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from typing import Dict, List, Any
import torch
import numpy as np
import torch.nn.functional as F
from serkan import SimpleUpscaleModel
class EndpointHandler():
def __init__(self, path="model_weights.pth"):
# load the optimized model
self.model = SimpleUpscaleModel()
self.model.load_state_dict(torch.load("model_weights.pth"))
def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
"""
Args:
data (:obj:):
includes the input data and the parameters for the inference.
Return:
A :obj:`list`:. The object returned should be a list of one list like [[{"label": 0.9939950108528137}]] containing :
- "label": A string representing what the label/class is. There can be multiple labels.
- "score": A score between 0 and 1 describing how confident the model is for this label/class.
"""
inputs = data.pop("inputs", data)
img = inputs["image"]
# Load the image
img = np.float32(img)
upscaled = self.model(img)
# postprocess the prediction
return "OKAY" |