File size: 1,851 Bytes
5ee43e9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | #!/usr/bin/env python3
# DPT (Dense Prediction Transformer) monocular depth estimation on Neuron
import argparse
import logging
import time
import torch
from transformers import DPTImageProcessor, DPTForDepthEstimation
from datasets import load_dataset
import torch_neuronx # ensures Neuron backend
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
def main():
parser = argparse.ArgumentParser(description="Run DPT depth estimation on Neuron")
parser.add_argument(
"--model",
type=str,
default="Intel/dpt-large",
help="DPT model name on Hugging Face Hub",
)
args = parser.parse_args()
torch.set_default_dtype(torch.float32)
torch.manual_seed(42)
# load dataset image
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
# load processor & DPT model
processor = DPTImageProcessor.from_pretrained(args.model)
model = DPTForDepthEstimation.from_pretrained(
args.model, torch_dtype=torch.float32, attn_implementation="eager"
).eval()
# preprocess
inputs = processor(images=image, return_tensors="pt")
# pre-run to lock shapes
with torch.no_grad():
_ = model(**inputs).predicted_depth
# compile
model.forward = torch.compile(model.forward, backend="neuron", fullgraph=False)
# warmup
warmup_start = time.time()
with torch.no_grad():
_ = model(**inputs)
warmup_time = time.time() - warmup_start
# benchmark run
run_start = time.time()
with torch.no_grad():
depth = model(**inputs).predicted_depth
run_time = time.time() - run_start
logger.info("Warmup: %.2f s, Run: %.4f s", warmup_time, run_time)
logger.info("Output depth shape: %s", depth.shape) # [B, 1, H, W]
if __name__ == "__main__":
main() |