[Examples] add Pytorch image demo

.gitignore

@@ -9,5 +9,8 @@ openvino-mobilenet-raw/mobilenet.bin
 openvino-mobilenet-raw/mobilenet.xml
 openvino-mobilenet-raw/tensor-1x224x224x3-f32.bgr
 
+pytorch-mobilenet-image/fixtures/input.jpg
+# pytorch-mobilenet-image/mobilenet.pt
+
 .DS_Store
 Cargo.lock

pytorch-mobilenet-image/README.md

@@ -0,0 +1,67 @@
+# Mobilenet example for WASI-NN
+
+This package is a high-level Rust bindings for [wasi-nn] example of Mobilenet.
+
+[wasi-nn]: https://github.com/WebAssembly/wasi-nn
+
+## Dependencies
+
+This crate depends on the `wasi-nn` in the `Cargo.toml`:
+
+```toml
+[dependencies]
+wasi-nn = "0.1.0"
+```
+
+## Build
+
+Compile the application to WebAssembly:
+
+```bash
+cargo build --target=wasm32-wasi --release
+```
+
+The output WASM file will be at `target/wasm32-wasi/release/wasmedge-wasinn-example-mobilenet-image.wasm`.
+To speed up the image processing, we can enable the AOT mode in WasmEdge with:
+
+```bash
+wasmedgec rust/target/wasm32-wasi/release/wasmedge-wasinn-example-mobilenet-image.wasm wasmedge-wasinn-example-mobilenet-image.wasm
+```
+
+## Run
+
+First generate the fixture of the pre-trained mobilenet with the script:
+
+```bash
+./download_data.sh fixtures && cd fixtures
+python -m pip install -r requirements.txt
+# generate the model fixture
+python generate_mobilenet.py
+```
+
+(or you can use the pre-generated fixture in `fixtures/mobilenet.pt`)
+
+The above will download a testing image `input.jpg`
+![](https://github.com/bytecodealliance/wasi-nn/raw/main/rust/images/1.jpg)
+as well as a pre-trained mobilenet model, then convert the model into the torchscript model for C++.
+
+And execute the WASM with the `wasmedge` with PyTorch supporting:
+
+```bash
+wasmedge --dir .:. wasmedge-wasinn-example-mobilenet-image.wasm fixtures/mobilenet.pt input.jpg
+```
+
+You will get the output:
+
+```console
+Read torchscript binaries, size in bytes: 14376924
+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](20.6681)banana
+   2.) [940](12.1483)spaghetti squash
+   3.) [951](11.5748)lemon
+   4.) [950](10.4899)orange
+   5.) [953](9.4834)pineapple, ananas
+```

pytorch-mobilenet-image/download_data.sh

@@ -0,0 +1,6 @@
+FIXTURE=https://github.com/intel/openvino-rs/raw/v0.3.3/crates/openvino/tests/fixtures/mobilenet
+TODIR=$1
+
+if [ ! -f $TODIR/input.jpg ]; then
+    wget --no-clobber https://github.com/bytecodealliance/wasi-nn/raw/main/rust/images/1.jpg -O $TODIR/input.jpg
+fi

pytorch-mobilenet-image/fixtures/generate_mobilenet.py

@@ -0,0 +1,57 @@
+
+# adapted from https://pytorch.org/hub/pytorch_vision_mobilenet_v2/
+
+import torch
+import struct
+# Download an example image from the pytorch website
+url, filename = (
+    "https://github.com/bytecodealliance/wasi-nn/raw/main/rust/images/1.jpg", "../input.jpg")
+# import urllib
+# try:
+#     urllib.URLopener().retrieve(url, filename)
+# except:
+#     urllib.request.urlretrieve(url, filename)
+
+# sample execution (requires torchvision)
+model = torch.hub.load('pytorch/vision:v0.10.0',
+                       'mobilenet_v2', pretrained=True)
+model.eval()
+
+from PIL import Image
+from torchvision import transforms
+input_image = Image.open(filename)
+print(input_image.mode)
+preprocess = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                         std=[0.229, 0.224, 0.225]),
+])
+input_tensor = preprocess(input_image)
+# create a mini-batch as expected by the model
+input_batch = input_tensor.unsqueeze(0)
+with open("image-1-3-244-244.rgb", 'wb') as f:
+    order_data = input_batch.reshape(-1)
+    for d in order_data:
+        d = d.item()
+        f.write(struct.pack('f', d))
+
+
+# move the input and model to GPU for speed if available
+if torch.cuda.is_available():
+    input_batch = input_batch.to('cuda')
+    model.to('cuda')
+
+with torch.no_grad():
+    output = model(input_batch)
+# Tensor of shape 1000, with confidence scores over Imagenet's 1000 classes
+# The output has unnormalized scores. To get probabilities, you can run a softmax on it.
+probabilities = torch.nn.functional.softmax(output[0], dim=0)
+# print(probabilities)
+
+with open("imagenet_classes.txt", "r") as f:
+    categories = [s.strip() for s in f.readlines()]
+# Show top categories per image
+top5_prob, top5_catid = torch.topk(probabilities, 5)
+for i in range(top5_prob.size(0)):
+    print(top5_catid[i], categories[top5_catid[i]], top5_prob[i].item())