
+            402,
+            449
+        ],
+        [
+            75,
+            451,
+            449
+        ],
+        [
+            81,
+            452,
+            448
+        ],
+        [
+            80,
+            452,
+            452
+        ],
+        [
+            56,
+            451,
+            450
+        ],
+        [
+            70,
+            455,
+            393
+        ],
+        [
+            80,
+            451,
+            452
+        ],
+        [
+            71,
+            451,
+            449
+        ],
+        [
+            80,
+            421,
+            449
+        ],
+        [
+            80,
+            448,
+            434
+        ],
+        [
+            73,
+            454,
+            393
+        ],
+        [
+            81,
+            449,
+            452
+        ],
+        [
+            75,
+            452,
+            434
+        ],
+        [
+            74,
+            449,
+            449
+        ],
+        [
+            82,
+            427,
+            396
+        ],
+        [
+            75,
+            398,
+            398
+        ],
+        [
+            80,
+            454,
+            430
+        ],
+        [
+            78,
+            451,
+            456
+        ],
+        [
+            81,
+            450,
+            451
+        ],
+        [
+            80,
+            449,
+            431
+        ],
+        [
+            79,
+            448,
+            454
+        ],
+        [
+            80,
+            451,
+            452
+        ],
+        [
+            79,
+            452,
+            454
+        ],
+        [
+            79,
+            450,
+            455
+        ],
+        [
+            82,
+            449,
+            447
+        ],
+        [
+            81,
+            450,
+            448
+        ],
+        [
+            81,
+            451,
+            451
+        ],
+        [
+            81,
+            449,
+            449
+        ],
+        [
+            79,
+            408,
+            457
+        ],
+        [
+            81,
+            449,
+            452
+        ],
+        [
+            80,
+            451,
+            453
+        ],
+        [
+            81,
+            448,
+            450
+        ],
+        [
+            77,
+            449,
+            457
+        ],
+        [
+            81,
+            450,
+            451
+        ],
+        [
+            81,
+            450,
+            450
+        ],
+        [
+            87,
+            310,
+            310
+        ],
+        [
+            81,
+            449,
+            451
+        ],
+        [
+            80,
+            453,
+            450
+        ],
+        [
+            80,
+            448,
+            452
+        ],
+        [
+            78,
+            451,
+            457
+        ],
+        [
+            81,
+            430,
+            391
+        ],
+        [
+            78,
+            449,
+            455
+        ],
+        [
+            81,
+            452,
+            412
+        ],
+        [
+            81,
+            449,
+            449
+        ],
+        [
+            80,
+            444,
+            432
+        ],
+        [
+            81,
+            449,
+            451
+        ],
+        [
+            81,
+            432,
+            450
+        ],
+        [
+            81,
+            448,
+            453
+        ],
+        [
+            79,
+            455,
+            449
+        ],
+        [
+            81,
+            449,
+            450
+        ],
+        [
+            81,
+            450,
+            385
+        ],
+        [
+            81,
+            434,
+            436
+        ],
+        [
+            82,
+            416,
+            449
+        ],
+        [
+            82,
+            442,
+            450
+        ],
+        [
+            81,
+            439,
+            451
+        ],
+        [
+            81,
+            447,
+            450
+        ],
+        [
+            81,
+            415,
+            450
+        ],
+        [
+            79,
+            450,
+            453
+        ],
+        [
+            76,
+            430,
+            450
+        ],
+        [
+            81,
+            450,
+            450
+        ],
+        [
+            81,
+            448,
+            450
+        ],
+        [
+            81,
+            448,
+            422
+        ],
+        [
+            82,
+            448,
+            448
+        ],
+        [
+            79,
+            454,
+            429
+        ],
+        [
+            81,
+            451,
+            420
+        ],
+        [
+            75,
+            449,
+            462
+        ],
+        [
+            82,
+            450,
+            450
+        ],
+        [
+            81,
+            450,
+            451
+        ],
+        [
+            82,
+            448,
+            451
+        ],
+        [
+            82,
+            448,
+            451
+        ],
+        [
+            75,
+            449,
+            448
+        ],
+        [
+            80,
+            451,
+            454
+        ],
+        [
+            80,
+            449,
+            453
+        ],
+        [
+            81,
+            449,
+            452
+        ],
+        [
+            82,
+            450,
+            450
+        ],
+        [
+            78,
+            450,
+            428
+        ],
+        [
+            80,
+            442,
+            453
+        ],
+        [
+            81,
+            440,
+            449
+        ],
+        [
+            82,
+            441,
+            449
+        ],
+        [
+            82,
+            450,
+            411
+        ],
+        [
+            81,
+            430,
+            449
+        ],
+        [
+            82,
+            413,
+            450
+        ],
+        [
+            81,
+            448,
+            404
+        ],
+        [
+            81,
+            450,
+            449
+        ],
+        [
+            81,
+            449,
+            451
+        ],
+        [
+            81,
+            450,
+            377
+        ],
+        [
+            80,
+            455,
+            453
+        ],
+        [
+            80,
+            454,
+            448
+        ],
+        [
+            82,
+            447,
+            449
+        ],
+        [
+            99,
+            452,
+            435
+        ],
+        [
+            68,
+            450,
+            430
+        ],
+        [
+            99,
+            314,
+            310
+        ],
+        [
+            99,
+            453,
+            452
+        ],
+        [
+            99,
+            312,
+            311
+        ],
+        [
+            75,
+            459,
+            414
+        ],
+        [
+            69,
+            432,
+            449
+        ],
+        [
+            99,
+            452,
+            434
+        ],
+        [
+            99,
+            456,
+            404
+        ],
+        [
+            71,
+            453,
+            400
+        ],
+        [
+            98,
+            310,
+            311
+        ],
+        [
+            99,
+            311,
+            309
+        ],
+        [
+            99,
+            309,
+            310
+        ],
+        [
+            99,
+            315,
+            308
+        ],
+        [
+            98,
+            308,
+            308
+        ],
+        [
+            99,
+            450,
+            453
+        ],
+        [
+            99,
+            383,
+            349
+        ],
+        [
+            99,
+            314,
+            310
+        ],
+        [
+            70,
+            452,
+            451
+        ],
+        [
+            69,
+            452,
+            447
+        ],
+        [
+            99,
+            452,
+            455
+        ],
+        [
+            68,
+            433,
+            447
+        ],
+        [
+            100,
+            308,
+            318
+        ],
+        [
+            98,
+            319,
+            309
+        ],
+        [
+            69,
+            452,
+            448
+        ],
+        [
+            99,
+            435,
+            454
+        ],
+        [
+            91,
+            315,
+            310
+        ],
+        [
+            99,
+            450,
+            424
+        ],
+        [
+            99,
+            452,
+            452
+        ],
+        [
+            98,
+            450,
+            448
+        ],
+        [
+            100,
+            455,
+            451
+        ],
+        [
+            91,
+            435,
+            452
+        ],
+        [
+            99,
+            313,
+            315
+        ],
+        [
+            99,
+            308,
+            310
+        ],
+        [
+            70,
+            450,
+            450
+        ],
+        [
+            99,
+            448,
+            448
+        ],
+        [
+            99,
+            309,
+            308
+        ],
+        [
+            79,
+            311,
+            307
+        ],
+        [
+            94,
+            312,
+            312
+        ],
+        [
+            99,
+            326,
+            315
+        ],
+        [
+            74,
+            467,
+            497
+        ],
+        [
+            71,
+            465,
+            499
+        ],
+        [
+            74,
+            467,
+            499
+        ],
+        [
+            73,
+            472,
+            499
+        ],
+        [
+            98,
+            448,
+            448
+        ],
+        [
+            100,
+            314,
+            316
+        ],
+        [
+            98,
+            431,
+            417
+        ],
+        [
+            100,
+            315,
+            309
+        ],
+        [
+            98,
+            307,
+            308
+        ],
+        [
+            98,
+            312,
+            319
+        ],
+        [
+            98,
+            308,
+            308
+        ],
+        [
+            98,
+            310,
+            265
+        ],
+        [
+            128,
+            390,
+            391
+        ],
+        [
+            68,
+            492,
+            473
+        ],
+        [
+            66,
+            497,
+            497
+        ],
+        [
+            67,
+            497,
+            498
+        ],
+        [
+            67,
+            474,
+            468
+        ],
+        [
+            66,
+            460,
+            493
+        ],
+        [
+            74,
+            445,
+            506
+        ],
+        [
+            70,
+            485,
+            468
+        ],
+        [
+            68,
+            501,
+            497
+        ],
+        [
+            67,
+            497,
+            498
+        ],
+        [
+            65,
+            498,
+            477
+        ],
+        [
+            72,
+            470,
+            503
+        ],
+        [
+            66,
+            498,
+            497
+        ],
+        [
+            67,
+            479,
+            498
+        ],
+        [
+            68,
+            499,
+            497
+        ],
+        [
+            69,
+            499,
+            501
+        ],
+        [
+            67,
+            497,
+            497
+        ],
+        [
+            69,
+            498,
+            499
+        ],
+        [
+            67,
+            497,
+            498
+        ],
+        [
+            68,
+            478,
+            500
+        ],
+        [
+            68,
+            501,
+            487
+        ],
+        [
+            70,
+            484,
+            500
+        ],
+        [
+            66,
+            473,
+            497
+        ],
+        [
+            66,
+            497,
+            497
+        ],
+        [
+            67,
+            499,
+            498
+        ],
+        [
+            67,
+            498,
+            497
+        ],
+        [
+            66,
+            498,
+            496
+        ],
+        [
+            67,
+            490,
+            497
+        ],
+        [
+            67,
+            498,
+            498
+        ],
+        [
+            66,
+            497,
+            497
+        ],
+        [
+            66,
+            479,
+            498
+        ],
+        [
+            67,
+            482,
+            499
+        ],
+        [
+            70,
+            478,
+            499
+        ],
+        [
+            68,
+            499,
+            500
+        ],
+        [
+            66,
+            497,
+            472
+        ],
+        [
+            70,
+            499,
+            482
+        ],
+        [
+            71,
+            479,
+            481
+        ],
+        [
+            79,
+            454,
+            499
+        ],
+        [
+            66,
+            497,
+            496
+        ],
+        [
+            67,
+            485,
+            500
+        ],
+        [
+            70,
+            499,
+            496
+        ],
+        [
+            59,
+            501,
+            498
+        ],
+        [
+            68,
+            471,
+            501
+        ],
+        [
+            64,
+            499,
+            498
+        ],
+        [
+            66,
+            498,
+            497
+        ],
+        [
+            67,
+            452,
+            469
+        ],
+        [
+            75,
+            435,
+            484
+        ],
+        [
+            66,
+            486,
+            492
+        ],
+        [
+            66,
+            467,
+            497
+        ],
+        [
+            75,
+            486,
+            498
+        ],
+        [
+            67,
+            498,
+            497
+        ],
+        [
+            67,
+            497,
+            497
+        ],
+        [
+            67,
+            498,
+            499
+        ],
+        [
+            66,
+            497,
+            497
+        ],
+        [
+            67,
+            498,
+            496
+        ],
+        [
+            66,
+            466,
+            498
+        ],
+        [
+            70,
+            482,
+            501
+        ],
+        [
+            69,
+            478,
+            479
+        ],
+        [
+            67,
+            500,
+            497
+        ],
+        [
+            71,
+            469,
+            502
+        ],
+        [
+            72,
+            466,
+            500
+        ],
+        [
+            66,
+            486,
+            497
+        ],
+        [
+            68,
+            497,
+            500
+        ],
+        [
+            67,
+            498,
+            498
+        ],
+        [
+            71,
+            481,
+            501
+        ],
+        [
+            70,
+            501,
+            499
+        ]
+    ],
+    "spacings": [
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ]
+    ]
+}

Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_0/checkpoint_final.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aaef86084b37113ab519ff31157a2fdb0ddedf6df7290e480c15b724f6b9f1da
+size 815581485

Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_0/validation/summary.json

@@ -0,0 +1,52 @@
+{
+    "mae": {
+        "mean": 55.60064848848964,
+        "std": 14.338210868800594,
+        "max": 105.47389638122534,
+        "min": 35.20833348049252,
+        "25pc": 44.134254115405234,
+        "50pc": 53.92539230685208,
+        "75pc": 62.27850375865349,
+        "count": 62
+    },
+    "psnr": {
+        "mean": 30.48458344031393,
+        "std": 2.38001206819551,
+        "max": 35.77302372613302,
+        "min": 25.190641684003367,
+        "25pc": 28.694927361710747,
+        "50pc": 29.964147419029842,
+        "75pc": 32.48056910462868,
+        "count": 62
+    },
+    "ms_ssim": {
+        "mean": 0.9189507095920884,
+        "std": 0.05408709159150134,
+        "max": 0.9836779174167589,
+        "min": 0.715160057895457,
+        "25pc": 0.8933213351088732,
+        "50pc": 0.9349496548077856,
+        "75pc": 0.9545689319733478,
+        "count": 62
+    },
+    "DICE": {
+        "mean": 0.7588220357162053,
+        "std": 0.15028017475619626,
+        "max": 0.9240115880966187,
+        "min": 0.12273936718702316,
+        "25pc": 0.7277467846870422,
+        "50pc": 0.8152719140052795,
+        "75pc": 0.8395445346832275,
+        "count": 62
+    },
+    "HD95": {
+        "mean": 5.62839973168295,
+        "std": 4.9812358838641115,
+        "max": 27.221315383911133,
+        "min": 1.3090169429779053,
+        "25pc": 2.8109169006347656,
+        "50pc": 4.001410961151123,
+        "75pc": 6.309501647949219,
+        "count": 62
+    }
+}

Dataset540/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/plans.json

@@ -0,0 +1,521 @@
+{
+    "dataset_name": "Dataset540_synthrad2025_task2_CBCT_AB_pre_v2r_stitched_masked_both",
+    "plans_name": "nnUNetResEncUNetLPlans",
+    "original_median_spacing_after_transp": [
+        3.0,
+        1.0,
+        1.0
+    ],
+    "original_median_shape_after_transp": [
+        81,
+        449,
+        449
+    ],
+    "image_reader_writer": "SimpleITKIO",
+    "transpose_forward": [
+        0,
+        1,
+        2
+    ],
+    "transpose_backward": [
+        0,
+        1,
+        2
+    ],
+    "configurations": {
+        "2d": {
+            "data_identifier": "nnUNetPlans_2d",
+            "preprocessor_name": "DefaultPreprocessor",
+            "batch_size": 13,
+            "patch_size": [
+                512,
+                448
+            ],
+            "median_image_size_in_voxels": [
+                449.0,
+                449.0
+            ],
+            "spacing": [
+                1.0,
+                1.0
+            ],
+            "normalization_schemes": [
+                "CTNormalizationClippingSynthrad2025"
+            ],
+            "use_mask_for_norm": [
+                false
+            ],
+            "resampling_fn_data": "resample_data_or_seg_to_shape",
+            "resampling_fn_seg": "resample_data_or_seg_to_shape",
+            "resampling_fn_data_kwargs": {
+                "is_seg": false,
+                "order": 3,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_seg_kwargs": {
+                "is_seg": true,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+            "resampling_fn_probabilities_kwargs": {
+                "is_seg": false,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "architecture": {
+                "network_class_name": "dynamic_network_architectures.architectures.unet.ResidualEncoderUNet",
+                "arch_kwargs": {
+                    "n_stages": 7,
+                    "features_per_stage": [
+                        32,
+                        64,
+                        128,
+                        256,
+                        512,
+                        512,
+                        512
+                    ],
+                    "conv_op": "torch.nn.modules.conv.Conv2d",
+                    "kernel_sizes": [
+                        [
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3
+                        ]
+                    ],
+                    "strides": [
+                        [
+                            1,
+                            1
+                        ],
+                        [
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2
+                        ]
+                    ],
+                    "n_blocks_per_stage": [
+                        1,
+                        3,
+                        4,
+                        6,
+                        6,
+                        6,
+                        6
+                    ],
+                    "n_conv_per_stage_decoder": [
+                        1,
+                        1,
+                        1,
+                        1,
+                        1,
+                        1
+                    ],
+                    "conv_bias": true,
+                    "norm_op": "torch.nn.modules.instancenorm.InstanceNorm2d",
+                    "norm_op_kwargs": {
+                        "eps": 1e-05,
+                        "affine": true
+                    },
+                    "dropout_op": null,
+                    "dropout_op_kwargs": null,
+                    "nonlin": "torch.nn.LeakyReLU",
+                    "nonlin_kwargs": {
+                        "inplace": true
+                    }
+                },
+                "_kw_requires_import": [
+                    "conv_op",
+                    "norm_op",
+                    "dropout_op",
+                    "nonlin"
+                ]
+            },
+            "batch_dice": true
+        },
+        "3d_lowres": {
+            "data_identifier": "nnUNetResEncUNetLPlans_3d_lowres",
+            "preprocessor_name": "DefaultPreprocessor",
+            "batch_size": 2,
+            "patch_size": [
+                56,
+                224,
+                192
+            ],
+            "median_image_size_in_voxels": [
+                81,
+                297,
+                297
+            ],
+            "spacing": [
+                3.0,
+                1.512589724855112,
+                1.512589724855112
+            ],
+            "normalization_schemes": [
+                "CTNormalizationClippingSynthrad2025"
+            ],
+            "use_mask_for_norm": [
+                false
+            ],
+            "resampling_fn_data": "resample_data_or_seg_to_shape",
+            "resampling_fn_seg": "resample_data_or_seg_to_shape",
+            "resampling_fn_data_kwargs": {
+                "is_seg": false,
+                "order": 3,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_seg_kwargs": {
+                "is_seg": true,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+            "resampling_fn_probabilities_kwargs": {
+                "is_seg": false,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "architecture": {
+                "network_class_name": "dynamic_network_architectures.architectures.unet.ResidualEncoderUNet",
+                "arch_kwargs": {
+                    "n_stages": 6,
+                    "features_per_stage": [
+                        32,
+                        64,
+                        128,
+                        256,
+                        320,
+                        320
+                    ],
+                    "conv_op": "torch.nn.modules.conv.Conv3d",
+                    "kernel_sizes": [
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ]
+                    ],
+                    "strides": [
+                        [
+                            1,
+                            1,
+                            1
+                        ],
+                        [
+                            2,
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2,
+                            2
+                        ],
+                        [
+                            1,
+                            2,
+                            2
+                        ],
+                        [
+                            1,
+                            2,
+                            2
+                        ]
+                    ],
+                    "n_blocks_per_stage": [
+                        1,
+                        3,
+                        4,
+                        6,
+                        6,
+                        6
+                    ],
+                    "n_conv_per_stage_decoder": [
+                        1,
+                        1,
+                        1,
+                        1,
+                        1
+                    ],
+                    "conv_bias": true,
+                    "norm_op": "torch.nn.modules.instancenorm.InstanceNorm3d",
+                    "norm_op_kwargs": {
+                        "eps": 1e-05,
+                        "affine": true
+                    },
+                    "dropout_op": null,
+                    "dropout_op_kwargs": null,
+                    "nonlin": "torch.nn.LeakyReLU",
+                    "nonlin_kwargs": {
+                        "inplace": true
+                    }
+                },
+                "_kw_requires_import": [
+                    "conv_op",
+                    "norm_op",
+                    "dropout_op",
+                    "nonlin"
+                ]
+            },
+            "batch_dice": false,
+            "next_stage": "3d_cascade_fullres"
+        },
+        "3d_fullres": {
+            "data_identifier": "nnUNetPlans_3d_fullres",
+            "preprocessor_name": "DefaultPreprocessor",
+            "batch_size": 2,
+            "patch_size": [
+                40,
+                224,
+                192
+            ],
+            "median_image_size_in_voxels": [
+                81.0,
+                449.0,
+                449.0
+            ],
+            "spacing": [
+                3.0,
+                1.0,
+                1.0
+            ],
+            "normalization_schemes": [
+                "CTNormalizationClippingSynthrad2025"
+            ],
+            "use_mask_for_norm": [
+                false
+            ],
+            "resampling_fn_data": "resample_data_or_seg_to_shape",
+            "resampling_fn_seg": "resample_data_or_seg_to_shape",
+            "resampling_fn_data_kwargs": {
+                "is_seg": false,
+                "order": 3,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_seg_kwargs": {
+                "is_seg": true,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+            "resampling_fn_probabilities_kwargs": {
+                "is_seg": false,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "architecture": {
+                "network_class_name": "dynamic_network_architectures.architectures.unet.ResidualEncoderUNet",
+                "arch_kwargs": {
+                    "n_stages": 6,
+                    "features_per_stage": [
+                        32,
+                        64,
+                        128,
+                        256,
+                        320,
+                        320
+                    ],
+                    "conv_op": "torch.nn.modules.conv.Conv3d",
+                    "kernel_sizes": [
+                        [
+                            1,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ]
+                    ],
+                    "strides": [
+                        [
+                            1,
+                            1,
+                            1
+                        ],
+                        [
+                            1,
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2,
+                            2
+                        ],
+                        [
+                            1,
+                            2,
+                            2
+                        ]
+                    ],
+                    "n_blocks_per_stage": [
+                        1,
+                        3,
+                        4,
+                        6,
+                        6,
+                        6
+                    ],
+                    "n_conv_per_stage_decoder": [
+                        1,
+                        1,
+                        1,
+                        1,
+                        1
+                    ],
+                    "conv_bias": true,
+                    "norm_op": "torch.nn.modules.instancenorm.InstanceNorm3d",
+                    "norm_op_kwargs": {
+                        "eps": 1e-05,
+                        "affine": true
+                    },
+                    "dropout_op": null,
+                    "dropout_op_kwargs": null,
+                    "nonlin": "torch.nn.LeakyReLU",
+                    "nonlin_kwargs": {
+                        "inplace": true
+                    }
+                },
+                "_kw_requires_import": [
+                    "conv_op",
+                    "norm_op",
+                    "dropout_op",
+                    "nonlin"
+                ]
+            },
+            "batch_dice": true
+        },
+        "3d_cascade_fullres": {
+            "inherits_from": "3d_fullres",
+            "previous_stage": "3d_lowres"
+        }
+    },
+    "experiment_planner_used": "nnUNetPlannerResEncL",
+    "label_manager": "LabelManager",
+    "foreground_intensity_properties_per_channel": {
+        "0": {
+            "max": 3071.0,
+            "mean": -239.3903045654297,
+            "median": -185.0,
+            "min": -1024.0,
+            "percentile_00_5": -1024.0,
+            "percentile_99_5": 479.0,
+            "std": 328.2787170410156
+        }
+    }
+}

Dataset542/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/dataset.json

@@ -0,0 +1,14 @@
+{
+    "labels": {
+        "label_001": "1",
+        "background": 0
+    },
+    "channel_names": {
+        "0": "CT_zscore_synthrad"
+    },
+    "numTraining": 325,
+    "file_ending": ".nii.gz",
+    "test_set": [
+        "case_0000"
+    ]
+}

Dataset542/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/dataset_fingerprint.json

