Parameter tuning

modules/maskrcnn_train.sh

@@ -1,7 +1,36 @@
 #!/bin/bash
 
-# Set whether to use GPU tuning or not
-USE_GPU_TUNING=true
+# Parameters
+USE_GPU_TUNING=true   # Set this to true to enable GPU tuning
+RESUME_TRAINING=false # Set this to true to resume training from last checkpoint
+MAX_ITER=100000        # Default maximum iterations
+CHECKPOINT_PERIOD=$(($MAX_ITER / 10))  # Set to 10% of MAX_ITER
+
+# Default values
+IMS_PER_BATCH=2
+BATCH_SIZE_PER_IMAGE=512
+BASE_LR=0.00025
+
+# If USE_GPU_TUNING is true, adjust parameters based on GPU memory
+if [ "$USE_GPU_TUNING" = true ]; then
+    # Get the GPU memory in MB
+    GPU_MEMORY=$(nvidia-smi --query-gpu=memory.total --format=csv,noheader,nounits | head -n 1)
+
+    # Set the batch size and learning rate based on GPU memory
+    if [ "$GPU_MEMORY" -ge 24576 ]; then
+        IMS_PER_BATCH=8   # For 24GB+ GPUs
+        BATCH_SIZE_PER_IMAGE=512
+    elif [ "$GPU_MEMORY" -ge 12288 ]; then
+        IMS_PER_BATCH=4   # For 12GB+ GPUs
+        BATCH_SIZE_PER_IMAGE=256
+    else
+        IMS_PER_BATCH=2   # For smaller GPUs
+        BATCH_SIZE_PER_IMAGE=128
+    fi
+
+    # Adjust learning rate based on batch size
+    BASE_LR=$(echo "0.00025 * $IMS_PER_BATCH / 2" | bc -l)
+fi
 
 # Get script dir
 SCRIPT_DIR=$(cd $(dirname $0); pwd)
@@ -32,7 +61,7 @@ if [ ! -f "$COCO_CONFIG_FILE" ]; then
 fi
 
 # Download the base config file if it doesn't exist
-if [ ! -f "$BASE_CONFIG_FILE" ];then
+if [ ! -f "$BASE_CONFIG_FILE" ]; then
     echo "Downloading Base-RCNN-FPN.yaml configuration file..."
     wget $BASE_CONFIG_URL -O $BASE_CONFIG_FILE
 fi
@@ -43,44 +72,28 @@ if [ ! -f "$TRAIN_NET_FILE" ]; then
     wget $TRAIN_NET_URL -O $TRAIN_NET_FILE
 fi
 
-# Default values
-IMS_PER_BATCH=2
-BATCH_SIZE_PER_IMAGE=512
-BASE_LR=0.00025
-
-# If USE_GPU_TUNING is true, adjust parameters based on GPU memory
-if [ "$USE_GPU_TUNING" = true ]; then
-    # Get the GPU memory in MB
-    GPU_MEMORY=$(nvidia-smi --query-gpu=memory.total --format=csv,noheader,nounits | head -n 1)
-
-    # Set the batch size and learning rate based on GPU memory
-    if [ "$GPU_MEMORY" -ge 24576 ]; then
-        IMS_PER_BATCH=8   # For 24GB+ GPUs
-        BATCH_SIZE_PER_IMAGE=512
-    elif [ "$GPU_MEMORY" -ge 12288 ]; then
-        IMS_PER_BATCH=4   # For 12GB+ GPUs
-        BATCH_SIZE_PER_IMAGE=256
-    else
-        IMS_PER_BATCH=2   # For smaller GPUs
-        BATCH_SIZE_PER_IMAGE=128
-    fi
-
-    # Adjust learning rate based on batch size
-    BASE_LR=$(echo "0.00025 * $IMS_PER_BATCH / 2" | bc -l)
-fi
-
-# Function to extract the number of classes from COCO annotation and run training
+# Python script to configure and run the training
 python3 - <<END
 import os
 import json
 from detectron2.data.datasets import register_coco_instances
 from detectron2.data import DatasetCatalog
+from detectron2.engine import DefaultTrainer
+from detectron2.config import get_cfg
 
-# Paths
+# Paths from Bash script
 train_annotation = "$TRAIN_ANNOTATION"
 train_image_dir = "$TRAIN_IMAGE_DIR"
 val_annotation = "$VAL_ANNOTATION"
 val_image_dir = "$VAL_IMAGE_DIR"
+resume_training = $([ "$RESUME_TRAINING" = true ] && echo True || echo False)  # Convert bash boolean to Python boolean
+max_iter = $MAX_ITER
+checkpoint_period = $CHECKPOINT_PERIOD
+output_dir = "$OUTPUT_DIR"
+coco_config_file = "$COCO_CONFIG_FILE"
+ims_per_batch = $IMS_PER_BATCH
+batch_size_per_image = $BATCH_SIZE_PER_IMAGE
+base_lr = $BASE_LR
 
 # Load the COCO annotation file to detect number of classes
 with open(train_annotation, 'r') as f:
@@ -98,25 +111,24 @@ register_coco_instances("coco_roboone_val", {}, val_annotation, val_image_dir)
 print("Datasets registered successfully.")
 print("Available datasets:", DatasetCatalog.list())
 
-# Import necessary modules for training
-from detectron2.engine import DefaultTrainer
-from detectron2.config import get_cfg
-
 # Set up configuration
 cfg = get_cfg()
-cfg.merge_from_file("$COCO_CONFIG_FILE")
+cfg.merge_from_file(coco_config_file)
 cfg.DATASETS.TRAIN = ("coco_roboone_train",)
 cfg.DATASETS.TEST = ("coco_roboone_val",)
-cfg.SOLVER.IMS_PER_BATCH = $IMS_PER_BATCH
-cfg.SOLVER.BASE_LR = $BASE_LR
-cfg.SOLVER.MAX_ITER = 1000
-cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = $BATCH_SIZE_PER_IMAGE
-cfg.MODEL.ROI_HEADS.NUM_CLASSES = num_classes  # Automatically set based on dataset
-cfg.OUTPUT_DIR = "$OUTPUT_DIR"
+cfg.MODEL.ROI_HEADS.NUM_CLASSES = num_classes
+cfg.OUTPUT_DIR = output_dir
+
+# Set solver parameters
+cfg.SOLVER.MAX_ITER = max_iter
+cfg.SOLVER.CHECKPOINT_PERIOD = checkpoint_period
+cfg.SOLVER.IMS_PER_BATCH = ims_per_batch
+cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = batch_size_per_image
+cfg.SOLVER.BASE_LR = base_lr
 
 # Train the model
 trainer = DefaultTrainer(cfg)
-trainer.resume_or_load(resume=False)
+trainer.resume_or_load(resume=resume_training)
 trainer.train()
 
 print("Mask R-CNN training completed.")