def _test_model_train(output_dirs: TestOutputDirectories,
image_channels: Any,
ground_truth_ids: Any,
no_mask_channel: bool = False) -> None:
def _check_patch_centers(epoch_results: List[MetricsDict],
should_equal: bool) -> None:
diagnostics_per_epoch = [
m.diagnostics[MetricType.PATCH_CENTER.value] for m in epoch_results
]
patch_centers_epoch1 = diagnostics_per_epoch[0]
for diagnostic in diagnostics_per_epoch[1:]:
assert np.array_equal(patch_centers_epoch1,
diagnostic) == should_equal
train_config = DummyModel()
train_config.local_dataset = base_path
train_config.set_output_to(output_dirs.root_dir)
train_config.image_channels = image_channels
train_config.ground_truth_ids = ground_truth_ids
train_config.mask_id = None if no_mask_channel else train_config.mask_id
train_config.random_seed = 42
train_config.class_weights = [0.5, 0.25, 0.25]
train_config.store_dataset_sample = True
expected_train_losses = [0.455538, 0.455213]
expected_val_losses = [0.455190, 0.455139]
expected_stats = "Epoch\tLearningRate\tTrainLoss\tTrainDice\tValLoss\tValDice\n" \
"1\t1.00e-03\t0.456\t0.242\t0.455\t0.000\n" \
"2\t5.36e-04\t0.455\t0.247\t0.455\t0.000"
expected_learning_rates = [[train_config.l_rate], [5.3589e-4]]
loss_absolute_tolerance = 1e-3
model_training_result = model_training.model_train(train_config)
assert isinstance(model_training_result, ModelTrainingResults)
# check to make sure training batches are NOT all the same across epochs
_check_patch_centers(model_training_result.train_results_per_epoch,
should_equal=False)
# check to make sure validation batches are all the same across epochs
_check_patch_centers(model_training_result.val_results_per_epoch,
should_equal=True)
assert isinstance(model_training_result.train_results_per_epoch[0],
MetricsDict)
actual_train_losses = [
m.get_single_metric(MetricType.LOSS)
for m in model_training_result.train_results_per_epoch
]
actual_val_losses = [
m.get_single_metric(MetricType.LOSS)
for m in model_training_result.val_results_per_epoch
]
print("actual_train_losses = {}".format(actual_train_losses))
print("actual_val_losses = {}".format(actual_val_losses))
assert np.allclose(actual_train_losses,
expected_train_losses,
atol=loss_absolute_tolerance)
assert np.allclose(actual_val_losses,
expected_val_losses,
atol=loss_absolute_tolerance)
assert np.allclose(model_training_result.learning_rates_per_epoch,
expected_learning_rates,
rtol=1e-6)
# check output files/directories
assert train_config.outputs_folder.is_dir()
assert train_config.logs_folder.is_dir()
# The train and val folder should contain Tensorflow event files
assert (train_config.logs_folder / "train").is_dir()
assert (train_config.logs_folder / "val").is_dir()
assert len([(train_config.logs_folder / "train").glob("*")]) == 1
assert len([(train_config.logs_folder / "val").glob("*")]) == 1
# Checkpoint folder
# With these settings, we should see a checkpoint only at epoch 2:
# That's the last epoch, and there should always be checkpoint at the last epoch)
assert train_config.save_start_epoch == 1
assert train_config.save_step_epochs == 100
assert train_config.num_epochs == 2
assert os.path.isdir(train_config.checkpoint_folder)
assert os.path.isfile(
os.path.join(train_config.checkpoint_folder,
"2" + CHECKPOINT_FILE_SUFFIX))
assert (train_config.outputs_folder / DATASET_CSV_FILE_NAME).is_file()
assert (train_config.outputs_folder /
STORED_CSV_FILE_NAMES[ModelExecutionMode.TRAIN]).is_file()
assert (train_config.outputs_folder /
STORED_CSV_FILE_NAMES[ModelExecutionMode.VAL]).is_file()
assert_file_contents(train_config.outputs_folder / TRAIN_STATS_FILE,
expected_stats)
# Test for saving of example images
assert os.path.isdir(train_config.example_images_folder)
example_files = os.listdir(train_config.example_images_folder)
assert len(example_files) == 3 * 2
def _test_model_train(output_dirs: OutputFolderForTests,
image_channels: Any,
ground_truth_ids: Any,
no_mask_channel: bool = False) -> None:
def _check_patch_centers(diagnostics_per_epoch: List[np.ndarray],
should_equal: bool) -> None:
patch_centers_epoch1 = diagnostics_per_epoch[0]
assert len(
diagnostics_per_epoch
) > 1, "Not enough data to check patch centers, need at least 2"
for diagnostic in diagnostics_per_epoch[1:]:
assert np.array_equal(patch_centers_epoch1,
diagnostic) == should_equal
def _check_voxel_count(results_per_epoch: List[Dict[str, float]],
expected_voxel_count_per_epoch: List[float],
prefix: str) -> None:
assert len(results_per_epoch) == len(expected_voxel_count_per_epoch)
for epoch, (results, voxel_count) in enumerate(
zip(results_per_epoch, expected_voxel_count_per_epoch)):
