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Runtime error
| from PIL import Image | |
| import numpy as np | |
| import torch | |
| from transformers import AutoImageProcessor, GLPNForDepthEstimation | |
| from modules import devices | |
| from modules.shared import opts | |
| class GLPNDetector: | |
| def __init__(self, model=None, processor=None): | |
| self.model = model | |
| self.processor = processor | |
| def __call__(self, input_image=None): | |
| from modules.control.processors import cache_dir | |
| if self.processor is None: | |
| self.processor = AutoImageProcessor.from_pretrained("vinvino02/glpn-kitti", cache_dir=cache_dir) | |
| if self.model is None: | |
| self.model = GLPNForDepthEstimation.from_pretrained("vinvino02/glpn-kitti", cache_dir=cache_dir) | |
| self.model.to(devices.device) | |
| with devices.inference_context(): | |
| inputs = self.processor(images=input_image, return_tensors="pt") | |
| inputs.to(devices.device) | |
| outputs = self.model(**inputs) | |
| predicted_depth = outputs.predicted_depth | |
| prediction = torch.nn.functional.interpolate( | |
| predicted_depth.unsqueeze(1), | |
| size=input_image.size[::-1], | |
| mode="bicubic", | |
| align_corners=False, | |
| ) | |
| output = prediction.squeeze().cpu().numpy() | |
| formatted = 255 - (output * 255 / np.max(output)).astype("uint8") | |
| if opts.control_move_processor: | |
| self.model.to('cpu') | |
| depth = Image.fromarray(formatted) | |
| depth = depth.convert('RGB') | |
| return depth | |