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rename to html

This commit is contained in:
Remi Cadene 2024-06-13 15:56:54 +00:00
parent 205e0c9dde
commit 12a1b8f55a
2 changed files with 151 additions and 715 deletions
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603
lerobot/scripts/visualize_dataset.py
View File

@ -30,46 +30,48 @@ Examples:
- Visualize data stored on a local machine:
```
local$ python lerobot/scripts/visualize_dataset.py \
--repo-id lerobot/pusht
local$ open http://localhost:9090
--repo-id lerobot/pusht \
--episode-index 0
```
- Visualize data stored on a distant machine with a local viewer:
```
distant$ python lerobot/scripts/visualize_dataset.py \
--repo-id lerobot/pusht
local$ ssh -L 9090:localhost:9090 distant # create a ssh tunnel
local$ open http://localhost:9090
```
- Select episodes to visualize:
```
python lerobot/scripts/visualize_dataset.py \
--repo-id lerobot/pusht \
--episode-indices 7 3 5 1 4
--episode-index 0 \
--save 1 \
--output-dir path/to/directory
local$ scp distant:path/to/directory/lerobot_pusht_episode_0.rrd .
local$ rerun lerobot_pusht_episode_0.rrd
```
- Visualize data stored on a distant machine through streaming:
(You need to forward the websocket port to the distant machine, with
`ssh -L 9087:localhost:9087 username@remote-host`)
```
distant$ python lerobot/scripts/visualize_dataset.py \
--repo-id lerobot/pusht \
--episode-index 0 \
--mode distant \
--ws-port 9087
local$ rerun ws://localhost:9087
```
"""
import argparse
import http.server
import gc
import logging
import os
import shutil
import socketserver
import time
from pathlib import Path
import rerun as rr
import torch
import tqdm
import yaml
from bs4 import BeautifulSoup
from huggingface_hub import snapshot_download
from safetensors.torch import load_file, save_file
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
from lerobot.common.policies.act.modeling_act import ACTPolicy
from lerobot.common.utils.utils import init_logging
class EpisodeSampler(torch.utils.data.Sampler):
@ -85,307 +87,33 @@ class EpisodeSampler(torch.utils.data.Sampler):
return len(self.frame_ids)
class NoCacheHTTPRequestHandler(http.server.SimpleHTTPRequestHandler):
def end_headers(self):
self.send_header("Cache-Control", "no-store, no-cache, must-revalidate")
self.send_header("Pragma", "no-cache")
self.send_header("Expires", "0")
super().end_headers()
def to_hwc_uint8_numpy(chw_float32_torch):
assert chw_float32_torch.dtype == torch.float32
assert chw_float32_torch.ndim == 3
c, h, w = chw_float32_torch.shape
assert c < h and c < w, f"expect channel first images, but instead {chw_float32_torch.shape}"
hwc_uint8_numpy = (chw_float32_torch * 255).type(torch.uint8).permute(1, 2, 0).numpy()
return hwc_uint8_numpy
def run_server(path, port):
# Change directory to serve 'index.html` as front page
os.chdir(path)
def visualize_dataset(
repo_id: str,
episode_index: int,
batch_size: int = 32,
num_workers: int = 0,
mode: str = "local",
web_port: int = 9090,
ws_port: int = 9087,
save: bool = False,
output_dir: Path | None = None,
) -> Path | None:
if save:
assert (
output_dir is not None
), "Set an output directory where to write .rrd files with `--output-dir path/to/directory`."
with socketserver.TCPServer(("", port), NoCacheHTTPRequestHandler) as httpd:
logging.info(f"Serving HTTP on 0.0.0.0 port {port} (http://0.0.0.0:{port}/) ...")
httpd.serve_forever()
def create_html_page(page_title: str):
"""Create a html page with beautiful soop with default doctype, meta, header and title."""
soup = BeautifulSoup("", "html.parser")
doctype = soup.new_tag("!DOCTYPE html")
soup.append(doctype)
html = soup.new_tag("html", lang="en")
soup.append(html)
head = soup.new_tag("head")
html.append(head)
meta_charset = soup.new_tag("meta", charset="UTF-8")
head.append(meta_charset)
meta_viewport = soup.new_tag(
"meta", attrs={"name": "viewport", "content": "width=device-width, initial-scale=1.0"}
)
head.append(meta_viewport)
title = soup.new_tag("title")
title.string = page_title
head.append(title)
body = soup.new_tag("body")
html.append(body)
main_div = soup.new_tag("div")
body.append(main_div)
return soup, head, body
def write_episode_data_csv(output_dir, file_name, episode_index, dataset, inference_results=None):
"""Write a csv file containg timeseries data of an episode (e.g. state and action).
