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Tester.py
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Tester.py
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import torch
import numpy as np
import os
import time
import torch.nn as nn
from tqdm import tqdm
from utils.util import AverageMeter, ensure_dir
import shutil
from PIL import Image
from utils.metrics import Evaluator_tensor
class Tester(object):
def __init__(self,
model,
config,
args,
test_data_loader,
class_name,
begin_time,
resume_file
):
# for general
self.config = config
self.args = args
self.device = torch.device('cpu') if self.args.gpu == -1 else torch.device('cuda:{}'.format(self.args.gpu))
self.class_name = class_name
# for Test
if isinstance(model, list):
self.model = []
for m in model:
m = m.to(self.device)
self.model.append(m)
else:
self.model = model.to(self.device)
self.models = []
# for time
self.begin_time = begin_time
# for data
self.test_data_loader = test_data_loader
# for resume/save path
self.history = {
"eval": {
"loss": [],
"acc": [],
"iou": [],
"time": [],
"prec": [],
"recall": [],
"f_score": [],
},
}
self.model_name = self.config.model_name
self.log_dir = os.path.join(self.args.output, self.model_name,
self.begin_time, 'log')
if not self.args.only_prediction:
self.test_log_path = os.path.join(self.args.output, 'test', 'log', self.model_name,
self.begin_time)
ensure_dir(self.test_log_path)
self.predict_path = os.path.join(self.args.output, 'test', 'predict', self.model_name,
self.begin_time)
ensure_dir(self.predict_path)
self.resume_ckpt_path = resume_file if resume_file is not None else \
os.path.join(self.config.save_dir, self.model_name,
self.begin_time, 'checkpoint-best.pth')
with open(os.path.join(self.predict_path, 'checkpoint.txt'), 'w') as f:
f.write(self.resume_ckpt_path)
self.model_name
self.evaluator = Evaluator_tensor(self.config.nb_classes, self.device)
def eval_and_predict(self):
self._resume_ckpt()
self.evaluator.reset()
if self.config.loss == 'crossentropy':
L_seg = nn.CrossEntropyLoss(ignore_index=255)
elif self.config.loss == 'bceloss':
L_seg = nn.BCEWithLogitsLoss()
ave_total_loss = AverageMeter()
self.model.eval()
with torch.no_grad():
tic = time.time()
for steps, (images, target, filenames) in tqdm(enumerate(self.test_data_loader, start=1)):
images = images.to(self.device, non_blocking=True)
target = target.to(self.device, non_blocking=True)
output = self.model(images)
if self.config.loss == 'bceloss':
logits = torch.squeeze(output["logits1"], 1)
probability = torch.sigmoid(logits)
elif self.config.loss == 'crossentropy':
logits = output["logits1"]
target = target.long()
loss = L_seg(logits, target)
if self.config.loss == 'bceloss':
probability[probability < 0.5] = 0
probability[probability >= 0.5] = 1
self._save_pred(probability, filenames)
pred = probability.view(-1).long()
elif self.config.loss == 'crossentropy':
pred = torch.argmax(logits, dim=1)
self._save_pred(pred, filenames)
pred = pred.view(-1).long()
label = target.view(-1).long()
self.evaluator.add_batch(label, pred)
ave_total_loss.update(loss.item())
total_time = time.time() - tic
acc = self.evaluator.Pixel_Accuracy().cpu().detach().numpy()
acc_class = self.evaluator.Pixel_Accuracy_Class().cpu().detach().numpy()
miou = self.evaluator.Mean_Intersection_over_Union().cpu().detach().numpy()
fwiou = self.evaluator.Frequency_Weighted_Intersection_over_Union().cpu().detach().numpy()
confusion_matrix1 = self.evaluator.get_confusion_matrix().cpu().detach().numpy()
TP, FP, FN, TN = self.evaluator.get_base_value()
iou = self.evaluator.get_iou().cpu().detach().numpy()
prec = self.evaluator.Pixel_Precision_Class().cpu().detach().numpy()
recall = self.evaluator.Pixel_Recall_Class().cpu().detach().numpy()
f1_score = self.evaluator.Pixel_F1_score_Class().cpu().detach().numpy()
kappa_coe = self.evaluator.Kapaa_coefficient().cpu().detach().numpy()
# display evaluation result at the end
print('Evaluation phase !\n'
'Accuracy: {:6.4f}, Loss: {:.6f}'.format(
acc, ave_total_loss.average()))
np.set_printoptions(formatter={'int': '{: 9}'.format})
print('Class: ', self.class_name, ' Average')
np.set_printoptions(formatter={'float': '{: 6.6f}'.format})
print('IoU: ', np.hstack((iou, np.average(iou))))
print('Precision:', np.hstack((prec, np.average(prec))))
print('Recall: ', np.hstack((recall, np.average(recall))))
print('F_Score: ', np.hstack((f1_score, np.average(f1_score))))
np.set_printoptions(formatter={'int': '{:14}'.format})
print('Confusion_matrix:')
print(confusion_matrix1)
# normalized confusion matrix
np.set_printoptions(formatter={'float': '{: 7.4f}'.format})
confusion_matrix_norm = confusion_matrix1 / np.sum(confusion_matrix1)
print('Normalized_confusion_matrix:')
print(confusion_matrix_norm)
print('Kappa_Coefficient:{:10.6f}'.format(kappa_coe))
print('Prediction Phase !\n'
'Total Time cost: {:.2f}s\n'
.format(total_time,
))
self.history["eval"]["loss"].append(ave_total_loss.average())
self.history["eval"]["acc"].append(acc.tolist())
self.history["eval"]["iou"].append(iou.tolist())
self.history["eval"]["time"].append(total_time)
self.history["eval"]["prec"].append(prec.tolist())
self.history["eval"]["recall"].append(recall.tolist())
self.history["eval"]["f_score"].append(f1_score.tolist())
# save results to log file
print(" + Saved history of evaluation phase !")
