def load_latest_checkpoint(checkpoint, directory, device):
ckpts = get_files(directory, "*.ckpt")
if ckpts:
last_ckpt = ckpts[-1]
checkpoint.restore(last_ckpt, device)
else:
raise ValueError("No checkpoints found in {}.".format(directory))
def _trim_checkpoints(self):
"""Trim older checkpoints until `max_to_keep` remain.
"""
# get a list of checkpoints in reverse
# chronological order
ckpts = get_files(self.directory, "*.ckpt")[::-1]
# remove until `max_to_keep` remain
num_remove = len(ckpts) - self.max_to_keep
while num_remove > 0:
ckpt_name = ckpts.pop()
os.remove(ckpt_name)
num_remove -= 1
def get_embs(embs_path, embodiment_names, num_traj):
emb_files = file_utils.get_files(osp.join(embs_path, "embs"),
"*.npy",
sort=False)
embs, frames, pos_neg_labels = {}, {}, {}
for i, emb_file in enumerate(emb_files):
emb_name = osp.basename(emb_file).split(".")[0]
if emb_name not in embodiment_names:
continue
with open(emb_file, "rb") as f:
data = np.load(f, allow_pickle=True).item()
embs[emb_name] = data["embs"][:num_traj]
frames[emb_name] = data["frames"][:num_traj]
pos_neg_labels[emb_name] = data["labels"][:num_traj]
return embs, frames, pos_neg_labels
def restore_or_initialize(self):
"""Restore items in checkpoint from the latest checkpoint file.
Returns:
The global iteration step. This is parsed from the latest
checkpoint file if one is found, else 0 is returned.
"""
ckpts = get_files(self.directory, "*.ckpt")
if ckpts:
last_ckpt = ckpts[-1]
status = self.checkpoint.restore(last_ckpt, self.device)
if not status:
logging.info("Could not restore latest checkpoint file.")
return 0
self.latest_checkpoint = last_ckpt
return int(osp.basename(last_ckpt).split(".")[0])
return 0
def main(args):
plt.switch_backend("Agg")
emb_files = file_utils.get_files(osp.join(args.embs_path, "embs"),
"*.npy",
sort=False)
print("Found files: {}".format(emb_files))
for i, emb_file in enumerate(emb_files):
embs, frames = [], []
with open(emb_file, "rb") as f:
query_dict = np.load(f, allow_pickle=True).item()
for j in range(len(query_dict["embs"])):
curr_embs = query_dict["embs"][j]
if args.l2_normalize:
curr_embs = [x / (np.linalg.norm(x) + 1e-7) for x in curr_embs]
embs.append(curr_embs)
frames.append(query_dict["frames"][j])
# generate video name
video_path = osp.join(args.embs_path, "videos")
file_utils.mkdir(video_path)
ext = ".mp4" if args.align else "_original.mp4"
video_path = osp.join(video_path,
osp.basename(emb_file).split(".")[0] + ext)
if not args.align:
print("Playing videos without alignment.")
create_original_videos(frames, video_path, args.interval)
else:
print("Aligning videos.")
create_video(
embs,
frames,
video_path,
args.use_dtw,
query=args.reference_video,
candidate=args.candidate_video,
interval=args.interval,
)
def videos_to_images(in_dir, out_dir, num_cores):
set_start_method("spawn")
with Pool(num_cores) as pool:
action_dirs = file_utils.get_subdirs(in_dir)
for action_dir in action_dirs:
print("Processing ", osp.basename(action_dir))
out_action = osp.join(out_dir, osp.basename(action_dir))
file_utils.mkdir(out_action)
videos = file_utils.get_subdirs(action_dir)
for video in videos:
out_action_video = osp.join(out_action, osp.basename(video))
file_utils.mkdir(out_action_video)
files = file_utils.get_files(video, "*.mp4", False)
func_args = [[f, out_action_video, video] for f in files]
for _ in tqdm(
pool.imap_unordered(process_video, func_args),
total=len(files),
):
pass
file_utils.copy_file(
osp.join(video, "joint_angles.txt"),
osp.join(out_action_video, "joint_angles.txt"),
)
def _load_frames(self, vid_dirs):
assert (
len(vid_dirs) > 1
), f"{self.__class__.__name__} can only operate on multiple videos at a time."
