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Repository: second-state/WasmEdge-WASINN-examples
Branch: master
Commit: ae14392ea47c
Files: 189
Total size: 52.4 MB
Directory structure:
gitextract_91qb3w5n/
├── .github/
│ └── workflows/
│ ├── build_openvino_mobilenet.yml
│ ├── build_openvino_road_seg_adas.yml
│ ├── build_pytorch_yolo.yml
│ ├── chatTTS.yml
│ ├── llama.yml
│ ├── piper.yml
│ ├── pytorch.yml
│ └── tflite.yml
├── .gitignore
├── LICENSE
├── README.md
├── openvino-mobilenet-image/
│ ├── README.md
│ ├── download_mobilenet.sh
│ ├── rust/
│ │ ├── Cargo.toml
│ │ └── src/
│ │ ├── imagenet_classes.rs
│ │ └── main.rs
│ └── wasmedge-wasinn-example-mobilenet-image.wasm
├── openvino-mobilenet-raw/
│ ├── README.md
│ ├── download_mobilenet.sh
│ ├── rust/
│ │ ├── Cargo.toml
│ │ └── src/
│ │ ├── imagenet_classes.rs
│ │ └── main.rs
│ └── wasmedge-wasinn-example-mobilenet.wasm
├── openvino-road-segmentation-adas/
│ ├── README.md
│ ├── model/
│ │ └── road-segmentation-adas-0001.xml
│ ├── openvino-road-seg-adas/
│ │ ├── Cargo.toml
│ │ └── src/
│ │ └── main.rs
│ ├── tensor/
│ │ ├── wasinn-openvino-inference-input-512x896x3xf32-bgr.tensor
│ │ └── wasinn-openvino-inference-output-1x4x512x896xf32.tensor
│ └── visualize_inference_result.ipynb
├── openvinogenai-raw/
│ ├── README.md
│ └── rust/
│ ├── Cargo.toml
│ └── src/
│ └── main.rs
├── pytorch-mobilenet-image/
│ ├── README.md
│ ├── gen_mobilenet_model.py
│ ├── gen_tensor.py
│ ├── image-1x3x224x224.rgb
│ ├── mobilenet.pt
│ ├── rust/
│ │ ├── Cargo.toml
│ │ └── src/
│ │ ├── imagenet_classes.rs
│ │ ├── main.rs
│ │ └── named_model.rs
│ ├── wasmedge-wasinn-example-mobilenet-image-named-model.wasm
│ └── wasmedge-wasinn-example-mobilenet-image.wasm
├── pytorch-yolo-image/
│ ├── README.md
│ ├── get_model.py
│ ├── rust/
│ │ ├── Cargo.toml
│ │ └── src/
│ │ ├── main.rs
│ │ └── yolo_classes.rs
│ └── yolov8n.torchscript
├── scripts/
│ ├── install_libtorch.sh
│ └── install_openvino.sh
├── tflite-birds_v1-image/
│ ├── README.md
│ ├── lite-model_aiy_vision_classifier_birds_V1_3.tflite
│ ├── rust/
│ │ ├── Cargo.toml
│ │ └── src/
│ │ ├── imagenet_classes.rs
│ │ └── main.rs
│ └── wasmedge-wasinn-example-tflite-bird-image.wasm
├── wasmedge-chatTTS/
│ ├── .gitignore
│ ├── Cargo.toml
│ ├── README.md
│ ├── assets/
│ │ └── demo.webm
│ ├── src/
│ │ └── main.rs
│ └── wasmedge-chattts.wasm
├── wasmedge-ggml/
│ ├── README.md
│ ├── basic/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ ├── src/
│ │ │ └── main.rs
│ │ └── wasmedge-ggml-basic.wasm
│ ├── chatml/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ ├── src/
│ │ │ └── main.rs
│ │ └── wasmedge-ggml-chatml.wasm
│ ├── command-r/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ ├── src/
│ │ │ └── main.rs
│ │ └── wasmedge-ggml-command-r.wasm
│ ├── embedding/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ ├── src/
│ │ │ └── main.rs
│ │ └── wasmedge-ggml-llama-embedding.wasm
│ ├── gemma/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ ├── src/
│ │ │ └── main.rs
│ │ └── wasmedge-ggml-gemma.wasm
│ ├── gemma-3/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ ├── src/
│ │ │ ├── base64.rs
│ │ │ └── main.rs
│ │ ├── wasmedge-ggml-gemma-3-base64.wasm
│ │ └── wasmedge-ggml-gemma-3.wasm
│ ├── gemma-4/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ ├── src/
│ │ │ └── main.rs
│ │ └── wasmedge-ggml-gemma-4.wasm
│ ├── grammar/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ ├── src/
│ │ │ └── main.rs
│ │ └── wasmedge-ggml-grammar.wasm
│ ├── json-schema/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ ├── src/
│ │ │ └── main.rs
│ │ └── wasmedge-ggml-json-schema.wasm
│ ├── llama/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ ├── src/
│ │ │ └── main.rs
│ │ └── wasmedge-ggml-llama.wasm
│ ├── llama-stream/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ ├── src/
│ │ │ └── main.rs
│ │ └── wasmedge-ggml-llama-stream.wasm
│ ├── llava/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ ├── src/
│ │ │ └── main.rs
│ │ └── wasmedge-ggml-llava.wasm
│ ├── llava-base64-stream/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ ├── src/
│ │ │ └── main.rs
│ │ └── wasmedge-ggml-llava-base64-stream.wasm
│ ├── multimodel/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ ├── src/
│ │ │ └── main.rs
│ │ └── wasmedge-ggml-multimodel.wasm
│ ├── nnrpc/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ ├── src/
│ │ │ └── main.rs
│ │ └── wasmedge-ggml-nnrpc.wasm
│ ├── qwen/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ └── src/
│ │ └── main.rs
│ ├── qwen2vl/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ ├── src/
│ │ │ └── main.rs
│ │ └── wasmedge-ggml-qwen2vl.wasm
│ ├── test/
│ │ ├── model-not-found/
│ │ │ ├── Cargo.toml
│ │ │ ├── README.md
│ │ │ ├── src/
│ │ │ │ └── main.rs
│ │ │ └── wasmedge-ggml-model-not-found.wasm
│ │ ├── phi-3/
│ │ │ ├── Cargo.toml
│ │ │ ├── README.md
│ │ │ ├── src/
│ │ │ │ └── main.rs
│ │ │ └── wasmedge-ggml-phi-3.wasm
│ │ ├── set-input-twice/
│ │ │ ├── Cargo.toml
│ │ │ ├── README.md
│ │ │ ├── src/
│ │ │ │ └── main.rs
│ │ │ └── wasmedge-ggml-set-input-twice.wasm
│ │ └── unload/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ ├── src/
│ │ │ └── main.rs
│ │ └── wasmedge-ggml-unload.wasm
│ ├── tts/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ ├── src/
│ │ │ └── main.rs
│ │ └── wasmedge-ggml-tts.wasm
│ └── whisper/
│ ├── Cargo.toml
│ ├── README.md
│ ├── src/
│ │ └── main.rs
│ └── whisper-basic.wasm
├── wasmedge-mlx/
│ ├── llama/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ └── src/
│ │ └── main.rs
│ ├── vlm/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ ├── decode.py
│ │ ├── encode.py
│ │ └── src/
│ │ └── main.rs
│ └── whisper/
│ ├── Cargo.toml
│ ├── README.md
│ └── src/
│ └── main.rs
├── wasmedge-piper/
│ ├── Cargo.toml
│ ├── README.md
│ ├── config.schema.json
│ ├── dependencies.d2
│ ├── json_input.schema.json
│ └── src/
│ └── main.rs
├── wasmedge-tf-llama/
│ ├── README.md
│ └── rust/
│ ├── Cargo.toml
│ └── src/
│ └── main.rs
├── wasmedge-tf-mobilenet_v2/
│ ├── README.md
│ ├── imagenet_slim_labels.txt
│ ├── mobilenet_v2_1.4_224_frozen.pb
│ ├── rust/
│ │ ├── Cargo.toml
│ │ └── src/
│ │ └── main.rs
│ └── wasmedge-tf-example-mobilenet.wasm
└── wasmedge-tf-mtcnn/
├── README.md
├── mtcnn.pb
├── rust/
│ ├── Cargo.toml
│ └── src/
│ └── main.rs
└── wasmedge-tf-example-mtcnn.wasm
================================================
FILE CONTENTS
================================================
================================================
FILE: .github/workflows/build_openvino_mobilenet.yml
================================================
name: OpenVINO Mobilenet Example
on:
schedule:
- cron: "0 0 * * *"
push:
branches: [master]
paths:
- ".github/workflows/build_openvino_mobilenet.yml"
- "openvino-mobilenet-raw/**"
- "openvino-mobilenet-image/**"
- "scripts/install_openvino.sh"
pull_request:
branches: [master]
paths:
- ".github/workflows/build_openvino_mobilenet.yml"
- "openvino-mobilenet-raw/**"
- "openvino-mobilenet-image/**"
- "scripts/install_openvino.sh"
merge_group:
env:
CARGO_TERM_COLOR: always
jobs:
build_openvino_examples:
runs-on: ubuntu-latest
strategy:
matrix:
rust: [1.84]
container:
image: wasmedge/wasmedge:ubuntu-build-clang
steps:
- name: Checkout Wasi-NN examples
uses: actions/checkout@v3
with:
fetch-depth: 0
- name: Install Rust-stable
uses: dtolnay/rust-toolchain@stable
with:
toolchain: ${{ matrix.rust }}
target: wasm32-wasip1
- name: Install dependencies
run: |
apt update
apt install -y libtbbmalloc2
- name: Install OpenVINO
working-directory: scripts
run: |
bash install_openvino.sh
- name: Install WasmEdge with Wasi-NN OpenVINO plugin
env:
CMAKE_BUILD_TYPE: "Release"
VERSION: "0.14.1"
run: |
curl -sSf https://raw.githubusercontent.com/WasmEdge/WasmEdge/master/utils/install.sh | bash -s -- -v $VERSION -p /usr/local --plugins wasi_nn-openvino
- name: Build and run openvino-mobilenet-raw
working-directory: openvino-mobilenet-raw
run: |
bash download_mobilenet.sh
cd rust
cargo build --target wasm32-wasip1 --release
cd ..
wasmedge --dir .:. ./rust/target/wasm32-wasip1/release/wasmedge-wasinn-example-mobilenet.wasm mobilenet.xml mobilenet.bin tensor-1x224x224x3-f32.bgr
- name: Build and run openvino-mobilenet-image
working-directory: openvino-mobilenet-image
run: |
bash download_mobilenet.sh
cd rust
cargo build --target wasm32-wasip1 --release
cd ..
wasmedge --dir .:. ./rust/target/wasm32-wasip1/release/wasmedge-wasinn-example-mobilenet-image.wasm mobilenet.xml mobilenet.bin input.jpg
================================================
FILE: .github/workflows/build_openvino_road_seg_adas.yml
================================================
name: OpenVINO Road Segmentation ADAS Example
on:
schedule:
- cron: "0 0 * * *"
push:
branches: [master]
paths:
- ".github/workflows/build_openvino_road_seg_adas.yml"
- "openvino-road-segmentation-adas/**"
- "scripts/install_openvino.sh"
pull_request:
branches: [master]
paths:
- ".github/workflows/build_openvino_road_seg_adas.yml"
- "openvino-road-segmentation-adas/**"
- "scripts/install_openvino.sh"
merge_group:
env:
CARGO_TERM_COLOR: always
jobs:
build_openvino_examples:
runs-on: ubuntu-latest
strategy:
matrix:
rust: [1.84]
container:
image: wasmedge/wasmedge:ubuntu-build-clang
steps:
- name: Checkout Wasi-NN examples
uses: actions/checkout@v3
with:
fetch-depth: 0
- name: Install Rust-stable
uses: dtolnay/rust-toolchain@stable
with:
toolchain: ${{ matrix.rust }}
target: wasm32-wasip1
- name: Install dependencies
run: |
apt update
apt install -y libtbbmalloc2
- name: Install OpenVINO
working-directory: scripts
run: |
bash install_openvino.sh
- name: Install WasmEdge with Wasi-NN OpenVINO plugin
env:
CMAKE_BUILD_TYPE: "Release"
VERSION: "0.14.1"
run: |
curl -sSf https://raw.githubusercontent.com/WasmEdge/WasmEdge/master/utils/install.sh | bash -s -- -v $VERSION -p /usr/local --plugins wasi_nn-openvino
- name: Build and run openvino-road-segmentation-adas
working-directory: openvino-road-segmentation-adas
run: |
cd openvino-road-seg-adas
cargo build --target=wasm32-wasip1 --release
cp target/wasm32-wasip1/release/openvino-road-seg-adas.wasm ..
cd ..
wasmedge --dir .:. openvino-road-seg-adas.wasm ./model/road-segmentation-adas-0001.xml ./model/road-segmentation-adas-0001.bin ./image/empty_road_mapillary.jpg
================================================
FILE: .github/workflows/build_pytorch_yolo.yml
================================================
name: Pytorch Yolo Detection
on:
schedule:
- cron: "0 0 * * *"
push:
branches: ['*']
paths:
- ".github/workflows/build_pytorch_yolo.yml"
- "pytorch-yolo-image/**"
- "scripts/install_libtorch.sh"
pull_request:
branches: ['*']
paths:
- ".github/workflows/build_pytorch_yolo.yml"
- "pytorch-yolo-image/**"
- "scripts/install_libtorch.sh"
merge_group:
env:
CARGO_TERM_COLOR: always
jobs:
build_pytorch_examples:
runs-on: ubuntu-latest
strategy:
matrix:
rust: [1.84]
container:
image: wasmedge/wasmedge:ubuntu-build-clang
steps:
- name: Checkout Wasi-NN examples
uses: actions/checkout@v3
with:
fetch-depth: 0
- name: Install Rust-stable
uses: dtolnay/rust-toolchain@stable
with:
toolchain: ${{ matrix.rust }}
target: wasm32-wasip1
- name: Install LibTorch
working-directory: scripts
run: |
set -e
bash install_libtorch.sh
cp ./libtorch/lib/* /lib/
- name: Install WasmEdge
env:
CMAKE_BUILD_TYPE: "Release"
VERSION: "0.13.4"
run: |
curl -sSf https://raw.githubusercontent.com/WasmEdge/WasmEdge/master/utils/install.sh | bash -s -- -v $VERSION --plugins wasi_nn-pytorch -p /usr/local
- name: Build and run pytorch-yolo-detection
run: |
cd pytorch-yolo-image
cd rust
cargo build --target=wasm32-wasip1 --release
cp target/wasm32-wasip1/release/wasmedge-wasinn-example-yolo-image.wasm ..
cd ..
wasmedge --dir .:. wasmedge-wasinn-example-yolo-image.wasm ./yolov8n.torchscript ./input.jpg
================================================
FILE: .github/workflows/chatTTS.yml
================================================
name: ChatTTS example
on:
schedule:
- cron: "0 0 * * *"
push:
paths:
- ".github/workflows/chatTTS.yml"
- "wasmedge-chatTTS/**"
pull_request:
paths:
- ".github/workflows/chatTTS.yml"
- "wasmedge-chatTTS/**"
merge_group:
jobs:
build:
runs-on: ubuntu-22.04
steps:
- name: Install Dependencies for building WasmEdge
run: |
sudo apt-get -y update
sudo apt-get -y install wget git curl software-properties-common build-essential python3 python3-dev python3-pip ninja-build
pip install chattts==0.1.1
- name: Install Rust target for wasm
run: |
rustup target add wasm32-wasip1
- name: Checkout WasmEdge
uses: actions/checkout@v4
with:
repository: WasmEdge/WasmEdge
path: WasmEdge
- name: Build WasmEdge with WASI-NN ChatTTS plugin
run: |
cmake -GNinja -Bbuild -DCMAKE_BUILD_TYPE=Release -DWASMEDGE_USE_LLVM=OFF -DWASMEDGE_PLUGIN_WASI_NN_BACKEND=ChatTTS
cmake --build build
working-directory: WasmEdge
- name: Checkout WasmEdge-WASINN-examples
uses: actions/checkout@v4
with:
path: WasmEdge-WASINN-examples
- name: Build wasm
run: cargo build --target wasm32-wasip1 --release
working-directory: WasmEdge-WASINN-examples/wasmedge-chatTTS
- name: Execute
run: WASMEDGE_PLUGIN_PATH=WasmEdge/build/plugins/wasi_nn WasmEdge/build/tools/wasmedge/wasmedge --dir .:. WasmEdge-WASINN-examples/wasmedge-chatTTS/target/wasm32-wasip1/release/wasmedge-chattts.wasm
- name: Verify output
run: test "$(file --brief output1.wav)" == 'RIFF (little-endian) data, WAVE audio, mono 24000 Hz'
================================================
FILE: .github/workflows/llama.yml
================================================
name: ggml llama2 examples
on:
schedule:
- cron: "0 0 * * *"
workflow_dispatch:
inputs:
logLevel:
description: 'Log level'
required: true
default: 'info'
push:
branches: [ '*' ]
paths:
- ".github/workflows/llama.yml"
- "wasmedge-ggml/**"
pull_request:
branches: [ '*' ]
paths:
- ".github/workflows/llama.yml"
- "wasmedge-ggml/**"
merge_group:
jobs:
build:
strategy:
matrix:
runner: [ubuntu-latest]
wasmedge: ["0.14.1"]
plugin: [wasi_nn-ggml]
job:
- name: Gemma 2B
shell: bash
run: |
test -f ~/.wasmedge/env && source ~/.wasmedge/env
cd wasmedge-ggml/gemma
curl -LO https://huggingface.co/second-state/Gemma-2b-it-GGUF/resolve/main/gemma-2b-it-Q5_K_M.gguf
cargo build --target wasm32-wasip1 --release
time wasmedge --dir .:. \
--env n_gpu_layers="$NGL" \
--nn-preload default:GGML:AUTO:gemma-2b-it-Q5_K_M.gguf \
target/wasm32-wasip1/release/wasmedge-ggml-gemma.wasm \
default \
'<start_of_turn>user Where is the capital of Japan? <end_of_turn><start_of_turn>model'
- name: Llama3 8B
shell: bash
run: |
test -f ~/.wasmedge/env && source ~/.wasmedge/env
cd wasmedge-ggml/llama
curl -LO https://huggingface.co/QuantFactory/Meta-Llama-3-8B-Instruct-GGUF/resolve/main/Meta-Llama-3-8B-Instruct.Q5_K_M.gguf
cargo build --target wasm32-wasip1 --release
time wasmedge --dir .:. \
--env n_gpu_layers="$NGL" \
--env llama3=true \
--nn-preload default:GGML:AUTO:Meta-Llama-3-8B-Instruct.Q5_K_M.gguf \
target/wasm32-wasip1/release/wasmedge-ggml-llama.wasm \
default \
$"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you do not know the answer to a question, please do not share false information.<|eot_id|>\n<|start_header_id|>user<|end_header_id|>\n\nWhat's the capital of Japan?<|eot_id|>\n<|start_header_id|>assistant<|end_header_id|>\n\n"
- name: Llama3 8B (Streaming)
shell: bash
run: |
test -f ~/.wasmedge/env && source ~/.wasmedge/env
cd wasmedge-ggml/llama-stream
curl -LO https://huggingface.co/QuantFactory/Meta-Llama-3-8B-Instruct-GGUF/resolve/main/Meta-Llama-3-8B-Instruct.Q5_K_M.gguf
cargo build --target wasm32-wasip1 --release
time wasmedge --dir .:. \
--env n_gpu_layers="$NGL" \
--env llama3=true \
--nn-preload default:GGML:AUTO:Meta-Llama-3-8B-Instruct.Q5_K_M.gguf \
target/wasm32-wasip1/release/wasmedge-ggml-llama-stream.wasm \
default \
$"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you do not know the answer to a question, please do not share false information.<|eot_id|>\n<|start_header_id|>user<|end_header_id|>\n\nWhat's the capital of Japan?<|eot_id|>\n<|start_header_id|>assistant<|end_header_id|>\n\n"
- name: Embedding Example (All-MiniLM)
shell: bash
run: |
test -f ~/.wasmedge/env && source ~/.wasmedge/env
cd wasmedge-ggml/embedding
curl -LO https://huggingface.co/second-state/All-MiniLM-L6-v2-Embedding-GGUF/resolve/main/all-MiniLM-L6-v2-ggml-model-f16.gguf
cargo build --target wasm32-wasip1 --release
time wasmedge --dir .:. \
--nn-preload default:GGML:AUTO:all-MiniLM-L6-v2-ggml-model-f16.gguf \
target/wasm32-wasip1/release/wasmedge-ggml-llama-embedding.wasm \
default \
'hello world'
- name: RPC Example
shell: bash
run: |
test -f ~/.wasmedge/env && source ~/.wasmedge/env
cd wasmedge-ggml/nnrpc
curl -LO https://huggingface.co/second-state/Llama-2-7B-Chat-GGUF/resolve/main/llama-2-7b-chat.Q5_K_M.gguf
cargo build --target wasm32-wasip1 --release
time wasmedge --dir .:. \
--env n_gpu_layers="$NGL" \
--nn-preload default:GGML:AUTO:llama-2-7b-chat.Q5_K_M.gguf \
target/wasm32-wasip1/release/wasmedge-ggml-nnrpc.wasm \
default \
$'[INST] <<SYS>>\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you do not know the answer to a question, please do not share false information.\n<</SYS>>\nWhat is the capital of Japan?[/INST]'
- name: Set Input Twice
shell: bash
run: |
test -f ~/.wasmedge/env && source ~/.wasmedge/env
