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eval.py
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import os
import argparse
import numpy as np
from tqdm import tqdm
import torch.utils.data
import torch.nn.functional as F
from util.dataloaders import get_eval_loaders
from util.common import check_eval_dirs, compute_p_r_f1_miou_oa, gpu_info, SaveResult, ScaleInOutput
from util.AverageMeter import AverageMeter, RunningMetrics
from util.utils import accuracy, SCDD_eval_all, AverageMeterSCD
from main_model import ChangeDetection
import time
def eval(opt):
os.environ["CUDA_VISIBLE_DEVICES"] = opt.cuda
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
gpu_info()
save_path, result_save_path = check_eval_dirs()
save_results = SaveResult(result_save_path)
save_results.prepare()
model = ChangeDetection(opt).cuda()
for ckp_path in opt.ckp_paths:
if os.path.isdir(ckp_path):
weight_file = os.listdir(ckp_path)
ckp_path = os.path.join(ckp_path, weight_file[0])
print("--Load model: {}".format(ckp_path))
model = torch.load(ckp_path, map_location=device)
eval_loader = get_eval_loaders(opt)
Fscd, IoU_mean, Sek, Acc = eval_for_metric(model, eval_loader)
save_results.show(Fscd, IoU_mean, Sek, Acc)
def eval_for_metric(model, eval_loader, input_size=512, num_classes=7):
running_metricsA = RunningMetrics(num_classes)
running_metricsB = RunningMetrics(num_classes)
acc_meter = AverageMeter()
avg_loss = 0
scale = ScaleInOutput(input_size)
model.eval()
preds_all = []
labels_all = []
start = time.time()
with torch.no_grad():
torch.cuda.empty_cache()
eval_tbar = tqdm(eval_loader)
for i, (batch_img1, batch_img2, batch_label1, batch_label2, _) in enumerate(eval_tbar):
eval_tbar.set_description("evaluating...eval_loss: {}".format(avg_loss))
batch_img1 = batch_img1.float().cuda()
batch_img2 = batch_img2.float().cuda()
labels_A = batch_label1.long().cuda()
labels_B = batch_label2.long().cuda()
batch_img1, batch_img2 = scale.scale_input((batch_img1, batch_img2))
outs = model(batch_img1, batch_img2)
outs = scale.scale_output(outs)
outputs_A, outputs_B, out = outs
labels_A = labels_A.cpu().detach().numpy()
labels_B = labels_B.cpu().detach().numpy()
outputs_A = outputs_A.cpu().detach()
outputs_B = outputs_B.cpu().detach()
change_mask = F.sigmoid(out).cpu().detach() > 0.5
preds_A = torch.argmax(outputs_A, dim=1)
preds_B = torch.argmax(outputs_B, dim=1)
preds_A = (preds_A * change_mask.squeeze().long()).numpy()
preds_B = (preds_B * change_mask.squeeze().long()).numpy()
for (pred_A, pred_B, label_A, label_B) in zip(preds_A, preds_B, labels_A, labels_B):
acc_A, valid_sum_A = accuracy(pred_A, label_A)
acc_B, valid_sum_B = accuracy(pred_B, label_B)
preds_all.append(pred_A)
preds_all.append(pred_B)
labels_all.append(label_A)
labels_all.append(label_B)
acc = (acc_A + acc_B) * 0.5
acc_meter.update(acc)
running_metricsA.update(labels_A, preds_A)
running_metricsB.update(labels_B, preds_B)
Fscd, IoU_mean, Sek = SCDD_eval_all(preds_all, labels_all, num_classes)
curr_time = time.time() - start
print('%.1fs Fscd: %.2f mIoU: %.2f Sek: %.2f Accuracy: %.2f' \
% (curr_time, Fscd * 100, IoU_mean * 100, Sek * 100, acc_meter.average() * 100))
scoreA = running_metricsA.get_scores()
scoreB = running_metricsB.get_scores()
iouA = scoreA['Mean_IoU']
iouB = scoreB['Mean_IoU']
iou = (iouA + iouB) / 2
F1 = (scoreA['F1_1'] + scoreA['F1_1']) / 2
# return iou,F1,Fscd, IoU_mean, Sek
return Fscd * 100, IoU_mean * 100, Sek * 100, acc_meter.average() * 100
if __name__ == "__main__":
parser = argparse.ArgumentParser('Change Detection eval')
parser.add_argument("--ckp-paths", type=str,
default=["./runs/train/3/best_ckp/",])
parser.add_argument("--backbone", type=str, default="msam_96")
parser.add_argument("--neck", type=str, default="fpn+drop")
parser.add_argument("--head", type=str, default="fcn")
parser.add_argument("--cuda", type=str, default="0")
parser.add_argument("--dataset-dir", type=str, default="SECOND-CD/")
parser.add_argument("--batch-size", type=int, default=2)
parser.add_argument("--num_classes", type=int, default=7)
parser.add_argument("--num-workers", type=int, default=8)
parser.add_argument("--input-size", type=int, default=512)
parser.add_argument("--pretrain", type=str, default="")
parser.add_argument("--loss", type=str, default="bce+dice")
opt = parser.parse_args()
print("\n" + "-" * 30 + "OPT" + "-" * 30)
print(opt)
eval(opt)