341 lines
11 KiB
Python
341 lines
11 KiB
Python
import argparse
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import json
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import os
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import os.path as osp
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import re
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from datetime import datetime
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from pathlib import Path
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from zipfile import ZipFile
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from modelindex.load_model_index import load
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from rich.console import Console
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from rich.syntax import Syntax
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from rich.table import Table
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console = Console()
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METRICS_MAP = {
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'Top 1 Accuracy': 'accuracy_top-1',
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'Top 5 Accuracy': 'accuracy_top-5'
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}
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def parse_args():
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parser = argparse.ArgumentParser(
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description='Train models (in bench_train.yml) and compare accuracy.')
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parser.add_argument(
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'partition', type=str, help='Cluster partition to use.')
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parser.add_argument(
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'--job-name',
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type=str,
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default='cls-train-benchmark',
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help='Slurm job name prefix')
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parser.add_argument('--port', type=int, default=29666, help='dist port')
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parser.add_argument(
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'--models', nargs='+', type=str, help='Specify model names to run.')
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parser.add_argument(
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'--work-dir',
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default='work_dirs/benchmark_train',
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help='the dir to save train log')
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parser.add_argument(
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'--run', action='store_true', help='run script directly')
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parser.add_argument(
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'--local',
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action='store_true',
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help='run at local instead of cluster.')
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parser.add_argument(
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'--mail', type=str, help='Mail address to watch train status.')
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parser.add_argument(
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'--mail-type',
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nargs='+',
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default=['BEGIN', 'END', 'FAIL'],
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choices=['NONE', 'BEGIN', 'END', 'FAIL', 'REQUEUE', 'ALL'],
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help='Mail address to watch train status.')
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parser.add_argument(
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'--quotatype',
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default=None,
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choices=['reserved', 'auto', 'spot'],
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help='Quota type, only available for phoenix-slurm>=0.2')
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parser.add_argument(
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'--summary',
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action='store_true',
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help='Summarize benchmark train results.')
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parser.add_argument(
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'--save',
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action='store_true',
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help='Save the summary and archive log files.')
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args = parser.parse_args()
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return args
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def create_train_job_batch(commands, model_info, args, port, script_name):
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fname = model_info.name
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assert 'Gpus' in model_info.data, \
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f"Haven't specify gpu numbers for {fname}"
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gpus = model_info.data['Gpus']
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config = Path(model_info.config)
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assert config.exists(), f'"{fname}": {config} not found.'
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job_name = f'{args.job_name}_{fname}'
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work_dir = Path(args.work_dir) / fname
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work_dir.mkdir(parents=True, exist_ok=True)
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if args.mail is not None and 'NONE' not in args.mail_type:
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mail_cfg = (f'#SBATCH --mail {args.mail}\n'
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f'#SBATCH --mail-type {args.mail_type}\n')
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else:
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mail_cfg = ''
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if args.quotatype is not None:
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quota_cfg = f'#SBATCH --quotatype {args.quotatype}\n'
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else:
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quota_cfg = ''
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launcher = 'none' if args.local else 'slurm'
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runner = 'python' if args.local else 'srun python'
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job_script = (f'#!/bin/bash\n'
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f'#SBATCH --output {work_dir}/job.%j.out\n'
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f'#SBATCH --partition={args.partition}\n'
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f'#SBATCH --job-name {job_name}\n'
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f'#SBATCH --gres=gpu:8\n'
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f'{mail_cfg}{quota_cfg}'
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f'#SBATCH --ntasks-per-node=8\n'
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f'#SBATCH --ntasks={gpus}\n'
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f'#SBATCH --cpus-per-task=5\n\n'
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f'{runner} -u {script_name} {config} '
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f'--work-dir={work_dir} --cfg-option '
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f'dist_params.port={port} '
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f'checkpoint_config.max_keep_ckpts=10 '
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f'--launcher={launcher}\n')
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with open(work_dir / 'job.sh', 'w') as f:
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f.write(job_script)
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commands.append(f'echo "{config}"')
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if args.local:
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commands.append(f'bash {work_dir}/job.sh')
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else:
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commands.append(f'sbatch {work_dir}/job.sh')
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return work_dir / 'job.sh'
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def train(args):
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models_cfg = load(str(Path(__file__).parent / 'bench_train.yml'))
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models_cfg.build_models_with_collections()
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models = {model.name: model for model in models_cfg.models}
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script_name = osp.join('tools', 'train.py')
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port = args.port
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commands = []
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if args.models:
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patterns = [re.compile(pattern) for pattern in args.models]
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filter_models = {}
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for k, v in models.items():
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if any([re.match(pattern, k) for pattern in patterns]):
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filter_models[k] = v
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if len(filter_models) == 0:
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print('No model found, please specify models in:')
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print('\n'.join(models.keys()))
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return
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models = filter_models
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for model_info in models.values():
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months = model_info.data.get('Months', range(1, 13))
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if datetime.now().month in months:
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script_path = create_train_job_batch(commands, model_info, args,
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port, script_name)
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port += 1
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command_str = '\n'.join(commands)
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preview = Table()
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preview.add_column(str(script_path))
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preview.add_column('Shell command preview')
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preview.add_row(
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Syntax.from_path(
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script_path,
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background_color='default',
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line_numbers=True,
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word_wrap=True),
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Syntax(
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command_str,
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'bash',
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background_color='default',
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line_numbers=True,