cd wasmedge-ggml/test/set-input-twice
curl -LO https://huggingface.co/second-state/Gemma-2b-it-GGUF/resolve/main/gemma-2b-it-Q5_K_M.gguf
cargo build --target wasm32-wasip1 --release
time wasmedge --dir .:. \
--env n_gpu_layers="$NGL" \
--nn-preload default:GGML:AUTO:gemma-2b-it-Q5_K_M.gguf \
target/wasm32-wasip1/release/wasmedge-ggml-set-input-twice.wasm \
default \
'<start_of_turn>user Where is the capital of Japan? <end_of_turn><start_of_turn>model'
- name: Model Not Found
shell: bash
run: |
test -f ~/.wasmedge/env && source ~/.wasmedge/env
cd wasmedge-ggml/test/model-not-found
cargo build --target wasm32-wasip1 --release
time wasmedge --dir .:. \
--nn-preload default:GGML:AUTO:model-not-found.gguf \
target/wasm32-wasip1/release/wasmedge-ggml-model-not-found.wasm \
default
- name: Unload
shell: bash
run: |
test -f ~/.wasmedge/env && source ~/.wasmedge/env
cd wasmedge-ggml/test/unload
curl -LO https://huggingface.co/second-state/Gemma-2b-it-GGUF/resolve/main/gemma-2b-it-Q5_K_M.gguf
cargo build --target wasm32-wasip1 --release
time wasmedge --dir .:. \
--nn-preload default:GGML:AUTO:gemma-2b-it-Q5_K_M.gguf \
target/wasm32-wasip1/release/wasmedge-ggml-unload.wasm \
default \
'<start_of_turn>user Where is the capital of Japan? <end_of_turn><start_of_turn>model'
- name: JSON Schema
shell: bash
run: |
test -f ~/.wasmedge/env && source ~/.wasmedge/env
cd wasmedge-ggml/json-schema
curl -LO https://huggingface.co/TheBloke/Llama-2-7b-Chat-GGUF/resolve/main/llama-2-7b-chat.Q5_K_M.gguf
cargo build --target wasm32-wasip1 --release
time wasmedge --dir .:. \
--env n_gpu_layers="$NGL" \
--nn-preload default:GGML:AUTO:llama-2-7b-chat.Q5_K_M.gguf \
target/wasm32-wasip1/release/wasmedge-ggml-json-schema.wasm \
default \
$'[INST] <<SYS>>\nYou are a helpful, respectful and honest assistant. Always output JSON format string.\n<</SYS>>\nGive me a JSON array of Apple products.[/INST]'
- name: Gemma-3 Vision
shell: bash
run: |
test -f ~/.wasmedge/env && source ~/.wasmedge/env
cd wasmedge-ggml/gemma-3
curl -LO https://huggingface.co/second-state/gemma-3-4b-it-GGUF/resolve/main/gemma-3-4b-it-Q5_K_M.gguf
curl -LO https://huggingface.co/second-state/gemma-3-4b-it-GGUF/resolve/main/gemma-3-4b-it-mmproj-f16.gguf
curl -LO https://llava-vl.github.io/static/images/monalisa.jpg
cargo build --target wasm32-wasip1 --release
time wasmedge --dir .:. \
--env n_gpu_layers="$NGL" \
--env image=monalisa.jpg \
--env mmproj=gemma-3-4b-it-mmproj-f16.gguf \
--nn-preload default:GGML:AUTO:gemma-3-4b-it-Q5_K_M.gguf \
target/wasm32-wasip1/release/wasmedge-ggml-gemma-3.wasm \
default \
$'<start_of_turn>user\n<start_of_image><image><end_of_image>Describe this image<end_of_turn>\n<start_of_turn>model\n'
- name: Build llama-stream
run: |
cd wasmedge-ggml/llama-stream
cargo build --target wasm32-wasip1 --release
- name: Build llava-base64-stream
run: |
cd wasmedge-ggml/llava-base64-stream
cargo build --target wasm32-wasip1 --release
name: ${{ matrix.runner == 'ubuntu-latest' && 'ubuntu:20.04' || matrix.runner }} - ${{ matrix.job.name }} - ${{ matrix.wasmedge }} - ${{ matrix.plugin }}
runs-on: ${{ matrix.runner }}
# set image to `ubuntu:20.04` if runner is `ubuntu-latest`
container: ${{ matrix.runner == 'ubuntu-latest' && fromJSON('{"image":"ubuntu:20.04"}') || null }}
steps:
- uses: actions/checkout@v4
- if: ${{ matrix.runner == 'ubuntu-latest' }}
name: Install apt-get packages
run: |
ACCEPT_EULA=Y apt-get update
ACCEPT_EULA=Y apt-get upgrade -y
apt-get install -y wget git curl software-properties-common build-essential
env:
DEBIAN_FRONTEND: noninteractive
- name: Install Rust target for wasm
uses: dtolnay/rust-toolchain@stable
with:
target: wasm32-wasip1
- name: Install WasmEdge + WASI-NN + GGML
run: |
curl -sSf https://raw.githubusercontent.com/WasmEdge/WasmEdge/master/utils/install.sh | bash -s -- -v ${{ matrix.wasmedge }} --plugins ${{ matrix.plugin }}
- name: Set environment variable
run: echo "NGL=${{ matrix.ngl || 0 }}" >> $GITHUB_ENV
- name: ${{ matrix.job.name }}
run: ${{ matrix.job.run }}
shell: bash
================================================
FILE: .github/workflows/piper.yml
================================================
name: Piper Example
on:
schedule:
- cron: "0 0 * * *"
push:
paths:
- ".github/workflows/piper.yml"
- "wasmedge-piper/**"
pull_request:
paths:
- ".github/workflows/piper.yml"
- "wasmedge-piper/**"
merge_group:
jobs:
build:
runs-on: ubuntu-22.04
steps:
- name: Install Dependencies for building WasmEdge
run: |
sudo apt-get update
sudo apt-get install ninja-build
- name: Checkout WasmEdge
uses: actions/checkout@v4
with:
repository: WasmEdge/WasmEdge
path: WasmEdge
- name: Install ONNX Runtime
run: sudo bash utils/wasi-nn/install-onnxruntime.sh
working-directory: WasmEdge
- name: Build WasmEdge with WASI-NN Piper plugin
run: |
cmake -GNinja -Bbuild -DCMAKE_BUILD_TYPE=Release -DWASMEDGE_USE_LLVM=OFF -DWASMEDGE_PLUGIN_WASI_NN_BACKEND=Piper
cmake --build build
working-directory: WasmEdge
- name: Install Rust target for wasm
run: rustup target add wasm32-wasip1
- name: Checkout WasmEdge-WASINN-examples
uses: actions/checkout@v4
with:
path: WasmEdge-WASINN-examples
- name: Build wasm
run: cargo build --target wasm32-wasip1 --release
working-directory: WasmEdge-WASINN-examples/wasmedge-piper
- name: Download model
run: curl -LO https://huggingface.co/rhasspy/piper-voices/resolve/main/en/en_US/lessac/medium/en_US-lessac-medium.onnx
- name: Download config
run: curl -LO https://huggingface.co/rhasspy/piper-voices/resolve/main/en/en_US/lessac/medium/en_US-lessac-medium.onnx.json
- name: Download espeak-ng-data
run: |
curl -LO https://github.com/rhasspy/piper/releases/download/2023.11.14-2/piper_linux_x86_64.tar.gz
tar -xzf piper_linux_x86_64.tar.gz piper/espeak-ng-data --strip-components=1
rm piper_linux_x86_64.tar.gz
- name: Execute
run: WASMEDGE_PLUGIN_PATH=WasmEdge/build/plugins/wasi_nn WasmEdge/build/tools/wasmedge/wasmedge --dir .:. WasmEdge-WASINN-examples/wasmedge-piper/target/wasm32-wasip1/release/wasmedge-piper.wasm
- name: Verify output
run: test "$(file --brief welcome.wav)" == 'RIFF (little-endian) data, WAVE audio, Microsoft PCM, 16 bit, mono 22050 Hz'
================================================
FILE: .github/workflows/pytorch.yml
================================================
name: PyTorch examples
on:
schedule:
- cron: "0 0 * * *"
workflow_dispatch:
inputs:
logLevel:
description: 'Log level'
required: true
default: 'info'
push:
branches: [ '*' ]
paths:
- ".github/workflows/pytorch.yml"
- "pytorch-mobilenet-image/**"
pull_request:
branches: [ '*' ]
paths:
- ".github/workflows/pytorch.yml"
- "pytorch-mobilenet-image/**"
merge_group:
jobs:
build:
runs-on: ubuntu-latest
container:
image: ubuntu:20.04
steps:
- uses: actions/checkout@v4
- name: Install apt-get packages
run: |
ACCEPT_EULA=Y apt-get update
ACCEPT_EULA=Y apt-get upgrade -y
apt-get install -y wget git curl software-properties-common build-essential unzip
env:
DEBIAN_FRONTEND: noninteractive
- name: Install Rust target for wasm
uses: dtolnay/rust-toolchain@stable
with:
target: wasm32-wasip1
- name: Install WasmEdge + WASI-NN + PyTorch
run: |
VERSION=0.13.4
curl -sSf https://raw.githubusercontent.com/WasmEdge/WasmEdge/master/utils/install.sh | bash -s -- -v $VERSION --plugins wasi_nn-pytorch -p /usr/local
export PYTORCH_VERSION="1.8.2"
# For the Ubuntu 20.04 or above, use the libtorch with cxx11 abi.
export PYTORCH_ABI="libtorch-cxx11-abi"
curl -s -L -O --remote-name-all https://download.pytorch.org/libtorch/lts/1.8/cpu/${PYTORCH_ABI}-shared-with-deps-${PYTORCH_VERSION}%2Bcpu.zip
unzip -q "${PYTORCH_ABI}-shared-with-deps-${PYTORCH_VERSION}%2Bcpu.zip"
rm -f "${PYTORCH_ABI}-shared-with-deps-${PYTORCH_VERSION}%2Bcpu.zip"
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:$(pwd)/libtorch/lib
- name: Example
run: |
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:$(pwd)/libtorch/lib
cd pytorch-mobilenet-image/rust
cargo build --target wasm32-wasip1 --release
cd ..
wasmedge compile rust/target/wasm32-wasip1/release/wasmedge-wasinn-example-mobilenet-image.wasm wasmedge-wasinn-example-mobilenet-image-aot.wasm
wasmedge compile rust/target/wasm32-wasip1/release/wasmedge-wasinn-example-mobilenet-image-named-model.wasm wasmedge-wasinn-example-mobilenet-image-named-model-aot.wasm
echo "Run without named model"
wasmedge --dir .:. wasmedge-wasinn-example-mobilenet-image-aot.wasm mobilenet.pt input.jpg
echo "Run with named model"
wasmedge --dir .:. --nn-preload demo:PyTorch:CPU:mobilenet.pt wasmedge-wasinn-example-mobilenet-image-named-model-aot.wasm demo input.jpg
================================================
FILE: .github/workflows/tflite.yml
================================================
name: TFlite examples
on:
schedule:
- cron: "0 0 * * *"
workflow_dispatch:
inputs:
logLevel:
description: 'Log level'
required: true
default: 'info'
push:
branches: [ '*' ]
paths:
- ".github/workflows/tflite.yml"
- "tflite-birds_v1-image/**"
pull_request:
branches: [ '*' ]
paths:
- ".github/workflows/tflite.yml"
- "tflite-birds_v1-image/**"
merge_group:
jobs:
build:
runs-on: ubuntu-latest
container:
image: ubuntu:20.04
steps:
- uses: actions/checkout@v4
- name: Install apt-get packages
run: |
ACCEPT_EULA=Y apt-get update
ACCEPT_EULA=Y apt-get upgrade -y
apt-get install -y wget git curl software-properties-common build-essential
env:
DEBIAN_FRONTEND: noninteractive
- name: Install Rust target for wasm
uses: dtolnay/rust-toolchain@stable
with:
target: wasm32-wasip1
- name: Install WasmEdge + WASI-NN + TFLite
run: |
VERSION=0.13.4
TFVERSION=2.12.0
curl -s -L -O --remote-name-all https://github.com/second-state/WasmEdge-tensorflow-deps/releases/download/TF-2.12.0-CC/WasmEdge-tensorflow-deps-TFLite-TF-$TFVERSION-CC-manylinux2014_x86_64.tar.gz
tar -zxf WasmEdge-tensorflow-deps-TFLite-TF-$TFVERSION-CC-manylinux2014_x86_64.tar.gz
rm -f WasmEdge-tensorflow-deps-TFLite-TF-$TFVERSION-CC-manylinux2014_x86_64.tar.gz
mv libtensorflowlite_c.so /usr/local/lib
mv libtensorflowlite_flex.so /usr/local/lib
curl -sSf https://raw.githubusercontent.com/WasmEdge/WasmEdge/master/utils/install.sh | bash -s -- -v $VERSION --plugins wasi_nn-tensorflowlite -p /usr/local
- name: Example
run: |
cd tflite-birds_v1-image/rust
cargo build --target wasm32-wasip1 --release
cd ..
wasmedge compile rust/target/wasm32-wasip1/release/wasmedge-wasinn-example-tflite-bird-image.wasm wasmedge-wasinn-example-tflite-bird-image.wasm
wasmedge --dir .:. wasmedge-wasinn-example-tflite-bird-image.wasm lite-model_aiy_vision_classifier_birds_V1_3.tflite bird.jpg
================================================
FILE: .gitignore
================================================
**/build
**/target
**/*.gguf
openvino-mobilenet-image/mobilenet.bin
openvino-mobilenet-image/mobilenet.xml
openvino-mobilenet-raw/mobilenet.bin
openvino-mobilenet-raw/mobilenet.xml
openvino-mobilenet-raw/tensor-1x224x224x3-f32.bgr
pytorch-yolo-image/*.pt
pytorch-yolo-image/*.onnx
wasmedge-ggml-llama/llama-2-7b-chat.ggmlv3.q4_0.bin
.DS_Store
Cargo.lock
================================================
FILE: LICENSE
================================================
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================================================
FILE: README.md
================================================
<div align="center">
<h1>WasmEdge WASI-NN Examples</h1>
<p><strong>High-level bindings for writing wasi-nn applications</strong></p>
<p>
<a href="https://github.com/second-state/WasmEdge-WASINN-examples/actions/workflows/llama.yml/">
<img src="https://github.com/second-state/WasmEdge-WASINN-examples/actions/workflows/llama.yml/badge.svg" alt="CI status - llama"/>
</a>
<a href="https://github.com/second-state/WasmEdge-WASINN-examples/actions/workflows/pytorch.yml/">
<img src="https://github.com/second-state/WasmEdge-WASINN-examples/actions/workflows/pytorch.yml/badge.svg" alt="CI status - pytorch"/>
</a>
<a href="https://github.com/second-state/WasmEdge-WASINN-examples/actions/workflows/tflite.yml/">
<img src="https://github.com/second-state/WasmEdge-WASINN-examples/actions/workflows/tflite.yml/badge.svg" alt="CI status - tflite"/>
</a>
</p>
</div>
### Introduction
This project provides the examples of high-level [wasi-nn] bindings and WasmEdge-TensorFlow plug-ins on Rust programming language. Developers can refer to this project to write their machine learning application in a high-level language using the bindings, compile it to WebAssembly, and run it with a WebAssembly runtime that supports the [wasi-nn] proposal, such as [WasmEdge].
### Prerequisites
#### OpenVINO Installation
Developers should install the [OpenVINO] first before build and run WasmEdge with wasi-nn and the examples.
For this project, we use the version `2023.0.0`. Please refer to [WasmEdge Docs](https://wasmedge.org/docs/contribute/source/plugin/wasi_nn) and [OpenVINO™](https://docs.openvino.ai/2023.0/openvino_docs_install_guides_installing_openvino_apt.html)(2023)
```bash
wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
sudo apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
echo "deb https://apt.repos.intel.com/openvino/2023 ubuntu20 main" | sudo tee /etc/apt/sources.list.d/intel-openvino-2023.list
sudo apt update
sudo apt-get -y install openvino
ldconfig
```
[OpenVINO]: https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/overview.html
#### Rust Installation
For building the WASM files from rust source, please refer to the [Rust Official Site](https://www.rust-lang.org/tools/install) for the Rust installation.
After the installation, developers should add the `wasm32-wasip1` target.
```bash
rustup target add wasm32-wasip1
```
#### Download the `wasi-nn` Rust Crate
In Rust, download the [crate from crates.io](https://crates.io/crates/wasi-nn) by adding `wasi-nn = "0.4.0"` as a Cargo dependency.
For using WasmEdge-TensorFlow plug-ins, please download the [crate from crates.io](https://crates.io/crates/wasmedge_tensorflow_interface) by adding `wasmedge_tensorflow_interface = "0.3.0"` as a Cargo dependency.
#### WasmEdge Installation
You can refer to [here to install WasmEdge](https://wasmedge.org/docs/start/install#install).
For the examples with different wasi-nn backends or using the WasmEdge-Tensorflow plug-ins, please install with plug-ins and their dependencies:
- [wasi-nn plug-in with OpenVINO backend](https://wasmedge.org/docs/start/install#wasi-nn-plug-in-with-openvino-backend)
- [wasi-nn plug-in with PyTorch backend](https://wasmedge.org/docs/start/install#wasi-nn-plug-in-with-pytorch-backend)
- [wasi-nn plug-in with TensorFlow-Lite backend](https://wasmedge.org/docs/start/install#wasi-nn-plug-in-with-tensorflow-lite-backend)
- [WasmEdge-Image plug-in](https://wasmedge.org/docs/start/install#wasmedge-image-plug-in)
- [WasmEdge-TensorFlow plug-in](https://wasmedge.org/docs/start/install#wasmedge-tensorflow-plug-in)
- [WasmEdge-TensorFlow-Lite plug-in](https://wasmedge.org/docs/start/install#wasmedge-tensorflow-lite-plug-in)
### Examples
[Mobilenet](mobilenet)
### Related Links
- [WASI]
- [wasi-nn]
- [WasmEdge]
- [WasmEdge-TensorFlow rust interface](https://crates.io/crates/wasmedge_tensorflow_interface)
- [wasi-nn-guest](https://github.com/radu-matei/wasi-nn-guest)
[WasmEdge]: https://wasmedge.org/
[wasi-nn]: https://github.com/WebAssembly/wasi-nn
[WASI]: https://github.com/WebAssembly/WASI
### License
This project is licensed under the Apache 2.0 license. See [LICENSE](LICENSE) for more details.
### Contribution
Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in this project by you, as defined in the Apache-2.0 license, shall be licensed as above, without any additional terms or conditions.
================================================
FILE: openvino-mobilenet-image/README.md
================================================
# Mobilenet example with WasmEdge WASI-NN OpenVINO plugin
This example demonstrates how to use WasmEdge WASI-NN OpenVINO plugin to perform an inference task with Mobilenet model.
## Set up the environment
- Install `rustup` and `Rust`
Go to the [official Rust webpage](https://www.rust-lang.org/tools/install) and follow the instructions to install `rustup` and `Rust`.
> It is recommended to use Rust 1.68 or above in the stable channel.
Then, add `wasm32-wasip1` target to the Rustup toolchain:
```bash
rustup target add wasm32-wasip1
```
- Clone the example repo
```bash
git clone https://github.com/second-state/WasmEdge-WASINN-examples.git
```
- Install OpenVINO
Please refer to [WasmEdge Docs](https://wasmedge.org/docs/contribute/source/plugin/wasi_nn) and [OpenVINO™](https://docs.openvino.ai/2023.0/openvino_docs_install_guides_installing_openvino_apt.html)(2023) for the installation process.
```bash
bash WasmEdge-WASINN-examples/scripts/install_openvino.sh
ldconfig
```
- Install WasmEdge with Wasi-NN OpenVINO plugin
```bash
export CMAKE_BUILD_TYPE=Release
export VERSION=0.13.2
curl -sSf https://raw.githubusercontent.com/WasmEdge/WasmEdge/master/utils/install.sh | bash -s -- -v $VERSION -p /usr/local --plugins wasi_nn-openvino
ldconfig
```
## Build and run `openvino-mobilenet-image` example
- Download `MobileNet` model file
```bash
cd openvino-mobilenet-image
bash download_mobilenet.sh
```
- Build and run the example
```bash
cd rust
cargo build --target wasm32-wasip1 --release
cd ..
