# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import glob
import logging
import os
import subprocess
from datetime import datetime
from pathlib import Path
from typing import List, Union
import mmengine
import openpyxl
import pandas as pd
import yaml
from torch.hub import download_url_to_file
from torch.multiprocessing import set_start_method
import mmdeploy.version
from mmdeploy.utils import (get_backend, get_codebase, get_root_logger,
is_dynamic_shape)
def parse_args():
parser = argparse.ArgumentParser(description='Regression Test')
parser.add_argument(
'--codebase',
nargs='+',
help='regression test yaml path.',
default=[
'mmcls', 'mmdet', 'mmseg', 'mmpose', 'mmocr', 'mmedit', 'mmrotate',
'mmdet3d'
])
parser.add_argument(
'-p',
'--performance',
default=False,
action='store_true',
help='test performance if it set')
parser.add_argument(
'--backends', nargs='+', help='test specific backend(s)')
parser.add_argument(
'--models', nargs='+', default=['all'], help='test specific model(s)')
parser.add_argument(
'--work-dir',
type=str,
help='the dir to save logs and models',
default='../mmdeploy_regression_working_dir')
parser.add_argument(
'--checkpoint-dir',
type=str,
help='the dir to save checkpoint for all model',
default='../mmdeploy_checkpoints')
parser.add_argument(
'--device',
type=str,
help='Device type, cuda:id or cpu, cuda:0 as default',
default='cuda:0')
parser.add_argument(
'--log-level',
help='set log level',
default='INFO',
choices=list(logging._nameToLevel.keys()))
args = parser.parse_args()
return args
def merge_report(work_dir: str, logger: logging.Logger):
"""Merge all the report into one report.
Args:
work_dir (str): Work dir that including all reports.
logger (logging.Logger): Logger handler.
"""
work_dir = Path(work_dir)
res_file = work_dir.joinpath(
f'mmdeploy_regression_test_{mmdeploy.version.__version__}.xlsx')
logger.info(f'Whole result report saving in {res_file}')
if res_file.exists():
# delete if it existed
res_file.unlink()
for report_file in work_dir.iterdir():
if report_file.name.startswith('.~'):
# skip unclosed temp file
continue
if '_report.xlsx' not in report_file.name or \
report_file.name == res_file.name or \
not report_file.is_file():
# skip other file
continue
# get info from report
logger.info(f'Merging {report_file}')
df = pd.read_excel(str(report_file))
df.rename(columns={'Unnamed: 0': 'Index'}, inplace=True)
# get key then convert to list
keys = list(df.keys())
values = df.values.tolist()
# sheet name
sheet_name = report_file.stem.split('_')[0]
# begin to write
if res_file.exists():
# load if it existed
wb = openpyxl.load_workbook(str(res_file))
else:
wb = openpyxl.Workbook()
# delete if sheet already exist
if sheet_name in wb.sheetnames:
wb.remove(wb[sheet_name])
# create sheet
wb.create_sheet(title=sheet_name, index=0)
# write in row
wb[sheet_name].append(keys)
for value in values:
wb[sheet_name].append(value)
# delete the blank sheet
for name in wb.sheetnames:
ws = wb[name]
if ws.cell(1, 1).value is None:
wb.remove(ws)
# save to file
wb.save(str(res_file))
logger.info('Report merge successful.')
def get_model_metafile_info(global_info: dict, model_info: dict,
logger: logging.Logger):
"""Get model metafile information.
Args:
global_info (dict): global info from deploy yaml.
model_info (dict): model info from deploy yaml.
logger (logging.Logger): Logger handler.
