def validate(val_loader, model, epoch, write_to_file=True):
average_meter = AverageMeter()
# switch to evaluate mode
model.eval()
end = time.time()
for i, (input, target) in enumerate(val_loader):
input, target = input.cuda(), target.cuda()
# torch.cuda.synchronize()
data_time = time.time() - end
# compute output
end = time.time()
with torch.no_grad():
pred = model(input)
# torch.cuda.synchronize()
gpu_time = time.time() - end
# measure accuracy and record loss
result = Result()
result.evaluate(pred.data, target.data)
average_meter.update(result, gpu_time, data_time, input.size(0))
end = time.time()
# save 8 images for visualization
skip = 50
if args.modality == 'd':
img_merge = None
else:
if args.modality == 'rgb':
rgb = input
elif args.modality == 'rgbd':
rgb = input[:, :3, :, :]
depth = input[:, 3:, :, :]
if i == 0:
if args.modality == 'rgbd':
img_merge = utils.merge_into_row_with_gt(
rgb, depth, target, pred)
else:
img_merge = utils.merge_into_row(rgb, target, pred)
elif (i < 8 * skip) and (i % skip == 0):
if args.modality == 'rgbd':
row = utils.merge_into_row_with_gt(rgb, depth, target,
pred)
else:
row = utils.merge_into_row(rgb, target, pred)
img_merge = utils.add_row(img_merge, row)
elif i == 8 * skip:
filename = output_directory + '/comparison_' + str(
epoch) + '.png'
utils.save_image(img_merge, filename)
if (i + 1) % args.print_freq == 0:
print('Test: [{0}/{1}]\t'
't_GPU={gpu_time:.3f}({average.gpu_time:.3f})\n\t'
'RMSE={result.rmse:.2f}({average.rmse:.2f}) '
'MAE={result.mae:.2f}({average.mae:.2f})\n\t'
'Delta1={result.delta1:.3f}({average.delta1:.3f}) '
'REL={result.absrel:.3f}({average.absrel:.3f}) '
'Lg10={result.lg10:.3f}({average.lg10:.3f}) '.format(
i + 1,
len(val_loader),
gpu_time=gpu_time,
result=result,
average=average_meter.average()))
avg = average_meter.average()
print('\n*\n'
'RMSE={average.rmse:.3f}\n'
'MAE={average.mae:.3f}\n'
'Delta1={average.delta1:.3f}\n'
'REL={average.absrel:.3f}\n'
'Lg10={average.lg10:.3f}\n'
't_GPU={time:.3f}\n'.format(average=avg, time=avg.gpu_time))
if write_to_file:
with open(test_csv, 'a') as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writerow({
'mse': avg.mse,
'rmse': avg.rmse,
'absrel': avg.absrel,
'lg10': avg.lg10,
'mae': avg.mae,
'delta1': avg.delta1,
'delta2': avg.delta2,
'delta3': avg.delta3,
'data_time': avg.data_time,
'gpu_time': avg.gpu_time
})
return avg, img_merge
def validate(val_loader, model, epoch, logger):
average_meter = AverageMeter()
model.eval() # switch to evaluate mode
end = time.time()
skip = len(val_loader) // 9 # save images every skip iters
for i, (input, target) in enumerate(val_loader):
input, target = input.cuda(), target.cuda()
torch.cuda.synchronize()
data_time = time.time() - end
# compute output
end = time.time()
with torch.no_grad():
pred = model(input)
torch.cuda.synchronize()
gpu_time = time.time() - end
# measure accuracy and record loss
result = Result()
result.evaluate(pred.data, target.data)
average_meter.update(result, gpu_time, data_time, input.size(0))
end = time.time()
# save 8 images for visualization
if args.dataset == 'kitti':
rgb = input[0]
pred = pred[0]
target = target[0]
else:
rgb = input
if i == 0:
img_merge = utils.merge_into_row(rgb, target, pred)
elif (i < 8 * skip) and (i % skip == 0):
row = utils.merge_into_row(rgb, target, pred)
img_merge = utils.add_row(img_merge, row)
elif i == 8 * skip:
filename = output_directory + '/comparison_' + str(epoch) + '.png'
utils.save_image(img_merge, filename)
if (i + 1) % args.print_freq == 0:
print('Test: [{0}/{1}]\t'
't_GPU={gpu_time:.3f}({average.gpu_time:.3f})\n\t'
'RMSE={result.rmse:.2f}({average.rmse:.2f}) '
'RML={result.absrel:.2f}({average.absrel:.2f}) '
'Log10={result.lg10:.3f}({average.lg10:.3f}) '
'Delta1={result.delta1:.3f}({average.delta1:.3f}) '
'Delta2={result.delta2:.3f}({average.delta2:.3f}) '
'Delta3={result.delta3:.3f}({average.delta3:.3f})'.format(
i + 1,
len(val_loader),
gpu_time=gpu_time,
result=result,
average=average_meter.average()))
avg = average_meter.average()
print('\n*\n'
'RMSE={average.rmse:.3f}\n'
'Rel={average.absrel:.3f}\n'
'Log10={average.lg10:.3f}\n'
'Delta1={average.delta1:.3f}\n'
'Delta2={average.delta2:.3f}\n'
'Delta3={average.delta3:.3f}\n'
't_GPU={time:.3f}\n'.format(average=avg, time=avg.gpu_time))
logger.add_scalar('Test/rmse', avg.rmse, epoch)
logger.add_scalar('Test/Rel', avg.absrel, epoch)
logger.add_scalar('Test/log10', avg.lg10, epoch)
logger.add_scalar('Test/Delta1', avg.delta1, epoch)
logger.add_scalar('Test/Delta2', avg.delta2, epoch)
logger.add_scalar('Test/Delta3', avg.delta3, epoch)
return avg, img_merge
rand_image, rand_depth = image.cuda(), depth.cuda()
rgb0 = model(real_image, real_depth, real_depth)
rgb1 = model(real_image, real_depth, rand_depth)
rgb2 = model(rand_image, rand_depth, real_depth)
rgb_ori = utils.output_rgb(real_image)
dep_ori = utils.output_depth(real_depth)
out_ori = utils.output_rgb(rgb0)
dep_ran = utils.output_depth(rand_depth)
out_dep = utils.output_rgb(rgb1)
rgb_ran = utils.output_rgb(rand_image)
