Rename modules

- Rename module "mlia.devices" into "mlia.target"
- Rename module "mlia.target.ethosu" into "mlia.target.ethos_u"
- Rename module "mlia.target.cortexa" into "mlia.target.cortex_a"
- Rename and update tests

Change-Id: I6dca7c8646d881f739fb6b5914d1cc7e45e63dc2

1 files changed, 0 insertions, 194 deletions

diff --git a/src/mlia/devices/ethosu/advisor.py b/src/mlia/devices/ethosu/advisor.py
deleted file mode 100644
index 2c25f6c..0000000
--- a/src/mlia/devices/ethosu/advisor.py
+++ /dev/null
@@ -1,194 +0,0 @@
-# SPDX-FileCopyrightText: Copyright 2022, Arm Limited and/or its affiliates.
-# SPDX-License-Identifier: Apache-2.0
-"""Ethos-U MLIA module."""
-from __future__ import annotations
-
-from pathlib import Path
-from typing import Any
-
-from mlia.core.advice_generation import AdviceProducer
-from mlia.core.advisor import DefaultInferenceAdvisor
-from mlia.core.advisor import InferenceAdvisor
-from mlia.core.common import AdviceCategory
-from mlia.core.context import Context
-from mlia.core.context import ExecutionContext
-from mlia.core.data_analysis import DataAnalyzer
-from mlia.core.data_collection import DataCollector
-from mlia.core.events import Event
-from mlia.core.typing import PathOrFileLike
-from mlia.devices.ethosu.advice_generation import EthosUAdviceProducer
-from mlia.devices.ethosu.advice_generation import EthosUStaticAdviceProducer
-from mlia.devices.ethosu.config import EthosUConfiguration
-from mlia.devices.ethosu.config import get_target
-from mlia.devices.ethosu.data_analysis import EthosUDataAnalyzer
-from mlia.devices.ethosu.data_collection import EthosUOperatorCompatibility
-from mlia.devices.ethosu.data_collection import EthosUOptimizationPerformance
-from mlia.devices.ethosu.data_collection import EthosUPerformance
-from mlia.devices.ethosu.events import EthosUAdvisorStartedEvent
-from mlia.devices.ethosu.handlers import EthosUEventHandler
-from mlia.nn.tensorflow.utils import is_tflite_model
-from mlia.utils.types import is_list_of
-
-
-class EthosUInferenceAdvisor(DefaultInferenceAdvisor):
-    """Ethos-U Inference Advisor."""
-
-    @classmethod
-    def name(cls) -> str:
-        """Return name of the advisor."""
-        return "ethos_u_inference_advisor"
-
-    def get_collectors(self, context: Context) -> list[DataCollector]:
-        """Return list of the data collectors."""
-        model = self.get_model(context)
-        device = self._get_device(context)
-        backends = self._get_backends(context)
-
-        collectors: list[DataCollector] = []
-
-        if AdviceCategory.OPERATORS in context.advice_category:
-            collectors.append(EthosUOperatorCompatibility(model, device))
-
-        # Performance and optimization are mutually exclusive.
-        # Decide which one to use (taking into account the model format).
-        if is_tflite_model(model):
-            # TensorFlow Lite models do not support optimization (only performance)!
-            if context.advice_category == AdviceCategory.OPTIMIZATION:
-                raise Exception(
-                    "Command 'optimization' is not supported for TensorFlow Lite files."
-                )
-            if AdviceCategory.PERFORMANCE in context.advice_category:
-                collectors.append(EthosUPerformance(model, device, backends))
-        else:
-            # Keras/SavedModel: Prefer optimization
-            if AdviceCategory.OPTIMIZATION in context.advice_category:
-                optimization_settings = self._get_optimization_settings(context)
-                collectors.append(
-                    EthosUOptimizationPerformance(
-                        model, device, optimization_settings, backends
-                    )
-                )
-            elif AdviceCategory.PERFORMANCE in context.advice_category:
-                collectors.append(EthosUPerformance(model, device, backends))
-
-        return collectors
-
-    def get_analyzers(self, context: Context) -> list[DataAnalyzer]:
-        """Return list of the data analyzers."""
-        return [
-            EthosUDataAnalyzer(),
-        ]
-
-    def get_producers(self, context: Context) -> list[AdviceProducer]:
-        """Return list of the advice producers."""
-        return [
-            EthosUAdviceProducer(),
-            EthosUStaticAdviceProducer(),
-        ]
-
-    def get_events(self, context: Context) -> list[Event]:
-        """Return list of the startup events."""
-        model = self.get_model(context)
-        device = self._get_device(context)
-
-        return [
-            EthosUAdvisorStartedEvent(device=device, model=model),
-        ]
-
-    def _get_device(self, context: Context) -> EthosUConfiguration:
-        """Get device."""
-        target_profile = self.get_target_profile(context)
-
-        return get_target(target_profile)
-
-    def _get_optimization_settings(self, context: Context) -> list[list[dict]]:
-        """Get optimization settings."""
-        return self.get_parameter(  # type: ignore
-            EthosUOptimizationPerformance.name(),
-            "optimizations",
-            expected_type=list,
-            expected=False,
-            context=context,
-        )
-
-    def _get_backends(self, context: Context) -> list[str] | None:
-        """Get list of backends."""
-        return self.get_parameter(  # type: ignore
-            self.name(),
-            "backends",
-            expected_type=list,
-            expected=False,
-            context=context,
-        )
-
-
-def configure_and_get_ethosu_advisor(
-    context: ExecutionContext,
-    target_profile: str,
-    model: str | Path,
-    output: PathOrFileLike | None = None,
-    **extra_args: Any,
-) -> InferenceAdvisor:
-    """Create and configure Ethos-U advisor."""
-    if context.event_handlers is None:
-        context.event_handlers = [EthosUEventHandler(output)]
-
-    if context.config_parameters is None:
-        context.config_parameters = _get_config_parameters(
-            model, target_profile, **extra_args
-        )
-
-    return EthosUInferenceAdvisor()
-
-
-_DEFAULT_OPTIMIZATION_TARGETS = [
-    {
-        "optimization_type": "pruning",
-        "optimization_target": 0.5,
-        "layers_to_optimize": None,
-    },
-    {
-        "optimization_type": "clustering",
-        "optimization_target": 32,
-        "layers_to_optimize": None,
-    },
-]
-
-
-def _get_config_parameters(
-    model: str | Path,
-    target_profile: str,
-    **extra_args: Any,
-) -> dict[str, Any]:
-    """Get configuration parameters for the advisor."""
-    advisor_parameters: dict[str, Any] = {
-        "ethos_u_inference_advisor": {
-            "model": model,
-            "target_profile": target_profile,
-        },
-    }
-
-    # Specifying backends is optional (default is used)
-    backends = extra_args.get("backends")
-    if backends is not None:
-        if not is_list_of(backends, str):
-            raise Exception("Backends value has wrong format")
-
-        advisor_parameters["ethos_u_inference_advisor"]["backends"] = backends
-
-    optimization_targets = extra_args.get("optimization_targets")
-    if not optimization_targets:
-        optimization_targets = _DEFAULT_OPTIMIZATION_TARGETS
-
-    if not is_list_of(optimization_targets, dict):
-        raise Exception("Optimization targets value has wrong format")
-
-    advisor_parameters.update(
-        {
-            "ethos_u_model_optimizations": {
-                "optimizations": [optimization_targets],
-            },
-        }
-    )
-
-    return advisor_parameters