hangX/lerobot
1
0
Fork 0
forked from tangger/lerobot
Code Pull Requests Activity

[pre-commit.ci] auto fixes from pre-commit.com hooks

Browse Source 
for more information, see https://pre-commit.ci
This commit is contained in:
pre-commit-ci[bot]
2025-03-24 13:41:27 +00:00
committed by Michel Aractingi
parent 2abbd60a0d
commit 0ea27704f6
123 changed files with 1161 additions and 3425 deletions
Download Patch File Download Diff File Expand all files Collapse all files

41
lerobot/common/utils/utils.py
View File

@@ -48,9 +48,7 @@ def auto_select_torch_device() -> torch.device:
logging.info("Metal backend detected, using cuda.")
return torch.device("mps")
else:
logging.warning(
"No accelerated backend detected. Using default cpu, this will be slow."
)
logging.warning("No accelerated backend detected. Using default cpu, this will be slow.")
return torch.device("cpu")
@@ -98,9 +96,7 @@ def is_torch_device_available(try_device: str) -> bool:
elif try_device == "cpu":
return True
else:
raise ValueError(
f"Unknown device {try_device}. Supported devices are: cuda, mps or cpu."
)
raise ValueError(f"Unknown device {try_device}. Supported devices are: cuda, mps or cpu.")
def is_amp_available(device: str):
@@ -158,10 +154,7 @@ def _relative_path_between(path1: Path, path2: Path) -> Path:
except ValueError: # most likely because path1 is not a subpath of path2
common_parts = Path(osp.commonpath([path1, path2])).parts
return Path(
"/".join(
[".."] * (len(path2.parts) - len(common_parts))
+ list(path1.parts[len(common_parts) :])
)
"/".join([".."] * (len(path2.parts) - len(common_parts)) + list(path1.parts[len(common_parts) :]))
)
@@ -172,26 +165,10 @@ def print_cuda_memory_usage():
gc.collect()
# Also clear the cache if you want to fully release the memory
torch.cuda.empty_cache()
print(
"Current GPU Memory Allocated: {:.2f} MB".format(
torch.cuda.memory_allocated(0) / 1024**2
)
)
print(
"Maximum GPU Memory Allocated: {:.2f} MB".format(
torch.cuda.max_memory_allocated(0) / 1024**2
)
)
print(
"Current GPU Memory Reserved: {:.2f} MB".format(
torch.cuda.memory_reserved(0) / 1024**2
)
)
print(
"Maximum GPU Memory Reserved: {:.2f} MB".format(
torch.cuda.max_memory_reserved(0) / 1024**2
)
)
print("Current GPU Memory Allocated: {:.2f} MB".format(torch.cuda.memory_allocated(0) / 1024**2))
print("Maximum GPU Memory Allocated: {:.2f} MB".format(torch.cuda.max_memory_allocated(0) / 1024**2))
print("Current GPU Memory Reserved: {:.2f} MB".format(torch.cuda.memory_reserved(0) / 1024**2))
print("Maximum GPU Memory Reserved: {:.2f} MB".format(torch.cuda.max_memory_reserved(0) / 1024**2))
def capture_timestamp_utc():
@@ -223,9 +200,7 @@ def say(text, blocking=False):
if blocking:
subprocess.run(cmd, check=True)
else:
subprocess.Popen(
cmd, creationflags=subprocess.CREATE_NO_WINDOW if system == "Windows" else 0
)
subprocess.Popen(cmd, creationflags=subprocess.CREATE_NO_WINDOW if system == "Windows" else 0)
def log_say(text, play_sounds, blocking=False):