diff --git a/.gitignore b/.gitignore index 6dccee0618b248854c7d12e84482690d39c13a47..73eeeecab52fcbb9b79a81125c017fc874d96aab 100644 --- a/.gitignore +++ b/.gitignore @@ -13,3 +13,7 @@ maskal_ws/results/ *.part + +export_coco/ + +no-hooks/ diff --git a/README.md b/README.md index c7a5d81657675eba182c1e5f2466ad380f1379f8..7e8a53fb4339d286bf80031fae825fb45e9d7cc8 100644 --- a/README.md +++ b/README.md @@ -3,6 +3,15 @@ ### Reference ### Setup +git clone git@hf.co:datasets/nyxrobotics/roboone_auto_dataset + +### Configuration +- `maskal_ws/config` + - `classes.yaml` + - `directory.yaml` + - `maskal.yaml` + - `meta.json` + - `network` ### Get PNG images from youtube videos - Create youtube public playlist on youtube website @@ -24,9 +33,17 @@ bash modules/move_png_to_ws.sh - Run maskal ```bash -run_maskal.sh +bash modules/run_maskal.sh +``` + +- Run labelme +```bash +bash modules/run_labelme.sh ``` +### Train dataset + + ### License --- license: apache-2.0 diff --git a/export_annotated/10th 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sha256:24b370808d9928ff563e2266a8fc3c119ae4a3bc270bfeb444616ac21c15a6c0 +size 1974035 diff --git a/maskal_ws/build/maskal.yaml b/maskal_ws/build/maskal.yaml index eaaca800e90d7a248fabbc0211ffe5d35b5f5cd0..e9a3ee6980facace713226da1596e50e97dd9475 100644 --- a/maskal_ws/build/maskal.yaml +++ b/maskal_ws/build/maskal.yaml @@ -1,8 +1,5 @@ # training-parameters -### Network Parameters -### Standard -# network_config: COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml -# pretrained_weights: COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml + classes: ['person', 'roboone'] # train-sampler minority_classes: ['person', 'roboone'] @@ -55,6 +52,13 @@ mcd_iterations: 10 iou_thres: 0.5 auto_annotate: True export_format: labelme +### Network Parameters +### Standard +# network_config: COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml +# pretrained_weights: COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml ### Light-weight +# network_config: COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml +# pretrained_weights: COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml network_config: COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml -pretrained_weights: COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml +### Pre-trained +pretrained_weights: /home/nyx/lib/roboone_auto_dataset/maskal_ws/weights/exp1/uncertainty/model_final.pth diff --git a/maskal_ws/config/network.yaml b/maskal_ws/config/network.yaml index 75c218ec535d203246ddad33f8328f3c72e4df3a..7b253fe6f828995ca3d83c059d0e38573e521c73 100644 --- a/maskal_ws/config/network.yaml +++ b/maskal_ws/config/network.yaml @@ -4,5 +4,9 @@ # pretrained_weights: COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml ### Light-weight +# network_config: COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml +# pretrained_weights: COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml + +### Pre-trained network_config: COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml -pretrained_weights: COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml +pretrained_weights: /home/nyx/lib/roboone_auto_dataset/maskal_ws/weights/exp1/uncertainty/model_final.pth diff --git a/modules/convert_labelme_nodata.sh b/modules/convert_labelme_nodata.sh new file mode 100644 index 0000000000000000000000000000000000000000..87cd0a73288d8b7b64468351d8b89faadbfddbf5 --- /dev/null +++ b/modules/convert_labelme_nodata.sh @@ -0,0 +1,14 @@ +#!