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# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import logging
import os
import os.path as osp
from mmengine.config import Config, DictAction
from mmengine.logging import print_log
from mmengine.runner import Runner
from mmseg.registry import RUNNERS
import torch
import json
import numpy as np
def initialize_model_stitching_layer(model, data_loader):
print(data_loader)
# print(next(iter(data_loader)))
dataiter = iter(data_loader)
images = []
total_samples = 50
batch_size = data_loader.batch_size
num_iter = total_samples // batch_size
for i in range(num_iter):
item = next(dataiter)
images.append(item['img'].data[0])
images = torch.cat(images, dim=0)
samples = images.to(model.device, non_blocking=True)
model.backbone.initialize_stitching_weights(samples)
def group_subnets_by_flops(data, flops_step=10):
sorted_data = {k: v for k, v in sorted(data.items(), key=lambda item: item[1])}
candidate_idx = []
grouped_cands = []
last_flops = 0
for cfg_id, flops in sorted_data.items():
# flops, _ = values
flops = flops // 1e9
if abs(last_flops - flops) > flops_step:
if len(candidate_idx) > 0:
grouped_cands.append(candidate_idx)
candidate_idx = [int(cfg_id)]
last_flops = flops
else:
candidate_idx.append(int(cfg_id))
if len(candidate_idx) > 0:
grouped_cands.append(candidate_idx)
return grouped_cands
def parse_args():
parser = argparse.ArgumentParser(description='Train a segmentor')
parser.add_argument('config', help='train config file path')
parser.add_argument('--work-dir', help='the dir to save logs and models')
parser.add_argument(
'--resume',
action='store_true',
default=False,
help='resume from the latest checkpoint in the work_dir automatically')
parser.add_argument(
'--amp',
action='store_true',
default=False,
help='enable automatic-mixed-precision training')
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(
'--launcher',
choices=['none', 'pytorch', 'slurm', 'mpi'],
default='none',
help='job launcher')
# When using PyTorch version >= 2.0.0, the `torch.distributed.launch`
# will pass the `--local-rank` parameter to `tools/train.py` instead
# of `--local_rank`.
parser.add_argument('--local_rank', '--local-rank', type=int, default=0)
args = parser.parse_args()
if 'LOCAL_RANK' not in os.environ:
os.environ['LOCAL_RANK'] = str(args.local_rank)
return args
def main():
args = parse_args()
# load config
cfg = Config.fromfile(args.config)
cfg.launcher = args.launcher
if args.cfg_options is not None:
cfg.merge_from_dict(args.cfg_options)
# work_dir is determined in this priority: CLI > segment in file > filename
if args.work_dir is not None:
# update configs according to CLI args if args.work_dir is not None
cfg.work_dir = args.work_dir
elif cfg.get('work_dir', None) is None:
# use config filename as default work_dir if cfg.work_dir is None
cfg.work_dir = osp.join('./work_dirs',
osp.splitext(osp.basename(args.config))[0])
# enable automatic-mixed-precision training
if args.amp is True:
optim_wrapper = cfg.optim_wrapper.type
if optim_wrapper == 'AmpOptimWrapper':
print_log(
'AMP training is already enabled in your config.',
logger='current',
level=logging.WARNING)
else:
assert optim_wrapper == 'OptimWrapper', (
'`--amp` is only supported when the optimizer wrapper type is '
f'`OptimWrapper` but got {optim_wrapper}.')
cfg.optim_wrapper.type = 'AmpOptimWrapper'
cfg.optim_wrapper.loss_scale = 'dynamic'
# resume training
cfg.resume = args.resume
# build the runner from config
if 'runner_type' not in cfg:
# build the default runner
runner = Runner.from_cfg(cfg)
else:
# build customized runner from the registry
# if 'runner_type' is set in the cfg
runner = RUNNERS.build(cfg)
# start training
runner.train()
if __name__ == '__main__':
main()
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