def test_mnist_estimator_const(tf2: bool) -> None:
config = conf.load_config(
conf.fixtures_path("mnist_estimator/single.yaml"))
config = conf.set_tf2_image(config) if tf2 else conf.set_tf1_image(config)
experiment_id = exp.run_basic_test_with_temp_config(
config, conf.official_examples_path("mnist_estimator"), 1)
trials = exp.experiment_trials(experiment_id)
assert len(trials) == 1
# Check validation metrics.
steps = trials[0]["steps"]
assert len(steps) == 1
step = steps[0]
assert "validation" in step
v_metrics = step["validation"]["metrics"]["validation_metrics"]
# GPU training is non-deterministic, but on CPU we can validate that we
# reach a consistent result.
if not cluster.running_on_gpu():
assert v_metrics["accuracy"] == 0.9125999808311462
# Check training metrics.
full_trial_metrics = exp.trial_metrics(trials[0]["id"])
for step in full_trial_metrics["steps"]:
metrics = step["metrics"]
batch_metrics = metrics["batch_metrics"]
assert len(batch_metrics) == 100
for batch_metric in batch_metrics:
assert batch_metric["loss"] > 0
def test_cifar10_byol_pytorch_accuracy() -> None:
config = conf.load_config(
conf.cv_examples_path("byol_pytorch/const-cifar10.yaml"))
# Limit convergence time, since was running over 30 minute limit.
config["searcher"]["max_length"]["epochs"] = 20
config["hyperparameters"]["classifier"]["train_epochs"] = 1
config = conf.set_random_seed(config, 1591280374)
experiment_id = exp.run_basic_test_with_temp_config(
config, conf.cv_examples_path("byol_pytorch"), 1)
trials = exp.experiment_trials(experiment_id)
trial_metrics = exp.trial_metrics(trials[0].trial.id)
validation_accuracies = [
step["validation"]["metrics"]["validation_metrics"]["test_accuracy"]
for step in trial_metrics["steps"] if step.get("validation")
]
# Accuracy reachable within limited convergence time -- goes higher given full training.
target_accuracy = 0.40
assert max(validation_accuracies) > target_accuracy, (
"cifar10_byol_pytorch did not reach minimum target accuracy {} in {} steps."
" full validation accuracy history: {}".format(
target_accuracy, len(trial_metrics["steps"]),
validation_accuracies))
def test_mnist_tp_accuracy() -> None:
config = conf.load_config(conf.official_examples_path("mnist_tp/const.yaml"))
experiment_id = exp.run_basic_test_with_temp_config(
config, conf.official_examples_path("mnist_tp"), 1
)
trials = exp.experiment_trials(experiment_id)
trial_metrics = exp.trial_metrics(trials[0]["id"])
validation_errors = []
# TODO (DET-3082): The validation metric names were modified by our trial reporting
# from accuracy to val_accuracy. We should probably remove the added prefix so
# the metric name is as specified.
validation_errors = [
step["validation"]["metrics"]["validation_metrics"]["val_accuracy"]
for step in trial_metrics["steps"]
if step.get("validation")
]
target_accuracy = 0.95
assert max(validation_errors) > target_accuracy, (
"mnist_tp did not reach minimum target accuracy {} in {} steps."
" full validation error history: {}".format(
target_accuracy, len(trial_metrics["steps"]), validation_errors
)
)
def test_metric_gathering() -> None:
"""
Confirm that metrics are gathered from the trial the way that we expect.
"""
experiment_id = exp.run_basic_test(
conf.fixtures_path("metric_maker/const.yaml"),
conf.fixtures_path("metric_maker"), 1)
trials = exp.experiment_trials(experiment_id)
assert len(trials) == 1
# Read the structure of the metrics directly from the config file
config = conf.load_config(conf.fixtures_path("metric_maker/const.yaml"))
base_value = config["hyperparameters"]["starting_base_value"]
gain_per_batch = config["hyperparameters"]["gain_per_batch"]
training_structure = config["hyperparameters"]["training_structure"]["val"]
validation_structure = config["hyperparameters"]["validation_structure"][
"val"]
