def main():
args = parse_args()
logger = logging.getLogger('demo')
if not logger.isEnabledFor(logging.INFO): # setup_logger is not called
setup_logger(output=args.output_dir, name='demo')
logger.info(pprint.pformat(args))
logger.info(config)
# cudnn related setting
cudnn.benchmark = config.CUDNN.BENCHMARK
cudnn.deterministic = config.CUDNN.DETERMINISTIC
cudnn.enabled = config.CUDNN.ENABLED
gpus = list(config.TEST.GPUS)
if len(gpus) > 1:
raise ValueError('Test only supports single core.')
device = torch.device('cuda:{}'.format(gpus[0]))
# build model
model = build_segmentation_model_from_cfg(config)
# Change ASPP image pooling
# output_stride = 2 ** (5 - sum(config.MODEL.BACKBONE.DILATION))
# train_crop_h, train_crop_w = config.TEST.CROP_SIZE
# scale = 1. / output_stride
# pool_h = int((float(train_crop_h) - 1.0) * scale + 1.0)
# pool_w = int((float(train_crop_w) - 1.0) * scale + 1.0)
# model.set_image_pooling((pool_h, pool_w))
logger.info("Model:\n{}".format(model))
model = model.to(device)
try:
# build data_loader
data_loader = build_test_loader_from_cfg(config)
meta_dataset = data_loader.dataset
save_intermediate_outputs = True
except:
logger.warning(
"Cannot build data loader, using default meta data. This will disable visualizing intermediate outputs"
)
if 'cityscapes' in config.DATASET.DATASET:
meta_dataset = CityscapesMeta()
else:
raise ValueError("Unsupported dataset: {}".format(
config.DATASET.DATASET))
save_intermediate_outputs = False
# load model
if config.TEST.MODEL_FILE:
model_state_file = config.TEST.MODEL_FILE
else:
model_state_file = os.path.join(config.OUTPUT_DIR, 'final_state.pth')
if os.path.isfile(model_state_file):
model_weights = torch.load(model_state_file)
if 'state_dict' in model_weights.keys():
model_weights = model_weights['state_dict']
logger.info('Evaluating a intermediate checkpoint.')
model.load_state_dict(model_weights, strict=False)
logger.info('Test model loaded from {}'.format(model_state_file))
else:
if not config.DEBUG.DEBUG:
raise ValueError('Cannot find test model.')
# load images
input_list = []
if os.path.exists(args.input_files):
if os.path.isfile(args.input_files):
# inference on a single file, extract extension
ext = os.path.splitext(os.path.basename(args.input_files))[1]
if ext in ['.png', '.jpg', '.jpeg']:
# image file
input_list.append(args.input_files)
elif ext in ['.mpeg']:
# video file
# TODO: decode video and convert to image list
raise NotImplementedError(
"Inference on video is not supported yet.")
else:
raise ValueError("Unsupported extension: {}.".format(ext))
else:
# inference on a directory
for fname in glob.glob(
os.path.join(args.input_files, '*' + args.extension)):
input_list.append(fname)
else:
raise ValueError('Input file or directory does not exists: {}'.format(
args.input_files))
if isinstance(input_list[0], str):
logger.info("Inference on images")
logger.info(input_list)
else:
logger.info("Inference on video")
# dir to save intermediate raw outputs
raw_out_dir = os.path.join(args.output_dir, 'raw')
PathManager.mkdirs(raw_out_dir)
# dir to save semantic outputs
semantic_out_dir = os.path.join(args.output_dir, 'semantic')
PathManager.mkdirs(semantic_out_dir)
# dir to save instance outputs
instance_out_dir = os.path.join(args.output_dir, 'instance')
PathManager.mkdirs(instance_out_dir)
# dir to save panoptic outputs
panoptic_out_dir = os.path.join(args.output_dir, 'panoptic')
PathManager.mkdirs(panoptic_out_dir)
# Test loop
model.eval()
# build image demo transform
transforms = T.Compose(
[T.ToTensor(),
T.Normalize(config.DATASET.MEAN, config.DATASET.STD)])
net_time = AverageMeter()
post_time = AverageMeter()
try:
with torch.no_grad():
for i, fname in enumerate(input_list):
if isinstance(fname, str):
# load image
raw_image = read_image(fname, 'RGB')
else:
NotImplementedError(
"Inference on video is not supported yet.")
