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import numpy as np
import torch
from mu_map.dataset.default import MuMapDataset
from mu_map.dataset.normalization import MeanNormTransform
from mu_map.models.unet import UNet
def mse(prediction: np.array, target: np.array):
pass
def nmae(prediction: np.array, target: np.array):
mae = np.absolute(prediction - target) / prediction.size
nmae = mae / (target.max() - target.min())
return nmae
device = torch.device("cpu")
model = UNet()
model.load_state_dict(torch.load("xx.pth", map_location=device))
model = model.eval()
dataset = MuMapDataset("data/initial/", transform_normalization=MeanNormTransform(), split_name="validation")
scores_mse = []
scores_nmae = []
for recon, mu_map in dataset:
recon = recon.unsqueeze(dim=0).to(device)
prediction = model(recon).squeeze().numpy()
mu_map = mu_map.squeeze().numpy()
scores_nmae.append(nmae(prediction, mu_map))
scores_mse.append(mse(prediction, mu_map))
scores_mse = np.array(scores_mse)
scores_nmae = np.array(scores_nmae)
mse_avg = scores_mse.mean()
mse_std = np.std(scores_mse)
nmae_avg = scores_nmae.mean()
nmae_std = np.std(scores_nmae)
print("Scores:")
print(f" - NMAE: {nmae_avg:.6f}±{nmae_std:.6f}")
print(f" - MSE: {mse_avg:.6f}±{mse_std:.6f}")