def launch_inference_from_filepath(args):
from frame_field_learning.inference_from_filepath import inference_from_filepath
# --- First step: figure out what run (experiment) is to be evaluated
# Option 1: the run_name argument is given in which case that's our run
run_name = None
config = None
if args.run_name is not None:
run_name = args.run_name
# Else option 2: Check if a config has been given to look for the run_name
if args.config is not None:
config = run_utils.load_config(args.config)
if config is not None and "run_name" in config and run_name is None:
run_name = config["run_name"]
# Else abort...
if run_name is None:
print_utils.print_error(
"ERROR: the run to evaluate could no be identified with the given arguments. "
"Please specify either the --run_name argument or the --config argument "
"linking to a config file that has a 'run_name' field filled with the name of "
"the run name to evaluate.")
sys.exit()
# --- Second step: get path to the run and if --config was not specified, load the config from the run's folder
run_dirpath = frame_field_learning.local_utils.get_run_dirpath(
args.runs_dirpath, run_name)
if config is None:
config = run_utils.load_config(config_dirpath=run_dirpath)
if config is None:
print_utils.print_error(
f"ERROR: the default run's config file at {run_dirpath} could not be loaded. "
f"Exiting now...")
sys.exit()
# --- Add command-line arguments
if args.batch_size is not None:
config["optim_params"]["batch_size"] = args.batch_size
if args.eval_batch_size is not None:
config["optim_params"]["eval_batch_size"] = args.eval_batch_size
else:
config["optim_params"][
"eval_batch_size"] = 2 * config["optim_params"]["batch_size"]
# --- Load params in config set as relative path to another JSON file
config = run_utils.load_defaults_in_config(
config, filepath_key="defaults_filepath")
config["eval_params"]["run_dirpath"] = run_dirpath
if args.eval_patch_size is not None:
config["eval_params"]["patch_size"] = args.eval_patch_size
if args.eval_patch_overlap is not None:
config["eval_params"]["patch_overlap"] = args.eval_patch_overlap
backbone = get_backbone(config["backbone_params"])
inference_from_filepath(config, args.in_filepath, backbone,
args.out_dirpath)
def launch_eval_coco(args):
# --- Init: fills mode-specific default command-line arguments
if args.fold is None:
fold = {"test"} # Default value for eval_coco
else:
fold = set(args.fold)
assert len(fold) == 1, \
"Argument fold when evaluating with COCO should be a single fold"
# --- Find which run and which config file to evaluate the run with
if args.run_name is None and args.config is None:
print_utils.print_error(
"ERROR: At least of one --run_name or --config has to be specified."
)
sys.exit()
elif args.run_name is None and args.config is not None:
# Load config
config = run_utils.load_config(args.config)
# Verify it has a run_name specified
if "run_name" not in config:
print_utils.print_error(
"ERROR: run_name was not found in the provided config file, you can specify it with --run_name"
)
sys.exit()
run_name = config["run_name"]
elif args.run_name is not None and args.config is None:
# Load run_name's config
run_dirpath = frame_field_learning.local_utils.get_run_dirpath(
args.runs_dirpath, args.run_name)
config = run_utils.load_config(config_dirpath=run_dirpath)
run_name = args.run_name
else:
# Load specified config and use specified run_name
config = run_utils.load_config(args.config)
run_name = args.run_name
# --- Load params in config set as relative path to another JSON file
config = run_utils.load_defaults_in_config(
config, filepath_key="defaults_filepath")
# --- Second step: Replace parameters in config file from command-line arguments
config["eval_params"]["run_name"] = run_name
if args.samples is not None:
config["samples"] = args.samples
config["fold"] = list(fold)
# Setup num_workers per process:
if config["num_workers"] is None:
config["num_workers"] = torch.multiprocessing.cpu_count()
eval_coco(config)
def launch_train(args):
assert args.config is not None, "Argument --config must be specified. Run 'python main.py --help' for help on arguments."
config = run_utils.load_config(args.config)
if config is None:
print_utils.print_error(
"ERROR: cannot continue without a config file. Exiting now...")
