Update repo name string in links checker

.github/workflows/link_checker.yml

@@ -17,19 +17,19 @@
 
 jobs:
   link-checker:
-    if: github.repository == 'sony/model_optimization'
+    if: github.repository == 'sonysemiconductorsolutions/mct-model-optimization'
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
       - name: Install Python 3
         uses: actions/setup-python@v5
         with:
           python-version: 3.10.*
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
           pip install requests==2.27.1
       - name: Run unittests
         run: |
           export CURL_CA_BUNDLE=''
           python -m unittest tests/doc_tests/test_docs_links.py -v

README.md

@@ -1,6 +1,6 @@
 <div align="center" markdown="1">
 <p>
-      <a href="https://sony.github.io/model_optimization/" target="_blank">
+      <a href="https://sonysemiconductorsolutions.github.io/mct-model-optimization/" target="_blank">
         <img src="https://raw.githubusercontent.com/sony/model_optimization/refs/heads/main/docsrc/images/mctHeader1-cropped.svg" width="1000"></a>
 </p>
 
@@ -17,9 +17,9 @@ ______________________________________________________________________
   <a href="#license">License</a>
 </p>
 <p align="center">
-  <a href="https://sony.github.io/model_optimization#prerequisites"><img src="https://img.shields.io/badge/pytorch-2.3%20%7C%202.4%20%7C%202.5%20%7C%202.6-blue" /></a>
-  <a href="https://sony.github.io/model_optimization#prerequisites"><img src="https://img.shields.io/badge/tensorflow-2.14%20%7C%202.15-blue" /></a>
-  <a href="https://sony.github.io/model_optimization#prerequisites"><img src="https://img.shields.io/badge/python-3.9%20%7C%203.10%20%7C%203.11%20%7C%203.12-blue" /></a>
+  <a href="https://sonysemiconductorsolutions.github.io/mct-model-optimization#prerequisites"><img src="https://img.shields.io/badge/pytorch-2.3%20%7C%202.4%20%7C%202.5%20%7C%202.6-blue" /></a>
+  <a href="https://sonysemiconductorsolutions.github.io/mct-model-optimization#prerequisites"><img src="https://img.shields.io/badge/tensorflow-2.14%20%7C%202.15-blue" /></a>
+  <a href="https://sonysemiconductorsolutions.github.io/mct-model-optimization#prerequisites"><img src="https://img.shields.io/badge/python-3.9%20%7C%203.10%20%7C%203.11%20%7C%203.12-blue" /></a>
   <a href="https://github.com/sony/model_optimization/releases"><img src="https://img.shields.io/github/v/release/sony/model_optimization" /></a>
   <a href="https://github.com/sony/model_optimization/blob/main/LICENSE.md"><img src="https://img.shields.io/badge/license-Apache%202.0-blue" /></a>
 
@@ -50,9 +50,9 @@ MCT supports various quantization methods as appears below.
 
