def __init__(self):
# Overrides the current default graph for the lifetime of the context
with tf.device('/gpu:0'): # Use GPU 0
# Count the number of eval data
num_data = utils.count_text_lines(args.test_filenames_file)
print('===> Test: There are totally %d Test files' % (num_data))
steps_per_epoch = np.ceil(num_data / args.batch_size).astype(
np.int32)
self.num_total_steps = 3 * steps_per_epoch # Test 3 epoches
# Load data
data_loader = Dataloader(test_dataloader_params,
shuffle=True) # No shuffle
I1_batch = data_loader.I1_batch
I2_batch = data_loader.I2_batch
I1_aug_batch = data_loader.I1_aug_batch
I2_aug_batch = data_loader.I2_aug_batch
I_batch = data_loader.I_batch
I_prime_batch = data_loader.I_prime_batch
pts1_batch = data_loader.pts1_batch
gt_batch = data_loader.gt_batch
patch_indices_batch = data_loader.patch_indices_batch
# Split on multiple GPU
I1_splits = tf.split(I1_batch, args.num_gpus, 0)
I2_splits = tf.split(I2_batch, args.num_gpus, 0)
I1_aug_splits = tf.split(I1_aug_batch, args.num_gpus, 0)
I2_aug_splits = tf.split(I2_aug_batch, args.num_gpus, 0)
I_splits = tf.split(I_batch, args.num_gpus, 0)
I_prime_splits = tf.split(I_prime_batch, args.num_gpus, 0)
pts1_splits = tf.split(pts1_batch, args.num_gpus, 0)
gt_splits = tf.split(gt_batch, args.num_gpus, 0)
patch_indices_splits = tf.split(patch_indices_batch, args.num_gpus,
0)
# Train on multiple GPU:
reuse_variables = None
h_losses = []
rec_losses = []
ssim_losses = []
l1_losses = []
l1_smooth_losses = []
num_fails = []
model_params = homography_model_params(
mode='test',
batch_size=int(args.batch_size / args.num_gpus),
patch_size=args.patch_size,
img_h=args.img_h,
img_w=args.img_w,
loss_type=args.loss_type,
use_batch_norm=args.use_batch_norm,
augment_list=args.augment_list,
leftright_consistent_weight=args.leftright_consistent_weight)
# Deal with sharable variables
with tf.variable_scope(tf.get_variable_scope()):
for i in range(args.num_gpus):
with tf.device('/gpu:%d' % i):
model = HomographyModel(
model_params,
I1_splits[i],
I2_splits[i],
I1_aug_splits[i],
I2_aug_splits[i],
I_splits[i],
I_prime_splits[i],
pts1_splits[i],
gt_splits[i],
patch_indices_splits[i],
reuse_variables=reuse_variables,
model_index=i)
# Debug test splits
#test_synthetic_dataloader(data_loader, True, I1_splits[i], I2_splits[i], I_splits[i], I_prime_splits[i], pts1_splits[i], gt_splits[i], patch_indices_splits[i])
reuse_variables = True
# In testing, use bounded_h_loss (under successful condition)
h_loss = model.bounded_h_loss
rec_loss = model.rec_loss
ssim_loss = model.ssim_loss
l1_loss = model.l1_loss
l1_smooth_loss = model.l1_smooth_loss
num_fail = model.num_fail
self.pred_I2 = model.pred_I2
self.I2 = model.I2
self.H_mat = model.H_mat
self.I1 = model.I1
self.I1_aug = model.I1_aug
self.I2_aug = model.I2_aug
self.I = model.I
self.I_prime = model.I_prime
self.pts1 = model.pts_1
self.gt = model.gt
self.pred_h4p = model.pred_h4p
h_losses.append(h_loss)
rec_losses.append(rec_loss)
ssim_losses.append(ssim_loss)
l1_losses.append(l1_loss)
l1_smooth_losses.append(l1_smooth_loss)
num_fails.append(num_fail)
self.total_h_loss = tf.reduce_mean(h_losses)
self.total_num_fail = tf.reduce_sum(num_fails)