This file will be loaded by Dygraph javascript to plot data in real time."""
from_idx = dataset.episode_data_index["from"][episode_index]
to_idx = dataset.episode_data_index["to"][episode_index]
has_state = "observation.state" in dataset.hf_dataset.features
has_action = "action" in dataset.hf_dataset.features
has_inference = inference_results is not None
# init header of csv with state and action names
header = ["timestamp"]
if has_state:
dim_state = len(dataset.hf_dataset["observation.state"][0])
header += [f"state_{i}" for i in range(dim_state)]
if has_action:
dim_action = len(dataset.hf_dataset["action"][0])
header += [f"action_{i}" for i in range(dim_action)]
if has_inference:
assert "actions" in inference_results
assert "loss" in inference_results
dim_pred_action = inference_results["actions"].shape[2]
header += [f"pred_action_{i}" for i in range(dim_pred_action)]
header += ["loss"]
columns = ["timestamp"]
if has_state:
columns += ["observation.state"]
if has_action:
columns += ["action"]
rows = []
data = dataset.hf_dataset.select_columns(columns)
for i in range(from_idx, to_idx):
row = [data[i]["timestamp"].item()]
if has_state:
row += data[i]["observation.state"].tolist()
if has_action:
row += data[i]["action"].tolist()
rows.append(row)
if has_inference:
num_frames = len(rows)
assert num_frames == inference_results["actions"].shape[0]
assert num_frames == inference_results["loss"].shape[0]
for i in range(num_frames):
rows[i] += inference_results["actions"][i, 0].tolist()
rows[i] += [inference_results["loss"][i].item()]
output_dir.mkdir(parents=True, exist_ok=True)
with open(output_dir / file_name, "w") as f:
f.write(",".join(header) + "\n")
for row in rows:
row_str = [str(col) for col in row]
f.write(",".join(row_str) + "\n")
def write_episode_data_js(output_dir, file_name, ep_csv_fname, dataset):
"""Write a javascript file containing logic to synchronize camera feeds and timeseries."""
s = ""
s += "document.addEventListener('DOMContentLoaded', function () {\n"
for i, key in enumerate(dataset.video_frame_keys):
s += f" const video{i} = document.getElementById('video_{key}');\n"
s += " const slider = document.getElementById('videoControl');\n"
s += " const playButton = document.getElementById('playButton');\n"
s += f" const dygraph = new Dygraph(document.getElementById('graph'), '{ep_csv_fname}', " + "{\n"
s += " pixelsPerPoint: 0.01,\n"
s += " legend: 'always',\n"
s += " labelsDiv: document.getElementById('labels'),\n"
s += " labelsSeparateLines: true,\n"
s += " labelsKMB: true,\n"
s += " highlightCircleSize: 1.5,\n"
s += " highlightSeriesOpts: {\n"
s += " strokeWidth: 1.5,\n"
s += " strokeBorderWidth: 1,\n"
s += " highlightCircleSize: 3\n"
s += " }\n"
s += " });\n"
s += "\n"
s += " // Function to play both videos\n"
s += " playButton.addEventListener('click', function () {\n"
for i in range(len(dataset.video_frame_keys)):
s += f" video{i}.play();\n"
s += " // playButton.disabled = true; // Optional: disable button after playing\n"
s += " });\n"
s += "\n"
s += " // Update the video time when the slider value changes\n"
s += " slider.addEventListener('input', function () {\n"
s += " const sliderValue = slider.value;\n"
for i in range(len(dataset.video_frame_keys)):
s += f" const time{i} = (video{i}.duration * sliderValue) / 100;\n"
for i in range(len(dataset.video_frame_keys)):
s += f" video{i}.currentTime = time{i};\n"
s += " });\n"
s += "\n"
s += " // Synchronize slider with the video's current time\n"
s += " const syncSlider = (video) => {\n"
s += " video.addEventListener('timeupdate', function () {\n"
s += " if (video.duration) {\n"
s += " const pc = (100 / video.duration) * video.currentTime;\n"
s += " slider.value = pc;\n"
s += " const index = Math.floor(pc * dygraph.numRows() / 100);\n"
s += " dygraph.setSelection(index, undefined, true, true);\n"
s += " }\n"
s += " });\n"
s += " };\n"
s += "\n"
for i in range(len(dataset.video_frame_keys)):
s += f" syncSlider(video{i});\n"
s += "\n"
s += "});\n"
output_dir.mkdir(parents=True, exist_ok=True)
with open(output_dir / file_name, "w", encoding="utf-8") as f:
f.write(s)
def write_episode_data_html(output_dir, file_name, js_fname, ep_index, dataset):
"""Write an html file containg video feeds and timeseries associated to an episode."""
