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Copyright (c) Meta Platforms, Inc. and affiliates.
"""
import os
from abc import ABCMeta, abstractmethod
# from glob import glob
import imageio
import numpy as np
import torch
# from transforms.input_transforms import full_segs_to_adj_maps
from utils.flow_utils import load_flow
from utils.manifold_utils import pathmgr
def local_path(path):
if "manifold" in path:
return pathmgr.get_local_path(path)
else:
return path
class ImgSeqDataset(torch.utils.data.Dataset, metaclass=ABCMeta):
def __init__(
self,
root,
full_seg_root,
key_obj_root=None,
name="",
input_transform=None,
co_transform=None,
ap_transform=None,
):
self.root = root
self.full_seg_root = full_seg_root
self.key_obj_root = key_obj_root
self.name = name
self.input_transform = input_transform
self.co_transform = co_transform
self.ap_transform = ap_transform
self.samples = self.collect_samples()
@abstractmethod
def collect_samples(self):
pass
def _load_sample(self, s):
imgs = []
full_segs = []
key_objs = []
for p in s["imgs"]:
image = (
imageio.imread(local_path(os.path.join(self.root, p))).astype(
np.float32
)
/ 255.0
)
imgs.append(image)
full_seg = imageio.imread(local_path(os.path.join(self.full_seg_root, p)))[
:, :, None
]
full_segs.append(full_seg)
if self.key_obj_root is not None:
key_obj = (
np.load(
local_path(os.path.join(self.key_obj_root, p[:-4] + ".npy"))
)
/ 255.0
)
key_objs.append(key_obj)
return imgs, full_segs, key_objs
def __len__(self):
return len(self.samples)
def __getitem__(self, idx):
imgs, full_segs, key_objs = self._load_sample(self.samples[idx])
data = {
"raw_size": imgs[0].shape[:2],
"img1_path": os.path.join(self.root, self.samples[idx]["imgs"][0]),
}
if self.co_transform is not None:
# In unsupervised learning, there is no need to change target with image
imgs, full_segs, key_objs, _ = self.co_transform(
imgs, full_segs, key_objs, {}
)
if self.input_transform is not None:
imgs, full_segs, key_objs = self.input_transform(
(imgs, full_segs, key_objs)
)
# adj_maps = full_segs_to_adj_maps(torch.stack(full_segs), win_size=9)
data.update(
{
"img1": imgs[0],
"img2": imgs[1],
"full_seg1": full_segs[0],
"full_seg2": full_segs[1],
}
)
# process key_objs to keep exactly three objects (to make sure the number of objects is fixed so that we can form batches)
if self.key_obj_root is not None:
place_holder = torch.full(
(1, *key_objs[0].shape[1:]), np.nan, dtype=torch.float32
)
if key_objs[0].shape[0] == 0:
key_obj = place_holder
else:
valid_key_obj = (
key_objs[0].mean(axis=(1, 2)) >= 0.005
) ## some objects may be too small after cropping
if valid_key_obj.sum() == 0:
key_obj = place_holder
else:
idx = np.random.choice(np.where(valid_key_obj)[0])
key_obj = key_objs[0][idx : idx + 1]
data["key_obj_mask"] = key_obj
if self.ap_transform is not None:
data["img1_ph"], data["img2_ph"] = self.ap_transform(
[imgs[0].clone(), imgs[1].clone()]
)
return data
class KITTIRawFile(ImgSeqDataset):
def __init__(
self,
root,
full_seg_root,
key_obj_root,
name="kitti-raw",
ap_transform=None,
input_transform=None,
co_transform=None,
):
super(KITTIRawFile, self).__init__(
root,
full_seg_root,
key_obj_root,
name,
input_transform=input_transform,
co_transform=co_transform,
ap_transform=ap_transform,
)
def collect_samples(self):
sp_file = os.path.join(self.root, "kitti_train_2f_sv.txt")
samples = []
with open(local_path(sp_file), "r") as f:
for line in f.readlines():
sp = line.split()
samples.append({"imgs": sp[0:2]})
samples.append({"imgs": sp[2:4]})
return samples
class KITTIFlowMV(ImgSeqDataset):
"""
This dataset is used for unsupervised training only
"""
def __init__(
self,
root,
full_seg_root,
key_obj_root,
name="",
input_transform=None,
co_transform=None,
ap_transform=None,
):
super(KITTIFlowMV, self).__init__(
root,
full_seg_root,
key_obj_root,
name,
input_transform=input_transform,
co_transform=co_transform,
ap_transform=ap_transform,
)
def collect_samples(self):
sp_file = os.path.join(self.root, "sample_list_mv.txt")
samples = []
with open(local_path(sp_file), "r") as f:
for line in f.readlines():
samples.append({"imgs": line.split()})
return samples
class KITTIFlowEval(ImgSeqDataset):
"""
This dataset is used for validation/test ONLY, so all files about target are stored as
file filepath and there is no transform about target.
