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# 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.
"""Loading pretrained models.
"""
import logging
from pathlib import Path
import typing as tp
#from dora.log import fatal
import logging
from diffq import DiffQuantizer
import torch.hub
from .model import Demucs
from .tasnet_v2 import ConvTasNet
from .utils import set_state
from .hdemucs import HDemucs
from .repo import RemoteRepo, LocalRepo, ModelOnlyRepo, BagOnlyRepo, AnyModelRepo, ModelLoadingError # noqa
logger = logging.getLogger(__name__)
ROOT_URL = "https://dl.fbaipublicfiles.com/demucs/mdx_final/"
REMOTE_ROOT = Path(__file__).parent / 'remote'
SOURCES = ["drums", "bass", "other", "vocals"]
def demucs_unittest():
model = HDemucs(channels=4, sources=SOURCES)
return model
def add_model_flags(parser):
group = parser.add_mutually_exclusive_group(required=False)
group.add_argument("-s", "--sig", help="Locally trained XP signature.")
group.add_argument("-n", "--name", default="mdx_extra_q",
help="Pretrained model name or signature. Default is mdx_extra_q.")
parser.add_argument("--repo", type=Path,
help="Folder containing all pre-trained models for use with -n.")
def _parse_remote_files(remote_file_list) -> tp.Dict[str, str]:
root: str = ''
models: tp.Dict[str, str] = {}
for line in remote_file_list.read_text().split('\n'):
line = line.strip()
if line.startswith('#'):
continue
elif line.startswith('root:'):
root = line.split(':', 1)[1].strip()
else:
sig = line.split('-', 1)[0]
assert sig not in models
models[sig] = ROOT_URL + root + line
return models
def get_model(name: str,
repo: tp.Optional[Path] = None):
"""`name` must be a bag of models name or a pretrained signature
from the remote AWS model repo or the specified local repo if `repo` is not None.
"""
if name == 'demucs_unittest':
return demucs_unittest()
model_repo: ModelOnlyRepo
if repo is None:
models = _parse_remote_files(REMOTE_ROOT / 'files.txt')
model_repo = RemoteRepo(models)
bag_repo = BagOnlyRepo(REMOTE_ROOT, model_repo)
else:
if not repo.is_dir():
fatal(f"{repo} must exist and be a directory.")
model_repo = LocalRepo(repo)
bag_repo = BagOnlyRepo(repo, model_repo)
any_repo = AnyModelRepo(model_repo, bag_repo)
model = any_repo.get_model(name)
model.eval()
return model
def get_model_from_args(args):
"""
Load local model package or pre-trained model.
"""
return get_model(name=args.name, repo=args.repo)
logger = logging.getLogger(__name__)
ROOT = "https://dl.fbaipublicfiles.com/demucs/v3.0/"
PRETRAINED_MODELS = {
'demucs': 'e07c671f',
'demucs48_hq': '28a1282c',
'demucs_extra': '3646af93',
'demucs_quantized': '07afea75',
'tasnet': 'beb46fac',
'tasnet_extra': 'df3777b2',
'demucs_unittest': '09ebc15f',
}
SOURCES = ["drums", "bass", "other", "vocals"]
def get_url(name):
sig = PRETRAINED_MODELS[name]
return ROOT + name + "-" + sig[:8] + ".th"
def is_pretrained(name):
return name in PRETRAINED_MODELS
def load_pretrained(name):
if name == "demucs":
return demucs(pretrained=True)
elif name == "demucs48_hq":
return demucs(pretrained=True, hq=True, channels=48)
elif name == "demucs_extra":
return demucs(pretrained=True, extra=True)
elif name == "demucs_quantized":
return demucs(pretrained=True, quantized=True)
elif name == "demucs_unittest":
return demucs_unittest(pretrained=True)
elif name == "tasnet":
return tasnet(pretrained=True)
elif name == "tasnet_extra":
return tasnet(pretrained=True, extra=True)
else:
raise ValueError(f"Invalid pretrained name {name}")
def _load_state(name, model, quantizer=None):
url = get_url(name)
state = torch.hub.load_state_dict_from_url(url, map_location='cpu', check_hash=True)
set_state(model, quantizer, state)
if quantizer:
quantizer.detach()
def demucs_unittest(pretrained=True):
model = Demucs(channels=4, sources=SOURCES)
if pretrained:
_load_state('demucs_unittest', model)
return model
def demucs(pretrained=True, extra=False, quantized=False, hq=False, channels=64):
if not pretrained and (extra or quantized or hq):
raise ValueError("if extra or quantized is True, pretrained must be True.")
model = Demucs(sources=SOURCES, channels=channels)
if pretrained:
name = 'demucs'
if channels != 64:
name += str(channels)
quantizer = None
if sum([extra, quantized, hq]) > 1:
raise ValueError("Only one of extra, quantized, hq, can be True.")
if quantized:
quantizer = DiffQuantizer(model, group_size=8, min_size=1)
name += '_quantized'
if extra:
name += '_extra'
if hq:
name += '_hq'
_load_state(name, model, quantizer)
return model
def tasnet(pretrained=True, extra=False):
if not pretrained and extra:
raise ValueError("if extra is True, pretrained must be True.")
model = ConvTasNet(X=10, sources=SOURCES)
if pretrained:
name = 'tasnet'
if extra:
name = 'tasnet_extra'
_load_state(name, model)
return model |