Returns:
Dict: Meta info of each model config
"""
# get info from global_info and model_info
checkpoint_dir = global_info.get('checkpoint_dir', None)
assert checkpoint_dir is not None
codebase_dir = global_info.get('codebase_dir', None)
assert codebase_dir is not None
codebase_name = global_info.get('codebase_name', None)
assert codebase_name is not None
model_config_files = model_info.get('model_configs', [])
assert len(model_config_files) > 0
# make checkpoint save directory
model_name = _filter_string(model_info.get('name', 'model'))
checkpoint_save_dir = Path(checkpoint_dir).joinpath(
codebase_name, model_name)
checkpoint_save_dir.mkdir(parents=True, exist_ok=True)
logger.info(f'Saving checkpoint in {checkpoint_save_dir}')
# get model metafile info
metafile_path = Path(codebase_dir).joinpath(model_info.get('metafile'))
if not metafile_path.exists():
logger.warning(f'Metafile not exists: {metafile_path}')
return [], '', ''
with open(metafile_path) as f:
metafile_info = yaml.load(f, Loader=yaml.FullLoader)
model_meta_info = dict()
for meta_model in metafile_info.get('Models'):
if str(meta_model.get('Config')) not in model_config_files:
# skip if the model not in model_config_files
logger.warning(f'{meta_model.get("Config")} '
f'not in {model_config_files}, pls check ! '
'Skip it...')
continue
# get meta info
model_meta_info.update({meta_model.get('Config'): meta_model})
# get weight url
weights_url = meta_model.get('Weights')
weights_name = str(weights_url).split('/')[-1]
weights_save_path = checkpoint_save_dir.joinpath(weights_name)
if weights_save_path.exists() and \
not global_info.get('checkpoint_force_download', False):
logger.info(f'model {weights_name} exist, skip download it...')
continue
# Download weight
logger.info(f'Downloading {weights_url} to {weights_save_path}')
download_url_to_file(
weights_url, str(weights_save_path), progress=True)
# check weather the weight download successful
if not weights_save_path.exists():
raise FileExistsError(f'Weight {weights_name} download fail')
logger.info('All models had been downloaded successful !')
return model_meta_info, checkpoint_save_dir, codebase_dir
def update_report(report_dict: dict, model_name: str, model_config: str,
task_name: str, checkpoint: str, dataset: str,
backend_name: str, deploy_config: str,
static_or_dynamic: str, precision_type: str,
conversion_result: str, fps: str, metric_info: list,
test_pass: str, report_txt_path: Path, codebase_name: str):
"""Update report information.
Args:
report_dict (dict): Report info dict.
model_name (str): Model name.
model_config (str): Model config name.
task_name (str): Task name.
checkpoint (str): Model checkpoint name.
dataset (str): Dataset name.
backend_name (str): Backend name.
deploy_config (str): Deploy config name.
static_or_dynamic (str): Static or dynamic.
precision_type (str): Precision type of the model.
conversion_result (str): Conversion result: Successful or Fail.
fps (str): Inference speed (ms/im).
metric_info (list): Metric info list of the ${modelName}.yml.
test_pass (str): Test result: Pass or Fail.
report_txt_path (Path): Report txt path.
codebase_name (str): Codebase name.
"""
# make model path shorter
if '.pth' in checkpoint:
checkpoint = Path(checkpoint).absolute().resolve()
checkpoint = str(checkpoint).split(f'/{codebase_name}/')[-1]
checkpoint = '${CHECKPOINT_DIR}' + f'/{codebase_name}/{checkpoint}'
else:
parent_dir, filename = os.path.split(checkpoint)
parent_dir = os.path.abspath(parent_dir)
work_dir = report_txt_path.parent.absolute().resolve()
parent_dir = parent_dir.replace(str(work_dir), '${WORK_DIR}')
checkpoint = os.path.join(parent_dir, filename)
# save to tmp file
tmp_str = f'{model_name},{model_config},{task_name},{checkpoint},' \
f'{dataset},{backend_name},{deploy_config},' \
f'{static_or_dynamic},{precision_type},{conversion_result},'
# save to report
report_dict.get('Model').append(model_name)
report_dict.get('Model Config').append(model_config)
report_dict.get('Task').append(task_name)
report_dict.get('Checkpoint').append(checkpoint)
report_dict.get('Dataset').append(dataset)
report_dict.get('Backend').append(backend_name)
report_dict.get('Deploy Config').append(deploy_config)
report_dict.get('Static or Dynamic').append(static_or_dynamic)
report_dict.get('Precision Type').append(precision_type)
report_dict.get('Conversion Result').append(conversion_result)
# report_dict.get('FPS').append(fps)
for metric in metric_info:
for metric_name, metric_value in metric.items():
metric_name = str(metric_name)
report_dict.get(metric_name).append(metric_value)
tmp_str += f'{metric_value},'
report_dict.get('Test Pass').append(test_pass)
tmp_str += f'{test_pass}\n'
with open(report_txt_path, 'a+', encoding='utf-8') as f:
f.write(tmp_str)
def get_pytorch_result(model_name: str, meta_info: dict, checkpoint_path: Path,
model_config_path: Path, model_config_name: str,
test_yaml_metric_info: dict, report_dict: dict,
logger: logging.Logger, report_txt_path: Path,
codebase_name: str):
"""Get metric from metafile info of the model.