wasmedge --dir .:. ./rust/target/wasm32-wasip1/release/wasmedge-wasinn-example-mobilenet.wasm mobilenet.xml mobilenet.bin input.jpg
```
If the commands above run successfully, you will get the output:
```bash
Read graph XML, size in bytes: 143525
Read graph weights, size in bytes: 13956476
Loaded graph into wasi-nn with ID: 0
Created wasi-nn execution context with ID: 0
Read input tensor, size in bytes: 602112
Executed graph inference
1.) [954](0.9789)banana
2.) [940](0.0074)spaghetti squash
3.) [951](0.0014)lemon
4.) [969](0.0005)eggnog
5.) [942](0.0005)butternut squash
```
================================================
FILE: openvino-mobilenet-image/download_mobilenet.sh
================================================
FIXTURE=https://github.com/intel/openvino-rs/raw/v0.3.3/crates/openvino/tests/fixtures/mobilenet
TODIR=$1
if [ ! -f $TODIR/mobilenet.bin ]; then
wget --no-clobber --directory-prefix=$TODIR $FIXTURE/mobilenet.bin
fi
if [ ! -f $TODIR/mobilenet.xml ]; then
wget --no-clobber --directory-prefix=$TODIR $FIXTURE/mobilenet.xml
fi
================================================
FILE: openvino-mobilenet-image/rust/Cargo.toml
================================================
[package]
name = "wasmedge-wasinn-example-mobilenet-image"
version = "0.1.0"
authors = ["Second-State"]
readme = "README.md"
edition = "2021"
publish = false
[dependencies]
image = { version = "0.23.14", default-features = false, features = ["gif", "jpeg", "ico", "png", "pnm", "tga", "tiff", "webp", "bmp", "hdr", "dxt", "dds", "farbfeld"] }
wasi-nn = { version = "0.6.0" }
[workspace]
================================================
FILE: openvino-mobilenet-image/rust/src/imagenet_classes.rs
================================================
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/* The code in this file is adapted from https://github.com/tensorflow/tfjs-models/blob/master/mobilenet/src/imagenet_classes.ts */
pub const IMAGENET_CLASSES: [&str; 1000] = [
"tench, Tinca tinca",
"goldfish, Carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias",
"tiger shark, Galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, Struthio camelus",
"brambling, Fringilla montifringilla",
"goldfinch, Carduelis carduelis",
"house finch, linnet, Carpodacus mexicanus",
"junco, snowbird",
"indigo bunting, indigo finch, indigo bird, Passerina cyanea",
"robin, American robin, Turdus migratorius",
"bulbul",
"jay",
"magpie",
"chickadee",
"water ouzel, dipper",
"kite",
"bald eagle, American eagle, Haliaeetus leucocephalus",
"vulture",
"great grey owl, great gray owl, Strix nebulosa",
"European fire salamander, Salamandra salamandra",
"common newt, Triturus vulgaris",
"eft",
"spotted salamander, Ambystoma maculatum",
"axolotl, mud puppy, Ambystoma mexicanum",
"bullfrog, Rana catesbeiana",
"tree frog, tree-frog",
"tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui",
"loggerhead, loggerhead turtle, Caretta caretta",
"leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea",
"mud turtle",
"terrapin",
"box turtle, box tortoise",
"banded gecko",
"common iguana, iguana, Iguana iguana",
"American chameleon, anole, Anolis carolinensis",
"whiptail, whiptail lizard",
"agama",
"frilled lizard, Chlamydosaurus kingi",
"alligator lizard",
"Gila monster, Heloderma suspectum",
"green lizard, Lacerta viridis",
"African chameleon, Chamaeleo chamaeleon",
"Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis",
"African crocodile, Nile crocodile, Crocodylus niloticus",
"American alligator, Alligator mississipiensis",
"triceratops",
"thunder snake, worm snake, Carphophis amoenus",
"ringneck snake, ring-necked snake, ring snake",
"hognose snake, puff adder, sand viper",
"green snake, grass snake",
"king snake, kingsnake",
"garter snake, grass snake",
"water snake",
"vine snake",
"night snake, Hypsiglena torquata",
"boa constrictor, Constrictor constrictor",
"rock python, rock snake, Python sebae",
"Indian cobra, Naja naja",
"green mamba",
"sea snake",
"horned viper, cerastes, sand viper, horned asp, Cerastes cornutus",
"diamondback, diamondback rattlesnake, Crotalus adamanteus",
"sidewinder, horned rattlesnake, Crotalus cerastes",
"trilobite",
"harvestman, daddy longlegs, Phalangium opilio",
"scorpion",
"black and gold garden spider, Argiope aurantia",
"barn spider, Araneus cavaticus",
"garden spider, Aranea diademata",
"black widow, Latrodectus mactans",
"tarantula",
"wolf spider, hunting spider",
"tick",
"centipede",
"black grouse",
"ptarmigan",
"ruffed grouse, partridge, Bonasa umbellus",
"prairie chicken, prairie grouse, prairie fowl",
"peacock",
"quail",
"partridge",
"African grey, African gray, Psittacus erithacus",
"macaw",
"sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita",
"lorikeet",
"coucal",
"bee eater",
"hornbill",
"hummingbird",
"jacamar",
"toucan",
"drake",
"red-breasted merganser, Mergus serrator",
"goose",
"black swan, Cygnus atratus",
"tusker",
"echidna, spiny anteater, anteater",
"platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus",
"wallaby, brush kangaroo",
"koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus",
"wombat",
"jelly fish",
"sea anemone, anemone",
"brain coral",
"flatworm, platyhelminth",
"nematode, nematode worm, roundworm",
"conch",
"snail",
"slug",
"sea slug, nudibranch",
"chiton, coat-of-mail shell, sea cradle, polyplacophore",
"chambered nautilus, pearly nautilus, nautilus",
"Dungeness crab, Cancer magister",
"rock crab, Cancer irroratus",
"fiddler crab",
"king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica",
"American lobster, Northern lobster, Maine lobster, Homarus americanus",
"spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish",
"crayfish, crawfish, crawdad, crawdaddy",
"hermit crab",
"isopod",
"white stork, Ciconia ciconia",
"black stork, Ciconia nigra",
"spoonbill",
"flamingo",
"little blue heron, Egretta caerulea",
"American egret, great white heron, Egretta albus",
"bittern",
"crane",
"limpkin, Aramus pictus",
"European gallinule, Porphyrio porphyrio",
"American coot, marsh hen, mud hen, water hen, Fulica americana",
"bustard",
"ruddy turnstone, Arenaria interpres",
"red-backed sandpiper, dunlin, Erolia alpina",
"redshank, Tringa totanus",
"dowitcher",
"oystercatcher, oyster catcher",
"pelican",
"king penguin, Aptenodytes patagonica",
"albatross, mollymawk",
"grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus",
"killer whale, killer, orca, grampus, sea wolf, Orcinus orca",
"dugong, Dugong dugon",
"sea lion",
"Chihuahua",
"Japanese spaniel",
"Maltese dog, Maltese terrier, Maltese",
"Pekinese, Pekingese, Peke",
"Shih-Tzu",
"Blenheim spaniel",
"papillon",
"toy terrier",
"Rhodesian ridgeback",
"Afghan hound, Afghan",
"basset, basset hound",
"beagle",
"bloodhound, sleuthhound",
"bluetick",
"black-and-tan coonhound",
"Walker hound, Walker foxhound",
"English foxhound",
"redbone",
"borzoi, Russian wolfhound",
"Irish wolfhound",
"Italian greyhound",
"whippet",
"Ibizan hound, Ibizan Podenco",
"Norwegian elkhound, elkhound",
"otterhound, otter hound",
"Saluki, gazelle hound",
"Scottish deerhound, deerhound",
"Weimaraner",
"Staffordshire bullterrier, Staffordshire bull terrier",
"American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier",
"Bedlington terrier",
"Border terrier",
"Kerry blue terrier",
"Irish terrier",
"Norfolk terrier",
"Norwich terrier",
"Yorkshire terrier",
"wire-haired fox terrier",
"Lakeland terrier",
"Sealyham terrier, Sealyham",
"Airedale, Airedale terrier",
"cairn, cairn terrier",
"Australian terrier",
"Dandie Dinmont, Dandie Dinmont terrier",
"Boston bull, Boston terrier",
"miniature schnauzer",
"giant schnauzer",
"standard schnauzer",
"Scotch terrier, Scottish terrier, Scottie",
"Tibetan terrier, chrysanthemum dog",
"silky terrier, Sydney silky",
"soft-coated wheaten terrier",
"West Highland white terrier",
"Lhasa, Lhasa apso",
"flat-coated retriever",
"curly-coated retriever",
"golden retriever",
"Labrador retriever",
"Chesapeake Bay retriever",
"German short-haired pointer",
"vizsla, Hungarian pointer",
"English setter",
"Irish setter, red setter",
"Gordon setter",
"Brittany spaniel",
"clumber, clumber spaniel",
"English springer, English springer spaniel",
"Welsh springer spaniel",
"cocker spaniel, English cocker spaniel, cocker",
"Sussex spaniel",
"Irish water spaniel",
"kuvasz",
"schipperke",
"groenendael",
"malinois",
"briard",
"kelpie",
"komondor",
"Old English sheepdog, bobtail",
"Shetland sheepdog, Shetland sheep dog, Shetland",
"collie",
"Border collie",
"Bouvier des Flandres, Bouviers des Flandres",
"Rottweiler",
"German shepherd, German shepherd dog, German police dog, alsatian",
"Doberman, Doberman pinscher",
"miniature pinscher",
"Greater Swiss Mountain dog",
"Bernese mountain dog",
"Appenzeller",
"EntleBucher",
"boxer",
"bull mastiff",
"Tibetan mastiff",
"French bulldog",
"Great Dane",
"Saint Bernard, St Bernard",
"Eskimo dog, husky",
"malamute, malemute, Alaskan malamute",
"Siberian husky",
"dalmatian, coach dog, carriage dog",
"affenpinscher, monkey pinscher, monkey dog",
"basenji",
"pug, pug-dog",
"Leonberg",
"Newfoundland, Newfoundland dog",
"Great Pyrenees",
"Samoyed, Samoyede",
"Pomeranian",
"chow, chow chow",
"keeshond",
"Brabancon griffon",
"Pembroke, Pembroke Welsh corgi",
"Cardigan, Cardigan Welsh corgi",
"toy poodle",
"miniature poodle",
"standard poodle",
"Mexican hairless",
"timber wolf, grey wolf, gray wolf, Canis lupus",
"white wolf, Arctic wolf, Canis lupus tundrarum",
"red wolf, maned wolf, Canis rufus, Canis niger",
"coyote, prairie wolf, brush wolf, Canis latrans",
"dingo, warrigal, warragal, Canis dingo",
"dhole, Cuon alpinus",
"African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus",
"hyena, hyaena",
"red fox, Vulpes vulpes",
"kit fox, Vulpes macrotis",
"Arctic fox, white fox, Alopex lagopus",
"grey fox, gray fox, Urocyon cinereoargenteus",
"tabby, tabby cat",
"tiger cat",
"Persian cat",
"Siamese cat, Siamese",
"Egyptian cat",
"cougar, puma, catamount, mountain lion, painter, panther, Felis concolor",
"lynx, catamount",
"leopard, Panthera pardus",
"snow leopard, ounce, Panthera uncia",
"jaguar, panther, Panthera onca, Felis onca",
"lion, king of beasts, Panthera leo",
"tiger, Panthera tigris",
"cheetah, chetah, Acinonyx jubatus",
"brown bear, bruin, Ursus arctos",
"American black bear, black bear, Ursus americanus, Euarctos americanus",
"ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus",
"sloth bear, Melursus ursinus, Ursus ursinus",
"mongoose",
"meerkat, mierkat",
"tiger beetle",
"ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle",
"ground beetle, carabid beetle",
"long-horned beetle, longicorn, longicorn beetle",
"leaf beetle, chrysomelid",
"dung beetle",
"rhinoceros beetle",
"weevil",
"fly",
"bee",
"ant, emmet, pismire",
"grasshopper, hopper",
"cricket",
"walking stick, walkingstick, stick insect",
"cockroach, roach",
"mantis, mantid",
"cicada, cicala",
"leafhopper",
"lacewing, lacewing fly",
"dragonfly, darning needle, devil\"s darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
"damselfly",
"admiral",
"ringlet, ringlet butterfly",
"monarch, monarch butterfly, milkweed butterfly, Danaus plexippus",
"cabbage butterfly",
"sulphur butterfly, sulfur butterfly",
"lycaenid, lycaenid butterfly",
"starfish, sea star",
"sea urchin",
"sea cucumber, holothurian",
"wood rabbit, cottontail, cottontail rabbit",
"hare",
"Angora, Angora rabbit",
"hamster",
"porcupine, hedgehog",
"fox squirrel, eastern fox squirrel, Sciurus niger",
"marmot",
"beaver",
"guinea pig, Cavia cobaya",
"sorrel",
"zebra",
"hog, pig, grunter, squealer, Sus scrofa",
"wild boar, boar, Sus scrofa",
"warthog",
"hippopotamus, hippo, river horse, Hippopotamus amphibius",
"ox",
"water buffalo, water ox, Asiatic buffalo, Bubalus bubalis",
"bison",
"ram, tup",
"bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis",
"ibex, Capra ibex",
"hartebeest",
"impala, Aepyceros melampus",
"gazelle",
"Arabian camel, dromedary, Camelus dromedarius",
"llama",
"weasel",
"mink",
"polecat, fitch, foulmart, foumart, Mustela putorius",
"black-footed ferret, ferret, Mustela nigripes",
"otter",
"skunk, polecat, wood pussy",
"badger",
"armadillo",
"three-toed sloth, ai, Bradypus tridactylus",
"orangutan, orang, orangutang, Pongo pygmaeus",
"gorilla, Gorilla gorilla",
"chimpanzee, chimp, Pan troglodytes",
"gibbon, Hylobates lar",
"siamang, Hylobates syndactylus, Symphalangus syndactylus",
"guenon, guenon monkey",
"patas, hussar monkey, Erythrocebus patas",
"baboon",
"macaque",
"langur",
"colobus, colobus monkey",
"proboscis monkey, Nasalis larvatus",
"marmoset",
"capuchin, ringtail, Cebus capucinus",
"howler monkey, howler",
"titi, titi monkey",
"spider monkey, Ateles geoffroyi",
"squirrel monkey, Saimiri sciureus",
"Madagascar cat, ring-tailed lemur, Lemur catta",
"indri, indris, Indri indri, Indri brevicaudatus",
"Indian elephant, Elephas maximus",
"African elephant, Loxodonta africana",
"lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens",
"giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca",
"barracouta, snoek",
"eel",
"coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch",
"rock beauty, Holocanthus tricolor",
"anemone fish",
"sturgeon",
"gar, garfish, garpike, billfish, Lepisosteus osseus",
"lionfish",
"puffer, pufferfish, blowfish, globefish",
"abacus",
"abaya",
"academic gown, academic robe, judge\"s robe",
"accordion, piano accordion, squeeze box",
"acoustic guitar",
"aircraft carrier, carrier, flattop, attack aircraft carrier",
"airliner",
"airship, dirigible",
"altar",
"ambulance",
"amphibian, amphibious vehicle",
"analog clock",
"apiary, bee house",
"apron",
"ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin",
"assault rifle, assault gun",
"backpack, back pack, knapsack, packsack, rucksack, haversack",
"bakery, bakeshop, bakehouse",
"balance beam, beam",
"balloon",
"ballpoint, ballpoint pen, ballpen, Biro",
"Band Aid",
"banjo",
"bannister, banister, balustrade, balusters, handrail",
"barbell",
"barber chair",
"barbershop",
"barn",
"barometer",
"barrel, cask",
"barrow, garden cart, lawn cart, wheelbarrow",
"baseball",
"basketball",
"bassinet",
"bassoon",
"bathing cap, swimming cap",
"bath towel",
"bathtub, bathing tub, bath, tub",
"beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon",
"beacon, lighthouse, beacon light, pharos",
"beaker",
"bearskin, busby, shako",
"beer bottle",
"beer glass",
"bell cote, bell cot",
"bib",
"bicycle-built-for-two, tandem bicycle, tandem",
"bikini, two-piece",
"binder, ring-binder",
"binoculars, field glasses, opera glasses",
"birdhouse",
"boathouse",
"bobsled, bobsleigh, bob",
"bolo tie, bolo, bola tie, bola",
"bonnet, poke bonnet",
"bookcase",
"bookshop, bookstore, bookstall",
"bottlecap",
"bow",
"bow tie, bow-tie, bowtie",
"brass, memorial tablet, plaque",
"brassiere, bra, bandeau",
"breakwater, groin, groyne, mole, bulwark, seawall, jetty",
"breastplate, aegis, egis",
"broom",
"bucket, pail",
"buckle",
"bulletproof vest",
"bullet train, bullet",
"butcher shop, meat market",
"cab, hack, taxi, taxicab",
"caldron, cauldron",
"candle, taper, wax light",
"cannon",
"canoe",
"can opener, tin opener",
"cardigan",
"car mirror",
"carousel, carrousel, merry-go-round, roundabout, whirligig",
"carpenter\"s kit, tool kit",
"carton",
"car wheel",
"cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM",
"cassette",
"cassette player",
"castle",
"catamaran",
"CD player",
"cello, violoncello",
"cellular telephone, cellular phone, cellphone, cell, mobile phone",
"chain",
"chainlink fence",
"chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour",
"chain saw, chainsaw",
"chest",
"chiffonier, commode",
"chime, bell, gong",
"china cabinet, china closet",
"Christmas stocking",
"church, church building",
"cinema, movie theater, movie theatre, movie house, picture palace",
"cleaver, meat cleaver, chopper",
"cliff dwelling",
"cloak",
"clog, geta, patten, sabot",
"cocktail shaker",
"coffee mug",
"coffeepot",
"coil, spiral, volute, whorl, helix",
"combination lock",
"computer keyboard, keypad",
"confectionery, confectionary, candy store",
"container ship, containership, container vessel",
"convertible",
"corkscrew, bottle screw",
"cornet, horn, trumpet, trump",
"cowboy boot",
"cowboy hat, ten-gallon hat",
"cradle",
"crane",
"crash helmet",
"crate",
"crib, cot",
"Crock Pot",
"croquet ball",
"crutch",
"cuirass",
"dam, dike, dyke",
"desk",
"desktop computer",
"dial telephone, dial phone",
"diaper, nappy, napkin",
"digital clock",
"digital watch",
"dining table, board",
"dishrag, dishcloth",
"dishwasher, dish washer, dishwashing machine",
"disk brake, disc brake",
"dock, dockage, docking facility",
"dogsled, dog sled, dog sleigh",
"dome",
"doormat, welcome mat",
"drilling platform, offshore rig",
"drum, membranophone, tympan",
"drumstick",
"dumbbell",
"Dutch oven",
"electric fan, blower",
"electric guitar",
"electric locomotive",
"entertainment center",
"envelope",
"espresso maker",
"face powder",
"feather boa, boa",
"file, file cabinet, filing cabinet",
"fireboat",
"fire engine, fire truck",
"fire screen, fireguard",
"flagpole, flagstaff",
"flute, transverse flute",
"folding chair",
"football helmet",
"forklift",
"fountain",
"fountain pen",
"four-poster",
"freight car",
"French horn, horn",
"frying pan, frypan, skillet",
"fur coat",
"garbage truck, dustcart",
"gasmask, respirator, gas helmet",
"gas pump, gasoline pump, petrol pump, island dispenser",
"goblet",
"go-kart",
"golf ball",
"golfcart, golf cart",
"gondola",
"gong, tam-tam",
"gown",
"grand piano, grand",
"greenhouse, nursery, glasshouse",
"grille, radiator grille",
"grocery store, grocery, food market, market",
"guillotine",
"hair slide",
"hair spray",
"half track",
"hammer",
"hamper",
"hand blower, blow dryer, blow drier, hair dryer, hair drier",
"hand-held computer, hand-held microcomputer",
"handkerchief, hankie, hanky, hankey",
"hard disc, hard disk, fixed disk",
"harmonica, mouth organ, harp, mouth harp",
"harp",
"harvester, reaper",
"hatchet",
"holster",
"home theater, home theatre",
"honeycomb",
"hook, claw",
"hoopskirt, crinoline",
"horizontal bar, high bar",
"horse cart, horse-cart",
"hourglass",
"iPod",
"iron, smoothing iron",
"jack-o\"-lantern",
"jean, blue jean, denim",
"jeep, landrover",
"jersey, T-shirt, tee shirt",
"jigsaw puzzle",
"jinrikisha, ricksha, rickshaw",
"joystick",
"kimono",
"knee pad",
"knot",
"lab coat, laboratory coat",
"ladle",
"lampshade, lamp shade",
"laptop, laptop computer",
"lawn mower, mower",
"lens cap, lens cover",
"letter opener, paper knife, paperknife",
"library",
"lifeboat",
"lighter, light, igniter, ignitor",
"limousine, limo",
"liner, ocean liner",
"lipstick, lip rouge",
"Loafer",
"lotion",
"loudspeaker, speaker, speaker unit, loudspeaker system, speaker system",
"loupe, jeweler\"s loupe",
"lumbermill, sawmill",
"magnetic compass",
"mailbag, postbag",
"mailbox, letter box",
"maillot",
"maillot, tank suit",
"manhole cover",
"maraca",
"marimba, xylophone",
"mask",
"matchstick",
"maypole",
"maze, labyrinth",
"measuring cup",
"medicine chest, medicine cabinet",
"megalith, megalithic structure",
"microphone, mike",
"microwave, microwave oven",
"military uniform",
"milk can",
"minibus",
"miniskirt, mini",
"minivan",
"missile",
"mitten",
"mixing bowl",
"mobile home, manufactured home",
"Model T",
"modem",
"monastery",
"monitor",
"moped",
"mortar",
"mortarboard",
"mosque",
"mosquito net",
"motor scooter, scooter",
"mountain bike, all-terrain bike, off-roader",
"mountain tent",
"mouse, computer mouse",
"mousetrap",
"moving van",
"muzzle",
"nail",
"neck brace",
"necklace",
"nipple",
"notebook, notebook computer",
"obelisk",
"oboe, hautboy, hautbois",
"ocarina, sweet potato",
"odometer, hodometer, mileometer, milometer",
"oil filter",
"organ, pipe organ",
"oscilloscope, scope, cathode-ray oscilloscope, CRO",
"overskirt",
"oxcart",
"oxygen mask",
"packet",
"paddle, boat paddle",
"paddlewheel, paddle wheel",
"padlock",
"paintbrush",
"pajama, pyjama, pj\"s, jammies",
"palace",
"panpipe, pandean pipe, syrinx",
"paper towel",
"parachute, chute",
"parallel bars, bars",
"park bench",
"parking meter",
"passenger car, coach, carriage",
"patio, terrace",
"pay-phone, pay-station",
"pedestal, plinth, footstall",
"pencil box, pencil case",
"pencil sharpener",
"perfume, essence",
"Petri dish",
"photocopier",
"pick, plectrum, plectron",
"pickelhaube",
"picket fence, paling",
"pickup, pickup truck",
"pier",
"piggy bank, penny bank",
"pill bottle",
"pillow",
"ping-pong ball",
"pinwheel",
"pirate, pirate ship",
"pitcher, ewer",
"plane, carpenter\"s plane, woodworking plane",
"planetarium",
"plastic bag",
"plate rack",
"plow, plough",
"plunger, plumber\"s helper",
"Polaroid camera, Polaroid Land camera",
"pole",
"police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria",
"poncho",
"pool table, billiard table, snooker table",
"pop bottle, soda bottle",
"pot, flowerpot",
"potter\"s wheel",
"power drill",
"prayer rug, prayer mat",
"printer",
"prison, prison house",
"projectile, missile",
"projector",
"puck, hockey puck",
"punching bag, punch bag, punching ball, punchball",
"purse",
"quill, quill pen",
"quilt, comforter, comfort, puff",
"racer, race car, racing car",
"racket, racquet",
"radiator",
"radio, wireless",
"radio telescope, radio reflector",
"rain barrel",
"recreational vehicle, RV, R.V.",
"reel",
"reflex camera",
"refrigerator, icebox",
"remote control, remote",
"restaurant, eating house, eating place, eatery",
"revolver, six-gun, six-shooter",
"rifle",
"rocking chair, rocker",
"rotisserie",
"rubber eraser, rubber, pencil eraser",
"rugby ball",
"rule, ruler",
"running shoe",
"safe",
"safety pin",
"saltshaker, salt shaker",
"sandal",
"sarong",
"sax, saxophone",
"scabbard",
"scale, weighing machine",
"school bus",
"schooner",
"scoreboard",
"screen, CRT screen",
"screw",
"screwdriver",
"seat belt, seatbelt",
"sewing machine",
"shield, buckler",
"shoe shop, shoe-shop, shoe store",
"shoji",
"shopping basket",
"shopping cart",
"shovel",
"shower cap",
"shower curtain",
"ski",
"ski mask",
"sleeping bag",
"slide rule, slipstick",
"sliding door",
"slot, one-armed bandit",
"snorkel",
"snowmobile",
"snowplow, snowplough",
"soap dispenser",
"soccer ball",
"sock",
"solar dish, solar collector, solar furnace",
"sombrero",
"soup bowl",
"space bar",
"space heater",
"space shuttle",
"spatula",
"speedboat",
"spider web, spider\"s web",
"spindle",
"sports car, sport car",
"spotlight, spot",
"stage",
"steam locomotive",
"steel arch bridge",
"steel drum",
"stethoscope",
"stole",
"stone wall",
"stopwatch, stop watch",
"stove",
"strainer",
"streetcar, tram, tramcar, trolley, trolley car",
"stretcher",
"studio couch, day bed",
"stupa, tope",
"submarine, pigboat, sub, U-boat",
"suit, suit of clothes",
"sundial",
"sunglass",
"sunglasses, dark glasses, shades",
"sunscreen, sunblock, sun blocker",
"suspension bridge",
"swab, swob, mop",
"sweatshirt",
"swimming trunks, bathing trunks",
"swing",
"switch, electric switch, electrical switch",
"syringe",
"table lamp",
"tank, army tank, armored combat vehicle, armoured combat vehicle",
"tape player",
"teapot",
"teddy, teddy bear",
"television, television system",
"tennis ball",
"thatch, thatched roof",
"theater curtain, theatre curtain",
"thimble",
"thresher, thrasher, threshing machine",
"throne",
"tile roof",
"toaster",
"tobacco shop, tobacconist shop, tobacconist",
"toilet seat",
"torch",
"totem pole",
"tow truck, tow car, wrecker",
"toyshop",
"tractor",
"trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi",
"tray",
"trench coat",
"tricycle, trike, velocipede",
"trimaran",
"tripod",
"triumphal arch",
"trolleybus, trolley coach, trackless trolley",
"trombone",
"tub, vat",
"turnstile",
"typewriter keyboard",
"umbrella",
"unicycle, monocycle",
"upright, upright piano",
"vacuum, vacuum cleaner",
"vase",
"vault",
"velvet",
"vending machine",
"vestment",
"viaduct",
"violin, fiddle",
"volleyball",
"waffle iron",
"wall clock",
"wallet, billfold, notecase, pocketbook",
"wardrobe, closet, press",
"warplane, military plane",
"washbasin, handbasin, washbowl, lavabo, wash-hand basin",
"washer, automatic washer, washing machine",
"water bottle",
"water jug",
"water tower",
"whiskey jug",
"whistle",
"wig",
"window screen",
"window shade",
"Windsor tie",
"wine bottle",
"wing",
"wok",
"wooden spoon",
"wool, woolen, woollen",
"worm fence, snake fence, snake-rail fence, Virginia fence",
"wreck",
"yawl",
"yurt",
"web site, website, internet site, site",
"comic book",
"crossword puzzle, crossword",
"street sign",
"traffic light, traffic signal, stoplight",
"book jacket, dust cover, dust jacket, dust wrapper",
"menu",
"plate",
"guacamole",
"consomme",
"hot pot, hotpot",
"trifle",
"ice cream, icecream",
"ice lolly, lolly, lollipop, popsicle",
"French loaf",
"bagel, beigel",
"pretzel",
"cheeseburger",
"hotdog, hot dog, red hot",
"mashed potato",
"head cabbage",
"broccoli",
"cauliflower",
"zucchini, courgette",
"spaghetti squash",
"acorn squash",
"butternut squash",
"cucumber, cuke",
"artichoke, globe artichoke",
"bell pepper",
"cardoon",
"mushroom",
"Granny Smith",
"strawberry",
"orange",
"lemon",
"fig",
"pineapple, ananas",
"banana",
"jackfruit, jak, jack",
"custard apple",
"pomegranate",
"hay",
"carbonara",
"chocolate sauce, chocolate syrup",
"dough",
"meat loaf, meatloaf",
"pizza, pizza pie",
"potpie",
"burrito",
"red wine",
"espresso",
"cup",
"eggnog",
"alp",
"bubble",
"cliff, drop, drop-off",
"coral reef",
"geyser",
"lakeside, lakeshore",
"promontory, headland, head, foreland",
"sandbar, sand bar",
"seashore, coast, seacoast, sea-coast",
"valley, vale",
"volcano",
"ballplayer, baseball player",
"groom, bridegroom",
"scuba diver",
"rapeseed",
"daisy",
"yellow lady\"s slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum",
"corn",
"acorn",
"hip, rose hip, rosehip",
"buckeye, horse chestnut, conker",
"coral fungus",
"agaric",
"gyromitra",
"stinkhorn, carrion fungus",
"earthstar",
"hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa",
"bolete",
"ear, spike, capitulum",
"toilet tissue, toilet paper, bathroom tissue"
];
================================================
FILE: openvino-mobilenet-image/rust/src/main.rs
================================================
use image::io::Reader;
use image::DynamicImage;
use std::env;
use wasi_nn;
mod imagenet_classes;
use wasi_nn::{ExecutionTarget, GraphBuilder, GraphEncoding, TensorType};
pub fn main() -> Result<(), Box<dyn std::error::Error>> {
let args: Vec<String> = env::args().collect();
let model_xml_path: &str = &args[1];
let model_bin_path: &str = &args[2];
let image_name: &str = &args[3];
print!("Load graph ...");
let graph = GraphBuilder::new(GraphEncoding::Openvino, ExecutionTarget::CPU)
.build_from_files([model_xml_path, model_bin_path])?;
println!("done");
print!("Init execution context ...");
let mut context = graph.init_execution_context()?;
println!("done");
print!("Set input tensor ...");
let input_dims = vec![1, 3, 224, 224];
let tensor_data = image_to_tensor(image_name.to_string(), 224, 224);
context.set_input(0, TensorType::F32, &input_dims, tensor_data)?;
println!("done");
print!("Perform graph inference ...");
context.compute()?;
println!("done");
print!("Retrieve the output ...");
// Copy output to abuffer.