self.total_rec_loss = tf.reduce_mean(rec_losses)
self.total_ssim_loss = tf.reduce_mean(ssim_losses)
self.total_l1_loss = tf.reduce_mean(l1_losses)
self.total_l1_smooth_loss = tf.reduce_mean(l1_smooth_losses)
with tf.name_scope('Losses'):
tf.summary.scalar('Total_h_loss', self.total_h_loss)
tf.summary.scalar('Total_rec_loss', self.total_rec_loss)
tf.summary.scalar('Total_ssim_loss', self.total_ssim_loss)
tf.summary.scalar('Total_l1_loss', self.total_l1_loss)
tf.summary.scalar('Total_l1_smooth_loss',
self.total_l1_smooth_loss)
self.summary_opt = tf.summary.merge_all()
def train():
# Overrides the current default graph for the lifetime of the context
with tf.Graph().as_default(), tf.device('/gpu:0'): # Use GPU 0
global_step = tf.Variable(0, trainable=False)
# Training parameters
# Count the number of training & eval data
num_data = utils.count_text_lines(args.filenames_file)
print('===> Train: There are totally %d training files' % (num_data))
num_total_steps = 150000
# Optimizer. Use exponential decay: decayed_lr = lr* decay_rate^ (global_steps/ decay_steps)
decay_rate = 0.96
decay_steps = (math.log(decay_rate) * num_total_steps) / math.log(
args.min_lr * 1.0 / args.lr)
print('args lr:', args.lr, args.min_lr)
print('===> Decay steps:', decay_steps)
learning_rate = tf.train.exponential_decay(args.lr,
global_step,
int(decay_steps),
decay_rate,
staircase=True)
# Due to slim.batch_norm docs:
# Note: when training, the moving_mean and moving_variance need to be updated.
# By default the update ops are placed in `tf.GraphKeys.UPDATE_OPS`, so they
# need to be added as a dependency to the `train_op`. For example:
# ```python
# update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
# with tf.control_dependencies(update_ops):
# train_op = optimizer.minimize(loss)
# ```
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
opt_step = tf.train.AdamOptimizer(learning_rate)
# Load data
data_loader = Dataloader(train_dataloader_params,
shuffle=True) # shuffle
I1_batch = data_loader.I1_batch
I2_batch = data_loader.I2_batch
I1_aug_batch = data_loader.I1_aug_batch
I2_aug_batch = data_loader.I2_aug_batch
I_batch = data_loader.I_batch
I_prime_batch = data_loader.I_prime_batch
pts1_batch = data_loader.pts1_batch
gt_batch = data_loader.gt_batch
patch_indices_batch = data_loader.patch_indices_batch
# Split on multiple GPU
I1_splits = tf.split(I1_batch, args.num_gpus, 0)
I2_splits = tf.split(I2_batch, args.num_gpus, 0)
I1_aug_splits = tf.split(I1_aug_batch, args.num_gpus, 0)
I2_aug_splits = tf.split(I2_aug_batch, args.num_gpus, 0)
I_splits = tf.split(I_batch, args.num_gpus, 0)
I_prime_splits = tf.split(I_prime_batch, args.num_gpus, 0)
pts1_splits = tf.split(pts1_batch, args.num_gpus, 0)
gt_splits = tf.split(gt_batch, args.num_gpus, 0)
patch_indices_splits = tf.split(patch_indices_batch, args.num_gpus, 0)
# Train on multiple GPU:
multi_grads = []
reuse_variables = None
h_losses = []
rec_losses = []
ssim_losses = []
l1_losses = []
l1_smooth_losses = []
ncc_losses = []
model_params = homography_model_params(
mode=args.mode,
batch_size=int(args.batch_size / args.num_gpus),
patch_size=args.patch_size,
img_h=args.img_h,
img_w=args.img_w,
loss_type=args.loss_type,