# pad image
raw_shape = raw_image.shape[:2]
raw_h = raw_shape[0]
raw_w = raw_shape[1]
new_h = (raw_h + 31) // 32 * 32 + 1
new_w = (raw_w + 31) // 32 * 32 + 1
input_image = np.zeros((new_h, new_w, 3), dtype=np.uint8)
input_image[:, :] = config.DATASET.MEAN
input_image[:raw_h, :raw_w, :] = raw_image
image, _ = transforms(input_image, None)
image = image.unsqueeze(0).to(device)
# network
start_time = time.time()
out_dict = model(image)
torch.cuda.synchronize(device)
net_time.update(time.time() - start_time)
# post-processing
start_time = time.time()
semantic_pred = get_semantic_segmentation(out_dict['semantic'])
panoptic_pred, center_pred = get_panoptic_segmentation(
semantic_pred,
out_dict['center'],
out_dict['offset'],
thing_list=meta_dataset.thing_list,
label_divisor=meta_dataset.label_divisor,
stuff_area=config.POST_PROCESSING.STUFF_AREA,
void_label=(meta_dataset.label_divisor *
meta_dataset.ignore_label),
threshold=config.POST_PROCESSING.CENTER_THRESHOLD,
nms_kernel=config.POST_PROCESSING.NMS_KERNEL,
top_k=config.POST_PROCESSING.TOP_K_INSTANCE,
foreground_mask=None)
torch.cuda.synchronize(device)
post_time.update(time.time() - start_time)
logger.info(
'[{}/{}]\t'
'Network Time: {net_time.val:.3f}s ({net_time.avg:.3f}s)\t'
'Post-processing Time: {post_time.val:.3f}s ({post_time.avg:.3f}s)\t'
.format(i,
len(input_list),
net_time=net_time,
post_time=post_time))
# save predictions
semantic_pred = semantic_pred.squeeze(0).cpu().numpy()
panoptic_pred = panoptic_pred.squeeze(0).cpu().numpy()
# crop predictions
semantic_pred = semantic_pred[:raw_h, :raw_w]
panoptic_pred = panoptic_pred[:raw_h, :raw_w]
if save_intermediate_outputs:
# Raw outputs
save_debug_images(
dataset=meta_dataset,
batch_images=image,
batch_targets={},
batch_outputs=out_dict,
out_dir=raw_out_dir,
iteration=i,
target_keys=[],
output_keys=['semantic', 'center', 'offset'],
is_train=False,
)
save_annotation(semantic_pred,
semantic_out_dir,
'semantic_pred_%d' % i,
add_colormap=True,
colormap=meta_dataset.create_label_colormap(),
image=raw_image if args.merge_image else None)
pan_to_sem = panoptic_pred // meta_dataset.label_divisor
save_annotation(pan_to_sem,
semantic_out_dir,
'panoptic_to_semantic_pred_%d' % i,
add_colormap=True,
colormap=meta_dataset.create_label_colormap(),
image=raw_image if args.merge_image else None)
ins_id = panoptic_pred % meta_dataset.label_divisor
pan_to_ins = panoptic_pred.copy()
pan_to_ins[ins_id == 0] = 0
save_instance_annotation(
pan_to_ins,
instance_out_dir,
'panoptic_to_instance_pred_%d' % i,
image=raw_image if args.merge_image else None)
save_panoptic_annotation(
panoptic_pred,
panoptic_out_dir,
'panoptic_pred_%d' % i,
label_divisor=meta_dataset.label_divisor,
colormap=meta_dataset.create_label_colormap(),
image=raw_image if args.merge_image else None)
except Exception:
logger.exception("Exception during demo:")
raise
finally:
logger.info("Demo finished.")
if save_intermediate_outputs:
logger.info("Intermediate outputs saved to {}".format(raw_out_dir))
logger.info(
"Semantic predictions saved to {}".format(semantic_out_dir))
logger.info(
"Instance predictions saved to {}".format(instance_out_dir))
logger.info(
"Panoptic predictions saved to {}".format(panoptic_out_dir))
def main():
# cudnn related setting
cudnn.benchmark = config.CUDNN.BENCHMARK
cudnn.deterministic = config.CUDNN.DETERMINISTIC
cudnn.enabled = config.CUDNN.ENABLED
gpus = list(config.TEST.GPUS)
if len(gpus) > 1:
raise ValueError('Test only supports single core.')
