def test_run_training_cloud_1(self, mocker):
mp_init = mocker.patch('imageatm.components.cloud.AWS.init')
mp_apply = mocker.patch('imageatm.components.cloud.AWS.apply')
mp_train = mocker.patch('imageatm.components.cloud.AWS.train')
mp_destroy = mocker.patch('imageatm.components.cloud.AWS.destroy')
mocker.patch('imageatm.components.cloud.AWS.__init__')
AWS.__init__.return_value = None
run_training_cloud(
image_dir=TEST_IMAGE_DIR,
job_dir=TEST_JOB_DIR,
provider='aws',
tf_dir=TEST_TF_DIR,
region=TEST_REGION,
instance_type=TEST_INSTANCE_TYPE,
vpc_id=TEST_VPC_ID,
bucket=TEST_S3_BUCKET,
destroy=False,
cloud_tag=TEST_CLOUD_TAG,
)
mp_init.assert_called_once()
mp_apply.assert_called_once()
mp_train.assert_called_with(job_dir=TEST_JOB_DIR, image_dir=TEST_IMAGE_DIR)
mp_destroy.assert_not_called()
AWS.__init__.assert_called_with(
tf_dir=TEST_TF_DIR,
region=TEST_REGION,
instance_type=TEST_INSTANCE_TYPE,
vpc_id=TEST_VPC_ID,
s3_bucket=TEST_S3_BUCKET,
job_dir=TEST_JOB_DIR,
cloud_tag=TEST_CLOUD_TAG,
)
def train(
config,
config_file: Optional[Path] = None,
job_dir: Optional[Path] = None,
image_dir: Optional[Path] = None,
provider: Optional[str] = None,
instance_type: Optional[str] = None,
region: Optional[str] = None,
vpc_id: Optional[str] = None,
bucket: Optional[str] = None,
tf_dir: Optional[Path] = None,
train_cloud: Optional[bool] = None,
destroy: Optional[bool] = None,
batch_size: Optional[int] = None,
learning_rate_dense: Optional[float] = None,
learning_rate_all: Optional[float] = None,
epochs_train_dense: Optional[int] = None,
epochs_train_all: Optional[int] = None,
base_model_name: Optional[str] = None,
cloud_tag: Optional[str] = None,
):
config = update_config(
config=config,
config_file=config_file,
job_dir=job_dir,
image_dir=image_dir,
provider=provider,
instance_type=instance_type,
region=region,
vpc_id=vpc_id,
bucket=bucket,
tf_dir=tf_dir,
train_cloud=train_cloud,
destroy=destroy,
batch_size=batch_size,
learning_rate_dense=learning_rate_dense,
learning_rate_all=learning_rate_all,
epochs_train_dense=epochs_train_dense,
epochs_train_all=epochs_train_all,
base_model_name=base_model_name,
cloud_tag=cloud_tag,
)
config.train['run'] = True
validate_config(config, ['train'])
if config.train.get('cloud'):
from imageatm.scripts import run_training_cloud
run_training_cloud(**{**config.cloud, **config.train})
else:
from imageatm.scripts import run_training
run_training(**config.train)
def test_run_training_cloud_2(self, mocker):
mp_init = mocker.patch('imageatm.components.cloud.AWS.init')
mp_apply = mocker.patch('imageatm.components.cloud.AWS.apply')
mp_train = mocker.patch('imageatm.components.cloud.AWS.train')
mp_destroy = mocker.patch('imageatm.components.cloud.AWS.destroy')
mocker.patch('imageatm.components.cloud.AWS.__init__')
AWS.__init__.return_value = None
run_training_cloud(
image_dir=TEST_IMAGE_DIR,
job_dir=TEST_JOB_DIR,
provider='aws',
tf_dir=TEST_TF_DIR,
region=TEST_REGION,
instance_type=TEST_INSTANCE_TYPE,
vpc_id=TEST_VPC_ID,
bucket=TEST_S3_BUCKET,
destroy=True,
cloud_tag=TEST_CLOUD_TAG,
epochs_train_dense=TEST_EPOCHS_TRAIN_DENSE,
epochs_train_all=TEST_EPOCHS_TRAIN_ALL,
learning_rate_dense=TEST_LEARNING_RATE_DENSE,
learning_rate_all=TEST_LEARNING_RATE_ALL,
batch_size=TEST_BATCH_SIZE,
dropout_rate=TEST_DROPOUT_RATE,
base_model_name=TEST_BASE_MODEL_NAME,
loss=TEST_LOSS,
)
mp_init.assert_called_once()
mp_apply.assert_called_once()