use_batch_norm=args.use_batch_norm,
augment_list=args.augment_list,
leftright_consistent_weight=args.leftright_consistent_weight)
# Deal with sharable variables
with tf.variable_scope(tf.get_variable_scope()):
for i in range(args.num_gpus):
with tf.device('/gpu:%d' % i):
model = HomographyModel(model_params,
I1_splits[i],
I2_splits[i],
I1_aug_splits[i],
I2_aug_splits[i],
I_splits[i],
I_prime_splits[i],
pts1_splits[i],
gt_splits[i],
patch_indices_splits[i],
reuse_variables=reuse_variables,
model_index=i)
h_loss = model.h_loss
rec_loss = model.rec_loss
ssim_loss = model.ssim_loss
l1_loss = model.l1_loss
l1_smooth_loss = model.l1_smooth_loss
ncc_loss = model.ncc_loss
pred_I2 = model.pred_I2
I2 = model.I2
H_mat = model.H_mat
I1 = model.I1
I = model.I
I1_aug = model.I1_aug
I2_aug = model.I2_aug
h_losses.append(h_loss)
rec_losses.append(rec_loss)
ssim_losses.append(ssim_loss)
l1_losses.append(l1_loss)
l1_smooth_losses.append(l1_smooth_loss)
ncc_losses.append(ncc_loss)
reuse_variables = True
if args.loss_type == 'h_loss':
grads = opt_step.compute_gradients(h_loss)
elif args.loss_type == 'rec_loss':
grads = opt_step.compute_gradients(rec_loss)
elif args.loss_type == 'ssim_loss':
grads = opt_step.compute_gradients(ssim_loss)
elif args.loss_type == 'l1_loss':
grads = opt_step.compute_gradients(l1_loss)
elif args.loss_type == 'l1_smooth_loss':
grads = opt_step.compute_gradients(l1_smooth_loss)
elif args.loss_type == 'ncc_loss':
grads = opt_step.compute_gradients(ncc_loss)
else:
print('===> Loss type does not exist!')
exit(0)
print('====> Use loss type: ', args.loss_type)
time.sleep(2)
multi_grads.append(grads)
# Take average of the grads
grads = utils.get_average_grads(multi_grads)
apply_grad_opt = opt_step.apply_gradients(grads,
global_step=global_step)
total_h_loss = tf.reduce_mean(h_losses)
total_rec_loss = tf.reduce_mean(rec_losses)
total_ssim_loss = tf.reduce_mean(ssim_losses)
total_l1_loss = tf.reduce_mean(l1_losses)
total_l1_smooth_loss = tf.reduce_mean(l1_smooth_losses)
total_ncc_loss = tf.reduce_mean(ncc_losses)
with tf.name_scope('Losses'):
tf.summary.scalar('Learning_rate', learning_rate)
tf.summary.scalar('Total_h_loss', total_h_loss)
tf.summary.scalar('Total_rec_loss', total_rec_loss)
tf.summary.scalar('Total_ssim_loss', total_ssim_loss)
tf.summary.scalar('Total_l1_loss', total_l1_loss)
tf.summary.scalar('Total_l1_smooth_loss', total_l1_smooth_loss)
tf.summary.scalar('Total_ncc_loss', total_ncc_loss)
summary_opt = tf.summary.merge_all()
# Create a session
gpu_options = tf.GPUOptions(
allow_growth=True
) # Does not pre-allocate large, increase if needed
config = tf.ConfigProto(
allow_soft_placement=True, gpu_options=gpu_options
) # soft_placement allows to work on CPUs if GPUs are not available
sess = tf.Session(config=config)
# Saver
log_name = args.loss_type
summary_writer = tf.summary.FileWriter(args.log_dir, sess.graph)
train_saver = tf.train.Saver(max_to_keep=5) # Keep maximum 5 models
# Initialize
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
# Threads coordinator
coordinator = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coordinator)
# Restore
if args.resume:
train_saver.restore(sess,
tf.train.latest_checkpoint(args.model_dir))
