TinyMyo: Update hand gesture classification experiment details

config/data_module/emg_pretrain_data_module.yaml

@@ -27,14 +27,36 @@ data_module:
   train_val_split_ratio: 0.8
   datasets:
     demo_dataset: null
-    emg2pose:
+    emg2pose_train:
       _target_: 'datasets.emg_pretrain_dataset.EMGPretrainDataset'
-      data_dir: "${env:DATA_PATH}/emg2pose/h5/"
-    db6:
+      hdf5_file: "${env:DATA_PATH}/emg2pose_data/h5/train.h5"
+    emg2pose_val:
       _target_: 'datasets.emg_pretrain_dataset.EMGPretrainDataset'
-      data_dir: "${env:DATA_PATH}/ninapro/DB6/h5/"
+      hdf5_file: "${env:DATA_PATH}/emg2pose_data/h5/val.h5"
+    emg2pose_test:
+      _target_: 'datasets.emg_pretrain_dataset.EMGPretrainDataset'
+      hdf5_file: "${env:DATA_PATH}/emg2pose_data/h5/test.h5"
+    db6_train:
+      _target_: 'datasets.emg_pretrain_dataset.EMGPretrainDataset'
+      hdf5_file: "${env:DATA_PATH}/ninapro/DB6/h5/train.h5"
+      pad_up_to_max_chans: 16
+    db6_val:
+      _target_: 'datasets.emg_pretrain_dataset.EMGPretrainDataset'
+      hdf5_file: "${env:DATA_PATH}/ninapro/DB6/h5/val.h5"
+      pad_up_to_max_chans: 16
+    db6_test:
+      _target_: 'datasets.emg_pretrain_dataset.EMGPretrainDataset'
+      hdf5_file: "${env:DATA_PATH}/ninapro/DB6/h5/test.h5"
+      pad_up_to_max_chans: 16
+    db7_train:
+      _target_: 'datasets.emg_pretrain_dataset.EMGPretrainDataset'
+      hdf5_file: "${env:DATA_PATH}/ninapro/DB7/h5/train.h5"
+      pad_up_to_max_chans: 16
+    db7_val:
+      _target_: 'datasets.emg_pretrain_dataset.EMGPretrainDataset'
+      hdf5_file: "${env:DATA_PATH}/ninapro/DB7/h5/val.h5"
       pad_up_to_max_chans: 16
-    db7:
+    db7_test:
       _target_: 'datasets.emg_pretrain_dataset.EMGPretrainDataset'
-      data_dir: "${env:DATA_PATH}/ninapro/DB7/h5/"
+      hdf5_file: "${env:DATA_PATH}/ninapro/DB7/h5/test.h5"
       pad_up_to_max_chans: 16

config/experiment/TinyMyo_finetune.yaml

@@ -21,7 +21,7 @@ tag: EMG_finetune
 
 gpus: -1
 num_nodes: 1
-num_workers: 8
+num_workers: 4
 batch_size: 32
 max_epochs: 50
 
@@ -32,7 +32,6 @@ finetune_pretrained: True
 resume: False
 
 layerwise_lr_decay: 0.90
-scheduler_type: cosine
 
 pretrained_checkpoint_path: null
 pretrained_safetensors_path: null
@@ -41,7 +40,7 @@ finetuning:
   freeze_layers: False
 
 io:
-  base_output_path: ${env:DATA_PATH}
+  base_output_path: ${env:LOG_DIR}
   checkpoint_dirpath: ${env:CHECKPOINT_DIR}/checkpoints
   version: 0
 
@@ -52,23 +51,16 @@ defaults:
   - override /task: finetune_task_TinyMyo
   - override /criterion: finetune_criterion
 
-masking:
-  patch_size: [1, 20]
-  masking_ratio: 0.50
-  unmasked_loss_coeff: 0.1
-
-input_normalization:
-  normalize: False
-
 model:
+  n_layer: 8
   num_classes: 6
-  classification_type: "ml"
+  task: "classification"
 
 trainer:
   accelerator: gpu
   num_nodes: ${num_nodes}
   devices: ${gpus}
-  strategy: auto
+  strategy: ddp_find_unused_parameters_true
   max_epochs: ${max_epochs}
 
 model_checkpoint:
@@ -89,7 +81,7 @@ optimizer:
   optim: 'AdamW'
   lr: 5e-4
   betas: [0.9, 0.98]
-  weight_decay: 0.01
+  weight_decay: 1e-2
 
