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eval.py
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eval.py
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import os
from glob import glob
import csv
import torch
import yaml
import scipy
from PIL import Image
from monai.data import decollate_batch
from monai.inferers import sliding_window_inference
from monai.metrics import DiceMetric, get_confusion_matrix, compute_confusion_matrix_metric
from transforms import val_imtrans_d, post_trans, val_trans_d_no_gt
from load_models import load_model
from data_loader import data_loader_d
with open('./config/eval_config.yaml', 'r') as config_file:
eval_config_params = yaml.safe_load(config_file)
def main():
eval_name = eval_config_params["eval_name"]
model_name = eval_config_params["model_name"]
model_backbone_network = eval_config_params["model_backbone_network"]
model_checkpoint_path = eval_config_params["model_checkpoint_path"]
output_base_dir = eval_config_params["output_base_dir"]
eval_type = eval_config_params["eval_type"]
eval_data_dir_0 = eval_config_params["eval_data_dir_0"]
eval_data_base_dir_1 = eval_config_params["eval_data_base_dir_1"]
images_path = eval_config_params["images_path"]
masks_path = eval_config_params["masks_path"]
eval_batch_size = eval_config_params["eval_batch_size"]
op_dir = os.path.join(output_base_dir, eval_name)
if eval_type == 0:
op_prefix = "external_valid"
elif eval_type == 1:
op_prefix = "internal_valid"
op_dir = op_dir + "_" + op_prefix
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if not os.path.exists(op_dir):
os.mkdir(op_dir)
eval_images = []
false_positive_error = 0
false_negative_error = 0
precision_metric = 0
recall_metric = 0
if eval_type == 0:
eval_images = sorted(glob(os.path.join(eval_data_dir_0, "*.jpg")))
eval_loader = data_loader_d(val_trans_d_no_gt(), eval_batch_size, eval_data_dir_0)
elif eval_type == 1:
eval_images = sorted(glob(os.path.join(os.path.join(eval_data_base_dir_1, images_path), "*.jpg")))
eval_loader = data_loader_d(val_imtrans_d(), eval_batch_size, os.path.join(eval_data_base_dir_1, images_path), os.path.join(eval_data_base_dir_1, masks_path))
fname_mapping_list = []
for img in eval_images:
fname_mapping_list.append(img.split('/')[-1])
dice_metric = DiceMetric(include_background=True, reduction="mean", get_not_nans=False)
post_infer_trans = post_trans()
model, source = load_model(model_name, model_backbone_network, device)
model.load_state_dict(torch.load(model_checkpoint_path)["model_state"])
model.eval()
with torch.no_grad():
idx = 0
list_for_csv = []
for eval_data in eval_loader:
eval_images = eval_data["img"].to(device)
if eval_type == 1:
eval_masks = eval_data["msk"].to(device)
if source == "monai":
roi_size = (640, 480)
sw_batch_size = 2
eval_outputs = sliding_window_inference(eval_images, roi_size, sw_batch_size, model)
eval_outputs = [post_infer_trans(i) for i in decollate_batch(eval_outputs)]
elif source == "torch":
eval_outputs = model(eval_images)
eval_outputs = post_trans()(list(eval_outputs.items())[0][1])
if eval_type == 1:
dice_score = dice_metric(y_pred=eval_outputs, y=eval_masks)
false_positive_error = compute_confusion_matrix_metric("fpr", get_confusion_matrix(torch.Tensor(eval_outputs[0]).reshape(1, 1, 640, 480), torch.Tensor(eval_masks)))
false_negative_error = compute_confusion_matrix_metric("fnr", get_confusion_matrix(torch.Tensor(eval_outputs[0]).reshape(1, 1, 640, 480), torch.Tensor(eval_masks)))
precision_metric = compute_confusion_matrix_metric("precision", get_confusion_matrix(torch.Tensor(eval_outputs[0]).reshape(1, 1, 640, 480), torch.Tensor(eval_masks)))
recall_metric = compute_confusion_matrix_metric("recall", get_confusion_matrix(torch.Tensor(eval_outputs[0]).reshape(1, 1, 640, 480), torch.Tensor(eval_masks)))
for eval_output in eval_outputs:
eval_op = scipy.ndimage.binary_fill_holes(eval_output[0].cpu().detach().numpy()).astype("uint8")
Image.fromarray(eval_op.astype("uint8") * 255).transpose(Image.Transpose.TRANSPOSE).save(os.path.join(op_dir,fname_mapping_list[idx].split(".")[0]+".png"))
if eval_type == 1:
list_for_csv.append([fname_mapping_list[idx].split(".")[0] + ".png", dice_score, false_positive_error, false_negative_error, precision_metric, recall_metric])
idx = idx + 1
# aggregate the final mean dice result
if eval_type == 1:
print("Final evaluation metric:", dice_metric.aggregate().item())
with open('outputs/csv_files/dice_scores_all' + eval_name + '.csv', 'w') as csv_file:
writer_csv = csv.writer(csv_file)
for items in list_for_csv:
writer_csv.writerow([items[0], items[1].item()])
if __name__ == "__main__":
main()