Update READMEs after keras quantizers refactor

model_compression_toolkit/qat/keras/README.md

@@ -1,38 +1,41 @@
 # QAT Quantizers
 
 ## Introduction
-[`BaseKerasQATTrainableQuantizer`](./quantizer/base_keras_qat_quantizer.py) is an interface that utilizes the Quantization Infrastructure's [`BaseKerasTrainableQuantizer`](../../trainable_infrastructure/keras/base_keras_quantizer.py) class to  enable easy development of quantizers dedicated to Quantization-Aware Training (QAT).
-All available training types for QAT are defined in the Enum `TrainingMethod`. See [Training Methods for QAT](./quantizer/README.md) for more details.
+All available training types for QAT are defined in the Enum [`TrainingMethod`](../../trainable_infrastructure/common/training_method.py).
+A trainable quantizer can be Weights Quantizer or Activation Quantizer.
+Any Activation Quantizer defined in [Activation Quantizers](../../trainable_infrastructure/keras/activation_quantizers) can be used for QAT.
 
 ## Make your own Keras trainable quantizers
-A trainable quantizer can be Weights Quantizer or Activation Quantizer.
 In order to make your new quantizer you need to create your quantizer class, `MyTrainingQuantizer` and do as follows:
-   - `MyTrainingQuantizer` should inherit from `BaseKerasQATTrainableQuantizer`.
-   - `MyTrainingQuantizer` should have [`init`](../../trainable_infrastructure/common/base_trainable_quantizer.py) function that gets `quantization_config` which is [`NodeWeightsQuantizationConfig`](../../core/common/quantization/node_quantization_config.py#L228) if you choose to implement weights quantizer or [`NodeActivationQuantizationConfig`](../../core/common/quantization/node_quantization_config.py#L63) if you choose activation quantizer.
-   - Implement [`initialize_quantization`](../../trainable_infrastructure/common/base_trainable_quantizer.py) where you can define your parameters for the quantizer.
-   - Implement [`__call__`](../../trainable_infrastructure/common/base_trainable_quantizer.py) method to quantize the given inputs while training. This is your custom quantization itself. 
-   - Implement [`convert2inferable`](../../trainable_infrastructure/common/base_trainable_quantizer.py) method. This method exports your quantizer for inference (deployment). For doing that you need to choose one of the available Inferable Quantizers from the [MCT Quantizers](https://github.com/sony/mct_quantizers) package, according to the target when implementing `convert2inferable`, and set your learned quantization parameters there.
-   - Decorate `MyTrainingQuantizer` class with the `@mark_quantizer` decorator (provided by the [MCT Quantizers](https://github.com/sony/mct_quantizers) package) and choose the appropriate properties to set for your quantizer. The "identifier" property for the decorator should be of type `TrainingMethod`  enum.  See explanation about `@mark_quantizer` and how to use it under the [Pytorch Quantization Infrastructure](../../trainable_infrastructure/keras/README.md).
+   - `MyTrainingQuantizer` should inherit from `BaseKerasQATWeightTrainableQuantizer` for weights quantizer or `BaseKerasActivationTrainableQuantizer` for activation quantizer
+   - `MyTrainingQuantizer` should have `__init__` method that accepts `quantization_config` of type `TrainableQuantizerWeightsConfig` for weights quantizer or `TrainableQuantizerActivationConfig` for activation quantizer.
+   - Implement `initialize_quantization` where you can define your parameters for the quantizer.
+   - Implement `__call__` method to quantize the given inputs while training. This is your custom quantization itself. 
+   - Implement `convert2inferable` method. This method exports your quantizer for inference (deployment). For doing that you need to choose one of the available Inferable Quantizers from the [MCT Quantizers](https://github.com/sony/mct_quantizers) package, according to the target when implementing `convert2inferable`, and set your learned quantization parameters there. 
+   - Decorate `MyTrainingQuantizer` class with the `@mark_quantizer` decorator (provided by the [MCT Quantizers](https://github.com/sony/mct_quantizers) package) and choose the appropriate properties to set for your quantizer. The "identifier" property for the decorator should be of the type `TrainingMethod`  enum. See explanation about `@mark_quantizer` and how to use it under the [Keras Quantization Infrastructure](../../trainable_infrastructure/keras/README.md).
+
+
 
