[pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
This commit is contained in:
pre-commit-ci[bot]
committed by
Michel Aractingi
Michel Aractingi
parent
bb69cb3c8c
commit
85fe8a3f4e
@@ -32,7 +32,11 @@ import numpy as np
|
||||
import pandas as pd
|
||||
import PIL
|
||||
import torch
|
||||
from skimage.metrics import mean_squared_error, peak_signal_noise_ratio, structural_similarity
|
||||
from skimage.metrics import (
|
||||
mean_squared_error,
|
||||
peak_signal_noise_ratio,
|
||||
structural_similarity,
|
||||
)
|
||||
from tqdm import tqdm
|
||||
|
||||
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
|
||||
@@ -81,7 +85,9 @@ def get_directory_size(directory: Path) -> int:
|
||||
return total_size
|
||||
|
||||
|
||||
def load_original_frames(imgs_dir: Path, timestamps: list[float], fps: int) -> torch.Tensor:
|
||||
def load_original_frames(
|
||||
imgs_dir: Path, timestamps: list[float], fps: int
|
||||
) -> torch.Tensor:
|
||||
frames = []
|
||||
for ts in timestamps:
|
||||
idx = int(ts * fps)
|
||||
@@ -94,7 +100,11 @@ def load_original_frames(imgs_dir: Path, timestamps: list[float], fps: int) -> t
|
||||
|
||||
|
||||
def save_decoded_frames(
|
||||
imgs_dir: Path, save_dir: Path, frames: torch.Tensor, timestamps: list[float], fps: int
|
||||
imgs_dir: Path,
|
||||
save_dir: Path,
|
||||
frames: torch.Tensor,
|
||||
timestamps: list[float],
|
||||
fps: int,
|
||||
) -> None:
|
||||
if save_dir.exists() and len(list(save_dir.glob("frame_*.png"))) == len(timestamps):
|
||||
return
|
||||
@@ -104,7 +114,10 @@ def save_decoded_frames(
|
||||
idx = int(ts * fps)
|
||||
frame_hwc = (frames[i].permute((1, 2, 0)) * 255).type(torch.uint8).cpu().numpy()
|
||||
PIL.Image.fromarray(frame_hwc).save(save_dir / f"frame_{idx:06d}_decoded.png")
|
||||
shutil.copyfile(imgs_dir / f"frame_{idx:06d}.png", save_dir / f"frame_{idx:06d}_original.png")
|
||||
shutil.copyfile(
|
||||
imgs_dir / f"frame_{idx:06d}.png",
|
||||
save_dir / f"frame_{idx:06d}_original.png",
|
||||
)
|
||||
|
||||
|
||||
def save_first_episode(imgs_dir: Path, dataset: LeRobotDataset) -> None:
|
||||
@@ -116,11 +129,17 @@ def save_first_episode(imgs_dir: Path, dataset: LeRobotDataset) -> None:
|
||||
hf_dataset = dataset.hf_dataset.with_format(None)
|
||||
|
||||
# We only save images from the first camera
|
||||
img_keys = [key for key in hf_dataset.features if key.startswith("observation.image")]
|
||||
img_keys = [
|
||||
key for key in hf_dataset.features if key.startswith("observation.image")
|
||||
]
|
||||
imgs_dataset = hf_dataset.select_columns(img_keys[0])
|
||||
|
||||
for i, item in enumerate(
|
||||
tqdm(imgs_dataset, desc=f"saving {dataset.repo_id} first episode images", leave=False)
|
||||
tqdm(
|
||||
imgs_dataset,
|
||||
desc=f"saving {dataset.repo_id} first episode images",
|
||||
leave=False,
|
||||
)
|
||||
):
|
||||
img = item[img_keys[0]]
|
||||
img.save(str(imgs_dir / f"frame_{i:06d}.png"), quality=100)
|
||||
@@ -129,7 +148,9 @@ def save_first_episode(imgs_dir: Path, dataset: LeRobotDataset) -> None:
|
||||
break
|
||||
|
||||
|
||||
def sample_timestamps(timestamps_mode: str, ep_num_images: int, fps: int) -> list[float]:
|
||||
def sample_timestamps(
|
||||
timestamps_mode: str, ep_num_images: int, fps: int
|
||||
) -> list[float]:
|
||||
# Start at 5 to allow for 2_frames_4_space and 6_frames
|
||||
idx = random.randint(5, ep_num_images - 1)
|
||||
match timestamps_mode:
|
||||
@@ -154,7 +175,9 @@ def decode_video_frames(
|
||||
backend: str,
|
||||
) -> torch.Tensor:
|
||||
if backend in ["pyav", "video_reader"]:
|
||||
return decode_video_frames_torchvision(video_path, timestamps, tolerance_s, backend)
|
||||
return decode_video_frames_torchvision(
|
||||
video_path, timestamps, tolerance_s, backend
|
||||
)
|
||||
else:
|
||||
raise NotImplementedError(backend)
|
||||
|
||||
@@ -181,7 +204,9 @@ def benchmark_decoding(
|
||||
}
|
||||
|
||||
with time_benchmark:
|
||||
frames = decode_video_frames(video_path, timestamps=timestamps, tolerance_s=5e-1, backend=backend)
|
||||
frames = decode_video_frames(
|
||||
