alzheimers_nn/ensemble_predict.py

import utils.ensemble as ens
import os
import tomli as toml
from utils.system import force_init_cudnn
from utils.data.datasets import prepare_datasets
import math
import torch

# CONFIGURATION
if os.getenv("ADL_CONFIG_PATH") is None:
    with open("config.toml", "rb") as f:
        config = toml.load(f)
else:
    with open(os.getenv("ADL_CONFIG_PATH"), "rb") as f:
        config = toml.load(f)

# Force cuDNN initialization
force_init_cudnn(config["training"]["device"])


ensemble_folder = config["paths"]["model_output"] + config["ensemble"]["name"] + "/"
models, model_descs = ens.load_models(ensemble_folder, config["training"]["device"])

# Load test data
test_dataset = prepare_datasets(
    config["paths"]["mri_data"],
    config["paths"]["xls_data"],
    config["dataset"]["validation_split"],
    0,
    config["training"]["device"],
)[2]

# Evaluate ensemble and uncertainty test set
correct = 0
total = 0
predictions = []
actual = []
stdevs = []
yes_votes = []
no_votes = []

for data, target in test_dataset:
    mri, xls = data
    mri = mri.unsqueeze(0)
    xls = xls.unsqueeze(0)
    data = (mri, xls)
    mean, variance = ens.ensemble_predict(models, data)
    _, yes_votes, no_votes = ens.ensemble_predict_strict_classes(models, data)
    stdevs.append(math.sqrt(variance.item()))
    predicted = torch.round(mean)
    expected = target[1]

    total += 1
    correct += (predicted == expected).item()

    out = mean.tolist()
    predictions.append(out)

    act = target[1].tolist()
    actual.append(act)


accuracy = correct / total

with open(ensemble_folder + "ensemble_test_results.txt", "w") as f:
    f.write("Accuracy: " + str(accuracy) + "\n")
    f.write("Correct: " + str(correct) + "\n")
    f.write("Total: " + str(total) + "\n")

    for exp, pred, stdev in zip(actual, predictions, stdevs):
        f.write(
            str(exp)
            + ", "
            + str(pred)
            + ", "
            + str(stdev)
            + ", "
            + str(yes_votes.item())
            + ", "
            + str(no_votes.item())
            + "\n"
        )