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# Copyright (c) Facebook, Inc. and its affiliates.
import json
import math
import os
import tempfile
import time
import unittest
from unittest import mock
import torch
from fvcore.common.checkpoint import Checkpointer
from torch import nn
from detectron2 import model_zoo
from detectron2.config import configurable, get_cfg
from detectron2.engine import DefaultTrainer, SimpleTrainer, default_setup, hooks
from detectron2.modeling.meta_arch import META_ARCH_REGISTRY
from detectron2.utils.events import CommonMetricPrinter, JSONWriter
@META_ARCH_REGISTRY.register()
class _SimpleModel(nn.Module):
@configurable
def __init__(self, sleep_sec=0):
super().__init__()
self.mod = nn.Linear(10, 20)
self.sleep_sec = sleep_sec
@classmethod
def from_config(cls, cfg):
return {}
def forward(self, x):
if self.sleep_sec > 0:
time.sleep(self.sleep_sec)
return {"loss": x.sum() + sum([x.mean() for x in self.parameters()])}
class TestTrainer(unittest.TestCase):
def _data_loader(self, device):
device = torch.device(device)
while True:
yield torch.rand(3, 3).to(device)
def test_simple_trainer(self, device="cpu"):
model = _SimpleModel().to(device=device)
trainer = SimpleTrainer(
model, self._data_loader(device), torch.optim.SGD(model.parameters(), 0.1)
)
trainer.train(0, 10)
def test_simple_trainer_reset_dataloader(self, device="cpu"):
model = _SimpleModel().to(device=device)
trainer = SimpleTrainer(
model, self._data_loader(device), torch.optim.SGD(model.parameters(), 0.1)
)
trainer.train(0, 10)
trainer.reset_data_loader(lambda: self._data_loader(device))
trainer.train(0, 10)
@unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
def test_simple_trainer_cuda(self):
self.test_simple_trainer(device="cuda")
def test_writer_hooks(self):
model = _SimpleModel(sleep_sec=0.1)
trainer = SimpleTrainer(
model, self._data_loader("cpu"), torch.optim.SGD(model.parameters(), 0.1)
)
max_iter = 50
with tempfile.TemporaryDirectory(prefix="detectron2_test") as d:
json_file = os.path.join(d, "metrics.json")
writers = [CommonMetricPrinter(max_iter), JSONWriter(json_file)]
trainer.register_hooks(
[hooks.EvalHook(0, lambda: {"metric": 100}), hooks.PeriodicWriter(writers)]
)
with self.assertLogs(writers[0].logger) as logs:
trainer.train(0, max_iter)
with open(json_file, "r") as f:
data = [json.loads(line.strip()) for line in f]
self.assertEqual([x["iteration"] for x in data], [19, 39, 49, 50])
# the eval metric is in the last line with iter 50
self.assertIn("metric", data[-1], "Eval metric must be in last line of JSON!")
# test logged messages from CommonMetricPrinter
self.assertEqual(len(logs.output), 3)
for log, iter in zip(logs.output, [19, 39, 49]):
self.assertIn(f"iter: {iter}", log)
self.assertIn("eta: 0:00:00", logs.output[-1], "Last ETA must be 0!")
def test_metric_gather_and_write(self):
gather_metric_period = 5
writer_period = 10
model = _SimpleModel(sleep_sec=0.1)
trainer = SimpleTrainer(
model,
self._data_loader("cpu"),
torch.optim.SGD(model.parameters(), 0.1),
gather_metric_period=gather_metric_period,
)
max_iter = 50
with tempfile.TemporaryDirectory(prefix="detectron2_test") as d:
json_file = os.path.join(d, "metrics.json")
writers = [JSONWriter(json_file, window_size=writer_period)]
trainer.register_hooks(
[
hooks.IterationTimer(),
hooks.PeriodicWriter(writers, period=writer_period),
]
)
trainer.train(0, max_iter)
with open(json_file, "r") as f:
data = [json.loads(line.strip()) for line in f]
self.assertEqual([x["iteration"] for x in data], [9, 19, 29, 39, 49])
self.assertEqual(len(trainer.storage.history("time").values()), 48)
for key in ["data_time", "total_loss"]:
history = trainer.storage.history(key).values()
history_iters = [h[1] for h in history]
self.assertEqual(history_iters, [4, 9, 14, 19, 24, 29, 34, 39, 44, 49])
for i in range(len(data)):
# written metric should equal to the median of 2 most recent logged metrics
logged1, logged2 = history[2 * i][0], history[2 * i + 1][0]
gt = data[i][key]
self.assertEqual(gt, (logged1 + logged2) / 2.0)
def test_async_write_metrics(self):
writer_period = 1
model = _SimpleModel(sleep_sec=0.1)
trainer = SimpleTrainer(
model,