let mut output_buffer = vec![0f32; 1001];
let size_in_bytes = context.get_output(0, &mut output_buffer)?;
println!("done");
println!("The size of the output buffer is {} bytes", size_in_bytes);
let results = sort_results(&output_buffer);
for i in 0..5 {
println!(
" {}.) [{}]({:.4}){}",
i + 1,
results[i].0,
results[i].1,
imagenet_classes::IMAGENET_CLASSES[results[i].0]
);
}
Ok(())
}
// Sort the buffer of probabilities. The graph places the match probability for each class at the
// index for that class (e.g. the probability of class 42 is placed at buffer[42]). Here we convert
// to a wrapping InferenceResult and sort the results.
fn sort_results(buffer: &[f32]) -> Vec<InferenceResult> {
let mut results: Vec<InferenceResult> = buffer
.iter()
.skip(1)
.enumerate()
.map(|(c, p)| InferenceResult(c, *p))
.collect();
results.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());
results
}
// Take the image located at 'path', open it, resize it to height x width, and then converts
// the pixel precision to FP32. The resulting BGR pixel vector is then returned.
fn image_to_tensor(path: String, height: u32, width: u32) -> Vec<u8> {
let pixels = Reader::open(path).unwrap().decode().unwrap();
let dyn_img: DynamicImage = pixels.resize_exact(width, height, image::imageops::Triangle);
let bgr_img = dyn_img.to_bgr8();
// Get an array of the pixel values
let raw_u8_arr: &[u8] = &bgr_img.as_raw()[..];
// Transpose from [height, width, 3] to [3, height, width]
let mut transposed: Vec<u8> = vec![0; raw_u8_arr.len()];
for ch in 0..3 {
for y in 0..height {
for x in 0..width {
let loc = y * height + x;
transposed[(ch * width * height + loc) as usize] =
raw_u8_arr[(loc * 3 + ch) as usize];
}
}
}
// Create an array to hold the f32 value of those pixels
let bytes_required = transposed.len() * 4;
let mut u8_f32_arr: Vec<u8> = vec![0; bytes_required];
for i in 0..transposed.len() {
// Read the number as a f32 and break it into u8 bytes
let u8_f32: f32 = transposed[i] as f32;
let u8_bytes = u8_f32.to_ne_bytes();
for j in 0..4 {
u8_f32_arr[(i * 4) + j] = u8_bytes[j];
}
}
return u8_f32_arr;
}
// A wrapper for class ID and match probabilities.
#[derive(Debug, PartialEq)]
struct InferenceResult(usize, f32);
================================================
FILE: openvino-mobilenet-raw/README.md
================================================
# Mobilenet example with WasmEdge WASI-NN OpenVINO plugin
This example demonstrates how to use WasmEdge WASI-NN OpenVINO plugin to perform an inference task with Mobilenet model.
## Set up the environment
- Install `rustup` and `Rust`
Go to the [official Rust webpage](https://www.rust-lang.org/tools/install) and follow the instructions to install `rustup` and `Rust`.
> It is recommended to use Rust 1.68 or above in the stable channel.
Then, add `wasm32-wasip1` target to the Rustup toolchain:
```bash
rustup target add wasm32-wasip1
```
- Clone the example repo
```bash
git clone https://github.com/second-state/WasmEdge-WASINN-examples.git
```
- Install OpenVINO
Please refer to [WasmEdge Docs](https://wasmedge.org/docs/contribute/source/plugin/wasi_nn) and [OpenVINO™](https://docs.openvino.ai/2023.0/openvino_docs_install_guides_installing_openvino_apt.html)(2023) for the installation process.
```bash
bash WasmEdge-WASINN-examples/scripts/install_openvino.sh
ldconfig
```
- Install WasmEdge with Wasi-NN OpenVINO plugin
```bash
export CMAKE_BUILD_TYPE=Release
export VERSION=0.13.2
curl -sSf https://raw.githubusercontent.com/WasmEdge/WasmEdge/master/utils/install.sh | bash -s -- -v $VERSION -p /usr/local --plugins wasi_nn-openvino
ldconfig
```
## Build and run `openvino-mobilenet-raw` example
- Download `MobileNet` model file
```bash
cd openvino-mobilenet-raw
bash download_mobilenet.sh
```
- Build and run the example
```bash
cd rust
cargo build --target wasm32-wasip1 --release
cd ..
wasmedge --dir .:. ./rust/target/wasm32-wasip1/release/wasmedge-wasinn-example-mobilenet-image.wasm mobilenet.xml mobilenet.bin input.jpg
```
If the commands above run successfully, you will get the output:
```bash
Read graph XML, size in bytes: 143525
Read graph weights, size in bytes: 13956476
Loaded graph into wasi-nn with ID: 0
Created wasi-nn execution context with ID: 0
Read input tensor, size in bytes: 602112
Executed graph inference
1.) [963](0.7113)pizza, pizza pie
2.) [762](0.0707)restaurant, eating house, eating place, eatery
3.) [909](0.0364)wok
4.) [926](0.0155)hot pot, hotpot
5.) [567](0.0153)frying pan, frypan, skillet
```
================================================
FILE: openvino-mobilenet-raw/download_mobilenet.sh
================================================
FIXTURE=https://github.com/intel/openvino-rs/raw/v0.3.3/crates/openvino/tests/fixtures/mobilenet
TODIR=$1
if [ ! -f $TODIR/mobilenet.bin ]; then
wget --no-clobber --directory-prefix=$TODIR $FIXTURE/mobilenet.bin
fi
if [ ! -f $TODIR/mobilenet.xml ]; then
wget --no-clobber --directory-prefix=$TODIR $FIXTURE/mobilenet.xml
fi
if [ ! -f $TODIR/tensor-1x224x224x3-f32.bgr ]; then
wget --no-clobber --directory-prefix=$TODIR $FIXTURE/tensor-1x224x224x3-f32.bgr
fi
================================================
FILE: openvino-mobilenet-raw/rust/Cargo.toml
================================================
[package]
name = "wasmedge-wasinn-example-mobilenet"
version = "0.1.0"
authors = ["Second-State"]
readme = "README.md"
edition = "2021"
publish = false
[dependencies]
wasi-nn = { version = "0.6.0" }
[workspace]
================================================
FILE: openvino-mobilenet-raw/rust/src/imagenet_classes.rs
================================================
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/* The code in this file is adapted from https://github.com/tensorflow/tfjs-models/blob/master/mobilenet/src/imagenet_classes.ts */
pub const IMAGENET_CLASSES: [&str; 1000] = [
"tench, Tinca tinca",
"goldfish, Carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias",
"tiger shark, Galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, Struthio camelus",
"brambling, Fringilla montifringilla",
"goldfinch, Carduelis carduelis",
"house finch, linnet, Carpodacus mexicanus",
"junco, snowbird",
"indigo bunting, indigo finch, indigo bird, Passerina cyanea",
"robin, American robin, Turdus migratorius",
"bulbul",
"jay",
"magpie",
"chickadee",
"water ouzel, dipper",
"kite",
"bald eagle, American eagle, Haliaeetus leucocephalus",
"vulture",
"great grey owl, great gray owl, Strix nebulosa",
"European fire salamander, Salamandra salamandra",
"common newt, Triturus vulgaris",
"eft",
"spotted salamander, Ambystoma maculatum",
"axolotl, mud puppy, Ambystoma mexicanum",
"bullfrog, Rana catesbeiana",
"tree frog, tree-frog",
"tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui",
"loggerhead, loggerhead turtle, Caretta caretta",
"leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea",
"mud turtle",
"terrapin",
"box turtle, box tortoise",
"banded gecko",
"common iguana, iguana, Iguana iguana",
"American chameleon, anole, Anolis carolinensis",
"whiptail, whiptail lizard",
"agama",
"frilled lizard, Chlamydosaurus kingi",
"alligator lizard",
"Gila monster, Heloderma suspectum",
"green lizard, Lacerta viridis",
"African chameleon, Chamaeleo chamaeleon",
"Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis",
"African crocodile, Nile crocodile, Crocodylus niloticus",
"American alligator, Alligator mississipiensis",
"triceratops",
"thunder snake, worm snake, Carphophis amoenus",
"ringneck snake, ring-necked snake, ring snake",
"hognose snake, puff adder, sand viper",
"green snake, grass snake",
"king snake, kingsnake",
"garter snake, grass snake",
"water snake",
"vine snake",
"night snake, Hypsiglena torquata",
"boa constrictor, Constrictor constrictor",
"rock python, rock snake, Python sebae",
"Indian cobra, Naja naja",
"green mamba",
"sea snake",
"horned viper, cerastes, sand viper, horned asp, Cerastes cornutus",
"diamondback, diamondback rattlesnake, Crotalus adamanteus",
"sidewinder, horned rattlesnake, Crotalus cerastes",
"trilobite",
"harvestman, daddy longlegs, Phalangium opilio",
"scorpion",
"black and gold garden spider, Argiope aurantia",
"barn spider, Araneus cavaticus",
"garden spider, Aranea diademata",
"black widow, Latrodectus mactans",
"tarantula",
"wolf spider, hunting spider",
"tick",
"centipede",
"black grouse",
"ptarmigan",
"ruffed grouse, partridge, Bonasa umbellus",
"prairie chicken, prairie grouse, prairie fowl",
"peacock",
"quail",
"partridge",
"African grey, African gray, Psittacus erithacus",
"macaw",
"sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita",
"lorikeet",
"coucal",
"bee eater",
"hornbill",
"hummingbird",
"jacamar",
"toucan",
"drake",
"red-breasted merganser, Mergus serrator",
"goose",
"black swan, Cygnus atratus",
"tusker",
"echidna, spiny anteater, anteater",
"platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus",
"wallaby, brush kangaroo",
"koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus",
"wombat",
"jelly fish",
"sea anemone, anemone",
"brain coral",
"flatworm, platyhelminth",
"nematode, nematode worm, roundworm",
"conch",
"snail",
"slug",
"sea slug, nudibranch",
"chiton, coat-of-mail shell, sea cradle, polyplacophore",
"chambered nautilus, pearly nautilus, nautilus",
"Dungeness crab, Cancer magister",
"rock crab, Cancer irroratus",
"fiddler crab",
"king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica",
"American lobster, Northern lobster, Maine lobster, Homarus americanus",
"spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish",
"crayfish, crawfish, crawdad, crawdaddy",
"hermit crab",
"isopod",
"white stork, Ciconia ciconia",
"black stork, Ciconia nigra",
"spoonbill",
"flamingo",
"little blue heron, Egretta caerulea",
"American egret, great white heron, Egretta albus",
"bittern",
"crane",
"limpkin, Aramus pictus",
"European gallinule, Porphyrio porphyrio",
"American coot, marsh hen, mud hen, water hen, Fulica americana",
"bustard",
"ruddy turnstone, Arenaria interpres",
"red-backed sandpiper, dunlin, Erolia alpina",
"redshank, Tringa totanus",
"dowitcher",
"oystercatcher, oyster catcher",
"pelican",
"king penguin, Aptenodytes patagonica",
"albatross, mollymawk",
"grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus",
"killer whale, killer, orca, grampus, sea wolf, Orcinus orca",
"dugong, Dugong dugon",
"sea lion",
"Chihuahua",
"Japanese spaniel",
"Maltese dog, Maltese terrier, Maltese",
"Pekinese, Pekingese, Peke",
"Shih-Tzu",
"Blenheim spaniel",
"papillon",
"toy terrier",
"Rhodesian ridgeback",
"Afghan hound, Afghan",
"basset, basset hound",
"beagle",
"bloodhound, sleuthhound",
"bluetick",
"black-and-tan coonhound",
"Walker hound, Walker foxhound",
"English foxhound",
"redbone",
"borzoi, Russian wolfhound",
"Irish wolfhound",
"Italian greyhound",
"whippet",
"Ibizan hound, Ibizan Podenco",
"Norwegian elkhound, elkhound",
"otterhound, otter hound",
"Saluki, gazelle hound",
"Scottish deerhound, deerhound",
"Weimaraner",
"Staffordshire bullterrier, Staffordshire bull terrier",
"American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier",
"Bedlington terrier",
"Border terrier",
"Kerry blue terrier",
"Irish terrier",
"Norfolk terrier",
"Norwich terrier",
"Yorkshire terrier",
"wire-haired fox terrier",
"Lakeland terrier",
"Sealyham terrier, Sealyham",
"Airedale, Airedale terrier",
"cairn, cairn terrier",
"Australian terrier",
"Dandie Dinmont, Dandie Dinmont terrier",
"Boston bull, Boston terrier",
"miniature schnauzer",
"giant schnauzer",
"standard schnauzer",
"Scotch terrier, Scottish terrier, Scottie",
"Tibetan terrier, chrysanthemum dog",
"silky terrier, Sydney silky",
"soft-coated wheaten terrier",
"West Highland white terrier",
"Lhasa, Lhasa apso",
"flat-coated retriever",
"curly-coated retriever",
"golden retriever",
"Labrador retriever",
"Chesapeake Bay retriever",
"German short-haired pointer",
"vizsla, Hungarian pointer",
"English setter",
"Irish setter, red setter",
"Gordon setter",
"Brittany spaniel",
"clumber, clumber spaniel",
"English springer, English springer spaniel",
"Welsh springer spaniel",
"cocker spaniel, English cocker spaniel, cocker",
"Sussex spaniel",
"Irish water spaniel",
"kuvasz",
"schipperke",
"groenendael",
"malinois",
"briard",
"kelpie",
"komondor",
"Old English sheepdog, bobtail",
"Shetland sheepdog, Shetland sheep dog, Shetland",
"collie",
"Border collie",
"Bouvier des Flandres, Bouviers des Flandres",
"Rottweiler",
"German shepherd, German shepherd dog, German police dog, alsatian",
"Doberman, Doberman pinscher",
"miniature pinscher",
"Greater Swiss Mountain dog",
"Bernese mountain dog",
"Appenzeller",
"EntleBucher",
"boxer",
"bull mastiff",
"Tibetan mastiff",
"French bulldog",
"Great Dane",
"Saint Bernard, St Bernard",
"Eskimo dog, husky",
"malamute, malemute, Alaskan malamute",
"Siberian husky",
"dalmatian, coach dog, carriage dog",
"affenpinscher, monkey pinscher, monkey dog",
"basenji",
"pug, pug-dog",
"Leonberg",
"Newfoundland, Newfoundland dog",
"Great Pyrenees",
"Samoyed, Samoyede",
"Pomeranian",
"chow, chow chow",
"keeshond",
"Brabancon griffon",
"Pembroke, Pembroke Welsh corgi",
"Cardigan, Cardigan Welsh corgi",
"toy poodle",
"miniature poodle",
"standard poodle",
"Mexican hairless",
"timber wolf, grey wolf, gray wolf, Canis lupus",
"white wolf, Arctic wolf, Canis lupus tundrarum",
"red wolf, maned wolf, Canis rufus, Canis niger",
"coyote, prairie wolf, brush wolf, Canis latrans",
"dingo, warrigal, warragal, Canis dingo",
"dhole, Cuon alpinus",
"African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus",
"hyena, hyaena",
"red fox, Vulpes vulpes",
"kit fox, Vulpes macrotis",
"Arctic fox, white fox, Alopex lagopus",
"grey fox, gray fox, Urocyon cinereoargenteus",
"tabby, tabby cat",
"tiger cat",
"Persian cat",
"Siamese cat, Siamese",
"Egyptian cat",
"cougar, puma, catamount, mountain lion, painter, panther, Felis concolor",
"lynx, catamount",
"leopard, Panthera pardus",
"snow leopard, ounce, Panthera uncia",
"jaguar, panther, Panthera onca, Felis onca",
"lion, king of beasts, Panthera leo",
"tiger, Panthera tigris",
"cheetah, chetah, Acinonyx jubatus",
"brown bear, bruin, Ursus arctos",
"American black bear, black bear, Ursus americanus, Euarctos americanus",
"ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus",
"sloth bear, Melursus ursinus, Ursus ursinus",
"mongoose",
"meerkat, mierkat",
"tiger beetle",
"ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle",
"ground beetle, carabid beetle",
"long-horned beetle, longicorn, longicorn beetle",
"leaf beetle, chrysomelid",
"dung beetle",
"rhinoceros beetle",
"weevil",
"fly",
"bee",
"ant, emmet, pismire",
"grasshopper, hopper",
"cricket",
"walking stick, walkingstick, stick insect",
"cockroach, roach",
"mantis, mantid",
"cicada, cicala",
"leafhopper",
"lacewing, lacewing fly",
"dragonfly, darning needle, devil\"s darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
"damselfly",
"admiral",
"ringlet, ringlet butterfly",
"monarch, monarch butterfly, milkweed butterfly, Danaus plexippus",
"cabbage butterfly",
"sulphur butterfly, sulfur butterfly",
"lycaenid, lycaenid butterfly",
"starfish, sea star",
"sea urchin",
"sea cucumber, holothurian",
"wood rabbit, cottontail, cottontail rabbit",
"hare",
"Angora, Angora rabbit",
"hamster",
"porcupine, hedgehog",
"fox squirrel, eastern fox squirrel, Sciurus niger",
"marmot",
"beaver",
"guinea pig, Cavia cobaya",
"sorrel",
"zebra",
"hog, pig, grunter, squealer, Sus scrofa",
"wild boar, boar, Sus scrofa",
"warthog",
"hippopotamus, hippo, river horse, Hippopotamus amphibius",
"ox",
"water buffalo, water ox, Asiatic buffalo, Bubalus bubalis",
"bison",
"ram, tup",
"bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis",
"ibex, Capra ibex",
"hartebeest",
"impala, Aepyceros melampus",
"gazelle",
"Arabian camel, dromedary, Camelus dromedarius",
"llama",
"weasel",
"mink",
"polecat, fitch, foulmart, foumart, Mustela putorius",
"black-footed ferret, ferret, Mustela nigripes",
"otter",
"skunk, polecat, wood pussy",
"badger",
"armadillo",
"three-toed sloth, ai, Bradypus tridactylus",
"orangutan, orang, orangutang, Pongo pygmaeus",
"gorilla, Gorilla gorilla",
"chimpanzee, chimp, Pan troglodytes",
"gibbon, Hylobates lar",
"siamang, Hylobates syndactylus, Symphalangus syndactylus",
"guenon, guenon monkey",
"patas, hussar monkey, Erythrocebus patas",
"baboon",
"macaque",
"langur",
"colobus, colobus monkey",
"proboscis monkey, Nasalis larvatus",
"marmoset",
"capuchin, ringtail, Cebus capucinus",
"howler monkey, howler",
"titi, titi monkey",
"spider monkey, Ateles geoffroyi",
"squirrel monkey, Saimiri sciureus",
"Madagascar cat, ring-tailed lemur, Lemur catta",
"indri, indris, Indri indri, Indri brevicaudatus",
"Indian elephant, Elephas maximus",
"African elephant, Loxodonta africana",
"lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens",
"giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca",
"barracouta, snoek",
"eel",
"coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch",
"rock beauty, Holocanthus tricolor",
"anemone fish",
"sturgeon",
"gar, garfish, garpike, billfish, Lepisosteus osseus",
"lionfish",
"puffer, pufferfish, blowfish, globefish",
"abacus",
"abaya",
"academic gown, academic robe, judge\"s robe",
"accordion, piano accordion, squeeze box",
"acoustic guitar",
"aircraft carrier, carrier, flattop, attack aircraft carrier",
"airliner",
"airship, dirigible",
"altar",
"ambulance",
"amphibian, amphibious vehicle",
"analog clock",
"apiary, bee house",
"apron",
"ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin",
"assault rifle, assault gun",
