init

finetune/tools/analysis_tools/get_flops.py

@@ -0,0 +1,124 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import tempfile
+from pathlib import Path
+
+import torch
+from mmengine import Config, DictAction
+from mmengine.logging import MMLogger
+from mmengine.model import revert_sync_batchnorm
+from mmengine.registry import init_default_scope
+
+from mmseg.models import BaseSegmentor
+from mmseg.registry import MODELS
+from mmseg.structures import SegDataSample
+
+try:
+    from mmengine.analysis import get_model_complexity_info
+    from mmengine.analysis.print_helper import _format_size
+except ImportError:
+    raise ImportError('Please upgrade mmengine >= 0.6.0 to use this script.')
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Get the FLOPs of a segmentor')
+    parser.add_argument('config', help='train config file path')
+    parser.add_argument(
+        '--shape',
+        type=int,
+        nargs='+',
+        default=[2048, 1024],
+        help='input image size')
+    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.')
+    args = parser.parse_args()
+    return args
+
+
+def inference(args: argparse.Namespace, logger: MMLogger) -> dict:
+    config_name = Path(args.config)
+
+    if not config_name.exists():
+        logger.error(f'Config file {config_name} does not exist')
+
+    cfg: Config = Config.fromfile(config_name)
+    cfg.work_dir = tempfile.TemporaryDirectory().name
+    cfg.log_level = 'WARN'
+    if args.cfg_options is not None:
+        cfg.merge_from_dict(args.cfg_options)
+
+    init_default_scope(cfg.get('scope', 'mmseg'))
+
+    if len(args.shape) == 1:
+        input_shape = (3, args.shape[0], args.shape[0])
+    elif len(args.shape) == 2:
+        input_shape = (3, ) + tuple(args.shape)
+    else:
+        raise ValueError('invalid input shape')
+    result = {}
+
+    model: BaseSegmentor = MODELS.build(cfg.model)
+    if hasattr(model, 'auxiliary_head'):
+        model.auxiliary_head = None
+    if torch.cuda.is_available():
+        model.cuda()
+    model = revert_sync_batchnorm(model)
+    result['ori_shape'] = input_shape[-2:]
+    result['pad_shape'] = input_shape[-2:]
+    data_batch = {
+        'inputs': [torch.rand(input_shape)],
+        'data_samples': [SegDataSample(metainfo=result)]
+    }
+    data = model.data_preprocessor(data_batch)
+    model.eval()
+    if cfg.model.decode_head.type in ['MaskFormerHead', 'Mask2FormerHead']:
+        # TODO: Support MaskFormer and Mask2Former
+        raise NotImplementedError('MaskFormer and Mask2Former are not '
+                                  'supported yet.')
+    outputs = get_model_complexity_info(
+        model,
+        input_shape=None,
+        inputs=data['inputs'],
+        show_table=False,
+        show_arch=False)
+    result['flops'] = _format_size(outputs['flops'])
+    result['params'] = _format_size(outputs['params'])
+    result['compute_type'] = 'direct: randomly generate a picture'
+    return result
+
+
+def main():
+
+    args = parse_args()
+    logger = MMLogger.get_instance(name='MMLogger')
+
+    result = inference(args, logger)
+    split_line = '=' * 30
+    ori_shape = result['ori_shape']
+    pad_shape = result['pad_shape']
+    flops = result['flops']
+    params = result['params']
+    compute_type = result['compute_type']
+
+    if pad_shape != ori_shape:
+        print(f'{split_line}\nUse size divisor set input shape '
+              f'from {ori_shape} to {pad_shape}')
+    print(f'{split_line}\nCompute type: {compute_type}\n'
+          f'Input shape: {pad_shape}\nFlops: {flops}\n'
+          f'Params: {params}\n{split_line}')
+    print('!!!Please be cautious if you use the results in papers. '
+          'You may need to check if all ops are supported and verify '
+          'that the flops computation is correct.')
+
+
+if __name__ == '__main__':
+    main()