def inference_kitti(self, data):
#crop kitti images into 3 parts
with torch.no_grad():
self.a_l_real = data['A_l'].cuda()
_, b_l_classes = self.depth_model(self.a_l_real)
self.b_l_fake = bins_to_depth(b_l_classes)
self.a_m_real = data['A_m'].cuda()
_, b_m_classes = self.depth_model(self.a_m_real)
self.b_m_fake = bins_to_depth(b_m_classes)
self.a_r_real = data['A_r'].cuda()
_, b_r_classes = self.depth_model(self.a_r_real)
self.b_r_fake = bins_to_depth(b_r_classes)
out = kitti_merge_imgs(self.b_l_fake, self.b_m_fake, self.b_r_fake,
torch.squeeze(data['B_raw']).shape,
data['crop_lmr'])
return {'b_fake': out}
def criterion(self, pred_softmax, pred_logit, data, epoch):
pred_depth = bins_to_depth(pred_softmax)
loss_metric = self.weight_cross_entropy_loss(pred_logit,
data['B_bins'],
data['B'].cuda())
loss_normal = self.virtual_normal_loss(data['B'].cuda(), pred_depth)
loss = {}
loss['metric_loss'] = loss_metric
loss['virtual_normal_loss'] = cfg.MODEL.DIFF_LOSS_WEIGHT * loss_normal
loss['total_loss'] = loss['metric_loss'] + loss['virtual_normal_loss']
return loss
def inference(self, data):
with torch.no_grad():
out = self.forward(data)
if cfg.MODEL.PREDICTION_METHOD == 'classification':
pred_depth = bins_to_depth(out['b_fake_softmax'])
elif cfg.MODEL.PREDICTION_METHOD == 'regression':
# for regression methods
pred_depth = torch.nn.functional.sigmoid(out['b_fake_logit'])
else:
raise ValueError("Unknown prediction methods")
out = pred_depth
return {'b_fake': out}
def run(self, imgfilelist, basedatasetpath):
pred_depths = []
num_lines = sum(1 for line in open(imgfilelist, 'r'))
with open(imgfilelist, 'r') as f:
for _, line in enumerate(tqdm(f, total=num_lines)):
imgname = line.split()[0]
fullpath = os.path.join(basedatasetpath, imgname)
with torch.no_grad():
# Load image
img = cv2.imread(fullpath)
# Scale image
img_resize = cv2.resize(
img, (int(img.shape[1]), int(img.shape[0])),
interpolation=cv2.INTER_LINEAR)
img_torch = scale_torch(img_resize, 255)
img_torch = img_torch[None, :, :, :].cuda()
# Run model
_, pred_depth_softmax = self.model.module.depth_model(
img_torch)
pred_depth = bins_to_depth(pred_depth_softmax)
pred_depth = pred_depth.cpu().numpy().squeeze()
# Store depth prediction and image path
if self.dataset == "kitti":
partial_image_path = os.sep.join(
os.path.normpath(fullpath).split(os.sep)[-5:])
else:
partial_image_path = os.sep.join(
os.path.normpath(fullpath).split(os.sep)[-2:])
outsample = {
'pred_depth': pred_depth,
'image_path': partial_image_path
}
pred_depths.append(outsample)
return pred_depths
out_dir, file.replace('leftImg8bit', 'VNL_Monocular'))
if not os.path.exists(out_dir):
os.makedirs(out_dir)
if not os.path.exists(out_path):
with torch.no_grad():
try:
img = Image.open(img_path)
img_resize = img.copy()
img_torch = scale_torch(img_resize, 255)
img_torch = img_torch[None, :, :, :].cuda()
_, pred_depth_softmax = model.module.depth_model(
img_torch)
pred_depth = bins_to_depth(pred_depth_softmax)
# Un-normalize using factor from vnl/data/kitti_dataset.py
pred_depth = 80 * pred_depth.cpu().numpy().squeeze()
# Convert to uint16 for storage
pred_depth_scaled = (pred_depth * 256).astype('uint16')
# Write out as uint16 png
with open(out_path, 'wb') as f:
writer = png.Writer(width=pred_depth.shape[1],
height=pred_depth.shape[0],
bitdepth=16,
greyscale=True)
z = pred_depth_scaled.tolist()
writer.write(f, z)
def inference(self, data):
with torch.no_grad():
out = self.forward(data)
pred_depth = bins_to_depth(out['b_fake_softmax'])
b_fake = pred_depth
return {'b_fake': b_fake}