Args:
model_name (str): Name of model.
meta_info (dict): Metafile info from model's metafile.yml.
checkpoint_path (Path): Checkpoint path.
model_config_path (Path): Model config path.
model_config_name (str): Name of model config in meta_info.
test_yaml_metric_info (dict): Metrics info from test yaml.
report_dict (dict): Report info dict.
logger (logging.Logger): Logger.
report_txt_path (Path): Report txt path.
codebase_name (str): Codebase name.
Returns:
Dict: metric info of the model
"""
if model_config_name not in meta_info:
logger.warning(
f'{model_config_name} not in meta_info, which is {meta_info}')
return {}
# get metric
model_info = meta_info[model_config_name]
metafile_metric_info = model_info['Results']
pytorch_metric = dict()
using_dataset = set()
using_task = set()
datasets = []
# Get metrics info from metafile
for metafile_metric in metafile_metric_info:
pytorch_metric.update(metafile_metric['Metrics'])
dataset = metafile_metric['Dataset']
task_name = metafile_metric['Task']
datasets.append(dataset)
using_task.add(task_name)
using_dataset.add(dataset)
dataset_type = '|'.join(list(using_dataset))
task_type = '|'.join(list(using_task))
metric_list = []
for metric, metric_info in test_yaml_metric_info.items():
value = '-'
if metric in pytorch_metric:
if 'dataset' in metric_info:
idx = datasets.index(metric_info['dataset'])
pytorch_metric.update(metafile_metric_info[idx]['Metrics'])
value = pytorch_metric[metric]
metric_list.append({metric: value})
valid_pytorch_metric = {
k: v
for k, v in pytorch_metric.items() if k in test_yaml_metric_info
}
# get pytorch fps value
fps_info = model_info.get('Metadata').get('inference time (ms/im)')
if fps_info is None:
fps = '-'
elif isinstance(fps_info, list):
fps = fps_info[0].get('value')
else:
fps = fps_info.get('value')
logger.info(f'Got metric_list = {metric_list} ')
logger.info(f'Got pytorch_metric = {pytorch_metric} ')
# update report
update_report(
report_dict=report_dict,
model_name=model_name,
model_config=str(model_config_path),
task_name=task_type,
checkpoint=str(checkpoint_path),
dataset=dataset_type,
backend_name='Pytorch',
deploy_config='-',
static_or_dynamic='-',
precision_type='-',
conversion_result='-',
fps=fps,
metric_info=metric_list,
test_pass='-',
report_txt_path=report_txt_path,
codebase_name=codebase_name)
logger.info(f'Got {model_config_path} metric: {valid_pytorch_metric}')
dataset_info = dict(dataset=dataset_type, task=task_type)
return valid_pytorch_metric, dataset_info
def parse_test_log(work_dir: str) -> dict:
"""Parse metrics result from output json file.
Args:
work_dir: work directory that has output json file.
Returns:
dict: metric results
"""
logger = get_root_logger()
json_files = glob.glob(os.path.join(work_dir, '*', '*.json'))
json_path = None
newest_date = None
# filter json and get latest json file
for f in json_files:
fname = os.path.split(f)[1].strip('.json')
try:
date = datetime.strptime(fname, '%Y%m%d_%H%M%S')
if newest_date is None:
newest_date = date
json_path = f
elif date > newest_date:
newest_date = date
json_path = f
except Exception:
pass
if (not os.path.exists(work_dir)) or json_path is None:
logger.warning(f'Not json files found in {work_dir}')
result = {}
else:
logger.info(f'Parse test result from {json_path}')
result = mmengine.load(json_path)
return result
def get_fps_metric(shell_res: int, pytorch_metric: dict, metric_info: dict,
work_path: Path):
"""Get fps and metric.