"""
def __init__(
self,
root,
full_seg_root,
key_obj_root,
name="",
input_transform=None,
test_mode=False,
):
self.test_mode = test_mode
super(KITTIFlowEval, self).__init__(
root, full_seg_root, key_obj_root, name, input_transform=input_transform
)
def __getitem__(self, idx):
data = super(KITTIFlowEval, self).__getitem__(idx)
if not self.test_mode:
# for validation; we do not load here because different samples have different sizes
data["flow_occ"] = os.path.join(self.root, self.samples[idx]["flow_occ"])
data["flow_noc"] = os.path.join(self.root, self.samples[idx]["flow_noc"])
return data
def collect_samples(self):
"""Will search in training folder for folders 'flow_noc' or 'flow_occ'
and 'colored_0' (KITTI 2012) or 'image_2' (KITTI 2015)"""
sp_file = os.path.join(self.root, "sample_list.txt")
samples = []
with open(local_path(sp_file), "r") as f:
for line in f.readlines():
samples.append({"imgs": line.split()})
if self.test_mode:
return samples
else:
for i, sample in enumerate(samples):
filename = os.path.basename(sample["imgs"][0])
samples[i].update(
{
"flow_occ": os.path.join("flow_occ", filename),
"flow_noc": os.path.join("flow_noc", filename),
}
)
return samples
class SintelRaw(ImgSeqDataset):
def __init__(
self,
root,
full_seg_root,
key_obj_root,
name="",
input_transform=None,
ap_transform=None,
co_transform=None,
):
super(SintelRaw, self).__init__(
root,
full_seg_root,
key_obj_root,
name,
input_transform=input_transform,
ap_transform=ap_transform,
co_transform=co_transform,
)
def collect_samples(self):
sp_file = os.path.join(self.root, "sample_list.txt")
samples = []
with open(local_path(sp_file), "r") as f:
for line in f.readlines():
samples.append({"imgs": line.split()})
return samples
class Sintel(ImgSeqDataset):
def __init__(
self,
root,
full_seg_root,
key_obj_root,
name="",
dataset_type="clean",
split="train",
subsplit="trainval",
with_flow=False,
input_transform=None,
co_transform=None,
ap_transform=None,
):
self.dataset_type = dataset_type
self.with_flow = with_flow
self.split = split
self.subsplit = subsplit
self.training_scenes = [
"alley_1",
"ambush_4",
"ambush_6",
"ambush_7",
"bamboo_2",
"bandage_2",
"cave_2",
"market_2",
"market_5",
"shaman_2",
"sleeping_2",
"temple_3",
] # Unofficial train-val split
super(Sintel, self).__init__(
root,
full_seg_root,
key_obj_root,
name,
input_transform=input_transform,
co_transform=co_transform,
ap_transform=ap_transform,
)
def __getitem__(self, idx):
data = super(Sintel, self).__getitem__(idx)
if self.with_flow:
data["flow_gt"] = load_flow(
pathmgr.get_local_path(self.samples[idx]["flow"])
).astype(np.float32)
data["occ_mask"] = (
imageio.imread(
pathmgr.get_local_path(self.samples[idx]["occ_mask"])
).astype(np.float32)[:, :, None]
/ 255.0
)
return data
def collect_samples(self):
samples = []
filename = self.split + "_" + self.dataset_type + "_images.txt"
sp_file = os.path.join(self.root, filename)
with open(local_path(sp_file), "r") as f:
for line in f.readlines():
img1, img2 = line[:-1].split(",")
path_split = img1.split("/")
scene = path_split[-2]
sample = {
"imgs": [
"/".join(img1.split("/")[-4:]),
"/".join(img2.split("/")[-4:]),
]
}
if self.with_flow:
sample["flow"] = os.path.join(
"/".join(path_split[:-3]),
"flow",
scene,
path_split[-1][:-4] + ".flo",
)
sample["occ_mask"] = os.path.join(
"/".join(path_split[:-3]),
"occlusions",
scene,
path_split[-1],
)
if self.subsplit == "trainval":
samples.append(sample)
elif self.subsplit == "train" and scene in self.training_scenes:
samples.append(sample)
elif self.subsplit == "val" and scene not in self.training_scenes:
samples.append(sample)
return samples
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