single-shot-robot

Josselin's group workspace

Group: 3-DICE-1-CLDICE

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of 10

Tags

Notes

Author

josselin

State

Finished

Start time

September 1st, 2023 2:24:06 PM

Runtime

31m 46s

Tracked hours

31m 39s

Run path

single-shot-robot/SKIL-Reachbot_vessels_combined_degraded_segmentation_prod/9vvz4p5z

Linux-5.15.0-79-generic-x86_64-with-glibc2.17

Python version

3.8.16

Git repository

git clone git@github.com:JosselinSomervilleRoberts/ReachBot-Predictor.git

Git state

git checkout -b "3-DICE-1-CLDICE_009Z7FC5WU" 59ba068a0aee228cdba8ffe22efbd7516ce801d2

Command

tools/train.py config_vessels_11.py --amp

System Hardware

CPU count	18
Logical CPU count	36
GPU count	1
GPU type	NVIDIA RTX A6000

W&B CLI Version

0.15.4

Group

3-DICE-1-CLDICE

Config parameters are your model's inputs. Learn more

▶
Config parameters:{} 54 keys
- CHECKPOINT:
  "pretrain/upernet_vit-b16_mln_512x512_80k_ade20k_20210624_130547-0403cee1_fix.pth"
- ▶
  crop_size:[] 2 items
  - 0:
    512
  - 1:
    512
- ▶
  data_preprocessor:{} 7 keys
- data_root:
  "../datasets/vessels/combined_degraded"
- DATASET:
  "vessels_combined_degraded"
- dataset_type:
  "ReachbotDataset"
- ▶
  datasets_mappings:{} 9 keys
- DEBUG_PATH:
  "/media/jsomerviller/SSD2/output"
- DEBUG_STEP_INTERVAL:
  -1
- ▶
  default_checkpoint:{} 1 key
  - vit_vit-b16_mln_upernet:
    "pretrain/upernet_vit-b16_mln_512x512_80k_ade20k_20210624_130547-0403cee1_fix.pth"
- ▶
  default_hooks:{} 5 keys
- default_scope:
  "mmseg"
- ▶
  env_cfg:{} 3 keys
  - cudnn_benchmark:
    true
  - ▶
    dist_cfg:{} 1 key
    - backend:
      "nccl"
  - ▶
    mp_cfg:{} 2 keys
    - mp_start_method:
      "fork"
    - opencv_num_threads:
      0
- EVAL_EPOCH_INTERVAL:
  5
- launcher:
  "none"
- load_from:
  null
- log_level:
  "INFO"
- ▶
  log_processor:{} 1 key
  - by_epoch:
    false
- LOG_STEP_INTERVAL:
  10
- ▶
  LOSSES:{} 2 keys
  - cl_dice:
    1
  - dice:
    3
- ▶
  losses_mappings:{} 5 keys
- LR:
  "aggressive"
- memory_available:
  47.05792236328125
- METRIC:
  "ReachbotMetric"
- MODEL:
  "vit_vit-b16_mln_upernet"
- ▶
  model:{} 9 keys
- ▶
  norm_cfg:{} 2 keys
  - requires_grad:
    true
  - type:
    "SyncBN"
- num_epochs:
  200
- num_training_samples:
  85
- ▶
  optim_wrapper:{} 4 keys
  - loss_scale:
    "dynamic"
  - ▶
    optimizer:{} 4 keys
    - ▶
      betas:[] 2 items
      - 0:
        0.9
      - 1:
        0.999
    - lr:
      0.00005
    - type:
      "AdamW"
    - weight_decay:
      0.01
  - ▶
    paramwise_cfg:{} 1 key
    - ▶
      custom_keys:{} 3 keys
      - ▶
        cls_token:{} 1 key
        decay_mult:
        0
      - ▶
        norm:{} 1 key
        decay_mult:
        0
      - ▶
        pos_embed:{} 1 key
        decay_mult:
        0
  - type:
    "AmpOptimWrapper"
- ▶
  param_scheduler:[] 2 items
  - ▶
    0:{} 5 keys
  - ▶
    1:{} 6 keys
- resume:
  false
- SAVE_EPOCH_INTERVAL:
  -1
- ▶
  schedules_mappings:{} 1 key
  - aggressive:
    "custom_configs.schedules.aggressive"
- tag:
  "009Z7FC5WU"
- ▶
  test_cfg:{} 1 key
  - type:
    "TestLoop"
- ▶
  test_dataloader:{} 5 keys
- ▶
  test_evaluator:{} 1 key
  - type:
    "ReachbotMetric"
- ▶
  test_pipeline:[] 4 items
  - ▶
    0:{} 1 key
    - type:
      "LoadImageFromFile"
  - ▶
    1:{} 3 keys
    - keep_ratio:
      true
    - ▶
      scale:[] 2 items
      - 0:
        1,024
      - 1:
        512
    - type:
      "Resize"
  - ▶
    2:{} 2 keys
    - reduce_zero_label:
      false
    - type:
      "LoadAnnotations"
  - ▶
    3:{} 1 key
    - type:
      "PackSegInputs"
- train_batch_size:
  8
- ▶
  train_cfg:{} 3 keys
  - max_iters:
    2,125
  - type:
    "IterBasedTrainLoop"
  - val_interval:
    53
- ▶
  train_dataloader:{} 5 keys
- ▶
  train_pipeline:[] 7 items
- ▶
  tta_model:{} 1 key
  - type:
    "SegTTAModel"
- USE_WANDB:
  true
- ▶
  val_cfg:{} 1 key
  - type:
    "ValLoop"
- ▶
  visualizer:{} 4 keys
  - alpha:
    0.6
  - name:
    "visualizer"
  - type:
    "SegLocalVisualizer"
  - ▶
    vis_backends:[] 2 items
    - ▶
      0:{} 1 key
      - type:
        "LocalVisBackend"
    - ▶
      1:{} 2 keys
      - ▶
        init_kwargs:{} 4 keys
        entity:
        "single-shot-robot"
        group:
        "3-DICE-1-CLDICE"
        name:
        "3-DICE-1-CLDICE_009Z7FC5WU"
        project:
        "vessels_combined_degraded_segmentation_prod"
      - type:
        "WandbVisBackend"
- WANDB_PROJECT_SUFFIX:
  "_prod"
- WANDB_RUN_NAME:
  "3-DICE-1-CLDICE"
- work_dir:
  "./work_dirs/config_vessels_11"

