def test_raises_on_bad_crop_sizes(self, crop_height, crop_width):
train_client_spec = client_spec.ClientSpec(num_epochs=2,
batch_size=10,
max_elements=3,
shuffle_buffer_size=5)
with self.assertRaisesRegex(
ValueError, 'The crop_height and crop_width '
'must be between 1 and 32.'):
image_classification_tasks.create_image_classification_task(
train_client_spec,
model_id='resnet18',
crop_height=crop_height,
crop_width=crop_width,
use_synthetic_data=True)
def test_constructs_with_no_eval_client_spec(self):
train_client_spec = client_spec.ClientSpec(num_epochs=2,
batch_size=10,
max_elements=3,
shuffle_buffer_size=5)
baseline_task_spec = image_classification_tasks.create_image_classification_task(
train_client_spec, model_id='resnet18', use_synthetic_data=True)
self.assertIsInstance(baseline_task_spec, baseline_task.BaselineTask)
def test_constructs_with_different_models(self, model_id):
train_client_spec = client_spec.ClientSpec(num_epochs=2,
batch_size=10,
max_elements=3,
shuffle_buffer_size=5)
baseline_task_spec = image_classification_tasks.create_image_classification_task(
train_client_spec,
model_id=model_id,
crop_height=3,
crop_width=3,
use_synthetic_data=True)
self.assertIsInstance(baseline_task_spec, baseline_task.BaselineTask)
def test_no_train_distortion_gives_deterministic_result(self):
train_client_spec = client_spec.ClientSpec(num_epochs=1,
batch_size=1,
max_elements=1,
shuffle_buffer_size=1)
task = image_classification_tasks.create_image_classification_task(
train_client_spec,
model_id='resnet18',
distort_train_images=False,
use_synthetic_data=True)
train_preprocess_fn = task.datasets.train_preprocess_fn
dataset = task.datasets.train_data.create_tf_dataset_from_all_clients()
tf.random.set_seed(0)
example1 = next(iter(train_preprocess_fn(dataset)))
tf.random.set_seed(1)
example2 = next(iter(train_preprocess_fn(dataset)))
self.assertAllClose(example1, example2)