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import copy
import os
import tempfile
import unittest
import torch
from mmengine import Config, MMLogger
from mmengine.dataset import Compose
from mmengine.model import BaseModel
from torch.utils.data import Dataset
from mmdet.registry import DATASETS, MODELS
from mmdet.utils import register_all_modules
from mmdet.utils.benchmark import (DataLoaderBenchmark, DatasetBenchmark,
InferenceBenchmark)
@MODELS.register_module()
class ToyDetector(BaseModel):
def __init__(self, *args, **kwargs):
super().__init__()
def forward(self, *args, **kwargs):
pass
@DATASETS.register_module()
class ToyDataset(Dataset):
METAINFO = dict() # type: ignore
data = torch.randn(12, 2)
label = torch.ones(12)
def __init__(self):
self.pipeline = Compose([lambda x: x])
def __len__(self):
return self.data.size(0)
def get_data_info(self, index):
return dict(inputs=self.data[index], data_sample=self.label[index])
def __getitem__(self, index):
return dict(inputs=self.data[index], data_sample=self.label[index])
@DATASETS.register_module()
class ToyFullInitDataset(Dataset):
METAINFO = dict() # type: ignore
data = torch.randn(12, 2)
label = torch.ones(12)
def __init__(self):
self.pipeline = Compose([lambda x: x])
def __len__(self):
return self.data.size(0)
def get_data_info(self, index):
return dict(inputs=self.data[index], data_sample=self.label[index])
def full_init(self):
pass
def __getitem__(self, index):
return dict(inputs=self.data[index], data_sample=self.label[index])
class TestInferenceBenchmark(unittest.TestCase):
def setUp(self) -> None:
register_all_modules()
self.cfg = Config(
dict(
model=dict(type='ToyDetector'),
test_dataloader=dict(
dataset=dict(type='ToyDataset'),
sampler=dict(type='DefaultSampler', shuffle=False),
batch_size=3,
num_workers=1),
env_cfg=dict(dist_cfg=dict(backend='nccl'))))
self.max_iter = 10
self.log_interval = 5
@unittest.skipIf(not torch.cuda.is_available(),
'test requires GPU and torch+cuda')
def test_init_and_run(self):
checkpoint_path = os.path.join(tempfile.gettempdir(), 'checkpoint.pth')
torch.save(ToyDetector().state_dict(), checkpoint_path)
cfg = copy.deepcopy(self.cfg)
inference_benchmark = InferenceBenchmark(cfg, checkpoint_path, False,
False, self.max_iter,
self.log_interval)
results = inference_benchmark.run()
self.assertTrue(isinstance(results, dict))
self.assertTrue('avg_fps' in results)
self.assertTrue('fps_list' in results)
self.assertEqual(len(results['fps_list']), 1)
self.assertTrue(inference_benchmark.data_loader.num_workers == 0)
self.assertTrue(inference_benchmark.data_loader.batch_size == 1)
results = inference_benchmark.run(1)
self.assertTrue('avg_fps' in results)
self.assertTrue('fps_list' in results)
self.assertEqual(len(results['fps_list']), 1)
self.assertTrue(inference_benchmark.data_loader.num_workers == 0)
self.assertTrue(inference_benchmark.data_loader.batch_size == 1)
# test repeat
results = inference_benchmark.run(3)
self.assertTrue('avg_fps' in results)
self.assertTrue('fps_list' in results)
self.assertEqual(len(results['fps_list']), 3)
# test cudnn_benchmark
cfg = copy.deepcopy(self.cfg)
cfg.env_cfg.cudnn_benchmark = True
inference_benchmark = InferenceBenchmark(cfg, checkpoint_path, False,
False, self.max_iter,
self.log_interval)
inference_benchmark.run(1)
# test mp_cfg
cfg = copy.deepcopy(self.cfg)
cfg.env_cfg.cudnn_benchmark = True
cfg.env_cfg.mp_cfg = {
'mp_start_method': 'fork',
'opencv_num_threads': 1
}
inference_benchmark = InferenceBenchmark(cfg, checkpoint_path, False,
False, self.max_iter,
self.log_interval)
inference_benchmark.run(1)
# test fp16
cfg = copy.deepcopy(self.cfg)
cfg.fp16 = True
inference_benchmark = InferenceBenchmark(cfg, checkpoint_path, False,
False, self.max_iter,
self.log_interval)
inference_benchmark.run(1)
# test logger
logger = MMLogger.get_instance(
'mmdet', log_file='temp.log', log_level='INFO')
inference_benchmark = InferenceBenchmark(
cfg,
checkpoint_path,
False,
False,
self.max_iter,
self.log_interval,
logger=logger)
inference_benchmark.run(1)
self.assertTrue(os.path.exists('temp.log'))
os.remove(checkpoint_path)
os.remove('temp.log')
