fix docsrc

docsrc/source/api/api_docs/index.rst

@@ -107,10 +107,10 @@ keras_load_quantized_model
 
 
 target_platform_capabilities
-================
+==============================
 - :ref:`target_platform_capabilities<ug-target_platform_capabilities>`: Module to create and model hardware-related settings to optimize the model according to, by the hardware the optimized model will use during inference.
 - :ref:`get_target_platform_capabilities<ug-get_target_platform_capabilities>`: A function to get a target platform model for Tensorflow and Pytorch.
-- :ref:`DefaultDict<ug-DefaultDict>`: Util class for creating a FrameworkQuantizationCapabilities.
+- :ref:`DefaultDict<ug-DefaultDict>`: Util class for creating a TargetPlatformCapabilities.
 
 
 Indices and tables

docsrc/source/api/api_docs/modules/target_platform.rst → docsrc/source/api/api_docs/modules/target_platform_capabilities.rst

@@ -3,9 +3,9 @@
 .. _ug-target_platform_capabilities:
 
 
-=================================
+=====================================
 target_platform_capabilities Module
-=================================
+=====================================
 
 MCT can be configured to quantize and optimize models for different hardware settings.
 For example, when using qnnpack backend for Pytorch model inference, Pytorch `quantization
@@ -24,7 +24,7 @@ Models for IMX500, TFLite and qnnpack can be observed `here <https://github.com/
 
 |
 
-The object MCT should get called FrameworkQuantizationCapabilities (or shortly TPC).
+The object MCT should get called TargetPlatformCapabilities (or shortly TPC).
 This diagram demonstrates the main components:
 
 .. image:: ../../../../images/tpc.jpg
@@ -42,62 +42,37 @@ QuantizationMethod
 
 OpQuantizationConfig
 ======================
-.. autoclass:: model_compression_toolkit.target_platform_capabilities.OpQuantizationConfig
+.. autoclass:: model_compression_toolkit.target_platform_capabilities.schema.mct_current_schema.OpQuantizationConfig
 
 
 
 AttributeQuantizationConfig
 ============================
-.. autoclass:: model_compression_toolkit.target_platform_capabilities.AttributeQuantizationConfig
+.. autoclass:: model_compression_toolkit.target_platform_capabilities.schema.mct_current_schema.AttributeQuantizationConfig
 
 
 QuantizationConfigOptions
 ============================
-.. autoclass:: model_compression_toolkit.target_platform_capabilities.QuantizationConfigOptions
+.. autoclass:: model_compression_toolkit.target_platform_capabilities.schema.mct_current_schema.QuantizationConfigOptions
 
 
 TargetPlatformCapabilities
-=======================
-.. autoclass:: model_compression_toolkit.target_platform_capabilities.TargetPlatformCapabilities
+============================
+.. autoclass:: model_compression_toolkit.target_platform_capabilities.schema.mct_current_schema.TargetPlatformCapabilities
 
 
 OperatorsSet
 ================
-.. autoclass:: model_compression_toolkit.target_platform_capabilities.OperatorsSet
+.. autoclass:: model_compression_toolkit.target_platform_capabilities.schema.mct_current_schema.OperatorsSet
 
 
 
 Fusing
 ==============
-.. autoclass:: model_compression_toolkit.target_platform_capabilities.Fusing
+.. autoclass:: model_compression_toolkit.target_platform_capabilities.schema.mct_current_schema.Fusing
 
 
 
 OperatorSetGroup
 ====================
-.. autoclass:: model_compression_toolkit.target_platform_capabilities.OperatorSetGroup
-
-
-OperationsToLayers
-=====================
-.. autoclass:: model_compression_toolkit.target_platform_capabilities.OperationsToLayers
-
-
-OperationsSetToLayers
-=========================
-.. autoclass:: model_compression_toolkit.target_platform_capabilities.OperationsSetToLayers
-
-
-LayerFilterParams
-=========================
-.. autoclass:: model_compression_toolkit.target_platform_capabilities.LayerFilterParams
-
-More filters and usage examples are detailed :ref:`here<ug-layer_filters>`.
-
-
-FrameworkQuantizationCapabilities
-=============================
-.. autoclass:: model_compression_toolkit.target_platform.FrameworkQuantizationCapabilities
-
-
-
+.. autoclass:: model_compression_toolkit.target_platform_capabilities.schema.mct_current_schema.OperatorSetGroup

docsrc/source/api/api_docs/modules/trainable_infrastructure.rst

@@ -55,7 +55,7 @@ For example, we can set a trainable weights quantizer with the following configu
 
 .. code-block:: python
 
-    from model_compression_toolkit.target_platform_capabilities import QuantizationMethod
+    from model_compression_toolkit.target_platform_capabilities.target_platform_capabilities import QuantizationMethod
     from model_compression_toolkit.constants import THRESHOLD, MIN_THRESHOLD
 
     TrainableQuantizerWeightsConfig(weights_quantization_method=QuantizationMethod.SYMMETRIC,
@@ -79,7 +79,7 @@ For example, we can set a trainable activation quantizer with the following conf
 
 .. code-block:: python
 
-    from model_compression_toolkit.target_platform_capabilities import QuantizationMethod
+    from model_compression_toolkit.target_platform_capabilities.target_platform_capabilities import QuantizationMethod
     from model_compression_toolkit.constants import THRESHOLD, MIN_THRESHOLD
 
     TrainableQuantizerActivationConfig(activation_quantization_method=QuantizationMethod.UNIFORM,

docsrc/source/api/api_docs/notes/tpc_note.rst

@@ -1,7 +1,7 @@
 
 .. note::
-   For now, some fields of :class:`model_compression_toolkit.target_platform_capabilities.schema.mct_current_schema.OpQuantizationConfig` are ignored during
+   For now, some fields of :class:`~model_compression_toolkit.target_platform_capabilities.OpQuantizationConfig` are ignored during
    the optimization process such as quantization_preserving, fixed_scale, and fixed_zero_point.
 
-   - MCT will use more information from :class:`~model_compression_toolkit.target_platform_capabilities.schema.mct_current_schema.OpQuantizationConfig`, in the future.
+   - MCT will use more information from :class:`~model_compression_toolkit.target_platform_capabilities.OpQuantizationConfig`, in the future.