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Copyright (c) Meta Platforms, Inc. and affiliates.
"""
import argparse
import os
import torch
from datasets.flow_datasets import KITTIFlowEval, Sintel
from models.get_model import get_model
from torchvision import transforms
from tqdm import tqdm
from transforms import input_transforms
from utils.config_parser import init_config
from utils.flow_utils import resize_flow, writeFlowKITTI, writeFlowSintel
from utils.manifold_utils import MANIFOLD_BUCKET, MANIFOLD_PATH, pathmgr
from utils.torch_utils import restore_model
parser = argparse.ArgumentParser(
description="create_submission",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
parser.add_argument(
"--model-folder",
required=True,
type=str,
help="the model folder (that contains the configuration file)",
)
parser.add_argument(
"--output-dir",
default=None,
type=str,
help="Output directory; default is test_flow under the folder of the model",
)
parser.add_argument(
"--trained-model",
required=True,
default="model_ckpt.pth.tar",
type=str,
help="trained model path in the model folder",
)
parser.add_argument(
"--dataset", type=str, choices=["sintel", "kitti"], help="sintel/kitti"
)
parser.add_argument(
"--subset", type=str, default="test", choices=["train", "test"], help="train/test"
)
def tensor2array(tensor):
return tensor.detach().cpu().numpy().transpose([0, 2, 3, 1])
@torch.no_grad()
def create_sintel_submission(model, args):
"""Create submission for the Sintel leaderboard"""
input_transform = transforms.Compose(
[
input_transforms.Zoom(args.img_height, args.img_width),
input_transforms.ArrayToTensor(),
]
)
# start inference
model.eval()
for dstype in ["final", "clean"]:
# ds_dir = os.path.join(args.output_dir, dstype)
ds_dir_local = os.path.join(args.output_local_dir, dstype)
ds_dir_bw_local = os.path.join(args.output_local_dir + "_bw", dstype)
# pathmgr.mkdirs(ds_dir)
os.makedirs(ds_dir_local, exist_ok=True)
os.makedirs(ds_dir_bw_local, exist_ok=True)
dataset = Sintel(
args.root_sintel,
args.full_seg_root_sintel,
None,
name="sintel-" + dstype,
dataset_type=dstype,
split=args.subset,
with_flow=False,
input_transform=input_transform,
)
data_loader = torch.utils.data.DataLoader(
dataset, batch_size=4, pin_memory=True, shuffle=False
)
for data in tqdm(data_loader):
img1, img2 = data["img1"].cuda(), data["img2"].cuda()
full_seg1, full_seg2 = data["full_seg1"].cuda(), data["full_seg2"].cuda()
# compute output
output = model(img1, img2, full_seg1, full_seg2, with_bk=True)
flow_pred = output["flows_12"][0]
flow_pred_bw = output["flows_21"][0]
for i in range(flow_pred.shape[0]):
h, w = data["raw_size"][0][i], data["raw_size"][1][i]
h, w = h.item(), w.item()
flow_pred_up = resize_flow(flow_pred[i : (i + 1)], (h, w))
scene, frame_id = data["img1_path"][i].split("/")[-2:]
filename = frame_id[:5] + frame_id[6:10] + ".flo"
# output_file = os.path.join(ds_dir, scene, filename)
output_file_local = os.path.join(ds_dir_local, scene, filename)
# wrtie to local and then move to manifold
writeFlowSintel(output_file_local, tensor2array(flow_pred_up)[0])
## also compute backward flow
flow_pred_bw_up = resize_flow(flow_pred_bw[i : (i + 1)], (h, w))
output_file_local = os.path.join(ds_dir_bw_local, scene, filename)
writeFlowSintel(output_file_local, tensor2array(flow_pred_bw_up)[0])
# if not pathmgr.exists(os.path.dirname(output_file)):
# pathmgr.mkdirs(os.path.dirname(output_file))
# pathmgr.copy_from_local(output_file_local, output_file)
print("Completed!")
