import argparse
import fnmatch
import logging
import os
import os.path as osp
import pickle
from collections import OrderedDict, defaultdict
from datetime import datetime
from pathlib import Path
from modelindex.load_model_index import load
from rich.console import Console
from rich.syntax import Syntax
from rich.table import Table
from utils import METRICS_MAP, MMCLS_ROOT, substitute_weights
# Avoid to import MMPretrain to accelerate speed to show summary
console = Console()
logger = logging.getLogger('test')
logger.addHandler(logging.StreamHandler())
logger.addHandler(logging.FileHandler('benchmark_test.log', mode='w'))
def parse_args():
parser = argparse.ArgumentParser(
description="Test all models' accuracy in model-index.yml")
parser.add_argument('checkpoint_root', help='Checkpoint file root path.')
parser.add_argument(
'--local', action='store_true', help='run at local instead of slurm.')
parser.add_argument(
'--models', nargs='+', type=str, help='Specify model names to run.')
parser.add_argument(
'--run', action='store_true', help='run script directly')
parser.add_argument(
'--summary',
action='store_true',
help='Summarize benchmark test results.')
parser.add_argument('--save', action='store_true', help='Save the summary')
parser.add_argument(
'--gpus', type=int, default=1, help='How many GPUS to use.')
parser.add_argument(
'--no-skip',
action='store_true',
help='Whether to skip models without results record in the metafile.')
parser.add_argument(
'--work-dir',
default='work_dirs/benchmark_test',
help='the dir to save metric')
parser.add_argument('--port', type=int, default=29666, help='dist port')
parser.add_argument(
'--partition',
type=str,
default='mm_model',
help='(for slurm) Cluster partition to use.')
parser.add_argument(
'--job-name',
type=str,
default='cls-test-benchmark',
help='(for slurm) Slurm job name prefix')
parser.add_argument(
'--quotatype',
default=None,
choices=['reserved', 'auto', 'spot'],
help='(for slurm) Quota type, only available for phoenix-slurm>=0.2')
parser.add_argument(
'--cfg-options',
nargs='+',
type=str,
default=[],
help='Config options for all config files.')
args = parser.parse_args()
return args
def create_test_job_batch(commands, model_info, args, port, script_name):
model_name = model_info.name
config = Path(model_info.config)
if model_info.weights is not None:
checkpoint = substitute_weights(model_info.weights,
args.checkpoint_root)
if checkpoint is None:
logger.warning(f'{model_name}: {checkpoint} not found.')
return None
else:
return None
job_name = f'{args.job_name}_{model_name}'
work_dir = Path(args.work_dir) / model_name
work_dir.mkdir(parents=True, exist_ok=True)
result_file = work_dir / 'result.pkl'
if args.quotatype is not None:
quota_cfg = f'#SBATCH --quotatype {args.quotatype}'
else:
quota_cfg = ''
if not args.local:
launcher = 'srun python'
runner = 'slurm'
elif args.gpus > 1:
launcher = 'pytorch'
runner = ('torchrun --master_addr="127.0.0.1" '
f'--master_port={port} --nproc_per_node={args.gpus}')
else:
launcher = 'none'
runner = 'python -u'
job_script = (f'#!/bin/bash\n'
f'#SBATCH --output {work_dir}/job.%j.out\n'
f'#SBATCH --partition={args.partition}\n'
f'#SBATCH --job-name {job_name}\n'
f'#SBATCH --gres=gpu:{min(8, args.gpus)}\n'
f'{quota_cfg}\n'
f'#SBATCH --ntasks-per-node={min(8, args.gpus)}\n'
f'#SBATCH --ntasks={args.gpus}\n'
f'#SBATCH --cpus-per-task=5\n\n'
f'{runner} {script_name} {config} {checkpoint} '
f'--work-dir={work_dir} --cfg-option '
f'env_cfg.dist_cfg.port={port} '
f'{" ".join(args.cfg_options)} '
f'--out={result_file} --out-item="metrics" '
f'--launcher={launcher}\n')
with open(work_dir / 'job.sh', 'w') as f:
f.write(job_script)
commands.append(f'echo "{config}"')
if args.local:
commands.append(f'bash {work_dir}/job.sh')
else:
commands.append(f'sbatch {work_dir}/job.sh')
return work_dir / 'job.sh'
def test(models, args):
script_name = osp.join('tools', 'test.py')
port = args.port
commands = []
preview_script = ''
for model_info in models.values():
if model_info.results is None:
# Skip pre-train model
continue
