[Feature] Support calculating FLOPs of segmentors

tools/analysis_tools/get_flops.py

@@ -1,10 +1,23 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import argparse
+import tempfile
+from pathlib import Path
 
-from mmcv.cnn import get_model_complexity_info
-from mmengine import Config
+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 build_segmentor
+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():
@@ -17,43 +30,94 @@ def parse_args():
         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 main():
+def inference(args: argparse.Namespace, logger: MMLogger) -> dict:
+    config_name = Path(args.config)
 
-    args = parse_args()
+    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 = {}
 
-    cfg = Config.fromfile(args.config)
-    cfg.model.pretrained = None
-    model = build_segmentor(
-        cfg.model,
-        train_cfg=cfg.get('train_cfg'),
-        test_cfg=cfg.get('test_cfg')).cuda()
+    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,
+        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
 
-    if hasattr(model, 'forward_dummy'):
-        model.forward = model.forward_dummy
-    else:
-        raise NotImplementedError(
-            'FLOPs counter is currently not currently supported with {}'.
-            format(model.__class__.__name__))
 
-    flops, params = get_model_complexity_info(model, input_shape)
+def main():
+
+    args = parse_args()
+    logger = MMLogger.get_instance(name='MMLogger')
+
+    result = inference(args, logger)
     split_line = '=' * 30
-    print('{0}\nInput shape: {1}\nFlops: {2}\nParams: {3}\n{0}'.format(
-        split_line, input_shape, flops, params))
+    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.')
+          'You may need to check if all ops are supported and verify '
+          'that the flops computation is correct.')
 
 
 if __name__ == '__main__':