sys.exit()
config["runs_dirpath"] = args.runs_dirpath
if args.run_name is not None:
config["run_name"] = args.run_name
config["new_run"] = args.new_run
config["init_run_name"] = args.init_run_name
if args.samples is not None:
config["samples"] = args.samples
if args.batch_size is not None:
config["optim_params"]["batch_size"] = args.batch_size
if args.max_epoch is not None:
config["optim_params"]["max_epoch"] = args.max_epoch
if args.fold is None:
if "fold" in config:
fold = set(config["fold"])
else:
fold = {"train"} # Default values for train
else:
fold = set(args.fold)
assert fold == {"train"} or fold == {"train", "val"}, \
"Argument fold when training should be either: ['train'] or ['train', 'val']"
config["fold"] = list(fold)
print_utils.print_info("Training on fold(s): {}".format(config["fold"]))
config["nodes"] = args.nodes
config["gpus"] = args.gpus
config["nr"] = args.nr
config["world_size"] = args.gpus * args.nodes
# --- Load params in config set as relative path to another JSON file
config = run_utils.load_defaults_in_config(
config, filepath_key="defaults_filepath")
# Setup num_workers per process:
if config["num_workers"] is None:
config["num_workers"] = int(torch.multiprocessing.cpu_count() /
config["gpus"])
# --- Distributed init:
os.environ['MASTER_ADDR'] = args.master_addr
os.environ['MASTER_PORT'] = args.master_port
manager = torch.multiprocessing.Manager()
shared_dict = manager.dict()
shared_dict["run_dirpath"] = None
shared_dict["init_checkpoints_dirpath"] = None
barrier = manager.Barrier(args.gpus)
torch.multiprocessing.spawn(train_process,
nprocs=args.gpus,
args=(config, shared_dict, barrier))
def main():
torch.manual_seed(0)
# --- Process args --- #
args = get_args()
# --- Setup run --- #
run_dirpath = local_utils.get_run_dirpath(args.runs_dirpath, args.run_name)
# Load run's config file:
config = run_utils.load_config(config_dirpath=run_dirpath)
if config is None:
print_utils.print_error(
"ERROR: cannot continue without a config file. Exiting now...")
sys.exit()
config["eval_params"]["run_dirpath"] = run_dirpath
if args.eval_patch_size is not None:
config["eval_params"]["patch_size"] = args.eval_patch_size
if args.eval_patch_overlap is not None:
config["eval_params"]["patch_overlap"] = args.eval_patch_overlap
backbone = get_backbone(config["backbone_params"])
polygonize_mask(config, args.filepath, backbone, args.out_ext)
def launch_eval(args):
# --- Init: fills mode-specific default command-line arguments
if args.fold is None:
fold = {"test"} # Default value for eval mode
else:
fold = set(args.fold)
assert len(fold) == 1, "Argument 'fold' must be a single fold in eval mode"
# --- First step: figure out what run (experiment) is to be evaluated
# Option 1: the run_name argument is given in which case that's our run
run_name = None
config = None
if args.run_name is not None:
run_name = args.run_name
# Else option 2: Check if a config has been given to look for the run_name
if args.config is not None:
config = run_utils.load_config(args.config)
if config is not None and "run_name" in config and run_name is None:
run_name = config["run_name"]
# Else abort...
if run_name is None:
print_utils.print_error(
"ERROR: the run to evaluate could no be identified with the given arguments. "
"Please specify either the --run_name argument or the --config argument "
"linking to a config file that has a 'run_name' field filled with the name of "
"the run name to evaluate.")
sys.exit()
# --- Second step: get path to the run and if --config was not specified, load the config from the run's folder
run_dirpath = frame_field_learning.local_utils.get_run_dirpath(
args.runs_dirpath, run_name)
if config is None:
config = run_utils.load_config(config_dirpath=run_dirpath)
if config is None:
print_utils.print_error(
f"ERROR: the default run's config file at {run_dirpath} could not be loaded. "
f"Exiting now...")