return
@torch.no_grad()
def create_kitti_submission(model, args):
"""Create submission for the KITTI leaderboard"""
input_transform = transforms.Compose(
[
input_transforms.Zoom(args.img_height, args.img_width),
input_transforms.ArrayToTensor(),
]
)
dataset_2012 = KITTIFlowEval(
os.path.join(args.root_kitti12, args.subset + "ing"),
os.path.join(args.full_seg_root_kitti12, args.subset + "ing"),
None,
name="kitti2012",
input_transform=input_transform,
test_mode=True,
)
dataset_2015 = KITTIFlowEval(
os.path.join(args.root_kitti15, args.subset + "ing"),
os.path.join(args.full_seg_root_kitti15, args.subset + "ing"),
None,
name="kitti2015",
input_transform=input_transform,
test_mode=True,
)
# start inference
model.eval()
for ds in [dataset_2015, dataset_2012]:
# ds_dir = os.path.join(args.output_dir, ds.name)
ds_dir_local = os.path.join(args.output_local_dir, ds.name)
ds_dir_bw_local = os.path.join(args.output_local_dir + "_bw", ds.name)
# pathmgr.mkdirs(os.path.join(ds_dir, "flow"))
os.makedirs(os.path.join(ds_dir_local, "flow"), exist_ok=True)
os.makedirs(os.path.join(ds_dir_bw_local, "flow"), exist_ok=True)
data_loader = torch.utils.data.DataLoader(
ds, batch_size=4, pin_memory=True, shuffle=False
)
for data in tqdm(data_loader):
img1, img2 = data["img1"].cuda(), data["img2"].cuda()
full_seg1, full_seg2 = data["full_seg1"].cuda(), data["full_seg2"].cuda()
# compute output
output = model(img1, img2, full_seg1, full_seg2, with_bk=True)
flow_pred = output["flows_12"][0]
flow_pred_bw = output["flows_21"][0]
for i in range(flow_pred.shape[0]):
h, w = data["raw_size"][0][i], data["raw_size"][1][i]
h, w = h.item(), w.item()
flow_pred_up = resize_flow(flow_pred[i : (i + 1)], (h, w))
filename = os.path.basename(data["img1_path"][i])
# output_file = os.path.join(ds_dir, "flow", filename)
output_file_local = os.path.join(ds_dir_local, "flow", filename)
# wrtie to local and then move to manifold
writeFlowKITTI(output_file_local, tensor2array(flow_pred_up)[0])
# pathmgr.copy_from_local(output_file_local, output_file)
## also compute backward flow
flow_pred_bw_up = resize_flow(flow_pred_bw[i : (i + 1)], (h, w))
output_file_local = os.path.join(ds_dir_bw_local, "flow", filename)
writeFlowKITTI(output_file_local, tensor2array(flow_pred_bw_up)[0])
print("Completed!")
return
@torch.no_grad()
def main():
args = parser.parse_args()
args.full_model_folder = os.path.join(
"memcache_manifold://", MANIFOLD_BUCKET, MANIFOLD_PATH, args.model_folder
)
if args.output_dir is None:
args.output_dir = os.path.join(
args.full_model_folder, args.subset + "_flow_" + args.dataset
)
args.output_local_dir = os.path.join(
YOUR_DIR,
args.model_folder,
args.subset + "_flow_" + args.dataset,
)
# pathmgr.mkdirs(args.output_dir)
os.makedirs(args.output_local_dir, exist_ok=True)
## set up the model
config_file = os.path.join(args.full_model_folder, "config.json")
model_file = os.path.join(args.full_model_folder, args.trained_model)
cfg = init_config(config_file)
model = get_model(cfg.model).cuda()
model = restore_model(model, model_file)
model.eval()
if args.dataset == "sintel":
args.img_height, args.img_width = 448, 1024
# Use local data to save time
args.root_sintel = YOUR_DIR
args.full_seg_root_sintel = YOUR_DIR
create_sintel_submission(model, args)
elif args.dataset == "kitti":
args.img_height, args.img_width = 256, 832
# Use local data to save time
args.root_kitti12 = YOUR_DIR
args.root_kitti15 = YOUR_DIR
args.full_seg_root_kitti12 = YOUR_DIR
args.full_seg_root_kitti15 = YOUR_DIR
create_kitti_submission(model, args)
if __name__ == "__main__":
main()
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