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import sys
import yaml
import torch
from torchvision import transforms
from fastai.vision.all import unet_learner, Path, resnet34, MSELossFlat, get_files, L, PILImageBW
from custom_data_loading import create_data
from eval_metric_calculation import compute_eval_metrics
from dagshub import dagshub_logger
from tqdm import tqdm
if __name__ == "__main__":
if len(sys.argv) < 2:
print("usage: %s <test_data_path>" % sys.argv[0], file=sys.stderr)
sys.exit(0)
with open(r"./src/code/params.yml") as f:
params = yaml.safe_load(f)
data_path = Path(sys.argv[1])
data = create_data(data_path)
arch = {'resnet34': resnet34}
loss = {'MSELossFlat': MSELossFlat()}
learner = unet_learner(data,
arch.get(params['architecture']),
n_out=int(params['num_outs']),
loss_func=loss.get(params['loss_func']),
path='src/',
model_dir='models')
learner = learner.load('model')
filenames = get_files(Path(data_path), extensions='.jpg')
test_files = L([Path(i) for i in filenames])
for i, sample in tqdm(enumerate(test_files.items),
desc="Predicting on test images",
total=len(test_files.items)):
pred = learner.predict(sample)[0]
pred = PILImageBW.create(pred).convert('L')
pred.save("src/eval/" + str(sample.stem) + "_pred.png")
if i < 10:
pred.save("src/eval/examples/" + str(sample.stem) + "_pred.png")
print("Calculating metrics...")
metrics = compute_eval_metrics(test_files)
with dagshub_logger(
metrics_path="logs/test_metrics.csv",
should_log_hparams=False
) as logger:
# Metric logging
logger.log_metrics(metrics)
print("Evaluation Done!")
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