soup, head, body = create_html_page("")
css_style = soup.new_tag("style")
css_style.string = ""
css_style.string += "#labels > span.highlight {\n"
css_style.string += " border: 1px solid grey;\n"
css_style.string += "}"
head.append(css_style)
# Add videos from camera feeds
videos_control_div = soup.new_tag("div")
body.append(videos_control_div)
videos_div = soup.new_tag("div")
videos_control_div.append(videos_div)
def create_video(id, src):
video = soup.new_tag("video", id=id, width="320", height="240", controls="")
source = soup.new_tag("source", src=src, type="video/mp4")
video.string = "Your browser does not support the video tag."
video.append(source)
return video
# get first frame of episode (hack to get video_path of the episode)
first_frame_idx = dataset.episode_data_index["from"][ep_index].item()
for key in dataset.video_frame_keys:
# Example of video_path: 'videos/observation.image_episode_000004.mp4'
video_path = dataset.hf_dataset.select_columns(key)[first_frame_idx][key]["path"]
videos_div.append(create_video(f"video_{key}", video_path))
# Add controls for videos and graph
control_div = soup.new_tag("div")
videos_control_div.append(control_div)
button_div = soup.new_tag("div")
control_div.append(button_div)
button = soup.new_tag("button", id="playButton")
button.string = "Play Videos"
button_div.append(button)
slider_div = soup.new_tag("div")
control_div.append(slider_div)
slider = soup.new_tag("input", type="range", id="videoControl", min="0", max="100", value="0", step="1")
control_div.append(slider)
# Add graph of states/actions, and its labels
graph_labels_div = soup.new_tag("div", style="display: flex;")
body.append(graph_labels_div)
graph_div = soup.new_tag("div", id="graph", style="flex: 1; width: 85%")
graph_labels_div.append(graph_div)
labels_div = soup.new_tag("div", id="labels", style="flex: 1; width: 15%")
graph_labels_div.append(labels_div)
# add dygraph library
script = soup.new_tag("script", type="text/javascript", src=js_fname)
body.append(script)
script_dygraph = soup.new_tag(
"script",
type="text/javascript",
src="https://cdn.jsdelivr.net/npm/dygraphs@2.1.0/dist/dygraph.min.js",
)
body.append(script_dygraph)
link_dygraph = soup.new_tag(
"link", rel="stylesheet", href="https://cdn.jsdelivr.net/npm/dygraphs@2.1.0/dist/dygraph.min.css"
)
body.append(link_dygraph)
# Write as a html file
output_dir.mkdir(parents=True, exist_ok=True)
with open(output_dir / file_name, "w", encoding="utf-8") as f:
f.write(soup.prettify())
def write_episodes_list_html(output_dir, file_name, ep_indices, ep_html_fnames, dataset):
"""Write an html file containing information related to the dataset and a list of links to
html pages of episodes."""
