Dev/r18 training rehydration

configs/experiment/m2/feat_v6.yaml

@@ -40,12 +40,12 @@ trainer:
   log_every_n_steps: 1
 
 model:
+  num_classes: 9  # number of activity classification classes
   compile: false
   net:
     # Length of feature vector for a single frame.
-    # Currently derived from feature version and other hyperparameters.
+    # Currently derived from the parameterization of dataset vectorizer.
     dim: 297
-  num_classes: 9
 
 data:
   coco_train_activities: "${paths.coco_file_root}/TRAIN-activity_truth.coco.json"
@@ -60,10 +60,12 @@ data:
   coco_test_objects: "${paths.coco_file_root}/TEST-object_detections.coco.json"
   coco_test_poses: "${paths.coco_file_root}/TEST-pose_estimates.coco.json"
 
-  batch_size: 512
+  # batch_size: 512
+  # batch_size: 8192
+  batch_size: 16384
   num_workers: 16
   target_framerate: 15  # BBN Hololens2 Framerate
-  epoch_length: 20000
+  epoch_length: 200000
 
   train_dataset:
     window_size: 25
@@ -106,13 +108,12 @@ data:
 
 paths:
   # root_dir: "/data/PTG/medical/training/activity_classifier/TCN_HPL/"
-  root_dir: "/home/local/KHQ/paul.tunison/data/darpa-ptg/train-TCN-M2_bbn_hololens/training_root"
+  root_dir: "/home/local/KHQ/paul.tunison/data/darpa-ptg/train-TCN-M2_bbn_hololens"
 
   # Convenience variable to where your train/val/test split COCO file datasets
   # are stored.
   coco_file_root: "/home/local/KHQ/paul.tunison/data/darpa-ptg/train-TCN-M2_bbn_hololens"
 
-#exp_name: "tcn_training_revive"
 #logger:
 #  aim:
 #    experiment: ${task_name}

configs/experiment/r18/OLD/feat_v6.yaml

@@ -0,0 +1,111 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=example
+topic: "medical"
+task: "r18"
+feature_version: 6
+
+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
+
+tags: ["r18", "ms_tcn", "debug"]
+
+seed: 12345
+
+trainer:
+  min_epochs: 50
+  max_epochs: 500
+  log_every_n_steps: 1
+
+
+model:
+  compile: false
+
+  net:
+    dim: 297 # length of feature vector when top_k_objects=1
+    #dim: 506 # length of feature vector when top_k_objects=2
+
+# LIVE HZ
+IMAGE_HZ: 30 # zed bags
+#IMAGE_HZ: 15 # BBN hololens live
+OBJECT_DET_HZ: 15
+POSE_HZ: 4
+
+# GENERATE TRAINING DATA
+data_gen:
+  top_k_objects: 1
+  pose_repeat_rate: 7.5 # ${IMAGE_HZ} / ${POSE_HZ}
+
+  data_type: "pro"
+  dataset_kwcoco: "/data/PTG/medical/training/yolo_object_detector/detect/r18_all/r18_all_all_obj_results_with_dets_and_pose.mscoco.json"
+  train_vid_ids: [1, 2, 4, 5, 6, 8, 9, 11, 12, 14, 15, 16, 17, 19, 20, 21,
+                  22, 23, 24, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40,
+                  42, 43, 44, 45, 46, 48, 49, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
+                  61, 62, 63, 64, 65, 66, 67, 68, 69, 70]
+  val_vid_ids: [3, 7, 10, 18, 27, 32, 41]
+  test_vid_ids: [50, 13, 47, 25]
+  names_black_gloves: []
+  names_blue_gloves: []
+  #data_type: "lab"
+  #dataset_kwcoco: "/data/PTG/medical/training/yolo_object_detector/detect/r18_all_bbn_lab_data/r18_all_bbn_lab_data_all_obj_results.mscoco.json"
+  #train_vid_ids: [1, 2, 3, 4, 5, 6, 7, 8]
+  #val_vid_ids: [9]
+  #test_vid_ids: [10]
+  #names_black_gloves: []
+  #names_blue_gloves: []
+
+  filter_black_gloves: false
+  filter_blue_gloves: false
+
+  activity_config_fn: "/home/local/KHQ/hannah.defazio/angel_system/config/activity_labels/medical/r18.yaml"
+
+  # This matches the folder name created in the data generator
+  exp_ext: "_NEW_ORDER_fix_overlap_gt" # anything unique about this run that isn't already in ``exp_name``
+  exp_name: "${task}_${data_gen.data_type}_data_top_${data_gen.top_k_objects}_objs_feat_v${feature_version}_pose_rate_${data_gen.pose_repeat_rate}${data_gen.exp_ext}"
+
+# TRAINING
+data:
+  num_classes: 6 # activities: includes background
+  batch_size: 512
+  num_workers: 12
+  epoch_length: 20000
+  window_size: 25
+  sample_rate: 2 # ${IMAGE_HZ} / ${OBJECT_DET_HZ}
+
+  # AUGMENTATIONS
+  all_transforms:
+    train_order: [] #["MoveCenterPts", "NormalizePixelPts"]
+    test_order: [] #["NormalizePixelPts"]
+    MoveCenterPts:
+        feat_version: ${feature_version}
+        num_obj_classes: 6 # not including background, includes hands
+        top_k_objects: ${data_gen.top_k_objects}
+    NormalizeFromCenter:
+      feat_version: ${feature_version}
+      num_obj_classes: 6 # not including background, includes hands
+      top_k_objects: ${data_gen.top_k_objects}
+    NormalizePixelPts:
+      feat_version: ${feature_version}
+      num_obj_classes: 6 # not including background, includes hands
+      top_k_objects: ${data_gen.top_k_objects}
+
+exp_name: ${data_gen.exp_name}_win_${data.window_size}_obj_sample_${data.sample_rate}
+
+paths:
+  data_dir: "/data/PTG/medical/training/activity_classifier/TCN_data/${task}/${data_gen.exp_name}"
+  root_dir: "/data/PTG/medical/training/activity_classifier/TCN_HPL/"
+
+logger:
+  aim:
+    experiment: ${exp_name}
+    capture_terminal_logs: true
+
+task_name: ${exp_name}

