def test_validation_step(self):
config = detr_cfg.DetrTask(
model=detr_cfg.Detr(
input_size=[1333, 1333, 3],
num_encoder_layers=1,
num_decoder_layers=1,
backbone=backbones.Backbone(
type='resnet',
resnet=backbones.ResNet(model_id=10, bn_trainable=False))
),
losses=detr_cfg.Losses(class_offset=1),
validation_data=detr_cfg.DataConfig(
tfds_name='coco/2017',
tfds_split='validation',
is_training=False,
global_batch_size=2,
))
with tfds.testing.mock_data(as_dataset_fn=_as_dataset):
task = detection.DetectionTask(config)
model = task.build_model()
metrics = task.build_metrics(training=False)
dataset = task.build_inputs(config.validation_data)
iterator = iter(dataset)
logs = task.validation_step(next(iterator), model, metrics)
state = task.aggregate_logs(step_outputs=logs)
task.reduce_aggregated_logs(state)
def test_train_step(self):
config = detr_cfg.DetrTask(
model=detr_cfg.Detr(
input_size=[1333, 1333, 3],
num_encoder_layers=1,
num_decoder_layers=1,
num_classes=81,
backbone=backbones.Backbone(
type='resnet',
resnet=backbones.ResNet(model_id=10, bn_trainable=False))
),
train_data=coco.COCODataConfig(
tfds_name='coco/2017',
tfds_split='validation',
is_training=True,
global_batch_size=2,
))
with tfds.testing.mock_data(as_dataset_fn=_as_dataset):
task = detection.DetectionTask(config)
model = task.build_model()
dataset = task.build_inputs(config.train_data)
iterator = iter(dataset)
opt_cfg = optimization.OptimizationConfig({
'optimizer': {
'type': 'detr_adamw',
'detr_adamw': {
'weight_decay_rate': 1e-4,
'global_clipnorm': 0.1,
}
},
'learning_rate': {
'type': 'stepwise',
'stepwise': {
'boundaries': [120000],
'values': [0.0001, 1.0e-05]
}
},
})
optimizer = detection.DetectionTask.create_optimizer(opt_cfg)
task.train_step(next(iterator), model, optimizer)