class TestDataLoaderBenchmark(unittest.TestCase):
def setUp(self) -> None:
register_all_modules()
self.cfg = Config(
dict(
model=dict(type='ToyDetector'),
train_dataloader=dict(
dataset=dict(type='ToyDataset'),
sampler=dict(type='DefaultSampler', shuffle=True),
batch_size=2,
num_workers=1),
val_dataloader=dict(
dataset=dict(type='ToyDataset'),
sampler=dict(type='DefaultSampler', shuffle=False),
batch_size=1,
num_workers=2),
test_dataloader=dict(
dataset=dict(type='ToyDataset'),
sampler=dict(type='DefaultSampler', shuffle=False),
batch_size=3,
num_workers=1),
env_cfg=dict(dist_cfg=dict(backend='nccl'))))
self.max_iter = 5
self.log_interval = 1
self.num_warmup = 1
def test_init_and_run(self):
cfg = copy.deepcopy(self.cfg)
dataloader_benchmark = DataLoaderBenchmark(cfg, False, 'train',
self.max_iter,
self.log_interval,
self.num_warmup)
results = dataloader_benchmark.run(1)
self.assertTrue('avg_fps' in results)
self.assertTrue('fps_list' in results)
self.assertEqual(len(results['fps_list']), 1)
self.assertTrue(dataloader_benchmark.data_loader.num_workers == 1)
self.assertTrue(dataloader_benchmark.data_loader.batch_size == 2)
# test repeat
results = dataloader_benchmark.run(3)
self.assertTrue('avg_fps' in results)
self.assertTrue('fps_list' in results)
self.assertEqual(len(results['fps_list']), 3)
# test dataset_type input parameters error
with self.assertRaises(AssertionError):
DataLoaderBenchmark(cfg, False, 'training', self.max_iter,
self.log_interval, self.num_warmup)
dataloader_benchmark = DataLoaderBenchmark(cfg, False, 'val',
self.max_iter,
self.log_interval,
self.num_warmup)
self.assertTrue(dataloader_benchmark.data_loader.num_workers == 2)
self.assertTrue(dataloader_benchmark.data_loader.batch_size == 1)
dataloader_benchmark = DataLoaderBenchmark(cfg, False, 'test',
self.max_iter,
self.log_interval,
self.num_warmup)
self.assertTrue(dataloader_benchmark.data_loader.num_workers == 1)
self.assertTrue(dataloader_benchmark.data_loader.batch_size == 3)
# test mp_cfg
cfg = copy.deepcopy(self.cfg)
cfg.env_cfg.mp_cfg = {
'mp_start_method': 'fork',
'opencv_num_threads': 1
}
dataloader_benchmark = DataLoaderBenchmark(cfg, False, 'train',
self.max_iter,
self.log_interval,
self.num_warmup)
dataloader_benchmark.run(1)
class TestDatasetBenchmark(unittest.TestCase):
def setUp(self) -> None:
register_all_modules()
self.cfg = Config(
dict(
model=dict(type='ToyDetector'),
train_dataloader=dict(
dataset=dict(type='ToyDataset'),
sampler=dict(type='DefaultSampler', shuffle=True),
batch_size=2,
num_workers=1),
val_dataloader=dict(
dataset=dict(type='ToyDataset'),
sampler=dict(type='DefaultSampler', shuffle=False),
batch_size=1,
num_workers=2),
test_dataloader=dict(
dataset=dict(type='ToyDataset'),
sampler=dict(type='DefaultSampler', shuffle=False),
batch_size=3,
num_workers=1)))
self.max_iter = 5
self.log_interval = 1
self.num_warmup = 1
def test_init_and_run(self):
cfg = copy.deepcopy(self.cfg)
dataset_benchmark = DatasetBenchmark(cfg, 'train', self.max_iter,
self.log_interval,
self.num_warmup)
results = dataset_benchmark.run(1)
self.assertTrue('avg_fps' in results)
self.assertTrue('fps_list' in results)
self.assertEqual(len(results['fps_list']), 1)
# test repeat
results = dataset_benchmark.run(3)
self.assertTrue('avg_fps' in results)
self.assertTrue('fps_list' in results)
self.assertEqual(len(results['fps_list']), 3)
# test test dataset
dataset_benchmark = DatasetBenchmark(cfg, 'test', self.max_iter,
self.log_interval,
self.num_warmup)
dataset_benchmark.run(1)
# test val dataset
dataset_benchmark = DatasetBenchmark(cfg, 'val', self.max_iter,
self.log_interval,
self.num_warmup)
dataset_benchmark.run(1)
# test dataset_type input parameters error
with self.assertRaises(AssertionError):
DatasetBenchmark(cfg, 'training', self.max_iter, self.log_interval,
self.num_warmup)
# test full_init
cfg = copy.deepcopy(self.cfg)
cfg.test_dataloader.dataset = dict(type='ToyFullInitDataset')
dataset_benchmark = DatasetBenchmark(cfg, 'train', self.max_iter,
self.log_interval,
self.num_warmup)
dataset_benchmark.run(1)
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