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import time
from urllib.request import urlopen
import cupy as cp
import numpy as np
import onnxruntime as ort
import torch
from PIL import Image
from imagenet_classes import IMAGENET2012_CLASSES
img = Image.open(
urlopen(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png"
)
)
def transforms_numpy(image: Image.Image):
image = image.convert("RGB")
image = image.resize((448, 448), Image.BICUBIC)
img_numpy = np.array(image).astype(np.float32) / 255.0
img_numpy = img_numpy.transpose(2, 0, 1)
mean = np.array([0.4815, 0.4578, 0.4082]).reshape(-1, 1, 1)
std = np.array([0.2686, 0.2613, 0.2758]).reshape(-1, 1, 1)
img_numpy = (img_numpy - mean) / std
img_numpy = np.expand_dims(img_numpy, axis=0)
img_numpy = img_numpy.astype(np.float32)
return img_numpy
def transforms_cupy(image: Image.Image):
# Convert image to RGB and resize
image = image.convert("RGB")
image = image.resize((448, 448), Image.BICUBIC)
# Convert to CuPy array and normalize
img_cupy = cp.array(image, dtype=cp.float32) / 255.0
img_cupy = img_cupy.transpose(2, 0, 1)
# Apply mean and std normalization
mean = cp.array([0.4815, 0.4578, 0.4082], dtype=cp.float32).reshape(-1, 1, 1)
std = cp.array([0.2686, 0.2613, 0.2758], dtype=cp.float32).reshape(-1, 1, 1)
img_cupy = (img_cupy - mean) / std
# Add batch dimension
img_cupy = cp.expand_dims(img_cupy, axis=0)
return img_cupy
# Create ONNX Runtime session with CPU provider
onnx_filename = "eva02_large_patch14_448.onnx"
session = ort.InferenceSession(onnx_filename, providers=["CUDAExecutionProvider"])
# Get input and output names
input_name = session.get_inputs()[0].name
output_name = session.get_outputs()[0].name
# Run inference
output = session.run([output_name], {input_name: transforms_numpy(img)})[0]
# Check the output
output = torch.from_numpy(output)
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
im_classes = list(IMAGENET2012_CLASSES.values())
class_names = [im_classes[i] for i in top5_class_indices[0]]
# Print class names and probabilities
for name, prob in zip(class_names, top5_probabilities[0]):
print(f"{name}: {prob:.2f}%")
# Run benchmark numpy
num_images = 100
start = time.perf_counter()
for i in range(num_images):
output = session.run([output_name], {input_name: transforms_numpy(img)})[0]
end = time.perf_counter()
time_taken = end - start
ms_per_image = time_taken / num_images * 1000
fps = num_images / time_taken
print(
f"Onnxruntime CUDA numpy transforms: {ms_per_image:.3f} ms per image, FPS: {fps:.2f}"
)
# Run benchmark cupy
num_images = 100
start = time.perf_counter()
for i in range(num_images):
img_cupy = transforms_cupy(img)
output = session.run([output_name], {input_name: cp.asnumpy(img_cupy)})[0]
end = time.perf_counter()
time_taken = end - start
ms_per_image = time_taken / num_images * 1000
fps = num_images / time_taken
print(
f"Onnxruntime CUDA cupy transforms: {ms_per_image:.3f} ms per image, FPS: {fps:.2f}"
)
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