device = torch.device('cuda:{}'.format(gpus[0]))
# build model
model = build_segmentation_model_from_cfg(config)
logger.info("Model:\n{}".format(model))
model = model.to(device)
# build data_loader
# TODO: still need it for thing_list
data_loader = build_test_loader_from_cfg(config)
# load model
if config.TEST.MODEL_FILE:
model_state_file = config.TEST.MODEL_FILE
else:
model_state_file = os.path.join(config.OUTPUT_DIR, 'final_state.pth')
if os.path.isfile(model_state_file):
model_weights = torch.load(model_state_file)
if 'state_dict' in model_weights.keys():
model_weights = model_weights['state_dict']
logger.info('Evaluating a intermediate checkpoint.')
model.load_state_dict(model_weights, strict=True)
logger.info('Test model loaded from {}'.format(model_state_file))
else:
if not config.DEBUG.DEBUG:
raise ValueError('Cannot find test model.')
# dir to save intermediate raw outputs
raw_out_dir = os.path.join(args.output_dir, 'raw')
PathManager.mkdirs(raw_out_dir)
# dir to save semantic outputs
semantic_out_dir = os.path.join(args.output_dir, 'semantic')
PathManager.mkdirs(semantic_out_dir)
# dir to save instance outputs
instance_out_dir = os.path.join(args.output_dir, 'instance')
PathManager.mkdirs(instance_out_dir)
# dir to save panoptic outputs
panoptic_out_dir = os.path.join(args.output_dir, 'panoptic')
PathManager.mkdirs(panoptic_out_dir)
# Test loop
model.eval()
# build image demo transform
net_time = AverageMeter()
post_time = AverageMeter()
# dataset
source = "/home/muyun99/Desktop/MyGithub/cnsoftbei-video/inference/input/video-clip_2-4.mp4"
imgsz = 1024
dataset = LoadImages(source, img_size=imgsz)
try:
with torch.no_grad():
for i, (path, image, im0s, vid_cap) in enumerate(dataset):
(_, raw_h, raw_w) = image.shape
image = torch.from_numpy(image).to(device)
image = image.float()
image /= 255.0 # 0 - 255 to 0.0 - 1.0
if image.ndimension() == 3:
image = image.unsqueeze(0)
# network
start_time = time.time()
out_dict = model(image)
torch.cuda.synchronize(device)
net_time.update(time.time() - start_time)
# post-processing
start_time = time.time()
semantic_pred = get_semantic_segmentation(out_dict['semantic'])
panoptic_pred, center_pred = get_panoptic_segmentation(
semantic_pred,
out_dict['center'],
out_dict['offset'],
thing_list=data_loader.dataset.thing_list,
label_divisor=data_loader.dataset.label_divisor,
stuff_area=config.POST_PROCESSING.STUFF_AREA,
void_label=(data_loader.dataset.label_divisor *
data_loader.dataset.ignore_label),
threshold=config.POST_PROCESSING.CENTER_THRESHOLD,
nms_kernel=config.POST_PROCESSING.NMS_KERNEL,
top_k=config.POST_PROCESSING.TOP_K_INSTANCE,
foreground_mask=None)
torch.cuda.synchronize(device)
post_time.update(time.time() - start_time)
logger.info(
'Network Time: {net_time.val:.3f}s ({net_time.avg:.3f}s)\t'
'Post-processing Time: {post_time.val:.3f}s ({post_time.avg:.3f}s)\t'
.format(net_time=net_time, post_time=post_time))
# save predictions
semantic_pred = semantic_pred.squeeze(0).cpu().numpy()
panoptic_pred = panoptic_pred.squeeze(0).cpu().numpy()
# crop predictions
semantic_pred = semantic_pred[:raw_h, :raw_w]
panoptic_pred = panoptic_pred[:raw_h, :raw_w]
# Raw outputs
save_debug_images(
dataset=data_loader.dataset,
batch_images=image,
batch_targets={},
batch_outputs=out_dict,
out_dir=raw_out_dir,
iteration=i,
target_keys=[],
output_keys=['semantic', 'center', 'offset'],
is_train=False,
)
save_annotation(
semantic_pred,
semantic_out_dir,
'semantic_pred_%d' % i,
add_colormap=True,
colormap=data_loader.dataset.create_label_colormap())
pan_to_sem = panoptic_pred // data_loader.dataset.label_divisor
save_annotation(
pan_to_sem,
semantic_out_dir,
'pan_to_sem_pred_%d' % i,
add_colormap=True,
colormap=data_loader.dataset.create_label_colormap())
ins_id = panoptic_pred % data_loader.dataset.label_divisor
pan_to_ins = panoptic_pred.copy()
pan_to_ins[ins_id == 0] = 0
save_instance_annotation(pan_to_ins, instance_out_dir,
'pan_to_ins_pred_%d' % i)
save_panoptic_annotation(
panoptic_pred,
panoptic_out_dir,
'panoptic_pred_%d' % i,
label_divisor=data_loader.dataset.label_divisor,
colormap=data_loader.dataset.create_label_colormap())
except Exception:
logger.exception("Exception during demo:")
raise
finally:
logger.info("Demo finished.")
