def eval_net(net, loader, device, n_classes):
"""Evaluation without the densecrf with the dice coefficient"""
net.eval()
mask_type = torch.float32 if n_classes == 1 else torch.long
n_val = len(loader) # the number of batch
tot = 0
with tqdm(total=n_val, desc='Validation round', unit='batch',
leave=False) as pbar:
for batch in loader:
imgs, true_masks = batch['image'], batch['mask']
true_biMasks = (true_masks > 0).int()
imgs = imgs.to(device=device, dtype=torch.float32)
true_biMasks = true_biMasks.to(device=device, dtype=mask_type)
with torch.no_grad():
#mask_pred = net(imgs)
mask_pred = crop_tile(net, imgs)
if n_classes > 1:
tot += F.cross_entropy(mask_pred, true_biMasks).item()
else:
# pred = torch.sigmoid(mask_pred)
# pred = (pred > 0.5).float()
# tot += dice_coeff(pred, true_biMasks).item()
tot += nn.BCEWithLogitsLoss(reduction='mean')(
mask_pred, true_biMasks).item()
pbar.update()
net.train()
return tot / n_val
def predict_img(net, full_img, gt, device, out_threshold=0.5):
net.eval()
img, gt, _ = preprocess_img_gt(full_img, gt, np.zeros(gt.shape), False)
gt = gt.squeeze(0)
img = img.unsqueeze(0)
img = img.to(device=device, dtype=torch.float32)
with torch.no_grad():
if CROP_FLAG:
output = crop_tile(net, img)
else:
output = net(img)
probs = torch.sigmoid(output)
full_mask = probs.squeeze(0)
bi_full_mask = (full_mask > out_threshold).int()
bi_mask = bi_full_mask[0]
bi_contour = bi_full_mask[1]
pred_mask = instancing(bi_mask.cpu().numpy(),
bi_contour.cpu().numpy(), img[0,
0].cpu().numpy())
result = jaccard(pred_mask, gt.numpy())
return result, pred_mask, bi_mask.cpu().numpy(), bi_contour.cpu().numpy()
def segmentor(models, img, device, crop_flag, vis_flag=False):
img[img > 255] = 255
# img = np.array(img, dtype=np.float)
img = np.array(img, dtype=np.uint8)
img = np.repeat(img[:, :, None], 3, axis=2)
img = all_transform(img)
img = img.unsqueeze(0)
img = img.to(device=device, dtype=torch.float32)
# img=img.repeat([3,1,1])
with torch.no_grad():
# output = net(img)
full_mask = torch.zeros([img.shape[2], img.shape[3]]).cuda()
for i, net in enumerate(models):
net.eval()
if crop_flag[i]:
output = crop_tile(net, img)
else:
output = net(img)
probs = torch.sigmoid(output)
full_mask += probs.squeeze(0).squeeze(0)
full_mask /= len(models)
bi_mask = (full_mask > 0.5).int()
pred_mask = instancing(bi_mask.cpu().numpy(), vis_flag=vis_flag)
return pred_mask, img, bi_mask.cpu().numpy()
def segmentor(net, img, device):
img, _, _ = preprocess_img_gt(img, np.zeros(img.shape),
np.zeros(img.shape), False)
# img[img>255]=255
# img = np.array(img, dtype=np.float)
# img = np.array(img, dtype=np.uint8)
# img = np.repeat(img[:,:,None],3,axis=2)
# img = all_transform(img)
img = img.unsqueeze(0)
img = img.to(device=device, dtype=torch.float32)
# img=img.repeat([3,1,1])
net.eval()
with torch.no_grad():
if CROP_FLAG:
output = crop_tile(net, img)
else:
output = net(img)
probs = torch.sigmoid(output)
full_mask = probs.squeeze(0)
bi_full_mask = (full_mask > 0.5).int()
bi_mask = bi_full_mask[0]
bi_contour = bi_full_mask[1]
pred_mask = instancing(bi_mask.cpu().numpy(),
bi_contour.cpu().numpy(), img[0,
0].cpu().numpy())
return pred_mask, bi_mask.cpu().numpy(), bi_contour.cpu().numpy()
def eval_net(net, loader, device, n_classes):
"""Evaluation without the densecrf with the dice coefficient"""
net.eval()
mask_type = torch.float32 if n_classes == 1 else torch.long
n_val = len(loader) # the number of batch
tot = 0
tot_contour = 0
score_num = 0
with tqdm(total=n_val, desc='Validation round', unit='batch',
leave=False) as pbar:
for batch in loader:
imgs, true_masks = batch['image'], batch['mask']
true_biMasks = (true_masks > 0).int()
contour_masks = batch['mask_contour']
imgs = imgs.to(device=device, dtype=torch.float32)
true_biMasks = true_biMasks.to(device=device, dtype=mask_type)
contour_masks = contour_masks.to(device=device, dtype=mask_type)
with torch.no_grad():
if CROP_FLAG:
mask_pred = crop_tile(net, imgs)
else:
mask_pred = net(imgs)
if n_classes > 1:
tot += F.cross_entropy(mask_pred, true_biMasks).item()
else:
pred = torch.sigmoid(mask_pred)
pred = (pred > 0.5).float()
pred_mask = pred[:, 0]
pred_contour = pred[:, 1]
tot += dice_coeff(pred_mask,
true_biMasks).item() * pred_mask.shape[0]
tot_contour += dice_coeff(
pred_contour, true_biMasks).item() * pred_mask.shape[0]
score_num += pred_mask.shape[0]
# tot += nn.BCEWithLogitsLoss(reduction='mean')(mask_pred, true_biMasks).item()
pbar.update()
net.train()
return tot / score_num, tot_contour / score_num
def predict_img(net, full_img, gt, device, out_threshold=0.5):
net.eval()
img, gt = preprocess_img_gt(full_img, gt, False)
gt = gt.squeeze(0)
img = img.unsqueeze(0)
img = img.to(device=device, dtype=torch.float32)
with torch.no_grad():
# output = net(img)
output = crop_tile(net, img)
probs = torch.sigmoid(output)
full_mask = probs.squeeze(0).squeeze(0)
bi_mask = (full_mask > out_threshold).int()
pred_mask = instancing(bi_mask.cpu().numpy())
result = jaccard(pred_mask, gt.numpy())
return result, pred_mask, bi_mask.cpu().numpy()
def segmentor(net, img, device):
img[img > 255] = 255
# img = np.array(img, dtype=np.float)
img = np.array(img, dtype=np.uint8)
img = np.repeat(img[:, :, None], 3, axis=2)
img = all_transform(img)
img = img.unsqueeze(0)
img = img.to(device=device, dtype=torch.float32)
# img=img.repeat([3,1,1])
with torch.no_grad():
if CROP_FLAG:
output = crop_tile(net, img)
else:
output = net(img)
probs = torch.sigmoid(output)
full_mask = probs.squeeze(0).squeeze(0)
bi_mask = (full_mask > 0.5).int()
pred_mask = instancing(bi_mask.cpu().numpy())
return pred_mask, img, bi_mask.cpu().numpy()
