mirror of
https://github.com/hanzoai/mugen.git
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88 lines
3.2 KiB
Python
88 lines
3.2 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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"""
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Evaluation with objective metrics for the pretrained MAGNeT models.
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This grid takes signature from the training grid and runs evaluation-only stage.
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When running the grid for the first time, please use:
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REGEN=1 dora grid magnet.magnet_pretrained_32khz_eval
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and re-use the REGEN=1 option when the grid is changed to force regenerating it.
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Note that you need the proper metrics external libraries setup to use all
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the objective metrics activated in this grid. Refer to the README for more information.
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"""
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import os
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from ..musicgen._explorers import GenerationEvalExplorer
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from ...environment import AudioCraftEnvironment
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from ... import train
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def eval(launcher, batch_size: int = 32):
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opts = {
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'dset': 'audio/musiccaps_32khz',
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'solver/musicgen/evaluation': 'objective_eval',
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'execute_only': 'evaluate',
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'+dataset.evaluate.batch_size': batch_size,
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'+metrics.fad.tf.batch_size': 16,
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}
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# binary for FAD computation: replace this path with your own path
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metrics_opts = {
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'metrics.fad.tf.bin': '/data/home/jadecopet/local/usr/opt/google-research'
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}
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sub = launcher.bind(opts)
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sub.bind_(metrics_opts)
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# base objective metrics
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sub()
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@GenerationEvalExplorer
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def explorer(launcher):
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partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global'])
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launcher.slurm_(gpus=4, partition=partitions)
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if 'REGEN' not in os.environ:
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folder = train.main.dora.dir / 'grids' / __name__.split('.', 2)[-1]
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with launcher.job_array():
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for sig in folder.iterdir():
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if not sig.is_symlink():
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continue
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xp = train.main.get_xp_from_sig(sig.name)
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launcher(xp.argv)
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return
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with launcher.job_array():
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magnet = launcher.bind(solver="magnet/magnet_32khz")
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fsdp = {'autocast': False, 'fsdp.use': True}
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segdur_10secs = {'dataset.segment_duration': 10,
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'generate.lm.decoding_steps': [20, 10, 10, 10]}
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# 10-second magnet models
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magnet_small_10secs = magnet.bind({'continue_from': '//pretrained/facebook/magnet-small-10secs'})
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magnet_small_10secs.bind_(segdur_10secs)
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eval(magnet_small_10secs, batch_size=128)
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magnet_medium_10secs = magnet.bind({'continue_from': '//pretrained/facebook/magnet-medium-10secs'})
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magnet_medium_10secs.bind_(segdur_10secs)
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magnet_medium_10secs.bind_({'model/lm/model_scale': 'medium'})
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magnet_medium_10secs.bind_(fsdp)
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eval(magnet_medium_10secs, batch_size=128)
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# 30-second magnet models
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magnet_small_30secs = magnet.bind({'continue_from': '//pretrained/facebook/magnet-small-30secs'})
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eval(magnet_small_30secs, batch_size=128)
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magnet_medium_30secs = magnet.bind({'continue_from': '//pretrained/facebook/magnet-medium-30secs'})
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magnet_medium_30secs.bind_({'model/lm/model_scale': 'medium'})
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magnet_medium_30secs.bind_(fsdp)
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eval(magnet_medium_30secs, batch_size=128)
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