soup, head, body = create_html_page("TODO")
h3 = soup.new_tag("h3")
h3.string = "TODO"
body.append(h3)
ul_info = soup.new_tag("ul")
body.append(ul_info)
li_info = soup.new_tag("li")
li_info.string = f"Number of samples/frames: {dataset.num_samples}"
ul_info.append(li_info)
li_info = soup.new_tag("li")
li_info.string = f"Number of episodes: {dataset.num_episodes}"
ul_info.append(li_info)
li_info = soup.new_tag("li")
li_info.string = f"Frames per second: {dataset.fps}"
ul_info.append(li_info)
# li_info = soup.new_tag("li")
# li_info.string = f"Size: {format_big_number(dataset.hf_dataset.info.size_in_bytes)}B"
# ul_info.append(li_info)
ul = soup.new_tag("ul")
body.append(ul)
for ep_idx, ep_html_fname in zip(ep_indices, ep_html_fnames, strict=False):
li = soup.new_tag("li")
ul.append(li)
a = soup.new_tag("a", href=ep_html_fname)
a.string = f"Episode number {ep_idx}"
li.append(a)
output_dir.mkdir(parents=True, exist_ok=True)
with open(output_dir / file_name, "w", encoding="utf-8") as f:
f.write(soup.prettify())
def run_inference(dataset, episode_index, policy, num_workers=4, batch_size=32, device="cuda"):
policy.eval()
policy.to(device)
logging.info("Loading dataset")
dataset = LeRobotDataset(repo_id)
logging.info("Loading dataloader")
episode_sampler = EpisodeSampler(dataset, episode_index)
@ -396,104 +124,70 @@ def run_inference(dataset, episode_index, policy, num_workers=4, batch_size=32,
sampler=episode_sampler,
)
logging.info("Running inference")
inference_results = {}
logging.info("Starting Rerun")
if mode not in ["local", "distant"]:
raise ValueError(mode)
spawn_local_viewer = mode == "local" and not save
rr.init(f"{repo_id}/episode_{episode_index}", spawn=spawn_local_viewer)
# Manually call python garbage collector after `rr.init` to avoid hanging in a blocking flush
# when iterating on a dataloader with `num_workers` > 0
# TODO(rcadene): remove `gc.collect` when rerun version 0.16 is out, which includes a fix
gc.collect()
if mode == "distant":
rr.serve(open_browser=False, web_port=web_port, ws_port=ws_port)
logging.info("Logging to Rerun")
for batch in tqdm.tqdm(dataloader, total=len(dataloader)):
batch = {k: v.to(device, non_blocking=True) for k, v in batch.items()}
with torch.inference_mode():
output_dict = policy.forward(batch)
# iterate over the batch
for i in range(len(batch["index"])):
rr.set_time_sequence("frame_index", batch["frame_index"][i].item())
rr.set_time_seconds("timestamp", batch["timestamp"][i].item())
for key in output_dict:
if key not in inference_results:
inference_results[key] = []
inference_results[key].append(output_dict[key].to("cpu"))
# display each camera image
for key in dataset.camera_keys:
# TODO(rcadene): add `.compress()`? is it lossless?
rr.log(key, rr.Image(to_hwc_uint8_numpy(batch[key][i])))
for key in inference_results:
inference_results[key] = torch.cat(inference_results[key])
# display each dimension of action space (e.g. actuators command)
if "action" in batch:
for dim_idx, val in enumerate(batch["action"][i]):
rr.log(f"action/{dim_idx}", rr.Scalar(val.item()))
return inference_results
# display each dimension of observed state space (e.g. agent position in joint space)
if "observation.state" in batch:
for dim_idx, val in enumerate(batch["observation.state"][i]):
rr.log(f"state/{dim_idx}", rr.Scalar(val.item()))
if "next.done" in batch:
rr.log("next.done", rr.Scalar(batch["next.done"][i].item()))
def visualize_dataset(
repo_id: str,
episode_indices: list[int] = None,
output_dir: Path | None = None,
serve: bool = True,
port: int = 9090,
force_overwrite: bool = True,
policy_repo_id: str | None = None,
policy_ckpt_path: Path | None = None,
batch_size: int = 32,
num_workers: int = 4,
) -> Path | None:
init_logging()
if "next.reward" in batch:
rr.log("next.reward", rr.Scalar(batch["next.reward"][i].item()))
has_policy = policy_repo_id or policy_ckpt_path
if "next.success" in batch:
rr.log("next.success", rr.Scalar(batch["next.success"][i].item()))
if has_policy:
logging.info("Loading policy")
if policy_repo_id:
pretrained_policy_path = Path(snapshot_download(policy_repo_id))
elif policy_ckpt_path:
pretrained_policy_path = Path(policy_ckpt_path)
policy = ACTPolicy.from_pretrained(pretrained_policy_path)
with open(pretrained_policy_path / "config.yaml") as f:
cfg = yaml.safe_load(f)
delta_timestamps = cfg["training"]["delta_timestamps"]
else:
delta_timestamps = None
if mode == "local" and save:
# save .rrd locally
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
repo_id_str = repo_id.replace("/", "_")
rrd_path = output_dir / f"{repo_id_str}_episode_{episode_index}.rrd"
rr.save(rrd_path)
return rrd_path
logging.info("Loading dataset")
dataset = LeRobotDataset(repo_id, delta_timestamps=delta_timestamps)
if not dataset.video:
raise NotImplementedError(f"Image datasets ({dataset.video=}) are currently not supported.")
