import logging import re import tempfile from argparse import ArgumentParser from collections import OrderedDict from functools import partial from pathlib import Path import numpy as np import pandas as pd import torch from mmengine import Config, DictAction from mmengine.analysis import get_model_complexity_info from mmengine.analysis.print_helper import _format_size from mmengine.fileio import FileClient from mmengine.logging import MMLogger from mmengine.model import revert_sync_batchnorm from mmengine.runner import Runner from modelindex.load_model_index import load from rich.console import Console from rich.table import Table from rich.text import Text from tqdm import tqdm from mmdet.registry import MODELS from mmdet.utils import register_all_modules console = Console() MMDET_ROOT = Path(__file__).absolute().parents[1] def parse_args(): parser = ArgumentParser(description='Valid all models in model-index.yml') parser.add_argument( '--shape', type=int, nargs='+', default=[1280, 800], help='input image size') parser.add_argument( '--checkpoint_root', help='Checkpoint file root path. If set, load checkpoint before test.') parser.add_argument('--img', default='demo/demo.jpg', help='Image file') parser.add_argument('--models', nargs='+', help='models name to inference') parser.add_argument( '--batch-size', type=int, default=1, help='The batch size during the inference.') parser.add_argument( '--flops', action='store_true', help='Get Flops and Params of models') parser.add_argument( '--flops-str', action='store_true', help='Output FLOPs and params counts in a string form.') parser.add_argument( '--cfg-options', nargs='+', action=DictAction, help='override some settings in the used config, the key-value pair ' 'in xxx=yyy format will be merged into config file. If the value to ' 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' 'Note that the quotation marks are necessary and that no white space ' 'is allowed.') parser.add_argument( '--size_divisor', type=int, default=32, help='Pad the input image, the minimum size that is divisible ' 'by size_divisor, -1 means do not pad the image.') args = parser.parse_args() return args def inference(config_file, checkpoint, work_dir, args, exp_name): logger = MMLogger.get_instance(name='MMLogger') logger.warning('if you want test flops, please make sure torch>=1.12') cfg = Config.fromfile(config_file) cfg.work_dir = work_dir cfg.load_from = checkpoint cfg.log_level = 'WARN' cfg.experiment_name = exp_name if args.cfg_options is not None: cfg.merge_from_dict(args.cfg_options) # forward the model result = {'model': config_file.stem} if args.flops: if len(args.shape) == 1: h = w = args.shape[0] elif len(args.shape) == 2: h, w = args.shape else: raise ValueError('invalid input shape') divisor = args.size_divisor if divisor > 0: h = int(np.ceil(h / divisor)) * divisor w = int(np.ceil(w / divisor)) * divisor input_shape = (3, h, w) result['resolution'] = input_shape try: cfg = Config.fromfile(config_file) if hasattr(cfg, 'head_norm_cfg'): cfg['head_norm_cfg'] = dict(type='SyncBN', requires_grad=True) cfg['model']['roi_head']['bbox_head']['norm_cfg'] = dict( type='SyncBN', requires_grad=True) cfg['model']['roi_head']['mask_head']['norm_cfg'] = dict( type='SyncBN', requires_grad=True) if args.cfg_options is not None: cfg.merge_from_dict(args.cfg_options) model = MODELS.build(cfg.model) input = torch.rand(1, *input_shape) if torch.cuda.is_available(): model.cuda() input = input.cuda() model = revert_sync_batchnorm(model) inputs = (input, ) model.eval() outputs = get_model_complexity_info( model, input_shape, inputs, show_table=False, show_arch=False) flops = outputs['flops'] params = outputs['params'] activations = outputs['activations'] result['Get Types'] = 'direct' except: # noqa 772 logger = MMLogger.get_instance(name='MMLogger') logger.warning( 'Direct get flops failed, try to get flops with data') cfg = Config.fromfile(config_file) if hasattr(cfg, 