Area Select

react-native-pytorch-core (core package)

Description

Hello! thanks for contributions!

I have a problem while developing my project. I need a function like torchvision.transforms.functional.perspective

Could you add this implementation for torchvision.transforms.functional.perspective? or Can i implement this function? There is no implementation of perspective function in playtorch docs

Another solution that i proceed is making pytorch mobile model for this function. This idea came from @raedle of this issue. but it has a error at react-native app like this:

{"message": "Calling torch.linalg.lstsq on a CPU tensor requires compiling PyTorch with LAPACK. Please use PyTorch built with LAPACK support.

  Debug info for handle(s): debug_handles:{-1}, was not found.

Exception raised from apply_lstsq at ../aten/src/ATen/native/BatchLinearAlgebraKernel.cpp:559 (most recent call first):
(no backtrace available)"}

Should i question to pytorch github about this error?

My perspective model is like this: This model is successful at python code.

import torch, torchvision
from typing import List, Dict
import torchvision.transforms.functional as F

class WrapPerspectiveCrop(torch.nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, inputs: torch.Tensor, points: List[List[int]]):
        size_points = [[0,0], [inputs.shape[2],0] , [inputs.shape[2],inputs.shape[1]], [0,inputs.shape[1]]]
        inputs = F.perspective(inputs, points, size_points)
        return inputs

    
crop = WrapPerspectiveCrop()
scripted_model = torch.jit.script(crop)
scripted_model.save("wrap_perspective.pt")


import torch
from torch.utils.mobile_optimizer import optimize_for_mobile

optimized_scripted_module=optimize_for_mobile(scripted_model)
optimized_scripted_module._save_for_lite_interpreter("wrap_perspective.ptl")

How can i solve this problem? Many many thanks for anyone help!

Version

1.1.0

Problem Area

react-native-pytorch-core (core package)

Steps to Reproduce

run model yolov5s.torchscript.ptl

Expected Results

get results (bbox, class)

Code example, screenshot, or link to repository

Error Possible Unhandled Promise Rejection (id: 0): Error: End of input at character 0 of promiseMethodWrapper

Did I follow this instruction to convert .pt to .ptl or am I doing something wrong? https://github.com/pytorch/android-demo-app/tree/master/ObjectDetection I ran android project pytorch mobile model worked there.

As I understand it, you need live.spec.json but where to get it for yolov5?

It's him ? I used it but it still gives an error or I need to project it? {'config.txt': '{"shape": [1, 3, 640, 640], "stride": 32, "names": ["person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"]}'}

here is the link to the issue

Version

0.2.0

Problem Area

Website/Documentation

Steps to Reproduce

1. https://playtorch.dev/docs/tutorials/snacks/image-classification/
2. Scan QR Code
3. Can't find variable: __torchlive__ Evaluacting react-native-pytorch-core.js

Expected Results

No error message

Code example, screenshot, or link to repository

Hi,

I noticed there was an issue for __torchlive__ that was opened for the CLI, but I wanted to also address the broken Snack demo in your documentation. Thanks!

Using an iPhone 13 Pro

https://snack.expo.dev/@playtorch/image-classification

Summary

The purpose of this pull request is to also create an AVD for the ARM architecture in order to adapt TorchLive for the M1 Macs.

We can see that the file AndroidEmulatorDeviceInstaller.ts will create a x86 Android Emulator:

Previous command to create the AVD:

const cmd = `echo "no" | ${cltPath} create avd --name "${AndroidVirtualDeviceName}" --device "pixel" --force --abi google_apis/x86_64 --package "system-images;android-29;google_apis;x86_64"`;

Previous keys in the config.ini file of the pytorch_live device:

'abi.type': 'x86_64',
'hw.cpu.arch': 'x86_64',

When we execute the command npx torchlive-cli setup-dev we don't get an error but instead an unusable AVD is created (see that the PyTorch Live AVD has a size of just 1MB):

Therefore, if we execute the command npx torchlive-cli run-android the terminal will print the android emulator version but it won't continue the execution as it can't execute a device designed to be executed in a x86 CPU architecture:

Things that were done to make this adaptation possible:

• Download the right system image depending on the CPU architecture (AndroidSDKManagerInstaller.ts file).
• Create the correct AVD depending on the CPU architecture (AndroidEmulatorDeviceInstaller.ts file).
• Change the config.ini file of the pytorch_live device (AndroidEmulatorDeviceInstaller.ts file).
• The doctor command has also been adapted to this change. Therefore, the check will change depending on the CPU architecture (Doctor.ts file).