if args.retrain:
sess.run(global_step.assign(0))
# Index of the image want to display
index = 0
h_total_loss_value = 0
rec_total_loss_value = 0
ssim_total_loss_value = 0
l1_total_loss_value = 0
l1_smooth_total_loss_value = 0
ncc_total_loss_value = 0
start_step = global_step.eval(session=sess)
print('===> Start step:', start_step)
# Start training
for step in range(start_step, start_step + num_total_steps):
if args.visual:
_, h_loss_value, rec_loss_value, ssim_loss_value, l1_loss_value, l1_smooth_loss_value, ncc_loss_value, lr_value, H_mat_value, pred_I2_value, I2_value, I1_value, I1_aug_value, I2_aug_value, I_value = sess.run(
[
apply_grad_opt, total_h_loss, total_rec_loss,
total_ssim_loss, total_l1_loss, total_l1_smooth_loss,
total_ncc_loss, learning_rate, H_mat, pred_I2, I2, I1,
I1_aug, I2_aug, I
])
elif args.loss_type == "l1_loss" and not args.visual:
_, h_loss_value, l1_loss_value, l1_smooth_loss_value, lr_value = sess.run(
[
apply_grad_opt, total_h_loss, total_l1_loss,
total_l1_smooth_loss, learning_rate
])
h_total_loss_value += h_loss_value
l1_total_loss_value += l1_loss_value
l1_smooth_total_loss_value += l1_smooth_loss_value
if step % 100 == 0:
total_time = utils.progress_bar(
step, num_total_steps + start_step - 1,
'Train: 1, h_loss %4.3f, l1_loss %.6f, l1_smooth_loss %.6f, lr %.6f'
%
(h_total_loss_value /
(step - start_step + 1), l1_total_loss_value /
(step - start_step + 1), l1_smooth_total_loss_value /
(step - start_step + 1), lr_value))
else:
_, h_loss_value, rec_loss_value, ssim_loss_value, l1_loss_value, l1_smooth_loss_value, ncc_loss_value, lr_value = sess.run(
[
apply_grad_opt, total_h_loss, total_rec_loss,
total_ssim_loss, total_l1_loss, total_l1_smooth_loss,
total_ncc_loss, learning_rate
])
h_total_loss_value += h_loss_value
rec_total_loss_value += rec_loss_value
ssim_total_loss_value += ssim_loss_value
l1_total_loss_value += l1_loss_value
l1_smooth_total_loss_value += l1_smooth_loss_value
ncc_total_loss_value += ncc_loss_value
if step % 100 == 0:
total_time = utils.progress_bar(
step, num_total_steps + start_step - 1,
'Train: 1, h_loss %4.3f, rec_loss %4.3f, ssim_loss %.6f. l1_loss %.6f, l1_smooth_loss %.6f, ncc_loss %.6f, lr %.6f'
%
(h_total_loss_value /
(step - start_step + 1), rec_total_loss_value /
(step - start_step + 1), ssim_total_loss_value /
(step - start_step + 1), l1_total_loss_value /
(step - start_step + 1), l1_smooth_total_loss_value /
(step - start_step + 1), ncc_total_loss_value /
(step - start_step + 1), lr_value))
# Tensorboard
if step % 1000 == 0:
summary_str = sess.run(summary_opt)
summary_writer.add_summary(summary_str, global_step=step)
if step and step % 1000 == 0:
train_saver.save(sess,
args.model_dir + args.model_name,
global_step=step)
if args.visual and step % 1 == 0:
if 'normalize' in args.augment_list:
pred_I2_sample_value = utils.denorm_img(
pred_I2_value[index, :, :, 0]).astype(np.uint8)
I2_sample_value = utils.denorm_img(
I2_value[index, :, :, 0]).astype(np.uint8)
I1_sample_value = utils.denorm_img(
I1_value[index, :, :, 0]).astype(np.uint8)
I1_aug_sample_value = utils.denorm_img(
I1_aug_value[index, :, :, 0]).astype(np.uint8)
I2_aug_sample_value = utils.denorm_img(