'head_norm_cfg'): cfg['head_norm_cfg'] = dict(type='SyncBN', requires_grad=True) cfg['model']['roi_head']['bbox_head']['norm_cfg'] = dict( type='SyncBN', requires_grad=True) cfg['model']['roi_head']['mask_head']['norm_cfg'] = dict( type='SyncBN', requires_grad=True) data_loader = Runner.build_dataloader(cfg.val_dataloader) data_batch = next(iter(data_loader)) model = MODELS.build(cfg.model) if torch.cuda.is_available(): model = model.cuda() model = revert_sync_batchnorm(model) model.eval() _forward = model.forward data = model.data_preprocessor(data_batch) del data_loader model.forward = partial( _forward, data_samples=data['data_samples']) outputs = get_model_complexity_info( model, input_shape, data['inputs'], show_table=False, show_arch=False) flops = outputs['flops'] params = outputs['params'] activations = outputs['activations'] result['Get Types'] = 'dataloader' if args.flops_str: flops = _format_size(flops) params = _format_size(params) activations = _format_size(activations) result['flops'] = flops result['params'] = params return result def show_summary(summary_data, args): table = Table(title='Validation Benchmark Regression Summary') table.add_column('Model') table.add_column('Validation') table.add_column('Resolution (c, h, w)') if args.flops: table.add_column('Flops', justify='right', width=11) table.add_column('Params', justify='right') for model_name, summary in summary_data.items(): row = [model_name] valid = summary['valid'] color = 'green' if valid == 'PASS' else 'red' row.append(f'[{color}]{valid}[/{color}]') if valid == 'PASS': row.append(str(summary['resolution'])) if args.flops: row.append(str(summary['flops'])) row.append(str(summary['params'])) table.add_row(*row) console.print(table) table_data = { x.header: [Text.from_markup(y).plain for y in x.cells] for x in table.columns } table_pd = pd.DataFrame(table_data) table_pd.to_csv('./mmdetection_flops.csv') # Sample test whether the inference code is correct def main(args): register_all_modules() model_index_file = MMDET_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}) logger = MMLogger( 'validation', logger_name='validation', log_file='benchmark_test_image.log', log_level=logging.INFO) if args.models: patterns = [ re.compile(pattern.replace('+', '_')) for pattern in args.models ] filter_models = {} for k, v in models.items(): k = k.replace('+', '_') if any([re.match(pattern, k) for pattern in patterns]): filter_models[k] = v if len(filter_models) == 0: print('No model found, please specify models in:') print('\n'.join(models.keys())) return models = filter_models summary_data = {} tmpdir = tempfile.TemporaryDirectory() for model_name, model_info in tqdm(models.items()): if model_info.config is None: continue model_info.config = model_info.config.replace('%2B', '+') config = Path(model_info.config) try: config.exists() except: # noqa 722 logger.error(f'{model_name}: {config} not found.') continue logger.info(f'Processing: {model_name}') http_prefix = 'https://download.openmmlab.com/mmdetection/' if args.checkpoint_root is not None: root = args.checkpoint_root if 's3://' in args.checkpoint_root: from petrel_client.common.exception import AccessDeniedError file_client = FileClient.infer_client(uri=root) checkpoint = file_client.join_path( root, model_info.weights[len(http_prefix):]) try: exists = file_client.exists(checkpoint) except AccessDeniedError: exists = False else: checkpoint = Path(root) / model_info.weights[len(http_prefix):] exists = checkpoint.exists() if exists: checkpoint = str(checkpoint) else: print(f'WARNING: {model_name}: {checkpoint} not found.') checkpoint = None else: checkpoint = None try: # build the model from a config file and a checkpoint file result = inference(MMDET_ROOT / config, checkpoint, tmpdir.name, args, model_name) result['valid'] = 'PASS' except Exception: # noqa 722 import traceback logger.error(f'"{config}" :\n{traceback.format_exc()}') result = {'valid': 'FAIL'} summary_data[model_name] = result tmpdir.cleanup() show_summary(summary_data, args) if __name__ == '__main__': args = parse_args() main(args)