vid_path = args.output_dir
pic2video(filelist=glob.glob(semantic_out_dir + "pan_to_sem_pred_*"),
vid_path=os.path.join(vid_path, "semantic.mp4"),
size=(1024, 576))
pic2video(filelist=glob.glob(instance_out_dir + "pan_to_ins_pred_*"),
vid_path=os.path.join(vid_path, "instance.mp4"),
size=(1024, 576))
pic2video(filelist=glob.glob(panoptic_out_dir + "panoptic_pred_*"),
vid_path=os.path.join(vid_path, "panoptic.mp4"),
size=(1024, 576))
logger.info("Video saved")
def main():
args = parse_args()
logger = logging.getLogger('segmentation')
if not logger.isEnabledFor(logging.INFO): # setup_logger is not called
setup_logger(output=config.OUTPUT_DIR,
distributed_rank=args.local_rank)
logger.info(pprint.pformat(args))
logger.info(config)
# cudnn related setting
cudnn.benchmark = config.CUDNN.BENCHMARK
cudnn.deterministic = config.CUDNN.DETERMINISTIC
cudnn.enabled = config.CUDNN.ENABLED
gpus = list(config.GPUS)
distributed = len(gpus) > 1
device = torch.device('cuda:{}'.format(args.local_rank))
if distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(
backend="nccl",
init_method="env://",
)
# build model
model = build_segmentation_model_from_cfg(config)
# logger.info("Model:\n{}".format(model))
logger.info("Rank of current process: {}. World size: {}".format(
comm.get_rank(), comm.get_world_size()))
if distributed:
model = nn.SyncBatchNorm.convert_sync_batchnorm(model)
model = model.to(device)
if comm.get_world_size() > 1:
model = DistributedDataParallel(model,
device_ids=[args.local_rank],
output_device=args.local_rank)
data_loader = build_train_loader_from_cfg(config)
optimizer = build_optimizer(config, model)
lr_scheduler = build_lr_scheduler(config, optimizer)
data_loader_iter = iter(data_loader)
start_iter = 0
max_iter = config.TRAIN.MAX_ITER
best_param_group_id = get_lr_group_id(optimizer)
# initialize model
if os.path.isfile(config.MODEL.WEIGHTS):
model_weights = torch.load(config.MODEL.WEIGHTS)
get_module(model, distributed).load_state_dict(model_weights,
strict=False)
logger.info('Pre-trained model from {}'.format(config.MODEL.WEIGHTS))
elif config.MODEL.BACKBONE.PRETRAINED:
if os.path.isfile(config.MODEL.BACKBONE.WEIGHTS):
pretrained_weights = torch.load(config.MODEL.BACKBONE.WEIGHTS)
get_module(model, distributed).backbone.load_state_dict(
pretrained_weights, strict=False)
logger.info('Pre-trained backbone from {}'.format(
config.MODEL.BACKBONE.WEIGHTS))
else:
logger.info(
'No pre-trained weights for backbone, training from scratch.')
