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# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import tempfile
from unittest import TestCase
import torch
import torch.nn as nn
from mmengine.evaluator import BaseMetric
from mmengine.model import BaseModel
from mmengine.optim import OptimWrapper
from mmengine.registry import MODEL_WRAPPERS
from mmengine.runner import Runner
from torch.utils.data import Dataset
from mmdet.registry import DATASETS
from mmdet.utils import register_all_modules
register_all_modules()
class ToyModel(nn.Module):
def __init__(self):
super().__init__()
self.linear = nn.Linear(2, 1)
def forward(self, inputs, data_samples, mode='tensor'):
labels = torch.stack(data_samples)
inputs = torch.stack(inputs)
outputs = self.linear(inputs)
if mode == 'tensor':
return outputs
elif mode == 'loss':
loss = (labels - outputs).sum()
outputs = dict(loss=loss)
return outputs
else:
return outputs
class ToyModel1(BaseModel, ToyModel):
def __init__(self):
super().__init__()
def forward(self, *args, **kwargs):
return super(BaseModel, self).forward(*args, **kwargs)
class ToyModel2(BaseModel):
def __init__(self):
super().__init__()
self.teacher = ToyModel1()
self.student = ToyModel1()
def forward(self, *args, **kwargs):
return self.student(*args, **kwargs)
@DATASETS.register_module(force=True)
class DummyDataset(Dataset):
METAINFO = dict() # type: ignore
data = torch.randn(12, 2)
label = torch.ones(12)
@property
def metainfo(self):
return self.METAINFO
def __len__(self):
return self.data.size(0)
def __getitem__(self, index):
return dict(inputs=self.data[index], data_samples=self.label[index])
class ToyMetric1(BaseMetric):
def __init__(self, collect_device='cpu', dummy_metrics=None):
super().__init__(collect_device=collect_device)
self.dummy_metrics = dummy_metrics
def process(self, data_batch, predictions):
result = {'acc': 1}
self.results.append(result)
def compute_metrics(self, results):
return dict(acc=1)
class TestMeanTeacherHook(TestCase):
def setUp(self):
self.temp_dir = tempfile.TemporaryDirectory()
def tearDown(self):
self.temp_dir.cleanup()
def test_mean_teacher_hook(self):
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
model = ToyModel2().to(device)
runner = Runner(
model=model,
train_dataloader=dict(
dataset=DummyDataset(),
sampler=dict(type='DefaultSampler', shuffle=True),
batch_size=3,
num_workers=0),
val_dataloader=dict(
dataset=DummyDataset(),
sampler=dict(type='DefaultSampler', shuffle=False),
batch_size=3,
num_workers=0),
val_evaluator=[ToyMetric1()],
work_dir=self.temp_dir.name,
default_scope='mmdet',
optim_wrapper=OptimWrapper(
torch.optim.Adam(ToyModel().parameters())),
train_cfg=dict(by_epoch=True, max_epochs=2, val_interval=1),
val_cfg=dict(),
default_hooks=dict(logger=None),
custom_hooks=[dict(type='MeanTeacherHook')],
experiment_name='test1')
runner.train()
self.assertTrue(
osp.exists(osp.join(self.temp_dir.name, 'epoch_2.pth')))
# checkpoint = torch.load(osp.join(self.temp_dir.name, 'epoch_2.pth'))
# load and testing
runner = Runner(
model=model,
test_dataloader=dict(
dataset=DummyDataset(),
sampler=dict(type='DefaultSampler', shuffle=True),
batch_size=3,
num_workers=0),
test_evaluator=[ToyMetric1()],
test_cfg=dict(),
work_dir=self.temp_dir.name,
default_scope='mmdet',
load_from=osp.join(self.temp_dir.name, 'epoch_2.pth'),
default_hooks=dict(logger=None),
custom_hooks=[dict(type='MeanTeacherHook')],
experiment_name='test2')
runner.test()
@MODEL_WRAPPERS.register_module()
class DummyWrapper(BaseModel):
def __init__(self, model):
super().__init__()
self.module = model
def forward(self, *args, **kwargs):
return self.module(*args, **kwargs)
# with model wrapper
runner = Runner(
model=DummyWrapper(ToyModel2()),
test_dataloader=dict(
dataset=DummyDataset(),
sampler=dict(type='DefaultSampler', shuffle=True),
batch_size=3,
num_workers=0),
test_evaluator=[ToyMetric1()],
test_cfg=dict(),
work_dir=self.temp_dir.name,
default_scope='mmdet',
load_from=osp.join(self.temp_dir.name, 'epoch_2.pth'),
default_hooks=dict(logger=None),
custom_hooks=[dict(type='MeanTeacherHook')],
experiment_name='test3')
runner.test()
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