   Quantization Method  | Complexity | Computational Cost | API | Tutorial 
 -------------------- | -----------|--------------------|---------|--------
-PTQ (Post Training Quantization)  | Low | Low (~1-10 CPU minutes) | [PyTorch API](https://sony.github.io/model_optimization/api/api_docs/methods/pytorch_post_training_quantization.html) / [Keras API](https://sony.github.io/model_optimization/api/api_docs/methods/keras_post_training_quantization.html) | <a href="https://colab.research.google.com/github/sony/model_optimization/blob/main/tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_post_training_quantization.ipynb"><img src="https://img.shields.io/badge/Pytorch-green"/></a> <a href="https://colab.research.google.com/github/sony/model_optimization/blob/main/tutorials/notebooks/mct_features_notebooks/keras/example_keras_post-training_quantization.ipynb"><img src="https://img.shields.io/badge/Keras-green"/></a>
-GPTQ (parameters fine-tuning using gradients)  | Moderate | Moderate (~1-3 GPU hours) | [PyTorch API](https://sony.github.io/model_optimization/api/api_docs/methods/pytorch_gradient_post_training_quantization.html) / [Keras API](https://sony.github.io/model_optimization/api/api_docs/methods/keras_gradient_post_training_quantization.html) | <a href="https://colab.research.google.com/github/sony/model_optimization/blob/main/tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_mobilenet_gptq.ipynb"><img src="https://img.shields.io/badge/PyTorch-green"/></a> <a href="https://colab.research.google.com/github/sony/model_optimization/blob/main/tutorials/notebooks/mct_features_notebooks/keras/example_keras_mobilenet_gptq.ipynb"><img src="https://img.shields.io/badge/Keras-green"/></a> 
-QAT (Quantization Aware Training)  | High | High (~12-36 GPU hours) | [QAT API](https://sony.github.io/model_optimization/api/api_docs/index.html#qat) | <a href="https://colab.research.google.com/github/sony/model_optimization/blob/main/tutorials/notebooks/mct_features_notebooks/keras/example_keras_qat.ipynb"><img src="https://img.shields.io/badge/Keras-green"/></a>
+PTQ (Post Training Quantization)  | Low | Low (~1-10 CPU minutes) | [PyTorch API](https://sonysemiconductorsolutions.github.io/mct-model-optimization/api/api_docs/methods/pytorch_post_training_quantization.html) / [Keras API](https://sonysemiconductorsolutions.github.io/mct-model-optimization/api/api_docs/methods/keras_post_training_quantization.html) | <a href="https://colab.research.google.com/github/sony/model_optimization/blob/main/tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_post_training_quantization.ipynb"><img src="https://img.shields.io/badge/Pytorch-green"/></a> <a href="https://colab.research.google.com/github/sony/model_optimization/blob/main/tutorials/notebooks/mct_features_notebooks/keras/example_keras_post-training_quantization.ipynb"><img src="https://img.shields.io/badge/Keras-green"/></a>
+GPTQ (parameters fine-tuning using gradients)  | Moderate | Moderate (~1-3 GPU hours) | [PyTorch API](https://sonysemiconductorsolutions.github.io/mct-model-optimization/api/api_docs/methods/pytorch_gradient_post_training_quantization.html) / [Keras API](https://sonysemiconductorsolutions.github.io/mct-model-optimization/api/api_docs/methods/keras_gradient_post_training_quantization.html) | <a href="https://colab.research.google.com/github/sony/model_optimization/blob/main/tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_mobilenet_gptq.ipynb"><img src="https://img.shields.io/badge/PyTorch-green"/></a> <a href="https://colab.research.google.com/github/sony/model_optimization/blob/main/tutorials/notebooks/mct_features_notebooks/keras/example_keras_mobilenet_gptq.ipynb"><img src="https://img.shields.io/badge/Keras-green"/></a> 
+QAT (Quantization Aware Training)  | High | High (~12-36 GPU hours) | [QAT API](https://sonysemiconductorsolutions.github.io/mct-model-optimization/api/api_docs/index.html#qat) | <a href="https://colab.research.google.com/github/sony/model_optimization/blob/main/tutorials/notebooks/mct_features_notebooks/keras/example_keras_qat.ipynb"><img src="https://img.shields.io/badge/Keras-green"/></a>
 
