def main():
# For reproducibility
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
np.random.seed(args.seed)
torch.backends.cudnn.benchmark = True
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Test loader
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD)])
test_data = dataloader.StereoDataset(data_dir=args.data_dir,
dataset_name=args.dataset_name,
mode=args.mode,
save_filename=True,
transform=test_transform)
test_loader = DataLoader(dataset=test_data, batch_size=args.batch_size, shuffle=False,
num_workers=args.num_workers, pin_memory=True, drop_last=False)
aanet = nets.AANet(args.max_disp,
num_downsample=args.num_downsample,
feature_type=args.feature_type,
no_feature_mdconv=args.no_feature_mdconv,
feature_pyramid=args.feature_pyramid,
feature_pyramid_network=args.feature_pyramid_network,
feature_similarity=args.feature_similarity,
aggregation_type=args.aggregation_type,
num_scales=args.num_scales,
num_fusions=args.num_fusions,
num_stage_blocks=args.num_stage_blocks,
num_deform_blocks=args.num_deform_blocks,
no_intermediate_supervision=args.no_intermediate_supervision,
refinement_type=args.refinement_type,
mdconv_dilation=args.mdconv_dilation,
deformable_groups=args.deformable_groups).to(device)
# print(aanet)
if os.path.exists(args.pretrained_aanet):
print('=> Loading pretrained AANet:', args.pretrained_aanet)
utils.load_pretrained_net(aanet, args.pretrained_aanet, no_strict=True)
else:
print('=> Using random initialization')
# Save parameters
num_params = utils.count_parameters(aanet)
print('=> Number of trainable parameters: %d' % num_params)
if torch.cuda.device_count() > 1:
print('=> Use %d GPUs' % torch.cuda.device_count())
aanet = torch.nn.DataParallel(aanet)
# Inference
aanet.eval()
inference_time = 0
num_imgs = 0
num_samples = len(test_loader)
print('=> %d samples found in the test set' % num_samples)
for i, sample in enumerate(test_loader):
if args.count_time and i == args.num_images: # testing time only
break
if i % 100 == 0:
print('=> Inferencing %d/%d' % (i, num_samples))
left = sample['left'].to(device) # [B, 3, H, W]
right = sample['right'].to(device)
# Pad
ori_height, ori_width = left.size()[2:]
if ori_height < args.img_height or ori_width < args.img_width:
top_pad = args.img_height - ori_height
right_pad = args.img_width - ori_width
# Pad size: (left_pad, right_pad, top_pad, bottom_pad)
left = F.pad(left, (0, right_pad, top_pad, 0))
right = F.pad(right, (0, right_pad, top_pad, 0))
# Warmup
if i == 0 and args.count_time:
with torch.no_grad():
for _ in range(10):
aanet(left, right)
num_imgs += left.size(0)
with torch.no_grad():
time_start = time.perf_counter()
pred_disp = aanet(left, right)[-1] # [B, H, W]
inference_time += time.perf_counter() - time_start
if pred_disp.size(-1) < left.size(-1):
pred_disp = pred_disp.unsqueeze(1) # [B, 1, H, W]
pred_disp = F.interpolate(pred_disp, (left.size(-2), left.size(-1)),
mode='bilinear', align_corners=True, recompute_scale_factor=True) * (left.size(-1) / pred_disp.size(-1))
pred_disp = pred_disp.squeeze(1) # [B, H, W]
# Crop
if ori_height < args.img_height or ori_width < args.img_width:
if right_pad != 0:
pred_disp = pred_disp[:, top_pad:, :-right_pad]
else:
pred_disp = pred_disp[:, top_pad:]
for b in range(pred_disp.size(0)):
disp = pred_disp[b].detach().cpu().numpy() # [H, W]
save_name = sample['left_name'][b]
save_name = os.path.join(args.output_dir, save_name)
utils.check_path(os.path.dirname(save_name))
if not args.count_time:
if args.save_type == 'pfm':
if args.visualize:
skimage.io.imsave(save_name, (disp * 256.).astype(np.uint16))
save_name = save_name[:-3] + 'pfm'
write_pfm(save_name, disp)
elif args.save_type == 'npy':
save_name = save_name[:-3] + 'npy'
np.save(save_name, disp)