"backpack, back pack, knapsack, packsack, rucksack, haversack",
"bakery, bakeshop, bakehouse",
"balance beam, beam",
"balloon",
"ballpoint, ballpoint pen, ballpen, Biro",
"Band Aid",
"banjo",
"bannister, banister, balustrade, balusters, handrail",
"barbell",
"barber chair",
"barbershop",
"barn",
"barometer",
"barrel, cask",
"barrow, garden cart, lawn cart, wheelbarrow",
"baseball",
"basketball",
"bassinet",
"bassoon",
"bathing cap, swimming cap",
"bath towel",
"bathtub, bathing tub, bath, tub",
"beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon",
"beacon, lighthouse, beacon light, pharos",
"beaker",
"bearskin, busby, shako",
"beer bottle",
"beer glass",
"bell cote, bell cot",
"bib",
"bicycle-built-for-two, tandem bicycle, tandem",
"bikini, two-piece",
"binder, ring-binder",
"binoculars, field glasses, opera glasses",
"birdhouse",
"boathouse",
"bobsled, bobsleigh, bob",
"bolo tie, bolo, bola tie, bola",
"bonnet, poke bonnet",
"bookcase",
"bookshop, bookstore, bookstall",
"bottlecap",
"bow",
"bow tie, bow-tie, bowtie",
"brass, memorial tablet, plaque",
"brassiere, bra, bandeau",
"breakwater, groin, groyne, mole, bulwark, seawall, jetty",
"breastplate, aegis, egis",
"broom",
"bucket, pail",
"buckle",
"bulletproof vest",
"bullet train, bullet",
"butcher shop, meat market",
"cab, hack, taxi, taxicab",
"caldron, cauldron",
"candle, taper, wax light",
"cannon",
"canoe",
"can opener, tin opener",
"cardigan",
"car mirror",
"carousel, carrousel, merry-go-round, roundabout, whirligig",
"carpenter\"s kit, tool kit",
"carton",
"car wheel",
"cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM",
"cassette",
"cassette player",
"castle",
"catamaran",
"CD player",
"cello, violoncello",
"cellular telephone, cellular phone, cellphone, cell, mobile phone",
"chain",
"chainlink fence",
"chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour",
"chain saw, chainsaw",
"chest",
"chiffonier, commode",
"chime, bell, gong",
"china cabinet, china closet",
"Christmas stocking",
"church, church building",
"cinema, movie theater, movie theatre, movie house, picture palace",
"cleaver, meat cleaver, chopper",
"cliff dwelling",
"cloak",
"clog, geta, patten, sabot",
"cocktail shaker",
"coffee mug",
"coffeepot",
"coil, spiral, volute, whorl, helix",
"combination lock",
"computer keyboard, keypad",
"confectionery, confectionary, candy store",
"container ship, containership, container vessel",
"convertible",
"corkscrew, bottle screw",
"cornet, horn, trumpet, trump",
"cowboy boot",
"cowboy hat, ten-gallon hat",
"cradle",
"crane",
"crash helmet",
"crate",
"crib, cot",
"Crock Pot",
"croquet ball",
"crutch",
"cuirass",
"dam, dike, dyke",
"desk",
"desktop computer",
"dial telephone, dial phone",
"diaper, nappy, napkin",
"digital clock",
"digital watch",
"dining table, board",
"dishrag, dishcloth",
"dishwasher, dish washer, dishwashing machine",
"disk brake, disc brake",
"dock, dockage, docking facility",
"dogsled, dog sled, dog sleigh",
"dome",
"doormat, welcome mat",
"drilling platform, offshore rig",
"drum, membranophone, tympan",
"drumstick",
"dumbbell",
"Dutch oven",
"electric fan, blower",
"electric guitar",
"electric locomotive",
"entertainment center",
"envelope",
"espresso maker",
"face powder",
"feather boa, boa",
"file, file cabinet, filing cabinet",
"fireboat",
"fire engine, fire truck",
"fire screen, fireguard",
"flagpole, flagstaff",
"flute, transverse flute",
"folding chair",
"football helmet",
"forklift",
"fountain",
"fountain pen",
"four-poster",
"freight car",
"French horn, horn",
"frying pan, frypan, skillet",
"fur coat",
"garbage truck, dustcart",
"gasmask, respirator, gas helmet",
"gas pump, gasoline pump, petrol pump, island dispenser",
"goblet",
"go-kart",
"golf ball",
"golfcart, golf cart",
"gondola",
"gong, tam-tam",
"gown",
"grand piano, grand",
"greenhouse, nursery, glasshouse",
"grille, radiator grille",
"grocery store, grocery, food market, market",
"guillotine",
"hair slide",
"hair spray",
"half track",
"hammer",
"hamper",
"hand blower, blow dryer, blow drier, hair dryer, hair drier",
"hand-held computer, hand-held microcomputer",
"handkerchief, hankie, hanky, hankey",
"hard disc, hard disk, fixed disk",
"harmonica, mouth organ, harp, mouth harp",
"harp",
"harvester, reaper",
"hatchet",
"holster",
"home theater, home theatre",
"honeycomb",
"hook, claw",
"hoopskirt, crinoline",
"horizontal bar, high bar",
"horse cart, horse-cart",
"hourglass",
"iPod",
"iron, smoothing iron",
"jack-o\"-lantern",
"jean, blue jean, denim",
"jeep, landrover",
"jersey, T-shirt, tee shirt",
"jigsaw puzzle",
"jinrikisha, ricksha, rickshaw",
"joystick",
"kimono",
"knee pad",
"knot",
"lab coat, laboratory coat",
"ladle",
"lampshade, lamp shade",
"laptop, laptop computer",
"lawn mower, mower",
"lens cap, lens cover",
"letter opener, paper knife, paperknife",
"library",
"lifeboat",
"lighter, light, igniter, ignitor",
"limousine, limo",
"liner, ocean liner",
"lipstick, lip rouge",
"Loafer",
"lotion",
"loudspeaker, speaker, speaker unit, loudspeaker system, speaker system",
"loupe, jeweler\"s loupe",
"lumbermill, sawmill",
"magnetic compass",
"mailbag, postbag",
"mailbox, letter box",
"maillot",
"maillot, tank suit",
"manhole cover",
"maraca",
"marimba, xylophone",
"mask",
"matchstick",
"maypole",
"maze, labyrinth",
"measuring cup",
"medicine chest, medicine cabinet",
"megalith, megalithic structure",
"microphone, mike",
"microwave, microwave oven",
"military uniform",
"milk can",
"minibus",
"miniskirt, mini",
"minivan",
"missile",
"mitten",
"mixing bowl",
"mobile home, manufactured home",
"Model T",
"modem",
"monastery",
"monitor",
"moped",
"mortar",
"mortarboard",
"mosque",
"mosquito net",
"motor scooter, scooter",
"mountain bike, all-terrain bike, off-roader",
"mountain tent",
"mouse, computer mouse",
"mousetrap",
"moving van",
"muzzle",
"nail",
"neck brace",
"necklace",
"nipple",
"notebook, notebook computer",
"obelisk",
"oboe, hautboy, hautbois",
"ocarina, sweet potato",
"odometer, hodometer, mileometer, milometer",
"oil filter",
"organ, pipe organ",
"oscilloscope, scope, cathode-ray oscilloscope, CRO",
"overskirt",
"oxcart",
"oxygen mask",
"packet",
"paddle, boat paddle",
"paddlewheel, paddle wheel",
"padlock",
"paintbrush",
"pajama, pyjama, pj\"s, jammies",
"palace",
"panpipe, pandean pipe, syrinx",
"paper towel",
"parachute, chute",
"parallel bars, bars",
"park bench",
"parking meter",
"passenger car, coach, carriage",
"patio, terrace",
"pay-phone, pay-station",
"pedestal, plinth, footstall",
"pencil box, pencil case",
"pencil sharpener",
"perfume, essence",
"Petri dish",
"photocopier",
"pick, plectrum, plectron",
"pickelhaube",
"picket fence, paling",
"pickup, pickup truck",
"pier",
"piggy bank, penny bank",
"pill bottle",
"pillow",
"ping-pong ball",
"pinwheel",
"pirate, pirate ship",
"pitcher, ewer",
"plane, carpenter\"s plane, woodworking plane",
"planetarium",
"plastic bag",
"plate rack",
"plow, plough",
"plunger, plumber\"s helper",
"Polaroid camera, Polaroid Land camera",
"pole",
"police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria",
"poncho",
"pool table, billiard table, snooker table",
"pop bottle, soda bottle",
"pot, flowerpot",
"potter\"s wheel",
"power drill",
"prayer rug, prayer mat",
"printer",
"prison, prison house",
"projectile, missile",
"projector",
"puck, hockey puck",
"punching bag, punch bag, punching ball, punchball",
"purse",
"quill, quill pen",
"quilt, comforter, comfort, puff",
"racer, race car, racing car",
"racket, racquet",
"radiator",
"radio, wireless",
"radio telescope, radio reflector",
"rain barrel",
"recreational vehicle, RV, R.V.",
"reel",
"reflex camera",
"refrigerator, icebox",
"remote control, remote",
"restaurant, eating house, eating place, eatery",
"revolver, six-gun, six-shooter",
"rifle",
"rocking chair, rocker",
"rotisserie",
"rubber eraser, rubber, pencil eraser",
"rugby ball",
"rule, ruler",
"running shoe",
"safe",
"safety pin",
"saltshaker, salt shaker",
"sandal",
"sarong",
"sax, saxophone",
"scabbard",
"scale, weighing machine",
"school bus",
"schooner",
"scoreboard",
"screen, CRT screen",
"screw",
"screwdriver",
"seat belt, seatbelt",
"sewing machine",
"shield, buckler",
"shoe shop, shoe-shop, shoe store",
"shoji",
"shopping basket",
"shopping cart",
"shovel",
"shower cap",
"shower curtain",
"ski",
"ski mask",
"sleeping bag",
"slide rule, slipstick",
"sliding door",
"slot, one-armed bandit",
"snorkel",
"snowmobile",
"snowplow, snowplough",
"soap dispenser",
"soccer ball",
"sock",
"solar dish, solar collector, solar furnace",
"sombrero",
"soup bowl",
"space bar",
"space heater",
"space shuttle",
"spatula",
"speedboat",
"spider web, spider\"s web",
"spindle",
"sports car, sport car",
"spotlight, spot",
"stage",
"steam locomotive",
"steel arch bridge",
"steel drum",
"stethoscope",
"stole",
"stone wall",
"stopwatch, stop watch",
"stove",
"strainer",
"streetcar, tram, tramcar, trolley, trolley car",
"stretcher",
"studio couch, day bed",
"stupa, tope",
"submarine, pigboat, sub, U-boat",
"suit, suit of clothes",
"sundial",
"sunglass",
"sunglasses, dark glasses, shades",
"sunscreen, sunblock, sun blocker",
"suspension bridge",
"swab, swob, mop",
"sweatshirt",
"swimming trunks, bathing trunks",
"swing",
"switch, electric switch, electrical switch",
"syringe",
"table lamp",
"tank, army tank, armored combat vehicle, armoured combat vehicle",
"tape player",
"teapot",
"teddy, teddy bear",
"television, television system",
"tennis ball",
"thatch, thatched roof",
"theater curtain, theatre curtain",
"thimble",
"thresher, thrasher, threshing machine",
"throne",
"tile roof",
"toaster",
"tobacco shop, tobacconist shop, tobacconist",
"toilet seat",
"torch",
"totem pole",
"tow truck, tow car, wrecker",
"toyshop",
"tractor",
"trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi",
"tray",
"trench coat",
"tricycle, trike, velocipede",
"trimaran",
"tripod",
"triumphal arch",
"trolleybus, trolley coach, trackless trolley",
"trombone",
"tub, vat",
"turnstile",
"typewriter keyboard",
"umbrella",
"unicycle, monocycle",
"upright, upright piano",
"vacuum, vacuum cleaner",
"vase",
"vault",
"velvet",
"vending machine",
"vestment",
"viaduct",
"violin, fiddle",
"volleyball",
"waffle iron",
"wall clock",
"wallet, billfold, notecase, pocketbook",
"wardrobe, closet, press",
"warplane, military plane",
"washbasin, handbasin, washbowl, lavabo, wash-hand basin",
"washer, automatic washer, washing machine",
"water bottle",
"water jug",
"water tower",
"whiskey jug",
"whistle",
"wig",
"window screen",
"window shade",
"Windsor tie",
"wine bottle",
"wing",
"wok",
"wooden spoon",
"wool, woolen, woollen",
"worm fence, snake fence, snake-rail fence, Virginia fence",
"wreck",
"yawl",
"yurt",
"web site, website, internet site, site",
"comic book",
"crossword puzzle, crossword",
"street sign",
"traffic light, traffic signal, stoplight",
"book jacket, dust cover, dust jacket, dust wrapper",
"menu",
"plate",
"guacamole",
"consomme",
"hot pot, hotpot",
"trifle",
"ice cream, icecream",
"ice lolly, lolly, lollipop, popsicle",
"French loaf",
"bagel, beigel",
"pretzel",
"cheeseburger",
"hotdog, hot dog, red hot",
"mashed potato",
"head cabbage",
"broccoli",
"cauliflower",
"zucchini, courgette",
"spaghetti squash",
"acorn squash",
"butternut squash",
"cucumber, cuke",
"artichoke, globe artichoke",
"bell pepper",
"cardoon",
"mushroom",
"Granny Smith",
"strawberry",
"orange",
"lemon",
"fig",
"pineapple, ananas",
"banana",
"jackfruit, jak, jack",
"custard apple",
"pomegranate",
"hay",
"carbonara",
"chocolate sauce, chocolate syrup",
"dough",
"meat loaf, meatloaf",
"pizza, pizza pie",
"potpie",
"burrito",
"red wine",
"espresso",
"cup",
"eggnog",
"alp",
"bubble",
"cliff, drop, drop-off",
"coral reef",
"geyser",
"lakeside, lakeshore",
"promontory, headland, head, foreland",
"sandbar, sand bar",
"seashore, coast, seacoast, sea-coast",
"valley, vale",
"volcano",
"ballplayer, baseball player",
"groom, bridegroom",
"scuba diver",
"rapeseed",
"daisy",
"yellow lady\"s slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum",
"corn",
"acorn",
"hip, rose hip, rosehip",
"buckeye, horse chestnut, conker",
"coral fungus",
"agaric",
"gyromitra",
"stinkhorn, carrion fungus",
"earthstar",
"hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa",
"bolete",
"ear, spike, capitulum",
"toilet tissue, toilet paper, bathroom tissue"
];
================================================
FILE: openvino-mobilenet-raw/rust/src/main.rs
================================================
use std::env;
use std::fs;
use wasi_nn;
mod imagenet_classes;
use wasi_nn::{ExecutionTarget, GraphBuilder, GraphEncoding, TensorType};
pub fn main() -> Result<(), Box<dyn std::error::Error>> {
let args: Vec<String> = env::args().collect();
let model_xml_path: &str = &args[1];
let model_bin_path: &str = &args[2];
let tensor_name: &str = &args[3];
print!("Load graph ...");
let graph = GraphBuilder::new(GraphEncoding::Openvino, ExecutionTarget::CPU)
.build_from_files([model_xml_path, model_bin_path])?;
println!("done");
print!("Init execution context ...");
let mut context = graph.init_execution_context()?;
println!("done");
// Load a tensor that precisely matches the graph input tensor (see
// `fixture/frozen_inference_graph.xml`).
print!("Set input tensor ...");
let input_dims = vec![1, 3, 224, 224];
let tensor_data = fs::read(tensor_name).unwrap();
context.set_input(0, TensorType::F32, &input_dims, tensor_data)?;
println!("done");
print!("Perform graph inference ...");
context.compute()?;
println!("done");
print!("Retrieve the output ...");
// Copy output to abuffer.
let mut output_buffer = vec![0f32; 1001];
let size_in_bytes = context.get_output(0, &mut output_buffer)?;
println!("done");
println!("The size of the output buffer is {} bytes", size_in_bytes);
let results = sort_results(&output_buffer);
for i in 0..5 {
println!(
" {}.) [{}]({:.4}){}",
i + 1,
results[i].0,
results[i].1,
imagenet_classes::IMAGENET_CLASSES[results[i].0]
);
}
Ok(())
}
// Sort the buffer of probabilities. The graph places the match probability for each class at the
// index for that class (e.g. the probability of class 42 is placed at buffer[42]). Here we convert
// to a wrapping InferenceResult and sort the results.
fn sort_results(buffer: &[f32]) -> Vec<InferenceResult> {
let mut results: Vec<InferenceResult> = buffer
.iter()
.skip(1)
.enumerate()
.map(|(c, p)| InferenceResult(c, *p))
.collect();
results.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());
results
}
// A wrapper for class ID and match probabilities.
#[derive(Debug, PartialEq)]
struct InferenceResult(usize, f32);
================================================
FILE: openvino-road-segmentation-adas/README.md
================================================
# OpenVINO Road Segmentation ADAS Example on WasmEdge Runtime
## Overview
In this example, we'll use `WasmEdge wasi-nn interfaces` to demonstrates a popular task used in the CV-based `Advanced Driver Assist Systems (ADAS)`: road segmentation.
| | |
| --------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------- |
| <img src="https://user-images.githubusercontent.com/36741649/127848003-9e45c8da-2e43-48ac-803f-9f51a8e9ea89.jpg" width=300> | <img src="https://user-images.githubusercontent.com/36741649/127847882-6305d483-f2ce-4c2f-a3b5-8573d1522d15.png" width=300> |
The model files and the images used for this demonstration are from the [Intel openvino_notebooks repo](https://github.com/openvinotoolkit/openvino_notebooks/blob/main/notebooks/003-hello-segmentation/README.md) on Github.
## Set up the environment
- Install `rustup` and `Rust`
Go to the [official Rust webpage](https://www.rust-lang.org/tools/install) and follow the instructions to install `rustup` and `Rust`.
> It is recommended to use Rust 1.68 or above in the stable channel.
Then, add `wasm32-wasip1` target to the Rustup toolchain:
```bash
rustup target add wasm32-wasip1
```
- Clone the example repo
```bash
git clone https://github.com/second-state/WasmEdge-WASINN-examples.git
```
- Install OpenVINO
Please refer to [WasmEdge Docs](https://wasmedge.org/docs/contribute/source/plugin/wasi_nn) and [OpenVINO™](https://docs.openvino.ai/2023.0/openvino_docs_install_guides_installing_openvino_apt.html)(2023) for the installation process.
```bash
bash WasmEdge-WASINN-examples/scripts/install_openvino.sh
ldconfig
```
- Install WasmEdge with Wasi-NN OpenVINO plugin
```bash
export CMAKE_BUILD_TYPE=Release
export VERSION=0.13.2
curl -sSf https://raw.githubusercontent.com/WasmEdge/WasmEdge/master/utils/install.sh | bash -s -- -v $VERSION -p /usr/local --plugins wasi_nn-openvino
ldconfig
```
## Build and run `openvino-road-segmentation-adas` example
- Download `MobileNet` model file
```bash
cd openvino-road-segmentation-adas
```
- Build and run the example
```bash
cd openvino-road-seg-adas
cargo build --target wasm32-wasip1 --release
cd ..
wasmedge --dir .:. ./openvino-road-seg-adas/target/wasm32-wasip1/release/openvino-road-seg-adas.wasm \
./model/road-segmentation-adas-0001.xml \
./model/road-segmentation-adas-0001.bin \
./image/empty_road_mapillary.jpg
```
If the commands run successfully, an output tensor will be generated and saved to `wasinn-openvino-inference-output-1x4x512x896xf32.tensor`.
## Visualize the inference result
To visualize the input image and the inference output tensor, you can use the `visualize_inference_result.ipynb` file. You may have to modify some file paths in the file, if the files used in the notebook are dumped in different directories. The following picture shows the inference result of road segmentation task.
