def validate_model(model,
data_val,
criterion,
epoch,
make_prediction=True,
save_folder_name='prediction'):
"""
Validation run
"""
# calculating validation loss
total_val_loss = 0
total_val_acc = 0
for batch, (images_v, masks_v, original_msk) in enumerate(data_val):
stacked_img = torch.Tensor([]).cuda()
for index in range(images_v.size()[1]):
with torch.no_grad():
image_v = Variable(images_v[:,
index, :, :].unsqueeze(0).cuda())
mask_v = Variable(masks_v[:, index, :, :].squeeze(1).cuda())
# print(image_v.shape, mask_v.shape)
output_v = model(image_v)
total_val_loss = total_val_loss + criterion(
output_v, mask_v).cpu().item()
# print('out', output_v.shape)
output_v = torch.argmax(output_v, dim=1).float()
stacked_img = torch.cat((stacked_img, output_v))
if make_prediction:
im_name = batch # TODO: Change this to real image name so we know
pred_msk = save_prediction_image(stacked_img, im_name, epoch,
save_folder_name)
acc_val = accuracy_check(original_msk, pred_msk)
total_val_acc = total_val_acc + acc_val
return total_val_acc / (batch + 1), total_val_loss / ((batch + 1) * 4)
def test_model(
model_path,
data_test,
epoch,
save_folder_name='prediction',
save_dir="./history/RMS/result_images_test",
save_file_name="./history/RMS/result_images_test/history_RMS3.csv"):
"""
Test run
"""
model = torch.load(model_path)
model = torch.nn.DataParallel(
model, device_ids=list(range(torch.cuda.device_count()))).cuda()
model.eval()
total_val_acc = 0
total_val_jac = 0
total_val_dice = 0
for batch, (images_v, masks_v, original_msk) in enumerate(data_test):
pdsz = 20
ori_shape = original_msk.shape
original_msk = original_msk[..., pdsz:-pdsz, pdsz:-pdsz, pdsz:-pdsz]
stacked_img = torch.Tensor([]).cuda()
stacked_reg = torch.Tensor([]).cuda()
for index in range(images_v.size()[1]):
with torch.no_grad():
image_v = Variable(images_v[:,
index, :, :].unsqueeze(0).cuda())
output_v, output_r_v = model(image_v)
output_v = torch.argmax(output_v, dim=1).float()
stacked_img = torch.cat((stacked_img, output_v))
output_r_v = torch.squeeze(output_r_v, dim=0)
stacked_reg = torch.cat((stacked_reg, output_r_v))
im_name = batch # TODO: Change this to real image name so we know
pred_msk = save_prediction_image(stacked_img, ori_shape[-3:], im_name,
epoch, 0, save_folder_name)
acc_val = accuracy_check(original_msk, pred_msk)
avg_dice, jac = dice_coeff(pred_msk, original_msk)
total_val_jac += jac
total_val_dice += avg_dice
total_val_acc = total_val_acc + acc_val
reconstruct_image(stacked_reg, ori_shape[-3:], epoch, save_folder_name)
print("total_val_acc is:%f. total_val_jac is:%f . total_val_dice is:%f "
"Finish Prediction!" % (total_val_acc / (batch + 1), total_val_jac /
(batch + 1), total_val_dice / (batch + 1)))
header = ['epoch', 'total_val_jac', 'total_val_dice', 'total_val_acc']
values = [
epoch, total_val_jac / (batch + 1), total_val_dice / (batch + 1),
total_val_acc / (batch + 1)
]
export_history(header, values, save_dir, save_file_name)
return total_val_acc / (batch + 1)
def validate_hard_model(model,
data_val,
criterion,
epoch,
make_prediction=True,
save_folder_name='prediction'):
"""
Validation run
"""
# calculating validation loss
model.eval()
total_val_loss = 0
total_val_acc = 0
total_val_dice = 0
total_val_jac = 0
count_val = 0
for batch, (images_v, masks_v, original_msk) in enumerate(data_val):
pdsz = 20
ori_shape = original_msk.shape
original_msk = original_msk[..., pdsz:-pdsz, pdsz:-pdsz, pdsz:-pdsz]
stacked_img = torch.Tensor([]).cuda()
for index in range(images_v.size()[1]):
with torch.no_grad():
image_v = Variable(images_v[:,
index, :, :].unsqueeze(0).cuda())
mask_v = Variable(masks_v[:, index, :, :].squeeze(1).cuda())
# print(image_v.shape, mask_v.shape)
output_v, output_r_v = model(image_v)
total_val_loss = total_val_loss + criterion(
output_v, mask_v).cpu().item()
# print('out', output_v.shape)
output_v = torch.argmax(output_v, dim=1).float()
stacked_img = torch.cat((stacked_img, output_v))
if make_prediction:
im_name = batch # TODO: Change this to real image name so we know
pred_msk = save_prediction_image(stacked_img, ori_shape[-3:],
im_name, epoch, 1,
save_folder_name)
acc_val = accuracy_check(original_msk, pred_msk)
dice, jac = dice_coeff(pred_msk, original_msk)
total_val_acc = total_val_acc + acc_val
total_val_dice = total_val_dice + dice
total_val_jac = total_val_jac + jac
count_val += 1
return total_val_acc/(count_val + 1), total_val_loss/((count_val + 1)*4),\
total_val_dice/(count_val + 1), total_val_jac/(count_val + 1)