/bin/bash +### get script dir +SCRIPT_DIR=$(cd $(dirname $0); pwd) +### Specify the directory containing JSON files +TARGET_DIR=$SCRIPT_DIR/../export_annotated + +# Process all JSON files in the target directory +for file in "$TARGET_DIR"/*.json; do + # Use jq to set imageData to null and overwrite the original file + jq '.imageData = null' "$file" > tmp && mv tmp "$file" + echo "Converted: $file" +done + +echo "All files have been converted." diff --git a/modules/labelme2coco.sh b/modules/labelme2coco.sh new file mode 100644 index 0000000000000000000000000000000000000000..b4fa9b85ab9a75e61013cf7de8e358813f38c5b2 --- /dev/null +++ b/modules/labelme2coco.sh @@ -0,0 +1,60 @@ +#!/bin/bash + +# Get script dir +SCRIPT_DIR=$(cd $(dirname $0); pwd) +LABELME_DIR=$SCRIPT_DIR/../export_annotated +COCO_DIR=$SCRIPT_DIR/../export_coco + +# Create necessary directories +mkdir -p $COCO_DIR/train +mkdir -p $COCO_DIR/val +mkdir -p $COCO_DIR/annotations + +# Clean train, val, and annotations directories by removing all files +rm -rf $COCO_DIR/train/* +rm -rf $COCO_DIR/val/* +rm -rf $COCO_DIR/annotations/* + +# Determine the number of files for validation (20%) +LABELME_FILES=$(find "$LABELME_DIR" -name "*.json") +NUM_FILES=$(echo "$LABELME_FILES" | wc -l) +VAL_COUNT=$(echo "$NUM_FILES * 0.2" | bc | awk '{print int($1+0.5)}') + +# Shuffle files and split into train and val sets +SHUFFLED_FILES=$(echo "$LABELME_FILES" | shuf) +VAL_FILES=$(echo "$SHUFFLED_FILES" | head -n $VAL_COUNT) +TRAIN_FILES=$(echo "$SHUFFLED_FILES" | tail -n +$(($VAL_COUNT + 1))) + +# Copy validation files and their corresponding images to val +echo "$VAL_FILES" | while read -r val_file; do + cp "$val_file" "$COCO_DIR/val/" + + # Copy the corresponding image file (assuming it has the same base name) + image_file="${val_file%.json}.png" # Change the extension to match your images + if [[ -f "$image_file" ]]; then + cp "$image_file" "$COCO_DIR/val/" + fi +done + +# Copy training files and their corresponding images to train +echo "$TRAIN_FILES" | while read -r train_file; do + cp "$train_file" "$COCO_DIR/train/" + + # Copy the corresponding image file (assuming it has the same base name) + image_file="${train_file%.json}.png" # Change the extension to match your images + if [[ -f "$image_file" ]]; then + cp "$image_file" "$COCO_DIR/train/" + fi +done + +# Convert Labelme JSON to COCO format for train and val +labelme2coco $COCO_DIR/train $COCO_DIR/annotations +mv $COCO_DIR/annotations/dataset.json $COCO_DIR/annotations/train.json +labelme2coco $COCO_DIR/val $COCO_DIR/annotations +mv $COCO_DIR/annotations/dataset.json $COCO_DIR/annotations/val.json + +# Remove the Labelme JSON files from train and val directories after conversion +find $COCO_DIR/train -name "*.json" -type f -delete +find $COCO_DIR/val -name "*.json" -type f -delete + +echo "Conversion to COCO format completed, and JSON files have been deleted from train and val directories." diff --git a/modules/maskrcnn_train.sh b/modules/maskrcnn_train.sh new file mode 100644 index 0000000000000000000000000000000000000000..aa13abd89634e6ab7498972a5ab0d2e6a9978198 --- /dev/null +++ b/modules/maskrcnn_train.sh @@ -0,0 +1,135 @@ +#!/bin/bash + +# Parameters +USE_GPU_TUNING=true # Set this to true to enable GPU tuning +RESUME_TRAINING=false # Set this to true to resume training from last checkpoint +MAX_ITER=100000 # Default maximum iterations +CHECKPOINT_PERIOD=$(($MAX_ITER / 10)) # Set to 10% of MAX_ITER + +# Default values +IMS_PER_BATCH=2 +BATCH_SIZE_PER_IMAGE=512 +BASE_LR=0.00025 + +# If USE_GPU_TUNING is true, adjust parameters based on GPU memory +if [ "$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 $0); pwd) + +# Set the paths for the dataset and configuration files +TRAIN_ANNOTATION=$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 + +# Python script to configure and run the training +python3 - <