Changelog

[TORCHLIVE-CLI][SETUP-DEV] - Create an AVD for the M1 Macs (avoids emulator failure)

Test Plan

After cloning the repository, you can go to the torchlive-cli folder, install the NPM packages and run the setup-dev command:

$ cd torchlive-cli
$ npm install
$ npm run start setup-dev

Now, if you open Android Studio and go to the AVD Manager, you will see that the column CPU/ABI of the pytorch live virtual device will be set according to your CPU architecture.

To really check if the emulator works, initialise a project using torchlive-cli and run it with android:

$ npx torchlive-cli init MyFirstProject
$ cd MyFirstProject
$ npx torchlive-cli run-android

After doing this, you can go to the Android Studio AVD Manager and you will see that the size of the pytorch_live emulator has a size of 17GB (instead of 1MB) and its CPU/ABI is correctly set to arm64:

Remarks

Newer MacBooks identification issue

A way to identify that the current device is a M1 MacBook is executing the following condition:

if(os.cpus()[0].model === 'Apple M1') {}

However, this only applies to the M1 MacBooks and I could not test this condition on the new M1 Pro & M1 Max MacBooks.

Tutorial Select

Prepare Custom Model

Feedback

Hello, always thanks for contributions! This is somewhat custom problem i think, so that i don't know that i can question. My simple tensor manipulation model is not working on my react-native app, but it is working successfully at my python code.

My adb logcat of several code trials sometimes got output memory consumptions problem or my java script code sometimes got output [Error: Exception in HostFunction: vector] or std::bad_alloc or just app crashes.

Can i get some help?..

Model export at python code:

import torch, torchvision
import os
from typing import List, Dict
import cv2
import torchvision.transforms.functional as F

class PostCD(torch.nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, inputs: torch.Tensor, iouThreshold: float):
        #shape e.g., "shape": [1, 25200, 16]
        
        inputs = inputs[inputs[:,:,4] > iouThreshold]
        max_class_tensor = torch.transpose(torch.argmax(inputs[:,5:], dim=1).unsqueeze(0),0,1)
        outputs = torch.cat((inputs[:,:5], max_class_tensor), 1).unsqueeze(0)
        return outputs

    
pcd = PostCD()
scripted_model = torch.jit.script(pcd)

data = [[247.37535095214844, 525.0855712890625, 129.0428924560547, 31.98747444152832, 0.8668637275695801, 0.0027807741425931454, 0.002542165108025074, 0.00628146156668663, 0.0026534167118370533, 0.002410427201539278, 0.0035515064373612404, 0.00348650268279016, 0.006360366474837065, 0.002872081473469734, 0.0036974542308598757, 0.9905574321746826],
        [247.37535095214844, 525.0855712890625, 129.0428924560547, 31.98747444152832, 0.8668637275695801, 0.9027807741425931454, 0.002542165108025074, 0.00628146156668663, 0.0026534167118370533, 0.002410427201539278, 0.0035515064373612404, 0.00348650268279016, 0.006360366474837065, 0.002872081473469734, 0.0036974542308598757, 0.005574321746826],
        [247.37535095214844, 525.0855712890625, 129.0428924560547, 31.98747444152832, 0.1, 0.0027807741425931454, 0.002542165108025074, 0.00628146156668663, 0.0026534167118370533, 0.002410427201539278, 0.0035515064373612404, 0.00348650268279016, 0.006360366474837065, 0.002872081473469734, 0.0036974542308598757, 0.9905574321746826],
        [247.37535095214844, 525.0855712890625, 129.0428924560547, 31.98747444152832, 0.1, 0.0027807741425931454, 0.9, 0.00628146156668663, 0.0026534167118370533, 0.002410427201539278, 0.0035515064373612404, 0.00348650268279016, 0.006360366474837065, 0.002872081473469734, 0.0036974542308598757, 0.0004000],
        [247.37535095214844, 525.0855712890625, 129.0428924560547, 31.98747444152832, 0.1, 0.0027807741425931454, 0.9, 0.00628146156668663, 0.0026534167118370533, 0.002410427201539278, 0.0035515064373612404, 0.00348650268279016, 0.006360366474837065, 0.002872081473469734, 0.0036974542308598757, 0.0004000],
        [247.37535095214844, 525.0855712890625, 129.0428924560547, 31.98747444152832, 0.1, 0.0027807741425931454, 0.9, 0.00628146156668663, 0.0026534167118370533, 0.002410427201539278, 0.0035515064373612404, 0.00348650268279016, 0.006360366474837065, 0.002872081473469734, 0.0036974542308598757, 0.0004000],
        ]
x_data = torch.tensor(data).unsqueeze(0)