out_rgb = utils.output_rgb(rgb2)
img_merge = utils.merge_into_row([
rgb_ori, dep_ori, out_ori, dep_ran, out_dep, rgb_ran,
out_rgb
])
elif (i < 8 * skip) and (i % skip == 0):
image, depth = batch['image'], batch['depth']
real_image, real_depth = image.cuda(), depth.cuda()
rgb0 = model(real_image, real_depth, real_depth)
rgb1 = model(real_image, real_depth, rand_depth)
rgb2 = model(rand_image, rand_depth, real_depth)
rgb_ori = utils.output_rgb(real_image)
dep_ori = utils.output_depth(real_depth)
out_ori = utils.output_rgb(rgb0)
dep_ran = utils.output_depth(rand_depth)
out_dep = utils.output_rgb(rgb1)
def validation(device,
data_loader,
model,
ord_loss,
output_dir,
epoch,
logger,
PRINT_FREQ,
BETA,
GAMMA,
ORD_NUM=80.0):
avg80 = AverageMeter()
avg50 = AverageMeter()
model.eval()
end = time.time()
skip = 1
img_list = []
evalbar = tqdm(total=len(data_loader))
for i, (_input, _sparse_depth, _dense_depth) in enumerate(data_loader):
_input, _sparse_depth, _dense_depth = _input.to(
device), _sparse_depth.to(device), _dense_depth.to(device)
torch.cuda.synchronize()
data_time = time.time() - end
# compute output
end = time.time()
with torch.no_grad():
_pred_prob, _pred_label = model(_input)
loss = ord_loss(_pred_prob, _dense_depth)
torch.cuda.synchronize()
gpu_time = time.time() - end
pred_depth = utils.label2depth_sid(_pred_label,
K=ORD_NUM,
alpha=1.0,
beta=BETA,
gamma=GAMMA)
abs_rel, sq_rel, rmse, rmse_log, a1, a2, a3 = compute_errors(
_sparse_depth, pred_depth.to(device))
# measure accuracy and record loss
result80 = Result()
result80.evaluate(pred_depth, _sparse_depth.data, cap=80)
result50 = Result()
result50.evaluate(pred_depth, _sparse_depth.data, cap=50)
avg80.update(result80, gpu_time, data_time, _input.size(0))
avg50.update(result50, gpu_time, data_time, _input.size(0))
end = time.time()
# save images for visualization
if i == 0:
img_merge = utils.merge_into_row(_input, _dense_depth, pred_depth)
elif (i < 8 * skip) and (i % skip == 0):
row = utils.merge_into_row(_input, _dense_depth, pred_depth)
img_merge = utils.add_row(img_merge, row)
elif i == 8 * skip:
filename = os.path.join(output_dir,
'eval_{}.png'.format(int(epoch)))
print('save validation figures at {}'.format(filename))
utils.save_image(img_merge, filename)
if (i + 1) % PRINT_FREQ == 0:
print('Test: [{0}/{1}]\t'
't_GPU={gpu_time:.3f}({average.gpu_time:.3f})\n\t'
'RMSE={result.rmse:.2f}({average.rmse:.2f}) '
'AbsRel={result.absrel:.2f}({average.absrel:.2f}) '
'SqRel={result.sqrel:.2f}({average.sqrel:.2f}) '
'Log10={result.lg10:.3f}({average.lg10:.3f}) '
'RMSE_log={result.rmse_log:.3f}({average.rmse_log:.3f}) '
'Delta1={result.delta1:.3f}({average.delta1:.3f}) '
'Delta2={result.delta2:.3f}({average.delta2:.3f}) '
'Delta3={result.delta3:.3f}({average.delta3:.3f})'.format(
i + 1,
len(val_loader),
gpu_time=gpu_time,
result=result80,
average=avg80.average()))
# update progress bar and show loss
evalbar.set_postfix(
ORD_LOSS=
'{:.2f},RMSE/log= {:.2f}/{:.2f},delta={:.2f}/{:.2f}/{:.2f},AbsRel/SqRe;={:.2f}/{:.2f}'
.format(loss, rmse, rmse_log, a1, a2, a3, abs_rel, sq_rel))
evalbar.update(1)
i = i + 1
print('\n**** CAP=80 ****\n'
'RMSE={average.rmse:.3f}\n'
'RMSE_log={average.rmse_log:.3f}\n'
'AbsRel={average.absrel:.3f}\n'
'SqRel={average.sqrel:.3f}\n'
'Log10={average.lg10:.3f}\n'
'Delta1={average.delta1:.3f}\n'
'Delta2={average.delta2:.3f}\n'
'Delta3={average.delta3:.3f}\n'
'iRMSE={average.irmse:.3f}\n'
'iMAE={average.imae:.3f}\n'
't_GPU={average.gpu_time:.3f}\n'.format(average=avg80.average()))
print('\n**** CAP=50 ****\n'
'RMSE={average.rmse:.3f}\n'
'RMSE_log={average.rmse_log:.3f}\n'
'AbsRel={average.absrel:.3f}\n'
'SqRel={average.sqrel:.3f}\n'
'Log10={average.lg10:.3f}\n'
'Delta1={average.delta1:.3f}\n'
'Delta2={average.delta2:.3f}\n'
'Delta3={average.delta3:.3f}\n'
'iRMSE={average.irmse:.3f}\n'
'iMAE={average.imae:.3f}\n'
't_GPU={average.gpu_time:.3f}\n'.format(average=avg50.average()))
logger.add_scalar('VAL_CAP80/RMSE', avg80.average().rmse, epoch)
logger.add_scalar('VAL_CAP80/RMSE_log', avg80.average().rmse_log, epoch)
logger.add_scalar('VAL_CAP80/AbsRel', avg80.average().absrel, epoch)
logger.add_scalar('VAL_CAP80/SqRel', avg80.average().sqrel, epoch)
logger.add_scalar('VAL_CAP80/Delta1', avg80.average().delta1, epoch)
logger.add_scalar('VAL_CAP80/Delta2', avg80.average().delta2, epoch)
logger.add_scalar('VAL_CAP80/Delta3', avg80.average().delta3, epoch)
logger.add_scalar('VAL_CAP50/RMSE', avg50.average().rmse, epoch)
logger.add_scalar('VAL_CAP50/RMSE_log', avg50.average().rmse_log, epoch)
logger.add_scalar('VAL_CAP50/AbsRel', avg50.average().absrel, epoch)
logger.add_scalar('VAL_CAP50/SqRel', avg50.average().sqrel, epoch)
logger.add_scalar('VAL_CAP50/Delta1', avg50.average().delta1, epoch)
logger.add_scalar('VAL_CAP50/Delta2', avg50.average().delta2, epoch)
logger.add_scalar('VAL_CAP50/Delta3', avg50.average().delta3, epoch)
def validate(val_loader, model, epoch, write_to_file=True):
average_meter = AverageMeter()
model.eval() # switch to evaluate mode
