mirror of
https://github.com/modelscope/modelscope.git
synced 2026-07-10 12:33:28 +02:00
34840fc5d8
1. Support `ReduceLROnPlateau` lr scheduler, and add `PlateauLrSchedulerHook` for it
2. Support custom `optimizer_hook` and `lr_scheduler_hook`
3. Remove function of save best ckpt from `EvaluationHook`, replace with `BestCkptSaverHook`
4. `evaluation_loop` return metric values directly，move metric computation to `single_gpu_test` and `multi_gpu_test`
Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/9584322
* [to #43627720] support ReduceLROnPlateau and fix lr scheduler
258 lines
8.2 KiB
Python
258 lines
8.2 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
|
|
import glob
|
|
import os
|
|
import shutil
|
|
import tempfile
|
|
import unittest
|
|
|
|
import json
|
|
import numpy as np
|
|
import torch
|
|
from torch import nn
|
|
from torch.optim import SGD
|
|
from torch.optim.lr_scheduler import StepLR
|
|
|
|
from modelscope.metrics.builder import MetricKeys
|
|
from modelscope.trainers import build_trainer
|
|
from modelscope.utils.constant import LogKeys, ModeKeys, ModelFile
|
|
from modelscope.utils.test_utils import (DistributedTestCase,
|
|
create_dummy_test_dataset, test_level)
|
|
|
|
dummy_dataset_small = create_dummy_test_dataset(
|
|
np.random.random(size=(5, )), np.random.randint(0, 4, (1, )), 20)
|
|
|
|
dummy_dataset_big = create_dummy_test_dataset(
|
|
np.random.random(size=(5, )), np.random.randint(0, 4, (1, )), 40)
|
|
|
|
|
|
class DummyModel(nn.Module):
|
|
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.linear = nn.Linear(5, 4)
|
|
self.bn = nn.BatchNorm1d(4)
|
|
|
|
def forward(self, feat, labels):
|
|
x = self.linear(feat)
|
|
|
|
x = self.bn(x)
|
|
loss = torch.sum(x)
|
|
return dict(logits=x, loss=loss)
|
|
|
|
|
|
def train_func(work_dir, dist=False):
|
|
json_cfg = {
|
|
'train': {
|
|
'work_dir': work_dir,
|
|
'dataloader': {
|
|
'batch_size_per_gpu': 2,
|
|
'workers_per_gpu': 1
|
|
},
|
|
'hooks': [{
|
|
'type': 'EvaluationHook',
|
|
'interval': 1
|
|
}]
|
|
},
|
|
'evaluation': {
|
|
'dataloader': {
|
|
'batch_size_per_gpu': 1,
|
|
'workers_per_gpu': 1,
|
|
'shuffle': False
|
|
},
|
|
'metrics': ['seq_cls_metric']
|
|
}
|
|
}
|
|
|
|
config_path = os.path.join(work_dir, ModelFile.CONFIGURATION)
|
|
with open(config_path, 'w') as f:
|
|
json.dump(json_cfg, f)
|
|
|
|
model = DummyModel()
|
|
optimmizer = SGD(model.parameters(), lr=0.01)
|
|
lr_scheduler = StepLR(optimmizer, 2)
|
|
trainer_name = 'EpochBasedTrainer'
|
|
kwargs = dict(
|
|
cfg_file=config_path,
|
|
model=model,
|
|
data_collator=None,
|
|