# In the test data, both structures "region" and "region_1" are read from the same nifti file, hence
# their voxel counts must be identical.
for structure in ["region", "region_1"]:
assert results[f"{MetricType.VOXEL_COUNT.value}/{structure}"] == pytest.approx(voxel_count, abs=1e-2), \
f"{prefix} voxel count mismatch for '{structure}' epoch {epoch}"
def _mean(a: List[float]) -> float:
return sum(a) / len(a)
def _mean_list(lists: List[List[float]]) -> List[float]:
return list(map(_mean, lists))
logging_to_stdout(log_level=logging.DEBUG)
train_config = DummyModel()
train_config.local_dataset = base_path
train_config.set_output_to(output_dirs.root_dir)
train_config.image_channels = image_channels
train_config.ground_truth_ids = ground_truth_ids
train_config.mask_id = None if no_mask_channel else train_config.mask_id
train_config.random_seed = 42
train_config.class_weights = [0.5, 0.25, 0.25]
train_config.store_dataset_sample = True
train_config.recovery_checkpoint_save_interval = 1
if machine_has_gpu:
expected_train_losses = [0.4553468, 0.454904]
expected_val_losses = [0.4553881, 0.4553041]
else:
expected_train_losses = [0.4553469, 0.4548947]
expected_val_losses = [0.4553880, 0.4553041]
loss_absolute_tolerance = 1e-6
expected_learning_rates = [train_config.l_rate, 5.3589e-4]
checkpoint_handler = get_default_checkpoint_handler(
model_config=train_config, project_root=Path(output_dirs.root_dir))
model_training_result = model_training.model_train(
train_config, checkpoint_handler=checkpoint_handler)
assert isinstance(model_training_result, ModelTrainingResults)
def assert_all_close(metric: str, expected: List[float],
**kwargs: Any) -> None:
actual = model_training_result.get_training_metric(metric)
assert np.allclose(
actual, expected, **kwargs
), f"Mismatch for {metric}: Got {actual}, expected {expected}"
# check to make sure training batches are NOT all the same across epochs
_check_patch_centers(model_training_result.train_diagnostics,
should_equal=False)
# check to make sure validation batches are all the same across epochs
_check_patch_centers(model_training_result.val_diagnostics,
should_equal=True)
assert_all_close(MetricType.SUBJECT_COUNT.value, [3.0, 3.0])
assert_all_close(MetricType.LEARNING_RATE.value,
expected_learning_rates,
rtol=1e-6)
if is_windows():
# Randomization comes out slightly different on Windows. Skip the rest of the detailed checks.
return
# Simple regression test: Voxel counts should be the same in both epochs on the validation set,
# and be the same across 'region' and 'region_1' because they derive from the same Nifti files.
# The following values are read off directly from the results of compute_dice_across_patches in the training loop
# This checks that averages are computed correctly, and that metric computers are reset after each epoch.
train_voxels = [[83092.0, 83212.0, 82946.0], [83000.0, 82881.0, 83309.0]]
val_voxels = [[82765.0, 83212.0], [82765.0, 83212.0]]
_check_voxel_count(model_training_result.train_results_per_epoch,
_mean_list(train_voxels), "Train")
_check_voxel_count(model_training_result.val_results_per_epoch,
_mean_list(val_voxels), "Val")
actual_train_losses = model_training_result.get_training_metric(
MetricType.LOSS.value)
actual_val_losses = model_training_result.get_validation_metric(
MetricType.LOSS.value)
print("actual_train_losses = {}".format(actual_train_losses))
print("actual_val_losses = {}".format(actual_val_losses))
assert np.allclose(actual_train_losses,
expected_train_losses,
atol=loss_absolute_tolerance), "Train losses"
assert np.allclose(actual_val_losses,
expected_val_losses,
atol=loss_absolute_tolerance), "Val losses"