print(x_data)
print(x_data.shape, end='\n\n')
    
outputs = scripted_model(x_data, 0.3)

print(outputs)
print(outputs.shape)

scripted_model.save("post_cd.pt")

import torch
from torch.utils.mobile_optimizer import optimize_for_mobile

optimized_scripted_module=optimize_for_mobile(scripted_model)
optimized_scripted_module._save_for_lite_interpreter("post_cd.ptl")

Python output:

tensor([[[2.4738e+02, 5.2509e+02, 1.2904e+02, 3.1987e+01, 8.6686e-01,
          2.7808e-03, 2.5422e-03, 6.2815e-03, 2.6534e-03, 2.4104e-03,
          3.5515e-03, 3.4865e-03, 6.3604e-03, 2.8721e-03, 3.6975e-03,
          9.9056e-01],
         [2.4738e+02, 5.2509e+02, 1.2904e+02, 3.1987e+01, 8.6686e-01,
          9.0278e-01, 2.5422e-03, 6.2815e-03, 2.6534e-03, 2.4104e-03,
          3.5515e-03, 3.4865e-03, 6.3604e-03, 2.8721e-03, 3.6975e-03,
          5.5743e-03],
         [2.4738e+02, 5.2509e+02, 1.2904e+02, 3.1987e+01, 1.0000e-01,
          2.7808e-03, 2.5422e-03, 6.2815e-03, 2.6534e-03, 2.4104e-03,
          3.5515e-03, 3.4865e-03, 6.3604e-03, 2.8721e-03, 3.6975e-03,
          9.9056e-01],
         [2.4738e+02, 5.2509e+02, 1.2904e+02, 3.1987e+01, 1.0000e-01,
          2.7808e-03, 9.0000e-01, 6.2815e-03, 2.6534e-03, 2.4104e-03,
          3.5515e-03, 3.4865e-03, 6.3604e-03, 2.8721e-03, 3.6975e-03,
          4.0000e-04],
         [2.4738e+02, 5.2509e+02, 1.2904e+02, 3.1987e+01, 1.0000e-01,
          2.7808e-03, 9.0000e-01, 6.2815e-03, 2.6534e-03, 2.4104e-03,
          3.5515e-03, 3.4865e-03, 6.3604e-03, 2.8721e-03, 3.6975e-03,
          4.0000e-04],
         [2.4738e+02, 5.2509e+02, 1.2904e+02, 3.1987e+01, 1.0000e-01,
          2.7808e-03, 9.0000e-01, 6.2815e-03, 2.6534e-03, 2.4104e-03,
          3.5515e-03, 3.4865e-03, 6.3604e-03, 2.8721e-03, 3.6975e-03,
          4.0000e-04]]])
torch.Size([1, 6, 16])

tensor([[[247.3754, 525.0856, 129.0429,  31.9875,   0.8669,  10.0000],
         [247.3754, 525.0856, 129.0429,  31.9875,   0.8669,   0.0000]]])
torch.Size([1, 2, 6])

This is app code:

const MODEL_URL = localPath;
let pcd_model = null;

async function testPCD(){
    if (pcd_model == null) {
        const filePath = await MobileModel.download(require(MODEL_URL));
        pcd_model = await torch.jit._loadForMobile(filePath);
        console.log('Model successfully loaded');
    }