configs/experiment/r18/feat_v6.yaml

@@ -12,9 +12,20 @@ defaults:
   - override /callbacks: default
   - override /trainer: gpu
   - override /paths: default
-  - override /logger: aim
+  #- override /logger: aim
+  - override /logger: csv
 
 # all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+
+# Change this name to something descriptive and unique for this experiment.
+# This will differentiate the run logs and output to be separate from other
+# experiments that may have been run under the configured
+# Setting this value influences:
+# - the name of the directory under `${paths.root_dir}/logs/` in which training
+#   run files are stored.
+# Default is "train" set in the "configs/train.yaml" file.
+#task_name:
 
 tags: ["r18", "ms_tcn", "debug"]
 
@@ -25,87 +36,81 @@ trainer:
   max_epochs: 500
   log_every_n_steps: 1
 
-
 model:
+  num_classes: 6  # number of activity classification classes
   compile: false
-
   net:
-    dim: 297 # length of feature vector when top_k_objects=1
-    #dim: 506 # length of feature vector when top_k_objects=2
-
-# LIVE HZ
-IMAGE_HZ: 30 # zed bags
-#IMAGE_HZ: 15 # BBN hololens live
-OBJECT_DET_HZ: 15
-POSE_HZ: 4
-
-# GENERATE TRAINING DATA
-data_gen:
-  top_k_objects: 1
-  pose_repeat_rate: 7.5 # ${IMAGE_HZ} / ${POSE_HZ}
-
-  data_type: "pro"
-  dataset_kwcoco: "/data/PTG/medical/training/yolo_object_detector/detect/r18_all/r18_all_all_obj_results_with_dets_and_pose.mscoco.json"
-  train_vid_ids: [1, 2, 4, 5, 6, 8, 9, 11, 12, 14, 15, 16, 17, 19, 20, 21,
-                  22, 23, 24, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40,
-                  42, 43, 44, 45, 46, 48, 49, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
-                  61, 62, 63, 64, 65, 66, 67, 68, 69, 70]
-  val_vid_ids: [3, 7, 10, 18, 27, 32, 41]
-  test_vid_ids: [50, 13, 47, 25]
-  names_black_gloves: []
-  names_blue_gloves: []
-  #data_type: "lab"
-  #dataset_kwcoco: "/data/PTG/medical/training/yolo_object_detector/detect/r18_all_bbn_lab_data/r18_all_bbn_lab_data_all_obj_results.mscoco.json"
-  #train_vid_ids: [1, 2, 3, 4, 5, 6, 7, 8]
-  #val_vid_ids: [9]
-  #test_vid_ids: [10]
-  #names_black_gloves: []
-  #names_blue_gloves: []
-
-  filter_black_gloves: false
-  filter_blue_gloves: false
-
-  activity_config_fn: "/home/local/KHQ/hannah.defazio/angel_system/config/activity_labels/medical/r18.yaml"
-
-  # This matches the folder name created in the data generator
-  exp_ext: "_NEW_ORDER_fix_overlap_gt" # anything unique about this run that isn't already in ``exp_name``
-  exp_name: "${task}_${data_gen.data_type}_data_top_${data_gen.top_k_objects}_objs_feat_v${feature_version}_pose_rate_${data_gen.pose_repeat_rate}${data_gen.exp_ext}"
+    # Length of feature vector for a single frame.
+    # Currently derived from the parameterization of dataset vectorizer.
+    dim: 297
 
