def metric_evaluation(dataTest,dataTestGT):
careRes=[]
resultImgs=[]
inputImgs=[]
model = UNet(1,depth=3)
model.load_state_dict(torch.load(MODEL_DIR+'/model.pt'))
model.eval()
## Testing on 5 images for demo purpose
for index in range(5):
im=dataTest[index]
gt=dataTestGT[0] # The ground truth is the same for all images
careResult = prediction.tiledPredict(im, model ,ps=256, overlap=48,device=device, noiseModel=None)
inputImgs.append(im)
rangePSNR=np.max(gt)-np.min(gt)
carePrior=PSNR(gt, careResult, rangePSNR )
careRes.append(carePrior)
print("Avg PSNR CARE:", np.mean(np.array(careRes) ), '+-(2SEM)',2*np.std(np.array(careRes) )/np.sqrt(float(len(careRes)) ) )
#### Logging the metric using mlflow #######
mlflow.log_metric("Avg_PSNR_CARE",np.mean(np.array(careRes)))
train_loader = torch.utils.data.DataLoader(dataset=isbi_dataset,
batch_size=1,
shuffle=True)
criterion = nn.BCEWithLogitsLoss()
# Select the device, if there is cuda use cuda, if not, use cpu
device = torch.device('cpu' if torch.cuda.is_available() else 'cpu')
# Load the network, the picture is single channel, classified as 1.
net = UNet(n_channels=1, n_classes=9)
# Copy the network to the deivce
net.to(device=device)
# Load model parameters
net.load_state_dict(
torch.load('/home/nonari/Descargas/best_model_v1.pth',
map_location=device))
# Test mode
net.eval()
img = cv2.imread("/home/nonari/Documentos/tfgdata/tfgoct/img_9_20.png")
lab = cv2.imread("/home/nonari/Documentos/tfgdata/tfgoct/seg_9_20.png")
# Convert to grayscale
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
lab = cv2.cvtColor(lab, cv2.COLOR_BGR2GRAY)
# Convert to batch as 1, channel as 1, size 512*512 array
# Convert to tensor
lab_tensor = im_to_tensor(lab)
lab_tensor = torch.unsqueeze(lab_tensor, 0)
img_tensor = im_trans(img)
img_tensor = torch.unsqueeze(img_tensor, 0)
# Copy tensor to device, only use cpu means copy to cpu, use cuda means copy to cuda.