@@ -0,0 +1,3268 @@
+{
+    "foreground_intensity_properties_per_channel": {
+        "0": {
+            "max": 3071.0,
+            "mean": -215.1082763671875,
+            "median": -133.0,
+            "min": -1024.0,
+            "percentile_00_5": -1024.0,
+            "percentile_99_5": 1345.0,
+            "std": 459.63665771484375
+        }
+    },
+    "median_relative_size_after_cropping": 1.0,
+    "shapes_after_crop": [
+        [
+            88,
+            307,
+            309
+        ],
+        [
+            83,
+            307,
+            311
+        ],
+        [
+            88,
+            309,
+            308
+        ],
+        [
+            79,
+            313,
+            314
+        ],
+        [
+            87,
+            309,
+            310
+        ],
+        [
+            88,
+            308,
+            308
+        ],
+        [
+            86,
+            309,
+            312
+        ],
+        [
+            88,
+            307,
+            308
+        ],
+        [
+            87,
+            309,
+            309
+        ],
+        [
+            87,
+            308,
+            309
+        ],
+        [
+            88,
+            309,
+            308
+        ],
+        [
+            88,
+            308,
+            307
+        ],
+        [
+            86,
+            313,
+            313
+        ],
+        [
+            82,
+            309,
+            309
+        ],
+        [
+            86,
+            308,
+            313
+        ],
+        [
+            86,
+            312,
+            307
+        ],
+        [
+            87,
+            307,
+            309
+        ],
+        [
+            87,
+            309,
+            309
+        ],
+        [
+            87,
+            307,
+            308
+        ],
+        [
+            87,
+            308,
+            309
+        ],
+        [
+            86,
+            309,
+            314
+        ],
+        [
+            88,
+            308,
+            310
+        ],
+        [
+            88,
+            307,
+            308
+        ],
+        [
+            87,
+            308,
+            307
+        ],
+        [
+            87,
+            311,
+            314
+        ],
+        [
+            87,
+            309,
+            309
+        ],
+        [
+            87,
+            307,
+            306
+        ],
+        [
+            86,
+            308,
+            310
+        ],
+        [
+            87,
+            307,
+            312
+        ],
+        [
+            87,
+            308,
+            311
+        ],
+        [
+            87,
+            307,
+            307
+        ],
+        [
+            51,
+            282,
+            311
+        ],
+        [
+            87,
+            308,
+            310
+        ],
+        [
+            88,
+            310,
+            309
+        ],
+        [
+            87,
+            310,
+            308
+        ],
+        [
+            88,
+            308,
+            308
+        ],
+        [
+            88,
+            308,
+            308
+        ],
+        [
+            87,
+            308,
+            309
+        ],
+        [
+            87,
+            308,
+            309
+        ],
+        [
+            87,
+            309,
+            310
+        ],
+        [
+            87,
+            308,
+            312
+        ],
+        [
+            82,
+            309,
+            310
+        ],
+        [
+            88,
+            310,
+            308
+        ],
+        [
+            88,
+            311,
+            308
+        ],
+        [
+            88,
+            309,
+            309
+        ],
+        [
+            87,
+            308,
+            312
+        ],
+        [
+            88,
+            309,
+            310
+        ],
+        [
+            86,
+            310,
+            312
+        ],
+        [
+            87,
+            310,
+            309
+        ],
+        [
+            87,
+            309,
+            310
+        ],
+        [
+            87,
+            307,
+            308
+        ],
+        [
+            87,
+            308,
+            310
+        ],
+        [
+            82,
+            288,
+            273
+        ],
+        [
+            88,
+            309,
+            309
+        ],
+        [
+            88,
+            307,
+            307
+        ],
+        [
+            87,
+            310,
+            307
+        ],
+        [
+            86,
+            311,
+            309
+        ],
+        [
+            87,
+            308,
+            311
+        ],
+        [
+            87,
+            309,
+            309
+        ],
+        [
+            87,
+            310,
+            308
+        ],
+        [
+            88,
+            225,
+            308
+        ],
+        [
+            87,
+            308,
+            307
+        ],
+        [
+            81,
+            311,
+            308
+        ],
+        [
+            87,
+            310,
+            310
+        ],
+        [
+            87,
+            309,
+            307
+        ],
+        [
+            88,
+            310,
+            308
+        ],
+        [
+            87,
+            313,
+            311
+        ],
+        [
+            88,
+            309,
+            309
+        ],
+        [
+            88,
+            309,
+            307
+        ],
+        [
+            88,
+            309,
+            308
+        ],
+        [
+            88,
+            309,
+            309
+        ],
+        [
+            87,
+            308,
+            312
+        ],
+        [
+            87,
+            308,
+            310
+        ],
+        [
+            88,
+            311,
+            309
+        ],
+        [
+            88,
+            308,
+            308
+        ],
+        [
+            88,
+            308,
+            308
+        ],
+        [
+            49,
+            307,
+            307
+        ],
+        [
+            88,
+            308,
+            309
+        ],
+        [
+            88,
+            310,
+            307
+        ],
+        [
+            86,
+            315,
+            315
+        ],
+        [
+            88,
+            306,
+            307
+        ],
+        [
+            87,
+            310,
+            311
+        ],
+        [
+            87,
+            311,
+            308
+        ],
+        [
+            88,
+            309,
+            308
+        ],
+        [
+            87,
+            310,
+            309
+        ],
+        [
+            88,
+            309,
+            309
+        ],
+        [
+            87,
+            312,
+            309
+        ],
+        [
+            87,
+            310,
+            311
+        ],
+        [
+            88,
+            310,
+            311
+        ],
+        [
+            88,
+            308,
+            309
+        ],
+        [
+            87,
+            309,
+            310
+        ],
+        [
+            87,
+            310,
+            311
+        ],
+        [
+            87,
+            312,
+            311
+        ],
+        [
+            88,
+            309,
+            311
+        ],
+        [
+            88,
+            308,
+            310
+        ],
+        [
+            88,
+            310,
+            309
+        ],
+        [
+            87,
+            308,
+            310
+        ],
+        [
+            87,
+            311,
+            308
+        ],
+        [
+            88,
+            309,
+            308
+        ],
+        [
+            87,
+            311,
+            311
+        ],
+        [
+            87,
+            307,
+            309
+        ],
+        [
+            87,
+            310,
+            311
+        ],
+        [
+            88,
+            309,
+            309
+        ],
+        [
+            82,
+            309,
+            308
+        ],
+        [
+            88,
+            309,
+            309
+        ],
+        [
+            87,
+            311,
+            308
+        ],
+        [
+            87,
+            310,
+            313
+        ],
+        [
+            88,
+            310,
+            309
+        ],
+        [
+            87,
+            309,
+            311
+        ],
+        [
+            88,
+            310,
+            309
+        ],
+        [
+            88,
+            308,
+            308
+        ],
+        [
+            87,
+            310,
+            309
+        ],
+        [
+            87,
+            309,
+            310
+        ],
+        [
+            87,
+            312,
+            312
+        ],
+        [
+            87,
+            309,
+            311
+        ],
+        [
+            87,
+            312,
+            311
+        ],
+        [
+            87,
+            312,
+            310
+        ],
+        [
+            87,
+            307,
+            309
+        ],
+        [
+            87,
+            312,
+            308
+        ],
+        [
+            88,
+            307,
+            309
+        ],
+        [
+            87,
+            308,
+            312
+        ],
+        [
+            87,
+            311,
+            307
+        ],
+        [
+            87,
+            312,
+            311
+        ],
+        [
+            81,
+            310,
+            311
+        ],
+        [
+            87,
+            308,
+            312
+        ],
+        [
+            88,
+            310,
+            308
+        ],
+        [
+            87,
+            311,
+            309
+        ],
+        [
+            87,
+            309,
+            310
+        ],
+        [
+            86,
+            311,
+            317
+        ],
+        [
+            88,
+            308,
+            308
+        ],
+        [
+            88,
+            311,
+            309
+        ],
+        [
+            51,
+            311,
+            311
+        ],
+        [
+            87,
+            311,
+            309
+        ],
+        [
+            86,
+            310,
+            307
+        ],
+        [
+            88,
+            311,
+            308
+        ],
+        [
+            88,
+            309,
+            309
+        ],
+        [
+            87,
+            311,
+            312
+        ],
+        [
+            87,
+            310,
+            307
+        ],
+        [
+            86,
+            309,
+            311
+        ],
+        [
+            88,
+            310,
+            311
+        ],
+        [
+            87,
+            312,
+            308
+        ],
+        [
+            86,
+            308,
+            311
+        ],
+        [
+            87,
+            311,
+            312
+        ],
+        [
+            85,
+            313,
+            314
+        ],
+        [
+            86,
+            311,
+            311
+        ],
+        [
+            88,
+            308,
+            309
+        ],
+        [
+            86,
+            314,
+            313
+        ],
+        [
+            87,
+            312,
+            312
+        ],
+        [
+            87,
+            314,
+            311
+        ],
+        [
+            86,
+            308,
+            312
+        ],
+        [
+            87,
+            308,
+            312
+        ],
+        [
+            88,
+            313,
+            308
+        ],
+        [
+            87,
+            307,
+            309
+        ],
+        [
+            87,
+            311,
+            308
+        ],
+        [
+            85,
+            317,
+            311
+        ],
+        [
+            87,
+            307,
+            311
+        ],
+        [
+            88,
+            308,
+            312
+        ],
+        [
+            87,
+            310,
+            310
+        ],
+        [
+            87,
+            311,
+            311
+        ],
+        [
+            52,
+            308,
+            310
+        ],
+        [
+            87,
