def prepareSave(args, data, pred_c, pred, recorder, log):
input_var, mask_var = data['img'], data['mask']
results = [input_var.data, mask_var.data, (data['normal'].data + 1) / 2]
if args.s1_est_d:
l_acc, data['dir_err'] = eval_utils.calDirsAcc(data['dirs'].data,
pred_c['dirs'].data,
args.batch)
recorder.updateIter('train', l_acc.keys(), l_acc.values())
if args.s1_est_i:
int_acc, data['int_err'] = eval_utils.calIntsAcc(
data['ints'].data, pred_c['intens'].data, args.batch)
recorder.updateIter('train', int_acc.keys(), int_acc.values())
if args.s2_est_n:
acc, error_map = eval_utils.calNormalAcc(data['normal'].data,
pred['normal'].data,
mask_var.data)
pred_n = (pred['normal'].data + 1) / 2
masked_pred = pred_n * mask_var.data.expand_as(pred['normal'].data)
res_n = [masked_pred, error_map['angular_map']]
results += res_n
recorder.updateIter('train', acc.keys(), acc.values())
nrow = input_var.shape[0] if input_var.shape[0] <= 32 else 32
return results, recorder, nrow
def prepareRes(args, data, pred, recorder, log, split):
data_batch = args.val_batch if split == 'val' else args.test_batch
iter_res = []
error = ''
if args.s1_est_d:
l_acc, data['dir_err'] = eval_utils.calDirsAcc(data['dirs'].data,
pred['dirs'].data,
data_batch)
recorder.updateIter(split, l_acc.keys(), l_acc.values())
iter_res.append(l_acc['l_err_mean'])
error += 'D_%.3f-' % (l_acc['l_err_mean'])
if args.s1_est_i:
int_acc, data['int_err'] = eval_utils.calIntsAcc(
data['ints'].data, pred['intens'].data, data_batch)
recorder.updateIter(split, int_acc.keys(), int_acc.values())
iter_res.append(int_acc['ints_ratio'])
error += 'I_%.3f-' % (int_acc['ints_ratio'])
if args.s1_est_n:
acc, error_map = eval_utils.calNormalAcc(data['normal'].data,
pred['normal'].data,
data['mask'].data)
recorder.updateIter(split, acc.keys(), acc.values())
iter_res.append(acc['n_err_mean'])
error += 'N_%.3f-' % (acc['n_err_mean'])
data['error_map'] = error_map['angular_map']
return recorder, iter_res, error
def prepareRes(args, data, pred_c, pred, random_loc, recorder, log, split):
mask_var = data['m']
data_batch = args.val_batch if split == 'val' else args.test_batch
iter_res = []
error = ''
if args.s1_est_d:
l_acc, data['dir_err'] = eval_utils.calDirsAcc(data['dirs'].data,
pred_c['dirs'].data,
data_batch)
recorder.updateIter(split, l_acc.keys(), l_acc.values())
iter_res.append(l_acc['l_err_mean'])
error += 'D_%.3f-' % (l_acc['l_err_mean'])
if args.s2_est_n:
random_x_loc, random_y_loc = random_loc
n_tar = data['n'][:, :, random_x_loc - 8:random_x_loc + 8,
random_y_loc - 8:random_y_loc + 8]
mask_var = mask_var[:, :, random_x_loc - 8:random_x_loc + 8,
random_y_loc - 8:random_y_loc + 8]
acc, error_map = eval_utils.calNormalAcc(n_tar.data, pred['n'].data,
mask_var.data)
recorder.updateIter(split, acc.keys(), acc.values())
iter_res.append(acc['n_err_mean'])
error += 'N_%.3f-' % (acc['n_err_mean'])
data['error_map'] = error_map['angular_map']
return recorder, iter_res, error
def prepareNormalRes(args, data, pred, recorder, log, split):
data_batch = args.val_batch if split == 'val' else args.test_batch
iter_res = []
error = ''
acc, error_map = eval_utils.calNormalAcc(data['normal'].data, pred['normal'].data, data['mask'].data)
recorder.updateIter(split, acc.keys(), acc.values())
iter_res.append(acc['n_err_mean'])
error += 'N_%.3f-' % (acc['n_err_mean'])
data['error_map'] = error_map['angular_map']
return recorder, iter_res, error
def test(args, split, loader, model, log, epoch, recorder, tf_writer):
model.eval()
print('---- Start %s Epoch %d: %d batches ----' %
(split, epoch, len(loader)))
timer = time_utils.Timer(args.time_sync)
disp_intv, save_intv = get_itervals(args, split)
with torch.no_grad():
for i, sample in enumerate(loader):
data = model_utils.parseData(args, sample, timer, split)
input = model_utils.getInput(args, data)
out_var = model(input)
timer.updateTime('Forward')
acc = eval_utils.calNormalAcc(data['tar'].data, out_var.data,
data['m'].data)
recorder.updateIter(split, acc.keys(), acc.values())
iters = i + 1
if iters % disp_intv == 0:
opt = {
'split': split,
'epoch': epoch,
'iters': iters,
'batch': len(loader),
'timer': timer,
'recorder': recorder
}
log.printItersSummary(opt)
for tag, value in acc.items():
tfboard.tensorboard_scalar(tf_writer, tag, value, iters)
if iters % save_intv == 0:
pred = (out_var.data + 1) / 2
masked_pred = pred * data['m'].data.expand_as(out_var.data)
log.saveNormalResults(masked_pred, split, epoch, iters)
opt = {'split': split, 'epoch': epoch, 'recorder': recorder}
log.printEpochSummary(opt)