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import numpy as np
from loguru import logger
import torch
from temps.temps_arch import EncoderPhotometry, MeasureZ
from temps.temps import TempsModule
def test():
nn_features = EncoderPhotometry()
nn_features.load_state_dict(
torch.load("data/models/modelF_DA.pt", map_location=torch.device("cpu"))
)
nn_z = MeasureZ(num_gauss=6)
nn_z.load_state_dict(
torch.load("data/models/modelZ_DA.pt", map_location=torch.device("cpu"))
)
temps_module = TempsModule(nn_features, nn_z)
col = np.array(
[0.54804805, 1.81142339, 0.63354394, 0.7356338, 1.3578122, 0.90108565]
)
ztrue = 0.4446
z, pz, odds = temps_module.get_pz(
input_data=torch.Tensor(col).unsqueeze(0), return_pz=True, return_flag=True
)
zdiff = np.abs(z - ztrue).mean()
logger.info(f"zdiff: {zdiff}")
logger.info("test passed")
assert zdiff < 0.01
test()
# %%
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