train_dataset=dummy_dataset_big,
|
|
eval_dataset=dummy_dataset_small,
|
|
optimizers=(optimmizer, lr_scheduler),
|
|
max_epochs=3,
|
|
device='gpu',
|
|
launcher='pytorch' if dist else None)
|
|
|
|
trainer = build_trainer(trainer_name, kwargs)
|
|
trainer.train()
|
|
|
|
|
|
@unittest.skipIf(not torch.cuda.is_available(), 'cuda unittest')
|
|
class TrainerTestSingleGpu(unittest.TestCase):
|
|
|
|
def setUp(self):
|
|
print(('Testing %s.%s' % (type(self).__name__, self._testMethodName)))
|
|
self.tmp_dir = tempfile.TemporaryDirectory().name
|
|
if not os.path.exists(self.tmp_dir):
|
|
os.makedirs(self.tmp_dir)
|
|
|
|
def tearDown(self):
|
|
super().tearDown()
|
|
shutil.rmtree(self.tmp_dir)
|
|
|
|
@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
|
|
def test_single_gpu(self):
|
|
train_func(self.tmp_dir)
|
|
|
|
results_files = os.listdir(self.tmp_dir)
|
|
json_files = glob.glob(os.path.join(self.tmp_dir, '*.log.json'))
|
|
self.assertEqual(len(json_files), 1)
|
|
|
|
with open(json_files[0], 'r') as f:
|
|
lines = [i.strip() for i in f.readlines()]
|
|
self.assertDictContainsSubset(
|
|
{
|
|
LogKeys.MODE: ModeKeys.TRAIN,
|
|
LogKeys.EPOCH: 1,
|
|
LogKeys.ITER: 10,
|
|
LogKeys.LR: 0.01
|
|
}, json.loads(lines[0]))
|
|
self.assertDictContainsSubset(
|
|
{
|
|
LogKeys.MODE: ModeKeys.TRAIN,
|
|
LogKeys.EPOCH: 1,
|
|
LogKeys.ITER: 20,
|
|
LogKeys.LR: 0.01
|
|
}, json.loads(lines[1]))
|
|
self.assertDictContainsSubset(
|
|
{
|
|
LogKeys.MODE: ModeKeys.EVAL,
|
|
LogKeys.EPOCH: 1,
|
|
LogKeys.ITER: 20
|
|
}, json.loads(lines[2]))
|
|
self.assertDictContainsSubset(
|
|
{
|
|
LogKeys.MODE: ModeKeys.TRAIN,
|
|
LogKeys.EPOCH: 2,
|
|
LogKeys.ITER: 10,
|
|
LogKeys.LR: 0.01
|
|
}, json.loads(lines[3]))
|
|
self.assertDictContainsSubset(
|
|
{
|
|
LogKeys.MODE: ModeKeys.TRAIN,
|
|
LogKeys.EPOCH: 2,
|
|
LogKeys.ITER: 20,
|
|
LogKeys.LR: 0.01
|
|
}, json.loads(lines[4]))
|
|
self.assertDictContainsSubset(
|
|
{
|
|
LogKeys.MODE: ModeKeys.EVAL,
|
|
LogKeys.EPOCH: 2,
|
|
LogKeys.ITER: 20
|
|
}, json.loads(lines[5]))
|
|
self.assertDictContainsSubset(
|
|
{
|
|
LogKeys.MODE: ModeKeys.TRAIN,
|
|
LogKeys.EPOCH: 3,
|
|
LogKeys.ITER: 10,
|
|
LogKeys.LR: 0.001
|
|
}, json.loads(lines[6]))
|
|
self.assertDictContainsSubset(
|
|
{
|
|
LogKeys.MODE: ModeKeys.TRAIN,
|
|
LogKeys.EPOCH: 3,
|
|
LogKeys.ITER: 20,
|
|
LogKeys.LR: 0.001
|
|
}, json.loads(lines[7]))
|
|
self.assertDictContainsSubset(
|
|
{
|
|
LogKeys.MODE: ModeKeys.EVAL,
|
|
LogKeys.EPOCH: 3,
|
|
LogKeys.ITER: 20
|
|
}, json.loads(lines[8]))
|
|
self.assertIn(f'{LogKeys.EPOCH}_1.pth', results_files)
|
|
