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test.py
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test.py
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import argparse
import glob
import os
import imageio
import numpy as np
import torch
import yaml
from PIL import Image
from sklearn.metrics import f1_score, mean_absolute_error
from torch.autograd import Variable
from torchvision import transforms
from data import image_loader
from utils import get_logger, create_dir
from model.pretrained_unet import preUnet
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
parser = argparse.ArgumentParser()
parser.add_argument('--batchsize', type=int, default=1, help='training batch size')
parser.add_argument('--trainsize', type=int, default=(512,288), help='training dataset size')
parser.add_argument('--dataset', type=str, default='kvasir', help='dataset name')
parser.add_argument('--threshold', type=float, default=0.5, help='threshold')
opt = parser.parse_args()
class Test(object):
def __init__(self):
self._init_configure()
self._init_logger()
self.model_1 = preUnet()
self.model_2 = preUnet()
def _init_configure(self):
with open('configs/config.yml') as fp:
self.cfg = yaml.safe_load(fp)
def _init_logger(self):
log_dir = 'logs/' + opt.dataset + '/test'
self.logger = get_logger(log_dir)
print('RUNDIR: {}'.format(log_dir))
self.save_path = log_dir
self.image_save_path_1 = log_dir + "/saved_images_1"
create_dir(self.image_save_path_1)
self.image_save_path_2 = log_dir + "/saved_images_2"
create_dir(self.image_save_path_2)
self.model_1_load_path = 'logs/' + opt.dataset + '/train/Checkpoints/Model_1.pth'
self.model_2_load_path = 'logs/' + opt.dataset + '/train/Checkpoints/Model_2.pth'
def visualize_val_input(self, var_map, i):
count = i
im = transforms.ToPILImage()(var_map.squeeze_(0).detach().cpu()).convert("RGB")
name = '{:02d}_input.png'.format(count)
imageio.imwrite(self.image_save_path_1 + "/val_" + name, im)
def visualize_gt(self, var_map, i):
count = i
for kk in range(var_map.shape[0]):
pred_edge_kk = var_map[kk, :, :, :]
pred_edge_kk = pred_edge_kk.detach().cpu().numpy().squeeze()
pred_edge_kk *= 255.0
pred_edge_kk = pred_edge_kk.astype(np.uint8)
name = '{:02d}_gt.png'.format(count)
imageio.imwrite(self.image_save_path_1 + "/val_" + name, pred_edge_kk)
imageio.imwrite(self.image_save_path_2 + "/val_" + name, pred_edge_kk)
def visualize_prediction1(self, var_map, i):
count = i
for kk in range(var_map.shape[0]):
pred_edge_kk = var_map[kk, :, :, :]
pred_edge_kk = pred_edge_kk.detach().cpu().numpy().squeeze()
pred_edge_kk *= 255.0
pred_edge_kk = pred_edge_kk.astype(np.uint8)
name = '{:02d}_pred_1.png'.format(count)
imageio.imwrite(self.image_save_path_1 + "/val_" + name, pred_edge_kk)
def visualize_prediction2(self, var_map, i):
count = i
for kk in range(var_map.shape[0]):
pred_edge_kk = var_map[kk, :, :, :]
pred_edge_kk = pred_edge_kk.detach().cpu().numpy().squeeze()
pred_edge_kk *= 255.0
pred_edge_kk = pred_edge_kk.astype(np.uint8)
name = '{:02d}_pred_2.png'.format(count)
imageio.imwrite(self.image_save_path_2 + "/val_" + name, pred_edge_kk)
def visualize_uncertainity(self, var_map, i):
count = i
for kk in range(var_map.shape[0]):
pred_edge_kk = var_map[kk, :, :, :]
pred_edge_kk = pred_edge_kk.detach().cpu().numpy().squeeze()
pred_edge_kk *= 255.0
pred_edge_kk = pred_edge_kk.astype(np.uint8)
name = '{:02d}_pred.png'.format(count)
imageio.imwrite(self.image_save_path_1 + "/uncertainity_" + name, pred_edge_kk)
def evaluate_model_1(self, image_dir):
target_list = np.array([])
output_list = np.array([])
output_pred_list = np.array([])
test_dir = image_dir
self.logger.info(test_dir)
pred_files = glob.glob(test_dir + 'val_*_pred_1.png')
gt_files = glob.glob(test_dir + 'val_*_gt.png')
for file in pred_files:
image = Image.open(file)
output = np.asarray(image)
output = output.flatten() / 255
output_pred = (output > opt.threshold)
output_list = np.concatenate((output_list, output), axis=None)
output_pred_list = np.concatenate((output_pred_list, output_pred), axis=None)
for file in gt_files:
image = Image.open(file)
target = np.asarray(image)
target = target.flatten() / 255
target = (target > opt.threshold)
target_list = np.concatenate((target_list, target), axis=None)
# F1 score
F1_score = f1_score(target_list, output_pred_list)
self.logger.info("Model 1 F1 score : {} ".format(F1_score))
# Mean Absolute Error
mae = mean_absolute_error(target_list, output_pred_list)
self.logger.info("Model 1 MAE : {} ".format(mae))
def evaluate_model_2(self, image_dir):
target_list = np.array([])
output_list = np.array([])
output_pred_list = np.array([])
test_dir = image_dir
self.logger.info(test_dir)
pred_files = glob.glob(test_dir + 'val_*_pred_2.png')
gt_files = glob.glob(test_dir + 'val_*_gt.png')
for file in pred_files:
image = Image.open(file)
output = np.asarray(image)
output = output.flatten() / 255
output_pred = (output > opt.threshold)
output_list = np.concatenate((output_list, output), axis=None)
output_pred_list = np.concatenate((output_pred_list, output_pred), axis=None)
for file in gt_files:
image = Image.open(file)
target = np.asarray(image)
target = target.flatten() / 255
target = (target > opt.threshold)
target_list = np.concatenate((target_list, target), axis=None)
# F1 score
F1_score = f1_score(target_list, output_pred_list)
self.logger.info("Model 2 F1 score : {} ".format(F1_score))
# Mean Absolute Error
mae = mean_absolute_error(target_list, output_pred_list)
self.logger.info("Model 2 MAE : {} ".format(mae))
def run(self):
# build models
self.model_1.load_state_dict(torch.load(self.model_1_load_path))
self.model_1.cuda()
self.model_2.load_state_dict(torch.load(self.model_2_load_path))
self.model_2.cuda()
image_root = './data/'+ opt.dataset +'/train/image/'
gt_root = './data/'+ opt.dataset +'/train/mask/'
val_img_root = './data/'+ opt.dataset +'/test/image/'
val_gt_root = './data/'+ opt.dataset +'/test/mask/'
_, _, _, val_loader = image_loader(image_root, gt_root,val_img_root,val_gt_root, opt.batchsize, opt.trainsize)
for i, pack in enumerate(val_loader, start=1):
with torch.no_grad():
images, gts = pack
images = Variable(images)
gts = Variable(gts)
images = images.cuda()
gts = gts.cuda()
feat_map_1 = self.model_1(images)
prediction1 = torch.sigmoid(feat_map_1)
feat_map_2 = self.model_2(images)
prediction2 = torch.sigmoid(feat_map_2)
self.visualize_val_input(images, i)
self.visualize_gt(gts, i)
self.visualize_prediction1(prediction1, i)
self.visualize_prediction2(prediction2, i)
self.evaluate_model_1('logs/kvasir/test/saved_images_1/')
self.evaluate_model_2('logs/kvasir/test/saved_images_2/')
if __name__ == '__main__':
Test_network = Test()
Test_network.run()