I2_aug_value[index, :, :, 0]).astype(np.uint8)
I_sample_value = utils.denorm_img(
I_value[index, ...]).astype(np.uint8)
else:
pred_I2_sample_value = pred_I2_value[index, :, :,
0].astype(np.uint8)
I2_sample_value = I2_value[index, :, :, 0].astype(np.uint8)
I1_sample_value = I1_value[index, :, :, 0].astype(np.uint8)
I1_aug_sample_value = I1_aug_value[index, :, :,
0].astype(np.uint8)
I2_aug_sample_value = I2_aug_value[index, :, :,
0].astype(np.uint8)
I_sample_value = I_value[index, ...].astype(np.uint8)
plt.subplot(3, 1, 1)
plt.imshow(np.concatenate(
[pred_I2_sample_value, I2_sample_value], 1),
cmap='gray')
plt.title('Pred I2 vs I2')
plt.subplot(3, 1, 2)
plt.imshow(np.concatenate(
[I1_aug_sample_value, I2_aug_sample_value], 1),
cmap='gray')
plt.title('I1_aug vs I2_aug')
plt.subplot(3, 1, 3)
plt.imshow(I_sample_value if I_sample_value.shape[2] ==
3 else I_sample_value[:, :, 0])
plt.title('I')
plt.show()
plt.pause(0.05)
# Save the final model
train_saver.save(sess,
args.model_dir + args.model_name,
global_step=step)
def train(args):
# Load data
TrainDataset = SyntheticDataset(data_path=args.data_path,
mode=args.mode,
img_h=args.img_h,
img_w=args.img_w,
patch_size=args.patch_size,
do_augment=args.do_augment)
train_loader = DataLoader(TrainDataset, batch_size=args.batch_size, shuffle=True, num_workers=4)
print('===> Train: There are totally {} training files'.format(len(TrainDataset)))
net = HomographyModel(args.use_batch_norm)
if args.resume:
model_path = os.path.join(args.model_dir, args.model_name)
ckpt = torch.load(model_path)
net.load_state_dict(ckpt.state_dict())
if torch.cuda.is_available():
net = net.cuda()
optimizer = optim.Adam(net.parameters(), lr=args.lr) # default as 0.0001
decay_rate = 0.96
step_size = (math.log(decay_rate) * args.max_epochs) / math.log(args.min_lr * 1.0 / args.lr)
print('args lr:', args.lr, args.min_lr)
print('===> Decay steps:', step_size)
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=int(step_size), gamma=0.96)
print("start training")
writer = SummaryWriter(logdir=args.log_dir, flush_secs=60)
score_print_fre = 100
summary_fre = 1000
model_save_fre = 4000
glob_iter = 0
t0 = time.time()
for epoch in range(args.max_epochs):
net.train()
epoch_start = time.time()
train_l1_loss = 0.0
train_l1_smooth_loss = 0.0
train_h_loss = 0.0
for i, batch_value in enumerate(train_loader):
I1_batch = batch_value[0].float()
I2_batch = batch_value[1].float()
I1_aug_batch = batch_value[2].float()
I2_aug_batch = batch_value[3].float()
I_batch = batch_value[4].float()
I_prime_batch = batch_value[5].float()
pts1_batch = batch_value[6].float()
gt_batch = batch_value[7].float()
patch_indices_batch = batch_value[8].float()
if torch.cuda.is_available():
I1_aug_batch = I1_aug_batch.cuda()
I2_aug_batch = I2_aug_batch.cuda()
I_batch = I_batch.cuda()
pts1_batch = pts1_batch.cuda()
gt_batch = gt_batch.cuda()
patch_indices_batch = patch_indices_batch.cuda()
# forward, backward, update weights
optimizer.zero_grad()
batch_out = net(I1_aug_batch, I2_aug_batch, I_batch, pts1_batch, gt_batch, patch_indices_batch)
h_loss = batch_out['h_loss']
rec_loss = batch_out['rec_loss']
ssim_loss = batch_out['ssim_loss']
l1_loss = batch_out['l1_loss']
