Uploading None in /Users/camille.brianceau/aramis/clinicadl/out_test54

environment.txt

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+abagen==0.1.3
+absl-py==1.4.0
+alembic==1.11.1
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+CacheControl==0.12.11
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+cattrs==1.10.0
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+cffi @ file:///private/var/folders/nz/j6p8yfhx1mv_0grj5xl4650h0000gp/T/abs_f9af_qffaa/croots/recipe/cffi_1659598650277/work
+cfgv==3.3.1
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+click-option-group==0.5.5
+clikit @ file:///tmp/build/80754af9/clikit_1596900189978/work
+clinica==0.7.5
+-e git+ssh://[email protected]/camillebrianceau/clinicadl.git@2ceab5b3ac7f80abc667b5ad8bffe779d669bd3c#egg=clinicadl
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+cryptography @ file:///private/var/folders/nz/j6p8yfhx1mv_0grj5xl4650h0000gp/T/abs_20erp7jjos/croot/cryptography_1665612656373/work
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+mypy-extensions==1.0.0
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+platformdirs==3.5.1
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+pyOpenSSL @ file:///opt/conda/conda-bld/pyopenssl_1643788558760/work
+pyparsing==3.0.9
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+PySocks @ file:///Users/ktietz/Code/oss/ci_pkgs/pysocks_1626781349491/work
+pytest==7.3.1
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+pytest-timeout==2.1.0
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+tomlkit @ file:///private/var/folders/nz/j6p8yfhx1mv_0grj5xl4650h0000gp/T/abs_0dfcgmelf8/croots/recipe/tomlkit_1658946892438/work
+torch==1.13.1
+torchio==0.18.91
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+traits==6.4.1
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+zope.event==4.5.0
+zope.interface==5.5.0

information.log

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+- Date :	09 Aug 2023, 16:00:24
+
+- Path :	/Users/camille.brianceau/aramis/clinicadl/out_test5
+
+- Model :	Sequential(
+  (0): Sequential(
+    (0): Conv3d(1, 8, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
+    (1): BatchNorm3d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+    (2): ReLU()
+    (3): PadMaxPool3d(
+      (pool): MaxPool3d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
+      (pad): ConstantPad3d(padding=(1, 0, 0, 0, 1, 0), value=0)
+    )
+    (4): Conv3d(8, 16, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
+    (5): BatchNorm3d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+    (6): ReLU()
+    (7): PadMaxPool3d(
+      (pool): MaxPool3d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
+      (pad): ConstantPad3d(padding=(0, 0, 0, 0, 1, 0), value=0)
+    )
+    (8): Conv3d(16, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
+    (9): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+    (10): ReLU()
+    (11): PadMaxPool3d(
+      (pool): MaxPool3d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
+      (pad): ConstantPad3d(padding=(1, 0, 0, 0, 1, 0), value=0)
+    )
+    (12): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
+    (13): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+    (14): ReLU()
+    (15): PadMaxPool3d(
+      (pool): MaxPool3d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
+      (pad): ConstantPad3d(padding=(1, 0, 0, 0, 0, 0), value=0)
+    )
+    (16): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
+    (17): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+    (18): ReLU()
+    (19): PadMaxPool3d(
+      (pool): MaxPool3d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
+      (pad): ConstantPad3d(padding=(0, 0, 1, 0, 1, 0), value=0)
+    )
+  )
+  (1): Sequential(
+    (0): Flatten(start_dim=1, end_dim=-1)
+    (1): Dropout(p=0.0, inplace=False)
+    (2): Linear(in_features=32256, out_features=1300, bias=True)
+    (3): ReLU()
+    (4): Linear(in_features=1300, out_features=50, bias=True)
+    (5): ReLU()
+    (6): Linear(in_features=50, out_features=1, bias=True)
+  )
+)
+

maps.json

@@ -0,0 +1,77 @@
+{
+    "architecture": "Conv5_FC3",
+    "multi_network": false,
+    "dropout": 0.0,
+    "latent_space_size": 128,
+    "feature_size": 1024,
+    "n_conv": 4,
+    "io_layer_channels": 8,
+    "recons_weight": 1,
+    "kl_weight": 1,
+    "normalization": "batch",
+    "selection_metrics": [
+        "loss"
+    ],
+    "label": "age",
+    "loss": "MSELoss",
+    "gpu": false,
+    "n_proc": 2,
+    "batch_size": 8,
+    "evaluation_steps": 0,
+    "amp": false,
+    "seed": 0,
+    "deterministic": false,
+    "compensation": "memory",
+    "transfer_path": "",
+    "transfer_selection_metric": "loss",
+    "nb_unfrozen_layer": 0,
+    "use_extracted_features": false,
+    "multi_cohort": false,
+    "diagnoses": [
+        "AD",
+        "CN"
+    ],
+    "baseline": false,
+    "normalize": true,
+    "data_augmentation": false,
+    "sampler": "random",
+    "size_reduction": false,
+    "size_reduction_factor": 2,
+    "n_splits": 2,
+    "optimizer": "Adam",
+    "epochs": 5,
+    "learning_rate": 0.0001,
+    "weight_decay": 0.0001,
+    "patience": 0,
+    "tolerance": 0.0,
+    "accumulation_steps": 1,
+    "profiler": false,
+    "network_task": "regression",
+    "caps_directory": "/Users/camille.brianceau/aramis/clinicadl_data_ci/data_ci/train/in/caps_image",
+    "tsv_path": "/Users/camille.brianceau/aramis/clinicadl_data_ci/data_ci/train/in/labels_list/2_fold",
+    "preprocessing_dict": {
+        "preprocessing": "t1-linear",
+        "mode": "image",
+        "use_uncropped_image": false,
+        "prepare_dl": false,
+        "extract_json": "t1-linear_mode-image.json",
+        "file_type": {
+            "pattern": "*space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_T1w.nii.gz",
+            "description": "T1W Image registered using t1-linear and cropped (matrix size 169\u00d7208\u00d7179, 1 mm isotropic voxels)",
+            "needed_pipeline": "t1-linear"
+        }
+    },
+    "mode": "image",
+    "split": [],
+    "validation": "KFoldSplit",
+    "selection_threshold": null,
+    "label_code": null,
+    "num_networks": 1,
+    "output_size": 1,
+    "input_size": [
+        1,
+        169,
+        208,
+        179
+    ]
+}

split-0_model5.pth.tar

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