return [get_files(vd, self._pattern) for vd in vid_dirs]
def _load_frames(self, vid_dirs):
assert (
len(vid_dirs) == 1
), f"{self.__class__.__name__} can only operate on a single video at a time."
return get_files(vid_dirs[0], self._pattern)
def main(args):
emb_files = file_utils.get_files(
osp.join(args.embs_path, "embs"), "*.npy", sort=False
)
print(f"Found {len(emb_files)} files.")
embodiment_names = [
"two_hands_two_fingers",
"tongs",
"rms",
# "ski_gloves",
# "quick_grip",
"one_hand_two_fingers",
"one_hand_five_fingers",
"double_quick_grip",
# "crab",
# "quick_grasp",
]
all_embs, all_names, phase_labels = [], [], []
for i, emb_file in enumerate(emb_files):
emb_name = osp.basename(emb_file).split(".")[0]
if emb_name not in embodiment_names:
continue
embs = []
with open(emb_file, "rb") as f:
query_dict = np.load(f, allow_pickle=True).item()
for j in range(len(query_dict["embs"])):
if j == args.num_traj:
break
curr_embs = query_dict["embs"][j]
if args.l2_normalize:
curr_embs = [x / (np.linalg.norm(x) + 1e-7) for x in curr_embs]
embs.append(curr_embs)
all_embs.append(embs)
all_names.append(emb_name)
phase_labels.append(query_dict["phase_labels"])
# split one_hand_five_fingers into 2
h_idx = all_names.index("one_hand_five_fingers")
h_emb = all_embs[h_idx]
h_phase_labels = phase_labels[h_idx]
all_names.pop(h_idx)
all_embs.pop(h_idx)
emb_one_hand_five_fivers_1 = []
emb_one_hand_five_fivers_2 = []
for emb, pl in zip(h_emb, h_phase_labels):
if len(np.unique(pl)) == 2:
emb_one_hand_five_fivers_1.append(emb)
else:
emb_one_hand_five_fivers_2.append(emb)
all_names = all_names + [
"one_hand_five_fingers_without_slide",
"one_hand_five_fingers_with_slide",
]
all_embs.append(emb_one_hand_five_fivers_1)
all_embs.append(emb_one_hand_five_fivers_2)
num_embs = len(all_embs)
chamfers = np.zeros((num_embs, num_embs))
for i in range(len(all_embs)):
query_embs = all_embs[i]
for j in range(len(all_embs)):
if i == j:
continue
cand_embs = all_embs[j]
query_chamfer = []
for query_emb in query_embs:
kd_tree_query = KDTree(query_emb)
chamfer_dist = []
for cand_emb in cand_embs:
chamfer_dist.append(
symmetric_chamfer(query_emb, cand_emb, kd_tree_query)
)
query_chamfer.append(np.mean(chamfer_dist))
chamfers[i, j] = np.mean(query_chamfer)
# determine row-wise min
mins = []
for i in range(num_embs):
mins.append(np.argsort(chamfers[i])[1])
# plot heatmap
fig, ax = plt.subplots(figsize=(15, 10))
im = ax.imshow(chamfers, cmap="RdBu", interpolation="nearest")
cbar = ax.figure.colorbar(im, ax=ax)
cbar.ax.set_ylabel("Chamfer Distance", rotation=-90, va="bottom")
ax.set_xticks(np.arange(num_embs))
ax.set_yticks(np.arange(num_embs))
ax.set_xticklabels(all_names)
ax.set_yticklabels(all_names)
ax.yaxis.set_tick_params(labelsize=7)
ax.xaxis.set_tick_params(labelsize=7)
plt.setp(
ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor"
)
for i in range(num_embs):
for j in range(num_embs):
if i == j:
continue
txt = "{:.5f}".format(chamfers[i, j])
if j == mins[i]:
txt += "_min"
_ = ax.text(
j, i, txt, ha="center", va="center", color="black", fontsize=7
)
task_name = args.embs_path.split("/")[-2]
ax.set_title(task_name)
name = "{}_heatmap.png".format(task_name)
plt.savefig(osp.join("tmp", name), format="png", dpi=300)
