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/'
)
models, model_descs = ens.load_models(ensemble_folder, config['training']['device'])
models, model_descs = ens.prune_models(
    models, model_descs, ensemble_folder, config['ensemble']['prune_threshold']
)

# Load test data
test_dataset = torch.load(
    config['paths']['model_output'] + config['ensemble']['name'] + '/test_dataset.pt'
)

# 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
    + f"ensemble_test_results_{config['ensemble']['prune_threshold']}.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)
            + ', '
            + str(no_votes)
            + '\n'
        )