integrated plot script, added tts-c task token to help the model be able to mix between normal VALL-E and VALL-E continuous
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@ -86,7 +86,9 @@ Two dataset formats are supported:
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### Plotting Metrics
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Included is a helper script to parse the training metrics. Simply invoke it with, for example: `python3 ./scripts/plot.py --log-dir ./training/valle/logs/1693675364 --out-dir ./data/ --xs=ar.engine_step --ys=ar.loss`
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Included is a helper script to parse the training metrics. Simply invoke it with, for example: `python3 -m vall_e.plot yaml="./training/valle/config.yaml"`
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You can specify what X and Y labels you want to plot against by passing `--xs tokens_processed --ys loss stats.acc`
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### Notices
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113
scripts/plot.py
113
scripts/plot.py
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@ -1,113 +0,0 @@
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#!/usr/bin/env python3
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import argparse
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import json
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import re
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from pathlib import Path
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import matplotlib.pyplot as plt
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import pandas as pd
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def plot(paths, args):
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dfs = []
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for path in paths:
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with open(path, "r") as f:
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text = f.read()
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rows = []
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pattern = r"(\{.+?\})\.\n"
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for row in re.findall(pattern, text, re.DOTALL):
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try:
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row = json.loads(row)
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except Exception as e:
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continue
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for x in args.xs:
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if x not in row:
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continue
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rows.append(row)
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break
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df = pd.DataFrame(rows)
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if "name" in df:
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df["name"] = df["name"].fillna("train")
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else:
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df["name"] = "train"
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df["group"] = str(path.parents[args.group_level])
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df["group"] = df["group"] + "/" + df["name"]
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dfs.append(df)
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df = pd.concat(dfs)
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if args.max_y is not None:
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for x in args.xs:
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df = df[df[x] < args.max_x]
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for gtag, gdf in sorted(
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df.groupby("group"),
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key=lambda p: (p[0].split("/")[-1], p[0]),
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):
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for x in args.xs:
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for y in args.ys:
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gdf = gdf.sort_values(x)
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if gdf[y].isna().all():
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continue
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if args.max_y is not None:
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gdf = gdf[gdf[y] < args.max_y]
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gdf[y] = gdf[y].ewm(10).mean()
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gdf.plot(
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x=x,
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y=y,
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label=f"{y}",
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ax=plt.gca(),
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marker="x" if len(gdf) < 100 else None,
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alpha=0.7,
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)
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plt.gca().legend(
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loc="center left",
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fancybox=True,
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shadow=True,
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bbox_to_anchor=(1.04, 0.5),
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)
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--xs", nargs="+", default="ar.engine_step")
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parser.add_argument("--ys", nargs="+", default="ar.loss")
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parser.add_argument("--log-dir", default="logs", type=Path)
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parser.add_argument("--out-dir", default="logs", type=Path)
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parser.add_argument("--filename", default="log.txt")
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parser.add_argument("--max-x", type=float, default=float("inf"))
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parser.add_argument("--max-y", type=float, default=float("inf"))
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parser.add_argument("--group-level", default=1)
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parser.add_argument("--model-name", type=str, default="ar")
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parser.add_argument("--filter", default=None)
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args = parser.parse_args()
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paths = args.log_dir.rglob(f"**/{args.filename}")
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if args.filter:
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paths = filter(lambda p: re.match(".*" + args.filter + ".*", str(p)), paths)
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plot(paths, args)
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name = "-".join(args.ys)
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out_path = (args.out_dir / name).with_suffix(".png")
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plt.savefig(out_path, bbox_inches="tight")
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if __name__ == "__main__":
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main()
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@ -46,6 +46,7 @@ def get_task_symmap():
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"<mask>": start + 4,
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"<eoe>": start + 5,
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"<svc>": start + 6,
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"<tts-c>": start + 7,
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}
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return symmap
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@ -58,6 +59,7 @@ def _get_quant_path(path):
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def _get_phone_path(path):
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return _replace_file_extension(path, ".phn.txt")
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@cfg.diskcache()
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def _load_paths(dataset, type="training"):