================================================
FILE: openvino-road-segmentation-adas/model/road-segmentation-adas-0001.xml
================================================
<?xml version="1.0" ?>
<net name="road-segmentation-adas-0001" version="10">
<layers>
<layer id="0" name="data" type="Parameter" version="opset1">
<data element_type="f32" shape="1, 3, 512, 896"/>
<output>
<port id="0" names="data" precision="FP32">
<dim>1</dim>
<dim>3</dim>
<dim>512</dim>
<dim>896</dim>
</port>
</output>
</layer>
<layer id="1" name="373" type="Const" version="opset1">
<data element_type="f32" offset="0" shape="16, 3, 3, 3" size="1728"/>
<output>
<port id="0" precision="FP32">
<dim>16</dim>
<dim>3</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</output>
</layer>
<layer id="2" name="L0000_Conv2d_BN/WithoutBiases" type="Convolution" version="opset1">
<data auto_pad="explicit" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" strides="2, 2"/>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>3</dim>
<dim>512</dim>
<dim>896</dim>
</port>
<port id="1" precision="FP32">
<dim>16</dim>
<dim>3</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>256</dim>
<dim>448</dim>
</port>
</output>
</layer>
<layer id="3" name="L0000_Conv2d_BN/Dims5753" type="Const" version="opset1">
<data element_type="f32" offset="1728" shape="1, 16, 1, 1" size="64"/>
<output>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="4" name="L0000_Conv2d_BN" type="Add" version="opset1">
<data auto_broadcast="numpy"/>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>256</dim>
<dim>448</dim>
</port>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" names="L0000_Conv2d_BN" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>256</dim>
<dim>448</dim>
</port>
</output>
</layer>
<layer id="5" name="L0001_ReLU" type="ReLU" version="opset1">
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>256</dim>
<dim>448</dim>
</port>
</input>
<output>
<port id="1" names="L0001_ReLU" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>256</dim>
<dim>448</dim>
</port>
</output>
</layer>
<layer id="6" name="555" type="Const" version="opset1">
<data element_type="f32" offset="1792" shape="8, 16, 1, 1" size="512"/>
<output>
<port id="0" precision="FP32">
<dim>8</dim>
<dim>16</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="7" name="L0002_Conv2d_BN/WithoutBiases" type="Convolution" version="opset1">
<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>256</dim>
<dim>448</dim>
</port>
<port id="1" precision="FP32">
<dim>8</dim>
<dim>16</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>8</dim>
<dim>256</dim>
<dim>448</dim>
</port>
</output>
</layer>
<layer id="8" name="L0002_Conv2d_BN/Dims6479" type="Const" version="opset1">
<data element_type="f32" offset="2304" shape="1, 8, 1, 1" size="32"/>
<output>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>8</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="9" name="L0002_Conv2d_BN" type="Add" version="opset1">
<data auto_broadcast="numpy"/>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>8</dim>
<dim>256</dim>
<dim>448</dim>
</port>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>8</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" names="L0002_Conv2d_BN" precision="FP32">
<dim>1</dim>
<dim>8</dim>
<dim>256</dim>
<dim>448</dim>
</port>
</output>
</layer>
<layer id="10" name="L0003_ReLU" type="ReLU" version="opset1">
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>8</dim>
<dim>256</dim>
<dim>448</dim>
</port>
</input>
<output>
<port id="1" names="L0003_ReLU" precision="FP32">
<dim>1</dim>
<dim>8</dim>
<dim>256</dim>
<dim>448</dim>
</port>
</output>
</layer>
<layer id="11" name="335" type="Const" version="opset1">
<data element_type="f32" offset="2336" shape="8, 1, 1, 3, 3" size="288"/>
<output>
<port id="0" precision="FP32">
<dim>8</dim>
<dim>1</dim>
<dim>1</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</output>
</layer>
<layer id="12" name="L0004_Conv2d_BN/WithoutBiases" type="GroupConvolution" version="opset1">
<data auto_pad="explicit" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" strides="1, 1"/>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>8</dim>
<dim>256</dim>
<dim>448</dim>
</port>
<port id="1" precision="FP32">
<dim>8</dim>
<dim>1</dim>
<dim>1</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>8</dim>
<dim>256</dim>
<dim>448</dim>
</port>
</output>
</layer>
<layer id="13" name="L0004_Conv2d_BN/Dims6323" type="Const" version="opset1">
<data element_type="f32" offset="2624" shape="1, 8, 1, 1" size="32"/>
<output>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>8</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="14" name="L0004_Conv2d_BN" type="Add" version="opset1">
<data auto_broadcast="numpy"/>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>8</dim>
<dim>256</dim>
<dim>448</dim>
</port>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>8</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" names="L0004_Conv2d_BN" precision="FP32">
<dim>1</dim>
<dim>8</dim>
<dim>256</dim>
<dim>448</dim>
</port>
</output>
</layer>
<layer id="15" name="L0005_ReLU" type="ReLU" version="opset1">
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>8</dim>
<dim>256</dim>
<dim>448</dim>
</port>
</input>
<output>
<port id="1" names="L0005_ReLU" precision="FP32">
<dim>1</dim>
<dim>8</dim>
<dim>256</dim>
<dim>448</dim>
</port>
</output>
</layer>
<layer id="16" name="397" type="Const" version="opset1">
<data element_type="f32" offset="2656" shape="16, 8, 1, 1" size="512"/>
<output>
<port id="0" precision="FP32">
<dim>16</dim>
<dim>8</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="17" name="L0006_Conv2d_BN/WithoutBiases" type="Convolution" version="opset1">
<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>8</dim>
<dim>256</dim>
<dim>448</dim>
</port>
<port id="1" precision="FP32">
<dim>16</dim>
<dim>8</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>256</dim>
<dim>448</dim>
</port>
</output>
</layer>
<layer id="18" name="L0006_Conv2d_BN/Dims5963" type="Const" version="opset1">
<data element_type="f32" offset="3168" shape="1, 16, 1, 1" size="64"/>
<output>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="19" name="L0006_Conv2d_BN" type="Add" version="opset1">
<data auto_broadcast="numpy"/>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>256</dim>
<dim>448</dim>
</port>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" names="L0006_Conv2d_BN" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>256</dim>
<dim>448</dim>
</port>
</output>
</layer>
<layer id="20" name="L0007_AddBackward1" type="Add" version="opset1">
<data auto_broadcast="numpy"/>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>256</dim>
<dim>448</dim>
</port>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>256</dim>
<dim>448</dim>
</port>
</input>
<output>
<port id="2" names="L0007_AddBackward1" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>256</dim>
<dim>448</dim>
</port>
</output>
</layer>
<layer id="21" name="L0008_ReLU" type="ReLU" version="opset1">
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>256</dim>
<dim>448</dim>
</port>
</input>
<output>
<port id="1" names="L0008_ReLU" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>256</dim>
<dim>448</dim>
</port>
</output>
</layer>
<layer id="22" name="351" type="Const" version="opset1">
<data element_type="f32" offset="3232" shape="8, 16, 1, 1" size="512"/>
<output>
<port id="0" precision="FP32">
<dim>8</dim>
<dim>16</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="23" name="L0009_Conv2d_BN/WithoutBiases" type="Convolution" version="opset1">
<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>256</dim>
<dim>448</dim>
</port>
<port id="1" precision="FP32">
<dim>8</dim>
<dim>16</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>8</dim>
<dim>256</dim>
<dim>448</dim>
</port>
</output>
</layer>
<layer id="24" name="L0009_Conv2d_BN/Dims5945" type="Const" version="opset1">
<data element_type="f32" offset="3744" shape="1, 8, 1, 1" size="32"/>
<output>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>8</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="25" name="L0009_Conv2d_BN" type="Add" version="opset1">
<data auto_broadcast="numpy"/>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>8</dim>
<dim>256</dim>
<dim>448</dim>
</port>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>8</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" names="L0009_Conv2d_BN" precision="FP32">
<dim>1</dim>
<dim>8</dim>
<dim>256</dim>
<dim>448</dim>
</port>
</output>
</layer>
<layer id="26" name="L0010_ReLU" type="ReLU" version="opset1">
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>8</dim>
<dim>256</dim>
<dim>448</dim>
</port>
</input>
<output>
<port id="1" names="L0010_ReLU" precision="FP32">
<dim>1</dim>
<dim>8</dim>
<dim>256</dim>
<dim>448</dim>
</port>
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</layer>
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<input>
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<layer id="168" name="501" type="Const" version="opset1">
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<layer id="169" name="L0071_Conv2d_BN/WithoutBiases" type="GroupConvolution" version="opset1">
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<outpu
gitextract_91qb3w5n/
├── .github/
│ └── workflows/
│ ├── build_openvino_mobilenet.yml
│ ├── build_openvino_road_seg_adas.yml
│ ├── build_pytorch_yolo.yml
│ ├── chatTTS.yml
│ ├── llama.yml
│ ├── piper.yml
│ ├── pytorch.yml
│ └── tflite.yml
├── .gitignore
├── LICENSE
├── README.md
├── openvino-mobilenet-image/
│ ├── README.md
│ ├── download_mobilenet.sh
│ ├── rust/
│ │ ├── Cargo.toml
│ │ └── src/
│ │ ├── imagenet_classes.rs
│ │ └── main.rs
│ └── wasmedge-wasinn-example-mobilenet-image.wasm
├── openvino-mobilenet-raw/
│ ├── README.md
│ ├── download_mobilenet.sh
│ ├── rust/
│ │ ├── Cargo.toml
│ │ └── src/
│ │ ├── imagenet_classes.rs
│ │ └── main.rs
│ └── wasmedge-wasinn-example-mobilenet.wasm
├── openvino-road-segmentation-adas/
│ ├── README.md
│ ├── model/
│ │ └── road-segmentation-adas-0001.xml
│ ├── openvino-road-seg-adas/
│ │ ├── Cargo.toml
│ │ └── src/
│ │ └── main.rs
│ ├── tensor/
│ │ ├── wasinn-openvino-inference-input-512x896x3xf32-bgr.tensor
│ │ └── wasinn-openvino-inference-output-1x4x512x896xf32.tensor
│ └── visualize_inference_result.ipynb
├── openvinogenai-raw/
│ ├── README.md
│ └── rust/
│ ├── Cargo.toml
│ └── src/
│ └── main.rs
├── pytorch-mobilenet-image/
│ ├── README.md
│ ├── gen_mobilenet_model.py
│ ├── gen_tensor.py
│ ├── image-1x3x224x224.rgb
│ ├── mobilenet.pt
│ ├── rust/
│ │ ├── Cargo.toml
│ │ └── src/
│ │ ├── imagenet_classes.rs
│ │ ├── main.rs
│ │ └── named_model.rs
│ ├── wasmedge-wasinn-example-mobilenet-image-named-model.wasm
│ └── wasmedge-wasinn-example-mobilenet-image.wasm
├── pytorch-yolo-image/
│ ├── README.md
│ ├── get_model.py
│ ├── rust/
│ │ ├── Cargo.toml
│ │ └── src/
│ │ ├── main.rs
│ │ └── yolo_classes.rs
│ └── yolov8n.torchscript
├── scripts/
│ ├── install_libtorch.sh
│ └── install_openvino.sh
├── tflite-birds_v1-image/
│ ├── README.md
│ ├── lite-model_aiy_vision_classifier_birds_V1_3.tflite
│ ├── rust/
│ │ ├── Cargo.toml
│ │ └── src/
│ │ ├── imagenet_classes.rs
│ │ └── main.rs
│ └── wasmedge-wasinn-example-tflite-bird-image.wasm
├── wasmedge-chatTTS/
│ ├── .gitignore
│ ├── Cargo.toml
│ ├── README.md
│ ├── assets/
│ │ └── demo.webm
│ ├── src/
│ │ └── main.rs
│ └── wasmedge-chattts.wasm
├── wasmedge-ggml/
│ ├── README.md
│ ├── basic/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ ├── src/
│ │ │ └── main.rs
│ │ └── wasmedge-ggml-basic.wasm
│ ├── chatml/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ ├── src/
│ │ │ └── main.rs
│ │ └── wasmedge-ggml-chatml.wasm
│ ├── command-r/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ ├── src/
│ │ │ └── main.rs
│ │ └── wasmedge-ggml-command-r.wasm
│ ├── embedding/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ ├── src/
│ │ │ └── main.rs
│ │ └── wasmedge-ggml-llama-embedding.wasm
│ ├── gemma/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ ├── src/
│ │ │ └── main.rs
│ │ └── wasmedge-ggml-gemma.wasm
│ ├── gemma-3/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ ├── src/
│ │ │ ├── base64.rs
│ │ │ └── main.rs
│ │ ├── wasmedge-ggml-gemma-3-base64.wasm
│ │ └── wasmedge-ggml-gemma-3.wasm
│ ├── gemma-4/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ ├── src/
│ │ │ └── main.rs
│ │ └── wasmedge-ggml-gemma-4.wasm
│ ├── grammar/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ ├── src/
│ │ │ └── main.rs
│ │ └── wasmedge-ggml-grammar.wasm
│ ├── json-schema/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ ├── src/
│ │ │ └── main.rs
│ │ └── wasmedge-ggml-json-schema.wasm
│ ├── llama/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ ├── src/
│ │ │ └── main.rs
│ │ └── wasmedge-ggml-llama.wasm
│ ├── llama-stream/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ ├── src/
│ │ │ └── main.rs
│ │ └── wasmedge-ggml-llama-stream.wasm
│ ├── llava/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ ├── src/
│ │ │ └── main.rs
│ │ └── wasmedge-ggml-llava.wasm
│ ├── llava-base64-stream/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ ├── src/
│ │ │ └── main.rs
│ │ └── wasmedge-ggml-llava-base64-stream.wasm
│ ├── multimodel/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ ├── src/
│ │ │ └── main.rs
│ │ └── wasmedge-ggml-multimodel.wasm
│ ├── nnrpc/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ ├── src/
│ │ │ └── main.rs
│ │ └── wasmedge-ggml-nnrpc.wasm
│ ├── qwen/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ └── src/
│ │ └── main.rs
│ ├── qwen2vl/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ ├── src/
│ │ │ └── main.rs
│ │ └── wasmedge-ggml-qwen2vl.wasm
│ ├── test/
│ │ ├── model-not-found/
│ │ │ ├── Cargo.toml
│ │ │ ├── README.md
│ │ │ ├── src/
│ │ │ │ └── main.rs
│ │ │ └── wasmedge-ggml-model-not-found.wasm
│ │ ├── phi-3/
│ │ │ ├── Cargo.toml
│ │ │ ├── README.md
│ │ │ ├── src/
│ │ │ │ └── main.rs
│ │ │ └── wasmedge-ggml-phi-3.wasm
│ │ ├── set-input-twice/
│ │ │ ├── Cargo.toml
│ │ │ ├── README.md
│ │ │ ├── src/
│ │ │ │ └── main.rs
│ │ │ └── wasmedge-ggml-set-input-twice.wasm
│ │ └── unload/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ ├── src/
│ │ │ └── main.rs
│ │ └── wasmedge-ggml-unload.wasm
│ ├── tts/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ ├── src/
│ │ │ └── main.rs
│ │ └── wasmedge-ggml-tts.wasm
│ └── whisper/
│ ├── Cargo.toml
│ ├── README.md
│ ├── src/
│ │ └── main.rs
│ └── whisper-basic.wasm
├── wasmedge-mlx/
│ ├── llama/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ └── src/
│ │ └── main.rs
│ ├── vlm/
│ │ ├── Cargo.toml
│ │ ├── README.md
│ │ ├── decode.py
│ │ ├── encode.py
│ │ └── src/
│ │ └── main.rs
│ └── whisper/
│ ├── Cargo.toml
│ ├── README.md
│ └── src/
│ └── main.rs
├── wasmedge-piper/
│ ├── Cargo.toml
│ ├── README.md
│ ├── config.schema.json
│ ├── dependencies.d2
│ ├── json_input.schema.json
│ └── src/
│ └── main.rs
├── wasmedge-tf-llama/
│ ├── README.md
│ └── rust/
│ ├── Cargo.toml
│ └── src/
│ └── main.rs
├── wasmedge-tf-mobilenet_v2/
│ ├── README.md
│ ├── imagenet_slim_labels.txt
│ ├── mobilenet_v2_1.4_224_frozen.pb
│ ├── rust/
│ │ ├── Cargo.toml
│ │ └── src/
│ │ └── main.rs
│ └── wasmedge-tf-example-mobilenet.wasm
└── wasmedge-tf-mtcnn/
├── README.md
├── mtcnn.pb
├── rust/
│ ├── Cargo.toml
│ └── src/
│ └── main.rs
└── wasmedge-tf-example-mtcnn.wasm
SYMBOL INDEX (240 symbols across 47 files)
FILE: openvino-mobilenet-image/rust/src/imagenet_classes.rs
constant IMAGENET_CLASSES (line 20) | pub const IMAGENET_CLASSES: [&str; 1000] = [
FILE: openvino-mobilenet-image/rust/src/main.rs
function main (line 9) | pub fn main() -> Result<(), Box<dyn std::error::Error>> {
function sort_results (line 58) | fn sort_results(buffer: &[f32]) -> Vec<InferenceResult> {
function image_to_tensor (line 71) | fn image_to_tensor(path: String, height: u32, width: u32) -> Vec<u8> {
type InferenceResult (line 108) | struct InferenceResult(usize, f32);
FILE: openvino-mobilenet-raw/rust/src/imagenet_classes.rs
constant IMAGENET_CLASSES (line 20) | pub const IMAGENET_CLASSES: [&str; 1000] = [
FILE: openvino-mobilenet-raw/rust/src/main.rs
function main (line 8) | pub fn main() -> Result<(), Box<dyn std::error::Error>> {
function sort_results (line 59) | fn sort_results(buffer: &[f32]) -> Vec<InferenceResult> {
type InferenceResult (line 72) | struct InferenceResult(usize, f32);
FILE: openvino-road-segmentation-adas/openvino-road-seg-adas/src/main.rs
function main (line 5) | fn main() -> Result<(), Box<dyn std::error::Error>> {
function dump (line 47) | fn dump<T>(
function image_to_tensor (line 70) | fn image_to_tensor(path: String, height: u32, width: u32) -> Vec<u8> {
FILE: openvinogenai-raw/rust/src/main.rs
function main (line 6) | pub fn main() -> Result<(), Box<dyn std::error::Error>> {
FILE: pytorch-mobilenet-image/rust/src/imagenet_classes.rs
constant IMAGENET_CLASSES (line 20) | pub const IMAGENET_CLASSES: [&str; 1000] = [
FILE: pytorch-mobilenet-image/rust/src/main.rs
function main (line 8) | pub fn main() {
function sort_results (line 52) | fn sort_results(buffer: &[f32]) -> Vec<InferenceResult> {
function image_to_tensor (line 64) | fn image_to_tensor(path: String, height: u32, width: u32) -> Vec<u8> {
type InferenceResult (line 96) | struct InferenceResult(usize, f32);
FILE: pytorch-mobilenet-image/rust/src/named_model.rs
function main (line 8) | pub fn main() {
function sort_results (line 52) | fn sort_results(buffer: &[f32]) -> Vec<InferenceResult> {
function image_to_tensor (line 64) | fn image_to_tensor(path: String, height: u32, width: u32) -> Vec<u8> {
type InferenceResult (line 96) | struct InferenceResult(usize, f32);
FILE: pytorch-yolo-image/rust/src/main.rs
function main (line 11) | pub fn main() {
constant SIZE (line 74) | const SIZE: usize = 640;
constant SIZE_U32 (line 75) | const SIZE_U32: u32 = 640;
type Channel (line 76) | type Channel = Vec<Vec<f32>>;
type ResizeScale (line 78) | struct ResizeScale(pub f32);
function pre_process_image (line 80) | fn pre_process_image(path: String) -> ([Channel; 3], ResizeScale) {
function post_process_results (line 131) | fn post_process_results(
function transpose (line 185) | fn transpose<T>(v: Vec<Vec<T>>) -> Vec<Vec<T>> {
type InferenceResult (line 204) | struct InferenceResult {
FILE: pytorch-yolo-image/rust/src/yolo_classes.rs
constant YOLO_CLASSES (line 1) | pub const YOLO_CLASSES: [&str; 80] = [
FILE: tflite-birds_v1-image/rust/src/imagenet_classes.rs
constant AIY_BIRDS_V1 (line 20) | pub const AIY_BIRDS_V1: [&str; 965] = [
FILE: tflite-birds_v1-image/rust/src/main.rs
function main (line 9) | pub fn main() -> Result<(), Box<dyn Error>> {
function sort_results (line 54) | fn sort_results(buffer: &[u8]) -> Vec<InferenceResult> {
function image_to_tensor (line 66) | fn image_to_tensor(path: String, height: u32, width: u32) -> Vec<u8> {
type InferenceResult (line 77) | struct InferenceResult(usize, u8);
FILE: wasmedge-chatTTS/src/main.rs
function get_data_from_context (line 7) | fn get_data_from_context(context: &GraphExecutionContext, index: usize) ...
function main (line 17) | fn main() {
FILE: wasmedge-ggml/basic/src/main.rs
function read_input (line 10) | fn read_input() -> String {
function get_options_from_env (line 22) | fn get_options_from_env() -> Value {
function set_data_to_context (line 52) | fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec<u8...
function set_metadata_to_context (line 57) | fn set_metadata_to_context(
function get_data_from_context (line 64) | fn get_data_from_context(context: &GraphExecutionContext, index: usize) ...
function get_output_from_context (line 76) | fn get_output_from_context(context: &GraphExecutionContext) -> String {
function get_metadata_from_context (line 81) | fn get_metadata_from_context(context: &GraphExecutionContext) -> Value {
function main (line 85) | fn main() {
FILE: wasmedge-ggml/chatml/src/main.rs
function read_input (line 9) | fn read_input() -> String {
function get_options_from_env (line 21) | fn get_options_from_env() -> Value {
function set_data_to_context (line 42) | fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec<u8...
function set_metadata_to_context (line 47) | fn set_metadata_to_context(
function get_data_from_context (line 54) | fn get_data_from_context(context: &GraphExecutionContext, index: usize) ...
function get_output_from_context (line 66) | fn get_output_from_context(context: &GraphExecutionContext) -> String {
function get_metadata_from_context (line 71) | fn get_metadata_from_context(context: &GraphExecutionContext) -> Value {
function main (line 75) | fn main() {
FILE: wasmedge-ggml/command-r/src/main.rs
function read_input (line 10) | fn read_input() -> String {
function get_options_from_env (line 22) | fn get_options_from_env() -> Value {
function set_data_to_context (line 41) | fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec<u8...
function set_metadata_to_context (line 46) | fn set_metadata_to_context(
function get_data_from_context (line 53) | fn get_data_from_context(context: &GraphExecutionContext, index: usize) ...
function get_output_from_context (line 65) | fn get_output_from_context(context: &GraphExecutionContext) -> String {
function get_metadata_from_context (line 69) | fn get_metadata_from_context(context: &GraphExecutionContext) -> Value {
function main (line 73) | fn main() {
FILE: wasmedge-ggml/embedding/src/main.rs
function read_input (line 9) | fn read_input() -> String {
function get_options_from_env (line 21) | fn get_options_from_env() -> Value {
function set_data_to_context (line 39) | fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec<u8...
function set_metadata_to_context (line 44) | fn set_metadata_to_context(
function get_data_from_context (line 51) | fn get_data_from_context(context: &GraphExecutionContext, index: usize) ...
function get_output_from_context (line 62) | fn get_output_from_context(context: &GraphExecutionContext) -> String {
function get_metadata_from_context (line 66) | fn get_metadata_from_context(context: &GraphExecutionContext) -> Value {
function get_embd_from_context (line 70) | fn get_embd_from_context(context: &GraphExecutionContext) -> Value {
function main (line 74) | fn main() {
FILE: wasmedge-ggml/gemma-3/src/base64.rs
function read_input (line 10) | fn read_input() -> String {
function get_options_from_env (line 22) | fn get_options_from_env() -> HashMap<&'static str, Value> {
function set_data_to_context (line 60) | fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec<u8...
function get_data_from_context (line 64) | fn get_data_from_context(context: &GraphExecutionContext, index: usize) ...
function get_output_from_context (line 76) | fn get_output_from_context(context: &GraphExecutionContext) -> String {
function get_metadata_from_context (line 80) | fn get_metadata_from_context(context: &GraphExecutionContext) -> Value {
function main (line 84) | fn main() {
FILE: wasmedge-ggml/gemma-3/src/main.rs
function read_input (line 10) | fn read_input() -> String {
function get_options_from_env (line 22) | fn get_options_from_env() -> HashMap<&'static str, Value> {
function set_data_to_context (line 66) | fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec<u8...
function get_data_from_context (line 70) | fn get_data_from_context(context: &GraphExecutionContext, index: usize) ...
function get_output_from_context (line 82) | fn get_output_from_context(context: &GraphExecutionContext) -> String {
function get_metadata_from_context (line 86) | fn get_metadata_from_context(context: &GraphExecutionContext) -> Value {
function main (line 90) | fn main() {
FILE: wasmedge-ggml/gemma-4/src/main.rs
constant MULTIMODAL_IMAGE_MARKER (line 10) | const MULTIMODAL_IMAGE_MARKER: &str = "<image>";
function read_input (line 12) | fn read_input() -> String {
function get_options_from_env (line 24) | fn get_options_from_env() -> HashMap<&'static str, Value> {
function set_data_to_context (line 73) | fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec<u8...
function get_data_from_context (line 77) | fn get_data_from_context(context: &GraphExecutionContext, index: usize) ...
function get_output_from_context (line 89) | fn get_output_from_context(context: &GraphExecutionContext) -> String {
function get_metadata_from_context (line 93) | fn get_metadata_from_context(context: &GraphExecutionContext) -> Value {
function get_system_prompt_from_env (line 97) | fn get_system_prompt_from_env() -> String {
function get_enable_thinking_from_env (line 101) | fn get_enable_thinking_from_env() -> bool {
function build_gemma4_system_turn (line 108) | fn build_gemma4_system_turn(system_prompt: &str, enable_thinking: bool) ...
function build_gemma4_user_turn (line 116) | fn build_gemma4_user_turn(user_content: &str) -> String {
function build_user_content (line 120) | fn build_user_content(user_input: &str, multimodal_enabled: bool) -> Str...
function strip_gemma4_thoughts (line 128) | fn strip_gemma4_thoughts(text: &str) -> String {
function strip_gemma4_turn_suffix (line 159) | fn strip_gemma4_turn_suffix(text: &str) -> String {
function parse_gemma4_output (line 167) | fn parse_gemma4_output(text: &str) -> (String, String) {
function main (line 179) | fn main() {
FILE: wasmedge-ggml/gemma/src/main.rs
function read_input (line 10) | fn read_input() -> String {
function get_options_from_env (line 22) | fn get_options_from_env() -> Value {
function set_data_to_context (line 47) | fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec<u8...
function set_metadata_to_context (line 52) | fn set_metadata_to_context(
function get_data_from_context (line 59) | fn get_data_from_context(context: &GraphExecutionContext, index: usize) ...
function get_output_from_context (line 71) | fn get_output_from_context(context: &GraphExecutionContext) -> String {
function get_metadata_from_context (line 76) | fn get_metadata_from_context(context: &GraphExecutionContext) -> Value {
function main (line 80) | fn main() {
FILE: wasmedge-ggml/grammar/src/main.rs
function read_input (line 10) | fn read_input() -> String {
function get_options_from_env (line 22) | fn get_options_from_env() -> Value {
function set_data_to_context (line 45) | fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec<u8...
function set_metadata_to_context (line 50) | fn set_metadata_to_context(
function get_data_from_context (line 57) | fn get_data_from_context(context: &GraphExecutionContext, index: usize) ...
function get_output_from_context (line 69) | fn get_output_from_context(context: &GraphExecutionContext) -> String {
function get_metadata_from_context (line 73) | fn get_metadata_from_context(context: &GraphExecutionContext) -> Value {
constant JSON_GRAMMAR (line 77) | const JSON_GRAMMAR: &str = r#"
function main (line 99) | fn main() {
FILE: wasmedge-ggml/json-schema/src/main.rs
function read_input (line 10) | fn read_input() -> String {
function get_options_from_env (line 22) | fn get_options_from_env() -> Value {
function set_data_to_context (line 48) | fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec<u8...
function set_metadata_to_context (line 53) | fn set_metadata_to_context(
function get_data_from_context (line 60) | fn get_data_from_context(context: &GraphExecutionContext, index: usize) ...
function get_output_from_context (line 72) | fn get_output_from_context(context: &GraphExecutionContext) -> String {
function get_metadata_from_context (line 76) | fn get_metadata_from_context(context: &GraphExecutionContext) -> Value {
constant JSON_SCHEMA (line 80) | const JSON_SCHEMA: &str = r#"
function main (line 111) | fn main() {
FILE: wasmedge-ggml/llama-stream/src/main.rs
function read_input (line 10) | fn read_input() -> String {
function get_options_from_env (line 22) | fn get_options_from_env() -> Value {
function set_data_to_context (line 53) | fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec<u8...
function set_metadata_to_context (line 58) | fn set_metadata_to_context(
function get_data_from_context (line 65) | fn get_data_from_context(context: &GraphExecutionContext, index: usize, ...
function get_output_from_context (line 84) | fn get_output_from_context(context: &GraphExecutionContext) -> String {
function get_single_output_from_context (line 88) | fn get_single_output_from_context(context: &GraphExecutionContext) -> St...
function get_metadata_from_context (line 93) | fn get_metadata_from_context(context: &GraphExecutionContext) -> Value {
function main (line 97) | fn main() {
FILE: wasmedge-ggml/llama/src/main.rs
function read_input (line 10) | fn read_input() -> String {
function get_options_from_env (line 22) | fn get_options_from_env() -> Value {
function set_data_to_context (line 51) | fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec<u8...