+            309,
+            307
+        ],
+        [
+            87,
+            308,
+            311
+        ],
+        [
+            87,
+            308,
+            310
+        ],
+        [
+            87,
+            310,
+            309
+        ],
+        [
+            88,
+            309,
+            309
+        ],
+        [
+            87,
+            309,
+            309
+        ],
+        [
+            86,
+            310,
+            314
+        ],
+        [
+            88,
+            309,
+            308
+        ],
+        [
+            86,
+            314,
+            312
+        ],
+        [
+            88,
+            308,
+            309
+        ],
+        [
+            87,
+            309,
+            310
+        ],
+        [
+            87,
+            309,
+            310
+        ],
+        [
+            87,
+            312,
+            309
+        ],
+        [
+            88,
+            310,
+            310
+        ],
+        [
+            86,
+            313,
+            311
+        ],
+        [
+            87,
+            311,
+            310
+        ],
+        [
+            87,
+            311,
+            309
+        ],
+        [
+            86,
+            311,
+            312
+        ],
+        [
+            87,
+            311,
+            310
+        ],
+        [
+            87,
+            309,
+            307
+        ],
+        [
+            85,
+            310,
+            311
+        ],
+        [
+            88,
+            312,
+            308
+        ],
+        [
+            85,
+            293,
+            319
+        ],
+        [
+            86,
+            308,
+            308
+        ],
+        [
+            87,
+            310,
+            309
+        ],
+        [
+            87,
+            309,
+            310
+        ],
+        [
+            53,
+            307,
+            310
+        ],
+        [
+            87,
+            308,
+            312
+        ],
+        [
+            54,
+            308,
+            307
+        ],
+        [
+            88,
+            309,
+            307
+        ],
+        [
+            88,
+            308,
+            307
+        ],
+        [
+            87,
+            307,
+            310
+        ],
+        [
+            88,
+            307,
+            308
+        ],
+        [
+            87,
+            310,
+            307
+        ],
+        [
+            77,
+            311,
+            311
+        ],
+        [
+            99,
+            315,
+            316
+        ],
+        [
+            128,
+            305,
+            307
+        ],
+        [
+            129,
+            300,
+            305
+        ],
+        [
+            128,
+            303,
+            305
+        ],
+        [
+            121,
+            299,
+            300
+        ],
+        [
+            128,
+            304,
+            301
+        ],
+        [
+            70,
+            300,
+            298
+        ],
+        [
+            70,
+            300,
+            303
+        ],
+        [
+            71,
+            304,
+            305
+        ],
+        [
+            71,
+            302,
+            303
+        ],
+        [
+            72,
+            302,
+            299
+        ],
+        [
+            70,
+            300,
+            301
+        ],
+        [
+            99,
+            308,
+            312
+        ],
+        [
+            70,
+            303,
+            299
+        ],
+        [
+            71,
+            300,
+            300
+        ],
+        [
+            70,
+            301,
+            299
+        ],
+        [
+            72,
+            307,
+            306
+        ],
+        [
+            70,
+            304,
+            304
+        ],
+        [
+            121,
+            303,
+            306
+        ],
+        [
+            119,
+            303,
+            303
+        ],
+        [
+            122,
+            308,
+            301
+        ],
+        [
+            130,
+            310,
+            301
+        ],
+        [
+            71,
+            300,
+            300
+        ],
+        [
+            128,
+            301,
+            301
+        ],
+        [
+            130,
+            392,
+            400
+        ],
+        [
+            73,
+            302,
+            302
+        ],
+        [
+            70,
+            301,
+            302
+        ],
+        [
+            70,
+            301,
+            301
+        ],
+        [
+            71,
+            299,
+            301
+        ],
+        [
+            71,
+            304,
+            303
+        ],
+        [
+            71,
+            302,
+            298
+        ],
+        [
+            70,
+            299,
+            300
+        ],
+        [
+            130,
+            371,
+            394
+        ],
+        [
+            123,
+            285,
+            306
+        ],
+        [
+            70,
+            300,
+            299
+        ],
+        [
+            70,
+            301,
+            299
+        ],
+        [
+            116,
+            302,
+            302
+        ],
+        [
+            129,
+            299,
+            302
+        ],
+        [
+            127,
+            304,
+            303
+        ],
+        [
+            115,
+            311,
+            311
+        ],
+        [
+            71,
+            299,
+            302
+        ],
+        [
+            70,
+            300,
+            301
+        ],
+        [
+            72,
+            304,
+            300
+        ],
+        [
+            72,
+            305,
+            300
+        ],
+        [
+            99,
+            290,
+            313
+        ],
+        [
+            70,
+            300,
+            299
+        ],
+        [
+            111,
+            302,
+            303
+        ],
+        [
+            128,
+            300,
+            298
+        ],
+        [
+            122,
+            305,
+            302
+        ],
+        [
+            70,
+            300,
+            299
+        ],
+        [
+            129,
+            303,
+            301
+        ],
+        [
+            121,
+            310,
+            305
+        ],
+        [
+            122,
+            301,
+            302
+        ],
+        [
+            110,
+            306,
+            298
+        ],
+        [
+            119,
+            310,
+            308
+        ],
+        [
+            121,
+            302,
+            304
+        ],
+        [
+            116,
+            304,
+            303
+        ],
+        [
+            124,
+            308,
+            313
+        ],
+        [
+            72,
+            304,
+            303
+        ],
+        [
+            69,
+            298,
+            299
+        ],
+        [
+            71,
+            299,
+            301
+        ],
+        [
+            122,
+            303,
+            299
+        ],
+        [
+            70,
+            301,
+            300
+        ],
+        [
+            131,
+            313,
+            310
+        ],
+        [
+            70,
+            302,
+            301
+        ],
+        [
+            72,
+            299,
+            298
+        ],
+        [
+            66,
+            496,
+            498
+        ],
+        [
+            71,
+            297,
+            298
+        ],
+        [
+            74,
+            305,
+            304
+        ],
+        [
+            72,
+            298,
+            297
+        ],
+        [
+            71,
+            297,
+            297
+        ],
+        [
+            71,
+            299,
+            297
+        ],
+        [
+            73,
+            298,
+            299
+        ],
+        [
+            71,
+            298,
+            297
+        ],
+        [
+            68,
+            457,
+            498
+        ],
+        [
+            71,
+            298,
+            297
+        ],
+        [
+            71,
+            298,
+            296
+        ],
+        [
+            72,
+            297,
+            298
+        ],
+        [
+            72,
+            297,
+            301
+        ],
+        [
+            66,
+            482,
+            498
+        ],
+        [
+            67,
+            496,
+            498
+        ],
+        [
+            70,
+            500,
+            498
+        ],
+        [
+            71,
+            298,
+            296
+        ],
+        [
+            71,
+            296,
+            295
+        ],
+        [
+            66,
+            497,
+            497
+        ],
+        [
+            73,
+            300,
+            299
+        ],
+        [
+            71,
+            297,
+            297
+        ],
+        [
+            71,
+            296,
+            297
+        ],
+        [
+            69,
+            480,
+            499
+        ],
+        [
+            71,
+            298,
+            298
+        ],
+        [
+            71,
+            297,
+            297
+        ],
+        [
+            73,
+            297,
+            303
+        ],
+        [
+            73,
+            299,
+            299
+        ],
+        [
+            72,
+            299,
+            298
+        ],
+        [
+            71,
+            297,
+            295
+        ],
+        [
+            71,
+            299,
+            299
+        ],
+        [
+            71,
+            298,
+            296
+        ],
+        [
+            70,
+            493,
+            499
+        ],
+        [
+            71,
+            296,
+            297
+        ],
+        [
+            68,
+            497,
+            500
+        ],
+        [
+            66,
+            496,
+            496
+        ],
+        [
+            71,
+            484,
+            501
+        ],
+        [
+            74,
+            297,
+            301
+        ],
+        [
+            73,
+            297,
+            297
+        ],
+        [
+            73,
+            300,
+            298
+        ],
+        [
+            72,
+            298,
+            298
+        ],
+        [
+            71,
+            499,
+            470
+        ],
+        [
+            71,
+            297,
+            298
+        ],
+        [
+            72,
+            498,
+            503
+        ],
+        [
+            71,
+            297,
+            296
+        ],
+        [
+            71,
+            300,
+            298
+        ],
+        [
+            71,
+            298,
+            296
+        ],
+        [
+            67,
+            497,
+            497
+        ],
+        [
+            71,
+            501,
+            499
+        ],
+        [
+            69,
+            498,
+            501
+        ],
+        [
+            70,
+            297,
+            297
+        ],
+        [
+            66,
+            498,
+            477
+        ],
+        [
+            71,
+            500,
+            500
+        ],
+        [
+            68,
+            483,
+            499
+        ],
+        [
+            66,
+            497,
+            498
+        ],
+        [
+            76,
+            306,
+            301
+        ],
+        [
+            73,
+            299,
+            298
+        ],
+        [
+            71,
+            296,
+            298
+        ],
+        [
+            70,
+            499,
+            498
+        ],
+        [
+            70,
+            499,
+            499
+        ],
+        [
+            72,
+            498,
+            504
+        ],
+        [
+            71,
+            490,
+            502
+        ],
+        [
+            71,
+            501,
+            500
+        ],
+        [
+            71,
+            501,
+            499
+        ],
+        [
+            71,
+            499,
+            501
+        ]
+    ],
+    "spacings": [
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ]
+    ]
+}