Args:
shell_res (int): Backend convert result: 0 is success.
pytorch_metric (dict): Metric info of pytorch metafile.
work_path (Path): Logger path.
metric_info (dict): Metric info.
Returns:
Float: fps: FPS of the model.
List: metric_list: metric result list.
Bool: test_pass: If the test pass or not.
"""
# check if converted successes or not.
fps = '-'
if shell_res != 0:
backend_results = {}
else:
backend_results = parse_test_log(work_path)
compare_results = {}
output_result = {}
for metric_name, metric_value in pytorch_metric.items():
metric_key = metric_info[metric_name]['metric_key']
tolerance = metric_info[metric_name]['tolerance']
multi_value = metric_info[metric_name].get('multi_value', 1.0)
compare_flag = False
output_result[metric_name] = 'x'
if metric_key in backend_results:
backend_value = backend_results[metric_key] * multi_value
output_result[metric_name] = backend_value
if backend_value >= metric_value - tolerance:
compare_flag = True
compare_results[metric_name] = compare_flag
if len(compare_results):
test_pass = all(list(compare_results.values()))
else:
test_pass = False
return fps, output_result, test_pass
def get_backend_fps_metric(deploy_cfg_path: str, model_cfg_path: Path,
convert_checkpoint_path: str, device_type: str,
logger: logging.Logger, pytorch_metric: dict,
metric_info: dict, backend_name: str,
precision_type: str, convert_result: bool,
report_dict: dict, infer_type: str, log_path: Path,
dataset_info: dict, report_txt_path: Path,
model_name: str):
"""Get backend fps and metric.
Args:
deploy_cfg_path (str): Deploy config path.
model_cfg_path (Path): Model config path.
convert_checkpoint_path (str): Converted checkpoint path.
device_type (str): Device for converting.
logger (logging.Logger): Logger handler.
pytorch_metric (dict): Pytorch metric info dict get from metafile.
metric_info (dict): Metric info from test yaml.
backend_name (str): Backend name.
precision_type (str): Precision type for evaluation.
convert_result (bool): Backend convert result.
report_dict (dict): Backend convert result.
infer_type (str): Infer type.
log_path (Path): Logger save path.
dataset_info (dict): Dataset info.
report_txt_path (Path): report txt save path.
model_name (str): Name of model in test yaml.
"""
work_dir = log_path.parent.joinpath('test_logs')
if not work_dir.exists():
work_dir.mkdir(parents=True, exist_ok=True)
cmd_lines = [
'python3 tools/test.py', f'{deploy_cfg_path}',
f'{str(model_cfg_path.absolute())}',
f'--model {convert_checkpoint_path}', f'--work-dir "{work_dir}"',
'--speed-test', f'--device {device_type}'
]
codebase_name = get_codebase(str(deploy_cfg_path)).value
# to stop Dataloader OOM in docker CI
if codebase_name not in ['mmedit', 'mmocr', 'mmcls']:
cfg_options = 'test_dataloader.num_workers=0 ' \
'test_dataloader.persistent_workers=False ' \
'val_dataloader.num_workers=0 ' \
'val_dataloader.persistent_workers=False '
cmd_lines.append(f'--cfg-options {cfg_options}')
# Test backend
return_code = run_cmd(cmd_lines, log_path)
fps, backend_metric, test_pass = get_fps_metric(return_code,
pytorch_metric,
metric_info, work_dir)
logger.info(f'test_pass= {test_pass}, results= {backend_metric}')
metric_list = []
for metric in metric_info:
value = '-'
if metric in backend_metric:
value = backend_metric[metric]
metric_list.append({metric: value})
dataset_type = dataset_info['dataset']
task_name = dataset_info['task']
# update the report
update_report(
report_dict=report_dict,
model_name=model_name,
model_config=str(model_cfg_path),
task_name=task_name,
checkpoint=convert_checkpoint_path,
dataset=dataset_type,
backend_name=backend_name,
deploy_config=str(deploy_cfg_path),
static_or_dynamic=infer_type,
precision_type=precision_type,
conversion_result=str(convert_result),
fps=fps,
metric_info=metric_list,
test_pass=str(test_pass),
report_txt_path=report_txt_path,
codebase_name=codebase_name)
def get_precision_type(deploy_cfg_name: str):
"""Get backend precision_type according to the name of deploy config.