plt.show()
if args.output_dir is not None:
file_utils.mkdir(args.output_dir)
else:
args.output_dir = args.input_dir
frame_dir = osp.join(args.input_dir, "frames")
label_dir = osp.join(args.input_dir, "labels")
if args.resize:
args.resize = (args.height, args.width)
logging.info("Resizing images to ({}, {}).".format(*args.resize))
else:
args.resize = None
# read all label files and figure out which
# videos belong to which class
label_files = file_utils.get_files(label_dir, "*.mat")
label_classes = [loadmat(lab)["action"][0] for lab in label_files]
# figure out train test splits
train_val_splits = [loadmat(lab)["train"][0][0] for lab in label_files]
# now figure out at which indices the class
# change occurs
change_idxs = []
for i in range(len(label_classes) - 1):
c_curr = label_classes[i]
c_next = label_classes[i + 1]
if c_curr != c_next:
change_idxs.append(i + 1)
change_idxs = [0, *change_idxs, len(label_files)]
def main(args):
# get a list of embedding files
# one for each action class
emb_files = file_utils.get_files(osp.join(args.embs_path, "embs"),
"*.npy",
sort=False)
print(f"Found {len(emb_files)} files.")
all_embs, all_names = [], []
for i, emb_file in enumerate(emb_files):
embs = []
with open(emb_file, "rb") as f:
query_dict = np.load(f, allow_pickle=True).item()
for j in range(len(query_dict["embs"])):
curr_embs = query_dict["embs"][j]
if args.l2_normalize:
curr_embs = [x / (np.linalg.norm(x) + 1e-7) for x in curr_embs]
embs.append(curr_embs)
all_embs.append(embs)
all_names.append(osp.basename(emb_file).split(".")[0])
query_name = "rms"
query_idx = all_names.index(query_name)
query_embs = all_embs[query_idx]
all_embs.pop(query_idx)
all_names.pop(query_idx)
# pick a random query trajectory
emb_query = query_embs[2] # np.random.choice(query_embs)
kd_tree_query = KDTree(emb_query)
# loop through all other embeddings
# and find the closest trajectory
# and its class
# chamfer_dists = []
class_min = -1
traj_min = -1
chamfer_dist = 100
for i, emb_cands in enumerate(all_embs):
for t, emb_cand in enumerate(emb_cands):
dist = symmetric_chamfer(emb_query, emb_cand, kd_tree_query)
if dist < chamfer_dist:
class_min = i
traj_min = t
chamfer_dist = dist
# chamfer_dists = np.array(chamfer_dists)
# class_min = chamfer_dists.min(axis=1).argmin()
# traj_min = chamfer_dists.argmin(axis=1)[class_min]
print(all_names[class_min], traj_min)
# artificially increase / decrease some trajectories
# emb_best = all_embs[class_min][traj_min]
emb_bad = all_embs[all_names.index("hand_5_fingers")][-9]
# emb_best_mod = modify_emb(emb_best)
# emb_bad_mod = modify_emb(emb_bad)
# compute dense reward between query emb
# and closest one as determined above
# reward_best = dense_reward(emb_query, emb_best, True)
reward_bad = dense_reward(emb_query, emb_bad, False)
# reward_best_mod = dense_reward(emb_query, emb_best_mod)
# reward_bad_mod = dense_reward(emb_query, emb_bad_mod)
plt.figure()
# plt.plot(reward_best, label='best')
plt.plot(reward_bad, label="random")
# plt.plot(reward_best_mod, label='best-updownsampled')
# plt.plot(reward_bad_mod, label='random-updownsampled')
plt.xlabel("Frame Index")
plt.xlabel("Reward")
plt.legend()
plt.savefig("./reward_rms.png", format="png", dpi=300)
plt.show()