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return { cfg.get_spkr( data_dir / "dummy" ): _load_paths_from_metadata( data_dir, type=type, validate=cfg.dataset.validate and type == "training" ) for data_dir in tqdm(dataset, desc=f"Parsing dataset: {type}") }
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@ -318,12 +320,16 @@ class Dataset(_Dataset):
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if task == "tts-c" and trim_length * 2 >= resps.shape[0]:
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task = "tts"
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task = "tts"
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# VALL-E continuous
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# ignore if target utterance is shorter than prompt duration
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# to-do: actually do this for the AR only as I don't think the paper trained the NAR for this
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if task == "tts-c":
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proms = resps[:trim_length, :]
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resps = resps[trim_length:, :]
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proms = torch.cat( [self.get_task_token(task), proms] )
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else:
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proms = self.sample_prompts(spkr_name, ignore=path) if random.random() < cfg.dataset.random_utterance else resps
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# noise suppression || speech removal
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@ -536,7 +542,6 @@ def _create_dataloader(dataset, training):
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sampler=sampler,
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)
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@cfg.diskcache()
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def create_datasets():
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train_dataset = Dataset( training=True )
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val_dataset = Dataset( phone_symmap=train_dataset.phone_symmap, training=False )
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112
vall_e/plot.py
Normal file
112
vall_e/plot.py
Normal file
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@ -0,0 +1,112 @@
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#!/usr/bin/env python3
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import argparse
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import json
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import re
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from pathlib import Path
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import matplotlib.pyplot as plt
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import pandas as pd
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from .config import cfg
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def plot(paths, args):
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dfs = []
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for path in paths:
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with open(path, "r") as f:
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text = f.read()
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rows = []
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pattern = r"(\{.+?\})\.\n"
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for row in re.findall(pattern, text, re.DOTALL):
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try:
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row = json.loads(row)
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except Exception as e:
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continue
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for model in args.models:
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if f'{model.name}.{args.xs}' not in row:
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continue
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rows.append(row)
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break
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df = pd.DataFrame(rows)
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if "name" in df:
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df["name"] = df["name"].fillna("train")
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else:
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df["name"] = "train"
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df["group"] = str(path.parents[args.group_level])
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df["group"] = df["group"] + "/" + df["name"]
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dfs.append(df)
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df = pd.concat(dfs)
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if args.max_y is not None:
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for model in args.models:
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df = df[df[f'{model.name}.{args.xs}'] < args.max_x]
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for gtag, gdf in sorted(
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df.groupby("group"),
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key=lambda p: (p[0].split("/")[-1], p[0]),
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):
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for model in args.models:
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x = f'{model.name}.{args.xs}'
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for ys in args.ys:
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y = f'{model.name}.{ys}'
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if gdf[y].isna().all():
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continue
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if args.max_y is not None:
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gdf = gdf[gdf[y] < args.max_y]
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gdf[y] = gdf[y].ewm(10).mean()
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gdf.plot(
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x=x,
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y=y,
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label=f"{y}",
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ax=plt.gca(),
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marker="x" if len(gdf) < 100 else None,
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alpha=0.7,
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)
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plt.gca().legend(
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loc="center left",
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fancybox=True,
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shadow=True,
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bbox_to_anchor=(1.04, 0.5),
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)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--xs", default="engine_step")
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parser.add_argument("--ys", nargs="+", default="")
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parser.add_argument("--model", nargs="+", default="*")
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parser.add_argument("--max-x", type=float, default=float("inf"))
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parser.add_argument("--max-y", type=float, default=float("inf"))
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parser.add_argument("--filename", default="log.txt")
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parser.add_argument("--group-level", default=1)
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args = parser.parse_args()
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path = cfg.relpath / "logs"
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paths = path.rglob(f"./*/{args.filename}")
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args.models = [ model for model in cfg.models.get() if model.training and (args.model == "*" or model.name in args.model) ]
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if args.ys == "":
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args.ys = ["loss"]
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plot(paths, args)
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out_path = cfg.relpath / "metrics.png"
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plt.savefig(out_path, bbox_inches="tight")
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