def test_revnet_creation(self, model_id):
"""Test creation of RevNet models."""
network = backbones.RevNet(model_id=model_id,
norm_momentum=0.99,
norm_epsilon=1e-5)
backbone_config = backbones_cfg.Backbone(
type='revnet', revnet=backbones_cfg.RevNet(model_id=model_id))
norm_activation_config = common_cfg.NormActivation(norm_momentum=0.99,
norm_epsilon=1e-5,
use_sync_bn=False)
factory_network = factory.build_backbone(
input_specs=tf.keras.layers.InputSpec(shape=[None, None, None, 3]),
backbone_config=backbone_config,
norm_activation_config=norm_activation_config)
network_config = network.get_config()
factory_network_config = factory_network.get_config()
self.assertEqual(network_config, factory_network_config)
def image_classification_imagenet_revnet() -> cfg.ExperimentConfig:
"""Returns a revnet config for image classification on imagenet."""
train_batch_size = 4096
eval_batch_size = 4096
steps_per_epoch = IMAGENET_TRAIN_EXAMPLES // train_batch_size
config = cfg.ExperimentConfig(
task=ImageClassificationTask(
model=ImageClassificationModel(
num_classes=1001,
input_size=[224, 224, 3],
backbone=backbones.Backbone(
type='revnet', revnet=backbones.RevNet(model_id=56)),
norm_activation=common.NormActivation(
norm_momentum=0.9, norm_epsilon=1e-5, use_sync_bn=False),
add_head_batch_norm=True),
losses=Losses(l2_weight_decay=1e-4),
train_data=DataConfig(
input_path=os.path.join(IMAGENET_INPUT_PATH_BASE, 'train*'),
is_training=True,
global_batch_size=train_batch_size),
validation_data=DataConfig(
input_path=os.path.join(IMAGENET_INPUT_PATH_BASE, 'valid*'),
is_training=False,
global_batch_size=eval_batch_size)),
trainer=cfg.TrainerConfig(
steps_per_loop=steps_per_epoch,
summary_interval=steps_per_epoch,
checkpoint_interval=steps_per_epoch,
train_steps=90 * steps_per_epoch,
validation_steps=IMAGENET_VAL_EXAMPLES // eval_batch_size,
validation_interval=steps_per_epoch,
optimizer_config=optimization.OptimizationConfig({
'optimizer': {
'type': 'sgd',
'sgd': {
'momentum': 0.9
}
},
'learning_rate': {
'type': 'stepwise',
'stepwise': {
'boundaries': [
30 * steps_per_epoch, 60 * steps_per_epoch,
80 * steps_per_epoch
],
'values': [0.8, 0.08, 0.008, 0.0008]
}
},
'warmup': {
'type': 'linear',
'linear': {
'warmup_steps': 5 * steps_per_epoch,
'warmup_learning_rate': 0
}
}
})),
restrictions=[
'task.train_data.is_training != None',
'task.validation_data.is_training != None'
])
return config