l1_smooth_loss = batch_out['l1_smooth_loss']
ncc_loss = batch_out['ncc_loss']
pred_I2 = batch_out['pred_I2']
loss = l1_loss
loss.backward()
optimizer.step()
train_l1_loss += loss.item()
train_l1_smooth_loss += l1_smooth_loss.item()
train_h_loss += h_loss.item()
if (i + 1) % score_print_fre == 0 or (i + 1) == len(train_loader):
print(
"Training: Epoch[{:0>3}/{:0>3}] Iter[{:0>3}]/[{:0>3}] l1 loss: {:.4f} "
"l1 smooth loss: {:.4f} h loss: {:.4f} lr={:.8f}".format(
epoch + 1, args.max_epochs, i + 1, len(train_loader), train_l1_loss / score_print_fre,
train_l1_smooth_loss / score_print_fre, train_h_loss / score_print_fre, scheduler.get_lr()[0]))
train_l1_loss = 0.0
train_l1_smooth_loss = 0.0
train_h_loss = 0.0
if glob_iter % summary_fre == 0:
writer.add_scalar('learning_rate', scheduler.get_lr()[0], glob_iter)
writer.add_scalar('h_loss', h_loss, glob_iter)
writer.add_scalar('rec_loss', rec_loss, glob_iter)
writer.add_scalar('ssim_loss', ssim_loss, glob_iter)
writer.add_scalar('l1_loss', l1_loss, glob_iter)
writer.add_scalar('l1_smooth_loss', l1_smooth_loss, glob_iter)
writer.add_scalar('ncc_loss', ncc_loss, glob_iter)
writer.add_image('I', utils.denorm_img(I_batch[0, ...].cpu().numpy()).astype(np.uint8)[:, :, ::-1],
glob_iter, dataformats='HWC')
writer.add_image('I_prime',
utils.denorm_img(I_prime_batch[0, ...].numpy()).astype(np.uint8)[:, :, ::-1],
glob_iter, dataformats='HWC')
writer.add_image('I1_aug', utils.denorm_img(I1_aug_batch[0, 0, ...].cpu().numpy()).astype(np.uint8),
glob_iter, dataformats='HW')
writer.add_image('I2_aug', utils.denorm_img(I2_aug_batch[0, 0, ...].cpu().numpy()).astype(np.uint8),
glob_iter, dataformats='HW')
writer.add_image('pred_I2',
utils.denorm_img(pred_I2[0, 0, ...].cpu().detach().numpy()).astype(np.uint8),
glob_iter, dataformats='HW')
writer.add_image('I2', utils.denorm_img(I2_batch[0, 0, ...].numpy()).astype(np.uint8), glob_iter,
dataformats='HW')
writer.add_image('I1', utils.denorm_img(I1_batch[0, 0, ...].numpy()).astype(np.uint8), glob_iter,
dataformats='HW')
# save model
if glob_iter % model_save_fre == 0 and glob_iter != 0:
filename = 'model' + '_iter_' + str(glob_iter) + '.pth'
model_save_path = os.path.join(args.model_dir, filename)
torch.save(net, model_save_path)
glob_iter += 1
scheduler.step()
print("Epoch: {} epoch time: {:.1f}s".format(epoch, time.time() - epoch_start))
elapsed_time = time.time() - t0
print("Finished Training in {:.0f}h {:.0f}m {:.0f}s.".format(
elapsed_time // 3600, (elapsed_time % 3600) // 60, (elapsed_time % 3600) % 60))
def __init__(self):
# Overrides the current default graph for the lifetime of the context
with tf.device('/gpu:0'): # Use GPU 0
# Count the number of eval data
num_data = utils.count_text_lines(args.test_filenames_file)
print('===> Test: There are totally %d Test files' % (num_data))
steps_per_epoch = np.ceil(num_data / args.batch_size).astype(
np.int32)
self.num_total_steps = 2 * steps_per_epoch # Test 2 epoches
# Load data
data_loader = Dataloader(test_dataloader_params,
shuffle=False) # No shuffle
# Debug test train_dataloader
# test_synthetic_dataloader(data_loader, True)
I1_batch = data_loader.I1_batch
I2_batch = data_loader.I2_batch
I1_aug_batch = data_loader.I1_aug_batch