self.assertIn(f'{LogKeys.EPOCH}_2.pth', results_files)
|
|
self.assertIn(f'{LogKeys.EPOCH}_3.pth', results_files)
|
|
for i in [0, 1, 3, 4, 6, 7]:
|
|
self.assertIn(LogKeys.DATA_LOAD_TIME, lines[i])
|
|
self.assertIn(LogKeys.ITER_TIME, lines[i])
|
|
for i in [2, 5, 8]:
|
|
self.assertIn(MetricKeys.ACCURACY, lines[i])
|
|
|
|
|
|
@unittest.skipIf(not torch.cuda.is_available()
|
|
or torch.cuda.device_count() <= 1, 'distributed unittest')
|
|
class TrainerTestMultiGpus(DistributedTestCase):
|
|
|
|
def setUp(self):
|
|
print(('Testing %s.%s' % (type(self).__name__, self._testMethodName)))
|
|
self.tmp_dir = tempfile.TemporaryDirectory().name
|
|
if not os.path.exists(self.tmp_dir):
|
|
os.makedirs(self.tmp_dir)
|
|
|
|
def tearDown(self):
|
|
super().tearDown()
|
|
shutil.rmtree(self.tmp_dir)
|
|
|
|
@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
|
|
def test_multi_gpus(self):
|
|
self.start(train_func, num_gpus=2, work_dir=self.tmp_dir, dist=True)
|
|
|
|
results_files = os.listdir(self.tmp_dir)
|
|
json_files = glob.glob(os.path.join(self.tmp_dir, '*.log.json'))
|
|
self.assertEqual(len(json_files), 1)
|
|
|
|
with open(json_files[0], 'r') as f:
|
|
lines = [i.strip() for i in f.readlines()]
|
|
|
|
self.assertDictContainsSubset(
|
|
{
|
|
LogKeys.MODE: ModeKeys.TRAIN,
|
|
LogKeys.EPOCH: 1,
|
|
LogKeys.ITER: 10,
|
|
LogKeys.LR: 0.01
|
|
}, json.loads(lines[0]))
|
|
self.assertDictContainsSubset(
|
|
{
|
|
LogKeys.MODE: ModeKeys.EVAL,
|
|
LogKeys.EPOCH: 1,
|
|
LogKeys.ITER: 10
|
|
}, json.loads(lines[1]))
|
|
self.assertDictContainsSubset(
|
|
{
|
|
LogKeys.MODE: ModeKeys.TRAIN,
|
|
LogKeys.EPOCH: 2,
|
|
LogKeys.ITER: 10,
|
|
LogKeys.LR: 0.01
|
|
}, json.loads(lines[2]))
|
|
self.assertDictContainsSubset(
|
|
{
|
|
LogKeys.MODE: ModeKeys.EVAL,
|
|
LogKeys.EPOCH: 2,
|
|
LogKeys.ITER: 10
|
|
}, json.loads(lines[3]))
|
|
self.assertDictContainsSubset(
|
|
{
|
|
LogKeys.MODE: ModeKeys.TRAIN,
|
|
LogKeys.EPOCH: 3,
|
|
LogKeys.ITER: 10,
|
|
LogKeys.LR: 0.001
|
|
}, json.loads(lines[4]))
|
|
self.assertDictContainsSubset(
|
|
{
|
|
LogKeys.MODE: ModeKeys.EVAL,
|
|
LogKeys.EPOCH: 3,
|
|
LogKeys.ITER: 10
|
|
}, json.loads(lines[5]))
|
|
self.assertIn(f'{LogKeys.EPOCH}_1.pth', results_files)
|
|
self.assertIn(f'{LogKeys.EPOCH}_2.pth', results_files)
|
|
self.assertIn(f'{LogKeys.EPOCH}_3.pth', results_files)
|
|
for i in [0, 2, 4]:
|
|
self.assertIn(LogKeys.DATA_LOAD_TIME, lines[i])
|
|
self.assertIn(LogKeys.ITER_TIME, lines[i])
|
|
for i in [1, 3, 5]:
|
|
self.assertIn(MetricKeys.ACCURACY, lines[i])
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|