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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)
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