vall-e/vall_e/plot.py

152 lines
3.4 KiB
Python

#!/usr/bin/env python3
import argparse
import json
import re
from pathlib import Path
import matplotlib.pyplot as plt
import pandas as pd
from .config import cfg
def plot(paths, args):
dfs = []
for path in paths:
with open(path, "r") as f:
text = f.read()
rows = []
pattern = r"(\{.+?\})\.\n"
for row in re.findall(pattern, text, re.DOTALL):
try:
row = json.loads(row)
except Exception as e:
continue
for model in args.models:
if f'{model.name}.{args.xs}' not in row:
continue
rows.append(row)
break
df = pd.DataFrame(rows)
if "name" in df:
df["name"] = df["name"].fillna("train")
else:
df["name"] = "train"
df["group"] = str(path.parents[args.group_level])
df["group"] = df["group"] + "/" + df["name"]
dfs.append(df)
df = pd.concat(dfs)
if args.min_x is not None:
for model in args.models:
df = df[args.min_x < df[f'{model.name}.{args.xs}']]
if args.max_x is not None:
for model in args.models:
df = df[df[f'{model.name}.{args.xs}'] < args.max_x]
for gtag, gdf in sorted(
df.groupby("group"),
key=lambda p: (p[0].split("/")[-1], p[0]),
):
for model in args.models:
x = f'{model.name}.{args.xs}'
for ys in args.ys:
y = f'{model.name}.{ys}'
if gdf[y].isna().all():
continue
if args.min_y is not None:
gdf = gdf[args.min_y < gdf[y]]
if args.max_y is not None:
gdf = gdf[gdf[y] < args.max_y]
if args.ewm:
gdf[y] = gdf[y].ewm(args.ewm).mean()
elif args.rolling:
gdf[y] = gdf[y].rolling(args.rolling).mean()
gdf.plot(
x=x,
y=y,
label=f"{y}",
ax=plt.gca(),
marker="x" if len(gdf) < 100 else None,
alpha=0.7,
)
plt.gca().legend(
#loc="center left",
fancybox=True,
shadow=True,
#bbox_to_anchor=(1.04, 0.5),
)
def plot_sample_metrics( metrics ):
"""
fig = plt.figure()
fig.set_figwidth( 16 * len(metrics) // cfg.dataset.frames_per_second )
"""
data = { key: [ e[0][key] for e in metrics ] for key in metrics[0][0].keys() }
df = pd.DataFrame(data)
df.plot()
plt.gca().legend(
#loc="center left",
fancybox=True,
shadow=True,
#bbox_to_anchor=(1.04, 0.5),
)
out_path = cfg.rel_path / "metrics.png"
plt.savefig(out_path, bbox_inches="tight")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--xs", default="engine_step")
parser.add_argument("--ys", nargs="+", default="")
parser.add_argument("--model", nargs="+", default="*")
parser.add_argument("--min-x", type=float, default=-float("inf"))
parser.add_argument("--min-y", type=float, default=-float("inf"))
parser.add_argument("--max-x", type=float, default=float("inf"))
parser.add_argument("--max-y", type=float, default=float("inf"))
parser.add_argument("--ewm", type=int, default=1024)
parser.add_argument("--rolling", type=int, default=None)
parser.add_argument("--size", type=str, default=None)
parser.add_argument("--filename", default="log.txt")
parser.add_argument("--group-level", default=1)
args, unknown = parser.parse_known_args()
path = cfg.rel_path / "logs"
paths = path.rglob(f"./*/{args.filename}")
args.models = [ model for model in cfg.model.get() if model.training and (args.model == "*" or model.name in args.model) ]
if args.ys == "":
args.ys = ["loss.nll"]
if args.size:
width, height = args.size.split("x")
plt.figure(figsize=(int(width), int(height)))
plot(paths, args)
out_path = cfg.rel_path / "metrics.png"
plt.savefig(out_path, bbox_inches="tight")