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# Copyright (c) OpenMMLab. All rights reserved.
import logging
import shutil
import sys
import tempfile
from unittest.mock import MagicMock, Mock, call, patch
import numpy as np
import pytest
import torch
import torch.nn as nn
from mmcv.runner import (CheckpointHook, IterTimerHook, PaviLoggerHook,
build_runner)
from torch.nn.init import constant_
from torch.utils.data import DataLoader, Dataset
from mmdet.core.hook import ExpMomentumEMAHook, YOLOXLrUpdaterHook
from mmdet.core.hook.sync_norm_hook import SyncNormHook
from mmdet.core.hook.sync_random_size_hook import SyncRandomSizeHook
def _build_demo_runner_without_hook(runner_type='EpochBasedRunner',
max_epochs=1,
max_iters=None,
multi_optimziers=False):
class Model(nn.Module):
def __init__(self):
super().__init__()
self.linear = nn.Linear(2, 1)
self.conv = nn.Conv2d(3, 3, 3)
def forward(self, x):
return self.linear(x)
def train_step(self, x, optimizer, **kwargs):
return dict(loss=self(x))
def val_step(self, x, optimizer, **kwargs):
return dict(loss=self(x))
model = Model()
if multi_optimziers:
optimizer = {
'model1':
torch.optim.SGD(model.linear.parameters(), lr=0.02, momentum=0.95),
'model2':
torch.optim.SGD(model.conv.parameters(), lr=0.01, momentum=0.9),
}
else:
optimizer = torch.optim.SGD(model.parameters(), lr=0.02, momentum=0.95)
tmp_dir = tempfile.mkdtemp()
runner = build_runner(
dict(type=runner_type),
default_args=dict(
model=model,
work_dir=tmp_dir,
optimizer=optimizer,
logger=logging.getLogger(),
max_epochs=max_epochs,
max_iters=max_iters))
return runner
def _build_demo_runner(runner_type='EpochBasedRunner',
max_epochs=1,
max_iters=None,
multi_optimziers=False):
log_config = dict(
interval=1, hooks=[
dict(type='TextLoggerHook'),
])
runner = _build_demo_runner_without_hook(runner_type, max_epochs,
max_iters, multi_optimziers)
runner.register_checkpoint_hook(dict(interval=1))
runner.register_logger_hooks(log_config)
return runner
@pytest.mark.parametrize('multi_optimziers', (True, False))
def test_yolox_lrupdater_hook(multi_optimziers):
"""xdoctest -m tests/test_hooks.py test_cosine_runner_hook."""
# Only used to prevent program errors
YOLOXLrUpdaterHook(0, min_lr_ratio=0.05)
sys.modules['pavi'] = MagicMock()
loader = DataLoader(torch.ones((10, 2)))
runner = _build_demo_runner(multi_optimziers=multi_optimziers)
hook_cfg = dict(
type='YOLOXLrUpdaterHook',
warmup='exp',
by_epoch=False,
warmup_by_epoch=True,
warmup_ratio=1,
warmup_iters=5, # 5 epoch
num_last_epochs=15,
min_lr_ratio=0.05)
runner.register_hook_from_cfg(hook_cfg)
runner.register_hook_from_cfg(dict(type='IterTimerHook'))
runner.register_hook(IterTimerHook())
# add pavi hook
hook = PaviLoggerHook(interval=1, add_graph=False, add_last_ckpt=True)
runner.register_hook(hook)
runner.run([loader], [('train', 1)])
shutil.rmtree(runner.work_dir)
# TODO: use a more elegant way to check values
assert hasattr(hook, 'writer')
if multi_optimziers:
calls = [
call(
'train', {
'learning_rate/model1': 8.000000000000001e-06,
'learning_rate/model2': 4.000000000000001e-06,
'momentum/model1': 0.95,
'momentum/model2': 0.9
}, 1),
call(
'train', {
'learning_rate/model1': 0.00039200000000000004,
'learning_rate/model2': 0.00019600000000000002,
'momentum/model1': 0.95,
'momentum/model2': 0.9
}, 7),
call(
'train', {
'learning_rate/model1': 0.0008000000000000001,
'learning_rate/model2': 0.0004000000000000001,
'momentum/model1': 0.95,
'momentum/model2': 0.9
}, 10)
]
else:
calls = [
call('train', {
'learning_rate': 8.000000000000001e-06,
'momentum': 0.95
}, 1),
call('train', {
'learning_rate': 0.00039200000000000004,
'momentum': 0.95
}, 7),
call('train', {
'learning_rate': 0.0008000000000000001,
'momentum': 0.95
}, 10)
]
hook.writer.add_scalars.assert_has_calls(calls, any_order=True)
def test_ema_hook():
"""xdoctest -m tests/test_hooks.py test_ema_hook."""
