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3404d44 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 | #!/bin/bash
# Run Experiment 2-A for all model types with proper conda environments
#
# Usage:
# ./run_exp2a.sh # Run all models
# ./run_exp2a.sh qwen # Run only Qwen
# ./run_exp2a.sh nvila # Run only NVILA
# ./run_exp2a.sh molmo # Run only Molmo
set -e
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
OUTPUT_DIR="/data/shared/Qwen/experiments/exp2a_results"
SAMPLES=50 # Samples per category
SEED=42
echo "=============================================="
echo "Experiment 2-A: Embedding Space Analysis"
echo "=============================================="
echo "Output directory: $OUTPUT_DIR"
echo "Samples per category: $SAMPLES"
echo ""
# Initialize conda
source /root/miniconda3/etc/profile.d/conda.sh
# Parse arguments
RUN_QWEN=false
RUN_NVILA=false
RUN_MOLMO=false
if [ $# -eq 0 ]; then
RUN_QWEN=true
RUN_NVILA=true
RUN_MOLMO=true
else
for arg in "$@"; do
case $arg in
qwen) RUN_QWEN=true ;;
nvila) RUN_NVILA=true ;;
molmo) RUN_MOLMO=true ;;
*) echo "Unknown model: $arg. Use qwen, nvila, or molmo." ;;
esac
done
fi
# Run for Qwen (no conda environment - deactivate all)
if [ "$RUN_QWEN" = true ]; then
echo "=============================================="
echo "Running Qwen2.5-VL experiments..."
echo "=============================================="
# Deactivate all conda environments to get to base system python
conda deactivate 2>/dev/null || true
conda deactivate 2>/dev/null || true
conda deactivate 2>/dev/null || true
# Use system python directly
/root/miniconda3/bin/python "$SCRIPT_DIR/exp2a_embedding_analysis.py" \
--model_type qwen \
--scales vanilla 80k 400k 800k 2m \
--output_dir "$OUTPUT_DIR" \
--samples_per_category $SAMPLES \
--seed $SEED
fi
# Run for NVILA (vila conda environment)
if [ "$RUN_NVILA" = true ]; then
echo "=============================================="
echo "Running NVILA experiments..."
echo "=============================================="
conda activate vila
python "$SCRIPT_DIR/exp2a_embedding_analysis.py" \
--model_type nvila \
--scales vanilla 80k 400k 800k 2m \
--output_dir "$OUTPUT_DIR" \
--samples_per_category $SAMPLES \
--seed $SEED
conda deactivate
fi
# Run for Molmo (molmo conda environment)
if [ "$RUN_MOLMO" = true ]; then
echo "=============================================="
echo "Running Molmo experiments..."
echo "=============================================="
conda activate molmo
python "$SCRIPT_DIR/exp2a_embedding_analysis.py" \
--model_type molmo \
--scales vanilla 80k 400k 800k \
--output_dir "$OUTPUT_DIR" \
--samples_per_category $SAMPLES \
--seed $SEED
conda deactivate
fi
echo ""
echo "=============================================="
echo "Experiments completed!"
echo "Results saved to: $OUTPUT_DIR"
echo "=============================================="
# Generate combined comparison plot (uses base python)
/root/miniconda3/bin/python - <<'EOF'
import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
output_dir = "/data/shared/Qwen/experiments/exp2a_results"
# Collect all results
all_results = []
for model_type in ['qwen', 'nvila', 'molmo']:
summary_path = os.path.join(output_dir, model_type, 'results_summary.csv')
if os.path.exists(summary_path):
df = pd.read_csv(summary_path)
all_results.append(df)
if all_results:
combined_df = pd.concat(all_results, ignore_index=True)
combined_df.to_csv(os.path.join(output_dir, 'all_results_summary.csv'), index=False)
# Create combined comparison plot
pairs = ['sim_above_far', 'sim_under_close', 'sim_left_right']
pair_labels = ['above-far', 'under-close', 'left-right']
fig, axes = plt.subplots(1, 3, figsize=(18, 6))
for ax, (pair, pair_label) in zip(axes, zip(pairs, pair_labels)):
for model_type in ['qwen', 'nvila', 'molmo']:
model_data = combined_df[combined_df['model'].str.startswith(model_type)]
if not model_data.empty:
scales = model_data['model'].str.replace(f'{model_type}_', '')
values = model_data[pair].values
ax.plot(scales, values, marker='o', label=model_type.upper(), linewidth=2)
ax.set_title(f'{pair_label}', fontsize=12, fontweight='bold')
ax.set_xlabel('Training Scale')
ax.set_ylabel('Cosine Similarity')
ax.legend()
ax.set_ylim(0, 1)
ax.axhline(y=0.5, color='gray', linestyle='--', alpha=0.5)
ax.tick_params(axis='x', rotation=45)
plt.suptitle('Spatial Concept Similarity Across Training Scales', fontsize=14, fontweight='bold')
plt.tight_layout()
plt.savefig(os.path.join(output_dir, 'all_models_comparison.png'), dpi=300, bbox_inches='tight')
plt.close()
print(f"\nCombined results saved to {output_dir}/all_results_summary.csv")
print(f"Combined plot saved to {output_dir}/all_models_comparison.png")
else:
print("No results found to combine.")
EOF
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