Merge pull request #19 from periakiva/main

Dev (#1)

.gitignore

@@ -143,6 +143,10 @@ dmypy.json
 *.h5
 *.tar
 *.tar.gz
+*.pth
+*.png
+*.jpg
+*.jpeg
 
 # Lightning-Hydra-Template
 configs/local/default.yaml

configs/callbacks/default.yaml

@@ -8,8 +8,10 @@ defaults:
 model_checkpoint:
   dirpath: ${paths.output_dir}/checkpoints
   filename: "epoch_{epoch:03d}"
-  monitor: "val/loss"
-  mode: "min"
+  # monitor: "val/loss"
+  # mode: "min"
+  monitor: "val/acc"
+  mode: "max"
   save_last: True
   auto_insert_metric_name: False
 

configs/callbacks/early_stopping.yaml

@@ -1,7 +1,7 @@
 # https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.EarlyStopping.html
 
 early_stopping:
-  _target_: lightning.pytorch.callbacks.EarlyStopping
+  _target_: pytorch_lightning.callbacks.EarlyStopping
   monitor: ??? # quantity to be monitored, must be specified !!!
   min_delta: 0. # minimum change in the monitored quantity to qualify as an improvement
   patience: 3 # number of checks with no improvement after which training will be stopped

configs/callbacks/model_checkpoint.yaml

@@ -1,13 +1,13 @@
 # https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.ModelCheckpoint.html
 
 model_checkpoint:
-  _target_: lightning.pytorch.callbacks.ModelCheckpoint
+  _target_: pytorch_lightning.callbacks.ModelCheckpoint
   dirpath: null # directory to save the model file
   filename: null # checkpoint filename
   monitor: null # name of the logged metric which determines when model is improving
   verbose: False # verbosity mode
   save_last: null # additionally always save an exact copy of the last checkpoint to a file last.ckpt
-  save_top_k: 1 # save k best models (determined by above metric)
+  save_top_k: 3 # save k best models (determined by above metric)
   mode: "min" # "max" means higher metric value is better, can be also "min"
   auto_insert_metric_name: True # when True, the checkpoints filenames will contain the metric name
   save_weights_only: False # if True, then only the model’s weights will be saved

configs/callbacks/model_summary.yaml

@@ -1,5 +1,5 @@
 # https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.RichModelSummary.html
 
 model_summary:
-  _target_: lightning.pytorch.callbacks.RichModelSummary
+  _target_: pytorch_lightning.callbacks.RichModelSummary
   max_depth: 1 # the maximum depth of layer nesting that the summary will include

configs/callbacks/rich_progress_bar.yaml

@@ -1,4 +1,4 @@
 # https://lightning.ai/docs/pytorch/latest/api/lightning.pytorch.callbacks.RichProgressBar.html
 
 rich_progress_bar:
-  _target_: lightning.pytorch.callbacks.RichProgressBar
+  _target_: pytorch_lightning.callbacks.RichProgressBar