end = time.time()
for i, (input, target) in enumerate(val_loader):
input, target = input.cuda(), target.cuda()
torch.cuda.synchronize()
data_time = time.time() - end
# compute output
end = time.time()
##############################################################
## Start of PnP-Depth modification ##
##############################################################
# Original inference
with torch.no_grad():
ori_pred = model.pnp_forward_front(model.pnp_forward_rear(
input)) # equivalent to `ori_pred = model(input)`
# Inference with PnP
sparse_target = input[:, -1:] # NOTE: written for rgbd input
criterion = criteria.MaskedL1Loss().cuda(
) # NOTE: criterion function defined here only for clarity
pnp_iters = 5 # number of iterations
pnp_alpha = 0.01 # update/learning rate
pnp_z = model.pnp_forward_front(input)
for pnp_i in range(pnp_iters):
if pnp_i != 0:
pnp_z = pnp_z - pnp_alpha * torch.sign(pnp_z_grad) # iFGM
pnp_z = Variable(pnp_z, requires_grad=True)
pred = model.pnp_forward_rear(pnp_z)
if pnp_i < pnp_iters - 1:
pnp_loss = criterion(pred, sparse_target)
pnp_z_grad = Grad([pnp_loss], [pnp_z], create_graph=True)[0]
##############################################################
## End of PnP-Depth modification ##
##############################################################
torch.cuda.synchronize()
gpu_time = time.time() - end
# measure accuracy and record loss
result = Result()
result.evaluate(pred.data, target.data)
average_meter.update(result, gpu_time, data_time, input.size(0))
end = time.time()
# save 8 images for visualization
skip = 50
if args.modality == 'd':
img_merge = None
else:
if args.modality == 'rgb':
rgb = input
elif args.modality == 'rgbd':
rgb = input[:, :3, :, :]
depth = input[:, 3:, :, :]
if i == 0:
if args.modality == 'rgbd':
img_merge = utils.merge_into_row_with_gt(
rgb, depth, target, pred)
else:
img_merge = utils.merge_into_row(rgb, target, pred)
elif (i < 8 * skip) and (i % skip == 0):
if args.modality == 'rgbd':
row = utils.merge_into_row_with_gt(rgb, depth, target,
pred)
else:
row = utils.merge_into_row(rgb, target, pred)
img_merge = utils.add_row(img_merge, row)
elif i == 8 * skip:
filename = output_directory + '/comparison_' + str(
epoch) + '.png'
utils.save_image(img_merge, filename)
if (i + 1) % args.print_freq == 0:
print('Test: [{0}/{1}]\t'
't_GPU={gpu_time:.3f}({average.gpu_time:.3f})\n\t'
'RMSE={result.rmse:.2f}({average.rmse:.2f}) '
'MAE={result.mae:.2f}({average.mae:.2f}) '
'Delta1={result.delta1:.3f}({average.delta1:.3f}) '
'REL={result.absrel:.3f}({average.absrel:.3f}) '
'Lg10={result.lg10:.3f}({average.lg10:.3f}) '.format(
i + 1,
len(val_loader),
gpu_time=gpu_time,
result=result,
average=average_meter.average()))
avg = average_meter.average()
print('\n*\n'
'RMSE={average.rmse:.3f}\n'
'MAE={average.mae:.3f}\n'
'Delta1={average.delta1:.3f}\n'
'REL={average.absrel:.3f}\n'
'Lg10={average.lg10:.3f}\n'
't_GPU={time:.3f}\n'.format(average=avg, time=avg.gpu_time))
if write_to_file:
with open(test_csv, 'a') as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writerow({
'mse': avg.mse,
'rmse': avg.rmse,
'absrel': avg.absrel,
'lg10': avg.lg10,
'mae': avg.mae,
'delta1': avg.delta1,
'delta2': avg.delta2,
'delta3': avg.delta3,
'data_time': avg.data_time,
'gpu_time': avg.gpu_time
})
return avg, img_merge
def demo(val_loader, model, epoch, write_to_file=True):
average_meter = AverageMeter()
model.eval() # switch to evaluate mode
end = time.time()
#viz.line([[0.,0.]],[0],win='demo2',opts=dict(title='resluts2',legend=['RMSE','MAE']))
viz.line([[0., 0., 0., 0., 0., 0.]], [0],
win='demo1',
opts=dict(
title='resluts1',
legend=['t_GPU', 'Delta1', 'REL', 'lG10', 'RMSE', 'MAE']))
for i, (input, target) in enumerate(val_loader):
input, target = input.cuda(), target.cuda()
# torch.cuda.synchronize()
data_time = time.time() - end
# compute output
end = time.time()
with torch.no_grad():
pred = model(input)
# torch.cuda.synchronize()
gpu_time = time.time() - end
# measure accuracy and record loss
result = Result()
result.evaluate(pred.data, target.data)
average_meter.update(result, gpu_time, data_time, input.size(0))
end = time.time()
# step 50 images for visualization
skip = 10
if args.modality == 'rgb':
rgb = input
img_merge = utils.merge_into_row(rgb, target, pred)
#row = utils.merge_into_row(rgb, target, pred)
#img_merge = utils.add_row(img_merge, row)
#filename = output_directory + '/comparison_' + str(epoch) + '.png'
#utils.save_image(img_merge, filename)
#print(img_merge)
if i % skip == 0:
img = np.transpose(img_merge, (2, 0, 1))
img = torch.from_numpy(img)
viz.images(img, win='depth_estimation')
t_GPU = float('{gpu_time:.3f}'.format(gpu_time=gpu_time))
RMSE = float('{result.rmse:.2f}'.format(result=result))
MAE = float('{result.mae:.2f}'.format(result=result))
Delta1 = float('{result.delta1:.3f}'.format(result=result))
REL = float('{result.absrel:.3f}'.format(result=result))
Lg10 = float('{result.lg10:.3f}'.format(result=result))
#print(t_GPU)
#viz.line([[RMSE,MAE]],[i],win='demo2',update='append')
