41 lines
1.1 KiB
Python
41 lines
1.1 KiB
Python
|
from typing import List, Tuple
|
||
|
|
import torch
|
||
|
|
import numpy as np
|
||
|
|
|
||
|
|
from ..a5.model import MmpNet
|
||
|
|
from ..a3.annotation import AnnotationRect
|
||
|
|
|
||
|
|
|
||
|
|
def batch_inference(
|
||
|
|
model: MmpNet, images: torch.Tensor, device: torch.device, anchor_grid: np.ndarray
|
||
|
|
) -> List[List[Tuple[AnnotationRect, float]]]:
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
|
||
|
|
def evaluate() -> float: # feel free to change the arguments
|
||
|
|
"""Evaluates a specified model on the whole validation dataset.
|
||
|
|
|
||
|
|
@return: AP for the validation set as a float.
|
||
|
|
|
||
|
|
You decide which arguments this function should receive
|
||
|
|
"""
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
|
||
|
|
def evaluate_test(): # feel free to change the arguments
|
||
|
|
"""Generates predictions on the provided test dataset.
|
||
|
|
This function saves the predictions to a text file.
|
||
|
|
|
||
|
|
You decide which arguments this function should receive
|
||
|
|
"""
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
|
||
|
|
def main():
|
||
|
|
"""Put the surrounding training code here. The code will probably look very similar to last assignment"""
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
main()
|