I2_aug_batch = data_loader.I2_aug_batch
I_batch = data_loader.I_batch
I_prime_batch = data_loader.I_prime_batch
full_I_batch = data_loader.full_I_batch
full_I_prime_batch = data_loader.full_I_prime_batch
pts1_batch = data_loader.pts1_batch
gt_batch = data_loader.gt_batch
patch_indices_batch = data_loader.patch_indices_batch
# Split on multiple GPU
I1_splits = tf.split(I1_batch, args.num_gpus, 0)
I2_splits = tf.split(I2_batch, args.num_gpus, 0)
I1_aug_splits = tf.split(I1_aug_batch, args.num_gpus, 0)
I2_aug_splits = tf.split(I2_aug_batch, args.num_gpus, 0)
I_splits = tf.split(I_batch, args.num_gpus, 0)
I_prime_splits = tf.split(I_prime_batch, args.num_gpus, 0)
pts1_splits = tf.split(pts1_batch, args.num_gpus, 0)
gt_splits = tf.split(gt_batch, args.num_gpus, 0)
patch_indices_splits = tf.split(patch_indices_batch, args.num_gpus,
0)
# Train on multiple GPU:
reuse_variables = None
rec_losses = []
ssim_losses = []
l1_losses = []
l1_smooth_losses = []
num_fails = []
model_params = homography_model_params(
mode='test',
batch_size=int(args.batch_size / args.num_gpus),
patch_size=args.patch_size,
img_h=args.img_h,
img_w=args.img_w,
loss_type=args.loss_type,
use_batch_norm=args.use_batch_norm,
augment_list=args.augment_list,
leftright_consistent_weight=args.leftright_consistent_weight)
# Deal with sharable variables
with tf.variable_scope(tf.get_variable_scope()):
for i in range(args.num_gpus):
with tf.device('/gpu:%d' % i):
# Note that ground truth gt_ here is the correspondences between pairs of images
# and are different from the actual delta movement of fourpoints that we want to find
# This ground truth is used for evaluating the estimated homography on real image data.
model = HomographyModel(
model_params,
I1_splits[i],
I2_splits[i],
I1_aug_splits[i],
I2_aug_splits[i],
I_splits[i],
I_prime_splits[i],
pts1_splits[i],
gt_splits[i],
patch_indices_splits[i],
reuse_variables=reuse_variables,
model_index=i)
# Debug test splits
#test_synthetic_dataloader(data_loader, True, I1_splits[i], I2_splits[i], I_splits[i], I_prime_splits[i], pts1_splits[i], gt_splits[i], patch_indices_splits[i])
reuse_variables = True
rec_loss = model.rec_loss
ssim_loss = model.ssim_loss
l1_loss = model.l1_loss
l1_smooth_loss = model.l1_smooth_loss
if i == 0:
self.pred_I2 = model.pred_I2
self.I = model.I
self.I_prime = model.I_prime
self.I1_aug = model.I1_aug
self.I2_aug = model.I2_aug
self.pred_h4p = model.pred_h4p
self.gt_corr = model.gt
self.pts1 = model.pts_1
else:
self.pred_I2 = tf.concat(
[self.pred_I2, model.pred_I2], axis=0)
self.I = tf.concat([self.I, model.I], axis=0)
self.I_prime = tf.concat(
[self.I_prime, model.I_prime], axis=0)
self.I1_aug = tf.concat(
[self.I1_aug, model.I1_aug], axis=0)
self.I2_aug = tf.concat(
[self.I2_aug, model.I2_aug], axis=0)
self.pred_h4p = tf.concat(
[self.pred_h4p, model.pred_h4p], axis=0)
self.gt_corr = tf.concat([self.gt_corr, model.gt],
axis=0)
self.pts1 = tf.concat([self.pts1, model.pts_1],
axis=0)
rec_losses.append(rec_loss)
ssim_losses.append(ssim_loss)
l1_losses.append(l1_loss)
l1_smooth_losses.append(l1_smooth_loss)
self.total_rec_loss = tf.reduce_mean(rec_losses)
self.total_ssim_loss = tf.reduce_mean(ssim_losses)