Dataset542/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_0/checkpoint_final.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ced0929a6ae2327f388ca4bb74dfca15acc111a68340b10442a2840895b5b5d8
+size 815577709

Dataset542/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_0/debug.json

@@ -0,0 +1,58 @@
+{
+    "_best_ema": "0.047194228",
+    "aim_run": "<Run#-1202935887677095771 name=7ec9a6e8af2847c58549736c repo=<Repo#-3891852473720853603 path=/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/results/runs_aim/.aim read_only=None>>",
+    "batch_size": "2",
+    "configuration_manager": "{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [48, 192, 192], 'median_image_size_in_voxels': [87.0, 308.0, 309.0], 'spacing': [3.0, 1.0, 1.0], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': False}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False}",
+    "configuration_name": "3d_fullres",
+    "cudnn_version": 90501,
+    "current_epoch": "27",
+    "dataloader_train": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x15543c17dc50>",
+    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D_MRCT_mask object at 0x1551f9793150>",
+    "dataloader_train.num_processes": "12",
+    "dataloader_train.transform": "Compose ( [RemoveLabelTransform( output_key = 'seg', input_key = 'seg', replace_with = 0, remove_label = -1 ), RenameTransform( delete_old = True, out_key = 'target', in_key = 'seg' ), NumpyToTensor( keys = ['data', 'target', 'mask'], cast_to = 'float' )] )",
+    "dataloader_val": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x15543c17f1d0>",
+    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D_MRCT_mask object at 0x1551f97937d0>",
+    "dataloader_val.num_processes": "6",
+    "dataloader_val.transform": "Compose ( [RemoveLabelTransform( output_key = 'seg', input_key = 'seg', replace_with = 0, remove_label = -1 ), RenameTransform( delete_old = True, out_key = 'target', in_key = 'seg' ), NumpyToTensor( keys = ['data', 'target', 'mask'], cast_to = 'float' )] )",
+    "dataset_json": "{'labels': {'label_001': '1', 'background': 0}, 'channel_names': {'0': 'CT_zscore_synthrad'}, 'numTraining': 325, 'file_ending': '.mha'}",
+    "debug_custom": "{'run_hash': 'de0b335742114ac0a04510ad'}",
+    "decoder_type": "standard",
+    "device": "cuda:0",
+    "disable_checkpointing": "False",
+    "enable_deep_supervision": "False",
+    "fold": "0",
+    "folder_with_segs_from_previous_stage": "None",
+    "gpu_name": "NVIDIA H100",
+    "grad_scaler": "<torch.amp.grad_scaler.GradScaler object at 0x15543db00690>",
+    "hostname": "g030",
+    "image_loss_weight": "0.5",
+    "inference_allowed_mirroring_axes": "None",
+    "initial_lr": "0.01",
+    "is_cascaded": "False",
+    "is_ddp": "False",
+    "label_manager": "<nnunetv2.utilities.label_handling.label_handling.LabelManager object at 0x15543db00790>",
+    "local_rank": "0",
+    "log_file": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/results/Dataset542_synthrad2025_task2_CBCT_HN_pre_v2r_stitched_masked_both/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_0/training_log_2025_8_14_18_25_01.txt",
+    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x15543e6adb50>",
+    "loss": "SynPerceptionLoss(\n  (seg_model): ResidualEncoderUNet(\n    (encoder): ResidualEncoder(\n      (stem): StackedConvBlocks(\n        (convs): Sequential(\n          (0): ConvDropoutNormReLU(\n            (conv): Conv3d(1, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n            (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n            (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n            (all_modules): Sequential(\n              (0): Conv3d(1, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n              (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n      )\n      (stages): Sequential(\n        (0): StackedResidualBlocks(\n          (blocks): Sequential(\n            (0): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                  (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                  (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n        (1): StackedResidualBlocks(\n          (blocks): Sequential(\n            (0): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              (skip): Sequential(\n                (0): AvgPool3d(kernel_size=[1, 2, 2], stride=[1, 2, 2], padding=0)\n                (1): ConvDropoutNormReLU(\n                  (conv): Conv3d(32, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(32, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n              )\n            )\n            (1): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (2): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n        (2): StackedResidualBlocks(\n          (blocks): Sequential(\n            (0): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              (skip): Sequential(\n                (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n                (1): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n              )\n            )\n            (1): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (2): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (3): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n        (3): StackedResidualBlocks(\n          (blocks): Sequential(\n            (0): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              (skip): Sequential(\n                (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n                (1): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n              )\n            )\n            (1): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (2): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (3): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (4): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (5): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n        (4): StackedResidualBlocks(\n          (blocks): Sequential(\n            (0): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(256, 320, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(256, 320, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              (skip): Sequential(\n                (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n                (1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 320, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 320, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n              )\n            )\n            (1): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (2): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (3): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (4): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (5): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n        (5): StackedResidualBlocks(\n          (blocks): Sequential(\n            (0): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              (skip): Sequential(\n                (0): AvgPool3d(kernel_size=[1, 2, 2], stride=[1, 2, 2], padding=0)\n              )\n            )\n            (1): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (2): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (3): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (4): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n            (5): BasicBlockD(\n              (conv1): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                )\n              )\n              (conv2): ConvDropoutNormReLU(\n                (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (all_modules): Sequential(\n                  (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                )\n              )\n              (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n            )\n          )\n        )\n      )\n    )\n    (decoder): UNetDecoder(\n      (encoder): ResidualEncoder(\n        (stem): StackedConvBlocks(\n          (convs): Sequential(\n            (0): ConvDropoutNormReLU(\n              (conv): Conv3d(1, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n              (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n              (all_modules): Sequential(\n                (0): Conv3d(1, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n        (stages): Sequential(\n          (0): StackedResidualBlocks(\n            (blocks): Sequential(\n              (0): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                  (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                    (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                  (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(32, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                    (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n          (1): StackedResidualBlocks(\n            (blocks): Sequential(\n              (0): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n                (skip): Sequential(\n                  (0): AvgPool3d(kernel_size=[1, 2, 2], stride=[1, 2, 2], padding=0)\n                  (1): ConvDropoutNormReLU(\n                    (conv): Conv3d(32, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (all_modules): Sequential(\n                      (0): Conv3d(32, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                      (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    )\n                  )\n                )\n              )\n              (1): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (2): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n          (2): StackedResidualBlocks(\n            (blocks): Sequential(\n              (0): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n                (skip): Sequential(\n                  (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n                  (1): ConvDropoutNormReLU(\n                    (conv): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (all_modules): Sequential(\n                      (0): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                      (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    )\n                  )\n                )\n              )\n              (1): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (2): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (3): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n          (3): StackedResidualBlocks(\n            (blocks): Sequential(\n              (0): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n                (skip): Sequential(\n                  (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n                  (1): ConvDropoutNormReLU(\n                    (conv): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (all_modules): Sequential(\n                      (0): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                      (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    )\n                  )\n                )\n              )\n              (1): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (2): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (3): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (4): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (5): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n          (4): StackedResidualBlocks(\n            (blocks): Sequential(\n              (0): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(256, 320, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(256, 320, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n                (skip): Sequential(\n                  (0): AvgPool3d(kernel_size=[2, 2, 2], stride=[2, 2, 2], padding=0)\n                  (1): ConvDropoutNormReLU(\n                    (conv): Conv3d(256, 320, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                    (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (all_modules): Sequential(\n                      (0): Conv3d(256, 320, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)\n                      (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    )\n                  )\n                )\n              )\n              (1): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (2): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (3): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (4): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (5): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n          (5): StackedResidualBlocks(\n            (blocks): Sequential(\n              (0): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n                (skip): Sequential(\n                  (0): AvgPool3d(kernel_size=[1, 2, 2], stride=[1, 2, 2], padding=0)\n                )\n              )\n              (1): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (2): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (3): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (4): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n              (5): BasicBlockD(\n                (conv1): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                    (2): LeakyReLU(negative_slope=0.01, inplace=True)\n                  )\n                )\n                (conv2): ConvDropoutNormReLU(\n                  (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                  (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  (all_modules): Sequential(\n                    (0): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                    (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                  )\n                )\n                (nonlin2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n      )\n      (stages): ModuleList(\n        (0): StackedConvBlocks(\n          (convs): Sequential(\n            (0): ConvDropoutNormReLU(\n              (conv): Conv3d(640, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n              (norm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n              (all_modules): Sequential(\n                (0): Conv3d(640, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (1): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n        (1): StackedConvBlocks(\n          (convs): Sequential(\n            (0): ConvDropoutNormReLU(\n              (conv): Conv3d(512, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n              (norm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n              (all_modules): Sequential(\n                (0): Conv3d(512, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (1): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n        (2): StackedConvBlocks(\n          (convs): Sequential(\n            (0): ConvDropoutNormReLU(\n              (conv): Conv3d(256, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n              (norm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n              (all_modules): Sequential(\n                (0): Conv3d(256, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n        (3): StackedConvBlocks(\n          (convs): Sequential(\n            (0): ConvDropoutNormReLU(\n              (conv): Conv3d(128, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n              (norm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n              (all_modules): Sequential(\n                (0): Conv3d(128, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n                (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n        (4): StackedConvBlocks(\n          (convs): Sequential(\n            (0): ConvDropoutNormReLU(\n              (conv): Conv3d(64, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n              (norm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n              (nonlin): LeakyReLU(negative_slope=0.01, inplace=True)\n              (all_modules): Sequential(\n                (0): Conv3d(64, 32, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1))\n                (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n                (2): LeakyReLU(negative_slope=0.01, inplace=True)\n              )\n            )\n          )\n        )\n      )\n      (transpconvs): ModuleList(\n        (0): ConvTranspose3d(320, 320, kernel_size=(1, 2, 2), stride=(1, 2, 2))\n        (1): ConvTranspose3d(320, 256, kernel_size=(2, 2, 2), stride=(2, 2, 2))\n        (2): ConvTranspose3d(256, 128, kernel_size=(2, 2, 2), stride=(2, 2, 2))\n        (3): ConvTranspose3d(128, 64, kernel_size=(2, 2, 2), stride=(2, 2, 2))\n        (4): ConvTranspose3d(64, 32, kernel_size=(1, 2, 2), stride=(1, 2, 2))\n      )\n      (seg_layers): ModuleList(\n        (0): Conv3d(320, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n        (1): Conv3d(256, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n        (2): Conv3d(128, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n        (3): Conv3d(64, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n        (4): Conv3d(32, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n      )\n    )\n  )\n  (L1): L1Loss()\n  (image_loss): myMaskedMSE(\n    (mse): myMSE()\n  )\n)",
+    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x15543c17d590>",
+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset542_synthrad2025_task2_CBCT_HN_pre_v2r_stitched_masked_both', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [3.0, 1.0, 1.0], 'original_median_shape_after_transp': [87, 308, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 30, 'patch_size': [320, 320], 'median_image_size_in_voxels': [308.0, 309.0], 'spacing': [1.0, 1.0], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [48, 192, 192], 'median_image_size_in_voxels': [87.0, 308.0, 309.0], 'spacing': [3.0, 1.0, 1.0], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False}}, 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 3071.0, 'mean': -215.1082763671875, 'median': -133.0, 'min': -1024.0, 'percentile_00_5': -1024.0, 'percentile_99_5': 1345.0, 'std': 459.63665771484375}}}, 'configuration': '3d_fullres', 'fold': 0, 'dataset_json': {'labels': {'label_001': '1', 'background': 0}, 'channel_names': {'0': 'CT_zscore_synthrad'}, 'numTraining': 325, 'file_ending': '.mha'}, 'unpack_dataset': True, 'device': device(type='cuda')}",
+    "network": "ResidualEncoderUNet",
+    "num_epochs": "1000",
+    "num_input_channels": "1",
+    "num_iterations_per_epoch": "250",
+    "num_val_iterations_per_epoch": "50",
+    "optimizer": "SGD (\nParameter Group 0\n    dampening: 0\n    differentiable: False\n    foreach: None\n    fused: None\n    initial_lr: 0.01\n    lr: 0.009765692859724779\n    maximize: False\n    momentum: 0.99\n    nesterov: True\n    weight_decay: 3e-05\n)",
+    "output_folder": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/results/Dataset542_synthrad2025_task2_CBCT_HN_pre_v2r_stitched_masked_both/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_0",
+    "output_folder_base": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/results/Dataset542_synthrad2025_task2_CBCT_HN_pre_v2r_stitched_masked_both/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres",
+    "oversample_foreground_percent": "0.33",
+    "perception_masked": "True",
+    "plans_manager": "{'dataset_name': 'Dataset542_synthrad2025_task2_CBCT_HN_pre_v2r_stitched_masked_both', 'plans_name': 'nnUNetResEncUNetLPlans', 'original_median_spacing_after_transp': [3.0, 1.0, 1.0], 'original_median_shape_after_transp': [87, 308, 309], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 30, 'patch_size': [320, 320], 'median_image_size_in_voxels': [308.0, 309.0], 'spacing': [1.0, 1.0], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [48, 192, 192], 'median_image_size_in_voxels': [87.0, 308.0, 309.0], 'spacing': [3.0, 1.0, 1.0], 'normalization_schemes': ['CTNormalizationClippingSynthrad2025'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False}}, 'experiment_planner_used': 'nnUNetPlannerResEncL', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 3071.0, 'mean': -215.1082763671875, 'median': -133.0, 'min': -1024.0, 'percentile_00_5': -1024.0, 'percentile_99_5': 1345.0, 'std': 459.63665771484375}}}",
+    "preprocessed_dataset_folder": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/preprocessed/Dataset542_synthrad2025_task2_CBCT_HN_pre_v2r_stitched_masked_both/nnUNetPlans_3d_fullres",
+    "preprocessed_dataset_folder_base": "/datasets/work/hb-synthrad2023/work/synthrad2025/bw_workplace/data/nnunet_struct/preprocessed/Dataset542_synthrad2025_task2_CBCT_HN_pre_v2r_stitched_masked_both",
+    "save_every": "50",
+    "torch_version": "2.7.1+cu126",
+    "unpack_dataset": "True",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}

Dataset542/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_0/validation/summary.json

@@ -0,0 +1,52 @@
+{
+    "mae": {
+        "mean": 59.6831403703124,
+        "std": 13.282268575250406,
+        "max": 135.26334083952912,
+        "min": 43.70561993673689,
+        "25pc": 50.90544320106635,
+        "50pc": 57.59766416973342,
+        "75pc": 63.61140017268492,
+        "count": 65
+    },
+    "psnr": {
+        "mean": 30.41879186477032,
+        "std": 1.7822310331910258,
+        "max": 33.303479514662435,
+        "min": 21.938598217631913,
+        "25pc": 29.489967207335592,
+        "50pc": 30.549653942101994,
+        "75pc": 31.678537891293097,
+        "count": 65
+    },
+    "ms_ssim": {
+        "mean": 0.972537866604284,
+        "std": 0.023400539772748585,
+        "max": 0.9896922348656185,
+        "min": 0.8256950304841117,
+        "25pc": 0.972346153083238,
+        "50pc": 0.9793511650351168,
+        "75pc": 0.9834134511089297,
+        "count": 65
+    },
+    "DICE": {
+        "mean": 0.8757181415191063,
+        "std": 0.04152858997642459,
+        "max": 0.9331912994384766,
+        "min": 0.7493325471878052,
+        "25pc": 0.8536831736564636,
+        "50pc": 0.885571300983429,
+        "75pc": 0.907253623008728,
+        "count": 65
+    },
+    "HD95": {
+        "mean": 1.9038109889397254,
+        "std": 0.8689346520839407,
+        "max": 4.7832865715026855,
+        "min": 1.1818181276321411,
+        "25pc": 1.3928370475769043,
+        "50pc": 1.54265558719635,
+        "75pc": 2.0603861808776855,
+        "count": 65
+    }
+}