# load model
if config.TRAIN.RESUME:
model_state_file = os.path.join(config.OUTPUT_DIR,
'checkpoint.pth.tar')
if os.path.isfile(model_state_file):
checkpoint = torch.load(model_state_file)
start_iter = checkpoint['start_iter']
get_module(model,
distributed).load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
logger.info('Loaded checkpoint (starting from iter {})'.format(
checkpoint['start_iter']))
data_time = AverageMeter()
batch_time = AverageMeter()
loss_meter = AverageMeter()
# Debug output.
if config.DEBUG.DEBUG:
debug_out_dir = os.path.join(config.OUTPUT_DIR, 'debug_train')
PathManager.mkdirs(debug_out_dir)
# Train loop.
try:
for i in range(start_iter, max_iter):
# data
start_time = time.time()
data = next(data_loader_iter)
if not distributed:
data = to_cuda(data, device)
data_time.update(time.time() - start_time)
image = data.pop('image')
out_dict = model(image, data)
loss = out_dict['loss']
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Get lr.
lr = optimizer.param_groups[best_param_group_id]["lr"]
lr_scheduler.step()
batch_time.update(time.time() - start_time)
loss_meter.update(loss.detach().cpu().item(), image.size(0))
if i == 0 or (i + 1) % config.PRINT_FREQ == 0:
msg = '[{0}/{1}] LR: {2:.7f}\t' \
'Time: {batch_time.val:.3f}s ({batch_time.avg:.3f}s)\t' \
'Data: {data_time.val:.3f}s ({data_time.avg:.3f}s)\t'.format(
i + 1, max_iter, lr, batch_time=batch_time, data_time=data_time)
msg += get_loss_info_str(
get_module(model, distributed).loss_meter_dict)
logger.info(msg)
if i == 0 or (i + 1) % config.DEBUG.DEBUG_FREQ == 0:
if comm.is_main_process() and config.DEBUG.DEBUG:
save_debug_images(dataset=data_loader.dataset,
batch_images=image,
batch_targets=data,
batch_outputs=out_dict,
out_dir=debug_out_dir,
iteration=i,
target_keys=config.DEBUG.TARGET_KEYS,
output_keys=config.DEBUG.OUTPUT_KEYS,
iteration_to_remove=i -
config.DEBUG.KEEP_INTERVAL)
if i == 0 or (i + 1) % config.CKPT_FREQ == 0:
if comm.is_main_process():
torch.save(
{
'start_iter':
i + 1,
'state_dict':
get_module(model, distributed).state_dict(),
'optimizer':
optimizer.state_dict(),
'lr_scheduler':
lr_scheduler.state_dict(),
}, os.path.join(config.OUTPUT_DIR,
'checkpoint.pth.tar'))
except Exception:
logger.exception("Exception during training:")
raise
finally:
if comm.is_main_process():
torch.save(
get_module(model, distributed).state_dict(),
os.path.join(config.OUTPUT_DIR, 'final_state.pth'))
logger.info("Training finished.")
def main():
args = parse_args()
logger = logging.getLogger('segmentation_test')
if not logger.isEnabledFor(logging.INFO): # setup_logger is not called
setup_logger(output=config.OUTPUT_DIR, name='segmentation_test')
logger.info(pprint.pformat(args))
logger.info(config)
# cudnn related setting
cudnn.benchmark = config.CUDNN.BENCHMARK
cudnn.deterministic = config.CUDNN.DETERMINISTIC
cudnn.enabled = config.CUDNN.ENABLED
gpus = list(config.TEST.GPUS)
if len(gpus) > 1:
raise ValueError('Test only supports single core.')
device = torch.device('cuda:{}'.format(gpus[0]))
# build model
model = build_segmentation_model_from_cfg(config)
# Change ASPP image pooling
output_stride = 2 ** (5 - sum(config.MODEL.BACKBONE.DILATION))
train_crop_h, train_crop_w = config.TEST.CROP_SIZE
scale = 1. / output_stride
pool_h = int((float(train_crop_h) - 1.0) * scale + 1.0)
pool_w = int((float(train_crop_w) - 1.0) * scale + 1.0)
model.set_image_pooling((pool_h, pool_w))
logger.info("Model:\n{}".format(model))
model = model.to(device)
# build data_loader
data_loader = build_test_loader_from_cfg(config)
# load model
if config.TEST.MODEL_FILE:
model_state_file = config.TEST.MODEL_FILE
else:
model_state_file = os.path.join(config.OUTPUT_DIR, 'final_state.pth')
if os.path.isfile(model_state_file):
model_weights = torch.load(model_state_file)
if 'state_dict' in model_weights.keys():
model_weights = model_weights['state_dict']
logger.info('Evaluating a intermediate checkpoint.')