self._data_loader("cpu"),
torch.optim.SGD(model.parameters(), 0.1),
async_write_metrics=True,
)
max_iter = 50
with tempfile.TemporaryDirectory(prefix="detectron2_test") as d:
json_file = os.path.join(d, "metrics.json")
writers = [JSONWriter(json_file, window_size=writer_period)]
trainer.register_hooks(
[
hooks.IterationTimer(),
hooks.PeriodicWriter(writers, period=writer_period),
]
)
trainer.train(0, max_iter)
self.assertEqual(len(trainer.storage.history("time").values()), 48)
for key in ["data_time", "total_loss"]:
history = trainer.storage.history(key).values()
history_iters = [h[1] for h in history]
self.assertEqual(history_iters, list(range(50)))
def test_default_trainer(self):
# TODO: this test requires manifold access, so changed device to CPU. see: T88318502
cfg = get_cfg()
cfg.MODEL.DEVICE = "cpu"
cfg.MODEL.META_ARCHITECTURE = "_SimpleModel"
cfg.DATASETS.TRAIN = ("coco_2017_val_100",)
with tempfile.TemporaryDirectory(prefix="detectron2_test") as d:
cfg.OUTPUT_DIR = d
trainer = DefaultTrainer(cfg)
# test property
self.assertIs(trainer.model, trainer._trainer.model)
trainer.model = _SimpleModel()
self.assertIs(trainer.model, trainer._trainer.model)
def test_checkpoint_resume(self):
model = _SimpleModel()
dataloader = self._data_loader("cpu")
opt = torch.optim.SGD(model.parameters(), 0.1)
scheduler = torch.optim.lr_scheduler.StepLR(opt, 3)
with tempfile.TemporaryDirectory(prefix="detectron2_test") as d:
trainer = SimpleTrainer(model, dataloader, opt)
checkpointer = Checkpointer(model, d, opt=opt, trainer=trainer)
trainer.register_hooks(
[
hooks.LRScheduler(scheduler=scheduler),
# checkpoint after scheduler to properly save the state of scheduler
hooks.PeriodicCheckpointer(checkpointer, 10),
]
)
trainer.train(0, 12)
self.assertAlmostEqual(opt.param_groups[0]["lr"], 1e-5)
self.assertEqual(scheduler.last_epoch, 12)
del trainer
opt = torch.optim.SGD(model.parameters(), 999) # lr will be loaded
trainer = SimpleTrainer(model, dataloader, opt)
scheduler = torch.optim.lr_scheduler.StepLR(opt, 3)
trainer.register_hooks(
[
hooks.LRScheduler(scheduler=scheduler),
]
)
checkpointer = Checkpointer(model, d, opt=opt, trainer=trainer)
checkpointer.resume_or_load("non_exist.pth")
self.assertEqual(trainer.iter, 11) # last finished iter number (0-based in Trainer)
# number of times `scheduler.step()` was called (1-based)
self.assertEqual(scheduler.last_epoch, 12)
self.assertAlmostEqual(opt.param_groups[0]["lr"], 1e-5)
def test_eval_hook(self):
model = _SimpleModel()
dataloader = self._data_loader("cpu")
opt = torch.optim.SGD(model.parameters(), 0.1)
for total_iter, period, eval_count in [(30, 15, 2), (31, 15, 3), (20, 0, 1)]:
test_func = mock.Mock(return_value={"metric": 3.0})
trainer = SimpleTrainer(model, dataloader, opt)
trainer.register_hooks([hooks.EvalHook(period, test_func)])
trainer.train(0, total_iter)
self.assertEqual(test_func.call_count, eval_count)
def test_best_checkpointer(self):
model = _SimpleModel()
dataloader = self._data_loader("cpu")
opt = torch.optim.SGD(model.parameters(), 0.1)
metric_name = "metric"
total_iter = 40
test_period = 10
test_cases = [
("max", iter([0.3, 0.4, 0.35, 0.5]), 3),
("min", iter([1.0, 0.8, 0.9, 0.9]), 2),
("min", iter([math.nan, 0.8, 0.9, 0.9]), 1),
]
for mode, metrics, call_count in test_cases:
trainer = SimpleTrainer(model, dataloader, opt)
with tempfile.TemporaryDirectory(prefix="detectron2_test") as d:
checkpointer = Checkpointer(model, d, opt=opt, trainer=trainer)
trainer.register_hooks(
[
hooks.EvalHook(test_period, lambda: {metric_name: next(metrics)}),
hooks.BestCheckpointer(test_period, checkpointer, metric_name, mode=mode),
]
)
with mock.patch.object(checkpointer, "save") as mock_save_method:
trainer.train(0, total_iter)
self.assertEqual(mock_save_method.call_count, call_count)
def test_setup_config(self):
with tempfile.TemporaryDirectory(prefix="detectron2_test") as d:
cfg = get_cfg()
cfg.OUTPUT_DIR = os.path.join(d, "yacs")
default_setup(cfg, {})
cfg = model_zoo.get_config("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.py")
cfg.train.output_dir = os.path.join(d, "omegaconf")
default_setup(cfg, {})
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