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Don’t see the model you want? Bring your own.

Deploy optimized models on real devices in minutes

Qualcomm® AI Hub simplifies deploying AI models for vision, audio, and speech applications to edge devices within minutes. This example shows how you can deploy your own PyTorch model on a real hosted device. See the documentation for more details. If you hit any issues with your model (performance, accuracy or otherwise), please file an issue here.

import qai_hub as hub
import torch
from torchvision.models import mobilenet_v2
import requests
import numpy as np
from PIL import Image

# Using pre-trained MobileNet
torch_model = mobilenet_v2(pretrained=True)
torch_model.eval()

# Step 1: Trace model
input_shape = (1, 3, 224, 224)
example_input = torch.rand(input_shape)
traced_torch_model = torch.jit.trace(torch_model, example_input)

# Step 2: Compile model
compile_job = hub.submit_compile_job(
    model=traced_torch_model,
    device=hub.Device("Snapdragon X Elite CRD"),
    input_specs=dict(image=input_shape),
    options="--target_runtime onnx",
)

# Step 3: Profile on cloud-hosted device
target_model = compile_job.get_target_model()
profile_job = hub.submit_profile_job(
    model=target_model,
    device=hub.Device("Snapdragon X Elite CRD"),
)

# Step 4: Run inference on cloud-hosted device
sample_image_url = (
    "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/apidoc/input_image1.jpg"
)
response = requests.get(sample_image_url, stream=True)
response.raw.decode_content = True
image = Image.open(response.raw).resize((224, 224))
input_array = np.expand_dims(
    np.transpose(np.array(image, dtype=np.float32) / 255.0, (2, 0, 1)), axis=0
)

# Run inference using the on-device model on the input image
inference_job = hub.submit_inference_job(
    model=target_model,
    device=hub.Device("Snapdragon X Elite CRD"),
    inputs=dict(image=[input_array]),
)
on_device_output = inference_job.download_output_data()

# Step 5: Post-processing the on-device output
output_name = list(on_device_output.keys())[0]
out = on_device_output[output_name][0]
on_device_probabilities = np.exp(out) / np.sum(np.exp(out), axis=1)

# Read the class labels for imagenet
sample_classes = "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/apidoc/imagenet_classes.txt"
response = requests.get(sample_classes, stream=True)
response.raw.decode_content = True
categories = [str(s.strip()) for s in response.raw]

# Print top five predictions for the on-device model
print("Top-5 On-Device predictions:")
top5_classes = np.argsort(on_device_probabilities[0], axis=0)[-5:]
for c in reversed(top5_classes):
    print(f"{c} {categories[c]:20s} {on_device_probabilities[0][c]:>6.1%}")

# Step 6: Download model
target_model = compile_job.get_target_model()
target_model.download("mobilenet_v2.onnx")
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