function set_metadata_to_context (line 56) | fn set_metadata_to_context(
function get_data_from_context (line 63) | fn get_data_from_context(context: &GraphExecutionContext, index: usize) ...
function get_output_from_context (line 75) | fn get_output_from_context(context: &GraphExecutionContext) -> String {
function get_metadata_from_context (line 79) | fn get_metadata_from_context(context: &GraphExecutionContext) -> Value {
function main (line 83) | fn main() {
FILE: wasmedge-ggml/llava-base64-stream/src/main.rs
function read_input (line 10) | fn read_input() -> String {
function get_options_from_env (line 22) | fn get_options_from_env() -> HashMap<&'static str, Value> {
function set_data_to_context (line 52) | fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec<u8...
function get_data_from_context (line 56) | fn get_data_from_context(context: &GraphExecutionContext, index: usize, ...
function get_output_from_context (line 74) | fn get_output_from_context(context: &GraphExecutionContext) -> String {
function get_single_output_from_context (line 78) | fn get_single_output_from_context(context: &GraphExecutionContext) -> St...
function main (line 82) | fn main() {
FILE: wasmedge-ggml/llava/src/main.rs
function read_input (line 10) | fn read_input() -> String {
function get_options_from_env (line 22) | fn get_options_from_env() -> HashMap<&'static str, Value> {
function set_data_to_context (line 58) | fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec<u8...
function get_data_from_context (line 62) | fn get_data_from_context(context: &GraphExecutionContext, index: usize) ...
function get_output_from_context (line 74) | fn get_output_from_context(context: &GraphExecutionContext) -> String {
function get_metadata_from_context (line 78) | fn get_metadata_from_context(context: &GraphExecutionContext) -> Value {
function main (line 82) | fn main() {
FILE: wasmedge-ggml/multimodel/src/main.rs
function read_input (line 10) | fn read_input() -> String {
function get_options_from_env (line 22) | fn get_options_from_env() -> Value {
function set_data_to_context (line 56) | fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec<u8...
function set_metadata_to_context (line 61) | fn set_metadata_to_context(
function get_data_from_context (line 68) | fn get_data_from_context(context: &GraphExecutionContext, index: usize) ...
function get_output_from_context (line 80) | fn get_output_from_context(context: &GraphExecutionContext) -> String {
function get_metadata_from_context (line 85) | fn get_metadata_from_context(context: &GraphExecutionContext) -> Value {
function main (line 89) | fn main() {
FILE: wasmedge-ggml/nnrpc/src/main.rs
function read_input (line 10) | fn read_input() -> String {
function get_options_from_env (line 22) | fn get_options_from_env() -> Value {
function set_data_to_context (line 41) | fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec<u8...
function set_metadata_to_context (line 46) | fn set_metadata_to_context(
function get_data_from_context (line 53) | fn get_data_from_context(context: &GraphExecutionContext, index: usize) ...
function get_output_from_context (line 65) | fn get_output_from_context(context: &GraphExecutionContext) -> String {
function get_metadata_from_context (line 70) | fn get_metadata_from_context(context: &GraphExecutionContext) -> Value {
function main (line 74) | fn main() {
FILE: wasmedge-ggml/qwen/src/main.rs
function read_input (line 10) | fn read_input() -> String {
function get_options_from_env (line 22) | fn get_options_from_env() -> Value {
function set_data_to_context (line 41) | fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec<u8...
function get_data_from_context (line 45) | fn get_data_from_context(context: &GraphExecutionContext, index: usize) ...
function get_output_from_context (line 59) | fn get_output_from_context(context: &GraphExecutionContext) -> String {
function main (line 63) | fn main() {
FILE: wasmedge-ggml/qwen2vl/src/main.rs
function read_input (line 10) | fn read_input() -> String {
function get_options_from_env (line 22) | fn get_options_from_env() -> HashMap<&'static str, Value> {
function set_data_to_context (line 58) | fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec<u8...
function get_data_from_context (line 62) | fn get_data_from_context(context: &GraphExecutionContext, index: usize) ...
function get_output_from_context (line 74) | fn get_output_from_context(context: &GraphExecutionContext) -> String {
function get_metadata_from_context (line 78) | fn get_metadata_from_context(context: &GraphExecutionContext) -> Value {
function main (line 82) | fn main() {
FILE: wasmedge-ggml/test/model-not-found/src/main.rs
function main (line 4) | fn main() {
FILE: wasmedge-ggml/test/phi-3/src/main.rs
function get_options_from_env (line 9) | fn get_options_from_env() -> Value {
function get_data_from_context (line 27) | fn get_data_from_context(context: &GraphExecutionContext, index: usize) ...
function get_output_from_context (line 39) | fn get_output_from_context(context: &GraphExecutionContext) -> String {
function main (line 43) | fn main() {
FILE: wasmedge-ggml/test/set-input-twice/src/main.rs
function get_options_from_env (line 8) | fn get_options_from_env() -> Value {
function get_data_from_context (line 27) | fn get_data_from_context(context: &GraphExecutionContext, index: usize) ...
function get_metadata_from_context (line 39) | fn get_metadata_from_context(context: &GraphExecutionContext) -> Value {
function main (line 43) | fn main() {
FILE: wasmedge-ggml/test/unload/src/main.rs
function get_options_from_env (line 9) | fn get_options_from_env() -> Value {
function get_data_from_context (line 27) | fn get_data_from_context(context: &GraphExecutionContext, index: usize) ...
function get_output_from_context (line 39) | fn get_output_from_context(context: &GraphExecutionContext) -> String {
function main (line 43) | fn main() {
FILE: wasmedge-ggml/tts/src/main.rs
function get_options_from_env (line 11) | fn get_options_from_env() -> HashMap<&'static str, Value> {
function set_data_to_context (line 82) | fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec<u8...
function get_data_from_context (line 86) | fn get_data_from_context(context: &GraphExecutionContext, index: usize) ...
function get_metadata_from_context (line 98) | fn get_metadata_from_context(context: &GraphExecutionContext) -> Value {
function main (line 103) | fn main() {
FILE: wasmedge-ggml/whisper/src/main.rs
function main (line 6) | pub fn main() -> Result<(), Box<dyn Error>> {
FILE: wasmedge-mlx/llama/src/main.rs
function get_data_from_context (line 6) | fn get_data_from_context(context: &GraphExecutionContext, index: usize) ...
function get_output_from_context (line 18) | fn get_output_from_context(context: &GraphExecutionContext) -> String {
function main (line 21) | fn main() {
FILE: wasmedge-mlx/vlm/decode.py
function _remove_space (line 6) | def _remove_space(x):
class Detokenizer (line 12) | class Detokenizer():
method __init__ (line 13) | def __init__(self, tokenizer, trim_space=True):
method add_token (line 27) | def add_token(self, token):
function decode (line 40) | def decode(token: list, model_path: str, **kwargs):
FILE: wasmedge-mlx/vlm/encode.py
function encode (line 8) | def encode(processor, image, prompts):
FILE: wasmedge-mlx/vlm/src/main.rs
function get_data_from_context (line 14) | fn get_data_from_context(context: &GraphExecutionContext, index: usize) ...
function get_output_from_context (line 26) | fn get_output_from_context(context: &GraphExecutionContext) -> NDTensorI...
function read_json (line 30) | fn read_json(path: &str) -> io::Result<Value> {
function main (line 38) | fn main() {
FILE: wasmedge-mlx/whisper/src/main.rs
function get_data_from_context (line 10) | fn get_data_from_context(context: &GraphExecutionContext, index: usize) ...
function get_output_from_context (line 22) | fn get_output_from_context(context: &GraphExecutionContext) -> String {
function read_json (line 26) | fn read_json(path: &str) -> io::Result<Value> {
function main (line 33) | fn main() {
FILE: wasmedge-piper/src/main.rs
function main (line 1) | fn main() {
FILE: wasmedge-tf-llama/rust/src/main.rs
type ChatbotError (line 14) | pub enum ChatbotError {
method from (line 26) | fn from(error: wasi_nn::Error) -> Self {
type Result (line 31) | type Result<T> = std::result::Result<T, ChatbotError>;
type Tokenizer (line 34) | struct Tokenizer {
method new (line 41) | fn new(initial_vocab: Vec<(&str, i32)>) -> Self {
method tokenize (line 67) | fn tokenize(&mut self, input: &str) -> Vec<i32> {
method tokenize_with_fixed_length (line 82) | fn tokenize_with_fixed_length(&mut self, input: &str, max_length: usiz...
method detokenize (line 94) | fn detokenize(&self, tokens: &[i32]) -> String {
function load_model (line 109) | fn load_model(model_path: &str) -> Result<wasi_nn::Graph> {
function init_context (line 118) | fn init_context(graph: wasi_nn::Graph) -> Result<wasi_nn::GraphExecution...
function main (line 122) | fn main() -> Result<()> {
function run_inference (line 217) | fn run_inference(ctx: &wasi_nn::GraphExecutionContext) -> Result<()> {
function get_model_output (line 222) | fn get_model_output(
FILE: wasmedge-tf-mobilenet_v2/rust/src/main.rs
function main (line 6) | fn main() {
FILE: wasmedge-tf-mtcnn/rust/src/main.rs
function main (line 10) | fn main() {
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"path": ".github/workflows/pytorch.yml",
"chars": 2622,
"preview": "name: PyTorch examples\n\non:\n schedule:\n - cron: \"0 0 * * *\"\n workflow_dispatch:\n inputs:\n logLevel:\n "
},
{
"path": ".github/workflows/tflite.yml",
"chars": 2158,
"preview": "name: TFlite examples\n\non:\n schedule:\n - cron: \"0 0 * * *\"\n workflow_dispatch:\n inputs:\n logLevel:\n "
},
{
"path": ".gitignore",
"chars": 360,
"preview": "**/build\n**/target\n**/*.gguf\n\nopenvino-mobilenet-image/mobilenet.bin\nopenvino-mobilenet-image/mobilenet.xml\n\nopenvino-mo"
},
{
"path": "LICENSE",
"chars": 12243,
"preview": "\n Apache License\n Version 2.0, January 2004\n "
},
{
"path": "README.md",
"chars": 4522,
"preview": "<div align=\"center\">\n <h1>WasmEdge WASI-NN Examples</h1>\n\n <p><strong>High-level bindings for writing wasi-nn applicat"
},
{
"path": "openvino-mobilenet-image/README.md",
"chars": 2213,
"preview": "# Mobilenet example with WasmEdge WASI-NN OpenVINO plugin\n\nThis example demonstrates how to use WasmEdge WASI-NN OpenVIN"
},
{
"path": "openvino-mobilenet-image/download_mobilenet.sh",
"chars": 333,
"preview": "FIXTURE=https://github.com/intel/openvino-rs/raw/v0.3.3/crates/openvino/tests/fixtures/mobilenet\nTODIR=$1\n\nif [ ! -f $TO"
},
{
"path": "openvino-mobilenet-image/rust/Cargo.toml",
"chars": 390,
"preview": "[package]\nname = \"wasmedge-wasinn-example-mobilenet-image\"\nversion = \"0.1.0\"\nauthors = [\"Second-State\"]\nreadme = \"README"
},
{
"path": "openvino-mobilenet-image/rust/src/imagenet_classes.rs",
"chars": 29571,
"preview": "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (th"
},
{
"path": "openvino-mobilenet-image/rust/src/main.rs",
"chars": 3705,
"preview": "use image::io::Reader;\nuse image::DynamicImage;\nuse std::env;\nuse wasi_nn;\nmod imagenet_classes;\n\nuse wasi_nn::{Executio"
},
{
"path": "openvino-mobilenet-raw/README.md",
"chars": 2271,
"preview": "# Mobilenet example with WasmEdge WASI-NN OpenVINO plugin\n\nThis example demonstrates how to use WasmEdge WASI-NN OpenVIN"
},
{
"path": "openvino-mobilenet-raw/download_mobilenet.sh",
"chars": 472,
"preview": "FIXTURE=https://github.com/intel/openvino-rs/raw/v0.3.3/crates/openvino/tests/fixtures/mobilenet\nTODIR=$1\n\nif [ ! -f $TO"
},
{
"path": "openvino-mobilenet-raw/rust/Cargo.toml",
"chars": 213,
"preview": "[package]\nname = \"wasmedge-wasinn-example-mobilenet\"\nversion = \"0.1.0\"\nauthors = [\"Second-State\"]\nreadme = \"README.md\"\ne"
},
{
"path": "openvino-mobilenet-raw/rust/src/imagenet_classes.rs",
"chars": 29571,
"preview": "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (th"
},
{
"path": "openvino-mobilenet-raw/rust/src/main.rs",
"chars": 2357,
"preview": "use std::env;\nuse std::fs;\nuse wasi_nn;\nmod imagenet_classes;\n\nuse wasi_nn::{ExecutionTarget, GraphBuilder, GraphEncodin"
},
{
"path": "openvino-road-segmentation-adas/README.md",
"chars": 3428,
"preview": "\n# OpenVINO Road Segmentation ADAS Example on WasmEdge Runtime\n\n## Overview\n\nIn this example, we'll use `WasmEdge wasi-n"
},
{
"path": "openvino-road-segmentation-adas/model/road-segmentation-adas-0001.xml",
"chars": 401509,
"preview": "<?xml version=\"1.0\" ?>\n<net name=\"road-segmentation-adas-0001\" version=\"10\">\n\t<layers>\n\t\t<layer id=\"0\" name=\"data\" type="
},
{
"path": "openvino-road-segmentation-adas/openvino-road-seg-adas/Cargo.toml",
"chars": 325,
"preview": "[package]\nauthors = [\"Sam Liu <sam@secondstate.io>\"]\nedition = \"2021\"\nname = \"openvino-road-seg-adas\"\nversion = \"0.2.0\"\n"
},
{
"path": "openvino-road-segmentation-adas/openvino-road-seg-adas/src/main.rs",
"chars": 3076,
"preview": "use image::{io::Reader, DynamicImage};\nuse std::env;\nuse wasi_nn::{ExecutionTarget, GraphBuilder, GraphEncoding, TensorT"
},
{
"path": "openvino-road-segmentation-adas/visualize_inference_result.ipynb",
"chars": 381327,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n"
},
{
"path": "openvinogenai-raw/README.md",
"chars": 3230,
"preview": "# Deepseek example with WasmEdge WASI-NN OpenVINO GenAI plugin\n\nThis example demonstrates how to use WasmEdge WASI-NN Op"
},
{
"path": "openvinogenai-raw/rust/Cargo.toml",
"chars": 297,
"preview": "[package]\nname = \"openvinogenai-deepseek-raw\"\nversion = \"0.1.0\"\nauthors = [\"Second-State\"]\nreadme = \"README.md\"\nedition "
},
{
"path": "openvinogenai-raw/rust/src/main.rs",
"chars": 1560,
"preview": "use std::env;\nuse std::io::{self, Write};\nuse wasmedge_wasi_nn;\nuse wasmedge_wasi_nn::{ExecutionTarget, GraphBuilder, Gr"
},
{
"path": "pytorch-mobilenet-image/README.md",
"chars": 3290,
"preview": "# Mobilenet Example For WASI-NN with PyTorch Backend\n\nThis package is a high-level Rust bindings for [wasi-nn] example o"
},
{
"path": "pytorch-mobilenet-image/gen_mobilenet_model.py",
"chars": 501,
"preview": "import os\nimport torch\nfrom torch import jit\n\nwith torch.no_grad():\n fake_input = torch.rand(1, 3, 224, 224)\n mode"
},
{
"path": "pytorch-mobilenet-image/gen_tensor.py",
"chars": 697,
"preview": "import sys\nimport struct\nfrom PIL import Image\nfrom torchvision import transforms\n\nif __name__ == '__main__':\n input_"
},
{
"path": "pytorch-mobilenet-image/rust/Cargo.toml",
"chars": 567,
"preview": "[package]\nname = \"wasmedge-wasinn-example-mobilenet-image\"\nversion = \"0.1.0\"\nauthors = [\"Second-State\"]\nreadme = \"README"
},
{
"path": "pytorch-mobilenet-image/rust/src/imagenet_classes.rs",
"chars": 29571,
"preview": "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (th"
},
{
"path": "pytorch-mobilenet-image/rust/src/main.rs",
"chars": 3470,
"preview": "use image;\nuse std::env;\nuse std::fs::File;\nuse std::io::Read;\nuse wasi_nn;\nmod imagenet_classes;\n\npub fn main() {\n l"
},
{
"path": "pytorch-mobilenet-image/rust/src/named_model.rs",
"chars": 3462,
"preview": "use image;\nuse std::env;\nuse std::fs::File;\nuse std::io::Read;\nuse wasi_nn;\nmod imagenet_classes;\n\npub fn main() {\n l"
},
{
"path": "pytorch-yolo-image/README.md",
"chars": 1125,
"preview": "# YOLOV8 detection Example For WASI-NN with PyTorch Backend\n\nCode for a working example of object detection using the yo"
},
{
"path": "pytorch-yolo-image/get_model.py",
"chars": 302,
"preview": "# requires ultralytics to be installed\nfrom ultralytics import YOLO\n\n# Load a model, saving it to disk\n# Note there are "
},
{
"path": "pytorch-yolo-image/rust/Cargo.toml",
"chars": 522,
"preview": "[package]\nname = \"wasmedge-wasinn-example-yolo-image\"\nversion = \"0.1.0\"\nauthors = [\"Second-State\"]\nreadme = \"README.md\"\n"
},
{
"path": "pytorch-yolo-image/rust/src/main.rs",
"chars": 6467,
"preview": "use image::{self, GenericImage, RgbImage};\nuse std::env;\nuse std::fs::File;\nuse std::io::Read;\nuse wasi_nn;\n\nuse crate::"
},
{
"path": "pytorch-yolo-image/rust/src/yolo_classes.rs",
"chars": 1227,
"preview": "pub const YOLO_CLASSES: [&str; 80] = [\n \"person\",\n \"bicycle\",\n \"car\",\n \"motorbike\",\n \"aeroplane\",\n \"bu"
},
{
"path": "scripts/install_libtorch.sh",
"chars": 797,
"preview": "#!/usr/bin/env bash\n# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception\n# SPDX-FileCopyrightText: 2019-2022 Second"
},
{
"path": "scripts/install_openvino.sh",
"chars": 471,
"preview": "#!/usr/bin/env bash\nset -e\necho \"Installing OpenVINO with version 2024.2.0\"\nKEY_FILE=GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB\nw"
},
{
"path": "tflite-birds_v1-image/README.md",
"chars": 1863,
"preview": "# Mobilenet Example For WASI-NN with Tensorflow Lite Backend\n\nThis package is a high-level Rust bindings for [wasi-nn] e"
},
{
"path": "tflite-birds_v1-image/rust/Cargo.toml",
"chars": 378,
"preview": "[package]\nname = \"wasmedge-wasinn-example-tflite-bird-image\"\nversion = \"0.1.0\"\nauthors = [\"Second-State\"]\nreadme = \"READ"
},
{
"path": "tflite-birds_v1-image/rust/src/imagenet_classes.rs",
"chars": 26261,
"preview": "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (th"
},
{
"path": "tflite-birds_v1-image/rust/src/main.rs",
"chars": 2794,
"preview": "use image::io::Reader;\nuse image::DynamicImage;\nuse std::env;\nuse std::error::Error;\nuse std::fs;\nuse wasi_nn::{Executio"
},
{
"path": "wasmedge-chatTTS/.gitignore",
"chars": 12,
"preview": "asset\nconfig"
},
{
"path": "wasmedge-chatTTS/Cargo.toml",
"chars": 218,
"preview": "[package]\nname = \"wasmedge-chattts\"\nversion = \"0.1.0\"\nedition = \"2021\"\n\n[dependencies]\nserde_json = \"1.0\"\nwasmedge-wasi-"
},
{
"path": "wasmedge-chatTTS/README.md",
"chars": 2236,
"preview": "# ChatTTS example with WasmEdge WASI-NN ChatTTS plugin\nThis example demonstrates how to use the WasmEdge WASI-NN ChatTTS"
},
{
"path": "wasmedge-chatTTS/src/main.rs",
"chars": 2112,
"preview": "use hound;\nuse serde_json::json;\nuse wasmedge_wasi_nn::{\n self, ExecutionTarget, GraphBuilder, GraphEncoding, GraphEx"
},
{
"path": "wasmedge-ggml/README.md",
"chars": 10927,
"preview": "# Llama Example For WASI-NN with GGML Backend\n\n[See it in action!](https://x.com/juntao/status/1705588244602114303)\n\n## "
},
{
"path": "wasmedge-ggml/basic/Cargo.toml",
"chars": 136,
"preview": "[package]\nname = \"wasmedge-ggml-basic\"\nversion = \"0.1.0\"\nedition = \"2021\"\n\n[dependencies]\nserde_json = \"1.0\"\nwasmedge-wa"
},
{
"path": "wasmedge-ggml/basic/README.md",
"chars": 3691,
"preview": "# Basic Example For WASI-NN with GGML Backend\n\n> [!NOTE]\n> Please refer to the [wasmedge-ggml/README.md](../README.md) f"
},
{
"path": "wasmedge-ggml/basic/src/main.rs",
"chars": 5657,
"preview": "use serde_json::json;\nuse serde_json::Value;\nuse std::env;\nuse std::io;\nuse wasmedge_wasi_nn::{\n self, BackendError, "
},
{
"path": "wasmedge-ggml/chatml/Cargo.toml",
"chars": 137,
"preview": "[package]\nname = \"wasmedge-ggml-chatml\"\nversion = \"0.1.0\"\nedition = \"2021\"\n\n[dependencies]\nserde_json = \"1.0\"\nwasmedge-w"
},
{
"path": "wasmedge-ggml/chatml/README.md",
"chars": 1692,