if output_dir is None:
output_dir = f"outputs/visualize_dataset/{repo_id}"
output_dir = Path(output_dir)
if force_overwrite and output_dir.exists():
shutil.rmtree(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
# Create a simlink from the dataset video folder containg mp4 files to the output directory
# so that the http server can get access to the mp4 files.
ln_videos_dir = output_dir / "videos"
if not ln_videos_dir.exists():
ln_videos_dir.symlink_to(dataset.videos_dir.resolve())
if episode_indices is None:
episode_indices = list(range(dataset.num_episodes))
logging.info("Writing html")
ep_html_fnames = []
for episode_index in tqdm.tqdm(episode_indices):
inference_results = None
if has_policy:
inference_results_path = output_dir / f"episode_{episode_index}.safetensors"
if inference_results_path.exists():
inference_results = load_file(inference_results_path)
else:
inference_results = run_inference(dataset, episode_index, policy)
save_file(inference_results, inference_results_path)
# write states and actions in a csv
ep_csv_fname = f"episode_{episode_index}.csv"
write_episode_data_csv(output_dir, ep_csv_fname, episode_index, dataset, inference_results)
js_fname = f"episode_{episode_index}.js"
write_episode_data_js(output_dir, js_fname, ep_csv_fname, dataset)
# write a html page to view videos and timeseries
ep_html_fname = f"episode_{episode_index}.html"
write_episode_data_html(output_dir, ep_html_fname, js_fname, episode_index, dataset)
ep_html_fnames.append(ep_html_fname)
write_episodes_list_html(output_dir, "index.html", episode_indices, ep_html_fnames, dataset)
if serve:
run_server(output_dir, port)
elif mode == "distant":
# stop the process from exiting since it is serving the websocket connection
try:
while True:
time.sleep(1)
except KeyboardInterrupt:
print("Ctrl-C received. Exiting.")
def main():
@ -503,51 +197,13 @@ def main():
"--repo-id",
type=str,
required=True,
help="Name of hugging face repositery containing a LeRobotDataset dataset (e.g. `lerobot/pusht` for https://huggingface.co/datasets/lerobot/pusht).",
help="Name of hugging face repositery containing a LeRobotDataset dataset (e.g. `lerobot/pusht`).",
)
parser.add_argument(
"--episode-indices",
"--episode-index",
type=int,
nargs="*",
default=None,
help="Episode indices to visualize (e.g. `0 1 5 6` to load episodes of index 0, 1, 5 and 6). By default loads all episodes.",
)
parser.add_argument(
"--output-dir",
type=str,
default=None,
help="Directory path to write html files and kickoff a web server. By default write them to 'outputs/visualize_dataset/REPO_ID'.",
)
parser.add_argument(
"--serve",
type=int,
default=1,
help="Launch web server.",
)
parser.add_argument(
"--port",
type=int,
default=9090,
help="Web port used by the http server.",
)
parser.add_argument(
"--force-overwrite",
type=int,
default=1,
help="Delete the output directory if it exists already.",
)
parser.add_argument(
"--policy-repo-id",
type=str,
default=None,
help="Name of hugging face repositery containing a pretrained policy (e.g. `lerobot/diffusion_pusht` for https://huggingface.co/lerobot/diffusion_pusht).",
)
parser.add_argument(
"--policy-ckpt-path",
type=str,
default=None,
help="Name of hugging face repositery containing a pretrained policy (e.g. `lerobot/diffusion_pusht` for https://huggingface.co/lerobot/diffusion_pusht).",
required=True,
help="Episode to visualize.",
)
parser.add_argument(
"--batch-size",
@ -561,6 +217,49 @@ def main():
default=4,
help="Number of processes of Dataloader for loading the data.",
)
parser.add_argument(
"--mode",
type=str,
default="local",
help=(
"Mode of viewing between 'local' or 'distant'. "
"'local' requires data to be on a local machine. It spawns a viewer to visualize the data locally. "
"'distant' creates a server on the distant machine where the data is stored. Visualize the data by connecting to the server with `rerun ws://localhost:PORT` on the local machine."
