model.py

from PIL import Image
import torch

# custom installation from this PR: https://github.com/huggingface/transformers/pull/34583
# !pip install git+https://github.com/geetu040/transformers.git@depth-pro-projects#egg=transformers
from transformers import DepthProImageProcessorFast, DepthProForDepthEstimation

# initialize processor and model
checkpoint = "geetu040/DepthPro"
revision = "project"
image_processor = DepthProImageProcessorFast.from_pretrained(checkpoint, revision=revision)
model = DepthProForDepthEstimation.from_pretrained(checkpoint, revision=revision)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)

def predict(image):
	# inference

	# prepare image for the model
	inputs = image_processor(images=image, return_tensors="pt")
	inputs = {k: v.to(device) for k, v in inputs.items()}

	with torch.no_grad():
		outputs = model(**inputs)

	# interpolate to original size
	post_processed_output = image_processor.post_process_depth_estimation(
		outputs, target_sizes=[(image.height, image.width)],
	)

	# visualize the prediction
	depth = post_processed_output[0]["predicted_depth"]
	depth = (depth - depth.min()) / depth.max()
	depth = depth * 255.
	depth = depth.detach().cpu().numpy()
	depth = Image.fromarray(depth.astype("uint8"))

	return depth