rm old eval script

scripts/exp/eval.py

@@ -1,124 +0,0 @@
-import glob
-import imp
-import os
-from pathlib import Path
-
-import argbind
-import audiotools
-import numpy as np
-import pandas as pd
-import torch
-from flatten_dict import flatten
-from rich.progress import track
-from torch.utils.tensorboard import SummaryWriter
-
-import wav2wav
-
-train = imp.load_source("train", str(Path(__file__).absolute().parent / "train.py"))
-
-
-@argbind.bind(without_prefix=True)
-def evaluate(
-    args,
-    model_tag: str = "ckpt/best",
-    device: str = "cuda",
-    exp: str = None,
-    overwrite: bool = False,
-):
-    assert exp is not None
-
-    sisdr_loss = audiotools.metrics.distance.SISDRLoss()
-    stft_loss = audiotools.metrics.spectral.MultiScaleSTFTLoss()
-    mel_loss = audiotools.metrics.spectral.MelSpectrogramLoss()
-
-    with audiotools.util.chdir(exp):
-        vampnet = wav2wav.modules.vampnet.transformer.VampNet.load(
-            f"{model_tag}/vampnet/package.pth"
-        )
-        vampnet = vampnet.to(device)
-        if vampnet.cond_dim > 0:
-            condnet = wav2wav.modules.condnet.transformer.CondNet.load(
-                f"{model_tag}/condnet/package.pth"
-            )
-            condnet = condnet.to(device)
-        else:
-            condnet = None
-
-        vqvae = wav2wav.modules.generator.Generator.load(
-            f"{model_tag}/vqvae/package.pth"
-        )
-
-    _, _, test_data = train.build_datasets(args, vqvae.sample_rate)
-
-    with audiotools.util.chdir(exp):
-        datasets = {
-            "test": test_data,
-        }
-
-        metrics_path = Path(f"{model_tag}/metrics")
-        metrics_path.mkdir(parents=True, exist_ok=True)
-
-        for key, dataset in datasets.items():
-            csv_path = metrics_path / f"{key}.csv"
-            if csv_path.exists() and not overwrite:
-                break
-            metrics = []
-            for i in track(range(len(dataset))):
-                # TODO: for coarse2fine
-                # grab the signal
-                # mask all the codebooks except the conditioning ones
-                # and infer
-                # then compute metrics
-                # for a baseline, just use the coarsest codebook
-
-                try:
-                    visqol = audiotools.metrics.quality.visqol(
-                        enhanced, clean, "audio"
-                    ).item()
-                except:
-                    visqol = None
-
-                sisdr = sisdr_loss(enhanced, clean)
-                stft = stft_loss(enhanced, clean)
-                mel = mel_loss(enhanced, clean)
-
-                metrics.append(
-                    {
-                        "visqol": visqol,
-                        "sisdr": sisdr.item(),
-                        "stft": stft.item(),
-                        "mel": mel.item(),
-                        "dataset": key,
-                        "condition": exp,
-                    }
-                )
-                print(metrics[-1])
-
-                transform_args = flatten(item["transform_args"], "dot")
-                for k, v in transform_args.items():
-                    if torch.is_tensor(v):
-                        if len(v.shape) == 0:
-                            metrics[-1][k] = v.item()
-
-            metrics = pd.DataFrame.from_dict(metrics)
-            with open(csv_path, "w") as f:
-                metrics.to_csv(f)
-
-        data = summary(model_tag).to_dict()
-        metrics = {}
-        for k1, v1 in data.items():
-            for k2, v2 in v1.items():
-                metrics[f"metrics/{k2}/{k1}"] = v2
-
-        # Number of steps to record
-        writer = SummaryWriter(log_dir=metrics_path)
-        num_steps = 10
-        for k, v in metrics.items():
-            for i in range(num_steps):
-                writer.add_scalar(k, v, i)
-
-
-if __name__ == "__main__":
-    args = argbind.parse_args()
-    with argbind.scope(args):
-        evaluate(args)