viz.line([[t_GPU, Delta1, REL, Lg10, RMSE, MAE]], [i],
win='demo1',
update='append')
time.sleep(0.2)
avg = average_meter.average()
viz.text('\n*\n'
'RMSE={result.rmse:.2f}({average.rmse:.3f})\n\t'
'MAE={result.mae:.2f}({average.mae:.3f})\n\t'
'Delta1={result.delta1:.3f}({average.delta1:.3f})\n\t'
'REL={result.absrel:.3f}({average.absrel:.3f})\n\t'
'Lg10={result.lg10:.3f}({average.lg10:.3f})\n\t'
't_GPU={gpu_time:.3f}{time:.3f}\n'.format(gpu_time=gpu_time,
average=avg,
time=avg.gpu_time,
result=result))
if write_to_file:
with open(test_csv, 'a') as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writerow({
'mse': avg.mse,
'rmse': avg.rmse,
'absrel': avg.absrel,
'lg10': avg.lg10,
'mae': avg.mae,
'delta1': avg.delta1,
'delta2': avg.delta2,
'delta3': avg.delta3,
'data_time': avg.data_time,
'gpu_time': avg.gpu_time
})
return avg, img_merge
def validate(val_loader, model, write_to_file=True):
average_meter = AverageMeter()
model.eval() # switch to evaluate mode
end = time.time()
print_freq = 10
for i, (input, target) in enumerate(val_loader):
input, target = input.cuda(), target.cuda()
torch.cuda.synchronize()
data_time = time.time() - end
# compute output
end = time.time()
with torch.no_grad():
pred = model(input)
torch.cuda.synchronize()
gpu_time = time.time() - end
# measure accuracy and record loss
result = Result()
result.evaluate(pred.data, target.data)
average_meter.update(result, gpu_time, data_time, input.size(0))
end = time.time()
# save 8 images for visualization
skip = 10
rgb = input
if i == 0:
import matplotlib.pyplot as plt
plt.imsave('pred.png', np.squeeze(pred.cpu().numpy()))
img_merge = utils.merge_into_row(rgb, target, pred)
elif (i < 8 * skip) and (i % skip == 0):
row = utils.merge_into_row(rgb, target, pred)
img_merge = utils.add_row(img_merge, row)
if (i + 1) % print_freq == 0:
print('Test: [{0}/{1}]\t'
't_GPU={gpu_time:.3f}({average.gpu_time:.3f})\n\t'
'RMSE={result.rmse:.2f}({average.rmse:.2f}) '
'MAE={result.mae:.2f}({average.mae:.2f}) '
'Delta1={result.delta1:.3f}({average.delta1:.3f}) '
'REL={result.absrel:.3f}({average.absrel:.3f}) '
'Lg10={result.lg10:.3f}({average.lg10:.3f}) '.format(
i + 1,
len(val_loader),
gpu_time=gpu_time,
result=result,
average=average_meter.average()))
avg = average_meter.average()
print('\n*\n'
'RMSE={average.rmse:.3f}\n'
'MAE={average.mae:.3f}\n'
'Delta1={average.delta1:.3f}\n'
'REL={average.absrel:.3f}\n'
'Lg10={average.lg10:.3f}\n'
't_GPU={time:.3f}\n'.format(average=avg, time=avg.gpu_time))
if write_to_file:
with open(test_csv, 'a') as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writerow({
'mse': avg.mse,
'rmse': avg.rmse,
'absrel': avg.absrel,
'lg10': avg.lg10,
'mae': avg.mae,
'delta1': avg.delta1,
'delta2': avg.delta2,
'delta3': avg.delta3,
'data_time': avg.data_time,
'gpu_time': avg.gpu_time
})
return avg, img_merge
def validate(val_loader, model, epoch, write_to_file=True):
average_meter = AverageMeter()
model.eval() # switch to evaluate mode
end = time.time()
for i, (input, target) in enumerate(val_loader):
# io.imshow(np.squeeze(input[:,3:,:,:].cpu().numpy()), interpolation='nearest')
# io.show()
#print(input.size())
input, target = input.cuda(), target.cuda()
torch.cuda.synchronize()
data_time = time.time() - end
# compute output
end = time.time()
with torch.no_grad():
pred = model(input)
torch.cuda.synchronize()
gpu_time = time.time() - end
# measure accuracy and record loss
result = Result()
result.evaluate(pred.data, target.data)
average_meter.update(result, gpu_time, data_time, input.size(0))
end = time.time()
if visualize:
if args.modality == 'd':
fig = plt.figure()
fig.suptitle(
'Error Percentage ' + str(round(result.absrel * 100, 2)) +
' GPU TIME ' + str(round(gpu_time, 2)) + ' FPS ' +
str(round(60.0 / (gpu_time + data_time), 2)),
fontsize=16)
plt.subplot(131)
plt.title("SPARSE (Input)")
plt.axis('off')
plt.imshow(np.squeeze(input.cpu().numpy()),
interpolation='nearest')
plt.subplot(132)
plt.title("TARGET (Ground Truth)")
plt.axis('off')
plt.imshow(np.squeeze(target.cpu().numpy()),
interpolation='nearest')
plt.colorbar(fraction=0.1, pad=0.04)
plt.subplot(133)
plt.title("PREDICTED")
plt.axis('off')
plt.imshow(np.squeeze(pred.cpu().numpy()),
interpolation='nearest')
plt.colorbar(fraction=0.1, pad=0.04)
# plt.waitforbuttonpress(timeout=2)
# plt.close()
plt.waitforbuttonpress()
plt.close()
if args.modality == 'rgbd':
# sparse = np.squeeze(input[:, 3:, :, :].cpu().numpy())
# print(sparse.shape)
# sleep(3)
fig = plt.figure()
fig.suptitle(
'Error Percentage ' + str(round(result.absrel * 100, 2)) +
' GPU TIME ' + str(round(gpu_time, 2)) + ' FPS ' +
str(round(60.0 / (gpu_time + data_time), 2)),
fontsize=16)
rgb1 = 255 * np.transpose(
np.squeeze(input[:, :3, :, :].cpu().numpy()),
(1, 2, 0)) # H, W, C
rgb1 = Image.fromarray(rgb1.astype('uint8'))
plt.subplot(221)
plt.title("RGB (Input)")
plt.axis('off')
plt.imshow(rgb1)
plt.subplot(222)
plt.title("SPARSE (Input)")
plt.axis('off')
plt.imshow(np.squeeze(input[:, 3:, :, :].cpu().numpy()),
interpolation='nearest')
