برنامج تعليمي عملي للترميز على نماذج Qualcomm AI Hub للتصنيف واكتشاف الكائنات والنشر المدرك للأجهزة

import subprocess, sys, os, glob, textwrap, traceback
import numpy as np, torch
from PIL import Image
import matplotlib.pyplot as plt
def pip_install(*pkgs):
   subprocess.run([sys.executable, "-m", "pip", "install", "-q", *pkgs], check=True)
pip_install("qai_hub_models")
OUT_DIR = "/content/qaihm_out"; os.makedirs(OUT_DIR, exist_ok=True)
torch.set_grad_enabled(False)
def to_nchw(value):
   arr = value[0] if isinstance(value, (list, tuple)) else value
   t = torch.from_numpy(np.asarray(arr, dtype=np.float32))
   if t.ndim == 3:
       t = t.unsqueeze(0)
   if t.ndim == 4 and t.shape[1] != 3 and t.shape[-1] == 3:
       t = t.permute(0, 3, 1, 2).contiguous()
   return t

import pkgutil, qai_hub_models.models as _m
model_ids = sorted(n for _, n, p in pkgutil.iter_modules(_m.__path__)
                  if p and not n.startswith("_"))
print(f">>> {len(model_ids)} models available. First 40:\n")
print(textwrap.fill(", ".join(model_ids[:40]), 100), "\n")
from qai_hub_models.models.mobilenet_v2 import Model as MobileNetV2
model = MobileNetV2.from_pretrained().eval()
spec = model.get_input_spec()
input_name = list(spec.keys())[0]
print(">>> Input:", input_name, spec[input_name].shape, spec[input_name].dtype)
from torchvision.models import MobileNet_V2_Weights
IMAGENET_CLASSES = MobileNet_V2_Weights.IMAGENET1K_V1.meta["categories"]
def top5(logits):
   if logits.ndim == 1: logits = logits.unsqueeze(0)
   probs = torch.softmax(logits, dim=1)[0]
   conf, idx = probs.topk(5)
   return [(IMAGENET_CLASSES[i], float(c)) for c, i in zip(conf, idx)]

sample = model.sample_inputs()
x = to_nchw(sample[input_name])
print(">>> fed tensor shape:", tuple(x.shape))
print("\n>>> Top-5 for the built-in sample input:")
for label, conf in top5(model(x)):
   print(f"    {conf:6.2%}  {label}")
from torchvision import transforms
preprocess = transforms.Compose([
   transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(),
])
img = None
try:
   import urllib.request
   p = os.path.join(OUT_DIR, "input.jpg")
   urllib.request.urlretrieve(
       "https://raw.githubusercontent.com/pytorch/hub/master/images/dog.jpg", p)
   img = Image.open(p).convert("RGB")
except Exception as e:
   print(">>> photo download skipped:", e)
if img is not None:
   preds = top5(model(preprocess(img).unsqueeze(0)))
   print("\n>>> Top-5 for the downloaded photo:")
   for label, conf in preds: print(f"    {conf:6.2%}  {label}")
   plt.figure(figsize=(5,5)); plt.imshow(img); plt.axis("off")
   plt.title(f"{preds[0][0]}  ({preds[0][1]:.1%})"); plt.show()

def run_demo(module, extra=None, timeout=900):
   cmd = [sys.executable, "-m", module, "--eval-mode", "fp",
          "--output-dir", OUT_DIR] + (extra or [])
   print(f"\n>>> {' '.join(cmd)}")
   try:
       r = subprocess.run(cmd, capture_output=True, text=True, timeout=timeout)
       print("\n".join((r.stdout + r.stderr).strip().splitlines()[-25:]))
   except Exception as e:
       print(">>> demo skipped:", e)
run_demo("qai_hub_models.models.mobilenet_v2.demo")
try:
   pip_install("qai_hub_models[yolov7]")
   run_demo("qai_hub_models.models.yolov7.demo")
   imgs = sorted(glob.glob(OUT_DIR + "/*.png") + glob.glob(OUT_DIR + "/*.jpg"),
                 key=os.path.getmtime)
   if imgs:
       plt.figure(figsize=(9,9)); plt.imshow(Image.open(imgs[-1]).convert("RGB"))
       plt.axis("off"); plt.title("YOLOv7 detections"); plt.show()
   else:
       print(">>> no output image found (results may have printed instead).")
except Exception:
   print(">>> YOLOv7 section skipped:\n", traceback.format_exc())

try:
   import qai_hub as hub
   devices = hub.get_devices()
   print(f"\n>>> Authenticated. {len(devices)} cloud devices available.")
   device = hub.Device("Samsung Galaxy S24 (Family)")
   sample = model.sample_inputs()
   nchw = to_nchw(sample[input_name])
   traced = torch.jit.trace(model, [nchw])
   cloud_inputs = {input_name: [nchw.numpy()]}
   cj = hub.submit_compile_job(model=traced, device=device,
                               input_specs=model.get_input_spec(),
                               options="--target_runtime tflite")
   target = cj.get_target_model(); print(">>> compiled:", cj.url)
   pj = hub.submit_profile_job(model=target, device=device); print(">>> profiling:", pj.url)
   ij = hub.submit_inference_job(model=target, device=device, inputs=cloud_inputs)
   out = ij.download_output_data()
   dev_logits = torch.from_numpy(np.asarray(list(out.values())[0][0]))
   print(">>> Top-5 from the REAL device:")
   for label, conf in top5(dev_logits): print(f"    {conf:6.2%}  {label}")
   target.download(os.path.join(OUT_DIR, "mobilenet_v2.tflite"))
   print(">>> saved compiled .tflite to", OUT_DIR)
except Exception as e:
   print("\n>>> Cloud (on-device) section skipped — no API token configured.")
   print("    Get one at workbench.aihub.qualcomm.com, then:")
   print("    !qai-hub configure --api_token YOUR_TOKEN")
   print("    detail:", (str(e).splitlines() or [type(e).__name__])[0])
print("\n>>> Tutorial complete. Outputs in:", OUT_DIR)