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Running
on
Zero
| # Copyright (c) Facebook, Inc. and its affiliates. | |
| # All rights reserved. | |
| # | |
| # This source code is licensed under the license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import gzip | |
| import sys | |
| from concurrent import futures | |
| import musdb | |
| import museval | |
| import torch as th | |
| import tqdm | |
| from scipy.io import wavfile | |
| from torch import distributed | |
| from .audio import convert_audio | |
| from .utils import apply_model | |
| def evaluate(model, | |
| musdb_path, | |
| eval_folder, | |
| workers=2, | |
| device="cpu", | |
| rank=0, | |
| save=False, | |
| shifts=0, | |
| split=False, | |
| overlap=0.25, | |
| is_wav=False, | |
| world_size=1): | |
| """ | |
| Evaluate model using museval. Run the model | |
| on a single GPU, the bottleneck being the call to museval. | |
| """ | |
| output_dir = eval_folder / "results" | |
| output_dir.mkdir(exist_ok=True, parents=True) | |
| json_folder = eval_folder / "results/test" | |
| json_folder.mkdir(exist_ok=True, parents=True) | |
| # we load tracks from the original musdb set | |
| test_set = musdb.DB(musdb_path, subsets=["test"], is_wav=is_wav) | |
| src_rate = 44100 # hardcoded for now... | |
| for p in model.parameters(): | |
| p.requires_grad = False | |
| p.grad = None | |
| pendings = [] | |
| with futures.ProcessPoolExecutor(workers or 1) as pool: | |
| for index in tqdm.tqdm(range(rank, len(test_set), world_size), file=sys.stdout): | |
| track = test_set.tracks[index] | |
| out = json_folder / f"{track.name}.json.gz" | |
| if out.exists(): | |
| continue | |
| mix = th.from_numpy(track.audio).t().float() | |
| ref = mix.mean(dim=0) # mono mixture | |
| mix = (mix - ref.mean()) / ref.std() | |
| mix = convert_audio(mix, src_rate, model.samplerate, model.audio_channels) | |
| estimates = apply_model(model, mix.to(device), | |
| shifts=shifts, split=split, overlap=overlap) | |
| estimates = estimates * ref.std() + ref.mean() | |
| estimates = estimates.transpose(1, 2) | |
| references = th.stack( | |
| [th.from_numpy(track.targets[name].audio).t() for name in model.sources]) | |
| references = convert_audio(references, src_rate, | |
| model.samplerate, model.audio_channels) | |
| references = references.transpose(1, 2).numpy() | |
| estimates = estimates.cpu().numpy() | |
| win = int(1. * model.samplerate) | |
| hop = int(1. * model.samplerate) | |
| if save: | |
| folder = eval_folder / "wav/test" / track.name | |
| folder.mkdir(exist_ok=True, parents=True) | |
| for name, estimate in zip(model.sources, estimates): | |
| wavfile.write(str(folder / (name + ".wav")), 44100, estimate) | |
| if workers: | |
| pendings.append((track.name, pool.submit( | |
| museval.evaluate, references, estimates, win=win, hop=hop))) | |
| else: | |
| pendings.append((track.name, museval.evaluate( | |
| references, estimates, win=win, hop=hop))) | |
| del references, mix, estimates, track | |
| for track_name, pending in tqdm.tqdm(pendings, file=sys.stdout): | |
| if workers: | |
| pending = pending.result() | |
| sdr, isr, sir, sar = pending | |
| track_store = museval.TrackStore(win=44100, hop=44100, track_name=track_name) | |
| for idx, target in enumerate(model.sources): | |
| values = { | |
| "SDR": sdr[idx].tolist(), | |
| "SIR": sir[idx].tolist(), | |
| "ISR": isr[idx].tolist(), | |
| "SAR": sar[idx].tolist() | |
| } | |
| track_store.add_target(target_name=target, values=values) | |
| json_path = json_folder / f"{track_name}.json.gz" | |
| gzip.open(json_path, "w").write(track_store.json.encode('utf-8')) | |
| if world_size > 1: | |
| distributed.barrier() | |