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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from unittest.mock import patch
import torch
import torch.nn as nn
from mmengine.model import BaseModel
from mmengine.registry import MODELS
@MODELS.register_module()
class ToyModel(BaseModel):
def __init__(self, *args, **kwargs):
super().__init__()
self.conv = nn.Conv2d(1, 1, 1)
def forward(self, *args, **kwargs):
return {'loss': torch.tensor(0.0)}
def update_params_step(self, loss):
pass
def runtimeinfo_step(self, runner, batch_idx, data_batch=None):
runner.message_hub.update_info('iter', runner.iter)
lr_dict = runner.optim_wrapper.get_lr()
for name, lr in lr_dict.items():
runner.message_hub.update_scalar(f'train/{name}', lr[0])
momentum_dict = runner.optim_wrapper.get_momentum()
for name, momentum in momentum_dict.items():
runner.message_hub.update_scalar(f'train/{name}', momentum[0])
@patch('mmengine.optim.optimizer.OptimWrapper.update_params',
update_params_step)
@patch('mmengine.hooks.RuntimeInfoHook.before_train_iter', runtimeinfo_step)
def fake_run(cfg):
from mmengine.runner import Runner
cfg.pop('model')
cfg.pop('visualizer')
cfg.pop('val_dataloader')
cfg.pop('val_evaluator')
cfg.pop('val_cfg')
cfg.pop('test_dataloader')
cfg.pop('test_evaluator')
cfg.pop('test_cfg')
extra_cfg = dict(
model=dict(type='ToyModel'),
visualizer=dict(
type='Visualizer',
vis_backends=[
dict(type='TensorboardVisBackend', save_dir='temp_dir')
]),
)
cfg.merge_from_dict(extra_cfg)
# build the runner from config
runner = Runner.from_cfg(cfg)
# start training
runner.train()
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