Dataset542/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/plans.json

@@ -0,0 +1,356 @@
+{
+    "dataset_name": "Dataset542_synthrad2025_task2_CBCT_HN_pre_v2r_stitched_masked_both",
+    "plans_name": "nnUNetResEncUNetLPlans",
+    "original_median_spacing_after_transp": [
+        3.0,
+        1.0,
+        1.0
+    ],
+    "original_median_shape_after_transp": [
+        87,
+        308,
+        309
+    ],
+    "image_reader_writer": "SimpleITKIO",
+    "transpose_forward": [
+        0,
+        1,
+        2
+    ],
+    "transpose_backward": [
+        0,
+        1,
+        2
+    ],
+    "configurations": {
+        "2d": {
+            "data_identifier": "nnUNetPlans_2d",
+            "preprocessor_name": "DefaultPreprocessor",
+            "batch_size": 30,
+            "patch_size": [
+                320,
+                320
+            ],
+            "median_image_size_in_voxels": [
+                308.0,
+                309.0
+            ],
+            "spacing": [
+                1.0,
+                1.0
+            ],
+            "normalization_schemes": [
+                "CTNormalizationClippingSynthrad2025"
+            ],
+            "use_mask_for_norm": [
+                false
+            ],
+            "resampling_fn_data": "resample_data_or_seg_to_shape",
+            "resampling_fn_seg": "resample_data_or_seg_to_shape",
+            "resampling_fn_data_kwargs": {
+                "is_seg": false,
+                "order": 3,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_seg_kwargs": {
+                "is_seg": true,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+            "resampling_fn_probabilities_kwargs": {
+                "is_seg": false,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "architecture": {
+                "network_class_name": "dynamic_network_architectures.architectures.unet.ResidualEncoderUNet",
+                "arch_kwargs": {
+                    "n_stages": 7,
+                    "features_per_stage": [
+                        32,
+                        64,
+                        128,
+                        256,
+                        512,
+                        512,
+                        512
+                    ],
+                    "conv_op": "torch.nn.modules.conv.Conv2d",
+                    "kernel_sizes": [
+                        [
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3
+                        ]
+                    ],
+                    "strides": [
+                        [
+                            1,
+                            1
+                        ],
+                        [
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2
+                        ]
+                    ],
+                    "n_blocks_per_stage": [
+                        1,
+                        3,
+                        4,
+                        6,
+                        6,
+                        6,
+                        6
+                    ],
+                    "n_conv_per_stage_decoder": [
+                        1,
+                        1,
+                        1,
+                        1,
+                        1,
+                        1
+                    ],
+                    "conv_bias": true,
+                    "norm_op": "torch.nn.modules.instancenorm.InstanceNorm2d",
+                    "norm_op_kwargs": {
+                        "eps": 1e-05,
+                        "affine": true
+                    },
+                    "dropout_op": null,
+                    "dropout_op_kwargs": null,
+                    "nonlin": "torch.nn.LeakyReLU",
+                    "nonlin_kwargs": {
+                        "inplace": true
+                    }
+                },
+                "_kw_requires_import": [
+                    "conv_op",
+                    "norm_op",
+                    "dropout_op",
+                    "nonlin"
+                ]
+            },
+            "batch_dice": true
+        },
+        "3d_fullres": {
+            "data_identifier": "nnUNetPlans_3d_fullres",
+            "preprocessor_name": "DefaultPreprocessor",
+            "batch_size": 2,
+            "patch_size": [
+                48,
+                192,
+                192
+            ],
+            "median_image_size_in_voxels": [
+                87.0,
+                308.0,
+                309.0
+            ],
+            "spacing": [
+                3.0,
+                1.0,
+                1.0
+            ],
+            "normalization_schemes": [
+                "CTNormalizationClippingSynthrad2025"
+            ],
+            "use_mask_for_norm": [
+                false
+            ],
+            "resampling_fn_data": "resample_data_or_seg_to_shape",
+            "resampling_fn_seg": "resample_data_or_seg_to_shape",
+            "resampling_fn_data_kwargs": {
+                "is_seg": false,
+                "order": 3,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_seg_kwargs": {
+                "is_seg": true,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+            "resampling_fn_probabilities_kwargs": {
+                "is_seg": false,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "architecture": {
+                "network_class_name": "dynamic_network_architectures.architectures.unet.ResidualEncoderUNet",
+                "arch_kwargs": {
+                    "n_stages": 6,
+                    "features_per_stage": [
+                        32,
+                        64,
+                        128,
+                        256,
+                        320,
+                        320
+                    ],
+                    "conv_op": "torch.nn.modules.conv.Conv3d",
+                    "kernel_sizes": [
+                        [
+                            1,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ]
+                    ],
+                    "strides": [
+                        [
+                            1,
+                            1,
+                            1
+                        ],
+                        [
+                            1,
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2,
+                            2
+                        ],
+                        [
+                            1,
+                            2,
+                            2
+                        ]
+                    ],
+                    "n_blocks_per_stage": [
+                        1,
+                        3,
+                        4,
+                        6,
+                        6,
+                        6
+                    ],
+                    "n_conv_per_stage_decoder": [
+                        1,
+                        1,
+                        1,
+                        1,
+                        1
+                    ],
+                    "conv_bias": true,
+                    "norm_op": "torch.nn.modules.instancenorm.InstanceNorm3d",
+                    "norm_op_kwargs": {
+                        "eps": 1e-05,
+                        "affine": true
+                    },
+                    "dropout_op": null,
+                    "dropout_op_kwargs": null,
+                    "nonlin": "torch.nn.LeakyReLU",
+                    "nonlin_kwargs": {
+                        "inplace": true
+                    }
+                },
+                "_kw_requires_import": [
+                    "conv_op",
+                    "norm_op",
+                    "dropout_op",
+                    "nonlin"
+                ]
+            },
+            "batch_dice": false
+        }
+    },
+    "experiment_planner_used": "nnUNetPlannerResEncL",
+    "label_manager": "LabelManager",
+    "foreground_intensity_properties_per_channel": {
+        "0": {
+            "max": 3071.0,
+            "mean": -215.1082763671875,
+            "median": -133.0,
+            "min": -1024.0,
+            "percentile_00_5": -1024.0,
+            "percentile_99_5": 1345.0,
+            "std": 459.63665771484375
+        }
+    }
+}

Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/dataset.json

@@ -0,0 +1,14 @@
+{
+    "labels": {
+        "label_001": "1",
+        "background": 0
+    },
+    "channel_names": {
+        "0": "CT_zscore_synthrad"
+    },
+    "numTraining": 321,
+    "file_ending": ".nii.gz",
+    "test_set": [
+        "case_0000"
+    ]
+}

Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/dataset_fingerprint.json

@@ -0,0 +1,3228 @@
+{
+    "foreground_intensity_properties_per_channel": {
+        "0": {
+            "max": 3071.0,
+            "mean": -330.8976135253906,
+            "median": -243.0,
+            "min": -1024.0,
+            "percentile_00_5": -1024.0,
+            "percentile_99_5": 563.0,
+            "std": 371.2642822265625
+        }
+    },
+    "median_relative_size_after_cropping": 1.0,
+    "shapes_after_crop": [
+        [
+            81,
+            311,
+            310
+        ],
+        [
+            78,
+            307,
+            308
+        ],
+        [
+            80,
+            359,
+            451
+        ],
+        [
+            81,
+            450,
+            450
+        ],
+        [
+            88,
+            310,
+            311
+        ],
+        [
+            81,
+            377,
+            451
+        ],
+        [
+            87,
+            309,
+            309
+        ],
+        [
+            80,
+            370,
+            451
+        ],
+        [
+            81,
+            370,
+            451
+        ],
+        [
+            80,
+            449,
+            453
+        ],
+        [
+            81,
+            380,
+            448
+        ],
+        [
+            87,
+            311,
+            311
+        ],
+        [
+            80,
+            429,
+            448
+        ],
+        [
+            87,
+            311,
+            307
+        ],
+        [
+            86,
+            316,
+            311
+        ],
+        [
+            87,
+            308,
+            311
+        ],
+        [
+            87,
+            312,
+            308
+        ],
+        [
+            88,
+            307,
+            308
+        ],
+        [
+            86,
+            314,
+            310
+        ],
+        [
+            79,
+            312,
+            310
+        ],
+        [
+            87,
+            308,
+            311
+        ],
+        [
+            87,
+            310,
+            312
+        ],
+        [
+            85,
+            310,
+            319
+        ],
+        [
+            87,
+            310,
+            309
+        ],
+        [
+            87,
+            310,
+            309
+        ],
+        [
+            81,
+            308,
+            309
+        ],
+        [
+            81,
+            290,
+            285
+        ],
+        [
+            88,
+            288,
+            291
+        ],
+        [
+            88,
+            310,
+            310
+        ],
+        [
+            88,
+            308,
+            297
+        ],
+        [
+            87,
+            310,
+            309
+        ],
+        [
+            86,
+            316,
+            309
+        ],
+        [
+            84,
+            308,
+            309
+        ],
+        [
+            87,
+            310,
+            311
+        ],
+        [
+            87,
+            311,
+            308
+        ],
+        [
+            86,
+            314,
+            309
+        ],
+        [
+            88,
+            310,
+            299
+        ],
+        [
+            87,
+            312,
+            309
+        ],
+        [
+            88,
+            309,
+            309
+        ],
+        [
+            87,
+            310,
+            289
+        ],
+        [
+            88,
+            308,
+            309
+        ],
+        [
+            74,
+            308,
+            313
+        ],
+        [
+            88,
+            308,
+            309
+        ],
+        [
+            82,
+            310,
+            308
+        ],
+        [
+            88,
+            309,
+            307
+        ],
+        [
+            88,
+            311,
+            309
+        ],
+        [
+            67,
+            309,
+            312
+        ],
+        [
+            86,
+            314,
+            309
+        ],
+        [
+            87,
+            309,
+            313
+        ],
+        [
+            80,
+            296,
+            286
+        ],
+        [
+            87,
+            308,
+            309
+        ],
+        [
+            81,
+            309,
+            311
+        ],
+        [
+            88,
+            308,
+            307
+        ],
+        [
+            87,
+            307,
+            309
+        ],
+        [
+            86,
+            308,
+            313
+        ],
+        [
+            86,
+            314,
+            309
+        ],
+        [
+            87,
+            312,
+            308
+        ],
+        [
+            87,
+            311,
+            311
+        ],
+        [
+            87,
+            311,
+            309
+        ],
+        [
+            86,
+            313,
+            312
+        ],
+        [
+            86,
+            317,
+            311
+        ],
+        [
+            87,
+            310,
+            312
+        ],
+        [
+            87,
+            313,
+            310
+        ],
+        [
+            87,
+            312,
+            309
+        ],
+        [
+            76,
+            307,
+            293
+        ],
+        [
+            78,
+            450,
+            434
+        ],
+        [
+            79,
+            450,
+            456
+        ],
+        [
+            77,
+            425,
+            446
+        ],
+        [
+            76,
+            452,
+            432
+        ],
+        [
+            82,
+            449,
+            449
+        ],
+        [
+            81,
+            451,
+            448
+        ],
+        [
+            80,
+            449,
+            425
+        ],
+        [
+            81,
+            452,
+            451
+        ],
+        [
+            79,
+            449,
+            455
+        ],
+        [
+            73,
+            440,
+            456
+        ],
+        [
+            77,
+            452,
+            442
+        ],
+        [
+            80,
+            449,
+            453
+        ],
+        [
+            78,
+            450,
+            436
+        ],
+        [
+            80,
+            451,
+            454
+        ],
+        [
+            81,
+            451,
+            450
+        ],
+        [
+            80,
+            452,
+            453
+        ],
+        [
+            80,
+            452,
+            449
+        ],
+        [
+            74,
+            454,
+            433
+        ],
+        [
+            79,
+            449,
+            455
+        ],
+        [
+            81,
+            448,
+            452
+        ],
+        [
+            80,
+            453,
+            450
+        ],
+        [
+            80,
+            449,
+            449
+        ],
+        [
+            81,
+            451,
+            450
+        ],
+        [
+            78,
+            437,
+            438
+        ],
+        [
+            79,
+            454,
+            429
+        ],
+        [
+            77,
+            454,
+            457
+        ],
+        [
+            81,
+            451,
+            433
+        ],
+        [
+            79,
+            455,
+            450
+        ],
+        [
+            79,
+            457,
+            452
+        ],
+        [
+            82,
+            451,
+            449
+        ],
+        [
+            80,
+            449,
+            455
+        ],
+        [
+            81,
+            450,
+            449
+        ],
+        [
+            79,
+            447,
+            448
+        ],
+        [
+            80,
+            450,
+            452
+        ],
+        [
+            81,
+            449,
+            451
+        ],
+        [
+            81,
+            450,
+            446
+        ],
+        [
+            81,
+            451,
+            450
+        ],
+        [
+            62,
+            451,
+            449
+        ],
+        [
+            80,
+            449,
+            454
+        ],
+        [
+            81,
+            450,
+            450
+        ],
+        [
+            80,
+            451,
+            453
+        ],
+        [
+            82,
+            450,
+            447
+        ],
+        [
+            82,
+            449,
+            449
+        ],
+        [
+            80,
+            450,
+            425
+        ],
+        [
+            78,
+            452,
+            457
+        ],
+        [
+            80,
+            430,
+            423
+        ],
+        [
+            77,
+            448,
+            429
+        ],
+        [
+            78,
+            449,
+            458
+        ],
+        [
+            80,
+            449,
+            451
+        ],
+        [
+            81,
+            451,
+            449
+        ],
+        [
+            81,
+            452,
+            442
+        ],
+        [
+            79,
+            454,
+            456
+        ],
+        [
+            79,
+            450,
+            402
+        ],
+        [
+            79,
+            450,
+            456
+        ],
+        [
+            81,
+            452,
+            449
+        ],
+        [
+            80,
+            453,
+            449
+        ],
+        [
+            77,
+            457,
+            453
+        ],
+        [
+            82,
+            450,
+            449
+        ],
+        [
+            79,
+            450,
+            442
+        ],
+        [
+            81,
+            450,
+            450
+        ],
+        [
+            80,
+            449,
+            432
+        ],
+        [
+            81,
+            448,
+            454
+        ],
+        [
+            70,
+            451,
+            457
+        ],
+        [
+            80,
+            451,
+            454
+        ],
+        [
+            80,
+            425,
+            449
+        ],
+        [
+            87,
+            310,
+            298
+        ],
+        [
+            87,
+            289,
+            309
+        ],
+        [
+            87,
+            293,
+            309
+        ],
+        [
+            86,
+            307,
+            314
+        ],
+        [
+            88,
+            308,
+            309
+        ],
+        [
+            88,
+            309,
+            308
+        ],
+        [
+            75,
+            449,
+            462
+        ],
+        [
+            88,
+            309,
+            309
+        ],
+        [
+            87,
+            311,
+            308
+        ],
+        [
+            87,
+            278,
+            315
+        ],
+        [
+            87,
+            308,
+            312
+        ],
+        [
+            81,
+            450,
+            451
+        ],
+        [
+            88,
+            289,
+            308
+        ],
+        [
+            85,
+            317,
+            314
+        ],
+        [
+            86,
+            311,
+            315
+        ],
+        [
+            82,
+            421,
+            450
+        ],
+        [
+            87,
+            309,
+            284
+        ],
+        [
+            81,
+            450,
+            447
+        ],
+        [
+            88,
+            311,
+            311
+        ],
+        [
+            88,
+            311,
+            312
+        ],
+        [
+            81,
+            422,
+            394
+        ],
+        [
+            87,
+            288,
+            312
+        ],
+        [
+            87,
+            312,
+            300
+        ],
+        [
+            81,
+            452,
+            451
+        ],
+        [
+            87,
+            311,
+            311
+        ],
+        [
+            84,
+            316,
+            318
+        ],
+        [
+            80,
+            451,
+            452
+        ],
+        [
+            87,
+            289,
+            311
+        ],
+        [
+            81,
+            450,
+            451
+        ],
+        [
+            86,
+            316,
+            309
+        ],
+        [
+            81,
+            448,
+            449
+        ],
+        [
+            86,
+            310,
+            302
+        ],
+        [
+            87,
+            314,
+            310
+        ],
+        [
+            81,
+            450,
+            448
+        ],
+        [
+            88,
+            309,
+            309
+        ],
+        [
+            86,
+            313,
+            314
+        ],
+        [
+            80,
+            448,
+            453
+        ],
+        [
+            87,
+            308,
+            314
+        ],
+        [
+            84,
+            320,
+            310
+        ],
+        [
+            81,
+            451,
+            451
+        ],
+        [
+            88,
+            310,
+            306
+        ],
+        [
+            82,
+            308,
+            309
+        ],
+        [
+            87,
+            290,
+            312
+        ],
+        [
+            88,
+            289,
+            280
+        ],
+        [
+            88,
+            309,
+            308
+        ],
+        [
+            75,
+            427,
+            452
+        ],
+        [
+            80,
+            435,
+            433
+        ],
+        [
+            86,
+            294,
+            310
+        ],
+        [
+            88,
+            310,
+            304
+        ],
+        [
+            80,
+            453,
+            451
+        ],
+        [
+            86,
+            297,
+            315
+        ],
+        [
+            85,
+            313,
+            314
+        ],
+        [
+            87,
+            311,
+            308
+        ],
+        [
+            88,
+            307,
+            309
+        ],
+        [
+            87,
+            308,
+            311
+        ],
+        [
+            84,
+            321,
+            315
+        ],
+        [
+            87,
+            309,
+            310
+        ],
+        [
+            85,
+            309,
+            299
+        ],
+        [
+            81,
+            309,
+            314
+        ],
+        [
+            74,
+            309,
+            308
+        ],
+        [
+            86,
+            297,
+            313
+        ],
+        [
+            87,
+            310,
+            298
+        ],
+        [
+            88,
+            310,
+            310
+        ],
+        [
+            99,
+            308,
+            308
+        ],
+        [
+            128,
+            384,
+            392
+        ],
+        [
+            70,
+            392,
+            393
+        ],
+        [
+            128,
+            392,
+            393
+        ],
+        [
+            71,
+            472,
+            499
+        ],
+        [
+            71,
+            463,
+            499
+        ],
+        [
+            75,
+            468,
+            494
+        ],
+        [
+            130,
+            471,
+            499
+        ],
+        [
+            133,
+            479,
+            499
+        ],
+        [
+            114,
+            394,
+            406
+        ],
+        [
+            137,
+            469,
+            499
+        ],
+        [
+            128,
+            467,
+            499
+        ],
+        [
+            70,
+            466,
+            498
+        ],
+        [
+            99,
+            310,
+            311
+        ],
+        [
+            131,
+            477,
+            499
+        ],
+        [
+            71,
+            467,
+            499
+        ],
+        [
+            74,
+            499,
+            496
+        ],
+        [
+            132,
+            469,
+            499
+        ],
+        [
+            99,
+            308,
+            307
+        ],
+        [
+            130,
+            472,
+            499
+        ],
+        [
+            93,
+            310,
+            309
+        ],
+        [
+            128,
+            465,
+            499
+        ],
+        [
+            98,
+            308,
+            315
+        ],
+        [
+            71,
+            467,
+            499
+        ],
+        [
+            91,
+            312,
+            307
+        ],
+        [
+            71,
+            468,
+            499
+        ],
+        [
+            75,
+            464,
+            499
+        ],
+        [
+            72,
+            469,
+            499
+        ],
+        [
+            123,
+            499,
+            499
+        ],
+        [
+            72,
+            469,
+            499
+        ],
+        [
+            131,
+            499,
+            499
+        ],
+        [
+            131,
+            467,
+            499
+        ],
+        [
+            71,
+            465,
+            499
+        ],
+        [
+            139,
+            467,
+            499
+        ],
+        [
+            137,
+            466,
+            499
+        ],
+        [
+            71,
+            465,
+            495
+        ],
+        [
+            129,
+            499,
+            499
+        ],
+        [
+            70,
+            466,
+            494
+        ],
+        [
+            133,
+            473,
+            499
+        ],
+        [
+            131,
+            468,
+            499
+        ],
+        [
+            131,
+            471,
+            499
+        ],
+        [
+            128,
+            467,
+            499
+        ],
+        [
+            131,
+            473,
+            499
+        ],
+        [
+            122,
+            499,
+            498
+        ],
+        [
+            132,
+            446,
+            499
+        ],
+        [
+            73,
+            468,
+            499
+        ],
+        [
+            99,
+            316,
+            308
+        ],
+        [
+            133,
+            474,
+            499
+        ],
+        [
+            131,
+            499,
+            487
+        ],
+        [
+            130,
+            470,
+            496
+        ],
+        [
+            79,
+            474,
+            499
+        ],
+        [
+            70,
+            467,
+            499
+        ],
+        [
+            128,
+            472,
+            499
+        ],
+        [
+            71,
+            471,
+            499
+        ],
+        [
+            72,
+            540,
+            539
+        ],
+        [
+            79,
+            467,
+            499
+        ],
+        [
+            75,
+            468,
+            492
+        ],
+        [
+            74,
+            470,
+            492
+        ],
+        [
+            72,
+            468,
+            499
+        ],
+        [
+            71,
+            466,
+            499
+        ],
+        [
+            132,
+            470,
+            499
+        ],
+        [
+            131,
+            472,
+            499
+        ],
+        [
+            130,
+            469,
+            499
+        ],
+        [
+            67,
+            497,
+            499
+        ],
+        [
+            66,
+            477,
+            497
+        ],
+        [
+            70,
+            478,
+            502
+        ],
+        [
+            69,
+            499,
+            500
+        ],
+        [
+            69,
+            497,
+            486
+        ],
+        [
+            66,
+            496,
+            498
+        ],
+        [
+            67,
+            449,
+            498
+        ],
+        [
+            71,
+            478,
+            503
+        ],
+        [
+            66,
+            496,
+            497
+        ],
+        [
+            68,
+            499,
+            499
+        ],
+        [
+            55,
+            480,
+            501
+        ],
+        [
+            66,
+            497,
+            498
+        ],
+        [
+            60,
+            477,
+            500
+        ],
+        [
+            69,
+            482,
+            479
+        ],
+        [
+            70,
+            497,
+            503
+        ],
+        [
+            68,
+            500,
+            495
+        ],
+        [
+            65,
+            500,
+            499
+        ],
+        [
+            70,
+            477,
+            502
+        ],
+        [
+            68,
+            499,
+            499
+        ],
+        [
+            67,
+            490,
+            497
+        ],
+        [
+            67,
+            496,
+            499
+        ],
+        [
+            65,
+            477,
+            497
+        ],
+        [
+            68,
+            492,
+            498
+        ],
+        [
+            69,
+            499,
+            498
+        ],
+        [
+            72,
+            440,
+            494
+        ],
+        [
+            73,
+            503,
+            501
+        ],
+        [
+            74,
+            505,
+            502
+        ],
+        [
+            67,
+            497,
+            478
+        ],
+        [
+            67,
+            499,
+            482
+        ],
+        [
+            70,
+            440,
+            482
+        ],
+        [
+            66,
+            498,
+            496
+        ],
+        [
+            67,
+            464,
+            499
+        ],
+        [
+            70,
+            481,
+            501
+        ],
+        [
+            66,
+            490,
+            478
+        ],
+        [
+            67,
+            498,
+            497
+        ],
+        [
+            65,
+            497,
+            496
+        ],
+        [
+            68,
+            499,
+            497
+        ],
+        [
+            67,
+            478,
+            498
+        ],
+        [
+            72,
+            501,
+            504
+        ],
+        [
+            70,
+            494,
+            507
+        ],
+        [
+            66,
+            498,
+            496
+        ],
+        [
+            71,
+            297,
+            296
+        ],
+        [
+            69,
+            500,
+            498
+        ],
+        [
+            69,
+            500,
+            501
+        ],
+        [
+            68,
+            498,
+            500
+        ],
+        [
+            69,
+            500,
+            499
+        ],
+        [
+            66,
+            497,
+            498
+        ],
+        [
+            68,
+            499,
+            498
+        ],
+        [
+            68,
+            477,
+            500
+        ],
+        [
+            75,
+            481,
+            500
+        ],
+        [
+            66,
+            488,
+            497
+        ],
+        [
+            73,
+            505,
+            500
+        ],
+        [
+            78,
+            512,
+            503
+        ],
+        [
+            67,
+            498,
+            499
+        ],
+        [
+            71,
+            500,
+            503
+        ],
+        [
+            72,
+            501,
+            502
+        ],
+        [
+            72,
+            503,
+            499
+        ],
+        [
+            72,
+            299,
+            297
+        ],
+        [
+            68,
+            500,
+            500
+        ],
+        [
+            68,
+            499,
+            499
+        ],
+        [
+            69,
+            493,
+            501
+        ],
+        [
+            70,
+            500,
+            501
+        ],
+        [
+            67,
+            499,
+            498
+        ],
+        [
+            67,
+            498,
+            497
+        ],
+        [
+            68,
+            498,
+            498
+        ]
+    ],
+    "spacings": [
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ],
+        [
+            3.0,
+            1.0,
+            1.0
+        ]
+    ]
+}

Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_0/checkpoint_final.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1695e6f921488dbc372d8c7600f748c8d56fcf28cf71ea3c14bcacc78cf2aabf
+size 815580525

Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/fold_0/validation/summary.json

@@ -0,0 +1,52 @@
+{
+    "mae": {
+        "mean": 52.80768787193642,
+        "std": 39.25411217734592,
+        "max": 349.4295323444315,
+        "min": 30.53782153506361,
+        "25pc": 39.67259547669727,
+        "50pc": 44.417437064864046,
+        "75pc": 56.57117028196328,
+        "count": 65
+    },
+    "psnr": {
+        "mean": 32.25481819350785,
+        "std": 2.9858789867732187,
+        "max": 36.561440579128075,
+        "min": 17.068229372936194,
+        "25pc": 30.947542147774367,
+        "50pc": 32.57369524597034,
+        "75pc": 34.17962467969445,
+        "count": 65
+    },
+    "ms_ssim": {
+        "mean": 0.9536834487302785,
+        "std": 0.08019347284318636,
+        "max": 0.9915810123537975,
+        "min": 0.3802523438307981,
+        "25pc": 0.9586479045957355,
+        "50pc": 0.9741513563984106,
+        "75pc": 0.9824337046998175,
+        "count": 65
+    },
+    "DICE": {
+        "mean": 0.8256282861416157,
+        "std": 0.06491444113684663,
+        "max": 0.9275574088096619,
+        "min": 0.5898815393447876,
+        "25pc": 0.7742491364479065,
+        "50pc": 0.8340623378753662,
+        "75pc": 0.8758383393287659,
+        "count": 65
+    },
+    "HD95": {
+        "mean": 4.208385368493887,
+        "std": 3.814029574946056,
+        "max": 30.388620376586914,
+        "min": 1.1522142887115479,
+        "25pc": 2.4057328701019287,
+        "50pc": 3.2774264812469482,
+        "75pc": 4.871885299682617,
+        "count": 65
+    }
+}

Dataset544/nnUNetTrainerMRCT_loss_masked_perception_masked__nnUNetResEncUNetLPlans__3d_fullres/plans.json

@@ -0,0 +1,521 @@
+{
+    "dataset_name": "Dataset544_synthrad2025_task2_CBCT_TH_pre_v2r_stitched_masked_both",
+    "plans_name": "nnUNetResEncUNetLPlans",
+    "original_median_spacing_after_transp": [
+        3.0,
+        1.0,
+        1.0
+    ],
+    "original_median_shape_after_transp": [
+        81,
+        450,
+        450
+    ],
+    "image_reader_writer": "SimpleITKIO",
+    "transpose_forward": [
+        0,
+        1,
+        2
+    ],
+    "transpose_backward": [
+        0,
+        1,
+        2
+    ],
+    "configurations": {
+        "2d": {
+            "data_identifier": "nnUNetPlans_2d",
+            "preprocessor_name": "DefaultPreprocessor",
+            "batch_size": 13,
+            "patch_size": [
+                512,
+                448
+            ],
+            "median_image_size_in_voxels": [
+                450.0,
+                450.0
+            ],
+            "spacing": [
+                1.0,
+                1.0
+            ],
+            "normalization_schemes": [
+                "CTNormalizationClippingSynthrad2025"
+            ],
+            "use_mask_for_norm": [
+                false
+            ],
+            "resampling_fn_data": "resample_data_or_seg_to_shape",
+            "resampling_fn_seg": "resample_data_or_seg_to_shape",
+            "resampling_fn_data_kwargs": {
+                "is_seg": false,
+                "order": 3,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_seg_kwargs": {
+                "is_seg": true,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+            "resampling_fn_probabilities_kwargs": {
+                "is_seg": false,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "architecture": {
+                "network_class_name": "dynamic_network_architectures.architectures.unet.ResidualEncoderUNet",
+                "arch_kwargs": {
+                    "n_stages": 7,
+                    "features_per_stage": [
+                        32,
+                        64,
+                        128,
+                        256,
+                        512,
+                        512,
+                        512
+                    ],
+                    "conv_op": "torch.nn.modules.conv.Conv2d",
+                    "kernel_sizes": [
+                        [
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3
+                        ]
+                    ],
+                    "strides": [
+                        [
+                            1,
+                            1
+                        ],
+                        [
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2
+                        ]
+                    ],
+                    "n_blocks_per_stage": [
+                        1,
+                        3,
+                        4,
+                        6,
+                        6,
+                        6,
+                        6
+                    ],
+                    "n_conv_per_stage_decoder": [
+                        1,
+                        1,
+                        1,
+                        1,
+                        1,
+                        1
+                    ],
+                    "conv_bias": true,
+                    "norm_op": "torch.nn.modules.instancenorm.InstanceNorm2d",
+                    "norm_op_kwargs": {
+                        "eps": 1e-05,
+                        "affine": true
+                    },
+                    "dropout_op": null,
+                    "dropout_op_kwargs": null,
+                    "nonlin": "torch.nn.LeakyReLU",
+                    "nonlin_kwargs": {
+                        "inplace": true
+                    }
+                },
+                "_kw_requires_import": [
+                    "conv_op",
+                    "norm_op",
+                    "dropout_op",
+                    "nonlin"
+                ]
+            },
+            "batch_dice": true
+        },
+        "3d_lowres": {
+            "data_identifier": "nnUNetResEncUNetLPlans_3d_lowres",
+            "preprocessor_name": "DefaultPreprocessor",
+            "batch_size": 2,
+            "patch_size": [
+                56,
+                224,
+                192
+            ],
+            "median_image_size_in_voxels": [
+                81,
+                298,
+                298
+            ],
+            "spacing": [
+                3.0,
+                1.512589724855112,
+                1.512589724855112
+            ],
+            "normalization_schemes": [
+                "CTNormalizationClippingSynthrad2025"
+            ],
+            "use_mask_for_norm": [
+                false
+            ],
+            "resampling_fn_data": "resample_data_or_seg_to_shape",
+            "resampling_fn_seg": "resample_data_or_seg_to_shape",
+            "resampling_fn_data_kwargs": {
+                "is_seg": false,
+                "order": 3,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_seg_kwargs": {
+                "is_seg": true,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+            "resampling_fn_probabilities_kwargs": {
+                "is_seg": false,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "architecture": {
+                "network_class_name": "dynamic_network_architectures.architectures.unet.ResidualEncoderUNet",
+                "arch_kwargs": {
+                    "n_stages": 6,
+                    "features_per_stage": [
+                        32,
+                        64,
+                        128,
+                        256,
+                        320,
+                        320
+                    ],
+                    "conv_op": "torch.nn.modules.conv.Conv3d",
+                    "kernel_sizes": [
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ]
+                    ],
+                    "strides": [
+                        [
+                            1,
+                            1,
+                            1
+                        ],
+                        [
+                            2,
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2,
+                            2
+                        ],
+                        [
+                            1,
+                            2,
+                            2
+                        ],
+                        [
+                            1,
+                            2,
+                            2
+                        ]
+                    ],
+                    "n_blocks_per_stage": [
+                        1,
+                        3,
+                        4,
+                        6,
+                        6,
+                        6
+                    ],
+                    "n_conv_per_stage_decoder": [
+                        1,
+                        1,
+                        1,
+                        1,
+                        1
+                    ],
+                    "conv_bias": true,
+                    "norm_op": "torch.nn.modules.instancenorm.InstanceNorm3d",
+                    "norm_op_kwargs": {
+                        "eps": 1e-05,
+                        "affine": true
+                    },
+                    "dropout_op": null,
+                    "dropout_op_kwargs": null,
+                    "nonlin": "torch.nn.LeakyReLU",
+                    "nonlin_kwargs": {
+                        "inplace": true
+                    }
+                },
+                "_kw_requires_import": [
+                    "conv_op",
+                    "norm_op",
+                    "dropout_op",
+                    "nonlin"
+                ]
+            },
+            "batch_dice": false,
+            "next_stage": "3d_cascade_fullres"
+        },
+        "3d_fullres": {
+            "data_identifier": "nnUNetPlans_3d_fullres",
+            "preprocessor_name": "DefaultPreprocessor",
+            "batch_size": 2,
+            "patch_size": [
+                40,
+                224,
+                192
+            ],
+            "median_image_size_in_voxels": [
+                81.0,
+                450.0,
+                450.0
+            ],
+            "spacing": [
+                3.0,
+                1.0,
+                1.0
+            ],
+            "normalization_schemes": [
+                "CTNormalizationClippingSynthrad2025"
+            ],
+            "use_mask_for_norm": [
+                false
+            ],
+            "resampling_fn_data": "resample_data_or_seg_to_shape",
+            "resampling_fn_seg": "resample_data_or_seg_to_shape",
+            "resampling_fn_data_kwargs": {
+                "is_seg": false,
+                "order": 3,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_seg_kwargs": {
+                "is_seg": true,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+            "resampling_fn_probabilities_kwargs": {
+                "is_seg": false,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "architecture": {
+                "network_class_name": "dynamic_network_architectures.architectures.unet.ResidualEncoderUNet",
+                "arch_kwargs": {
+                    "n_stages": 6,
+                    "features_per_stage": [
+                        32,
+                        64,
+                        128,
+                        256,
+                        320,
+                        320
+                    ],
+                    "conv_op": "torch.nn.modules.conv.Conv3d",
+                    "kernel_sizes": [
+                        [
+                            1,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ],
+                        [
+                            3,
+                            3,
+                            3
+                        ]
+                    ],
+                    "strides": [
+                        [
+                            1,
+                            1,
+                            1
+                        ],
+                        [
+                            1,
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2,
+                            2
+                        ],
+                        [
+                            2,
+                            2,
+                            2
+                        ],
+                        [
+                            1,
+                            2,
+                            2
+                        ]
+                    ],
+                    "n_blocks_per_stage": [
+                        1,
+                        3,
+                        4,
+                        6,
+                        6,
+                        6
+                    ],
+                    "n_conv_per_stage_decoder": [
+                        1,
+                        1,
+                        1,
+                        1,
+                        1
+                    ],
+                    "conv_bias": true,
+                    "norm_op": "torch.nn.modules.instancenorm.InstanceNorm3d",
+                    "norm_op_kwargs": {
+                        "eps": 1e-05,
+                        "affine": true
+                    },
+                    "dropout_op": null,
+                    "dropout_op_kwargs": null,
+                    "nonlin": "torch.nn.LeakyReLU",
+                    "nonlin_kwargs": {
+                        "inplace": true
+                    }
+                },
+                "_kw_requires_import": [
+                    "conv_op",
+                    "norm_op",
+                    "dropout_op",
+                    "nonlin"
+                ]
+            },
+            "batch_dice": true
+        },
+        "3d_cascade_fullres": {
+            "inherits_from": "3d_fullres",
+            "previous_stage": "3d_lowres"
+        }
+    },
+    "experiment_planner_used": "nnUNetPlannerResEncL",
+    "label_manager": "LabelManager",
+    "foreground_intensity_properties_per_channel": {
+        "0": {
+            "max": 3071.0,
+            "mean": -330.8976135253906,
+            "median": -243.0,
+            "min": -1024.0,
+            "percentile_00_5": -1024.0,
+            "percentile_99_5": 563.0,
+            "std": 371.2642822265625
+        }
+    }
+}