    var data = [[247.37535095214844, 525.0855712890625, 129.0428924560547, 31.98747444152832, 0.8668637275695801, 0.0027807741425931454, 0.002542165108025074, 0.00628146156668663, 0.0026534167118370533, 0.002410427201539278, 0.0035515064373612404, 0.00348650268279016, 0.006360366474837065, 0.002872081473469734, 0.0036974542308598757, 0.9905574321746826],
                [247.37535095214844, 525.0855712890625, 129.0428924560547, 31.98747444152832, 0.8668637275695801, 0.9027807741425931454, 0.002542165108025074, 0.00628146156668663, 0.0026534167118370533, 0.002410427201539278, 0.0035515064373612404, 0.00348650268279016, 0.006360366474837065, 0.002872081473469734, 0.0036974542308598757, 0.005574321746826],
                [247.37535095214844, 525.0855712890625, 129.0428924560547, 31.98747444152832, 0.1, 0.0027807741425931454, 0.002542165108025074, 0.00628146156668663, 0.0026534167118370533, 0.002410427201539278, 0.0035515064373612404, 0.00348650268279016, 0.006360366474837065, 0.002872081473469734, 0.0036974542308598757, 0.9905574321746826],
                [247.37535095214844, 525.0855712890625, 129.0428924560547, 31.98747444152832, 0.1, 0.0027807741425931454, 0.9, 0.00628146156668663, 0.0026534167118370533, 0.002410427201539278, 0.0035515064373612404, 0.00348650268279016, 0.006360366474837065, 0.002872081473469734, 0.0036974542308598757, 0.0004000],
                [247.37535095214844, 525.0855712890625, 129.0428924560547, 31.98747444152832, 0.1, 0.0027807741425931454, 0.9, 0.00628146156668663, 0.0026534167118370533, 0.002410427201539278, 0.0035515064373612404, 0.00348650268279016, 0.006360366474837065, 0.002872081473469734, 0.0036974542308598757, 0.0004000],
                [247.37535095214844, 525.0855712890625, 129.0428924560547, 31.98747444152832, 0.1, 0.0027807741425931454, 0.9, 0.00628146156668663, 0.0026534167118370533, 0.002410427201539278, 0.0035515064373612404, 0.00348650268279016, 0.006360366474837065, 0.002872081473469734, 0.0036974542308598757, 0.0004000],
                ]
    var x_data = torch.tensor(data).unsqueeze(0);
    console.log(x_data);
    console.log(x_data.shape);

    try{
        const startInferencTime = global.performance.now();

        const outputs = await pcd_model.forward(x_data, 0.3);

        console.log(outputs);
        console.log(outputs.shape);

        const inferenceTime = global.performance.now() - startInferencTime;
        console.log(`inference time ${inferenceTime.toFixed(3)} ms`);
    }
    catch(err){
        console.log(err);
    }
}

Node.js output:

 LOG  Model successfully loaded
 LOG  {"abs": [Function abs], "add": [Function add], "argmax": [Function argmax], "argmin": [Function argmin], "clamp": [Function clamp], "contiguous": [Function contiguous], "data": [Function data], "div": [Function div], "dtype": "float32", "expand": [Function expand], "flip": [Function flip], "item": [Function item], "mul": [Function mul], "permute": [Function permute], "reshape": [Function reshape], "shape": [1, 6, 16], "size": [Function size], "softmax": [Function softmax], "sqrt": [Function sqrt], "squeeze": [Function squeeze], "stride": [Function stride], "sub": [Function sub], "sum": [Function sum], "to": [Function to], "toString": [Function toString], "topk": [Function topk], "unsqueeze": [Function unsqueeze]}
 LOG  [1, 6, 16]

-> App crashes

Version

No response

Problem Area

react-native-pytorch-core (core package)

Steps to Reproduce

1. Test it on Android
2. Do all the steps from here https://pytorch.org/live/docs/tutorials/add-package/
3. Use the Android Studio, run clean build, rebuild, and run
4. You will see the following Errors

Related: https://stackoverflow.com/questions/44485941/lib-so-missing-and-no-known-rule-to-make-it

Expected Results

5. Build command failed. Error while executing process D:\ADK\cmake\3.10.2.4988404\bin\ninja.exe with arguments {-C D:\XXXXXXXXXXX\Main\Project\Project\XXXXXXX\src\mobile\node_modules\react-native-pytorch-core\android.cxx\cmake\debug\armeabi-v7a torchlive} ninja: Entering directory `D:\XXXXXXXXXXX\Project\Project\XXXXXXX\src\mobile\node_modules\react-native-pytorch-core\android.cxx\cmake\debug\armeabi-v7a'

ninja: error: '../../../../build/react-native-0.64.3.aar/jni/armeabi-v7a/libreactnativejni.so', needed by '../../../../build/intermediates/cmake/debug/obj/armeabi-v7a/libtorchlive.so', missing and no known rule to make it

