From 9eb83cba99f53a7dfbbac3e2b9763aa564f63f04 Mon Sep 17 00:00:00 2001 From: Lior Dikstein <78903511+lior-dikstein@users.noreply.github.com> Date: Sun, 29 Sep 2024 11:43:30 +0300 Subject: [PATCH] Fix small bug in Pytorch xquant notebook (#1234) * Fix small bug in PyTorch xquant notebook * Fix broken links --------- Co-authored-by: liord --- README.md | 2 +- tutorials/README.md | 2 +- .../mct_features_notebooks/pytorch/example_pytorch_xquant.ipynb | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index a4f5787ba..8c3479381 100644 --- a/README.md +++ b/README.md @@ -42,7 +42,7 @@ Explore the Model Compression Toolkit (MCT) through our tutorials, covering compression techniques for Keras and PyTorch models. Access interactive [notebooks](https://github.com/sony/model_optimization/blob/main/tutorials/README.md) for hands-on learning. For example: * [Keras MobileNetV2 post training quantization](https://github.com/sony/model_optimization/blob/main/tutorials/notebooks/imx500_notebooks/keras/example_keras_mobilenetv2_for_imx500.ipynb) -* [Post training quantization with PyTorch](https://github.com/sony/model_optimization/blob/main/tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_ptq_mnist.ipynb) +* [Post training quantization with PyTorch](https://github.com/sony/model_optimization/blob/main/tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_post_training_quantization.ipynb) * [Data Generation for ResNet18 with PyTorch](https://github.com/sony/model_optimization/blob/main/tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_data_generation.ipynb). diff --git a/tutorials/README.md b/tutorials/README.md index 72cbc7f61..0796e8169 100644 --- a/tutorials/README.md +++ b/tutorials/README.md @@ -7,7 +7,7 @@ Access interactive Jupyter notebooks for hands-on learning. ## Getting started Learn how to quickly quantize pre-trained models using MCT's post-training quantization technique for both Keras and PyTorch models. - [Post training quantization with Keras](notebooks/imx500_notebooks/keras/example_keras_mobilenetv2_for_imx500.ipynb) -- [Post training quantization with PyTorch](notebooks/mct_features_notebooks/pytorch/example_pytorch_ptq_mnist.ipynb) +- [Post training quantization with PyTorch](notebooks/mct_features_notebooks/pytorch/example_pytorch_post_training_quantization.ipynb) ## MCT Features This set of tutorials covers all the quantization tools provided by MCT. diff --git a/tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_xquant.ipynb b/tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_xquant.ipynb index f5c7f5de4..802c3261e 100644 --- a/tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_xquant.ipynb +++ b/tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_xquant.ipynb @@ -153,7 +153,7 @@ "result = xquant_report_pytorch_experimental(\n", " float_model,\n", " quantized_model,\n", - " repr_dataset,\n", + " random_data_gen,\n", " validation_dataset,\n", " xquant_config\n", " )"