hist_path = os.path.join(self.test_log_path, "history1.txt")
with open(hist_path, 'w') as f:
f.write(str(self.history).replace("'", '"'))
f.write('\nKappa_Coefficient:{:10.6f}'.format(kappa_coe))
f.write('\nConfusion_matrix:\n')
f.write(str(confusion_matrix1))
np.set_printoptions(formatter={'float': '{: 6.3f}'.format})
f.write('\n Normalized_confusion_matrix:\n')
f.write(str(confusion_matrix_norm))
np.set_printoptions(formatter={'int': '{: 9}'.format})
f.write('\nClass: ' + str(self.class_name) + ' Average')
np.set_printoptions(formatter={'float': '{: 6.6f}'.format})
format_iou = np.hstack((iou, np.average(iou)))
format_prec = np.hstack((prec, np.average(prec)))
format_recall = np.hstack((recall, np.average(recall)))
format_f1_score = np.hstack((f1_score, np.average(f1_score)))
f.write('\nIoU: ' + str(format_iou))
f.write('\nPrecision:' + str(format_prec))
f.write('\nRecall: ' + str(format_recall))
f.write('\nF1_score: ' + str(format_f1_score))
test_log_path1 = os.path.join(self.args.output, 'test', 'log', self.model_name, "history1.txt")
if os.path.exists(test_log_path1):
os.remove(test_log_path1)
shutil.copy(hist_path, test_log_path1)
if not self.args.is_test:
hist_test_log_path = os.path.join(self.log_dir, "history1-test.txt")
shutil.copy(hist_path, hist_test_log_path)
else:
input_dir_path=os.path.dirname(self.resume_ckpt_path)
input_file_name=os.path.basename(self.resume_ckpt_path)
output_dir=os.path.join(input_dir_path, 'batch_test')
ensure_dir(output_dir)
output_file_path=os.path.join(output_dir, input_file_name+'.txt')
shutil.copy(hist_path, output_file_path)
return iou[1], ave_total_loss.average()
def predict(self):
self._resume_ckpt()
self.evaluator.reset()
self.model.eval()
with torch.no_grad():
for steps, (images, filenames) in tqdm(enumerate(self.test_data_loader, start=1)):
# images
images = images.to(self.device, non_blocking=True)
output = self.model(images)
pred = torch.argmax(output["logits1"], dim=1)
self._save_pred(pred, filenames)
print("Predicting and Saving Done!\n")
def _resume_ckpt(self):
print(" + Loading ckpt path : {} ...".format(self.resume_ckpt_path))
checkpoint = torch.load(self.resume_ckpt_path)
self.model.load_state_dict(checkpoint['state_dict'], strict=False)
print(" + Model State Loaded ! :D ")
print(" + Checkpoint file: '{}' , Loaded ! \n"
" + Prepare to test ! ! !"
.format(self.resume_ckpt_path))
def _save_pred(self, binary_map, filenames):
"""
save binary_map
"""
for index, map in enumerate(binary_map):
map = np.asarray(map.cpu(), dtype=np.uint8) * 255
map = Image.fromarray(map)
filename = filenames[index].split('\\')[-1].split('.')
save_filename = filename[0] + '_binary.tif'
save_path = os.path.join(self.predict_path, save_filename)
map.save(save_path, compression='tiff_lzw')