 </p>    
 </div>
@@ -95,13 +95,13 @@ Generates synthetic images based on the statistics stored in the model's batch n
 The specifications of the method are detailed in the paper: _"**Data Generation for Hardware-Friendly Post-Training Quantization**"_ [5].
 __________________________________________________________________________________________________________
 ### Structured Pruning [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/sony/model_optimization/blob/main/tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_pruning_mnist.ipynb)
-Reduces model size/complexity and ensures better channels utilization by removing redundant input channels from layers and reconstruction of layer weights. Read more ([Pytorch API](https://sony.github.io/model_optimization/api/api_docs/methods/pytorch_pruning_experimental.html) / [Keras API](https://sony.github.io/model_optimization/api/api_docs/methods/keras_pruning_experimental.html)).
+Reduces model size/complexity and ensures better channels utilization by removing redundant input channels from layers and reconstruction of layer weights. Read more ([Pytorch API](https://sonysemiconductorsolutions.github.io/mct-model-optimization/api/api_docs/methods/pytorch_pruning_experimental.html) / [Keras API](https://sonysemiconductorsolutions.github.io/mct-model-optimization/api/api_docs/methods/keras_pruning_experimental.html)).
 __________________________________________________________________________________________________________
 ### **Debugging and Visualization**
 **🎛️ Network Editor (Modify Quantization Configurations)** [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/sony/model_optimization/blob/main/tutorials/notebooks/mct_features_notebooks/keras/example_keras_network_editor.ipynb). 
 Modify your model's quantization configuration for specific layers or apply a custom edit rule (e.g adjust layer's bit-width) using MCT’s network editor.
 
-**🖥️ Visualization**. Observe useful information for troubleshooting the quantized model's performance using TensorBoard. [Read more](https://sony.github.io/model_optimization/guidelines/visualization.html).
+**🖥️ Visualization**. Observe useful information for troubleshooting the quantized model's performance using TensorBoard. [Read more](https://sonysemiconductorsolutions.github.io/mct-model-optimization/guidelines/visualization.html).
 
 **🔑 XQuant (Explainable Quantization)** [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/sony/model_optimization/blob/main/tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_xquant.ipynb). Get valuable insights regarding the quality and success of the quantization process of your model. The report includes histograms and similarity metrics between the original float model and the quantized model in key points of the model. The report can be visualized using TensorBoard.
 __________________________________________________________________________________________________________
@@ -111,15 +111,15 @@ The specifications of the algorithm are detailed in the paper: _"**EPTQ: Enhance
 More details on how to use EPTQ via MCT can be found in the [GPTQ guidelines](https://github.com/sony/model_optimization/blob/main/model_compression_toolkit/gptq/README.md).
 
 ## <div align="center">Resources</div>
-* [User Guide](https://sony.github.io/model_optimization/index.html)  contains detailed information about MCT and guides you from installation through optimizing models for your edge AI applications.
+* [User Guide](https://sonysemiconductorsolutions.github.io/mct-model-optimization/index.html)  contains detailed information about MCT and guides you from installation through optimizing models for your edge AI applications.
 
-* MCT's [API Docs](https://sony.github.io/model_optimization/api/api_docs/) is separated per quantization methods:  
+* MCT's [API Docs](https://sonysemiconductorsolutions.github.io/mct-model-optimization/api/api_docs/) is separated per quantization methods:  
 
-  * [Post-training quantization](https://sony.github.io/model_optimization/api/api_docs/index.html#ptq) | PTQ API docs
-  * [Gradient-based post-training quantization](https://sony.github.io/model_optimization/api/api_docs/index.html#gptq) | GPTQ API docs
-  * [Quantization-aware training](https://sony.github.io/model_optimization/api/api_docs/index.html#qat) | QAT API docs
+  * [Post-training quantization](https://sonysemiconductorsolutions.github.io/mct-model-optimization/api/api_docs/index.html#ptq) | PTQ API docs
+  * [Gradient-based post-training quantization](https://sonysemiconductorsolutions.github.io/mct-model-optimization/api/api_docs/index.html#gptq) | GPTQ API docs
+  * [Quantization-aware training](https://sonysemiconductorsolutions.github.io/mct-model-optimization/api/api_docs/index.html#qat) | QAT API docs
 
-* [Debug](https://sony.github.io/model_optimization/guidelines/visualization.html) – modify optimization process or generate an explainable report
+* [Debug](https://sonysemiconductorsolutions.github.io/mct-model-optimization/guidelines/visualization.html) – modify optimization process or generate an explainable report
 
 * [Release notes](https://github.com/sony/model_optimization/releases)
 

model_compression_toolkit/ptq/keras/quantization_facade.py

@@ -122,7 +122,7 @@ def keras_post_training_quantization(in_model: Model,
 
             >>> quantized_model, quantization_info = mct.ptq.keras_post_training_quantization(model, repr_datagen, ru, core_config=config)
 
-            For more configuration options, please take a look at our `API documentation <https://sony.github.io/model_optimization/api/api_docs/modules/mixed_precision_quantization_config.html>`_.
+            For more configuration options, please take a look at our `API documentation <https://sonysemiconductorsolutions.github.io/mct-model-optimization/api/api_docs/modules/mixed_precision_quantization_config.html>`_.
 
