85 lines
3.5 KiB
Python
85 lines
3.5 KiB
Python
import json
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import os
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import sys
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import re
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def extract_metrics_from_log(log_file_path):
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all_metrics = []
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pattern = re.compile(
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r"\{'agilex_sample_mse':\s*([0-9.eE+-]+),\s*'agilex_sample_l2err':\s*([0-9.eE+-]+),\s*'overall_avg_sample_mse':\s*([0-9.eE+-]+),\s*'overall_avg_sample_l2err':\s*([0-9.eE+-]+)\}"
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)
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try:
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with open(log_file_path, 'r', encoding='utf-8') as f:
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for line in f:
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m = pattern.search(line)
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if m:
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metrics = (
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float(m.group(1)),
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float(m.group(2)),
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float(m.group(3)),
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float(m.group(4))
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)
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all_metrics.append(metrics)
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print(f"Find Metrics: agilex_sample_mse={metrics[0]}, agilex_sample_l2err={metrics[1]}, "
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f"overall_avg_sample_mse={metrics[2]}, overall_avg_sample_l2err={metrics[3]}")
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except Exception as e:
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print(f"Failed to read log: {e}")
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return (None, None, None, None)
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if not all_metrics:
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print("No metrics found in the log file")
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return (None, None, None, None)
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print(f"\nTotal {len(all_metrics)} metrics found in the log file")
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best_agilex_mse = min(m[0] for m in all_metrics)
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best_agilex_l2err = min(m[1] for m in all_metrics)
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best_overall_mse = min(m[2] for m in all_metrics)
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best_overall_l2err = min(m[3] for m in all_metrics)
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print(f"\nBest metrics:")
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print(f" agilex_sample_mse: {best_agilex_mse}")
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print(f" agilex_sample_l2err: {best_agilex_l2err}")
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print(f" overall_avg_sample_mse: {best_overall_mse}")
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print(f" overall_avg_sample_l2err: {best_overall_l2err}")
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return (best_agilex_mse, best_agilex_l2err, best_overall_mse, best_overall_l2err)
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def generate_output_json(input_config_file, output_dir, runtime):
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with open(input_config_file, 'r') as f:
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config = json.load(f)
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log_file = os.path.join(output_dir, 'output.log')
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agilex_sample_mse, agilex_sample_l2err, overall_avg_sample_mse, overall_avg_sample_l2err = extract_metrics_from_log(log_file)
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if None in [agilex_sample_mse, agilex_sample_l2err, overall_avg_sample_mse, overall_avg_sample_l2err]:
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print("Warning: Some metrics are missing in the log file.")
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output_json = {
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"task_id": config.get("task_id"),
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"model_type": "RDT-170M",
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"model_name": config.get("model_name") if "model_name" in config else config.get("train", {}).get("model"),
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"gpu_id": config.get("gpu_id"),
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"runtime": runtime,
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"log_path": log_file,
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"output_dir": output_dir,
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"model_path": os.path.join(output_dir, 'pytorch_model.bin'),
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"metrics": {
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"agilex_sample_mse": agilex_sample_mse,
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"agilex_sample_l2err": agilex_sample_l2err,
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"overall_avg_sample_mse": overall_avg_sample_mse,
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"overall_avg_sample_l2err": overall_avg_sample_l2err
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}
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}
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# 写入 output.json，格式化输出、确保null与规范json一致
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output_json_path = os.path.join(output_dir, 'output.json')
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with open(output_json_path, 'w') as f:
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json.dump(output_json, f, indent=4, ensure_ascii=False)
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if __name__ == "__main__":
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if len(sys.argv) != 4:
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print("Usage: python generate_output_json.py <input_config_file> <output_dir> <runtime>")
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sys.exit(1)
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generate_output_json(sys.argv[1], sys.argv[2], sys.argv[3])
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