plt.subplot(223)
plt.title("TARGET (Ground Truth)")
plt.axis('off')
plt.imshow(np.squeeze(target.cpu().numpy()),
interpolation='nearest')
plt.colorbar(fraction=0.1, pad=0.04)
plt.subplot(224)
plt.title("PREDICTED")
plt.axis('off')
plt.imshow(np.squeeze(pred.cpu().numpy()),
interpolation='nearest')
plt.colorbar(fraction=0.1, pad=0.04)
# plt.waitforbuttonpress(timeout=2)
# plt.close()
plt.waitforbuttonpress()
plt.close()
#
# save 8 images for visualization
skip = 50
if args.modality == 'd':
img_merge = None
else:
if args.modality == 'rgb':
rgb = input
elif args.modality == 'rgbd':
rgb = input[:, :3, :, :]
depth = input[:, 3:, :, :]
if i == 0:
if args.modality == 'rgbd':
img_merge = utils.merge_into_row_with_gt(
rgb, depth, target, pred)
else:
img_merge = utils.merge_into_row(rgb, target, pred)
elif (i < 8 * skip) and (i % skip == 0):
if args.modality == 'rgbd':
row = utils.merge_into_row_with_gt(rgb, depth, target,
pred)
else:
row = utils.merge_into_row(rgb, target, pred)
img_merge = utils.add_row(img_merge, row)
elif i == 8 * skip:
filename = output_directory + '/comparison_' + str(
epoch) + '.png'
utils.save_image(img_merge, filename)
if (i + 1) % args.print_freq == 0:
print('Test: [{0}/{1}]\t'
't_GPU={gpu_time:.3f}({average.gpu_time:.3f})\n\t'
'RMSE={result.rmse:.2f}({average.rmse:.2f}) '
'MAE={result.mae:.2f}({average.mae:.2f}) '
'Delta1={result.delta1:.3f}({average.delta1:.3f}) '
'REL={result.absrel:.3f}({average.absrel:.3f}) '
'Lg10={result.lg10:.3f}({average.lg10:.3f}) '.format(
i + 1,
len(val_loader),
gpu_time=gpu_time,
result=result,
average=average_meter.average()))
avg = average_meter.average()
print('\n*\n'
'RMSE={average.rmse:.3f}\n'
'MAE={average.mae:.3f}\n'
'Delta1={average.delta1:.3f}\n'
'REL={average.absrel:.3f}\n'
'Lg10={average.lg10:.3f}\n'
't_GPU={time:.3f}\n'.format(average=avg, time=avg.gpu_time))
if write_to_file:
with open(test_csv, 'a') as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writerow({
'mse': avg.mse,
'rmse': avg.rmse,
'absrel': avg.absrel,
'lg10': avg.lg10,
'mae': avg.mae,
'delta1': avg.delta1,
'delta2': avg.delta2,
'delta3': avg.delta3,
'data_time': avg.data_time,
'gpu_time': avg.gpu_time
})
return avg, img_merge
def validate(val_loader, model, epoch, write_to_file=True, logger=None):
average_meter = AverageMeter()
if args.arch in multistage_group:
average_meter_stage1 = AverageMeter()
# Include daynight info and rain condition
avg_meter_day = AverageMeter()
avg_meter_night = AverageMeter()
# day, night, sun, rain combinations
avg_meter_day_sun = AverageMeter()
avg_meter_day_rain = AverageMeter()
avg_meter_night_sun = AverageMeter()
avg_meter_night_rain = AverageMeter()
# sun and rain
avg_meter_sun = AverageMeter()
avg_meter_rain = AverageMeter()
model.eval() # switch to evaluate mode
end = time.time()
# Save something to draw??
if logger is None:
import h5py
output_path = os.path.join(output_directory, "results.h5")
h5_writer = h5py.File(output_path, "w", libver="latest", swmr=True)
for i, data in enumerate(val_loader):
# Add compatibility for nuscenes
if args.data != "nuscenes":
inputs, target = data[0].cuda(), data[1].cuda()
else:
inputs, target = data["inputs"].cuda(), data["labels"].cuda()
torch.cuda.synchronize()
data_time = time.time() - end
# Compute output
end = time.time()
with torch.no_grad():
if args.arch in multistage_group:
pred_ = model(inputs)
pred1 = pred_["stage1"]
pred = pred_["stage2"]
else:
pred = model(inputs)
pred_ = None
torch.cuda.synchronize()
gpu_time = time.time() - end
# Record for qualitative results
if (logger is None) and (i % 5 == 0):
pred_np = {}
if pred_ is None:
pred_np = pred.cpu().numpy()
else:
for key in pred_.keys():
pred_np[key] = pred_[key][0, ...].cpu().numpy()
res = {
"inputs": data["inputs"][0, ...].cpu().numpy(),
"lidar_depth": data["lidar_depth"][0, ...].cpu().numpy(),
"radar_depth": data["radar_depth"][0, ...].cpu().numpy(),
"pred": pred_np
}
file_key = "%05d"%(i)
f_group = h5_writer.create_group(file_key)
# Store data
for key, output_data in res.items():
if isinstance(output_data, dict):
for key, data_ in output_data.items():
if key in res.keys():
key = key + "*"
f_group.create_dataset(key, data=data_, compression="gzip")
elif output_data is None:
pass
else:
f_group.create_dataset(key, data=output_data, compression="gzip")
# Measure accuracy and record loss
result = Result()
result.evaluate(pred.data, target.data)
average_meter.update(result, gpu_time, data_time, inputs.size(0))
if args.arch in multistage_group:
result_stage1 = Result()
result_stage1.evaluate(pred1.data, target.data)
average_meter_stage1.update(result_stage1, gpu_time, data_time, inputs.size(0))
end = time.time()
# Record the day, night, rain info
assert inputs.size(0) == 1
daynight_info = data["daynight_info"][0]
if ("day" in daynight_info) and ("rain" in daynight_info):
avg_meter_day_rain.update(result, gpu_time, data_time, inputs.size(0))
avg_meter_day.update(result, gpu_time, data_time, inputs.size(0))
avg_meter_rain.update(result, gpu_time, data_time, inputs.size(0))
