def test_savig_best_states(self):
fsm = FileStructManager(base_dir=self.base_dir, is_continue=False)
model = SimpleModel()
metrics_processor = MetricsProcessor()
stages = [TrainStage(TestDataProducer([[{'data': torch.rand(1, 3), 'target': torch.rand(1)}
for _ in list(range(20))]]), metrics_processor)]
trainer = Trainer(TrainConfig(model, stages, SimpleLoss(), torch.optim.SGD(model.parameters(), lr=0.1)),
fsm).set_epoch_num(3).enable_best_states_saving(lambda: np.mean(stages[0].get_losses()))
checkpoint_file = os.path.join(self.base_dir, 'checkpoints', 'last', 'last_checkpoint.zip')
best_checkpoint_file = os.path.join(self.base_dir, 'checkpoints', 'best', 'best_checkpoint.zip')
class Val:
def __init__(self):
self.v = None
first_val = Val()
def on_epoch_end(val):
if val.v is not None and np.mean(stages[0].get_losses()) < val.v:
self.assertTrue(os.path.exists(best_checkpoint_file))
os.remove(best_checkpoint_file)
val.v = np.mean(stages[0].get_losses())
return
val.v = np.mean(stages[0].get_losses())
self.assertTrue(os.path.exists(checkpoint_file))
self.assertFalse(os.path.exists(best_checkpoint_file))
os.remove(checkpoint_file)
trainer.add_on_epoch_end_callback(lambda: on_epoch_end(first_val))
trainer.train()
def test_lr_decaying(self):
fsm = FileStructManager(base_dir=self.base_dir, is_continue=False)
model = SimpleModel()
metrics_processor = MetricsProcessor()
stages = [
TrainStage(
TestDataProducer([[{
'data': torch.rand(1, 3),
'target': torch.rand(1)
} for _ in list(range(20))]]), metrics_processor),
ValidationStage(
TestDataProducer([[{
'data': torch.rand(1, 3),
'target': torch.rand(1)
} for _ in list(range(20))]]), metrics_processor)
]
trainer = Trainer(
model,
TrainConfig(stages, SimpleLoss(),
torch.optim.SGD(model.parameters(), lr=0.1)),
fsm).set_epoch_num(10)
def target_value_clbk() -> float:
return 1
trainer.enable_lr_decaying(0.5, 3, target_value_clbk)
trainer.train()
self.assertAlmostEqual(trainer.data_processor().get_lr(),
0.1 * (0.5**3),
delta=1e-6)
def test_savig_states(self):
fsm = FileStructManager(base_dir=self.base_dir, is_continue=False)
model = SimpleModel()
metrics_processor = MetricsProcessor()
stages = [
TrainStage(
TestDataProducer([[{
'data': torch.rand(1, 3),
'target': torch.rand(1)
} for _ in list(range(20))]]), metrics_processor)
]
trainer = Trainer(
model,
TrainConfig(stages, SimpleLoss(),
torch.optim.SGD(model.parameters(), lr=0.1)),
fsm).set_epoch_num(3)
checkpoint_file = os.path.join(self.base_dir, 'checkpoints', 'last',
'last_checkpoint.zip')
def on_epoch_end():
self.assertTrue(os.path.exists(checkpoint_file))
os.remove(checkpoint_file)
trainer.add_on_epoch_end_callback(on_epoch_end)
trainer.train()
def test_train(self):
fsm = FileStructManager(base_dir=self.base_dir, is_continue=False)
model = SimpleModel()
metrics_processor = MetricsProcessor()
stages = [TrainStage(TestDataProducer([[{'data': torch.rand(1, 3), 'target': torch.rand(1)}
for _ in list(range(20))]]), metrics_processor),
ValidationStage(TestDataProducer([[{'data': torch.rand(1, 3), 'target': torch.rand(1)}
for _ in list(range(20))]]), metrics_processor)]
Trainer(TrainConfig(model, stages, SimpleLoss(), torch.optim.SGD(model.parameters(), lr=1)), fsm) \
.set_epoch_num(1).train()
class SegmentationMetricsProcessor(MetricsProcessor):
def __init__(self, stage_name: str):
super().__init__()
self.add_metrics_group(MetricsGroup(stage_name).add(JaccardMetric()).add(DiceMetric()))
###################################
# define train config and train model
###################################
train_data_producer = DataProducer([train_dataset], batch_size=2, num_workers=3)
val_data_producer = DataProducer([val_dataset], batch_size=2, num_workers=3)
train_stage = TrainStage(train_data_producer, SegmentationMetricsProcessor('train')).enable_hard_negative_mining(0.1)
val_metrics_processor = SegmentationMetricsProcessor('validation')
val_stage = ValidationStage(val_data_producer, val_metrics_processor)
def train():
model = resnet18(classes_num=1, in_channels=3, pretrained=True)
train_config = TrainConfig(model, [train_stage, val_stage], torch.nn.BCEWithLogitsLoss(),
torch.optim.Adam(model.parameters(), lr=1e-4))
file_struct_manager = FileStructManager(base_dir='data', is_continue=False)
trainer = Trainer(train_config, file_struct_manager, torch.device('cuda:0')).set_epoch_num(2)
tensorboard = TensorboardMonitor(file_struct_manager, is_continue=False, network_name='PortraitSegmentation')
log = LogMonitor(file_struct_manager).write_final_metrics()