Args:
deploy_cfg_name (str): Name of the deploy config.
Returns:
Str: precision_type: Precision type of the deployment name.
"""
if 'int8' in deploy_cfg_name:
precision_type = 'int8'
elif 'fp16' in deploy_cfg_name:
precision_type = 'fp16'
else:
precision_type = 'fp32'
return precision_type
def replace_top_in_pipeline_json(backend_output_path: Path,
logger: logging.Logger):
"""Replace `topk` with `num_classes` in `pipeline.json`.
Args:
backend_output_path (Path): Backend convert result path.
logger (logger.Logger): Logger handler.
"""
sdk_pipeline_json_path = backend_output_path.joinpath('pipeline.json')
sdk_pipeline_json = mmengine.load(sdk_pipeline_json_path)
pipeline_tasks = sdk_pipeline_json.get('pipeline', {}).get('tasks', [])
for index, task in enumerate(pipeline_tasks):
if task.get('name', '') != 'postprocess':
continue
num_classes = task.get('params', {}).get('num_classes', 0)
if 'topk' not in task.get('params', {}):
continue
sdk_pipeline_json['pipeline']['tasks'][index]['params']['topk'] = \
num_classes
logger.info(f'sdk_pipeline_json = {sdk_pipeline_json}')
mmengine.dump(
sdk_pipeline_json, sdk_pipeline_json_path, sort_keys=False, indent=4)
logger.info('replace done')
def gen_log_path(backend_output_path: Path, log_name: str):
if not backend_output_path.exists():
backend_output_path.mkdir(parents=True, exist_ok=True)
log_path = backend_output_path.joinpath(log_name).absolute().resolve()
if log_path.exists():
os.remove(str(log_path))
return log_path
def run_cmd(cmd_lines: List[str], log_path: Path):
"""
Args:
cmd_lines: (list[str]): A command in multiple line style.
log_path (Path): Path to log file.
Returns:
int: error code.
"""
import platform
system = platform.system().lower()
if system == 'windows':
sep = r'`'
else: # 'Linux', 'Darwin'
sep = '\\'
cmd_for_run = ' '.join(cmd_lines)
cmd_for_log = f' {sep}\n'.join(cmd_lines) + '\n'
parent_path = log_path.parent
if not parent_path.exists():
parent_path.mkdir(parents=True, exist_ok=True)
logger = get_root_logger()
logger.info(100 * '-')
logger.info(f'Start running cmd\n{cmd_for_log}')
logger.info(f'Logging log to \n{log_path}')
with open(log_path, 'w', encoding='utf-8') as file_handler:
# write cmd
file_handler.write(f'Command:\n{cmd_for_log}\n')
file_handler.flush()
process_res = subprocess.Popen(
cmd_for_run,
cwd=str(Path(__file__).absolute().parent.parent),
shell=True,
stdout=file_handler,
stderr=file_handler)
process_res.wait()
return_code = process_res.returncode
if return_code != 0:
logger.error(f'Got shell return code={return_code}')
with open(log_path, 'r') as f:
content = f.read()
logger.error(f'Log error message\n{content}')
return return_code
def get_backend_result(pipeline_info: dict, model_cfg_path: Path,
checkpoint_path: Path, work_dir: Path, device_type: str,
pytorch_metric: dict, metric_info: dict,
report_dict: dict, test_type: str,
logger: logging.Logger, backend_file_name: Union[str,
list],
report_txt_path: Path, metafile_dataset: str,
model_name: str):
"""Convert model to onnx and then get metric.
Args:
pipeline_info (dict): Pipeline info of test yaml.
model_cfg_path (Path): Model config file path.
checkpoint_path (Path): Checkpoints path.
work_dir (Path): A working directory.
device_type (str): A string specifying device, defaults to 'cuda'.
pytorch_metric (dict): All pytorch metric info.
metric_info (dict): Metrics info.
report_dict (dict): Report info dict.
test_type (str): Test type. 'precision' or 'convert'.
logger (logging.Logger): Logger.
backend_file_name (str | list): backend file save name.
report_txt_path (Path): report txt path.
metafile_dataset (str): Dataset type get from metafile.
model_name (str): Name of model in test yaml.