),
)
parser.add_argument(
"--web-port",
type=int,
default=9090,
help="Web port for rerun.io when `--mode distant` is set.",
)
parser.add_argument(
"--ws-port",
type=int,
default=9087,
help="Web socket port for rerun.io when `--mode distant` is set.",
)
parser.add_argument(
"--save",
type=int,
default=0,
help=(
"Save a .rrd file in the directory provided by `--output-dir`. "
"It also deactivates the spawning of a viewer. ",
"Visualize the data by running `rerun path/to/file.rrd` on your local machine.",
),
)
parser.add_argument(
"--output-dir",
type=str,
help="Directory path to write a .rrd file when `--save 1` is set.",
)
parser.add_argument(
"--root",
type=str,
help="Root directory for a dataset stored on a local machine.",
)
args = parser.parse_args()
visualize_dataset(**vars(args))

263
lerobot/scripts/visualize_dataset_rerun.py
View File

@ -1,263 +0,0 @@
#!/usr/bin/env python
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Visualize data of **all** frames of any episode of a dataset of type LeRobotDataset.
Note: The last frame of the episode doesnt always correspond to a final state.
That's because our datasets are composed of transition from state to state up to
the antepenultimate state associated to the ultimate action to arrive in the final state.
However, there might not be a transition from a final state to another state.
Note: This script aims to visualize the data used to train the neural networks.
~What you see is what you get~. When visualizing image modality, it is often expected to observe
lossly compression artifacts since these images have been decoded from compressed mp4 videos to
save disk space. The compression factor applied has been tuned to not affect success rate.
Examples:
- Visualize data stored on a local machine:
```
local$ python lerobot/scripts/visualize_dataset.py \
--repo-id lerobot/pusht \
--episode-index 0
```
- Visualize data stored on a distant machine with a local viewer:
```
distant$ python lerobot/scripts/visualize_dataset.py \
--repo-id lerobot/pusht \
--episode-index 0 \
--save 1 \
--output-dir path/to/directory
local$ scp distant:path/to/directory/lerobot_pusht_episode_0.rrd .
local$ rerun lerobot_pusht_episode_0.rrd
```
- Visualize data stored on a distant machine through streaming:
(You need to forward the websocket port to the distant machine, with
`ssh -L 9087:localhost:9087 username@remote-host`)
```
distant$ python lerobot/scripts/visualize_dataset.py \
--repo-id lerobot/pusht \
--episode-index 0 \
--mode distant \
--ws-port 9087
local$ rerun ws://localhost:9087
```
"""
import argparse
import gc
import logging
import time
from pathlib import Path
import rerun as rr
import torch
import tqdm
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
class EpisodeSampler(torch.utils.data.Sampler):
def __init__(self, dataset, episode_index):
from_idx = dataset.episode_data_index["from"][episode_index].item()
to_idx = dataset.episode_data_index["to"][episode_index].item()
self.frame_ids = range(from_idx, to_idx)
def __iter__(self):
return iter(self.frame_ids)
def __len__(self):
return len(self.frame_ids)
def to_hwc_uint8_numpy(chw_float32_torch):
assert chw_float32_torch.dtype == torch.float32
assert chw_float32_torch.ndim == 3
c, h, w = chw_float32_torch.shape
assert c < h and c < w, f"expect channel first images, but instead {chw_float32_torch.shape}"
hwc_uint8_numpy = (chw_float32_torch * 255).type(torch.uint8).permute(1, 2, 0).numpy()
return hwc_uint8_numpy
def visualize_dataset(
repo_id: str,
episode_index: int,
batch_size: int = 32,
num_workers: int = 0,
mode: str = "local",
web_port: int = 9090,
ws_port: int = 9087,
save: bool = False,
output_dir: Path | None = None,
) -> Path | None:
if save:
assert (
output_dir is not None
), "Set an output directory where to write .rrd files with `--output-dir path/to/directory`."