mp_train.assert_called_with(
job_dir=TEST_JOB_DIR,
image_dir=TEST_IMAGE_DIR,
epochs_train_dense=TEST_EPOCHS_TRAIN_DENSE,
epochs_train_all=TEST_EPOCHS_TRAIN_ALL,
learning_rate_dense=TEST_LEARNING_RATE_DENSE,
learning_rate_all=TEST_LEARNING_RATE_ALL,
batch_size=TEST_BATCH_SIZE,
dropout_rate=TEST_DROPOUT_RATE,
base_model_name=TEST_BASE_MODEL_NAME,
loss=TEST_LOSS,
)
mp_destroy.assert_called_once()
AWS.__init__.assert_called_with(
tf_dir=TEST_TF_DIR,
region=TEST_REGION,
instance_type=TEST_INSTANCE_TYPE,
vpc_id=TEST_VPC_ID,
s3_bucket=TEST_S3_BUCKET,
job_dir=TEST_JOB_DIR,
cloud_tag=TEST_CLOUD_TAG,
)
def pipeline(
config: Config,
config_file: Path,
job_dir: Optional[Path] = None,
image_dir: Optional[Path] = None,
samples_file: Optional[Path] = None,
provider: Optional[str] = None,
instance_type: Optional[str] = None,
region: Optional[str] = None,
vpc_id: Optional[str] = None,
bucket: Optional[str] = None,
tf_dir: Optional[Path] = None,
train_cloud: Optional[bool] = None,
destroy: Optional[bool] = None,
resize: Optional[bool] = None,
batch_size: Optional[int] = None,
learning_rate_dense: Optional[float] = None,
learning_rate_all: Optional[float] = None,
epochs_train_dense: Optional[int] = None,
epochs_train_all: Optional[int] = None,
base_model_name: Optional[str] = None,
cloud_tag: Optional[str] = None,
create_report: Optional[bool] = None,
kernel_name: Optional[str] = None,
export_html: Optional[bool] = None,
export_pdf: Optional[bool] = None,
):
"""Runs the entire pipeline based on config file."""
config = update_config(
config=config,
config_file=config_file,
job_dir=job_dir,
image_dir=image_dir,
samples_file=samples_file,
provider=provider,
instance_type=instance_type,
region=region,
vpc_id=vpc_id,
bucket=bucket,
tf_dir=tf_dir,
train_cloud=train_cloud,
destroy=destroy,
resize=resize,
batch_size=batch_size,
learning_rate_dense=learning_rate_dense,
learning_rate_all=learning_rate_all,
epochs_train_dense=epochs_train_dense,
epochs_train_all=epochs_train_all,
base_model_name=base_model_name,
cloud_tag=cloud_tag,
create_report=create_report,
kernel_name=kernel_name,
export_html=export_html,
export_pdf=export_pdf,
)
validate_config(config, config.pipeline)
Path(config.job_dir).resolve().mkdir(parents=True, exist_ok=True)
logger = get_logger(__name__, config.job_dir) # type: ignore
if 'dataprep' in config.pipeline:
from imageatm.scripts import run_dataprep
logger.info('\n********************************\n'
'******* Data preparation *******\n'
'********************************')
dp = run_dataprep(**config.dataprep)
# update image_dir if images were resized
if config.dataprep.get('resize', False):
config.image_dir = dp.image_dir # type: ignore
config = update_component_configs(config)
if 'train' in config.pipeline:
logger.info('\n********************************\n'
'*********** Training ***********\n'
'********************************')
if config.train.get('cloud'):
from imageatm.scripts import run_training_cloud
run_training_cloud(**{**config.cloud, **config.train})
else:
from imageatm.scripts import run_training
run_training(**config.train)
if 'evaluate' in config.pipeline:
from imageatm.scripts import run_evaluation
logger.info('\n********************************\n'
'********** Evaluation **********\n'
'********************************')
run_evaluation(**config.evaluate)
if 'cloud' in config.pipeline:
from imageatm.scripts import run_cloud
run_cloud(**config.cloud)