self.total_l1_loss = tf.reduce_mean(l1_losses)
self.total_l1_smooth_loss = tf.reduce_mean(l1_smooth_losses)
self.full_I = full_I_batch
self.full_I_prime = full_I_prime_batch
with tf.name_scope('Losses'):
tf.summary.scalar('Total_rec_loss', self.total_rec_loss)
tf.summary.scalar('Total_ssim_loss', self.total_ssim_loss)
tf.summary.scalar('Total_l1_loss', self.total_l1_loss)
tf.summary.scalar('Total_l1_smooth_loss',
self.total_l1_smooth_loss)
self.summary_opt = tf.summary.merge_all()
def test(args):
# Load data
TestDataset = SyntheticDataset(data_path=args.data_path,
mode=args.mode,
img_h=args.img_h,
img_w=args.img_w,
patch_size=args.patch_size,
do_augment=args.do_augment)
test_loader = DataLoader(TestDataset, batch_size=1)
print('===> Test: There are totally {} testing files'.format(len(TestDataset)))
# Load model
net = HomographyModel()
model_path = os.path.join(args.model_dir, args.model_name)
state = torch.load(model_path)
net.load_state_dict(state.state_dict())
if torch.cuda.is_available():
net = net.cuda()
print("start testing")
with torch.no_grad():
net.eval()
test_l1_loss = 0.0
test_h_loss = 0.0
h_losses_array = []
for i, batch_value in enumerate(test_loader):
I1_aug_batch = batch_value[2].float()
I2_aug_batch = batch_value[3].float()
I_batch = batch_value[4].float()
I_prime_batch = batch_value[5].float()
pts1_batch = batch_value[6].float()
gt_batch = batch_value[7].float()
patch_indices_batch = batch_value[8].float()
if torch.cuda.is_available():
I1_aug_batch = I1_aug_batch.cuda()
I2_aug_batch = I2_aug_batch.cuda()
I_batch = I_batch.cuda()
pts1_batch = pts1_batch.cuda()
gt_batch = gt_batch.cuda()
patch_indices_batch = patch_indices_batch.cuda()
batch_out = net(I1_aug_batch, I2_aug_batch, I_batch, pts1_batch, gt_batch, patch_indices_batch)
h_loss = batch_out['h_loss']
rec_loss = batch_out['rec_loss']
ssim_loss = batch_out['ssim_loss']
l1_loss = batch_out['l1_loss']
pred_h4p_value = batch_out['pred_h4p']
test_h_loss += h_loss.item()
test_l1_loss += l1_loss.item()
h_losses_array.append(h_loss.item())
if args.save_visual:
I_sample = utils.denorm_img(I_batch[0].cpu().numpy()).astype(np.uint8)
I_prime_sample = utils.denorm_img(I_prime_batch[0].numpy()).astype(np.uint8)
pts1_sample = pts1_batch[0].cpu().numpy().reshape([4, 2]).astype(np.float32)
gt_h4p_sample = gt_batch[0].cpu().numpy().reshape([4, 2]).astype(np.float32)
pts2_sample = pts1_sample + gt_h4p_sample
pred_h4p_sample = pred_h4p_value[0].cpu().numpy().reshape([4, 2]).astype(np.float32)
pred_pts2_sample = pts1_sample + pred_h4p_sample
# Save
visual_file_name = ('%s' % i).zfill(4) + '.jpg'
utils.save_correspondences_img(I_prime_sample, I_sample, pts1_sample, pts2_sample, pred_pts2_sample,
args.results_dir, visual_file_name)
print("Testing: h_loss: {:4.3f}, rec_loss: {:4.3f}, ssim_loss: {:4.3f}, l1_loss: {:4.3f}".format(
h_loss.item(), rec_loss.item(), ssim_loss.item(), l1_loss.item()
))
print('|Test size | h_loss | l1_loss |')
print(len(test_loader), test_h_loss / len(test_loader), test_l1_loss / len(test_loader))
tops_list = utils.find_percentile(h_losses_array)
print('===> Percentile Values: (20, 50, 80, 100):')
print(tops_list)
print('======> End! ====================================')