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Running on Zero
File size: 2,047 Bytes
64ec292 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | """
Script to print metrics for checkpoint file of new format
"""
import argparse
import torch
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--start_check_point",
type=str,
default="",
help="Initial checkpoint to start training",
)
args = parser.parse_args()
return args
def main():
args = parse_args()
checkpoint = torch.load(
args.start_check_point, weights_only=False, map_location="cpu"
)
all_metrics = checkpoint["all_metrics"]
from typing import Any, Dict, List
import numpy as np
def _fmt_list(xs: List[float], n: int, p: int) -> str:
if not xs:
return "[]"
xs = [float(x) for x in xs]
head = ", ".join(f"{v:.{p}f}" for v in xs[:n])
if len(xs) > n:
return f"[{head}, … {len(xs) - n} more]"
return f"[{head}]"
def pretty_metrics(
all_time_all_metrics: Dict[str, Any], *, precision: int = 4, show_items: int = 6
) -> str:
lines = []
for epoch_key in sorted(
all_time_all_metrics.keys(), key=lambda k: int(k.split("_")[-1])
):
m = all_time_all_metrics[epoch_key]
lines.append(f"\n=== {epoch_key} ===")
for metric_name, per_instr in m.items():
lines.append(f"{metric_name}:")
for instr, values in per_instr.items():
arr = np.array(values, dtype=float)
mean = np.mean(arr) if arr.size else float("nan")
std = np.std(arr) if arr.size else float("nan")
preview = _fmt_list(values, show_items, precision)
lines.append(
f" {instr:>10s}: mean={mean:.{precision}f} std={std:.{precision}f} "
f"n={arr.size} values={preview}"
)
return "\n".join(lines)
print(pretty_metrics(all_metrics, precision=4, show_items=6))
if __name__ == "__main__":
main()
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