Summary metrics are your model's outputs. Learn more

▶
Summary metrics:{} 20 keys
- aux.acc_seg:
  79.50171661376953
- aux.loss_cl_dice:
  0.23118370920419692
- aux.loss_dice:
  0.4456812649965286
- avg:
  0.5251258467101148
- data_time:
  0.06353394738559065
- decode.acc_seg:
  88.31737518310547
- decode.loss_cl_dice:
  0.3562017172574997
- decode.loss_dice:
  0.8602105438709259
- dice:
  0.4141260492816426
- iou:
  0.2660842370672693
- iter:
  2,120
- length:
  0.562938117889496
- line_distance:
  0.6118844893865665
- loss:
  1.8932772517204284
- lr:
  0.00000015693659761456
- memory:
  18,858
- time:
  0.4883551679808518
- ▶
  val_17.png:{} 7 keys
- ▶
  val_26.png:{} 7 keys
- width:
  0.472508140716864

This run consumed these artifacts as inputs. Learn more

job

job-git_github.com_JosselinSomervilleRoberts_ReachBot-Predictor.git_mmsegmentation_tools_train.py:v5

This run produced these artifacts as outputs. Total: 2. Learn more

code

source-vessels_combined_degraded_segmentation_prod-mmsegmentation_tools_train.py:v45

wandb-history

run-9vvz4p5z-history:v0