| 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 |
|
|