configs/experiment/m2/feat_v6.yaml

@@ -0,0 +1,109 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=example
+task: "m2"
+defaults:
+  - override /data: ptg
+  - override /model: ptg
+  - override /callbacks: default
+  - override /trainer: gpu
+  - override /paths: default
+  - override /logger: aim
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+# exp_name: "p_m2_tqt_data_test_feat_v6_with_pose" #[_v2_aug_False]
+# exp_name: "p_m2_tqt_data_test_feat_v6_with_pose_v3_aug_False_reshuffle_True" #[_v2_aug_False]
+exp_name: "p_m2_feat_v6_with_pose_v3_aug_False_reshuffle_True"
+# exp_name: "p_m2_feat_v6_no_pose_v3_aug_False_reshuffle_True"
+# exp_name: "p_m2_tqt_data_test_feat_v6_with_pose_v2_aug_False_reshuffle_False" #[_v2_aug_False]
+# exp_name: "p_m2_tqt_data_test_feat_v6_with_pose_v2_aug_True_reshuffle_True" #[_v2_aug_False]
+# exp_name: "p_m2_tqt_data_test_feat_v6_with_pose_v2_aug_True_reshuffle_False" #[_v2_aug_False]
+
+
+tags: ["m2", "ms_tcn"]
+
+seed: 12345
+
+trainer:
+  min_epochs: 50
+  max_epochs: 500
+
+
+model:
+  compile: false
+
+  net:
+    dim: 209 # length of feature vector
+
+data:
+  num_classes: 9 # activities: includes background
+  batch_size: 512
+  num_workers: 16
+  epoch_length: 20000
+  window_size: 30
+  sample_rate: 1
+
+  all_transforms:
+    train_order: [] #["MoveCenterPts", "NormalizePixelPts"]
+    test_order: [] #["NormalizePixelPts"]
+
+    MoveCenterPts:
+        feat_version: 6
+        num_obj_classes: 9 # not including background
+    NormalizeFromCenter:
+      feat_version: 6
+    NormalizePixelPts:
+      feat_version: 6
+      num_obj_classes: 9 # not including background
+
+data_gen:
+  reshuffle_datasets: true
+  augment: false
+  num_augs: 5
+  feat_type: "with_pose" #[no_pose, with_pose, only_hands_joints, only_objects_joints]
+  filter_black_gloves: false
+  filter_blue_gloves: false
+  train_vid_ids: [1, 2, 4, 8, 9, 10, 11, 12, 16, 17,18, 20, 19, 30, 31, 32, 33, 34,35,36,
+                    7,132,133,50,51,54,56,52,61,53,57,65,66,67,68,69,58,60,64,125,126,
+                    127,129,131,134,135,136,119,122,124,70,72,92,93,94,95,97,98,100,
+                    101,102,103,104,105,107,108,112,114,117,118,73]
+  val_vid_ids: [5, 59,106,130,138, 77, 123, 71]
+  test_vid_ids: [3,14,55,62,96,109,128,137,139, 120, 75, 21, 13]
+  names_black_gloves: [22,23,26,24,25,27,29,28,41,42,43,44,45,46,47,48,49,78,
+                       79,84,88,90,80,81,82,83,85,86,87,89,91,99,110,111,121,113,115,116]
+  names_blue_gloves: [132,133,50,51,54,55,56,52,61,59,53,57,62,65,66,67,68,69,
+                      58,60,63,64,125,126,127,129,131,134,135,136,128,130,137,
+                      138,139]
+
+
+paths:
+  # data_dir: "/data/PTG/TCN_data/m2/p_m2_tqt_data_test_feat_v6_with_pose_v2_aug_False_reshuffle_True" #[_v2_aug_False]
+  data_dir: "/data/PTG/TCN_data/m2/p_m2_feat_v6_with_pose_v3_aug_False_reshuffle_True" #[_v2_aug_False]
+  # data_dir: "/data/PTG/TCN_data/m2/p_m2_feat_v6_no_pose_v3_aug_False_reshuffle_True" #[_v2_aug_False]
+  # data_dir: "/data/PTG/TCN_data/m2/p_m2_feat_v6_no_pose_v3_aug_False_reshuffle_True" #[_v2_aug_False]
+  # data_dir: "/data/PTG/TCN_data/m2/p_m2_tqt_data_test_feat_v6_with_pose_v2_aug_False_reshuffle_False" #[_v2_aug_False]
+  # data_dir: "/data/PTG/TCN_data/m2/p_m2_tqt_data_test_feat_v6_with_pose_v2_aug_True_reshuffle_True" #[_v2_aug_False]
+  # data_dir: "/data/PTG/TCN_data/m2/p_m2_tqt_data_test_feat_v6_with_pose_v2_aug_True_reshuffle_False" #[_v2_aug_False]
+
+  # root_dir: "/data/PTG/medical/training/activity_classifier/TCN_HPL"
+  root_dir: "/data/users/peri.akiva/PTG/medical/training/activity_classifier/TCN_HPL"
+
+  # dataset_kwcoco: "/data/users/peri.akiva/datasets/ptg/m2_good_images_only_no_amputation_stump_train_activity_obj_results_with_dets_and_pose.mscoco.json"
+  dataset_kwcoco: "/data/users/peri.akiva/datasets/ptg/m2_all_all_obj_results_with_dets_and_pose.mscoco.json"
+  activity_config_root: "/home/local/KHQ/peri.akiva/projects/angel_system/config/activity_labels"
+  activity_config_fn: "${paths.activity_config_root}/${task}"
+  ptg_root: "/home/local/KHQ/peri.akiva/angel_system"
+  activity_config_path: "${paths.ptg_root}/config/activity_labels/medical"
+  output_data_dir_root: "/data/PTG/TCN_data"
+  # bbn_data_root: "{bbn_data_dir}/Release_v0.5/v0.56"
+  bbn_data_dir: "/data/PTG/medical/bbn_data"
+
+
+logger:
+  aim:
+    experiment: ${exp_name}
+    capture_terminal_logs: true
+
+task_name: ${exp_name}