 scheduler:
   trainer: ${trainer}
@@ -98,3 +90,10 @@ scheduler:
   warmup_epochs: 5
   total_training_opt_steps: ${max_epochs}
   t_in_epochs: True
+
+wandb:
+  entity: "TinyMyo"
+  project: "TinyMyo"
+  save_dir: ${env:LOG_DIR}
+  run_name: "TinyMyo-Finetuning"
+  offline: True

config/experiment/TinyMyo_pretrain.yaml

@@ -22,15 +22,15 @@ tag: EMG_pretrain
 gpus: -1
 num_nodes: 1
 num_workers: 8
-batch_size: 128
-max_epochs: 50
+batch_size: 512
+max_epochs: 30
 
 final_validate: True
 final_test: False
 
 pretrained_checkpoint_path: null
 io:
-  base_output_path: ${env:DATA_PATH}
+  base_output_path: ${env:LOG_DIR}
   checkpoint_dirpath: ${env:CHECKPOINT_DIR}/checkpoints
   version: 0
 
@@ -49,22 +49,23 @@ masking:
 input_normalization:
   normalize: True
 
-scheduler:
-  trainer: ${trainer}
-  min_lr: 1e-6
-  warmup_lr_init: 1e-6
-  warmup_epochs: 10
-  total_training_opt_steps: ${max_epochs}
-  t_in_epochs: True
+model:
+  n_layer: 8
+  drop_path: 0.0 # Stochastic depth disabled for pretraining
+  num_classes: 0 # No classification head for pretraining
+  task: pretraining
+
+criterion:
+  loss_type: 'smooth_l1'
 
 trainer:
   accelerator: gpu
   num_nodes: ${num_nodes}
   devices: ${gpus}
   strategy: auto
+  precision: "bf16-mixed"
   max_epochs: ${max_epochs}
-  gradient_clip_val: 3
-  accumulate_grad_batches: 8
+  gradient_clip_val: 1
 
 model_checkpoint:
   save_last: True
@@ -73,7 +74,21 @@ model_checkpoint:
   save_top_k: 1
 
 optimizer:
-  optim: 'AdamW'
-  lr: 1e-4
+  lr: 5e-4
   betas: [0.9, 0.98]
-  weight_decay: 0.01
+  weight_decay: 1e-2
+
+scheduler:
+  trainer: ${trainer}
+  min_lr: 1e-6
+  warmup_lr_init: 1e-6
+  warmup_epochs: 3
+  total_training_opt_steps: ${max_epochs}
+  t_in_epochs: True
+
+wandb:
+  entity: "TinyMyo"
+  project: "TinyMyo"
+  save_dir: ${env:LOG_DIR}
+  run_name: "TinyMyo-Pretraining"
+  offline: True

datasets/__init__.py

@@ -0,0 +1 @@
+"""Local dataset implementations for BioFoundation."""

datasets/emg_finetune_dataset.py

@@ -16,130 +16,62 @@
 # *                                                                            *
 # * Author:  Matteo Fasulo                                                     *
 # *----------------------------------------------------------------------------*
-from collections import deque
 from typing import Tuple, Union
 
 import h5py
+import numpy as np
 import torch
 
 
 class EMGDataset(torch.utils.data.Dataset):
-    """
-    A PyTorch Dataset class for loading EMG (Electromyography) data from HDF5 files.
-    This dataset supports lazy loading of data from HDF5 files, with optional caching
-    to improve performance during training. It can be used for both fine-tuning (with labels)
-    and inference (without labels) modes. The class handles data preprocessing, such as
-    converting to tensors and optional unsqueezing.
+    """PyTorch Dataset for loading EMG data from HDF5 files.
+
     Attributes:
-        hdf5_file (str): Path to the HDF5 file containing the dataset.
-        unsqueeze (bool): Whether to add an extra dimension to the input data (default: False).
-        finetune (bool): If True, loads both data and labels; if False, loads only data (default: True).
-        cache_size (int): Maximum number of samples to cache in memory (default: 1500).
-        use_cache (bool): Whether to use caching for faster access (default: True).
-        regression (bool): If True, treats labels as regression targets (float); else, classification (long) (default: False).
-        num_samples (int): Total number of samples in the dataset, determined from HDF5 file.
-        data (h5py.File or None): Handle to the opened HDF5 file (lazy-loaded).
-        X_ds (h5py.Dataset or None): Dataset handle for input data.
-        Y_ds (h5py.Dataset or None): Dataset handle for labels (if finetune is True).
-        cache (dict): Dictionary for caching data items (if use_cache is True).
-        cache_queue (deque): Queue to track the order of cached items for LRU eviction.
-    Note:
-        - The HDF5 file is expected to have 'data' and 'label' datasets.
-        - Caching uses an LRU (Least Recently Used) eviction policy.
-        - Suitable for use with PyTorch DataLoader for batched loading.
+        hdf5_file (str): Path to the HDF5 source file.
+        finetune (bool): If True, returns (data, label). If False, returns data only.
+        regression (bool): If True, labels are treated as floats. Else, longs.
     """
-
     def __init__(
         self,
         hdf5_file: str,
-        unsqueeze: bool = False,
         finetune: bool = True,
-        cache_size: int = 1500,
-        use_cache: bool = True,
         regression: bool = False,
+        verbose: bool = False,
     ):
         self.hdf5_file = hdf5_file
-        self.unsqueeze = unsqueeze
-        self.cache_size = cache_size
         self.finetune = finetune
-        self.use_cache = use_cache
         self.regression = regression
 