elif "day" in daynight_info:
avg_meter_day_sun.update(result, gpu_time, data_time, inputs.size(0))
avg_meter_day.update(result, gpu_time, data_time, inputs.size(0))
avg_meter_sun.update(result, gpu_time, data_time, inputs.size(0))
if ("night" in daynight_info) and ("rain" in daynight_info):
avg_meter_night_rain.update(result, gpu_time, data_time, inputs.size(0))
avg_meter_night.update(result, gpu_time, data_time, inputs.size(0))
avg_meter_rain.update(result, gpu_time, data_time, inputs.size(0))
elif "night" in daynight_info:
avg_meter_night_sun.update(result, gpu_time, data_time, inputs.size(0))
avg_meter_night.update(result, gpu_time, data_time, inputs.size(0))
avg_meter_sun.update(result, gpu_time, data_time, inputs.size(0))
# save 8 images for visualization
skip = 50
if args.modality == 'd':
img_merge = None
else:
if args.modality == 'rgb':
rgb = inputs
elif args.modality == 'rgbd':
rgb = inputs[:,:3,:,:]
depth = inputs[:,3:,:,:]
if i == 0:
if args.modality == 'rgbd':
img_merge = utils.merge_into_row_with_gt(rgb, depth, target, pred)
else:
img_merge = utils.merge_into_row(rgb, target, pred)
elif (i < 8*skip) and (i % skip == 0):
if args.modality == 'rgbd':
row = utils.merge_into_row_with_gt(rgb, depth, target, pred)
else:
row = utils.merge_into_row(rgb, target, pred)
img_merge = utils.add_row(img_merge, row)
elif i == 8*skip:
filename = output_directory + '/comparison_' + str(epoch) + '.png'
utils.save_image(img_merge, filename)
if (i+1) % args.print_freq == 0:
print('Test: [{0}/{1}]\t'
't_GPU={gpu_time:.3f}({average.gpu_time:.3f})\n\t'
'RMSE={result.rmse:.2f}({average.rmse:.2f}) '
'MAE={result.mae:.2f}({average.mae:.2f}) '
'Delta1={result.delta1:.3f}({average.delta1:.3f}) '
'REL={result.absrel:.3f}({average.absrel:.3f}) '
'Lg10={result.lg10:.3f}({average.lg10:.3f}) '.format(
i+1, len(val_loader), gpu_time=gpu_time, result=result, average=average_meter.average()))
# Save the result to pkl file
if logger is None:
h5_writer.close()
avg = average_meter.average()
if args.arch in multistage_group:
avg_stage1 = average_meter_stage1.average()
if logger is not None:
record_test_scalar_summary(avg_stage1, epoch, logger, "Test_stage1")
print('\n*\n'
'RMSE={average.rmse:.3f}\n'
'Rel={average.absrel:.3f}\n'
'Log10={average.lg10:.3f}\n'
'Delta1={average.delta1:.3f}\n'
'Delta2={average.delta2:.3f}\n'
'Delta3={average.delta3:.3f}\n'
't_GPU={time:.3f}\n'.format(
average=avg, time=avg.gpu_time))
if logger is not None:
# Record summaries
record_test_scalar_summary(avg, epoch, logger, "Test")
print('\n*\n'
'RMSE={average.rmse:.3f}\n'
'MAE={average.mae:.3f}\n'
'Delta1={average.delta1:.3f}\n'
'REL={average.absrel:.3f}\n'
'Lg10={average.lg10:.3f}\n'
't_GPU={time:.3f}\n'.format(
average=avg, time=avg.gpu_time))
if write_to_file:
with open(test_csv, 'a') as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writerow({'mse': avg.mse, 'rmse': avg.rmse, 'absrel': avg.absrel, 'lg10': avg.lg10,
'mae': avg.mae, 'delta1': avg.delta1, 'delta2': avg.delta2, 'delta3': avg.delta3,
'data_time': avg.data_time, 'gpu_time': avg.gpu_time})
return avg, img_merge
def validate(val_loader, model, epoch):
average_meter = AverageMeter()
model.eval() # switch to evaluate mode
end = time.time()
skip = len(val_loader) // 8 # save images every skip iters
count_b = 0
count = 0
x = random.randint(0, len(val_loader))
for i, (input, target, label, mask) in enumerate(val_loader):
input, target = input.cuda(), target.cuda()
input = torch.squeeze(input, 0)
mask = mask.cuda()
torch.cuda.synchronize()
data_time = time.time() - end
# compute output
end = time.time()
with torch.no_grad():
pred, pred_mask, c1, c2, c3 = model(input)
# pred,c1,c2,c3 = model(input)
b, c, h, w = pred.size()
# temp0 = torch.zeros_like(pred_mask)
# temp1 = torch.ones_like(pred_mask)
# pred_mask2 = torch.where(pred_mask>0.5,temp1,temp0)
#pred[:,1,:,:] = pred[:,1,:,:] * pred_mask2
temp0_2 = torch.zeros_like(c1)
temp1_2 = torch.ones_like(c1)
c1_2 = torch.where(c1 > 0.5, temp1_2, temp0_2)
c2_2 = torch.where(c2 > 0.5, temp1_2, temp0_2)
c3_2 = torch.where(c3 > 0.5, temp1_2, temp0_2)
torch.cuda.synchronize()
gpu_time = time.time() - end
target = torch.squeeze(target, 1)
# measure accuracy and record loss
c1_2 = c1_2.cpu().numpy()
c2_2 = c2_2.cpu().numpy()
c3_2 = c3_2.cpu().numpy()
l = label.numpy()
for k in range(l.shape[0]):
if c1_2[k] == 0 and c2_2[k] == 0 and c3_2[k] == 0 and l[k] == -1:
count_b += 1
if c1_2[k] == 1 and c2_2[k] == 0 and c3_2[k] == 0 and l[k] == 0:
count_b += 1
if c1_2[k] == 0 and c2_2[k] == 1 and c3_2[k] == 0 and l[k] == 1:
count_b += 1
if c1_2[k] == 0 and c2_2[k] == 0 and c3_2[k] == 1 and l[k] == 2:
count_b += 1
count += l.shape[0]
result = Result()
result.evaluate(pred, target, label)
average_meter.update(result, gpu_time, data_time, input.size(0))
end = time.time()
# save 8 images for visualization
rgb = input
# print(rgb.size(),target.size(),pred.size())
# exit(0)
# if i == x:
# img_merge = utils.merge_into_row(rgb, target, pred, pred_mask2,label)
# filename = output_directory + '/comparison_' + str(epoch) + '.png'
# utils.save_image(img_merge, filename)
if i == 0:
img_merge = utils.merge_into_row(
rgb, target, pred, target,
label) # (rgb, target, pred, pred_mask2,label)
elif (i < 8 * skip) and (i % skip == 0):
row = utils.merge_into_row(rgb, target, pred, target, label)
img_merge = utils.add_row(img_merge, row)
elif i == 8 * skip:
filename = output_directory + '/comparison_' + str(epoch) + '.png'
utils.save_image(img_merge, filename)
if (i + 1) % args.print_freq == 0:
print("acc: %f" % (count_b * 1.0 / count))
print('Test: [{0}/{1}]\t'
't_GPU={gpu_time:.3f}({average.gpu_time:.3f})\n\t'
'MAE={result.mae:.2f}({average.mae:.2f}) '.format(
i + 1,
len(val_loader),
gpu_time=gpu_time,
result=result,
average=average_meter.average()))
avg = average_meter.average()
print("epoch: %d, acc: %f" % (epoch, count_b * 1.0 / count))
print('\n*\n'
'MAE={average.mae:.3f}\n'
't_GPU={time:.3f}\n'.format(average=avg, time=avg.gpu_time))
return avg, img_merge
def validate(val_loader, model, epoch, write_to_file=True):
average_meter = AverageMeter()
model.eval() # switch to evaluate mode
end = time.time()
for i, (input, target) in enumerate(val_loader):
input, target = input.cuda(), target.cuda()
# torch.cuda.synchronize()
data_time = time.time() - end
# compute output
end = time.time()
with torch.no_grad():
pred = model(input)
#print("Predicted shape ",pred.shape) #(1,1,224,224)
# torch.cuda.synchronize()
gpu_time = time.time() - end
# measure accuracy and record loss
result = Result()
result.evaluate(pred.data, target.data)
average_meter.update(result, gpu_time, data_time, input.size(0))
end = time.time()
# save 8 images for visualization
skip = 50
if args.modality == 'rgb':
rgb = input
output_dir = "/content/drive/MyDrive/Code/fast-depth/results2/"
filename_gt = output_dir + "gt_" + str(i) + ".jpg"
filename_predicted = output_dir + "predicted_" + str(i) + ".jpg"
gt, predicted = utils.get_depth_map(rgb, target, pred)
print("Predicted shape ", predicted.shape)
import scipy.misc
#scipy.misc.toimage(gt).save(filename_gt)
#scipy.misc.toimage(predicted).save(filename_predicted)
if i == 0:
img_merge = utils.merge_into_row(rgb, target, pred)
elif (i < 8 * skip) and (i % skip == 0):
row = utils.merge_into_row(rgb, target, pred)
img_merge = utils.add_row(img_merge, row)
elif i == 8 * skip:
filename = output_directory + '/comparison_' + str(epoch) + '.png'
utils.save_image(img_merge, filename)
if (i + 1) % args.print_freq == 0:
print('Test: [{0}/{1}]\t'
't_GPU={gpu_time:.3f}({average.gpu_time:.3f})\n\t'
'RMSE={result.rmse:.2f}({average.rmse:.2f}) '
'MAE={result.mae:.2f}({average.mae:.2f}) '
'Delta1={result.delta1:.3f}({average.delta1:.3f}) '
'REL={result.absrel:.3f}({average.absrel:.3f}) '
'Lg10={result.lg10:.3f}({average.lg10:.3f}) '.format(
i + 1,
len(val_loader),
gpu_time=gpu_time,
result=result,
average=average_meter.average()))
avg = average_meter.average()
print('\n*\n'
'RMSE={average.rmse:.3f}\n'
'MAE={average.mae:.3f}\n'
'Delta1={average.delta1:.3f}\n'
'REL={average.absrel:.3f}\n'
'Lg10={average.lg10:.3f}\n'
't_GPU={time:.3f}\n'.format(average=avg, time=avg.gpu_time))
if write_to_file:
with open(test_csv, 'a') as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writerow({
'mse': avg.mse,
'rmse': avg.rmse,
'absrel': avg.absrel,
'lg10': avg.lg10,
'mae': avg.mae,
'delta1': avg.delta1,
'delta2': avg.delta2,
'delta3': avg.delta3,
'data_time': avg.data_time,
'gpu_time': avg.gpu_time
})
return avg, img_merge
def validate_coarse(val_loader, model, epoch):
average_meter = AverageMeter()
model.eval() # switch to evaluate mode
end = time.time()
for i, (input, target) in enumerate(val_loader):
input, target = input.cuda(), target.cuda()
torch.cuda.synchronize()
data_time = time.time() - end
# compute output
end = time.time()
with torch.no_grad():
pred = model(input)
torch.cuda.synchronize()
gpu_time = time.time() - end
# measure accuracy and record loss
result = Result()
result.evaluate(pred.data, target.data)
average_meter.update(result, gpu_time, data_time, input.size(0))
end = time.time()
# save 8 images for visualization
skip = 50
if args.modality == 'd':
img_merge = None
else:
rgb = input
upsmaple_size = (rgb.size()[2], rgb.size()[3])
upsmaple = torch.nn.Upsample(size=upsmaple_size,
mode='bilinear',
align_corners=True)
target = upsmaple(target)
pred = upsmaple(pred)
if i == 0:
img_merge = utils.merge_into_row(rgb, target, pred)
elif (i < 8 * skip) and (i % skip == 0):
row = utils.merge_into_row(rgb, target, pred)
img_merge = utils.add_row(img_merge, row)
elif i == 8 * skip:
filename = output_directory + '/coarse_comparison_' + str(
epoch) + '.png'
utils.save_image(img_merge, filename)
if (i + 1) % args.print_freq == 0:
print('Test: [{0}/{1}]\t'
't_GPU={gpu_time:.3f}({average.gpu_time:.3f})\n\t'
'RMSE={result.rmse:.2f}({average.rmse:.2f}) '
'MAE={result.mae:.2f}({average.mae:.2f}) '
'Delta1={result.delta1:.3f}({average.delta1:.3f}) '
'REL={result.absrel:.3f}({average.absrel:.3f}) '
