Add files using upload-large-folder tool

archives/datasets.tar.part-00

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+size 5368709120

archives/datasets.tar.part-01

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+size 5368709120

archives/datasets.tar.part-02

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archives/datasets.tar.part-03

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archives/datasets.tar.part-04

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+size 2897561600

archives/outputs.tar.part-00

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configs/data/amos.yaml

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+data:
+  manifest: data_json/amos_ct2mr_shared13_sfda.json
+  in_channels: 1
+  num_classes: 14
+  patch_size: [96, 96, 96]
+  batch_size: 1
+  val_batch_size: 1
+  intensity:
+    clip: null
+    normalize: zscore_nonzero

configs/data/crossmoda.yaml

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+data:
+  manifest: data_json/crossmoda_t1_to_t2.json
+  in_channels: 1
+  # background, vestibular schwannoma, cochlea
+  num_classes: 3
+  patch_size: [96, 96, 96]
+  batch_size: 1
+  val_batch_size: 1
+  intensity:
+    clip: null
+    normalize: zscore_nonzero

configs/data/mmwhs.yaml

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+data:
+  manifest: data_json/mmwhs_ct2mr.json
+  in_channels: 1
+  # Common public-subset MMWHS protocols often use 4 foreground cardiac structures plus background.
+  # Set num_classes to match your prepared labels.
+  num_classes: 5
+  patch_size: [96, 96, 96]
+  batch_size: 1
+  val_batch_size: 1
+  intensity:
+    clip: null
+    normalize: zscore_nonzero

configs/defaults.yaml

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+seed: 1337
+output_dir: outputs/default
+amp: true
+compile: false
+channels_last_3d: false
+num_workers: 4
+pin_memory: true
+wandb:
+  enabled: true
+  project: SACFlow-FM
+  entity: null
+  mode: online
+  tags: []
+  log_images_every: 250
+  watch_model: false
+
+distributed:
+  backend: nccl
+  find_unused_parameters: false
+
+data:
+  manifest: data_json/mmwhs_ct2mr.json
+  spacing: null
+  patch_size: [96, 96, 96]
+  batch_size: 1
+  val_batch_size: 1
+  cache_rate: 0.0
+  in_channels: 1
+  num_classes: 5
+  foreground_labels: null
+  intensity:
+    clip: null
+    normalize: zscore_nonzero
+  augmentation:
+    random_flip: true
+    random_intensity_shift: 0.10
+    random_intensity_scale: 0.10
+
+model:
+  name: unet3d
+  in_channels: 1
+  num_classes: 5
+  base_channels: 32
+  levels: 4
+  norm: instance
+  dropout: 0.0
+  deep_supervision: false
+  adapter:
+    enabled: false
+    bottleneck_ratio: 0.25
+    locations: [prelogit]
+
+optim:
+  optimizer: adamw
+  lr: 2.0e-4
+  weight_decay: 1.0e-4
+  betas: [0.9, 0.999]
+  scheduler: cosine
+  warmup_steps: 500
+  max_steps: 30000
+  grad_clip_norm: 12.0
+
+train:
+  mode: source_train
+  epochs: 100
+  steps_per_epoch: 250
+  val_every: 1
+  save_every: 5
+  log_every: 20
+  resume: null
+  source_checkpoint: null
+  memory_path: null
+  ema_decay: 0.99
+  pseudo_conf_threshold: 0.70
+  class_balanced_thresholds: false
+  loss:
+    ce: 1.0
+    dice: 1.0
+    entropy: 0.0
+    consistency: 0.0
+
+eval:
+  checkpoint: null
+  split: target_test
+  save_predictions: false
+  sliding_window: true
+  roi_size: [96, 96, 96]
+  sw_batch_size: 1
+  overlap: 0.5

environment.yml

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+name: sacflow
+channels:
+  - conda-forge
+  - defaults
+dependencies:
+  - python=3.10
+  - pip
+  - numpy
+  - scipy
+  - pandas
+  - scikit-image
+  - scikit-learn
+  - nibabel
+  - tqdm
+  - pyyaml
+  - rich
+  - matplotlib
+  - tensorboard
+  - pip:
+      - monai>=1.3.0
+      - wandb>=0.17.0
+      - einops>=0.7.0
+      - SimpleITK>=2.3.0
+      - connected-components-3d>=3.12.0
+      - medpy>=0.5.2
+      # Install torch/torchvision/torchaudio separately for your ROCm version.
+      # Example for ROCm 6.2/6.3 clusters, check official PyTorch selector:
+      # pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.2

restore_archives.sh

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+#!/usr/bin/env bash
+set -euo pipefail
+
+cd "$(dirname "$0")"
+
+if ls archives/datasets.tar.part-* >/dev/null 2>&1; then
+  cat archives/datasets.tar.part-* | tar -xf -
+fi
+
+if ls archives/outputs.tar.part-* >/dev/null 2>&1; then
+  cat archives/outputs.tar.part-* | tar -xf -
+fi
+
+if ls archives/wandb.tar.part-* >/dev/null 2>&1; then
+  cat archives/wandb.tar.part-* | tar -xf -
+fi
+
+echo "Restored archived folders."