6. If you run the cmake on clion, you will see this warning. I am not sure it is the reason.

CMake Error: The following variables are used in this project, but they are set to NOTFOUND. Please set them or make sure they are set and tested correctly in the CMake files: FBJNI_LIBRARY linked by target "torchlive" in directory /cygdrive/d/XXXXXXXXXXX Main/Project/Project/XXXXXXX/src/mobile/node_modules/react-native-pytorch-core/android PYTORCH_LIBRARY linked by target "torchlive" in directory /cygdrive/d/XXXXXXXXXXX Main/Project/Project/XXXXXXX/src/mobile/node_modules/react-native-pytorch-core/android REACT_NATIVE_JNI_LIB linked by target "torchlive" in directory /cygdrive/d/XXXXXXXXXXX Main/Project/Project/XXXXXXX/src/mobile/node_modules/react-native-pytorch-core/android REACT_NATIVE_UTILS_LIB linked by target "torchlive" in directory /cygdrive/d/XXXXXXXXXXX Main/Project/Project/XXXXXXX/src/mobile/node_modules/react-native-pytorch-core/android

Code example, screenshot, or link to repository

No response

Version

0.2.1

Problem Area

react-native-pytorch-core (core package)

Steps to Reproduce

1. repeat rendering to Canvas

Error message: Message from debugger: Terminated due to memory issue

just before the crush: W0821 11:40:23.873222 1881387008 JSIExecutor.cpp:381] Memory warning (pressure level: 1) received by JS VM, unrecognized pressure level

The crush doesn't occur when this commit is ignored. v0.2.0 doesn't crush app.

What kind of measures should I take?

Expected Results

No response

Code example, screenshot, or link to repository

No response

Tutorial Select

Prepare Custom Model

Feedback

Hi Playtorch community,

I am trying to implement this model. It is based on U2-net but does the clothes segmentation. I converted it in the same way as I did for the usual U2-net model, using the tutorial I have previously posted.

I am using the U2-net snack snack as core, which perfectly works on my device using Playtorch app. Then I change the path to the converted model (https://cdn-128.anonfiles.com/v5l75ez1yc/431fccf2-1658318807/cloth_segm_live.ptl) in ImageMask.ts When I take a picture, nothing happens, I just see the camera UI.

Here is the link to my expo snack for cloth segmentation model.

I would appreciate any help with this issue.

After install package, I got this error when run yarn start. I add package to existing app which using bare react native. Current RN ver: 0.66.0 React ver: 17.0.2

Version

No response

Problem Area

react-native-pytorch-core (core package)

Steps to Reproduce

I am trying to run Image Classification model on Android. I had followed manual instructions for setup on linux machine. Everything is fine till the last step of installation and app gets succesfully installed on my android phone running android v9(Pie). But app crashes as soon as it is launched at start.

Expected Results

No response

Code example, screenshot, or link to repository

No response

Version

No response

Problem Area

react-native-pytorch-core (core package)

Steps to Reproduce

I am trying to install PyTorchLive on Windows 10 machine.

1. Already have Python installed.
2. I followed steps in https://pytorch.org/live/docs/tutorials/get-started-manually/ to install reactnative development environment
3. Am able to successfully run the react-native sample application.
4. npx react-native init MyFirstProject --template react-native-template-pytorch-live give the following error: Welcome to React Native! Learn once, write anywhere

√ Downloading template √ Copying template √ Processing template √ Executing post init script × Installing dependencies

error Error: Command failed: npm install
npm ERR! code ERESOLVE
npm ERR! ERESOLVE unable to resolve dependency tree
npm ERR!
npm ERR! While resolving: [email protected]
npm ERR! Found: [email protected]
npm ERR! node_modules/react
npm ERR!   react@"17.0.1" from the root project
npm ERR!
npm ERR! Could not resolve dependency:
npm ERR! peer react@"^16.0" from @react-native-community/[email protected]
npm ERR! node_modules/@react-native-community/masked-view
npm ERR!   @react-native-community/masked-view@"^0.1.10" from the root project
npm ERR!
npm ERR! Fix the upstream dependency conflict, or retry
npm ERR! this command with --force, or --legacy-peer-deps
npm ERR! to accept an incorrect (and potentially broken) dependency resolution.

