train data

modules/maskrcnn_train.sh

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+#!/bin/bash
+
+# Get script dir
+SCRIPT_DIR=$(cd $(dirname $0); pwd)
+
+# Set the paths for the dataset and configuration files
+TRAIN_ANNOTATION=$SCRIPT_DIR/../export_coco/annotations/train.json
+TRAIN_IMAGE_DIR=$SCRIPT_DIR/../export_coco/train
+VAL_ANNOTATION=$SCRIPT_DIR/../export_coco/annotations/val.json
+VAL_IMAGE_DIR=$SCRIPT_DIR/../export_coco/val
+COCO_CONFIG_FILE=$SCRIPT_DIR/../export_coco/config/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml
+BASE_CONFIG_FILE=$SCRIPT_DIR/../export_coco/config/Base-RCNN-FPN.yaml  # Base config file path
+CONFIG_URL="https://raw.githubusercontent.com/facebookresearch/detectron2/main/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"
+BASE_CONFIG_URL="https://raw.githubusercontent.com/facebookresearch/detectron2/main/configs/Base-RCNN-FPN.yaml"  # Base config URL
+TRAIN_NET_DIR=$SCRIPT_DIR/../export_coco/detectron2
+TRAIN_NET_FILE=$TRAIN_NET_DIR/train_net.py
+TRAIN_NET_URL="https://raw.githubusercontent.com/facebookresearch/detectron2/main/tools/train_net.py"
+OUTPUT_DIR=$SCRIPT_DIR/../export_coco/output
+
+# Create necessary directories
+mkdir -p $(dirname $COCO_CONFIG_FILE)
+mkdir -p $TRAIN_NET_DIR
+mkdir -p $OUTPUT_DIR
+
+# Download the COCO config file if it doesn't exist
+if [ ! -f "$COCO_CONFIG_FILE" ]; then
+    echo "Downloading Mask R-CNN configuration file..."
+    wget $CONFIG_URL -O $COCO_CONFIG_FILE
+fi
+
+# Download the base config file if it doesn't exist
+if [ ! -f "$BASE_CONFIG_FILE" ]; then
+    echo "Downloading Base-RCNN-FPN.yaml configuration file..."
+    wget $BASE_CONFIG_URL -O $BASE_CONFIG_FILE
+fi
+
+# Download train_net.py if it doesn't exist
+if [ ! -f "$TRAIN_NET_FILE" ]; then
+    echo "Downloading train_net.py file..."
+    wget $TRAIN_NET_URL -O $TRAIN_NET_FILE
+fi
+
+# Function to extract the number of classes from COCO annotation
+NUM_CLASSES=$(python3 - <<END
+import json
+
+# Load the COCO annotation file
+with open("$TRAIN_ANNOTATION", 'r') as f:
+    coco_data = json.load(f)
+
+# Extract the number of unique categories
+num_classes = len(coco_data['categories'])
+print(num_classes)
+END
+)
+
+echo "Detected $NUM_CLASSES classes from the dataset."
+
+# Register the datasets with Detectron2 (assuming Detectron2 environment is set up)
+python3 - <<END
+import os
+from detectron2.data.datasets import register_coco_instances
+
+# Register COCO datasets
+train_annotation = "$TRAIN_ANNOTATION"
+train_image_dir = "$TRAIN_IMAGE_DIR"
+val_annotation = "$VAL_ANNOTATION"
+val_image_dir = "$VAL_IMAGE_DIR"
+
+# Register with the name coco_roboone_train and coco_roboone_val
+register_coco_instances("coco_roboone_train", {}, train_annotation, train_image_dir)
+register_coco_instances("coco_roboone_val", {}, val_annotation, val_image_dir)
+
+print("Datasets registered successfully.")
+END
+
+# Run the training with Detectron2
+python3 $TRAIN_NET_FILE \
+  --config-file $COCO_CONFIG_FILE \
+  --num-gpus 1 \
+  OUTPUT_DIR $OUTPUT_DIR \
+  DATASETS.TRAIN "('coco_roboone_train',)" \
+  DATASETS.TEST "('coco_roboone_val',)" \
+  SOLVER.IMS_PER_BATCH 2 \
+  SOLVER.BASE_LR 0.00025 \
+  SOLVER.MAX_ITER 1000 \
+  MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE 512 \
+  MODEL.ROI_HEADS.NUM_CLASSES $NUM_CLASSES  # Automatically set based on dataset
+
+echo "Mask R-CNN training completed."