else:
skimage.io.imsave(save_name, (disp * 256.).astype(np.uint16))
print('=> Mean inference time for %d images: %.3fs' % (num_imgs, inference_time / num_imgs))
def main():
# For reproducibility
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
np.random.seed(args.seed)
torch.backends.cudnn.benchmark = True
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Test loader
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD)
])
aanet = nets.AANet(
args.max_disp,
num_downsample=args.num_downsample,
feature_type=args.feature_type,
no_feature_mdconv=args.no_feature_mdconv,
feature_pyramid=args.feature_pyramid,
feature_pyramid_network=args.feature_pyramid_network,
feature_similarity=args.feature_similarity,
aggregation_type=args.aggregation_type,
num_scales=args.num_scales,
num_fusions=args.num_fusions,
num_stage_blocks=args.num_stage_blocks,
num_deform_blocks=args.num_deform_blocks,
no_intermediate_supervision=args.no_intermediate_supervision,
refinement_type=args.refinement_type,
mdconv_dilation=args.mdconv_dilation,
deformable_groups=args.deformable_groups).to(device)
if os.path.exists(args.pretrained_aanet):
print('=> Loading pretrained AANet:', args.pretrained_aanet)
utils.load_pretrained_net(aanet, args.pretrained_aanet, no_strict=True)
else:
print('=> Using random initialization')
if torch.cuda.device_count() > 1:
print('=> Use %d GPUs' % torch.cuda.device_count())
aanet = torch.nn.DataParallel(aanet)
# Inference
aanet.eval()
if args.data_dir.endswith('/'):
args.data_dir = args.data_dir[:-1]
# all_samples = sorted(glob(args.data_dir + '/*left.png'))
all_samples = sorted(glob(args.data_dir + '/left/*.png'))
num_samples = len(all_samples)
print('=> %d samples found in the data dir' % num_samples)
for i, sample_name in enumerate(all_samples):
if i % 100 == 0:
print('=> Inferencing %d/%d' % (i, num_samples))
left_name = sample_name
right_name = left_name.replace('left', 'right')
left = read_img(left_name)
right = read_img(right_name)
sample = {'left': left, 'right': right}
sample = test_transform(sample) # to tensor and normalize
left = sample['left'].to(device) # [3, H, W]
left = left.unsqueeze(0) # [1, 3, H, W]
right = sample['right'].to(device)
right = right.unsqueeze(0)
# Pad
ori_height, ori_width = left.size()[2:]
# Automatic
factor = 48
args.img_height = math.ceil(ori_height / factor) * factor
args.img_width = math.ceil(ori_width / factor) * factor
if ori_height < args.img_height or ori_width < args.img_width:
top_pad = args.img_height - ori_height
right_pad = args.img_width - ori_width
# Pad size: (left_pad, right_pad, top_pad, bottom_pad)
left = F.pad(left, (0, right_pad, top_pad, 0))
right = F.pad(right, (0, right_pad, top_pad, 0))
with torch.no_grad():
pred_disp = aanet(left, right)[-1] # [B, H, W]
if pred_disp.size(-1) < left.size(-1):
pred_disp = pred_disp.unsqueeze(1) # [B, 1, H, W]
pred_disp = F.interpolate(
pred_disp, (left.size(-2), left.size(-1)),
mode='bilinear') * (left.size(-1) / pred_disp.size(-1))
pred_disp = pred_disp.squeeze(1) # [B, H, W]
# Crop
if ori_height < args.img_height or ori_width < args.img_width:
if right_pad != 0:
pred_disp = pred_disp[:, top_pad:, :-right_pad]
else:
pred_disp = pred_disp[:, top_pad:]
disp = pred_disp[0].detach().cpu().numpy() # [H, W]
save_name = os.path.basename(
left_name)[:-4] + '_' + args.save_suffix + '.png'
save_name = os.path.join(args.output_dir, save_name)
if args.save_type == 'pfm':
if args.visualize:
skimage.io.imsave(save_name, (disp * 256.).astype(np.uint16))
save_name = save_name[:-3] + 'pfm'
write_pfm(save_name, disp)
elif args.save_type == 'npy':
save_name = save_name[:-3] + 'npy'
np.save(save_name, disp)
else:
skimage.io.imsave(save_name, (disp * 256.).astype(np.uint16))