Expected Results

No response

Code example, screenshot, or link to repository

No response

Area Select

react-native-pytorch-core (core package)

Description

Hello, always thanks for contributions.

My test app needs an image with good resolution, but if the camera resolution size is larger than that of the playtorch tutorial, it will be slower than before to change the camera screen to the loading screen after pressing the capture button. I also tested it in the tutorial on android. i modified targetResolution={{ width: 1080, height: 1920 }} to targetResolution={{ width: 3000, height: 4000 }}. The image result is good in resolution, but the loading screen changes slowly on the camera screen. How can i improve the changing speed?

I saw the code in CamerView.tsx, but it doesn't seem to help me.

Stack created with Sapling. Best reviewed with ReviewStack.

• #180
• -> #179

Install PyTorch Tutorial Basic tutorial for how to install the Python PyTorch dependency

Stack created with Sapling. Best reviewed with ReviewStack.

• -> #178

Basic instructions to build the PlayTorch app Adding basic instructions to build the PlayTorch app locally

Summary: Upgrade app to use Expo SDK 47.

Additional Changes:

• Updated minimum deployment target to iOS 13
• Updated snack-runtime, which required using the custom build raedle/[email protected] (see changes in forked expo/snack repo: https://github.com/raedle/snack/tree/playtorch-expo-sdk-47)

Differential Revision: D41605720

Version

LibTorch-Lite (1.12.0), react-native-pytorch-core (0.2.2)

Problem Area

react-native-pytorch-core (core package)

Steps to Reproduce

Environment:

• MacBook Air m1 Ventura 13.0
• Darwin MacBook-Air.local 22.1.0 Darwin Kernel Version 22.1.0: Sun Oct 9 20:14:30 PDT 2022; root:xnu-8792.41.9~2/RELEASE_ARM64_T8103 arm64
• npx --version 8.3.1
• packaage.json

{
  "name": "myapp",
  "version": "0.0.1",
  "private": true,
  "scripts": {
    "android": "react-native run-android",
    "ios": "react-native run-ios",
    "start": "react-native start",
    "test": "jest",
    "lint": "eslint ."
  },
  "dependencies": {
    "react": "18.1.0",
    "react-native": "0.70.5",
    "react-native-pytorch-core": "^0.2.2"
  },
  "devDependencies": {
    "@babel/core": "^7.12.9",
    "@babel/runtime": "^7.12.5",
    "@react-native-community/eslint-config": "^2.0.0",
    "babel-jest": "^26.6.3",
    "eslint": "^7.32.0",
    "jest": "^26.6.3",
    "metro-react-native-babel-preset": "0.72.3",
    "react-test-renderer": "18.1.0"
  },
  "jest": {
    "preset": "react-native"
  }
}

1. npx react-native init myapp
2. cd myapp/ios && pod install
3. cd ..
4. npm install react-native-pytorch-core
5. cd ios && pod install

Integrating client project Pod installation complete! There are 60 dependencies from the Podfile and 51 total pods installed.

[!] Can't merge user_target_xcconfig for pod targets: ["LibTorch-Lite", "Core", "Torch", "hermes-engine"]. Singular build setting CLANG_CXX_LANGUAGE_STANDARD has different values.

[!] Can't merge user_target_xcconfig for pod targets: ["LibTorch-Lite", "Core", "Torch", "hermes-engine"]. Singular build setting CLANG_CXX_LIBRARY has different values.

[!] Can't merge user_target_xcconfig for pod targets: ["LibTorch-Lite", "Core", "Torch", "hermes-engine"]. Singular build setting CLANG_CXX_LANGUAGE_STANDARD has different values.

[!] Can't merge user_target_xcconfig for pod targets: ["LibTorch-Lite", "Core", "Torch", "hermes-engine"]. Singular build setting CLANG_CXX_LIBRARY has different values.

[!] Can't merge user_target_xcconfig for pod targets: ["LibTorch-Lite", "Core", "Torch", "hermes-engine"]. Singular build setting CLANG_CXX_LANGUAGE_STANDARD has different values.

[!] Can't merge user_target_xcconfig for pod targets: ["LibTorch-Lite", "Core", "Torch", "hermes-engine"]. Singular build setting CLANG_CXX_LIBRARY has different values.

Expected Results

Dont expect to see the "Can't merge user_target_xcconfig for pod targets" messagesDon't

Code example, screenshot, or link to repository

No response