-        self.data = None
-        self.X_ds = None
-        self.Y_ds = None
-
-        # Open once to get length, then close immediately
         with h5py.File(self.hdf5_file, "r") as f:
-            self.num_samples = f["data"].shape[0]
-
-        if self.use_cache:
-            self.cache: dict[int, Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = {}
-            self.cache_queue = deque()
-
-    def _open_file(self) -> None:
-        # 'rdcc_nbytes' to increase the raw data chunk cache size
-        self.data = h5py.File(self.hdf5_file, "r", rdcc_nbytes=1024 * 1024 * 4)
-        if self.data is not None:
-            self.X_ds = self.data["data"]
-            self.Y_ds = self.data["label"]
-
-    def __len__(self) -> int:
-        return self.num_samples
-
-    def __getitem__(self, index):
-        # Check Cache
-        if self.use_cache and index in self.cache:
-            return self._process_data(self.cache[index])
-
-        # Open file (Lazy Loading for Multiprocessing)
-        if self.data is None:
-            self._open_file()
-
-        # Read Data, HDF5 slicing returns numpy array
-        X_np = self.X_ds[index]
-        X = torch.from_numpy(X_np).float()
+            X_np = f["data"][:]
+            Y_np = f["label"][:] if self.finetune else None
 
+        self.X_tensor = torch.from_numpy(X_np).float().contiguous()
         if self.finetune:
-            Y_np = self.Y_ds[index]
             if self.regression:
-                Y = torch.from_numpy(Y_np).float()
+                self.Y_tensor = torch.from_numpy(Y_np).float().contiguous()
             else:
-                # Ensure scalar is converted properly
-                Y = torch.tensor(Y_np, dtype=torch.long)
-
-            data_item = (X, Y)
-        else:
-            data_item = X
+                self.Y_tensor = torch.from_numpy(Y_np).long().contiguous()
+                if verbose:
+                    uniq, cnt = np.unique(Y_np, return_counts=True)
+                    print(
+                        f"[EMGDataset] {self.hdf5_file}: label min={Y_np.min()}, max={Y_np.max()}, classes={len(uniq)}"
+                    )
+                    print(f"[EMGDataset] {self.hdf5_file}: class hist={dict(zip(uniq.tolist(), cnt.tolist()))}")
 
-        # Update Cache
-        if self.use_cache:
-            # If cache is full, remove oldest item from dict AND queue
-            if len(self.cache) >= self.cache_size:
-                oldest_index = self.cache_queue.popleft()
-                del self.cache[oldest_index]
+        self.num_samples = self.X_tensor.shape[0] # [N, C, T]
 
-            self.cache[index] = data_item
-            self.cache_queue.append(index)
-
-        return self._process_data(data_item)
+    def __len__(self) -> int:
+        """Returns the total number of samples in the dataset."""
+        return self.num_samples
 
-    def _process_data(self, data_item):
-        """Helper to handle squeezing/returning uniformly."""
-        if self.finetune:
-            X, Y = data_item
-        else:
-            X = data_item
-            Y = None
+    def __getitem__(self, index: int) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        """Retrieves the EMG data and optional label at the specified index."""
 
-        if self.unsqueeze:
-            X = X.unsqueeze(0)
+        X = self.X_tensor[index]
 
         if self.finetune:
+            Y = self.Y_tensor[index]
             return X, Y
-        else:
-            return X
 
-    def __del__(self):
-        if self.data is not None:
-            self.data.close()
+        return X