          """
 

model_compression_toolkit/qat/keras/quantization_facade.py

@@ -167,7 +167,7 @@ def keras_quantization_aware_training_init_experimental(in_model: Model,
 
              >>> quantized_model = tf.keras.models.load_model(model_file, custom_objects=custom_objects)
 
-             For more configuration options, please take a look at our `API documentation <https://sony.github.io/model_optimization/api/api_docs/modules/mixed_precision_quantization_config.html>`_.
+             For more configuration options, please take a look at our `API documentation <https://sonysemiconductorsolutions.github.io/mct-model-optimization/api/api_docs/modules/mixed_precision_quantization_config.html>`_.
 
          """
 

model_compression_toolkit/qat/pytorch/quantization_facade.py

@@ -136,7 +136,7 @@ def pytorch_quantization_aware_training_init_experimental(in_model: Module,
 
              >>> quantized_model, quantization_info = mct.qat.pytorch_quantization_aware_training_init_experimental(model, repr_datagen, core_config=config)
 
-             For more configuration options, please take a look at our `API documentation <https://sony.github.io/model_optimization/api/api_docs/modules/mixed_precision_quantization_config.html>`_.
+             For more configuration options, please take a look at our `API documentation <https://sonysemiconductorsolutions.github.io/mct-model-optimization/api/api_docs/modules/mixed_precision_quantization_config.html>`_.
 
          """
         Logger.warning(

model_compression_toolkit/target_platform_capabilities/README.md

@@ -27,7 +27,7 @@ One may view the full default target-platform model and its parameters [here](./
 
 ## Usage
 
-The simplest way to initiate a TPC and use it in MCT is by using the function [get_target_platform_capabilities](https://sony.github.io/model_optimization/api/api_docs/methods/get_target_platform_capabilities.html#ug-get-target-platform-capabilities).
+The simplest way to initiate a TPC and use it in MCT is by using the function [get_target_platform_capabilities](https://sonysemiconductorsolutions.github.io/mct-model-optimization/api/api_docs/methods/get_target_platform_capabilities.html#ug-get-target-platform-capabilities).
 
 For example:
 
@@ -50,4 +50,4 @@ quantized_model, quantization_info = mct.ptq.keras_post_training_quantization(Mo
 
 Similarly, you can retrieve IMX500, TFLite and QNNPACK target-platform models for Keras and PyTorch frameworks.
 
-For more information and examples, we highly recommend you to visit our [project website](https://sony.github.io/model_optimization/api/api_docs/modules/target_platform_capabilities.html#ug-target-platform-capabilities).
+For more information and examples, we highly recommend you to visit our [project website](https://sonysemiconductorsolutions.github.io/mct-model-optimization/api/api_docs/modules/target_platform_capabilities.html#ug-target-platform-capabilities).

tutorials/notebooks/mct_features_notebooks/keras/example_keras_activation_threshold_search.ipynb

@@ -276,7 +276,7 @@
    "cell_type": "markdown",
    "source": [
     "## Target Platform Capabilities\n",
-    "MCT optimizes the model for dedicated hardware. This is done using TPC (for more details, please visit our [documentation](https://sony.github.io/model_optimization/api/api_docs/modules/target_platform_capabilities.html)). Here, we use the default Tensorflow TPC:"
+    "MCT optimizes the model for dedicated hardware. This is done using TPC (for more details, please visit our [documentation](https://sonysemiconductorsolutions.github.io/mct-model-optimization/api/api_docs/modules/target_platform_capabilities.html)). Here, we use the default Tensorflow TPC:"
    ],
    "metadata": {
     "collapsed": false

tutorials/notebooks/mct_features_notebooks/keras/example_keras_activation_z_score_threshold.ipynb