scheduling_unit = 100
# Check training metrics.
full_trial_metrics = exp.trial_metrics(trials[0].trial.id)
batches_trained = 0
for step in full_trial_metrics["steps"]:
metrics = step["metrics"]
actual = metrics["batch_metrics"]
assert len(actual) == scheduling_unit
first_base_value = base_value + batches_trained
batch_values = first_base_value + gain_per_batch * np.arange(
scheduling_unit)
expected = [
structure_to_metrics(value, training_structure)
for value in batch_values
]
assert structure_equal(expected, actual)
batches_trained = step["total_batches"]
# Check validation metrics.
validation_workloads = exp.workloads_with_validation(trials[0].workloads)
for validation in validation_workloads:
actual = validation.metrics
batches_trained = validation.totalBatches
value = base_value + batches_trained
expected = structure_to_metrics(value, validation_structure)
assert structure_equal(expected, actual)
def test_metric_gathering() -> None:
"""
Confirm that metrics are gathered from the trial the way that we expect.
"""
experiment_id = exp.run_basic_test(
conf.fixtures_path("metric_maker/const.yaml"),
conf.fixtures_path("metric_maker"), 1)
trials = exp.experiment_trials(experiment_id)
assert len(trials) == 1
# Read the structure of the metrics directly from the config file
config = conf.load_config(conf.fixtures_path("metric_maker/const.yaml"))
base_value = config["hyperparameters"]["starting_base_value"]
gain_per_batch = config["hyperparameters"]["gain_per_batch"]
training_structure = config["hyperparameters"]["training_structure"]["val"]
validation_structure = config["hyperparameters"]["validation_structure"][
"val"]
scheduling_unit = 100
# Check training metrics.
full_trial_metrics = exp.trial_metrics(trials[0]["id"])
for step in full_trial_metrics["steps"]:
metrics = step["metrics"]
assert metrics["num_inputs"] == scheduling_unit
actual = metrics["batch_metrics"]
assert len(actual) == scheduling_unit
first_base_value = base_value + (step["id"] - 1) * scheduling_unit
batch_values = first_base_value + gain_per_batch * np.arange(
scheduling_unit)
expected = [
structure_to_metrics(value, training_structure)
for value in batch_values
]
assert structure_equal(expected, actual)
# Check validation metrics.
for step in trials[0]["steps"]:
validation = step["validation"]
metrics = validation["metrics"]
actual = metrics["validation_metrics"]
value = base_value + step["id"] * scheduling_unit
expected = structure_to_metrics(value, validation_structure)
assert structure_equal(expected, actual)
def test_mnist_pytorch_accuracy() -> None:
config = conf.load_config(conf.tutorials_path("mnist_pytorch/const.yaml"))
experiment_id = exp.run_basic_test_with_temp_config(
config, conf.tutorials_path("mnist_pytorch"), 1)
trials = exp.experiment_trials(experiment_id)
trial_metrics = exp.trial_metrics(trials[0]["id"])
validation_errors = [
step["validation"]["metrics"]["validation_metrics"]["accuracy"]
for step in trial_metrics["steps"] if step.get("validation")
]
target_accuracy = 0.97
assert max(validation_errors) > target_accuracy, (
"mnist_pytorch did not reach minimum target accuracy {} in {} steps."
" full validation error history: {}".format(
target_accuracy, len(trial_metrics["steps"]), validation_errors))
def test_cifar10_pytorch_accuracy() -> None:
config = conf.load_config(
conf.official_examples_path("cifar10_cnn_pytorch/const.yaml"))
experiment_id = exp.run_basic_test_with_temp_config(
config, conf.official_examples_path("cifar10_cnn_pytorch"), 1)
trials = exp.experiment_trials(experiment_id)
trial_metrics = exp.trial_metrics(trials[0]["id"])
validation_errors = [
step.validation.metrics["validation_metrics"]["validation_accuracy"]
for step in trial_metrics["steps"] if step.validation
]
target_accuracy = 0.745
assert max(validation_errors) > target_accuracy, (
"cifar10_cnn_pytorch did not reach minimum target accuracy {} in {} steps."