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word_wrap=True))
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console.print(preview)
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if args.run:
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os.system(command_str)
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else:
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console.print('Please set "--run" to start the job')
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def save_summary(summary_data, models_map, work_dir):
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date = datetime.now().strftime('%Y%m%d-%H%M%S')
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zip_path = work_dir / f'archive-{date}.zip'
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zip_file = ZipFile(zip_path, 'w')
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summary_path = work_dir / 'benchmark_summary.md'
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file = open(summary_path, 'w')
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headers = [
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'Model', 'Top-1 Expected(%)', 'Top-1 (%)', 'Top-1 best(%)',
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'best epoch', 'Top-5 Expected (%)', 'Top-5 (%)', 'Config', 'Log'
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]
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file.write('# Train Benchmark Regression Summary\n')
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file.write('| ' + ' | '.join(headers) + ' |\n')
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file.write('|:' + ':|:'.join(['---'] * len(headers)) + ':|\n')
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for model_name, summary in summary_data.items():
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if len(summary) == 0:
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# Skip models without results
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continue
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row = [model_name]
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if 'Top 1 Accuracy' in summary:
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metric = summary['Top 1 Accuracy']
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row.append(f"{metric['expect']:.2f}")
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row.append(f"{metric['last']:.2f}")
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row.append(f"{metric['best']:.2f}")
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row.append(f"{metric['best_epoch']:.2f}")
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else:
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row.extend([''] * 4)
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if 'Top 5 Accuracy' in summary:
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metric = summary['Top 5 Accuracy']
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row.append(f"{metric['expect']:.2f}")
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row.append(f"{metric['last']:.2f}")
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else:
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row.extend([''] * 2)
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model_info = models_map[model_name]
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row.append(model_info.config)
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row.append(str(summary['log_file'].relative_to(work_dir)))
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zip_file.write(summary['log_file'])
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file.write('| ' + ' | '.join(row) + ' |\n')
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file.close()
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zip_file.write(summary_path)
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zip_file.close()
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print('Summary file saved at ' + str(summary_path))
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print('Log files archived at ' + str(zip_path))
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def show_summary(summary_data):
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table = Table(title='Train Benchmark Regression Summary')
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table.add_column('Model')
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for metric in METRICS_MAP:
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table.add_column(f'{metric} (expect)')
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table.add_column(f'{metric}')
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table.add_column(f'{metric} (best)')
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def set_color(value, expect):
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if value > expect:
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return 'green'
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elif value > expect - 0.2:
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return 'white'
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else:
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return 'red'
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for model_name, summary in summary_data.items():
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row = [model_name]
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for metric_key in METRICS_MAP:
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if metric_key in summary:
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metric = summary[metric_key]
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expect = metric['expect']
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last = metric['last']
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last_color = set_color(last, expect)
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best = metric['best']
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best_color = set_color(best, expect)
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best_epoch = metric['best_epoch']
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row.append(f'{expect:.2f}')
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row.append(f'[{last_color}]{last:.2f}[/{last_color}]')
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row.append(
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f'[{best_color}]{best:.2f}[/{best_color}] ({best_epoch})')
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table.add_row(*row)
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console.print(table)
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def summary(args):
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models_cfg = load(str(Path(__file__).parent / 'bench_train.yml'))
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models = {model.name: model for model in models_cfg.models}
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work_dir = Path(args.work_dir)
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dir_map = {p.name: p for p in work_dir.iterdir() if p.is_dir()}
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if args.models:
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patterns = [re.compile(pattern) for pattern in args.models]
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filter_models = {}
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for k, v in models.items():
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if any([re.match(pattern, k) for pattern in patterns]):
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filter_models[k] = v
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if len(filter_models) == 0:
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print('No model found, please specify models in:')
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print('\n'.join(models.keys()))
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return
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models = filter_models
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summary_data = {}
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for model_name, model_info in models.items():
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# Skip if not found any log file.
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if model_name not in dir_map:
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summary_data[model_name] = {}
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continue
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sub_dir = dir_map[model_name]
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log_files = list(sub_dir.glob('*.log.json'))
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if len(log_files) == 0:
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continue
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log_file = sorted(log_files)[-1]
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# parse train log
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with open(log_file) as f:
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json_logs = [json.loads(s) for s in f.readlines()]
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val_logs = [
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log for log in json_logs
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if 'mode' in log and log['mode'] == 'val'
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]
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if len(val_logs) == 0:
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continue
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expect_metrics = model_info.results[0].metrics
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# extract metrics
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summary = {'log_file': log_file}
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for key_yml, key_res in METRICS_MAP.items():
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if key_yml in expect_metrics:
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assert key_res in val_logs[-1], \
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f'{model_name}: No metric "{key_res}"'
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expect_result = float(expect_metrics[key_yml])
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last = float(val_logs[-1][key_res])
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best_log = sorted(val_logs, key=lambda x: x[key_res])[-1]
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best = float(best_log[key_res])
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best_epoch = int(best_log['epoch'])
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summary[key_yml] = dict(
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expect=expect_result,
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last=last,
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best=best,
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best_epoch=best_epoch)
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summary_data[model_name] = summary
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show_summary(summary_data)
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if args.save:
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save_summary(summary_data, models, work_dir)
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def main():
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args = parse_args()
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if args.summary:
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summary(args)
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else:
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train(args)
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if __name__ == '__main__':
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main()
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