script_path = create_test_job_batch(commands, model_info, args, port,
script_name)
preview_script = script_path or preview_script
port += 1
command_str = '\n'.join(commands)
preview = Table()
preview.add_column(str(preview_script))
preview.add_column('Shell command preview')
preview.add_row(
Syntax.from_path(
preview_script,
background_color='default',
line_numbers=True,
word_wrap=True),
Syntax(
command_str,
'bash',
background_color='default',
line_numbers=True,
word_wrap=True))
console.print(preview)
if args.run:
os.system(command_str)
else:
console.print('Please set "--run" to start the job')
def save_summary(summary_data, work_dir):
summary_path = work_dir / 'test_benchmark_summary.csv'
file = open(summary_path, 'w')
columns = defaultdict(list)
for model_name, summary in summary_data.items():
if len(summary) == 0:
# Skip models without results
continue
columns['Name'].append(model_name)
for metric_key in METRICS_MAP:
if metric_key in summary:
metric = summary[metric_key]
expect = round(metric['expect'], 2)
result = round(metric['result'], 2)
columns[f'{metric_key} (expect)'].append(str(expect))
columns[f'{metric_key}'].append(str(result))
else:
columns[f'{metric_key} (expect)'].append('')
columns[f'{metric_key}'].append('')
columns = {
field: column
for field, column in columns.items() if ''.join(column)
}
file.write(','.join(columns.keys()) + '\n')
for row in zip(*columns.values()):
file.write(','.join(row) + '\n')
file.close()
logger.info('Summary file saved at ' + str(summary_path))
def show_summary(summary_data):
table = Table(title='Test Benchmark Regression Summary')
table.add_column('Name')
for metric in METRICS_MAP:
table.add_column(f'{metric} (expect)')
table.add_column(f'{metric}')
table.add_column('Date')
def set_color(value, expect):
if value > expect + 0.01:
return 'green'
elif value >= expect - 0.01:
return 'white'
else:
return 'red'
for model_name, summary in summary_data.items():
row = [model_name]
for metric_key in METRICS_MAP:
if metric_key in summary:
metric = summary[metric_key]
expect = round(metric['expect'], 2)
result = round(metric['result'], 2)
color = set_color(result, expect)
row.append(f'{expect:.2f}')
row.append(f'[{color}]{result:.2f}[/{color}]')
else:
row.extend([''] * 2)
if 'date' in summary:
row.append(summary['date'])
else:
row.append('')
table.add_row(*row)
# Remove empty columns
table.columns = [
column for column in table.columns if ''.join(column._cells)
]
console.print(table)
def summary(models, args):
work_dir = Path(args.work_dir)
summary_data = {}
for model_name, model_info in models.items():
if model_info.results is None and not args.no_skip:
continue
# Skip if not found result file.
result_file = work_dir / model_name / 'result.pkl'
if not result_file.exists():
summary_data[model_name] = {}
continue
with open(result_file, 'rb') as file:
results = pickle.load(file)
date = datetime.fromtimestamp(result_file.lstat().st_mtime)
expect_metrics = model_info.results[0].metrics
# extract metrics
summary = {'date': date.strftime('%Y-%m-%d')}
for key_yml, key_res in METRICS_MAP.items():
if key_yml in expect_metrics and key_res in results:
expect_result = float(expect_metrics[key_yml])
result = float(results[key_res])
summary[key_yml] = dict(expect=expect_result, result=result)
summary_data[model_name] = summary
show_summary(summary_data)
if args.save:
save_summary(summary_data, work_dir)
def main():
args = parse_args()
# parse model-index.yml
model_index_file = MMCLS_ROOT / 'model-index.yml'
model_index = load(str(model_index_file))
model_index.build_models_with_collections()
models = OrderedDict({model.name: model for model in model_index.models})
if args.models:
filter_models = {}
for pattern in args.models:
filter_models.update({
name: models[name]
for name in fnmatch.filter(models, pattern + '*')
})
if len(filter_models) == 0:
logger.error('No model found, please specify models in:\n' +
'\n'.join(models.keys()))
return
models = filter_models
if args.summary:
summary(models, args)
else:
test(models, args)
if __name__ == '__main__':
main()