updated model download links with S3 links

Author: jeffxtang

ImageSegmentation/README.md

@@ -17,7 +17,7 @@ To Test Run the Image Segmentation iOS App, follow the steps below:
 
 ### 1. Prepare the Model
 
-If you don't have the PyTorch environment set up to run the script below to generate the model file, you can download it to the `ios-demo-app/ImageSegmentation` folder using the link [here](https://drive.google.com/file/d/1_guNVutt8eTvO_YhGxkAe1uReBhNaC4f/view?usp=sharing).
+If you don't have the PyTorch environment set up to run the script below to generate the model file, you can download it to the `ios-demo-app/ImageSegmentation` folder using the link [here](https://pytorch-mobile-demo-apps.s3.us-east-2.amazonaws.com/deeplabv3_scripted.ptl).
 
 Open a Mac Terminal, first install PyTorch 1.9 and torchvision 0.10 using command like `pip install torch torchvision`, then run the following commands:
 

ObjectDetection/README.md

@@ -17,7 +17,7 @@ To Test Run the Object Detection iOS App, follow the steps below:
 
 ### 1. Prepare the Model
 
-If you don't have the PyTorch environment set up to run the script, you can download the model file [here](https://drive.google.com/file/d/1_MF7NVi9Csm1lizoSCp1wCtUUUpuhwet/view?usp=sharing) to the `ios-demo-app/ObjectDetection/ObjectDetection` folder, then skip the rest of this step and go to step 2 directly.
+If you don't have the PyTorch environment set up to run the script, you can download the model file [here](https://pytorch-mobile-demo-apps.s3.us-east-2.amazonaws.com/yolov5s.torchscript.ptl) to the `ios-demo-app/ObjectDetection/ObjectDetection` folder, then skip the rest of this step and go to step 2 directly.
 
 The Python script `export.py` in the `models` folder of the [YOLOv5 repo](https://github.com/ultralytics/yolov5) is used to generate a TorchScript-formatted YOLOv5 model named `yolov5s.torchscript.ptl` for mobile apps.
 

QuestionAnswering/README.md

@@ -19,7 +19,7 @@ To Test Run the iOS QA demo app, run the following commands on a Terminal:
 
 ### 1. Prepare the Model
 
-If you don't have PyTorch installed or want to have a quick try of the demo app, you can download the scripted QA model compressed in a zip file [here](https://drive.google.com/file/d/1PgD3pAEf0riUiT3BfwHOm6UEGk8FfJzI/view?usp=sharing), then unzip, drag and drop it to the project, and continue to Step 2.
+If you don't have PyTorch installed or want to have a quick try of the demo app, you can download the scripted QA model compressed in a zip file [here](https://pytorch-mobile-demo-apps.s3.us-east-2.amazonaws.com/qa360_quantized.ptl), then unzip, drag and drop it to the project, and continue to Step 2.
 
 Be aware that the downloadable model file was created with PyTorch 1.9.0, matching the iOS LibTorch library 1.9.0 specified in the `Podfile`. If you use a different version of PyTorch to create your model by following the instructions below, make sure you specify the same iOS LibTorch version in the `Podfile` to avoid possible errors caused by the version mismatch. Furthermore, if you want to use the latest prototype features in the PyTorch master branch to create the model, follow the steps at [Building PyTorch iOS Libraries from Source](https://pytorch.org/mobile/ios/#build-pytorch-ios-libraries-from-source) on how to use the model in iOS.
 

Seq2SeqNMT/README.md

@@ -23,7 +23,7 @@ To Test Run the Object Detection iOS App, follow the steps below:
 
 ### 1. Prepare the Model
 
-If you don't have the PyTorch environment set up to run the script, you can download the PyTorch trained and optimized NMT encoder and decoder models compressed in a zip [here](https://drive.google.com/file/d/1Azj1AI3-clVJ7ub_FUVSm3ja7TIn43Kl/view?usp=sharing), unzip it, copy to the iOS app project folder, and continue to Step 2.
+If you don't have the PyTorch environment set up to run the script, you can download the PyTorch trained and optimized NMT encoder and decoder models compressed in a zip [here](https://pytorch-mobile-demo-apps.s3.us-east-2.amazonaws.com/Seq2SeqNMT_models.zip), unzip it, copy to the iOS app project folder, and continue to Step 2.
 
 If you have a good GPU and want to train your model from scratch, uncomment the line `trainIters(encoder, decoder, 450100, print_every=5000)` in `seq2seq_nmt.py` before running `python seq2seq2_nmt.py` to go through the whole process of training, saving, loading, optimizing and saving the final mobile-ready models. Otherwise, run the script to create `optimized_encoder_150k.ptl` and `optimized_decoder_150k.ptl`, and copy them to the iOS app. Note that dynamic quantization is applied to the decoder in `seq2seq2_nmt.py` for its `nn.Linear` parameters to reduce the decoder model size from 29MB to 18MB.
 

SpeechRecognition/README.md

@@ -24,7 +24,7 @@ git clone https://github.com/pytorch/ios-demo-app
 cd ios-demo-app/SpeechRecognition
 ```
 
-If you don't have PyTorch 1.9 and torchaudio 0.9 installed or want to have a quick try of the demo app, you can download the quantized scripted wav2vec2 model file [here](https://drive.google.com/file/d/1xMh-BZMSIeoohBfZvQFYcemmh5zUn_gh/view?usp=sharing), then drag and drop to the project, and continue to Step 3.
+If you don't have PyTorch 1.9 and torchaudio 0.9 installed or want to have a quick try of the demo app, you can download the quantized scripted wav2vec2 model file [here](https://pytorch-mobile-demo-apps.s3.us-east-2.amazonaws.com/wav2vec2.ptl), then drag and drop to the project, and continue to Step 3.
 
 ### 2. Prepare the Model
 

TorchVideo/README.md

@@ -16,7 +16,7 @@ The newly released open-sourced [PyTorchVideo](https://github.com/facebookresear
 
 ### 1. Prepare the Model (Optional)
 
-If you don't have the PyTorch environment set up to run the script, you can download the model file `video_classification.pt` [here](https://drive.google.com/file/d/1ti8Eb59L5BZV3YJa-c0AUx6XCgEvsqlV/view?usp=sharing) to the `ios-demo-app/TorchVideo/TorchVideo` folder, then skip the rest of this step and go to step 2 directly.
+If you don't have the PyTorch environment set up to run the script, you can download the model file `video_classification.pt` [here](https://pytorch-mobile-demo-apps.s3.us-east-2.amazonaws.com/video_classification.ptl) to the `ios-demo-app/TorchVideo/TorchVideo` folder, then skip the rest of this step and go to step 2 directly.
 
 To create the model yourself, simply run the following commands:
 ```