 ## Example: Symmetric Weights Quantizer
 To create custom `MyWeightsTrainingQuantizer` which is a symmetric weights training quantizer you need to set
-`qi.QuantizationTarget.Weights` as target and `qi.QuantizationMethod.SYMMETRIC` as method.
+`QuantizationTarget.Weights` as target and `QuantizationMethod.SYMMETRIC` as method.
 Assume that the quantizer has a new training method called `MyTrainig` which is defined in the `TrainingMethod` Enum.
 
 ```python
 import tensorflow as tf
-from model_compression_toolkit.trainable_infrastructure import TrainingMethod
+
+from model_compression_toolkit.trainable_infrastructure import TrainingMethod, TrainableQuantizerWeightsConfig
 from mct_quantizers import mark_quantizer, QuantizationMethod, QuantizationTarget
-from model_compression_toolkit.qat.keras.quantizer.base_keras_qat_quantizer import BaseKerasQATTrainableQuantizer
+from mct_quantizers.keras.quantizers import WeightsUniformInferableQuantizer
+from model_compression_toolkit.qat.keras.quantizer.base_keras_qat_weight_quantizer import BaseKerasQATWeightTrainableQuantizer
 
 NEW_PARAM = "new_param_name"
 
-
 @mark_quantizer(quantization_target=QuantizationTarget.Weights,
                 quantization_method=[QuantizationMethod.SYMMETRIC],
                 identifier=TrainingMethod.MyTraining)
-class MyWeightsTrainingQuantizer(BaseKerasQATTrainableQuantizer):
-    def __init__(self, quantization_config: NodeWeightsQuantizationConfig):
+class MyWeightsTrainingQuantizer(BaseKerasQATWeightTrainableQuantizer):
+    def __init__(self, quantization_config: TrainableQuantizerWeightsConfig):
         super(MyWeightsTrainingQuantizer, self).__init__(quantization_config)
         # Define your new params here:
         self.new_param = ...
@@ -56,28 +59,30 @@ class MyWeightsTrainingQuantizer(BaseKerasQATTrainableQuantizer):
         return quantized_inputs
 
     def convert2inferable(self):
-        return qi.WeightsUniformInferableQuantizer(...)
+        return WeightsUniformInferableQuantizer(...)
+
 ```
 
 ## Example: Symmetric Activations Quantizer
-To create custom `MyActivationsTrainingQuantizer` which is a symmetric activations training quantizer you need to set `qi.QuantizationTarget.Activation` as target and `qi.QuantizationMethod.SYMMETRIC` as method.
+To create custom `MyActivationsTrainingQuantizer` which is a symmetric activations training quantizer you need to set `QuantizationTarget.Activation` as target and `QuantizationMethod.SYMMETRIC` as method.
 Assume that the quantizer has a new training method called `MyTrainig` which is defined in the `TrainingMethod` Enum.
 
 ```python
 import tensorflow as tf
 
-NEW_PARAM = "new_param_name"
-from model_compression_toolkit.trainable_infrastructure import TrainingMethod
+from mct_quantizers.keras.quantizers import ActivationUniformInferableQuantizer
+from model_compression_toolkit.trainable_infrastructure import TrainingMethod, TrainableQuantizerActivationConfig
 from model_compression_toolkit.target_platform_capabilities.target_platform import QuantizationMethod
 from mct_quantizers import mark_quantizer, QuantizationTarget
-from model_compression_toolkit.qat.keras.quantizer.base_keras_qat_quantizer import BaseKerasQATTrainableQuantizer
+from model_compression_toolkit.qat.keras.quantizer.base_keras_qat_weight_quantizer import BaseKerasQATWeightTrainableQuantizer
 
+NEW_PARAM = "new_param_name"
 
 @mark_quantizer(quantization_target=QuantizationTarget.Activation,
                 quantization_method=[QuantizationMethod.SYMMETRIC],
                 identifier=TrainingMethod.TrainingMethod)
-class MyActivationsTrainingQuantizer(BaseKerasQATTrainableQuantizer):
-    def __init__(self, quantization_config: NodeActivationQuantizationConfig):
+class MyActivationsTrainingQuantizer(BaseKerasQATWeightTrainableQuantizer):
+    def __init__(self, quantization_config: TrainableQuantizerActivationConfig):
         super(MyActivationsTrainingQuantizer, self).__init__(quantization_config)
         # Define your new params here:
         self.new_param = ...
@@ -101,5 +106,5 @@ class MyActivationsTrainingQuantizer(BaseKerasQATTrainableQuantizer):
         return quantized_inputs
 
     def convert2inferable(self):
-        return qi.ActivationUniformInferableQuantizer(...)
+        return ActivationUniformInferableQuantizer(...)
 ```

model_compression_toolkit/trainable_infrastructure/keras/README.md

@@ -2,7 +2,7 @@
 
 [`BaseKerasTrainableQuantizer`](base_keras_quantizer.py) is an interface that enables easy quantizers development and training. 
 Using this base class makes it simple to implement new quantizers for training and inference for weights or activations.
-[`BaseKerasTrainableQuantizer`](base_keras_quantizer.py) constitutes a base class for trainable quantizers of specific of specific tasks - currently, [`BaseKerasQATTrainableQuantizer`](../../qat/keras/quantizer/base_keras_qat_quantizer.py) for Quantization-Aware Training.
+[`BaseKerasTrainableQuantizer`](base_keras_quantizer.py) constitutes a base class for trainable quantizers of specific of specific tasks, such as QAT.
 
 ### The mark_quantizer Decorator
 
@@ -14,5 +14,5 @@ The [`@mark_quantizer`](https://github.com/sony/mct_quantizers/blob/main/mct_qua
 Note that the `@mark_quantizer` decorator, and the `QuantizationTarget` and `QuantizationMethod` enums are provided by the external [MCT Quantizers](https://github.com/sony/mct_quantizers/) package.
 
 ## Examples and Fully implementation quantizers
-For fully reference, check our QAT quantizers here:
-[QAT Quantizers](../../qat/keras/README.md)
+Examples of Trainable Activation quantizers can be found here [Activation Quantizers](./activation_quantizers) and Trainable Weight quantizers here
+[QAT Weight Quantizers](../../qat/keras/quantizer)