" full validation error history: {}".format(
target_accuracy, len(trial_metrics["steps"]), validation_errors))
def test_mnist_tf_layers_accuracy() -> None:
config = conf.load_config(
conf.cv_examples_path("mnist_tf_layers/const.yaml"))
experiment_id = exp.run_basic_test_with_temp_config(
config, conf.cv_examples_path("mnist_tf_layers"), 1)
trials = exp.experiment_trials(experiment_id)
trial_metrics = exp.trial_metrics(trials[0]["id"])
validation_errors = [
step["validation"]["metrics"]["validation_metrics"]["error"]
for step in trial_metrics["steps"] if step.get("validation")
]
target_error = 0.04
assert min(validation_errors) < target_error, (
"mnist_estimator did not reach minimum target error {} in {} steps."
" full validation error history: {}".format(
target_error, len(trial_metrics["steps"]), validation_errors))
def test_nan_metrics() -> None:
"""
Confirm that NaN and Infinity metrics are gathered from the trial.
"""
exp_id = exp.run_basic_test(conf.fixtures_path("metric_maker/nans.yaml"),
conf.fixtures_path("metric_maker"), 1)
trials = exp.experiment_trials(exp_id)
config = conf.load_config(conf.fixtures_path("metric_maker/nans.yaml"))
base_value = config["hyperparameters"]["starting_base_value"]
gain_per_batch = config["hyperparameters"]["gain_per_batch"]
# Infinity and NaN cannot be processed in the YAML->JSON deserializer
# Add them to expected values here
training_structure = config["hyperparameters"]["training_structure"]["val"]
training_structure["inf"] = "Infinity"
training_structure["nan"] = "NaN"
training_structure["nanarray"] = ["NaN", "NaN"]
validation_structure = config["hyperparameters"]["validation_structure"][
"val"]
validation_structure["neg_inf"] = "-Infinity"
# Check training metrics.
full_trial_metrics = exp.trial_metrics(trials[0].trial.id)
batches_trained = 0
for step in full_trial_metrics["steps"]:
metrics = step["metrics"]
actual = metrics["batch_metrics"]
first_base_value = base_value + batches_trained
batch_values = first_base_value + gain_per_batch * np.arange(5)
expected = [
structure_to_metrics(value, training_structure)
for value in batch_values
]
assert structure_equal(expected, actual)
batches_trained = step["total_batches"]
# Check validation metrics.
validation_workloads = exp.workloads_with_validation(trials[0].workloads)
for validation in validation_workloads:
actual = validation.metrics
batches_trained = validation.totalBatches
expected = structure_to_metrics(base_value, validation_structure)
assert structure_equal(expected, actual)
def test_imagenet_pytorch() -> None:
config = conf.load_config(
conf.tutorials_path("imagenet_pytorch/const_cifar.yaml"))
experiment_id = exp.run_basic_test_with_temp_config(
config, conf.tutorials_path("imagenet_pytorch"), 1)
trials = exp.experiment_trials(experiment_id)
trial_metrics = exp.trial_metrics(trials[0].trial.id)
validation_loss = [
step["validation"]["metrics"]["validation_metrics"]["val_loss"]
for step in trial_metrics["steps"] if step.get("validation")
]
target_loss = 1.55
assert max(validation_loss) < target_loss, (
"imagenet_pytorch did not reach minimum target loss {} in {} steps."
" full validation accuracy history: {}".format(
target_loss, len(trial_metrics["steps"]), validation_loss))
def test_fashion_mnist_tf_keras() -> None:
config = conf.load_config(
conf.official_examples_path("trial/fashion_mnist_tf_keras/const.yaml"))
config = conf.set_random_seed(config, 1591110586)
experiment_id = exp.run_basic_test_with_temp_config(
config, conf.official_examples_path("trial/fashion_mnist_tf_keras"), 1)
trials = exp.experiment_trials(experiment_id)
trial_metrics = exp.trial_metrics(trials[0]["id"])
validation_errors = [
step["validation"]["metrics"]["validation_metrics"]["val_accuracy"]
for step in trial_metrics["steps"] if step.get("validation")
]
accuracy = 0.85
assert max(validation_errors) > accuracy, (
"fashion_mnist_tf_keras did not reach minimum target accuracy {} in {} steps."
