| import torch | |
| from torchvision import transforms | |
| from huggingface_hub import hf_hub_download | |
| import json | |
| import io | |
| import base64 | |
| from PIL import Image | |
| from omegaconf import OmegaConf | |
| from model import Generator | |
| class EndpointHandler: | |
| def __init__(self, path=''): | |
| self.transform = transforms.Compose([ | |
| transforms.ToTensor() | |
| ]) | |
| repo_id = "Kiwinicki/sat2map-generator" | |
| generator_path = hf_hub_download(repo_id=repo_id, filename="generator.pth") | |
| config_path = hf_hub_download(repo_id=repo_id, filename="config.json") | |
| model_path = hf_hub_download(repo_id=repo_id, filename="model.py") | |
| with open(config_path, "r") as f: | |
| config_dict = json.load(f) | |
| cfg = OmegaConf.create(config_dict) | |
| self.generator = Generator(cfg) | |
| self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| self.generator.load_state_dict(torch.load(generator_path, map_location=self.device)) | |
| self.generator.eval() | |
| def __call__(self, data: dict[str, any]) -> dict[str, str]: | |
| base64_image = data.get('inputs') | |
| input_tensor = self._decode_base64_image(base64_image) | |
| print('Input tensor shape: ' + str(input_tensor.shape)) | |
| output_tensor = self.generator(input_tensor.to(self.device)) | |
| output_tensor = output_tensor.squeeze(0) | |
| output_image = transforms.ToPILImage()(output_tensor) | |
| output_image = output_image.convert('RGB') | |
| output_buffer = io.BytesIO() | |
| output_image.save(output_buffer, format="png") | |
| base64_output = base64.b64encode(output_buffer.getvalue()).decode('utf-8') | |
| return {"output": base64_output} | |
| def _decode_base64_image(self, base64_image: str) -> torch.Tensor: | |
| image_decoded = base64.b64decode(base64_image) | |
| image = Image.open(io.BytesIO(image_decoded)).convert('RGB') | |
| image_tensor: torch.Tensor = self.transform(image) | |
| image_tensor = image_tensor.unsqueeze(0) | |
| return image_tensor | |