logging.info("Loading dataset")
dataset = LeRobotDataset(repo_id)
logging.info("Loading dataloader")
episode_sampler = EpisodeSampler(dataset, episode_index)
dataloader = torch.utils.data.DataLoader(
dataset,
num_workers=num_workers,
batch_size=batch_size,
sampler=episode_sampler,
)
logging.info("Starting Rerun")
if mode not in ["local", "distant"]:
raise ValueError(mode)
spawn_local_viewer = mode == "local" and not save
rr.init(f"{repo_id}/episode_{episode_index}", spawn=spawn_local_viewer)
# Manually call python garbage collector after `rr.init` to avoid hanging in a blocking flush
# when iterating on a dataloader with `num_workers` > 0
# TODO(rcadene): remove `gc.collect` when rerun version 0.16 is out, which includes a fix
gc.collect()
if mode == "distant":
rr.serve(open_browser=False, web_port=web_port, ws_port=ws_port)
logging.info("Logging to Rerun")
for batch in tqdm.tqdm(dataloader, total=len(dataloader)):
# iterate over the batch
for i in range(len(batch["index"])):
rr.set_time_sequence("frame_index", batch["frame_index"][i].item())
rr.set_time_seconds("timestamp", batch["timestamp"][i].item())
# display each camera image
for key in dataset.camera_keys:
# TODO(rcadene): add `.compress()`? is it lossless?
rr.log(key, rr.Image(to_hwc_uint8_numpy(batch[key][i])))
# display each dimension of action space (e.g. actuators command)
if "action" in batch:
for dim_idx, val in enumerate(batch["action"][i]):
rr.log(f"action/{dim_idx}", rr.Scalar(val.item()))
# display each dimension of observed state space (e.g. agent position in joint space)
if "observation.state" in batch:
for dim_idx, val in enumerate(batch["observation.state"][i]):
rr.log(f"state/{dim_idx}", rr.Scalar(val.item()))
if "next.done" in batch:
rr.log("next.done", rr.Scalar(batch["next.done"][i].item()))
if "next.reward" in batch:
rr.log("next.reward", rr.Scalar(batch["next.reward"][i].item()))
if "next.success" in batch:
rr.log("next.success", rr.Scalar(batch["next.success"][i].item()))
if mode == "local" and save:
# save .rrd locally
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
repo_id_str = repo_id.replace("/", "_")
rrd_path = output_dir / f"{repo_id_str}_episode_{episode_index}.rrd"
rr.save(rrd_path)
return rrd_path
elif mode == "distant":
# stop the process from exiting since it is serving the websocket connection
try:
while True:
time.sleep(1)
except KeyboardInterrupt:
print("Ctrl-C received. Exiting.")
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--repo-id",
type=str,
required=True,
help="Name of hugging face repositery containing a LeRobotDataset dataset (e.g. `lerobot/pusht`).",
)
parser.add_argument(
"--episode-index",
type=int,
required=True,
help="Episode to visualize.",
)
parser.add_argument(
"--batch-size",
type=int,
default=32,
help="Batch size loaded by DataLoader.",
)
parser.add_argument(
"--num-workers",
type=int,
default=4,
help="Number of processes of Dataloader for loading the data.",
)
parser.add_argument(
"--mode",
type=str,
default="local",
help=(
"Mode of viewing between 'local' or 'distant'. "
"'local' requires data to be on a local machine. It spawns a viewer to visualize the data locally. "
"'distant' creates a server on the distant machine where the data is stored. Visualize the data by connecting to the server with `rerun ws://localhost:PORT` on the local machine."
),
)
parser.add_argument(
"--web-port",
type=int,
default=9090,
help="Web port for rerun.io when `--mode distant` is set.",
)
parser.add_argument(
"--ws-port",
type=int,
default=9087,
help="Web socket port for rerun.io when `--mode distant` is set.",
)
parser.add_argument(
"--save",
type=int,
default=0,
help=(
"Save a .rrd file in the directory provided by `--output-dir`. "
"It also deactivates the spawning of a viewer. ",
"Visualize the data by running `rerun path/to/file.rrd` on your local machine.",
),
)
parser.add_argument(
"--output-dir",
type=str,
help="Directory path to write a .rrd file when `--save 1` is set.",
)
args = parser.parse_args()
visualize_dataset(**vars(args))
if __name__ == "__main__":
main()