import torch.utils.data
from utils.parser import get_parser_with_args
from utils.helpers import get_test_loaders
from tqdm import tqdm
from sklearn.metrics import confusion_matrix
parser, metadata = get_parser_with_args()
opt = parser.parse_args()
dev = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
test_loader = get_test_loaders(opt)
path = 'weights/cam32_e99.pt' # the path of the model
model = torch.load(path)
c_matrix = {'tn': 0, 'fp': 0, 'fn': 0, 'tp': 0}
model.eval()
with torch.no_grad():
tbar = tqdm(test_loader)
for batch_img1, batch_img2, labels in tbar:
batch_img1 = batch_img1.float().to(dev)
batch_img2 = batch_img2.float().to(dev)
labels = labels.long().to(dev)
# Get predictions and calculate loss
cd_preds = model(batch_img1, batch_img2)
cd_preds = cd_preds[-1]
_, cd_preds = torch.max(cd_preds, 1)
import torch.utils.data
from utils.parser import get_parser_with_args
from utils.helpers import get_test_loaders, initialize_metrics
import os
from tqdm import tqdm
import cv2
if not os.path.exists('./output_img'):
os.mkdir('./output_img')
parser, metadata = get_parser_with_args()
opt = parser.parse_args()
dev = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
test_loader = get_test_loaders(opt, batch_size=1)
path = 'weights/snunet-32.pt' # the path of the model
model = torch.load(path)
model.eval()
index_img = 0
test_metrics = initialize_metrics()
with torch.no_grad():
tbar = tqdm(test_loader)
for batch_img1, batch_img2, labels in tbar:
batch_img1 = batch_img1.float().to(dev)
batch_img2 = batch_img2.float().to(dev)
labels = labels.long().to(dev)