init

finetune/tools/misc/browse_dataset.py

@@ -0,0 +1,73 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import os.path as osp
+
+from mmengine import Config, DictAction
+from mmengine.registry import init_default_scope
+from mmengine.utils import ProgressBar
+
+from mmseg.registry import DATASETS, VISUALIZERS
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Browse a dataset')
+    parser.add_argument('config', help='train config file path')
+    parser.add_argument(
+        '--output-dir',
+        default=None,
+        type=str,
+        help='If there is no display interface, you can save it')
+    parser.add_argument('--not-show', default=False, action='store_true')
+    parser.add_argument(
+        '--show-interval',
+        type=float,
+        default=2,
+        help='the interval of show (s)')
+    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():
+    args = parse_args()
+    cfg = Config.fromfile(args.config)
+    if args.cfg_options is not None:
+        cfg.merge_from_dict(args.cfg_options)
+
+    # register all modules in mmseg into the registries
+    init_default_scope('mmseg')
+
+    dataset = DATASETS.build(cfg.train_dataloader.dataset)
+    cfg.visualizer['save_dir'] = args.output_dir
+    visualizer = VISUALIZERS.build(cfg.visualizer)
+    visualizer.dataset_meta = dataset.METAINFO
+
+    progress_bar = ProgressBar(len(dataset))
+    for item in dataset:
+        img = item['inputs'].permute(1, 2, 0).numpy()
+        data_sample = item['data_samples'].numpy()
+        img_path = osp.basename(item['data_samples'].img_path)
+
+        img = img[..., [2, 1, 0]]  # bgr to rgb
+
+        visualizer.add_datasample(
+            osp.basename(img_path),
+            img,
+            data_sample,
+            show=not args.not_show,
+            wait_time=args.show_interval)
+
+        progress_bar.update()
+
+
+if __name__ == '__main__':
+    main()

finetune/tools/misc/print_config.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import warnings
+
+from mmengine import Config, DictAction
+
+from mmseg.apis import init_model
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Print the whole config')
+    parser.add_argument('config', help='config file path')
+    parser.add_argument(
+        '--graph', action='store_true', help='print the models graph')
+    parser.add_argument(
+        '--options',
+        nargs='+',
+        action=DictAction,
+        help="--options is deprecated in favor of --cfg_options' and it will "
+        'not be supported in version v0.22.0. 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(
+        '--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()
+
+    if args.options and args.cfg_options:
+        raise ValueError(
+            '--options and --cfg-options cannot be both '
+            'specified, --options is deprecated in favor of --cfg-options. '
+            '--options will not be supported in version v0.22.0.')
+    if args.options:
+        warnings.warn('--options is deprecated in favor of --cfg-options, '
+                      '--options will not be supported in version v0.22.0.')
+        args.cfg_options = args.options
+
+    return args
+
+
+def main():
+    args = parse_args()
+
+    cfg = Config.fromfile(args.config)
+    if args.cfg_options is not None:
+        cfg.merge_from_dict(args.cfg_options)
+    print(f'Config:\n{cfg.pretty_text}')
+    # dump config
+    cfg.dump('example.py')
+    # dump models graph
+    if args.graph:
+        model = init_model(args.config, device='cpu')
+        print(f'Model graph:\n{str(model)}')
+        with open('example-graph.txt', 'w') as f:
+            f.writelines(str(model))
+
+
+if __name__ == '__main__':
+    main()

finetune/tools/misc/publish_model.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import subprocess
+from hashlib import sha256
+
+import torch
+
+BLOCK_SIZE = 128 * 1024
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Process a checkpoint to be published')
+    parser.add_argument('in_file', help='input checkpoint filename')
+    parser.add_argument('out_file', help='output checkpoint filename')
+    args = parser.parse_args()
+    return args
+
+
+def sha256sum(filename: str) -> str:
+    """Compute SHA256 message digest from a file."""
+    hash_func = sha256()
+    byte_array = bytearray(BLOCK_SIZE)
+    memory_view = memoryview(byte_array)
+    with open(filename, 'rb', buffering=0) as file:
+        for block in iter(lambda: file.readinto(memory_view), 0):
+            hash_func.update(memory_view[:block])
+    return hash_func.hexdigest()
+
+
+def process_checkpoint(in_file, out_file):
+    checkpoint = torch.load(in_file, map_location='cpu')
+    # remove optimizer for smaller file size
+    if 'optimizer' in checkpoint:
+        del checkpoint['optimizer']
+    # if it is necessary to remove some sensitive data in checkpoint['meta'],
+    # add the code here.
+    torch.save(checkpoint, out_file)
+    sha = sha256sum(in_file)
+    final_file = out_file.rstrip('.pth') + f'-{sha[:8]}.pth'
+    subprocess.Popen(['mv', out_file, final_file])
+
+
+def main():
+    args = parse_args()
+    process_checkpoint(args.in_file, args.out_file)
+
+
+if __name__ == '__main__':
+    main()