configs/experiment/m2/feat_v6_no_pose.yaml

@@ -0,0 +1,64 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=example
+
+defaults:
+  - override /data: ptg
+  - override /model: ptg
+  - override /callbacks: default
+  - override /trainer: gpu
+  - override /paths: default
+  - override /logger: aim
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+exp_name: "p_m2_tqt_data_test_feat_v6_no_pose" #[p_m2_tqt_data_test_feat_v6]
+
+tags: ["m2", "ms_tcn"]
+
+seed: 12345
+
+trainer:
+  min_epochs: 50
+  max_epochs: 400
+
+
+model:
+  compile: false
+
+  net:
+    dim: 288 # length of feature vector
+
+data:
+  num_classes: 9 # activities: includes background
+  batch_size: 256
+  num_workers: 0
+  epoch_length: 20000
+  window_size: 30
+  #sample_rate: 2
+
+  all_transforms:
+    train_order: [] #["MoveCenterPts", "NormalizePixelPts"]
+    test_order: [] #["NormalizePixelPts"]
+
+    MoveCenterPts:
+        feat_version: 5
+        num_obj_classes: 12 # not including background
+    NormalizeFromCenter:
+      feat_version: 5
+    NormalizePixelPts:
+      feat_version: 5
+      num_obj_classes: 12 # not including background
+
+paths:
+  data_dir: "/data/PTG/TCN_data/m2/p_m2_tqt_data_test_feat_v6_no_pose" #[p_m2_tqt_data_test_feat_v6]
+  # root_dir: "/data/PTG/medical/training/activity_classifier/TCN_HPL"
+  root_dir: "/data/users/peri.akiva/PTG/medical/training/activity_classifier/TCN_HPL"
+
+logger:
+  aim:
+    experiment: ${exp_name}
+    capture_terminal_logs: true
+
+task_name: ${exp_name}

configs/experiment/m2/feat_v6_only_hands_joints.yaml

@@ -0,0 +1,64 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=example
+
+defaults:
+  - override /data: ptg
+  - override /model: ptg
+  - override /callbacks: default
+  - override /trainer: gpu
+  - override /paths: default
+  - override /logger: aim
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+exp_name: "p_m2_tqt_data_test_feat_v6_only_hands_joints_v2" #[p_m2_tqt_data_test_feat_v6]
+
+tags: ["m2", "ms_tcn"]
+
+seed: 12345
+
+trainer:
+  min_epochs: 50
+  max_epochs: 400
+
+
+model:
+  compile: false
+
+  net:
+    dim: 332 # length of feature vector
+
+data:
+  num_classes: 9 # activities: includes background
+  batch_size: 256
+  num_workers: 0
+  epoch_length: 20000
+  window_size: 30
+  #sample_rate: 2
+
+  all_transforms:
+    train_order: [] #["MoveCenterPts", "NormalizePixelPts"]
+    test_order: [] #["NormalizePixelPts"]
+
+    MoveCenterPts:
+        feat_version: 6
+        num_obj_classes: 12 # not including background
+    NormalizeFromCenter:
+      feat_version: 6
+    NormalizePixelPts:
+      feat_version: 6
+      num_obj_classes: 12 # not including background
+
+paths:
+  data_dir: "/data/PTG/TCN_data/m2/p_m2_tqt_data_test_feat_v6_only_hands_joints" #[p_m2_tqt_data_test_feat_v6]
+  # root_dir: "/data/PTG/medical/training/activity_classifier/TCN_HPL"
+  root_dir: "/data/users/peri.akiva/PTG/medical/training/activity_classifier/TCN_HPL"
+
+logger:
+  aim:
+    experiment: ${exp_name}
+    capture_terminal_logs: true
+
+task_name: ${exp_name}

configs/experiment/m2/feat_v6_only_object_joints.yaml

@@ -0,0 +1,64 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=example
+
+defaults:
+  - override /data: ptg
+  - override /model: ptg
+  - override /callbacks: default
+  - override /trainer: gpu
+  - override /paths: default
+  - override /logger: aim
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+exp_name: "p_m2_tqt_data_test_feat_v6_only_objects_joints" #[p_m2_tqt_data_test_feat_v6]
+
+tags: ["m2", "ms_tcn"]
+
+seed: 12345
+
+trainer:
+  min_epochs: 50
+  max_epochs: 400
+
+
+model:
+  compile: false
+
+  net:
+    dim: 310 # length of feature vector
+
+data:
+  num_classes: 9 # activities: includes background
+  batch_size: 256
+  num_workers: 0
+  epoch_length: 20000
+  window_size: 30
+  #sample_rate: 2
+
+  all_transforms:
+    train_order: [] #["MoveCenterPts", "NormalizePixelPts"]
+    test_order: [] #["NormalizePixelPts"]
+
+    MoveCenterPts:
+        feat_version: 6
+        num_obj_classes: 12 # not including background
+    NormalizeFromCenter:
+      feat_version: 6
+    NormalizePixelPts:
+      feat_version: 6
+      num_obj_classes: 12 # not including background
+
+paths:
+  data_dir: "/data/PTG/TCN_data/m2/p_m2_tqt_data_test_feat_v6_only_objects_joints" #[p_m2_tqt_data_test_feat_v6]
+  # root_dir: "/data/PTG/medical/training/activity_classifier/TCN_HPL"
+  root_dir: "/data/users/peri.akiva/PTG/medical/training/activity_classifier/TCN_HPL"
+
+logger:
+  aim:
+    experiment: ${exp_name}
+    capture_terminal_logs: true
+
+task_name: ${exp_name}