model.load_state_dict(model_weights, strict=True)
logger.info('Test model loaded from {}'.format(model_state_file))
else:
if not config.DEBUG.DEBUG:
raise ValueError('Cannot find test model.')
data_time = AverageMeter()
net_time = AverageMeter()
post_time = AverageMeter()
timing_warmup_iter = 10
semantic_metric = SemanticEvaluator(
num_classes=data_loader.dataset.num_classes,
ignore_label=data_loader.dataset.ignore_label,
output_dir=os.path.join(config.OUTPUT_DIR, config.TEST.SEMANTIC_FOLDER),
train_id_to_eval_id=data_loader.dataset.train_id_to_eval_id()
)
instance_metric = None
panoptic_metric = None
if config.TEST.EVAL_INSTANCE:
if 'cityscapes' in config.DATASET.DATASET:
instance_metric = CityscapesInstanceEvaluator(
output_dir=os.path.join(config.OUTPUT_DIR, config.TEST.INSTANCE_FOLDER),
train_id_to_eval_id=data_loader.dataset.train_id_to_eval_id(),
gt_dir=os.path.join(config.DATASET.ROOT, 'gtFine', config.DATASET.TEST_SPLIT)
)
elif 'coco' in config.DATASET.DATASET:
instance_metric = COCOInstanceEvaluator(
output_dir=os.path.join(config.OUTPUT_DIR, config.TEST.INSTANCE_FOLDER),
train_id_to_eval_id=data_loader.dataset.train_id_to_eval_id(),
gt_dir=os.path.join(config.DATASET.ROOT, 'annotations',
'instances_{}.json'.format(config.DATASET.TEST_SPLIT))
)
else:
raise ValueError('Undefined evaluator for dataset {}'.format(config.DATASET.DATASET))
if config.TEST.EVAL_PANOPTIC:
if 'cityscapes' in config.DATASET.DATASET:
panoptic_metric = CityscapesPanopticEvaluator(
output_dir=os.path.join(config.OUTPUT_DIR, config.TEST.PANOPTIC_FOLDER),
train_id_to_eval_id=data_loader.dataset.train_id_to_eval_id(),
label_divisor=data_loader.dataset.label_divisor,
void_label=data_loader.dataset.label_divisor * data_loader.dataset.ignore_label,
gt_dir=config.DATASET.ROOT,
split=config.DATASET.TEST_SPLIT,
num_classes=data_loader.dataset.num_classes
)
elif 'coco' in config.DATASET.DATASET:
panoptic_metric = COCOPanopticEvaluator(
output_dir=os.path.join(config.OUTPUT_DIR, config.TEST.PANOPTIC_FOLDER),
train_id_to_eval_id=data_loader.dataset.train_id_to_eval_id(),
label_divisor=data_loader.dataset.label_divisor,
void_label=data_loader.dataset.label_divisor * data_loader.dataset.ignore_label,
gt_dir=config.DATASET.ROOT,
split=config.DATASET.TEST_SPLIT,
num_classes=data_loader.dataset.num_classes
)
else:
raise ValueError('Undefined evaluator for dataset {}'.format(config.DATASET.DATASET))
foreground_metric = None
if config.TEST.EVAL_FOREGROUND:
foreground_metric = SemanticEvaluator(
num_classes=2,
ignore_label=data_loader.dataset.ignore_label,
output_dir=os.path.join(config.OUTPUT_DIR, config.TEST.FOREGROUND_FOLDER)
)
image_filename_list = [
os.path.splitext(os.path.basename(ann))[0] for ann in data_loader.dataset.ann_list]
# Debug output.
if config.TEST.DEBUG:
debug_out_dir = os.path.join(config.OUTPUT_DIR, 'debug_test')
PathManager.mkdirs(debug_out_dir)