" full validation error history: {}".format(
accuracy, len(trial_metrics["steps"]), validation_errors))
def test_fasterrcnn_coco_pytorch_accuracy() -> None:
config = conf.load_config(
conf.cv_examples_path("fasterrcnn_coco_pytorch/const.yaml"))
config = conf.set_random_seed(config, 1590497309)
experiment_id = exp.run_basic_test_with_temp_config(
config, conf.cv_examples_path("fasterrcnn_coco_pytorch"), 1)
trials = exp.experiment_trials(experiment_id)
trial_metrics = exp.trial_metrics(trials[0]["id"])
validation_iou = [
step["validation"]["metrics"]["validation_metrics"]["val_avg_iou"]
for step in trial_metrics["steps"] if step.get("validation")
]
target_iou = 0.42
assert max(validation_iou) > target_iou, (
"fasterrcnn_coco_pytorch did not reach minimum target accuracy {} in {} steps."
" full validation avg_iou history: {}".format(
target_iou, len(trial_metrics["steps"]), validation_iou))
def test_data_layer_mnist_estimator_accuracy() -> None:
config = conf.load_config(
conf.data_layer_examples_path("data_layer_mnist_estimator/const.yaml"))
experiment_id = exp.run_basic_test_with_temp_config(
config, conf.data_layer_examples_path("data_layer_mnist_estimator"), 1)
trials = exp.experiment_trials(experiment_id)
trial_metrics = exp.trial_metrics(trials[0]["id"])
validation_accuracies = [
step["validation"]["metrics"]["validation_metrics"]["accuracy"]
for step in trial_metrics["steps"] if step.get("validation")
]
target_accuracy = 0.93
assert max(validation_accuracies) > target_accuracy, (
"data_layer_mnist_estimator did not reach minimum target accuracy {} in {} steps."
" full validation accuracy history: {}".format(
target_accuracy, len(trial_metrics["steps"]),
validation_accuracies))
def test_unets_tf_keras_accuracy() -> None:
config = conf.load_config(
conf.cv_examples_path("unets_tf_keras/const.yaml"))
config = conf.set_random_seed(config, 1591280374)
experiment_id = exp.run_basic_test_with_temp_config(
config, conf.cv_examples_path("unets_tf_keras"), 1)
trials = exp.experiment_trials(experiment_id)
trial_metrics = exp.trial_metrics(trials[0]["id"])
validation_accuracies = [
step["validation"]["metrics"]["validation_metrics"]["val_accuracy"]
for step in trial_metrics["steps"] if step.get("validation")
]
target_accuracy = 0.85
assert max(validation_accuracies) > target_accuracy, (
"unets_tf_keras did not reach minimum target accuracy {} in {} steps."
" full validation accuracy history: {}".format(
target_accuracy, len(trial_metrics["steps"]),
validation_accuracies))
def test_cifar10_tf_keras_accuracy() -> None:
config = conf.load_config(
conf.cv_examples_path("cifar10_tf_keras/const.yaml"))
config = conf.set_random_seed(config, 1591110586)
experiment_id = exp.run_basic_test_with_temp_config(
config, conf.cv_examples_path("cifar10_tf_keras"), 1, None, 6000)
trials = exp.experiment_trials(experiment_id)
trial_metrics = exp.trial_metrics(trials[0].trial.id)
validation_accuracies = [
step["validation"]["metrics"]["validation_metrics"]
["val_categorical_accuracy"] for step in trial_metrics["steps"]
if step.get("validation")
]
target_accuracy = 0.73
assert max(validation_accuracies) > target_accuracy, (
"cifar10_pytorch did not reach minimum target accuracy {} in {} steps."
" full validation accuracy history: {}".format(
target_accuracy, len(trial_metrics["steps"]),
validation_accuracies))
def test_text_classification_tf_keras_accuracy() -> None:
config = conf.load_config(
conf.features_examples_path("text_classification_tf_keras/const.yaml")
)
experiment_id = exp.run_basic_test_with_temp_config(
config, conf.features_examples_path("text_classification_tf_keras"), 1
)
trials = exp.experiment_trials(experiment_id)
trial_metrics = exp.trial_metrics(trials[0]["id"])
validation_accuracies = [
step["validation"]["metrics"]["validation_metrics"]["val_sparse_categorical_accuracy"]
for step in trial_metrics["steps"]
if step.get("validation")
]
target_accuracy = 0.95
assert max(validation_accuracies) > target_accuracy, (
"text_classification_tf_keras did not reach minimum target accuracy {} in {} steps."
" full validation accuracy history: {}".format(
target_accuracy, len(trial_metrics["steps"]), validation_accuracies
)
)