def test_write_results():
true_event_handler_time = 0.125
true_max_epochs = 3
true_num_iters = 2
test_folder = "./test_log_folder"
if os.path.exists(test_folder):
shutil.rmtree(test_folder)
os.makedirs(test_folder)
profiler = BasicTimeProfiler()
dummy_trainer = Engine(_do_nothing_update_fn)
profiler.attach(dummy_trainer)
@dummy_trainer.on(Events.STARTED)
def delay_start(engine):
time.sleep(true_event_handler_time)
@dummy_trainer.on(Events.COMPLETED)
def delay_complete(engine):
time.sleep(true_event_handler_time)
@dummy_trainer.on(Events.EPOCH_STARTED)
def delay_epoch_start(engine):
time.sleep(true_event_handler_time)
@dummy_trainer.on(Events.EPOCH_COMPLETED)
def delay_epoch_complete(engine):
time.sleep(true_event_handler_time)
@dummy_trainer.on(Events.ITERATION_STARTED)
def delay_iter_start(engine):
time.sleep(true_event_handler_time)
@dummy_trainer.on(Events.ITERATION_COMPLETED)
def delay_iter_complete(engine):
time.sleep(true_event_handler_time)
@dummy_trainer.on(Events.GET_BATCH_STARTED)
def delay_get_batch_started(engine):
time.sleep(true_event_handler_time)
@dummy_trainer.on(Events.GET_BATCH_COMPLETED)
def delay_get_batch_completed(engine):
time.sleep(true_event_handler_time)
dummy_trainer.run(range(true_num_iters), max_epochs=true_max_epochs)
profiler.write_results(test_folder + "/test_log.csv")
assert os.path.isfile(test_folder + "/test_log.csv")
file_length = 0
with open(test_folder + "/test_log.csv") as f:
for l in f:
file_length += 1
assert file_length == (true_max_epochs * true_num_iters) + 1
# cleanup test log directory
shutil.rmtree(test_folder)
def test_profilers_wrong_inputs():
profiler = BasicTimeProfiler()
with pytest.raises(TypeError, match=r"Argument engine should be ignite.engine.Engine"):
profiler.attach(None)
with pytest.raises(RuntimeError, match=r"Need pandas to write results as files"):
with patch.dict("sys.modules", {"pandas": None}):
profiler.write_results("")
profiler = HandlersTimeProfiler()
with pytest.raises(TypeError, match=r"Argument engine should be ignite.engine.Engine"):
profiler.attach(None)
with pytest.raises(RuntimeError, match=r"Need pandas to write results as files"):
with patch.dict("sys.modules", {"pandas": None}):
profiler.write_results("")
def test_write_results_basic_profiler(dirname):
true_event_handler_time = 0.125
true_max_epochs = 3
true_num_iters = 2
profiler = BasicTimeProfiler()
dummy_trainer = get_prepared_engine_for_basic_profiler(true_event_handler_time)
profiler.attach(dummy_trainer)
dummy_trainer.run(range(true_num_iters), max_epochs=true_max_epochs)
fp = os.path.join(dirname, "test_log.csv")
profiler.write_results(fp)
assert os.path.isfile(fp)
file_length = 0
with open(fp) as f:
for _ in f:
file_length += 1
assert file_length == (true_max_epochs * true_num_iters) + 1
def create_trainer(loader, model, opt, loss_fn, device, args):
def _update(engine, batch):
model.train()
x = batch['x'].to(engine.state.device, non_blocking=True)
y = batch['y'].to(engine.state.device, non_blocking=True)
m = batch['m'].to(engine.state.device, non_blocking=True)
opt.zero_grad()
y_pred = model(x)
softmax = nn.Softmax()
masked_loss = softmax(y_pred)
#masked_loss = y_pred*m
loss = loss_fn(masked_loss, y)
if m.sum().item() / m.numel() > 0.7:
loss.backward()
opt.step()
masked_loss = (masked_loss>0.5).float()
acc = accuracy_segmentation(masked_loss[:,1,:,:,:],y[:,1,:,:,:])
return {
'x': x.detach(),
'y': y.detach(),
'm': m.detach(),
'y_pred': y_pred.detach(),
'loss': loss.item(),
'acc' : acc
}
def _inference(engine, batch):
model.eval()
with th.no_grad():
x = batch['x'].to(engine.state.device, non_blocking=True)
y = batch['y'].to(engine.state.device, non_blocking=True)
m = batch['m'].to(engine.state.device, non_blocking=True)
y_pred = model(x)
softmax = nn.Softmax(dim=1)
masked_loss = softmax(y_pred)
#masked_loss = y_pred*m
loss = loss_fn(masked_loss, y)
masked_loss = (masked_loss[-3:]>0.5).float()
acc = accuracy_segmentation(masked_loss[:,1,:,:,:],y[:,1,:,:,:])
return {
'x': x.detach(),
'y': y.detach(),