'Lg10={result.lg10:.3f}({average.lg10:.3f}) '.format(
i + 1,
len(val_loader),
gpu_time=gpu_time,
result=result,
average=average_meter.average()))
avg = average_meter.average()
print('\n*\n'
'RMSE={average.rmse:.3f}\n'
'MAE={average.mae:.3f}\n'
'Delta1={average.delta1:.3f}\n'
'REL={average.absrel:.3f}\n'
'Lg10={average.lg10:.3f}\n'
't_GPU={time:.3f}\n'.format(average=avg, time=avg.gpu_time))
return avg, img_merge
def validate(val_loader, model, epoch, write_to_file=True):
average_meter = AverageMeter()
# switch to evaluate mode
model.eval()
end = time.time()
for i, (input, target) in enumerate(val_loader):
input, target = input.cuda(), target.cuda(
) # 从后面看，这里的target应该是深度图的ground truth
input_var = torch.autograd.Variable(input)
target_var = torch.autograd.Variable(target)
torch.cuda.synchronize()
data_time = time.time() - end
# compute output
end = time.time()
depth_pred = model(input_var)
torch.cuda.synchronize()
gpu_time = time.time() - end
# measure accuracy and record loss
result = Result()
output1 = torch.index_select(depth_pred.data, 1,
torch.cuda.LongTensor([0]))
result.evaluate(output1, target)
average_meter.update(result, gpu_time, data_time, input.size(0))
end = time.time()
# save 8 images for visualization
skip = 50
if args.modality == 'd':
img_merge = None
else:
if args.modality == 'rgb':
rgb = input
elif args.modality == 'rgbd':
rgb = input[:, :3, :, :]
if i == 0:
img_merge = utils.merge_into_row(rgb, target, depth_pred)
# 隔50个图片抽一张作为可视化结果
elif (i < 8 * skip) and (i % skip == 0): # and等同于C++中的&&
row = utils.merge_into_row(rgb, target, depth_pred)
img_merge = utils.add_row(img_merge, row) # 添加一行
elif i == 8 * skip: # 只保存8张图片，保存够8张后输出
filename = output_directory + '/comparison_' + str(
epoch) + '.png' # str()：将()中的对象转换为字符串
utils.save_image(img_merge,
filename) # 建议：把这种常用的功能写到特定的脚本文件中，再像这样调用
if (i + 1) % args.print_freq == 0:
print('Test: [{0}/{1}]\t'
't_GPU={gpu_time:.3f}({average.gpu_time:.3f})\t'
'RMSE={result.rmse:.2f}({average.rmse:.2f}) '
'MAE={result.mae:.2f}({average.mae:.2f}) '
'Delta1={result.delta1:.3f}({average.delta1:.3f}) '
'REL={result.absrel:.3f}({average.absrel:.3f}) '
'Lg10={result.lg10:.3f}({average.lg10:.3f}) '.format(
i + 1,
len(val_loader),
gpu_time=gpu_time,
result=result,
average=average_meter.average()))
avg = average_meter.average()
print('\n*\n'
'RMSE={average.rmse:.3f}\n'
'MAE={average.mae:.3f}\n'
'Delta1={average.delta1:.3f}\n'
'REL={average.absrel:.3f}\n'
'Lg10={average.lg10:.3f}\n'
't_GPU={time:.3f}\n'.format(average=avg, time=avg.gpu_time))
if write_to_file:
with open(test_csv, 'a') as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writerow({
'mse': avg.mse,
'rmse': avg.rmse,
'absrel': avg.absrel,
'lg10': avg.lg10,
'mae': avg.mae,
'delta1': avg.delta1,
'delta2': avg.delta2,
'delta3': avg.delta3,
'data_time': avg.data_time,
'gpu_time': avg.gpu_time
})
return avg, img_merge
def inference(model, rgb_path, sparse_path, b_gpu):
# sparse_files = glob(sparse_path+"*.tiff")
# print(sparse_files)
sparse_files = glob(sparse_path + "*.txt")
print(sparse_files)
postfix = sparse_files[0].split('/')[-2]
for sparse in sparse_files:
#get frame
# time_stamp = sparse.split('/')[-1].split('_')[0]
time_stamp = sparse.split('/')[-1].split('.')[0]
print(time_stamp)
filename_rgb = rgb_path + time_stamp + ".png"
print(filename_rgb)
model.eval()
rgb = cv2.imread(filename_rgb)
if rgb is None:
continue
# rgb=cv2.flip( rgb, 1 )
rgb = cv2.cvtColor(rgb, cv2.COLOR_BGR2RGB)
# depth = cv2.imread(sparse, cv2.IMREAD_UNCHANGED)
depth, cloud = get_depth(sparse, (228,304))
if depth is None:
continue
np.save("res/"+postfix+"/"+time_stamp+".npz", cloud)
#transform
# transform = transforms.Compose([
# transforms.Resize(240.0 / 480),
# transforms.CenterCrop((228, 304)),
# ])
# rgb_np = transform(rgb)
rgb_np = cv2.resize(rgb,(304,228))
# cv2.imwrite("res/"+postfix+"/"+time_stamp+"_test.png", rgb_np)
rgb_np = np.asfarray(rgb_np, dtype='float') / 255
# depth_np = transform(depth)
# depth_np = cv2.resize(depth,(304,228), interpolation=cv2.INTER_NEAREST)
# depth_np = np.asfarray(depth_np, dtype='float')
depth_np = depth
print(rgb_np.shape)
print(depth_np.shape)
input_np = np.append(rgb_np, np.expand_dims(depth_np, axis=2), axis=2)
print(input_np.shape)
# input_np = np.transpose(input_np, (1, 2, 0))
# print(input_np.shape)
input_tensor = to_tensor(input_np)
while input_tensor.dim() < 4:
input_tensor = input_tensor.unsqueeze(0)
if b_gpu:
input_tensor = input_tensor.cuda()
with torch.no_grad():
print(input_tensor.size())
pred = model(input_tensor)
if b_gpu:
torch.cuda.synchronize()
depth_pred_cpu = np.squeeze(pred.data.cpu().numpy())
# print(depth_pred_cpu)
# print("====")
# print(np.max(depth_pred_cpu))
res = depth_pred_cpu / np.max(depth_pred_cpu) * 255
res = cv2.resize(res,(640,480))
# res = res.astype(np.uint16)
print(np.unique(res))
cv2.imwrite("res/"+postfix+"/"+time_stamp+"_dense.png", res)
img_merge = utils.merge_into_row(input_tensor[:,:3,:,:], input_tensor[:,3,:,:], pred)
cv2.imwrite("res/"+postfix+"/"+time_stamp+"_comp.png", img_merge)