class DemoModel(nn.Module):
def __init__(self):
super().__init__()
self.conv = nn.Conv2d(
in_channels=1,
out_channels=2,
kernel_size=1,
padding=1,
bias=True)
self.bn = nn.BatchNorm2d(2)
self._init_weight()
def _init_weight(self):
constant_(self.conv.weight, 0)
constant_(self.conv.bias, 0)
constant_(self.bn.weight, 0)
constant_(self.bn.bias, 0)
def forward(self, x):
return self.bn(self.conv(x)).sum()
def train_step(self, x, optimizer, **kwargs):
return dict(loss=self(x))
def val_step(self, x, optimizer, **kwargs):
return dict(loss=self(x))
loader = DataLoader(torch.ones((1, 1, 1, 1)))
runner = _build_demo_runner()
demo_model = DemoModel()
runner.model = demo_model
ema_hook = ExpMomentumEMAHook(
momentum=0.0002,
total_iter=1,
skip_buffers=True,
interval=2,
resume_from=None)
checkpointhook = CheckpointHook(interval=1, by_epoch=True)
runner.register_hook(ema_hook, priority='HIGHEST')
runner.register_hook(checkpointhook)
runner.run([loader, loader], [('train', 1), ('val', 1)])
checkpoint = torch.load(f'{runner.work_dir}/epoch_1.pth')
num_eam_params = 0
for name, value in checkpoint['state_dict'].items():
if 'ema' in name:
num_eam_params += 1
value.fill_(1)
assert num_eam_params == 4
torch.save(checkpoint, f'{runner.work_dir}/epoch_1.pth')
work_dir = runner.work_dir
resume_ema_hook = ExpMomentumEMAHook(
momentum=0.5,
total_iter=10,
skip_buffers=True,
interval=1,
resume_from=f'{work_dir}/epoch_1.pth')
runner = _build_demo_runner(max_epochs=2)
runner.model = demo_model
runner.register_hook(resume_ema_hook, priority='HIGHEST')
checkpointhook = CheckpointHook(interval=1, by_epoch=True)
runner.register_hook(checkpointhook)
runner.run([loader, loader], [('train', 1), ('val', 1)])
checkpoint = torch.load(f'{runner.work_dir}/epoch_2.pth')
num_eam_params = 0
desired_output = [0.9094, 0.9094]
for name, value in checkpoint['state_dict'].items():
if 'ema' in name:
num_eam_params += 1
assert value.sum() == 2
else:
if ('weight' in name) or ('bias' in name):
np.allclose(value.data.cpu().numpy().reshape(-1),
desired_output, 1e-4)
assert num_eam_params == 4
shutil.rmtree(runner.work_dir)
shutil.rmtree(work_dir)
def test_sync_norm_hook():
# Only used to prevent program errors
SyncNormHook()
loader = DataLoader(torch.ones((5, 2)))
runner = _build_demo_runner()
runner.register_hook_from_cfg(dict(type='SyncNormHook'))
runner.run([loader, loader], [('train', 1), ('val', 1)])
shutil.rmtree(runner.work_dir)
def test_sync_random_size_hook():
# Only used to prevent program errors
SyncRandomSizeHook()
class DemoDataset(Dataset):
def __getitem__(self, item):
return torch.ones(2)
def __len__(self):
return 5
def update_dynamic_scale(self, dynamic_scale):
pass
loader = DataLoader(DemoDataset())
runner = _build_demo_runner()
runner.register_hook_from_cfg(
dict(type='SyncRandomSizeHook', device='cpu'))
runner.run([loader, loader], [('train', 1), ('val', 1)])
shutil.rmtree(runner.work_dir)
if torch.cuda.is_available():
runner = _build_demo_runner()
runner.register_hook_from_cfg(
dict(type='SyncRandomSizeHook', device='cuda'))
runner.run([loader, loader], [('train', 1), ('val', 1)])
shutil.rmtree(runner.work_dir)
@pytest.mark.parametrize('set_loss', [
dict(set_loss_nan=False, set_loss_inf=False),
dict(set_loss_nan=True, set_loss_inf=False),
dict(set_loss_nan=False, set_loss_inf=True)
])
def test_check_invalid_loss_hook(set_loss):
