commit 5304a28f26daf5e5d0c387fbfc9037e8c9092f75
parent 1d354a00dc75719e49624cb5af296ee66d5e5155
Author: Steven Atkinson <[email protected]>
Date: Sat, 1 Apr 2023 14:22:43 -0700
Metadata in model files, loudness as first metadatum (#168)
Define metadata, store loudness (#137)
Diffstat:
11 files changed, 59 insertions(+), 31 deletions(-)
diff --git a/nam/_version.py b/nam/_version.py
@@ -1 +1 @@
-__version__ = "0.5.0"
+__version__ = "0.5.1"
diff --git a/nam/models/_activations.py b/nam/models/_activations.py
@@ -6,4 +6,4 @@ import torch.nn as nn
def get_activation(name: str) -> nn.Module:
- return getattr(nn, name)()
-\ No newline at end of file
+ return getattr(nn, name)()
diff --git a/nam/models/_base.py b/nam/models/_base.py
@@ -10,7 +10,6 @@ steps)
import abc
import math
import pkg_resources
-from pathlib import Path
from typing import Any, Optional, Tuple
import numpy as np
@@ -38,17 +37,21 @@ class _Base(nn.Module, InitializableFromConfig, Exportable):
def forward(self, *args, **kwargs) -> torch.Tensor:
pass
- def _loudness(self, gain: float=1.0) -> float:
+ def _loudness(self, gain: float = 1.0) -> float:
"""
How loud is this model when given a standardized input?
In dB
:param gain: Multiplies input signal
"""
- x = wav_to_tensor(pkg_resources.resource_filename("nam", "models/_resources/loudness_input.wav"))
+ x = wav_to_tensor(
+ pkg_resources.resource_filename(
+ "nam", "models/_resources/loudness_input.wav"
+ )
+ )
y = self._at_nominal_settings(gain * x)
return 10.0 * torch.log10(torch.mean(torch.square(y))).item()
-
+
def _at_nominal_settings(self, x: torch.Tensor) -> torch.Tensor:
# parametric?...
raise NotImplementedError()
@@ -59,7 +62,7 @@ class _Base(nn.Module, InitializableFromConfig, Exportable):
The true forward method.
:param x: (N,L1)
- :return: (N,L1-RF+1)
+ :return: (N,L1-RF+1)
"""
pass
@@ -84,10 +87,15 @@ class _Base(nn.Module, InitializableFromConfig, Exportable):
)
# Use pad start to ensure same length as requested by ._export_input_output()
return (
- x.detach().cpu().numpy(),
- self(*args, x, pad_start=True).detach().cpu().numpy()
+ x.detach().cpu().numpy(),
+ self(*args, x, pad_start=True).detach().cpu().numpy(),
)
-
+
+ def _get_export_dict(self):
+ d = super()._get_export_dict()
+ d["metadata"] = {}
+ return d
+
class BaseNet(_Base):
def forward(self, x: torch.Tensor, pad_start: Optional[bool] = None):
@@ -101,7 +109,7 @@ class BaseNet(_Base):
if scalar:
y = y[0]
return y
-
+
def _at_nominal_settings(self, x: torch.Tensor) -> torch.Tensor:
return self(x)
@@ -111,10 +119,15 @@ class BaseNet(_Base):
The true forward method.
:param x: (N,L1)
- :return: (N,L1-RF+1)
+ :return: (N,L1-RF+1)
"""
pass
+ def _get_export_dict(self):
+ d = super()._get_export_dict()
+ d["metadata"]["loudness"] = self._loudness()
+ return d
+
class ParametricBaseNet(_Base):
"""
@@ -143,6 +156,6 @@ class ParametricBaseNet(_Base):
:param params: (N,D)
:param x: (N,L1)
- :return: (N,L1-RF+1)
+ :return: (N,L1-RF+1)
"""
pass
diff --git a/nam/models/_exportable.py b/nam/models/_exportable.py
@@ -39,12 +39,7 @@ class Exportable(abc.ABC):
self.eval()
with open(Path(outdir, modelname + ".nam"), "w") as fp:
json.dump(
- {
- "version": __version__,
- "architecture": self.__class__.__name__,
- "config": self._export_config(),
- "weights": self._export_weights().tolist(),
- },
+ self._get_export_dict(),
fp,
indent=4,
)
@@ -103,3 +98,11 @@ class Exportable(abc.ABC):
Flatten the weights out to a 1D array
"""
pass
+
+ def _get_export_dict(self):
+ return {
+ "version": __version__,
+ "architecture": self.__class__.__name__,
+ "config": self._export_config(),
+ "weights": self._export_weights().tolist(),
+ }
diff --git a/nam/models/parametric/__init__.py b/nam/models/parametric/__init__.py
@@ -1,4 +1,3 @@
