diff options
Diffstat (limited to 'src/mlia')
3 files changed, 68 insertions, 1 deletions
diff --git a/src/mlia/resources/optimization_profiles/optimization.toml b/src/mlia/resources/optimization_profiles/optimization.toml index 623a763..42b64f0 100644 --- a/src/mlia/resources/optimization_profiles/optimization.toml +++ b/src/mlia/resources/optimization_profiles/optimization.toml @@ -7,5 +7,6 @@ learning_rate = 1e-3 show_progress = true steps = 48000 learning_rate_schedule = "cosine" +augmentations = "gaussian" num_procs = 1 num_threads = 0 diff --git a/src/mlia/resources/optimization_profiles/optimization_custom_augmentation.toml b/src/mlia/resources/optimization_profiles/optimization_custom_augmentation.toml new file mode 100644 index 0000000..5d1f917 --- /dev/null +++ b/src/mlia/resources/optimization_profiles/optimization_custom_augmentation.toml @@ -0,0 +1,13 @@ +# SPDX-FileCopyrightText: Copyright 2024, Arm Limited and/or its affiliates. +# SPDX-License-Identifier: Apache-2.0 + +[training] +batch_size = 32 +learning_rate = 1e-3 +show_progress = true +steps = 48000 +learning_rate_schedule = "cosine" +num_procs = 1 +num_threads = 0 +augmentations.gaussian_strength = 0.1 +augmentations.mixup_strength = 0.1 diff --git a/src/mlia/target/common/optimization.py b/src/mlia/target/common/optimization.py index 1423189..a139a7d 100644 --- a/src/mlia/target/common/optimization.py +++ b/src/mlia/target/common/optimization.py @@ -17,6 +17,7 @@ from mlia.core.errors import FunctionalityNotSupportedError from mlia.core.performance import estimate_performance from mlia.core.performance import P from mlia.core.performance import PerformanceEstimator +from mlia.nn.rewrite.core.train import AUGMENTATION_PRESETS from mlia.nn.select import get_optimizer from mlia.nn.select import OptimizationSettings from mlia.nn.tensorflow.config import get_keras_model @@ -218,7 +219,54 @@ _DEFAULT_OPTIMIZATION_TARGETS = [ ] -def add_common_optimization_params(advisor_parameters: dict, extra_args: dict) -> None: +def parse_augmentations( + augmentations: dict | str | None, +) -> tuple[float | None, float | None]: + """Parse augmentations from optimization-profile and return a valid tuple.""" + if isinstance(augmentations, str): + match_augmentation = AUGMENTATION_PRESETS.get(augmentations) + if not match_augmentation: + match_augmentation = AUGMENTATION_PRESETS["none"] + return match_augmentation + if isinstance(augmentations, dict): + augmentation_keys_test_for_valid = list(augmentations.keys()) + augmentation_keys_test_for_float = list(augmentations.keys()) + valid_keys = ["mixup_strength", "gaussian_strength"] + tuple_to_return = [] + for valid_key in valid_keys.copy(): + if augmentations.get(valid_key): + del augmentation_keys_test_for_valid[ + augmentation_keys_test_for_valid.index(valid_key) + ] + if isinstance(augmentations.get(valid_key), float): + tuple_to_return.append(augmentations[valid_key]) + del augmentation_keys_test_for_float[ + augmentation_keys_test_for_float.index(valid_key) + ] + else: + tuple_to_return.append(None) + else: + tuple_to_return.append(None) + + if len(augmentation_keys_test_for_valid) > 0: + logger.warning( + "Warning! Expected augmentation parameters to be 'gaussian_strength' " + "and/or 'mixup_strength' got %s. " + "Removing invalid augmentations", + str(list(augmentations.keys())), + ) + elif len(augmentation_keys_test_for_float) > 0: + logger.warning( + "Warning! Not all augmentation parameters were floats, " + "removing non-float augmentations" + ) + return (tuple_to_return[0], tuple_to_return[1]) + return AUGMENTATION_PRESETS["none"] + + +def add_common_optimization_params( # pylint: disable=too-many-branches + advisor_parameters: dict, extra_args: dict +) -> None: """Add common optimization parameters.""" optimization_targets = extra_args.get("optimization_targets") if not optimization_targets: @@ -234,6 +282,11 @@ def add_common_optimization_params(advisor_parameters: dict, extra_args: dict) - raise TypeError("Training Parameter values has wrong format.") training_parameters = extra_args["optimization_profile"].get("training") + if training_parameters: + training_parameters["augmentations"] = parse_augmentations( + training_parameters.get("augmentations") + ) + advisor_parameters.update( { "common_optimizations": { |