def testTaskWithUnstructuredSparsity(self, config_name):
config = exp_factory.get_exp_config(config_name)
config.task.train_data.global_batch_size = 2
task = img_cls_task.ImageClassificationTask(config.task)
model = task.build_model()
metrics = task.build_metrics()
strategy = tf.distribute.get_strategy()
dataset = orbit.utils.make_distributed_dataset(strategy, task.build_inputs,
config.task.train_data)
iterator = iter(dataset)
opt_factory = optimization.OptimizerFactory(config.trainer.optimizer_config)
optimizer = opt_factory.build_optimizer(opt_factory.build_learning_rate())
if isinstance(optimizer, optimization.ExponentialMovingAverage
) and not optimizer.has_shadow_copy:
optimizer.shadow_copy(model)
if config.task.pruning:
# This is an auxilary initialization required to prune a model which is
# originally done in the train library.
actions.PruningAction(
export_dir=tempfile.gettempdir(), model=model, optimizer=optimizer)
# Check all layers and target weights are successfully pruned.
self._validate_model_pruned(model, config_name)
logs = task.train_step(next(iterator), model, optimizer, metrics=metrics)
self._validate_metrics(logs, metrics)
logs = task.validation_step(next(iterator), model, metrics=metrics)
self._validate_metrics(logs, metrics)
def testTaskWithStructuredSparsity(self, config_name):
test_tfrecord_file = os.path.join(self.get_temp_dir(),
'cls_test.tfrecord')
self._create_test_tfrecord(test_tfrecord_file=test_tfrecord_file,
num_samples=10,
input_image_size=[224, 224])
config = exp_factory.get_exp_config(config_name)
config.task.train_data.global_batch_size = 2
config.task.validation_data.input_path = test_tfrecord_file
config.task.train_data.input_path = test_tfrecord_file
# Add structured sparsity
config.task.pruning.sparsity_m_by_n = (2, 4)
config.task.pruning.frequency = 1
task = img_cls_task.ImageClassificationTask(config.task)
model = task.build_model()
metrics = task.build_metrics()
strategy = tf.distribute.get_strategy()
dataset = orbit.utils.make_distributed_dataset(strategy,
task.build_inputs,
config.task.train_data)
iterator = iter(dataset)
opt_factory = optimization.OptimizerFactory(
config.trainer.optimizer_config)
optimizer = opt_factory.build_optimizer(
opt_factory.build_learning_rate())
if isinstance(optimizer, optimization.ExponentialMovingAverage
) and not optimizer.has_shadow_copy:
optimizer.shadow_copy(model)
# This is an auxiliary initialization required to prune a model which is
# originally done in the train library.
pruning_actions = actions.PruningAction(
export_dir=tempfile.gettempdir(), model=model, optimizer=optimizer)
# Check all layers and target weights are successfully pruned.
self._validate_model_pruned(model, config_name)
logs = task.train_step(next(iterator),
model,
optimizer,
metrics=metrics)
self._validate_metrics(logs, metrics)
logs = task.validation_step(next(iterator), model, metrics=metrics)
self._validate_metrics(logs, metrics)
pruning_actions.update_pruning_step.on_epoch_end(batch=None)
# Check whether the weights are pruned in 2x4 pattern.
self._check_2x4_sparsity(model)
