| import onnxruntime as ort |
| import numpy as np |
| import time |
| import os |
| from PIL import Image |
| from transformers import AutoImageProcessor |
|
|
| |
| processor = AutoImageProcessor.from_pretrained("microsoft/resnet-18") |
|
|
| |
| models = { |
| "ONNX": "models/resnet18.onnx", |
| "ONNX Quantized": "models/resnet18_quantized.onnx" |
| } |
|
|
| |
| if not os.path.exists("test.jpg"): |
| img = Image.new("RGB", (224, 224), color=(128, 64, 32)) |
| img.save("test.jpg") |
|
|
| img = Image.open("test.jpg").convert("RGB") |
| inputs = processor(images=img, return_tensors="np") |
| pixel_values = inputs["pixel_values"].astype(np.float32) |
|
|
| for name, model_path in models.items(): |
| if not os.path.exists(model_path): |
| print(f"Skipping {name}: {model_path} not found") |
| continue |
| |
| session = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"]) |
| |
| times = [] |
| for _ in range(100): |
| t0 = time.perf_counter() |
| _ = session.run(["logits"], {"pixel_values": pixel_values}) |
| times.append(time.perf_counter() - t0) |
| |
| print(f"\n{name}:") |
| print(f" Avg Latency: {sum(times)/len(times)*1000:.2f} ms") |
| print(f" P95 Latency: {sorted(times)[94]*1000:.2f} ms") |
| print(f" File Size: {os.path.getsize(model_path)/1e6:.2f} MB") |
|
|