# File: __init__.py
# Created Date: Sunday July 17th 2022
# Author: Steven Atkinson ([email protected])
-
diff --git a/nam/models/parametric/catnets.py b/nam/models/parametric/catnets.py
@@ -8,6 +8,7 @@ input samples
"""
import abc
+import logging
from enum import Enum
from contextlib import contextmanager
from pathlib import Path
@@ -21,6 +22,8 @@ from ..recurrent import LSTM
from ..wavenet import WaveNet
from .params import Param
+logger = logging.getLogger(__name__)
+
class _ShapeType(Enum):
CONV = "conv" # (B,C,L)
@@ -49,7 +52,7 @@ class _CatMixin(ParametricBaseNet):
@abc.abstractproperty
def _single_class(self):
- """"
+ """ "
The class for the non-parametric model that this is extending
"""
# TODO verify that single class satisfies requirements
@@ -99,8 +102,11 @@ class _CatMixin(ParametricBaseNet):
def _export_cpp_header_parametric(self, config):
if config is None:
return self._single_class._export_cpp_head_parametric(self, config)
- s_parametric = ['nlohmann::json PARAMETRIC = nlohmann::json::parse(R"(\n', " {\n"]
- for i, (key, val) in enumerate(config.items(), 1):
+ s_parametric = [
+ 'nlohmann::json PARAMETRIC = nlohmann::json::parse(R"(\n',
+ " {\n",
+ ]
+ for i, (key, val) in enumerate(config.items(), 1):
s_parametric.append(f' "{key}": ' "{\n")
for j, (k2, v2) in enumerate(val.items(), 1):
v_str = f'"{v2}"' if isinstance(v2, str) else str(v2)
@@ -112,7 +118,6 @@ class _CatMixin(ParametricBaseNet):
s_parametric.append(')");\n')
return tuple(s_parametric)
-
def _export_input_output_args(self) -> Tuple[torch.Tensor]:
return (self._sidedoor_params_to_tensor(),)
@@ -186,6 +191,15 @@ class CatLSTM(_CatMixin, LSTM):
dim=2,
)
+ def _at_nominal_settings(self, x: torch.Tensor) -> torch.Tensor:
+ if self._input_size != 1:
+ logger.warning(
+ "Nominal settings aren't defined for parametric models; outputting unity"
+ )
+ return x
+ params = torch.zeros(()).to(x.device)
+ return self(params, x)
+
def _get_initial_state(self) -> Tuple[torch.Tensor, torch.Tensor]:
inputs = self._append_default_params(torch.zeros((1, 48_000)))
return super()._get_initial_state(inputs=inputs)
@@ -199,4 +213,3 @@ class CatWaveNet(_CatMixin, WaveNet):
@property
def _single_class(self):
return WaveNet
-
-\ No newline at end of file
diff --git a/nam/train/_version.py b/nam/train/_version.py
@@ -6,6 +6,7 @@
Version utility
"""
+
class Version:
def __init__(self, major: int, minor: int, patch: int):
self.major = major
diff --git a/tests/test_nam/test_models/base.py b/tests/test_nam/test_models/base.py
@@ -28,4 +28,3 @@ class Base(abc.ABC):
args = args if args is not None else self._args
kwargs = kwargs if kwargs is not None else self._kwargs
return C(*args, **kwargs)
-
diff --git a/tests/test_nam/test_models/test_base.py b/tests/test_nam/test_models/test_base.py
@@ -40,6 +40,7 @@ class _MockBaseNet(_base.BaseNet):
return self.gain * x
+
def test_loudness():
obj = _MockBaseNet(1.0)
y = obj._loudness()
diff --git a/tests/test_nam/test_models/test_parametric/__init__.py b/tests/test_nam/test_models/test_parametric/__init__.py
@@ -1,4 +1,3 @@
# File: __init__.py
# Created Date: Sunday July 17th 2022
# Author: Steven Atkinson ([email protected])
-
diff --git a/tests/test_nam/test_models/test_recurrent.py b/tests/test_nam/test_models/test_recurrent.py
@@ -47,12 +47,14 @@ class TestLSTM(Base):
]
input_names = [z.name for z in session.get_inputs()]
- onnx_inputs = {i: z.detach().cpu().numpy() for i, z in zip(input_names, (x, hin, cin))}
+ onnx_inputs = {
+ i: z.detach().cpu().numpy() for i, z in zip(input_names, (x, hin, cin))
+ }
y_actual, hout_actual, cout_actual = session.run([], onnx_inputs)
def approx(val):
return pytest.approx(val, rel=1.0e-6, abs=1.0e-6)
-
+
assert y_expected == approx(y_actual)
assert hout_expected == approx(hout_actual)
assert cout_expected == approx(cout_actual)