"""
# get backend_test info
backend_test = pipeline_info.get('backend_test', False)
# get convert_image info
convert_image_info = pipeline_info.get('convert_image', None)
if convert_image_info is not None:
input_img_path = \
convert_image_info.get('input_img', './tests/data/tiger.jpeg')
test_img_path = convert_image_info.get('test_img', None)
else:
input_img_path = './tests/data/tiger.jpeg'
test_img_path = None
# get sdk_cfg info
sdk_config = pipeline_info.get('sdk_config', None)
if sdk_config is not None:
sdk_config = Path(sdk_config)
# Overwrite metric tolerance
metric_tolerance = pipeline_info.get('metric_tolerance', None)
if metric_tolerance is not None:
for metric, new_tolerance in metric_tolerance.items():
if metric not in metric_info:
logger.debug(f'{metric} not in {metric_info},'
'skip compare it...')
continue
if new_tolerance is None:
logger.debug('new_tolerance is None, skip it ...')
continue
metric_info[metric]['tolerance'] = new_tolerance
if backend_test is False and sdk_config is None:
test_type = 'convert'
deploy_cfg_path = Path(pipeline_info.get('deploy_config'))
backend_name = str(get_backend(str(deploy_cfg_path)).name).lower()
# change device_type for special case
if backend_name in ['ncnn', 'openvino']:
device_type = 'cpu'
elif backend_name == 'onnxruntime' and device_type != 'cpu':
import onnxruntime as ort
if ort.get_device() != 'GPU':
device_type = 'cpu'
logger.warning('Device type is forced to cpu '
'since onnxruntime-gpu is not installed')
infer_type = \
'dynamic' if is_dynamic_shape(str(deploy_cfg_path)) else 'static'
precision_type = get_precision_type(deploy_cfg_path.name)
codebase_name = get_codebase(str(deploy_cfg_path)).value
backend_output_path = Path(work_dir). \
joinpath(Path(checkpoint_path).parent.parent.name,
Path(checkpoint_path).parent.name,
backend_name,
infer_type,
precision_type,
Path(checkpoint_path).stem)
backend_output_path.mkdir(parents=True, exist_ok=True)
# convert cmd lines
cmd_lines = [
'python3 ./tools/deploy.py',
f'{str(deploy_cfg_path.absolute().resolve())}',
f'{str(model_cfg_path.absolute().resolve())}',
f'"{str(checkpoint_path.absolute().resolve())}"',
f'"{input_img_path}" ', f'--work-dir "{backend_output_path}" ',
f'--device {device_type} ', '--log-level INFO'
]
if sdk_config is not None and test_type == 'precision':
cmd_lines += ['--dump-info']
if test_img_path is not None:
cmd_lines += [f'--test-img {test_img_path}']
if precision_type == 'int8':
calib_dataset_cfg = pipeline_info.get('calib_dataset_cfg', None)
if calib_dataset_cfg is not None:
cmd_lines += [f'--calib-dataset-cfg {calib_dataset_cfg}']
convert_log_path = backend_output_path.joinpath('convert.log')
return_code = run_cmd(cmd_lines, convert_log_path)
convert_result = return_code == 0
logger.info(f'Got convert_result = {convert_result}')
if isinstance(backend_file_name, list):
report_checkpoint = backend_output_path.joinpath(backend_file_name[0])
convert_checkpoint_path = ''
for backend_file in backend_file_name:
backend_path = backend_output_path.joinpath(backend_file)
backend_path = str(backend_path.absolute().resolve())
convert_checkpoint_path += f'{str(backend_path)} '
else:
report_checkpoint = backend_output_path.joinpath(backend_file_name)
convert_checkpoint_path = \
str(backend_output_path.joinpath(backend_file_name))
# Test the model
if convert_result and test_type == 'precision':
# test the model metric
if backend_test:
log_path = \
gen_log_path(backend_output_path.joinpath('backend'),
'test.log')
get_backend_fps_metric(
deploy_cfg_path=str(deploy_cfg_path),
model_cfg_path=model_cfg_path,
convert_checkpoint_path=convert_checkpoint_path,
device_type=device_type,
logger=logger,
pytorch_metric=pytorch_metric,
metric_info=metric_info,
backend_name=backend_name,
precision_type=precision_type,
convert_result=convert_result,