# Check that the metric we track for Hyperdrive runs is actually written.
assert TrackedMetrics.Val_Loss.value.startswith(VALIDATION_PREFIX)
tracked_metric = TrackedMetrics.Val_Loss.value[len(VALIDATION_PREFIX):]
for val_result in model_training_result.val_results_per_epoch:
assert tracked_metric in val_result
# The following values are read off directly from the results of compute_dice_across_patches in the
# training loop. Results are slightly different for CPU, hence use a larger tolerance there.
dice_tolerance = 1e-4 if machine_has_gpu else 4.5e-4
train_dice_region = [[0.0, 0.0, 4.0282e-04], [0.0309, 0.0334, 0.0961]]
train_dice_region1 = [[0.4806, 0.4800, 0.4832], [0.4812, 0.4842, 0.4663]]
# There appears to be some amount of non-determinism here: When using a tolerance of 1e-4, we get occasional
# test failures on Linux in the cloud (not on Windows, not on AzureML) Unclear where it comes from. Even when
# failing here, the losses match up to the expected tolerance.
assert_all_close("Dice/region",
_mean_list(train_dice_region),
atol=dice_tolerance)
assert_all_close("Dice/region_1",
_mean_list(train_dice_region1),
atol=dice_tolerance)
expected_average_dice = [
_mean(train_dice_region[i] + train_dice_region1[i]) # type: ignore
for i in range(len(train_dice_region))
]
assert_all_close("Dice/AverageAcrossStructures",
expected_average_dice,
atol=dice_tolerance)
# check output files/directories
assert train_config.outputs_folder.is_dir()
assert train_config.logs_folder.is_dir()
# Tensorboard event files go into a Lightning subfolder (Pytorch Lightning default)
assert (train_config.logs_folder / "Lightning").is_dir()
assert len([(train_config.logs_folder / "Lightning").glob("events*")]) == 1
assert train_config.num_epochs == 2
# Checkpoint folder
assert train_config.checkpoint_folder.is_dir()
actual_checkpoints = list(train_config.checkpoint_folder.rglob("*.ckpt"))
assert len(
actual_checkpoints) == 2, f"Actual checkpoints: {actual_checkpoints}"
assert (train_config.checkpoint_folder /
RECOVERY_CHECKPOINT_FILE_NAME_WITH_SUFFIX).is_file()
assert (train_config.checkpoint_folder /
BEST_CHECKPOINT_FILE_NAME_WITH_SUFFIX).is_file()
assert (train_config.outputs_folder / DATASET_CSV_FILE_NAME).is_file()
assert (train_config.outputs_folder /
STORED_CSV_FILE_NAMES[ModelExecutionMode.TRAIN]).is_file()
assert (train_config.outputs_folder /
STORED_CSV_FILE_NAMES[ModelExecutionMode.VAL]).is_file()
# Path visualization: There should be 3 slices for each of the 2 subjects
sampling_folder = train_config.outputs_folder / PATCH_SAMPLING_FOLDER
assert sampling_folder.is_dir()