# Check whether loss is valid during training.
class DemoModel(nn.Module):
def __init__(self, set_loss_nan=False, set_loss_inf=False):
super().__init__()
self.set_loss_nan = set_loss_nan
self.set_loss_inf = set_loss_inf
self.linear = nn.Linear(2, 1)
def forward(self, x):
return self.linear(x)
def train_step(self, x, optimizer, **kwargs):
if self.set_loss_nan:
return dict(loss=torch.tensor(float('nan')))
elif self.set_loss_inf:
return dict(loss=torch.tensor(float('inf')))
else:
return dict(loss=self(x))
loader = DataLoader(torch.ones((5, 2)))
runner = _build_demo_runner()
demo_model = DemoModel(**set_loss)
runner.model = demo_model
runner.register_hook_from_cfg(
dict(type='CheckInvalidLossHook', interval=1))
if not set_loss['set_loss_nan'] \
and not set_loss['set_loss_inf']:
# check loss is valid
runner.run([loader], [('train', 1)])
else:
# check loss is nan or inf
with pytest.raises(AssertionError):
runner.run([loader], [('train', 1)])
shutil.rmtree(runner.work_dir)
def test_set_epoch_info_hook():
"""Test SetEpochInfoHook."""
class DemoModel(nn.Module):
def __init__(self):
super().__init__()
self.epoch = 0
self.linear = nn.Linear(2, 1)
def forward(self, x):
return self.linear(x)
def train_step(self, x, optimizer, **kwargs):
return dict(loss=self(x))
def set_epoch(self, epoch):
self.epoch = epoch
loader = DataLoader(torch.ones((5, 2)))
runner = _build_demo_runner(max_epochs=3)
demo_model = DemoModel()
runner.model = demo_model
runner.register_hook_from_cfg(dict(type='SetEpochInfoHook'))
runner.run([loader], [('train', 1)])
assert demo_model.epoch == 2
def test_memory_profiler_hook():
from collections import namedtuple
# test ImportError without psutil and memory_profiler
with pytest.raises(ImportError):
from mmdet.core.hook import MemoryProfilerHook
MemoryProfilerHook(1)
# test ImportError without memory_profiler
sys.modules['psutil'] = MagicMock()
with pytest.raises(ImportError):
from mmdet.core.hook import MemoryProfilerHook
MemoryProfilerHook(1)
sys.modules['memory_profiler'] = MagicMock()
def _mock_virtual_memory():
virtual_memory_type = namedtuple(
'virtual_memory', ['total', 'available', 'percent', 'used'])
return virtual_memory_type(
total=270109085696,
available=250416816128,
percent=7.3,
used=17840881664)
def _mock_swap_memory():
swap_memory_type = namedtuple('swap_memory', [
'total',
'used',
'percent',
])
return swap_memory_type(total=8589930496, used=0, percent=0.0)
def _mock_memory_usage():
return [40.22265625]
mock_virtual_memory = Mock(return_value=_mock_virtual_memory())
mock_swap_memory = Mock(return_value=_mock_swap_memory())
mock_memory_usage = Mock(return_value=_mock_memory_usage())
@patch('psutil.swap_memory', mock_swap_memory)
@patch('psutil.virtual_memory', mock_virtual_memory)
@patch('memory_profiler.memory_usage', mock_memory_usage)
def _test_memory_profiler_hook():
from mmdet.core.hook import MemoryProfilerHook
hook = MemoryProfilerHook(1)
runner = _build_demo_runner()
assert not mock_memory_usage.called
assert not mock_swap_memory.called
assert not mock_memory_usage.called
hook.after_iter(runner)
assert mock_memory_usage.called
assert mock_swap_memory.called
assert mock_memory_usage.called
_test_memory_profiler_hook()
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