report_dict=report_dict,
infer_type=infer_type,
log_path=log_path,
dataset_info=metafile_dataset,
report_txt_path=report_txt_path,
model_name=model_name)
if sdk_config is not None:
if codebase_name == 'mmcls' or codebase_name == 'mmaction':
replace_top_in_pipeline_json(backend_output_path, logger)
log_path = gen_log_path(
backend_output_path.joinpath('sdk'), 'test.log')
if backend_name == 'onnxruntime':
# sdk only support onnxruntime of cpu
device_type = 'cpu'
# sdk test
get_backend_fps_metric(
deploy_cfg_path=str(sdk_config),
model_cfg_path=model_cfg_path,
convert_checkpoint_path=str(backend_output_path),
device_type=device_type,
logger=logger,
pytorch_metric=pytorch_metric,
metric_info=metric_info,
backend_name=f'SDK-{backend_name}',
precision_type=precision_type,
convert_result=convert_result,
report_dict=report_dict,
infer_type=infer_type,
log_path=log_path,
dataset_info=metafile_dataset,
report_txt_path=report_txt_path,
model_name=model_name)
else:
logger.info('Only test convert, saving to report...')
metric_list = [{metric: '-'} for metric in metric_info]
fps = '-'
test_pass = convert_result
dataset_type = metafile_dataset['dataset']
task_name = metafile_dataset['task']
# update the report
update_report(
report_dict=report_dict,
model_name=model_name,
model_config=str(model_cfg_path),
task_name=task_name,
checkpoint=str(report_checkpoint),
dataset=dataset_type,
backend_name=backend_name,
deploy_config=str(deploy_cfg_path),
static_or_dynamic=infer_type,
precision_type=precision_type,
conversion_result=str(convert_result),
fps=fps,
metric_info=metric_list,
test_pass=str(test_pass),
report_txt_path=report_txt_path,
codebase_name=codebase_name)
def save_report(report_info: dict, report_save_path: Path,
logger: logging.Logger):
"""Convert model to onnx and then get metric.
Args:
report_info (dict): Report info dict.
report_save_path (Path): Report save path.
logger (logging.Logger): Logger.
"""
logger.info('Saving regression test report to '
f'{report_save_path.absolute().resolve()}, pls wait...')
try:
df = pd.DataFrame(report_info)
df.to_excel(report_save_path)
except ValueError:
logger.info(f'Got error report_info = {report_info}')
logger.info('Saved regression test report to '
f'{report_save_path.absolute().resolve()}.')
def _filter_string(inputs):
"""Remove non alpha&number character from input string.
Args:
inputs(str): Input string.
Returns:
str: Output of only alpha&number string.
"""
outputs = ''.join([i.lower() for i in inputs if i.isalnum()])
return outputs
def main():
args = parse_args()
set_start_method('spawn')
logger = get_root_logger(log_level=args.log_level)
test_type = 'precision' if args.performance else 'convert'
logger.info(f'Processing regression test in {test_type} mode.')
backend_file_info = {
'onnxruntime': 'end2end.onnx',
'tensorrt': 'end2end.engine',
'openvino': 'end2end.xml',
'ncnn': ['end2end.param', 'end2end.bin'],
'pplnn': ['end2end.onnx', 'end2end.json'],
'torchscript': 'end2end.pt'
}
backend_list = args.backends
if backend_list is None:
backend_list = [
'onnxruntime', 'tensorrt', 'openvino', 'ncnn', 'pplnn',
'torchscript'
]
assert isinstance(backend_list, list)
logger.info(f'Regression test backend list = {backend_list}')
args.models = [_filter_string(s) for s in args.models]
logger.info(f'Regression test models list = {args.models}')
assert ' ' not in args.work_dir, \
f'No empty space included in {args.work_dir}'
assert ' ' not in args.checkpoint_dir, \
f'No empty space included in {args.checkpoint_dir}'
work_dir = Path(args.work_dir)
work_dir.mkdir(parents=True, exist_ok=True)
deploy_yaml_list = [
f'./tests/regression/{codebase}.yml' for codebase in args.codebase
]
for deploy_yaml in deploy_yaml_list:
if not Path(deploy_yaml).exists():
raise FileNotFoundError(f'deploy_yaml {deploy_yaml} not found, '
'please check !')