assert train_config.show_patch_sampling > 0
assert len(list(sampling_folder.rglob(
"*.png"))) == 3 * train_config.show_patch_sampling
# Time per epoch: Test that we have all these times logged.
model_training_result.get_training_metric(
MetricType.SECONDS_PER_EPOCH.value)
model_training_result.get_validation_metric(
MetricType.SECONDS_PER_EPOCH.value)
model_training_result.get_validation_metric(
MetricType.SECONDS_PER_BATCH.value)
model_training_result.get_training_metric(
MetricType.SECONDS_PER_BATCH.value)
def _test_model_train(output_dirs: OutputFolderForTests,
image_channels: Any,
ground_truth_ids: Any,
no_mask_channel: bool = False) -> None:
def _check_patch_centers(diagnostics_per_epoch: List[np.ndarray],
should_equal: bool) -> None:
patch_centers_epoch1 = diagnostics_per_epoch[0]
assert len(
diagnostics_per_epoch
) > 1, "Not enough data to check patch centers, need at least 2"
for diagnostic in diagnostics_per_epoch[1:]:
assert np.array_equal(patch_centers_epoch1,
diagnostic) == should_equal
def _check_voxel_count(results_per_epoch: List[Dict[str, float]],
expected_voxel_count_per_epoch: List[float],
prefix: str) -> None:
assert len(results_per_epoch) == len(expected_voxel_count_per_epoch)
for epoch, (results, voxel_count) in enumerate(
zip(results_per_epoch, expected_voxel_count_per_epoch)):
# In the test data, both structures "region" and "region_1" are read from the same nifti file, hence
# their voxel counts must be identical.
for structure in ["region", "region_1"]:
assert results[f"{MetricType.VOXEL_COUNT.value}/{structure}"] == pytest.approx(voxel_count, abs=1e-2), \
f"{prefix} voxel count mismatch for '{structure}' epoch {epoch}"
def _mean(a: List[float]) -> float:
return sum(a) / len(a)
def _mean_list(lists: List[List[float]]) -> List[float]:
return list(map(_mean, lists))
logging_to_stdout(log_level=logging.DEBUG)
train_config = DummyModel()
train_config.local_dataset = base_path
train_config.set_output_to(output_dirs.root_dir)
train_config.image_channels = image_channels
train_config.ground_truth_ids = ground_truth_ids
train_config.mask_id = None if no_mask_channel else train_config.mask_id
train_config.random_seed = 42
train_config.class_weights = [0.5, 0.25, 0.25]
train_config.store_dataset_sample = no_mask_channel
train_config.check_exclusive = False
if machine_has_gpu:
expected_train_losses = [0.4554231, 0.4550124]
expected_val_losses = [0.4553894, 0.4553061]
else:
expected_train_losses = [0.4554231, 0.4550112]
expected_val_losses = [0.4553893, 0.4553061]
loss_absolute_tolerance = 1e-6
expected_learning_rates = [train_config.l_rate, 5.3589e-4]
model_training_result, _ = model_train_unittest(train_config,
output_folder=output_dirs)
assert isinstance(model_training_result, StoringLogger)
# Check that all metrics from the BatchTimeCallback are present
# # TODO: re-enable once the BatchTimeCallback is fixed
# for epoch, epoch_results in model_training_result.results_per_epoch.items():
# for prefix in [TRAIN_PREFIX, VALIDATION_PREFIX]:
# for metric_type in [BatchTimeCallback.EPOCH_TIME,
# BatchTimeCallback.BATCH_TIME + " avg",
# BatchTimeCallback.BATCH_TIME + " max",
# BatchTimeCallback.EXCESS_LOADING_TIME]:
# expected = BatchTimeCallback.METRICS_PREFIX + prefix + metric_type
# assert expected in epoch_results, f"Expected {expected} in results for epoch {epoch}"
# # Excess loading time can be zero because that only measure batches over the threshold
# if metric_type != BatchTimeCallback.EXCESS_LOADING_TIME:
# value = epoch_results[expected]
# assert isinstance(value, float)
# assert value > 0.0, f"Time for {expected} should be > 0"
actual_train_losses = model_training_result.get_train_metric(
MetricType.LOSS.value)
actual_val_losses = model_training_result.get_val_metric(
MetricType.LOSS.value)
print("actual_train_losses = {}".format(actual_train_losses))
print("actual_val_losses = {}".format(actual_val_losses))
def assert_all_close(metric: str, expected: List[float],
**kwargs: Any) -> None:
actual = model_training_result.get_train_metric(metric)
assert np.allclose(
actual, expected, **kwargs
), f"Mismatch for {metric}: Got {actual}, expected {expected}"
# check to make sure training batches are NOT all the same across epochs
_check_patch_centers(model_training_result.train_diagnostics,
should_equal=False)
# check to make sure validation batches are all the same across epochs
_check_patch_centers(model_training_result.val_diagnostics,
should_equal=True)
assert_all_close(MetricType.SUBJECT_COUNT.value, [3.0, 3.0])
assert_all_close(MetricType.LEARNING_RATE.value,
expected_learning_rates,
rtol=1e-6)
if is_windows():