 # TRAINING
 data:
-  num_classes: 6 # activities: includes background
-  batch_size: 512
-  num_workers: 12
-  epoch_length: 20000
-  window_size: 25
-  sample_rate: 2 # ${IMAGE_HZ} / ${OBJECT_DET_HZ}
-
-  # AUGMENTATIONS
-  all_transforms:
-    train_order: [] #["MoveCenterPts", "NormalizePixelPts"]
-    test_order: [] #["NormalizePixelPts"]
-    MoveCenterPts:
-        feat_version: ${feature_version}
-        num_obj_classes: 6 # not including background, includes hands
-        top_k_objects: ${data_gen.top_k_objects}
-    NormalizeFromCenter:
-      feat_version: ${feature_version}
-      num_obj_classes: 6 # not including background, includes hands
-      top_k_objects: ${data_gen.top_k_objects}
-    NormalizePixelPts:
-      feat_version: ${feature_version}
-      num_obj_classes: 6 # not including background, includes hands
-      top_k_objects: ${data_gen.top_k_objects}
-
-exp_name: ${data_gen.exp_name}_win_${data.window_size}_obj_sample_${data.sample_rate}
+  coco_train_activities: "${paths.coco_file_root}/TRAIN-activity_truth.coco.json"
+  coco_train_objects: "${paths.coco_file_root}/TRAIN-object_detections.coco.json"
+  coco_train_poses: "${paths.coco_file_root}/TRAIN-pose_estimations.coco.json"
+
+  coco_validation_activities: "${paths.coco_file_root}/VALIDATION-activity_truth.coco.json"
+  coco_validation_objects: "${paths.coco_file_root}/VALIDATION-object_detections.coco.json"
+  coco_validation_poses: "${paths.coco_file_root}/VALIDATION-pose_estimations.coco.json"
+
+  coco_test_activities: "${paths.coco_file_root}/TEST-activity_truth.coco.json"
+  coco_test_objects: "${paths.coco_file_root}/TEST-object_detections.coco.json"
+  coco_test_poses: "${paths.coco_file_root}/TEST-pose_estimations.coco.json"
+
+  batch_size: 16384
+  num_workers: 16
+  target_framerate: 15  # BBN Hololens2 Framerate
+  epoch_length: 80000
+
+  train_dataset:
+    window_size: 25
+    vectorizer:
+      _target_: tcn_hpl.data.vectorize.classic.Classic
+      feat_version: 6
+      top_k: 1
+      num_classes: 7
+      background_idx: 0
+      hand_left_idx: 5
+      hand_right_idx: 6
+    transform:
+      transforms: []  # no transforms
+#        - _target_: tcn_hpl.data.components.augmentations.MoveCenterPts
+#          hand_dist_delta: 0.05
+#          obj_dist_delta: 0.05
+#          joint_dist_delta: 0.025
+#          im_w: 1280
+#          im_h: 720
+#          num_obj_classes: 42
+#          feat_version: 2
+#          top_k_objects: 1
+#        - _target_: tcn_hpl.data.components.augmentations.NormalizePixelPts
+#          im_w: 1280
+#          im_h: 720
+#          num_obj_classes: 42
+#          feat_version: 2
+#          top_k_objects: 1
+  val_dataset:
+    transform:
+      transforms: []  # no transforms
+#        - _target_: tcn_hpl.data.components.augmentations.NormalizePixelPts
+#          im_w: 1280
+#          im_h: 720
+#          num_obj_classes: 42
+#          feat_version: 2
+#          top_k_objects: 1
+  # Test dataset usually configured the same as val, unless there is some
+  # different set of transforms that should be used during test/prediction.
 
 paths:
-  data_dir: "/data/PTG/medical/training/activity_classifier/TCN_data/${task}/${data_gen.exp_name}"
-  root_dir: "/data/PTG/medical/training/activity_classifier/TCN_HPL/"
+  # root_dir: "/data/PTG/medical/training/activity_classifier/TCN_HPL/"
+  root_dir: "/data/paul.tunison/data/darpa-ptg/train-TCN-R18_bbn_hololens-yolo_v7-mmpose"
 
-logger:
-  aim:
-    experiment: ${exp_name}
-    capture_terminal_logs: true
+  # Convenience variable to where your train/val/test split COCO file datasets
+  # are stored.
+  coco_file_root: ${paths.root_dir}
 
-task_name: ${exp_name}
+#logger:
+#  aim:
+#    experiment: ${task_name}
+#    capture_terminal_logs: true

tcn_hpl/data/ptg_datamodule.py

@@ -142,8 +142,7 @@ def __init__(
         # this line allows to access init params with 'self.hparams' attribute
         # also ensures init params will be stored in ckpt
         self.save_hyperparameters(
-            logger=False,
-            ignore=["train_dataset", "val_dataset", "test_dataset"]
+            logger=False, ignore=["train_dataset", "val_dataset", "test_dataset"]
         )
 
         self.data_train: Optional[TCNDataset] = train_dataset