"preview": "# `chatml`\n\n## Execute\n\n````console\n$ wasmedge --dir .:. \\\n --nn-preload default:GGML:AUTO:mixtral-8x7b-instruct-v0.1.Q"
},
{
"path": "wasmedge-ggml/chatml/src/main.rs",
"chars": 6939,
"preview": "use serde_json::{json, Value};\nuse std::env;\nuse std::io;\nuse wasmedge_wasi_nn::{\n self, BackendError, Error, Executi"
},
{
"path": "wasmedge-ggml/command-r/Cargo.toml",
"chars": 140,
"preview": "[package]\nname = \"wasmedge-ggml-command-r\"\nversion = \"0.1.0\"\nedition = \"2021\"\n\n[dependencies]\nserde_json = \"1.0\"\nwasmedg"
},
{
"path": "wasmedge-ggml/command-r/README.md",
"chars": 1412,
"preview": "# Command-R Example For WASI-NN with GGML Backend\n\n> [!NOTE]\n> Please refer to the [wasmedge-ggml/README.md](../README.m"
},
{
"path": "wasmedge-ggml/command-r/src/main.rs",
"chars": 9784,
"preview": "use serde_json::json;\nuse serde_json::Value;\nuse std::env;\nuse std::io;\nuse wasmedge_wasi_nn::{\n self, BackendError, "
},
{
"path": "wasmedge-ggml/embedding/Cargo.toml",
"chars": 146,
"preview": "[package]\nname = \"wasmedge-ggml-llama-embedding\"\nversion = \"0.1.0\"\nedition = \"2021\"\n\n[dependencies]\nserde_json = \"1.0\"\nw"
},
{
"path": "wasmedge-ggml/embedding/README.md",
"chars": 1392,
"preview": "# Embedding Example For WASI-NN with GGML Backend\n\n> [!NOTE]\n> Please refer to the [wasmedge-ggml/README.md](../README.m"
},
{
"path": "wasmedge-ggml/embedding/src/main.rs",
"chars": 6475,
"preview": "use serde_json::{json, Value};\nuse std::env;\nuse std::io::{self};\nuse wasmedge_wasi_nn::{\n self, BackendError, Error,"
},
{
"path": "wasmedge-ggml/gemma/Cargo.toml",
"chars": 136,
"preview": "[package]\nname = \"wasmedge-ggml-gemma\"\nversion = \"0.1.0\"\nedition = \"2021\"\n\n[dependencies]\nserde_json = \"1.0\"\nwasmedge-wa"
},
{
"path": "wasmedge-ggml/gemma/README.md",
"chars": 2544,
"preview": "# `gemma`\n\n## Get Model\n\n```console\nwget https://huggingface.co/second-state/Gemma-2b-it-GGUF/resolve/main/gemma-2b-it-Q"
},
{
"path": "wasmedge-ggml/gemma/src/main.rs",
"chars": 7127,
"preview": "use serde_json::json;\nuse serde_json::Value;\nuse std::env;\nuse std::io;\nuse wasmedge_wasi_nn::{\n self, BackendError, "
},
{
"path": "wasmedge-ggml/gemma-3/Cargo.toml",
"chars": 269,
"preview": "[package]\nname = \"wasmedge-ggml-gemma-3\"\nversion = \"0.1.0\"\nedition = \"2021\"\n\n[dependencies]\nserde_json = \"1.0\"\nwasmedge-"
},
{
"path": "wasmedge-ggml/gemma-3/README.md",
"chars": 5480,
"preview": "# Gemma-3 Example For WASI-NN with GGML Backend\n\n> [!NOTE]\n> Please refer to the [wasmedge-ggml/README.md](../README.md)"
},
{
"path": "wasmedge-ggml/gemma-3/src/base64.rs",
"chars": 729766,
"preview": "use serde_json::Value;\nuse std::collections::HashMap;\nuse std::env;\nuse std::io;\nuse wasmedge_wasi_nn::{\n self, Backe"
},
{
"path": "wasmedge-ggml/gemma-3/src/main.rs",
"chars": 7212,
"preview": "use serde_json::Value;\nuse std::collections::HashMap;\nuse std::env;\nuse std::io;\nuse wasmedge_wasi_nn::{\n self, Backe"
},
{
"path": "wasmedge-ggml/gemma-4/Cargo.toml",
"chars": 198,
"preview": "[package]\nname = \"wasmedge-ggml-gemma-4\"\nversion = \"0.1.0\"\nedition = \"2021\"\n\n[dependencies]\nserde_json = \"1.0\"\nwasmedge-"
},
{
"path": "wasmedge-ggml/gemma-4/README.md",
"chars": 2673,
"preview": "# Gemma-4 Example For WASI-NN With GGML Backend\n## Requirements\n\n- WasmEdge with the WASI-NN GGML plugin installed\n- Rus"
},
{
"path": "wasmedge-ggml/gemma-4/src/main.rs",
"chars": 9744,
"preview": "use serde_json::Value;\nuse std::collections::HashMap;\nuse std::env;\nuse std::io;\nuse wasmedge_wasi_nn::{\n self, Backe"
},
{
"path": "wasmedge-ggml/grammar/Cargo.toml",
"chars": 138,
"preview": "[package]\nname = \"wasmedge-ggml-grammar\"\nversion = \"0.1.0\"\nedition = \"2021\"\n\n[dependencies]\nserde_json = \"1.0\"\nwasmedge-"
},
{
"path": "wasmedge-ggml/grammar/README.md",
"chars": 1505,
"preview": "# Grammar Example For WASI-NN with GGML Backend\n\n> [!NOTE]\n> Please refer to the [wasmedge-ggml/README.md](../README.md)"
},
{
"path": "wasmedge-ggml/grammar/src/main.rs",
"chars": 6479,
"preview": "use serde_json::json;\nuse serde_json::Value;\nuse std::env;\nuse std::io;\nuse wasmedge_wasi_nn::{\n self, BackendError, "
},
{
"path": "wasmedge-ggml/json-schema/Cargo.toml",
"chars": 142,
"preview": "[package]\nname = \"wasmedge-ggml-json-schema\"\nversion = \"0.1.0\"\nedition = \"2021\"\n\n[dependencies]\nserde_json = \"1.0\"\nwasme"
},
{
"path": "wasmedge-ggml/json-schema/README.md",
"chars": 1672,
"preview": "# JSON Schema Example For WASI-NN with GGML Backend\n\n> [!NOTE]\n> Please refer to the [wasmedge-ggml/README.md](../README"
},
{
"path": "wasmedge-ggml/json-schema/src/main.rs",
"chars": 6689,
"preview": "use serde_json::json;\nuse serde_json::Value;\nuse std::env;\nuse std::io;\nuse wasmedge_wasi_nn::{\n self, BackendError, "
},
{
"path": "wasmedge-ggml/llama/Cargo.toml",
"chars": 136,
"preview": "[package]\nname = \"wasmedge-ggml-llama\"\nversion = \"0.1.0\"\nedition = \"2021\"\n\n[dependencies]\nserde_json = \"1.0\"\nwasmedge-wa"
},
{
"path": "wasmedge-ggml/llama/README.md",
"chars": 1681,
"preview": "# `llama`\n\n## Execute - llama 3\n\n### Model Download Link\n\n```console\nwget https://huggingface.co/QuantFactory/Meta-Llama"
},
{
"path": "wasmedge-ggml/llama/src/main.rs",
"chars": 7863,
"preview": "use serde_json::json;\nuse serde_json::Value;\nuse std::env;\nuse std::io;\nuse wasmedge_wasi_nn::{\n self, BackendError, "
},
{
"path": "wasmedge-ggml/llama-stream/Cargo.toml",
"chars": 143,
"preview": "[package]\nname = \"wasmedge-ggml-llama-stream\"\nversion = \"0.1.0\"\nedition = \"2021\"\n\n[dependencies]\nserde_json = \"1.0\"\nwasm"
},
{
"path": "wasmedge-ggml/llama-stream/README.md",
"chars": 1702,
"preview": "# `llama-stream`\n\n## Execute - llama 3\n\n### Model Download Link\n\n```console\nwget https://huggingface.co/QuantFactory/Met"
},
{
"path": "wasmedge-ggml/llama-stream/src/main.rs",
"chars": 10655,
"preview": "use serde_json::json;\nuse serde_json::Value;\nuse std::env;\nuse std::io::{self, Write};\nuse wasmedge_wasi_nn::{\n self,"
},
{
"path": "wasmedge-ggml/llava/Cargo.toml",
"chars": 136,
"preview": "[package]\nname = \"wasmedge-ggml-llava\"\nversion = \"0.1.0\"\nedition = \"2021\"\n\n[dependencies]\nserde_json = \"1.0\"\nwasmedge-wa"
},
{
"path": "wasmedge-ggml/llava/README.md",
"chars": 4085,
"preview": "# Llava Example For WASI-NN with GGML Backend\n\n> [!NOTE]\n> Please refer to the [wasmedge-ggml/README.md](../README.md) f"
},
{
"path": "wasmedge-ggml/llava/src/main.rs",
"chars": 6940,
"preview": "use serde_json::Value;\nuse std::collections::HashMap;\nuse std::env;\nuse std::io;\nuse wasmedge_wasi_nn::{\n self, Backe"
},
{
"path": "wasmedge-ggml/llava-base64-stream/Cargo.toml",
"chars": 150,
"preview": "[package]\nname = \"wasmedge-ggml-llava-base64-stream\"\nversion = \"0.1.0\"\nedition = \"2021\"\n\n[dependencies]\nserde_json = \"1."
},
{
"path": "wasmedge-ggml/llava-base64-stream/README.md",
"chars": 1438,
"preview": "# Llava Example For WASI-NN with GGML Backend\n\n> [!NOTE]\n> Please refer to the [wasmedge-ggml/README.md](../README.md) f"
},
{
"path": "wasmedge-ggml/llava-base64-stream/src/main.rs",
"chars": 1086763,
"preview": "use serde_json::Value;\nuse std::collections::HashMap;\nuse std::env;\nuse std::io::{self, Write};\nuse wasmedge_wasi_nn::{\n"
},
{
"path": "wasmedge-ggml/multimodel/Cargo.toml",
"chars": 141,
"preview": "[package]\nname = \"wasmedge-ggml-multimodel\"\nversion = \"0.1.0\"\nedition = \"2021\"\n\n[dependencies]\nserde_json = \"1.0\"\nwasmed"
},
{
"path": "wasmedge-ggml/multimodel/README.md",
"chars": 2626,
"preview": "\n# Multiple Models Example For WASI-NN with GGML Backend\n\n> [!NOTE]\n> Please refer to the [wasmedge-ggml/README.md](../R"
},
{
"path": "wasmedge-ggml/multimodel/src/main.rs",
"chars": 6797,
"preview": "use serde_json::json;\nuse serde_json::Value;\nuse std::env;\nuse std::io;\nuse wasmedge_wasi_nn::{\n self, BackendError, "
},
{
"path": "wasmedge-ggml/nnrpc/Cargo.toml",
"chars": 136,
"preview": "[package]\nname = \"wasmedge-ggml-nnrpc\"\nversion = \"0.1.0\"\nedition = \"2021\"\n\n[dependencies]\nserde_json = \"1.0\"\nwasmedge-wa"
},
{
"path": "wasmedge-ggml/nnrpc/README.md",
"chars": 1399,
"preview": "# RPC Example For WASI-NN with GGML Backend\n\n> [!NOTE]\n> Please refer to the [wasmedge-ggml/README.md](../README.md) for"
},
{
"path": "wasmedge-ggml/nnrpc/src/main.rs",
"chars": 6682,
"preview": "use serde_json::json;\nuse serde_json::Value;\nuse std::env;\nuse std::io;\nuse wasmedge_wasi_nn::{\n self, BackendError, "
},
{
"path": "wasmedge-ggml/qwen/Cargo.toml",
"chars": 233,
"preview": "[package]\nname = \"wasmedge-ggml-qwen\"\nversion = \"0.1.0\"\nedition = \"2021\"\n\n# See more keys and their definitions at https"
},
{
"path": "wasmedge-ggml/qwen/README.md",
"chars": 959,
"preview": "# `通义千问`\n\n## Execute - Tong Yi Qwen\n\n### Model Download Link\n\n```console\nwget https://huggingface.co/Qwen/Qwen1.5-0.5B-C"
},
{
"path": "wasmedge-ggml/qwen/src/main.rs",
"chars": 4330,
"preview": "use serde_json::json;\nuse serde_json::Value;\nuse std::env;\nuse std::io;\nuse wasmedge_wasi_nn::{\n self, BackendError, "
},
{
"path": "wasmedge-ggml/qwen2vl/Cargo.toml",
"chars": 138,
"preview": "[package]\nname = \"wasmedge-ggml-qwen2vl\"\nversion = \"0.1.0\"\nedition = \"2021\"\n\n[dependencies]\nserde_json = \"1.0\"\nwasmedge-"
},
{
"path": "wasmedge-ggml/qwen2vl/README.md",
"chars": 1585,
"preview": "# Qwen-2VL Example For WASI-NN with GGML Backend\n\n> [!NOTE]\n> Please refer to the [wasmedge-ggml/README.md](../README.md"
},
{
"path": "wasmedge-ggml/qwen2vl/src/main.rs",
"chars": 7122,
"preview": "use serde_json::Value;\nuse std::collections::HashMap;\nuse std::env;\nuse std::io;\nuse wasmedge_wasi_nn::{\n self, Backe"
},
{
"path": "wasmedge-ggml/test/model-not-found/Cargo.toml",
"chars": 146,
"preview": "[package]\nname = \"wasmedge-ggml-model-not-found\"\nversion = \"0.1.0\"\nedition = \"2021\"\n\n[dependencies]\nserde_json = \"1.0\"\nw"
},
{
"path": "wasmedge-ggml/test/model-not-found/README.md",
"chars": 249,
"preview": "# `model-not-found`\n\nEnsure that we get the `ModelNotFound` error when the model does not exist.\n\n## Execute\n\n```console"
},
{
"path": "wasmedge-ggml/test/model-not-found/src/main.rs",
"chars": 694,
"preview": "use std::env;\nuse wasmedge_wasi_nn::{self, BackendError, Error, ExecutionTarget, GraphBuilder, GraphEncoding};\n\nfn main("
},
{
"path": "wasmedge-ggml/test/phi-3/Cargo.toml",
"chars": 136,
"preview": "[package]\nname = \"wasmedge-ggml-phi-3\"\nversion = \"0.1.0\"\nedition = \"2021\"\n\n[dependencies]\nserde_json = \"1.0\"\nwasmedge-wa"
},
{
"path": "wasmedge-ggml/test/phi-3/README.md",
"chars": 349,
"preview": "# `phi-3-mini`\n\nEnsure that we can use the `phi-3-mini` model.\n\n## Execute\n\n```console\n$ curl -LO https://huggingface.co"
},
{
"path": "wasmedge-ggml/test/phi-3/src/main.rs",
"chars": 3440,
"preview": "use serde_json::json;\nuse serde_json::Value;\nuse std::env;\nuse wasmedge_wasi_nn::{\n self, BackendError, Error, Execut"
},
{
"path": "wasmedge-ggml/test/set-input-twice/Cargo.toml",
"chars": 146,
"preview": "[package]\nname = \"wasmedge-ggml-set-input-twice\"\nversion = \"0.1.0\"\nedition = \"2021\"\n\n[dependencies]\nserde_json = \"1.0\"\nw"
},
{
"path": "wasmedge-ggml/test/set-input-twice/README.md",
"chars": 365,
"preview": "# `set-input-twice`\n\nEnsure that we get the same result from executing `set_input` twice.\n\n## Execute\n\n```console\n$ curl"
},
{
"path": "wasmedge-ggml/test/set-input-twice/src/main.rs",
"chars": 3986,
"preview": "use serde_json::json;\nuse serde_json::Value;\nuse std::env;\nuse wasmedge_wasi_nn::{\n self, ExecutionTarget, GraphBuild"
},
{
"path": "wasmedge-ggml/test/unload/Cargo.toml",
"chars": 137,
"preview": "[package]\nname = \"wasmedge-ggml-unload\"\nversion = \"0.1.0\"\nedition = \"2021\"\n\n[dependencies]\nserde_json = \"1.0\"\nwasmedge-w"
},
{
"path": "wasmedge-ggml/test/unload/README.md",
"chars": 358,
"preview": "# `unload`\n\nEnsure that we can unload and reload the graph multiple times without errors.\n\n## Execute\n\n```console\n$ curl"
},
{
"path": "wasmedge-ggml/test/unload/src/main.rs",
"chars": 3765,
"preview": "use serde_json::json;\nuse serde_json::Value;\nuse std::env;\nuse wasmedge_wasi_nn::{\n self, BackendError, Error, Execut"
},
{
"path": "wasmedge-ggml/tts/Cargo.toml",
"chars": 134,
"preview": "[package]\nname = \"wasmedge-ggml-tts\"\nversion = \"0.1.0\"\nedition = \"2021\"\n\n[dependencies]\nserde_json = \"1.0\"\nwasmedge-wasi"
},
{
"path": "wasmedge-ggml/tts/README.md",
"chars": 1016,
"preview": "# `tts` - Text-to-Speech Example\n\n## Model Download\n\n```console\nwget https://huggingface.co/second-state/OuteTTS-0.2-500"
},
{
"path": "wasmedge-ggml/tts/src/main.rs",
"chars": 5977,
"preview": "use serde_json::Value;\nuse std::collections::HashMap;\nuse std::env;\nuse std::fs::File;\nuse std::io::Write;\nuse wasmedge_"
},
{
"path": "wasmedge-ggml/whisper/Cargo.toml",
"chars": 111,
"preview": "[package]\nname = \"whisper-basic\"\nversion = \"0.1.0\"\nedition = \"2021\"\n\n[dependencies]\nwasmedge-wasi-nn = \"0.8.0\"\n"
},
{
"path": "wasmedge-ggml/whisper/README.md",
"chars": 2273,
"preview": "# Basic Example For WASI-NN with Whisper Backend\n\nThis example is for a basic audio recognition with WASI-NN whisper bac"
},
{
"path": "wasmedge-ggml/whisper/src/main.rs",
"chars": 1184,
"preview": "use std::env;\nuse std::error::Error;\nuse std::fs;\nuse wasmedge_wasi_nn::{ExecutionTarget, GraphBuilder, GraphEncoding, T"
},
{
"path": "wasmedge-mlx/llama/Cargo.toml",
"chars": 129,
"preview": "[package]\nname = \"wasmedge-mlx\"\nversion = \"0.1.0\"\nedition = \"2021\"\n\n[dependencies]\nserde_json = \"1.0\"\nwasmedge-wasi-nn ="
},
{
"path": "wasmedge-mlx/llama/README.md",
"chars": 3083,
"preview": "# MLX example with WasmEdge WASI-NN MLX plugin\n\nThis example demonstrates using WasmEdge WASI-NN MLX plugin to perform a"
},
{
"path": "wasmedge-mlx/llama/src/main.rs",
"chars": 1818,
"preview": "use serde_json::json;\nuse std::env;\nuse wasmedge_wasi_nn::{\n self, ExecutionTarget, GraphBuilder, GraphEncoding, Grap"
},
{
"path": "wasmedge-mlx/vlm/Cargo.toml",
"chars": 250,
"preview": "[package]\nname = \"wasmedge-vlm\"\nversion = \"0.1.0\"\nedition = \"2021\"\n\n[dependencies]\nserde_json = \"1.0\"\nwasmedge-wasi-nn ="
},
{
"path": "wasmedge-mlx/vlm/README.md",
"chars": 2481,
"preview": "# VLM example with WasmEdge WASI-NN MLX plugin\n\nThis example demonstrates using WasmEdge WASI-NN MLX plugin to perform a"
},
{
"path": "wasmedge-mlx/vlm/decode.py",
"chars": 1527,
"preview": "from transformers import AutoProcessor\nimport mlx.core as mx\nimport sys\n\n\ndef _remove_space(x):\n if x and x[0] == \" \""
},
{
"path": "wasmedge-mlx/vlm/encode.py",
"chars": 1641,
"preview": "\nfrom transformers import AutoProcessor\nimport mlx.core as mx\nfrom PIL import Image, ImageOps\nimport sys\n\n\ndef encode(pr"
},
{
"path": "wasmedge-mlx/vlm/src/main.rs",
"chars": 3853,
"preview": "use rust_processor::auto::processing_auto::AutoProcessor;\nuse rust_processor::gemma3::detokenizer::decode;\nuse rust_proc"
},
{
"path": "wasmedge-mlx/whisper/Cargo.toml",
"chars": 133,
"preview": "[package]\nname = \"wasmedge-whisper\"\nversion = \"0.1.0\"\nedition = \"2024\"\n\n[dependencies]\nserde_json = \"1.0\"\nwasmedge-wasi-"
},
{
"path": "wasmedge-mlx/whisper/README.md",
"chars": 1830,
"preview": "# Whisper example with WasmEdge WASI-NN MLX plugin\n\nThis example demonstrates using WasmEdge WASI-NN MLX plugin to perfo"
},
{
"path": "wasmedge-mlx/whisper/src/main.rs",
"chars": 1932,
"preview": "use std::env;\nuse wasmedge_wasi_nn::{\n self, ExecutionTarget, GraphBuilder, GraphEncoding, GraphExecutionContext, Ten"
},
{
"path": "wasmedge-piper/Cargo.toml",
"chars": 233,
"preview": "[package]\nname = \"wasmedge-piper\"\nversion = \"0.1.0\"\nedition = \"2021\"\n\n# See more keys and their definitions at https://d"
},
{
"path": "wasmedge-piper/README.md",
"chars": 5086,
"preview": "# Text to speech example with WasmEdge WASI-NN Piper plugin\n\nThis example demonstrates how to use WasmEdge WASI-NN Piper"
},
{
"path": "wasmedge-piper/config.schema.json",
"chars": 2423,
"preview": "{\n \"$schema\": \"http://json-schema.org/draft-07/schema#\",\n \"properties\": {\n \"model\": {\n \"descript"
},
{
"path": "wasmedge-piper/dependencies.d2",
"chars": 181,
"preview": "direction: right\nWasmEdge WASI-NN Piper plugin -> piper\npiper -> piper-phonemize\npiper -> espeak-ng\npiper -> onnxruntime"
},
{
"path": "wasmedge-piper/json_input.schema.json",
"chars": 531,
"preview": "{\n \"$schema\": \"http://json-schema.org/draft-07/schema#\",\n \"properties\": {\n \"text\": {\n \"descripti"
},
{
"path": "wasmedge-piper/src/main.rs",
"chars": 1244,
"preview": "fn main() {\n // create graph with the config\n let config = serde_json::json!({\n \"model\": \"en_US-lessac-medi"
},
{
"path": "wasmedge-tf-llama/README.md",
"chars": 1728,
"preview": "# Llama Example For WasmEdge-Tensorflow plug-in\n\nThis package is a high-level Rust bindings for [WasmEdge-TensorFlow plu"
},
{
"path": "wasmedge-tf-llama/rust/Cargo.toml",
"chars": 360,
"preview": "[package]\nname = \"wasmedge-tf-example-llama\"\nversion = \"0.1.0\"\nauthors = [\"victoryang00\"]\nreadme = \"README.md\"\nedition ="
},
{
"path": "wasmedge-tf-llama/rust/src/main.rs",
"chars": 6798,
"preview": "#![feature(str_as_str)]\nuse bytemuck::{cast_slice, cast_slice_mut};\nuse std::collections::HashMap;\nuse std::env;\nuse std"
},
{
"path": "wasmedge-tf-mobilenet_v2/README.md",
"chars": 1788,
"preview": "# Mobilenet Example For WasmEdge-Tensorflow plug-in\n\nThis package is a high-level Rust bindings for [WasmEdge-TensorFlow"
},
{
"path": "wasmedge-tf-mobilenet_v2/imagenet_slim_labels.txt",
"chars": 10480,
"preview": "dummy\ntench\ngoldfish\ngreat white shark\ntiger shark\nhammerhead\nelectric ray\nstingray\ncock\nhen\nostrich\nbrambling\ngoldfinch"
},
{
"path": "wasmedge-tf-mobilenet_v2/rust/Cargo.toml",
"chars": 217,
"preview": "[package]\nname = \"wasmedge-tf-example-mobilenet\"\nversion = \"0.1.0\"\nauthors = [\"Second-State\"]\nreadme = \"README.md\"\nediti"
},
{
"path": "wasmedge-tf-mobilenet_v2/rust/src/main.rs",
"chars": 1873,
"preview": "use std::env;\nuse std::fs::File;\nuse std::io::Read;\nuse wasmedge_tensorflow_interface;\n\nfn main() {\n let args: Vec<St"
},
{
"path": "wasmedge-tf-mtcnn/README.md",
"chars": 1548,
"preview": "# Mobilenet Example For WasmEdge-Tensorflow plug-in\n\nThis package is a high-level Rust bindings for [WasmEdge-TensorFlow"
},
{
"path": "wasmedge-tf-mtcnn/rust/Cargo.toml",
"chars": 326,
"preview": "[package]\nname = \"wasmedge-tf-example-mtcnn\"\nversion = \"0.1.0\"\nauthors = [\"Second-State\"]\nreadme = \"README.md\"\nedition ="
},
{
"path": "wasmedge-tf-mtcnn/rust/src/main.rs",
"chars": 2248,
"preview": "use std::env;\nuse std::fs::File;\nuse std::io::Read;\nuse wasmedge_tensorflow_interface::TFSession;\n\nuse image::{GenericIm"
}
]
// ... and 40 more files (download for full content)
About this extraction
This page contains the full source code of the second-state/WasmEdge-WASINN-examples GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 189 files (52.4 MB), approximately 774.1k tokens, and a symbol index with 240 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
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