@@ -260,7 +260,7 @@
    "cell_type": "markdown",
    "source": [
     "## Target Platform Capabilities\n",
-    "MCT optimizes the model for dedicated hardware. This is done using TPC (for more details, please visit our [documentation](https://sony.github.io/model_optimization/api/api_docs/modules/target_platform_capabilities.html)). Here, we use the default Tensorflow TPC:"
+    "MCT optimizes the model for dedicated hardware. This is done using TPC (for more details, please visit our [documentation](https://sonysemiconductorsolutions.github.io/mct-model-optimization/api/api_docs/modules/target_platform_capabilities.html)). Here, we use the default Tensorflow TPC:"
    ],
    "metadata": {
     "collapsed": false

tutorials/notebooks/mct_features_notebooks/keras/example_keras_mobilenet_mixed_precision.ipynb

@@ -243,7 +243,7 @@
    "source": [
     "## Target Platform Capabilities (TPC)\n",
     "In addition, MCT optimizes models for dedicated hardware platforms using Target Platform Capabilities (TPC). \n",
-    "**Note:** To apply mixed-precision quantization to specific layers, the TPC must define different bit-width options for those layers. For more details, please refer to our [documentation](https://sony.github.io/model_optimization/api/api_docs/modules/target_platform_capabilities.html). In this example, we use the default Tensorflow TPC, which supports 2, 4, and 8-bit options for convolution and linear layers"
+    "**Note:** To apply mixed-precision quantization to specific layers, the TPC must define different bit-width options for those layers. For more details, please refer to our [documentation](https://sonysemiconductorsolutions.github.io/mct-model-optimization/api/api_docs/modules/target_platform_capabilities.html). In this example, we use the default Tensorflow TPC, which supports 2, 4, and 8-bit options for convolution and linear layers"
    ],
    "metadata": {
     "collapsed": false

tutorials/notebooks/mct_features_notebooks/keras/example_keras_post-training_quantization.ipynb

@@ -237,7 +237,7 @@
    "cell_type": "markdown",
    "source": [
     "## Target Platform Capabilities\n",
-    "MCT optimizes the model for dedicated hardware. This is done using TPC (for more details, please visit our [documentation](https://sony.github.io/model_optimization/api/api_docs/modules/target_platform_capabilities.html)). Here, we use the default Tensorflow TPC:"
+    "MCT optimizes the model for dedicated hardware. This is done using TPC (for more details, please visit our [documentation](https://sonysemiconductorsolutions.github.io/mct-model-optimization/api/api_docs/modules/target_platform_capabilities.html)). Here, we use the default Tensorflow TPC:"
    ],
    "metadata": {
     "collapsed": false

tutorials/notebooks/mct_features_notebooks/keras/example_keras_pruning_mnist.ipynb

@@ -204,7 +204,7 @@
     "## MCT Structured Pruning\n",
     "\n",
     "### Target Platform Capabilities (TPC)\n",
-    "MCT optimizes models for dedicated hardware using Target Platform Capabilities (TPC). For more details, please refer to our [documentation](https://sony.github.io/model_optimization/api/api_docs/modules/target_platform_capabilities.html)). First, we'll configure the TPC to define each layer's SIMD (Single Instruction, Multiple Data) size.\n",
+    "MCT optimizes models for dedicated hardware using Target Platform Capabilities (TPC). For more details, please refer to our [documentation](https://sonysemiconductorsolutions.github.io/mct-model-optimization/api/api_docs/modules/target_platform_capabilities.html)). First, we'll configure the TPC to define each layer's SIMD (Single Instruction, Multiple Data) size.\n",
     "\n",
     "In MCT, SIMD plays a key role in channel grouping, influencing the pruning process by considering channel importance within each SIMD group.\n",
     "\n",