video_path, timestamps=timestamps, tolerance_s=5e-1, backend=backend
|
||||
)
|
||||
result["load_time_video_ms"] = time_benchmark.result_ms / num_frames
|
||||
|
||||
with time_benchmark:
|
||||
@@ -190,12 +215,18 @@ def benchmark_decoding(
|
||||
|
||||
frames_np, original_frames_np = frames.numpy(), original_frames.numpy()
|
||||
for i in range(num_frames):
|
||||
result["mse_values"].append(mean_squared_error(original_frames_np[i], frames_np[i]))
|
||||
result["mse_values"].append(
|
||||
mean_squared_error(original_frames_np[i], frames_np[i])
|
||||
)
|
||||
result["psnr_values"].append(
|
||||
peak_signal_noise_ratio(original_frames_np[i], frames_np[i], data_range=1.0)
|
||||
peak_signal_noise_ratio(
|
||||
original_frames_np[i], frames_np[i], data_range=1.0
|
||||
)
|
||||
)
|
||||
result["ssim_values"].append(
|
||||
structural_similarity(original_frames_np[i], frames_np[i], data_range=1.0, channel_axis=0)
|
||||
structural_similarity(
|
||||
original_frames_np[i], frames_np[i], data_range=1.0, channel_axis=0
|
||||
)
|
||||
)
|
||||
|
||||
if save_frames and sample == 0:
|
||||
@@ -215,7 +246,9 @@ def benchmark_decoding(
|
||||
# As these samples are independent, we run them in parallel threads to speed up the benchmark.
|
||||
with ThreadPoolExecutor(max_workers=num_workers) as executor:
|
||||
futures = [executor.submit(process_sample, i) for i in range(num_samples)]
|
||||
for future in tqdm(as_completed(futures), total=num_samples, desc="samples", leave=False):
|
||||
for future in tqdm(
|
||||
as_completed(futures), total=num_samples, desc="samples", leave=False
|
||||
):
|
||||
result = future.result()
|
||||
load_times_video_ms.append(result["load_time_video_ms"])
|
||||
load_times_images_ms.append(result["load_time_images_ms"])
|
||||
@@ -275,9 +308,13 @@ def benchmark_encoding_decoding(
|
||||
random.seed(seed)
|
||||
benchmark_table = []
|
||||
for timestamps_mode in tqdm(
|
||||
decoding_cfg["timestamps_modes"], desc="decodings (timestamps_modes)", leave=False
|
||||
decoding_cfg["timestamps_modes"],
|
||||
desc="decodings (timestamps_modes)",
|
||||
leave=False,
|
||||
):
|
||||
for backend in tqdm(decoding_cfg["backends"], desc="decodings (backends)", leave=False):
|
||||
for backend in tqdm(
|
||||
decoding_cfg["backends"], desc="decodings (backends)", leave=False
|
||||
):
|
||||
benchmark_row = benchmark_decoding(
|
||||
imgs_dir,
|
||||
video_path,
|
||||
@@ -355,14 +392,23 @@ def main(
|
||||
imgs_dir = output_dir / "images" / dataset.repo_id.replace("/", "_")
|
||||
# We only use the first episode
|
||||
save_first_episode(imgs_dir, dataset)
|
||||
for key, values in tqdm(encoding_benchmarks.items(), desc="encodings (g, crf)", leave=False):
|
||||
for key, values in tqdm(
|
||||
encoding_benchmarks.items(), desc="encodings (g, crf)", leave=False
|
||||
):
|
||||
for value in tqdm(values, desc=f"encodings ({key})", leave=False):
|
||||
encoding_cfg = BASE_ENCODING.copy()
|
||||
encoding_cfg["vcodec"] = video_codec
|
||||
encoding_cfg["pix_fmt"] = pixel_format
|
||||
encoding_cfg[key] = value
|
||||
args_path = Path("_".join(str(value) for value in encoding_cfg.values()))
|
||||
video_path = output_dir / "videos" / args_path / f"{repo_id.replace('/', '_')}.mp4"
|
||||
args_path = Path(
|
||||
"_".join(str(value) for value in encoding_cfg.values())
|
||||
)
|
||||
video_path = (
|
||||
output_dir
|
||||
/ "videos"
|
||||
/ args_path
|
||||
/ f"{repo_id.replace('/', '_')}.mp4"
|
||||
)
|
||||
benchmark_table += benchmark_encoding_decoding(
|
||||
dataset,
|
||||
video_path,
|
||||
@@ -388,7 +434,9 @@ def main(
|
||||
# Concatenate all results
|
||||
df_list = [pd.read_csv(csv_path) for csv_path in file_paths]
|
||||
concatenated_df = pd.concat(df_list, ignore_index=True)
|
||||
concatenated_path = output_dir / f"{now:%Y-%m-%d}_{now:%H-%M-%S}_all_{num_samples}-samples.csv"
|
||||
concatenated_path = (
|
||||
output_dir / f"{now:%Y-%m-%d}_{now:%H-%M-%S}_all_{num_samples}-samples.csv"
|
||||
)
|
||||
concatenated_df.to_csv(concatenated_path, header=True, index=False)
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user