Merge branch 'main' into reuvenperetz-patch-2

model_compression_toolkit/target_platform_capabilities/README.md

@@ -13,7 +13,7 @@ in some operator for its weights/activations, fusing patterns, etc.)
 ## Supported Target Platform Models 
 
 Currently, MCT contains three target-platform models
-(new models can be created and used by users as demonstrated [here](https://sony.github.io/model_optimization/api/api_docs/modules/target_platform.html#targetplatformmodel-code-example)):
+(new models can be created and used by users as demonstrated [here](https://github.com/sony/model_optimization/blob/main/model_compression_toolkit/target_platform_capabilities/tpc_models/imx500_tpc/v1/tpc.py)):
 - [IMX500](https://developer.sony.com/develop/imx500/)
 - [TFLite](https://www.tensorflow.org/lite/performance/quantization_spec)
 - [QNNPACK](https://github.com/pytorch/QNNPACK)
@@ -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.html#ug-target-platform).
+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).

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.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://sony.github.io/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.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://sony.github.io/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.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://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"
    ],
    "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.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://sony.github.io/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.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://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",
     "\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",

tutorials/notebooks/mct_features_notebooks/keras/example_keras_qat.ipynb

@@ -171,7 +171,7 @@
    "source": [
     "## Preparing the Model for Hardware-Friendly Quantization Aware Training with MCT\n",
     "## 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.html)). In this tutorial, we use a TPC configuration that applies 2-bit quantization for weights and 3-bit quantization for activations.\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)). In this tutorial, we use a TPC configuration that applies 2-bit quantization for weights and 3-bit quantization for activations.\n",
     "\n",
     "If desired, you can skip this step and directly use the pre-configured [`get_target_platform_capabilities`](https://sony.github.io/model_optimization/api/api_docs/methods/get_target_platform_capabilities.html) function to obtain an initialized TPC."
    ],

tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_activation_threshold_search.ipynb

@@ -219,7 +219,7 @@
    },
    "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.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://sony.github.io/model_optimization/api/api_docs/modules/target_platform_capabilities.html)). Here, we use the default Tensorflow TPC:"
    ]
   },
   {

tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_data_generation.ipynb

@@ -216,7 +216,7 @@
     "In order to evaulate our generated images, we will use them to quantize the model using MCT's PTQ.This is referred to as **\"Zero-Shot Quantization (ZSQ)\"** or **\"Data-Free Quantization\"** because no real data is used in the quantization process. Next we will define configurations for MCT's PTQ.\n",
     "\n",
     "### Target Platform Capabilities (TPC)\n",
-    "MCT optimizes the model for dedicated hardware platforms. This is done using TPC (for more details, please visit our [documentation](https://sony.github.io/model_optimization/api/api_docs/modules/target_platform.html)). Here, we use the default Pytorch TPC:"
+    "MCT optimizes the model for dedicated hardware platforms. 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 Pytorch TPC:"
    ],
    "metadata": {
     "collapsed": false

tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_mixed_precision_ptq.ipynb

@@ -181,7 +181,7 @@
    "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.html). In this example, we use the default PyTorch 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://sony.github.io/model_optimization/api/api_docs/modules/target_platform_capabilities.html). In this example, we use the default PyTorch TPC, which supports 2, 4, and 8-bit options for convolution and linear layers."
    ],
    "metadata": {
     "collapsed": false

tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_post_training_quantization.ipynb

@@ -180,7 +180,7 @@
    "cell_type": "markdown",
    "source": [
     "## Target Platform Capabilities (TPC)\n",
-    "In addition, MCT optimizes the model for dedicated hardware platforms. This is done using TPC (for more details, please visit our [documentation](https://sony.github.io/model_optimization/api/api_docs/modules/target_platform.html)). Here, we use the default Pytorch TPC:"
+    "In addition, MCT optimizes the model for dedicated hardware platforms. 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 Pytorch TPC:"
    ],
    "metadata": {
     "collapsed": false