# Randomization comes out slightly different on Windows. Skip the rest of the detailed checks.
return
# Simple regression test: Voxel counts should be the same in both epochs on the validation set,
# and be the same across 'region' and 'region_1' because they derive from the same Nifti files.
# The following values are read off directly from the results of compute_dice_across_patches in the training loop
# This checks that averages are computed correctly, and that metric computers are reset after each epoch.
train_voxels = [[82765.0, 83212.0, 82740.0], [82831.0, 82647.0, 83255.0]]
val_voxels = [[82765.0, 83212.0], [82765.0, 83212.0]]
_check_voxel_count(model_training_result.train_results_per_epoch(),
_mean_list(train_voxels), "Train")
_check_voxel_count(model_training_result.val_results_per_epoch(),
_mean_list(val_voxels), "Val")
assert np.allclose(actual_train_losses,
expected_train_losses,
atol=loss_absolute_tolerance), "Train losses"
assert np.allclose(actual_val_losses,
expected_val_losses,
atol=loss_absolute_tolerance), "Val losses"
# Check that the metric we track for Hyperdrive runs is actually written.
assert TrackedMetrics.Val_Loss.value.startswith(VALIDATION_PREFIX)
tracked_metric = TrackedMetrics.Val_Loss.value[len(VALIDATION_PREFIX):]
for val_result in model_training_result.val_results_per_epoch():
assert tracked_metric in val_result
# The following values are read off directly from the results of compute_dice_across_patches in the
# training loop. Results are slightly different for GPU, hence use a larger tolerance there.
dice_tolerance = 1e-3 if machine_has_gpu else 4.5e-4
train_dice_region = [[0.0, 0.0, 0.0], [0.0376, 0.0343, 0.1017]]
train_dice_region1 = [[0.4845, 0.4814, 0.4829], [0.4822, 0.4747, 0.4426]]
# There appears to be some amount of non-determinism here: When using a tolerance of 1e-4, we get occasional
# test failures on Linux in the cloud (not on Windows, not on AzureML) Unclear where it comes from. Even when
# failing here, the losses match up to the expected tolerance.
assert_all_close("Dice/region",
_mean_list(train_dice_region),
atol=dice_tolerance)
assert_all_close("Dice/region_1",
_mean_list(train_dice_region1),
atol=dice_tolerance)
expected_average_dice = [
_mean(train_dice_region[i] + train_dice_region1[i]) # type: ignore
for i in range(len(train_dice_region))
]
assert_all_close("Dice/AverageAcrossStructures",
expected_average_dice,
atol=dice_tolerance)
# check output files/directories
assert train_config.outputs_folder.is_dir()
assert train_config.logs_folder.is_dir()
# Tensorboard event files go into a Lightning subfolder (Pytorch Lightning default)
assert (train_config.logs_folder / "Lightning").is_dir()
assert len([(train_config.logs_folder / "Lightning").glob("events*")]) == 1
assert train_config.num_epochs == 2
# Checkpoint folder
assert train_config.checkpoint_folder.is_dir()
actual_checkpoints = list(train_config.checkpoint_folder.rglob("*.ckpt"))
assert len(
actual_checkpoints) == 1, f"Actual checkpoints: {actual_checkpoints}"
assert (train_config.checkpoint_folder /
LAST_CHECKPOINT_FILE_NAME_WITH_SUFFIX).is_file()
assert (train_config.outputs_folder / DATASET_CSV_FILE_NAME).is_file()
assert (train_config.outputs_folder /
STORED_CSV_FILE_NAMES[ModelExecutionMode.TRAIN]).is_file()
assert (train_config.outputs_folder /
STORED_CSV_FILE_NAMES[ModelExecutionMode.VAL]).is_file()
# Path visualization: There should be 3 slices for each of the 2 subjects
sampling_folder = train_config.outputs_folder / PATCH_SAMPLING_FOLDER
assert sampling_folder.is_dir()
assert train_config.show_patch_sampling > 0
assert len(list(sampling_folder.rglob(
"*.png"))) == 3 * train_config.show_patch_sampling
# # Test for saving of example images
assert train_config.example_images_folder.is_dir(
) if train_config.store_dataset_sample else True
example_files = list(train_config.example_images_folder.rglob("*.*"))
assert len(example_files) == (3 * 2 *
2 if train_config.store_dataset_sample else 0
) # images x epochs x patients