sys.exit()
# --- Third step: Replace parameters in config file from command-line arguments
if args.dataset_params is not None:
config["dataset_params"] = python_utils.load_json(args.dataset_params)
if args.samples is not None:
config["samples"] = args.samples
if args.batch_size is not None:
config["optim_params"]["batch_size"] = args.batch_size
if args.eval_batch_size is not None:
config["optim_params"]["eval_batch_size"] = args.eval_batch_size
else:
config["optim_params"][
"eval_batch_size"] = 2 * config["optim_params"]["batch_size"]
config["fold"] = list(fold)
config["nodes"] = args.nodes
config["gpus"] = args.gpus
config["nr"] = args.nr
config["world_size"] = args.gpus * args.nodes
# --- Load params in config set as relative path to another JSON file
config = run_utils.load_defaults_in_config(
config, filepath_key="defaults_filepath")
config["eval_params"]["run_dirpath"] = run_dirpath
if args.eval_patch_size is not None:
config["eval_params"]["patch_size"] = args.eval_patch_size
if args.eval_patch_overlap is not None:
config["eval_params"]["patch_overlap"] = args.eval_patch_overlap
# Setup num_workers per process:
if config["num_workers"] is None:
config["num_workers"] = int(torch.multiprocessing.cpu_count() /
config["gpus"])
# --- Distributed init:
os.environ['MASTER_ADDR'] = args.master_addr
os.environ['MASTER_PORT'] = args.master_port
manager = torch.multiprocessing.Manager()
shared_dict = manager.dict()
shared_dict["name_list"] = manager.list()
shared_dict["iou_list"] = manager.list()
shared_dict["seg_coco_list"] = manager.list()
shared_dict["poly_coco_list"] = manager.list()
barrier = manager.Barrier(args.gpus)
torch.multiprocessing.spawn(eval_process,
nprocs=args.gpus,
args=(config, shared_dict, barrier))
def polygonize_masks(run_dirpath, images_dirpath, gt_filepath, in_filepath,
out_filepath, batch_size, batch_size_mult):
coco_gt = COCO(gt_filepath)
coco_dt = coco_gt.loadRes(in_filepath)
# --- Load model --- #
# Load run's config file:
config = run_utils.load_config(config_dirpath=run_dirpath)
if config is None:
print_utils.print_error(
"ERROR: cannot continue without a config file. Exiting now...")
sys.exit()
config["backbone_params"][
"pretrained"] = False # Don't load pretrained model
backbone = get_backbone(config["backbone_params"])
eval_online_cuda_transform = data_transforms.get_eval_online_cuda_transform(
config)
model = FrameFieldModel(config,
backbone=backbone,
eval_transform=eval_online_cuda_transform)
model.to(config["device"])
checkpoints_dirpath = run_utils.setup_run_subdir(
run_dirpath, config["optim_params"]["checkpoints_dirname"])
model = inference.load_checkpoint(model, checkpoints_dirpath,
config["device"])
model.eval()
# --- Polygonize input COCO mask detections --- #
img_ids = coco_dt.getImgIds()
# img_ids = sorted(img_ids)[:1] # TODO: rm limit
output_annotations = []
model_data_list = [
] # Used to accumulate inputs and run model inference on it.
poly_data_list = [
] # Used to accumulate inputs and run polygonization on it.
for img_id in tqdm(img_ids, desc="Polygonizing"):
# Load image
img = coco_gt.loadImgs(img_id)[0]
image = skimage.io.imread(
os.path.join(images_dirpath, img["file_name"]))
# Draw mask from input COCO mask annotations
mask_image = np.zeros((img["height"], img["width"]))
score_image = np.zeros((img["height"], img["width"]))
dts = coco_dt.loadAnns(coco_dt.getAnnIds(imgIds=img_id))
for dt in dts:
dt_mask = cocomask.decode(dt["segmentation"])
mask_image = np.maximum(mask_image, dt_mask)
score_image = np.maximum(score_image, dt_mask * dt["score"])
# Accumulate inputs into the current batch
sample_data = {
"img_id": [img_id],
"mask_image":
torch_lydorn.torchvision.transforms.functional.to_tensor(
mask_image)[None, ...].float(),
"score_image":
torch_lydorn.torchvision.transforms.functional.to_tensor(
score_image)[None, ...].float(),
"image":
torch_lydorn.torchvision.transforms.functional.to_tensor(image)[
None, ...],
"image_mean":
torch.tensor(image_mean)[None, ...],
"image_std":
torch.tensor(image_std)[None, ...]
}
# Accumulate batch for running the model
model_data_list.append(sample_data)
if len(model_data_list) == batch_size:
# Run model
tile_data = run_model(config, model, model_data_list)
model_data_list = [] # Empty model batch
# Accumulate batch for running the polygonization
poly_data_list.append(tile_data)
if len(poly_data_list) == batch_size_mult:
coco_ann = run_polygonization(poly_data_list)
output_annotations.extend(coco_ann)
poly_data_list = []
# Finish with incomplete batches
if len(model_data_list):
tile_data = run_model(config, model, model_data_list)
poly_data_list.append(tile_data)
if len(poly_data_list):
coco_ann = run_polygonization(poly_data_list)
output_annotations.extend(coco_ann)
print("Saving output...")
with open(out_filepath, 'w') as outfile:
json.dump(output_annotations, outfile)