# Train loop.
try:
model.eval()
with torch.no_grad():
for i, data in enumerate(data_loader):
if i == timing_warmup_iter:
data_time.reset()
net_time.reset()
post_time.reset()
# data
start_time = time.time()
for key in data.keys():
try:
data[key] = data[key].to(device)
except:
pass
image = data.pop('image')
torch.cuda.synchronize(device)
data_time.update(time.time() - start_time)
start_time = time.time()
# out_dict = model(image, data)
out_dict = model(image)
torch.cuda.synchronize(device)
net_time.update(time.time() - start_time)
start_time = time.time()
semantic_pred = get_semantic_segmentation(out_dict['semantic'])
if 'foreground' in out_dict:
foreground_pred = get_semantic_segmentation(out_dict['foreground'])
else:
foreground_pred = None
# Oracle experiment
if config.TEST.ORACLE_SEMANTIC:
# Use predicted semantic for foreground
foreground_pred = torch.zeros_like(semantic_pred)
for thing_class in data_loader.dataset.thing_list:
foreground_pred[semantic_pred == thing_class] = 1
# Use gt semantic
semantic_pred = data['semantic']
# Set it to a stuff label
stuff_label = 0
while stuff_label in data_loader.dataset.thing_list:
stuff_label += 1
semantic_pred[semantic_pred == data_loader.dataset.ignore_label] = stuff_label
if config.TEST.ORACLE_FOREGROUND:
foreground_pred = data['foreground']
if config.TEST.ORACLE_CENTER:
out_dict['center'] = data['center']
if config.TEST.ORACLE_OFFSET:
out_dict['offset'] = data['offset']
if config.TEST.EVAL_INSTANCE or config.TEST.EVAL_PANOPTIC:
panoptic_pred, center_pred = get_panoptic_segmentation(
semantic_pred,
out_dict['center'],
out_dict['offset'],
thing_list=data_loader.dataset.thing_list,
label_divisor=data_loader.dataset.label_divisor,
stuff_area=config.POST_PROCESSING.STUFF_AREA,
void_label=(
data_loader.dataset.label_divisor *
data_loader.dataset.ignore_label),
threshold=config.POST_PROCESSING.CENTER_THRESHOLD,
nms_kernel=config.POST_PROCESSING.NMS_KERNEL,
top_k=config.POST_PROCESSING.TOP_K_INSTANCE,
foreground_mask=foreground_pred)
else:
panoptic_pred = None
torch.cuda.synchronize(device)
post_time.update(time.time() - start_time)
logger.info('[{}/{}]\t'
'Data Time: {data_time.val:.3f}s ({data_time.avg:.3f}s)\t'
'Network Time: {net_time.val:.3f}s ({net_time.avg:.3f}s)\t'
'Post-processing Time: {post_time.val:.3f}s ({post_time.avg:.3f}s)\t'.format(
i, len(data_loader), data_time=data_time, net_time=net_time, post_time=post_time))
semantic_pred = semantic_pred.squeeze(0).cpu().numpy()
if panoptic_pred is not None:
panoptic_pred = panoptic_pred.squeeze(0).cpu().numpy()
if foreground_pred is not None:
foreground_pred = foreground_pred.squeeze(0).cpu().numpy()
# Crop padded regions.
image_size = data['size'].squeeze(0).cpu().numpy()
semantic_pred = semantic_pred[:image_size[0], :image_size[1]]
if panoptic_pred is not None:
panoptic_pred = panoptic_pred[:image_size[0], :image_size[1]]
if foreground_pred is not None:
foreground_pred = foreground_pred[:image_size[0], :image_size[1]]
# Resize back to the raw image size.
raw_image_size = data['raw_size'].squeeze(0).cpu().numpy()
if raw_image_size[0] != image_size[0] or raw_image_size[1] != image_size[1]:
semantic_pred = cv2.resize(semantic_pred.astype(np.float), (raw_image_size[1], raw_image_size[0]),
interpolation=cv2.INTER_NEAREST).astype(np.int32)
if panoptic_pred is not None:
panoptic_pred = cv2.resize(panoptic_pred.astype(np.float),
(raw_image_size[1], raw_image_size[0]),
interpolation=cv2.INTER_NEAREST).astype(np.int32)
if foreground_pred is not None:
foreground_pred = cv2.resize(foreground_pred.astype(np.float),
(raw_image_size[1], raw_image_size[0]),
interpolation=cv2.INTER_NEAREST).astype(np.int32)