with open(deploy_yaml) as f:
yaml_info = yaml.load(f, Loader=yaml.FullLoader)
report_save_path = \
work_dir.joinpath(Path(deploy_yaml).stem + '_report.xlsx')
report_txt_path = report_save_path.with_suffix('.txt')
report_dict = {
'Model': [],
'Model Config': [],
'Task': [],
'Checkpoint': [],
'Dataset': [],
'Backend': [],
'Deploy Config': [],
'Static or Dynamic': [],
'Precision Type': [],
'Conversion Result': [],
# 'FPS': []
}
global_info = yaml_info.get('globals')
metric_info = global_info.get('metric_info', {})
for metric_name in metric_info:
report_dict.update({metric_name: []})
report_dict.update({'Test Pass': []})
global_info.update({'checkpoint_dir': args.checkpoint_dir})
global_info.update(
{'codebase_name': Path(deploy_yaml).stem.split('_')[0]})
with open(report_txt_path, 'w') as f_report:
title_str = ''
for key in report_dict:
title_str += f'{key},'
title_str = title_str[:-1] + '\n'
f_report.write(title_str) # clear the report tmp file
models_info = yaml_info.get('models')
for models in models_info:
model_name_origin = models.get('name', 'model')
model_name_new = _filter_string(model_name_origin)
if 'model_configs' not in models:
logger.warning('Can not find field "model_configs", '
f'skipping {model_name_origin}...')
continue
if args.models != ['all'] and model_name_new not in args.models:
logger.info(
f'Test specific model mode, skip {model_name_origin}...')
continue
try:
model_metafile_info, checkpoint_save_dir, codebase_dir = \
get_model_metafile_info(global_info, models, logger)
except Exception as e:
logger.error(f'Failed to get meta info {e}')
continue
for model_config in model_metafile_info:
logger.info(f'Processing test for {model_config}...')
# Get backends info
pipelines_info = models.get('pipelines', None)
if pipelines_info is None:
logger.warning('pipelines_info is None, skip it...')
continue
# Get model config path
model_cfg_path = Path(codebase_dir).joinpath(model_config)
assert model_cfg_path.exists()
# Get checkpoint path
checkpoint_name = Path(
model_metafile_info.get(model_config).get('Weights')).name
checkpoint_path = Path(checkpoint_save_dir, checkpoint_name)
assert checkpoint_path.exists()
# Get pytorch from metafile.yml
pytorch_metric, metafile_dataset = get_pytorch_result(
model_name_origin, model_metafile_info, checkpoint_path,
model_cfg_path, model_config, metric_info, report_dict,
logger, report_txt_path, global_info.get('codebase_name'))
for pipeline in pipelines_info:
deploy_config = pipeline.get('deploy_config')
backend_name = get_backend(deploy_config).name.lower()
if backend_name not in backend_list:
logger.warning(f'backend_name ({backend_name}) not '
f'in {backend_list}, skip it...')
continue
backend_file_name = \
backend_file_info.get(backend_name, None)
if backend_file_name is None:
logger.warning('backend_file_name is None, '
'skip it...')
continue
get_backend_result(pipeline, model_cfg_path,
checkpoint_path, work_dir, args.device,
pytorch_metric, metric_info,
report_dict, test_type, logger,
backend_file_name, report_txt_path,
metafile_dataset, model_name_origin)
if len(report_dict.get('Model')) > 0:
save_report(report_dict, report_save_path, logger)
else:
logger.info(f'No model for {deploy_yaml}, not saving report.')
# merge report
merge_report(str(work_dir), logger)
logger.info('All done.')
if __name__ == '__main__':
main()