'm': m.detach(),
'y_pred': y_pred.detach(),
'loss': loss.item(),
'acc' : acc
}
#wandb.watch(model, log ='all')
trainer = Engine(_update)
evaluator = Engine(_inference)
profiler = BasicTimeProfiler()
profiler.attach(trainer)
logdir = args.logdir
save_ = (not args.devrun) and (not args.nosave)
# initialize trainer state
trainer.state.device = device
trainer.state.hparams = args
trainer.state.save = save_
trainer.state.logdir = logdir
trainer.state.df = defaultdict(dict)
trainer.state.metrics = dict()
trainer.state.val_metrics = dict()
trainer.state.best_metrics = defaultdict(list)
trainer.state.gradnorm = defaultdict(dict)
# initialize evaluator state
evaluator.logger = setup_logger('evaluator')
evaluator.state.device = device
evaluator.state.df = defaultdict(dict)
evaluator.state.metrics = dict()
pbar = ProgressBar(persist=True)
ebar = ProgressBar(persist=False)
pbar.attach(trainer, ['loss'])
ebar.attach(evaluator, ['loss'])
pbar.attach(trainer,['acc'])
ebar.attach(evaluator,['acc'])
# model summary
if args.model_summary:
trainer.add_event_handler(
Events.STARTED,
print_model_summary, model
)
# terminate on nan
trainer.add_event_handler(
Events.ITERATION_COMPLETED,
TerminateOnNan(lambda x: x['loss'])
)
# metrics
trainer.add_event_handler(
Events.ITERATION_COMPLETED,
_metrics
)
evaluator.add_event_handler(
Events.ITERATION_COMPLETED,
_metrics
)
trainer.add_event_handler(
Events.EPOCH_COMPLETED,
_metrics_mean
)
evaluator.add_event_handler(
Events.COMPLETED,
_metrics_mean
)
trainer.add_event_handler(
#Events.STARTED | Events.EPOCH_COMPLETED,
Events.EPOCH_COMPLETED,
_evaluate, evaluator, loader
)
# logging
trainer.add_event_handler(
Events.EPOCH_COMPLETED,
_log_metrics
)
# early stopping
if args.early_stopping > 0:
es_p = args.early_stopping
es_s = lambda engine: -engine.state.metrics['loss']
evaluator.add_event_handler(
Events.COMPLETED,
EarlyStopping(patience=es_p, score_function=es_s, trainer=trainer)
)
# lr schedulers
if args.epoch_length is None:
el = len(loader['train'])
else:
el = args.epoch_length
if args.lr_scheduler is not None:
lr_sched = create_lr_scheduler(opt, args, num_steps=el)
if args.lr_scheduler != 'plateau':
def _sched_fun(engine):
lr_sched.step()
else:
def _sched_fun(engine):
e = engine.state.epoch
v = engine.state.val_metrics[e]['nmse']
lr_sched.step(v)
if args.lr_scheduler == 'linearcycle':
trainer.add_event_handler(Events.ITERATION_STARTED, lr_sched)
else:
trainer.add_event_handler(Events.EPOCH_COMPLETED, _sched_fun)
# FIXME: warmup is modifying opt base_lr -> must create last
if args.lr_warmup > 0:
wsched = create_lr_scheduler(opt, args, 'warmup', num_steps=el)
wsts = wsched.total_steps
trainer.add_event_handler(
Events.ITERATION_COMPLETED(event_filter=lambda _, i: i <= wsts),
lambda _: wsched.step()
)
# saving
if save_:
to_save = {
'model': model,
'optimizer': opt,
'trainer': trainer,
'evaluator': evaluator
}
trainer.add_event_handler(
Events.EPOCH_COMPLETED,
Checkpoint(to_save, DiskSaver(logdir), n_saved=3)
)
# handler = Checkpoint(
# {'model': model},
# DiskSaver(logdir),
# n_saved = 3,
# filename_prefix = 'best',
# score_function = lambda engine: -engine.state.metrics['nmae'],
# score_name = 'val_nmae',
# )
# evaluator.add_event_handler(
# Events.COMPLETED,
# handler
# )
# handler = Checkpoint(
# {'model': model},
# DiskSaver(logdir),
# n_saved = 3,
# filename_prefix = 'best',
# score_function = lambda engine: -engine.state.metrics['nmse'],
# score_name = 'val_nmse',
# )
# evaluator.add_event_handler(
# Events.COMPLETED,
# handler
# )
# handler = Checkpoint(
# {'model': model},
# DiskSaver(logdir),
# n_saved = 3,
# filename_prefix = 'best',
# score_function = lambda engine: engine.state.metrics['R2'],
# score_name = 'val_R2',
# )
# evaluator.add_event_handler(
# Events.COMPLETED,
# handler
# )
trainer.add_event_handler(
Events.EPOCH_COMPLETED,
_save_metrics
)
# timer
trainer.add_event_handler(
Events.COMPLETED | Events.TERMINATE,
lambda _: profiler.write_results(logdir + '/time.csv')
)
return trainer