# Evaluates semantic segmentation.
semantic_metric.update(semantic_pred,
data['raw_label'].squeeze(0).cpu().numpy(),
image_filename_list[i])
# Optional: evaluates instance segmentation.
if instance_metric is not None:
raw_semantic = F.softmax(out_dict['semantic'][:, :, :image_size[0], :image_size[1]], dim=1)
center_hmp = out_dict['center'][:, :, :image_size[0], :image_size[1]]
if raw_image_size[0] != image_size[0] or raw_image_size[1] != image_size[1]:
raw_semantic = F.interpolate(raw_semantic,
size=(raw_image_size[0], raw_image_size[1]),
mode='bilinear',
align_corners=False) # Consistent with OpenCV.
center_hmp = F.interpolate(center_hmp,
size=(raw_image_size[0], raw_image_size[1]),
mode='bilinear',
align_corners=False) # Consistent with OpenCV.
raw_semantic = raw_semantic.squeeze(0).cpu().numpy()
center_hmp = center_hmp.squeeze(1).squeeze(0).cpu().numpy()
instances = get_cityscapes_instance_format(panoptic_pred,
raw_semantic,
center_hmp,
label_divisor=data_loader.dataset.label_divisor,
score_type=config.TEST.INSTANCE_SCORE_TYPE)
instance_metric.update(instances, image_filename_list[i])
# Optional: evaluates panoptic segmentation.
if panoptic_metric is not None:
image_id = '_'.join(image_filename_list[i].split('_')[:3])
panoptic_metric.update(panoptic_pred,
image_filename=image_filename_list[i],
image_id=image_id)
# Optional: evaluates foreground segmentation.
if foreground_metric is not None:
semantic_label = data['raw_label'].squeeze(0).cpu().numpy()
foreground_label = np.zeros_like(semantic_label)
for sem_lab in np.unique(semantic_label):
# Both `stuff` and `ignore` are background.
if sem_lab in data_loader.dataset.thing_list:
foreground_label[semantic_label == sem_lab] = 1
# Use semantic segmentation as foreground segmentation.
if foreground_pred is None:
foreground_pred = np.zeros_like(semantic_pred)
for sem_lab in np.unique(semantic_pred):
if sem_lab in data_loader.dataset.thing_list:
foreground_pred[semantic_pred == sem_lab] = 1
foreground_metric.update(foreground_pred,
foreground_label,
image_filename_list[i])
if config.TEST.DEBUG:
# Raw outputs
save_debug_images(
dataset=data_loader.dataset,
batch_images=image,
batch_targets=data,
batch_outputs=out_dict,
out_dir=debug_out_dir,
iteration=i,
target_keys=config.DEBUG.TARGET_KEYS,
output_keys=config.DEBUG.OUTPUT_KEYS,
is_train=False,
)
if panoptic_pred is not None:
# Processed outputs
save_annotation(semantic_pred, debug_out_dir, 'semantic_pred_%d' % i,
add_colormap=True, colormap=data_loader.dataset.create_label_colormap())
pan_to_sem = panoptic_pred // data_loader.dataset.label_divisor
save_annotation(pan_to_sem, debug_out_dir, 'pan_to_sem_pred_%d' % i,
add_colormap=True, colormap=data_loader.dataset.create_label_colormap())
ins_id = panoptic_pred % data_loader.dataset.label_divisor
pan_to_ins = panoptic_pred.copy()
pan_to_ins[ins_id == 0] = 0
save_instance_annotation(pan_to_ins, debug_out_dir, 'pan_to_ins_pred_%d' % i)
save_panoptic_annotation(panoptic_pred, debug_out_dir, 'panoptic_pred_%d' % i,
label_divisor=data_loader.dataset.label_divisor,
colormap=data_loader.dataset.create_label_colormap())
except Exception:
logger.exception("Exception during testing:")
raise
finally:
logger.info("Inference finished.")
semantic_results = semantic_metric.evaluate()
logger.info(semantic_results)
if instance_metric is not None:
instance_results = instance_metric.evaluate()
logger.info(instance_results)
if panoptic_metric is not None:
panoptic_results = panoptic_metric.evaluate()
logger.info(panoptic_results)
if foreground_metric is not None:
foreground_results = foreground_metric.evaluate()
logger.info(foreground_results)