commit 50c247a39d20ba5325ebdecc7541b06871c6105e
parent ced3ec4355568faa1b89ff722afdf2987f219be2
Author: Steven Atkinson <steven@atkinson.mn>
Date: Sat, 3 Sep 2022 19:35:22 -0400
Version 0.4.0 (#48)
* Fix bug exporting parametric models (#41)
* Pre-emphasis filter and loss (#42)
* WaveNet and CatWaveNet models (#43)
* Remove redundant `"nx"` in data set config; let the model provide it w/ its receptive field.
* Faster data indexing in `ConcatDataset`s with many data sets (#45)
* Improvements to the LSTM model.
* Version bump to 0.4.0.
Squash commit notes:
* Pencil in requirements
* Python package workflow
* install package, trigger on dev branch
* Quick ConvNet tests
* Colab Notebook test
* Fix Issue 15
* Update colab.ipynb
* Bump to version 0.2.1
* Should be good
* Update README.md
* Update README.md
* Parametric model (#26)
* Parametric data and Hypernet
* Validte data print path if fail
* Affine instead of layer norm, batchnorm momentum
* Tweak batchnorm momentum
* Fix test so data doesn't clip
* Exporting Hypernet models (#28)
* Export for HyperConvNet, tests
* Fix bool in CPP header
* DC loss (#32)
Closes #31
Implement DC loss of Eq. (19) of https://www.mdpi.com/2076-3417/10/3/766/htm, but without denominator term.
* ESR Loss (#33)
Closes #10
Energy-signal ratio loss metric of Eq. (10) of https://www.mdpi.com/2076-3417/10/3/766/htm
* tqdm loading data
* LSTM (#35)
Closes #24
* Implement an LSTM model.
* Config with some decent starting parameters as well as some hints.
* Some improvements to the training script
* Some refactoring to model exporting
Squash of:
* Better callbacks
* Rearrange training input JSONs
* np_to_wav, expose REQUIRED_RATE
* LSTM model
* Version bump to 0.3.0
* Conditional LSTM (#38)
Closes #36
* A conditional LSTM where the input signal is concatenated with the parametric inputs.
* Example configurations with helpful tips commented.
* Quality of life improvements in the trainer script including functionality for `ConcatDataset`-type validation datasets
* Slicing of a single pair of WAV files into datasets at different parametric settings.
Squash of:
* Better callbacks
* Rearrange training input JSONs
* np_to_wav, expose REQUIRED_RATE
* LSTM model
* Version bump to 0.3.0
* Plot ConcatDatasets
* Flatten datasets inside a ConcatDataset
* CatLSTM
* Tests
* Config for CatLSTM
* Better error message on invalid nx
* Fix export for parametric models (#40)
* Fix export for parametric models
* Version bump to 0.3.1
* Improve docstring
* WaveNet and other improvements (#46)
Smashing together a few things...
* Implements the WaveNet architecture (#43) (and the parametric version, concatenating the knobs as additional inputs i.e. "CatWaveNet")
* Speed up `ConcatDataset` access (#45)
* Deprecate use of `"nx"` in dataset config (#44)
* Increment version to 0.4.0
Commit notes:
* WaveNet
* Fix invalid broadcasting, rechannel needed
* CatWaveNet
* Faster lookup of data in ConcatDataset
* Gated, exporting weights work
* Fix bugs
* Fix export for parametric models
* Version bump to 0.3.1
* Exporting
* Move bias from the input mixer to the dilated conv, which is always used.
* Fix redundant conv in WaveNet head
* Fix bugs; works with plugin
* Automatically add nx to data config from model, check fewer than once per epoch
* Refactor for multiple layer sets
* Zero out through-connection for init (learn direct paths)
* Layer send to head before 1x1, remove zeroing init
* Fix bug: reintroduce out_length in head term
* Fix receptive field w/ non-2 kernel sizes
* Fix up WaveNet export
* cpp headers for WaveNet and CatWaveNet
* Improve docstring
* LSTM tweaks, etc (#47)
* Tweak default parameters of LSTM configs
* Pre-emphasis filtered loss (#42)
* LSTM export C++ header
* Improve printing on the training figures
* Update config files
* Update README.md
* Update README.md
* Update Colab notebook
* Update Colab notebook
Diffstat:
22 files changed, 34652 insertions(+), 1378 deletions(-)
diff --git a/.gitignore b/.gitignore
@@ -130,7 +130,5 @@ dmypy.json
# Pyre type checker
.pyre/
-# etc
-data/
-.vscode/
-lightning_logs/
+# Training outputs
+./bin/train/outputs/
diff --git a/README.md b/README.md
@@ -1,27 +1,45 @@
# NAM: neural amp modeler
-This is the training part of NAM.
+This repository handles training, reamping, and exporting the weights of a model.
For the code to create the plugin with a trained model, see my
[iPlug2 fork](https://github.com/sdatkinson/iPlug2).
-This repository handles training, reamping, and exporting the weights of a model (to
-use with [the iPlug2 plugin]())
-
## How to use (Google Colab)
If you don't have a good computer for training ML models, you can run the
notebook located at `bin/train/colab.ipynb` in the cloud using Google Colab--no
local installation required!
-Go to [colab.research.google.com](https://colab.research.google.com), open the
-notebook using the "GitHub" tab, and go!
+Go to [colab.research.google.com](https://colab.research.google.com), select the
+"GitHub" tab, paste in the URL for the notebook
+(https://github.com/sdatkinson/neural-amp-modeler/blob/dev/bin/train/colab.ipynb),
+and go!
+
+**Pros:**
+* No local installation required!
+* Decent GPUs are available if you don't have one on your computer.
+
+**Cons:**
+* Uploading your data can take a long time.
+* The session will time out after an hour or so (for free accounts), so extended
+ training runs aren't really feasible. I've tried to set you up with a good
+ model that should train quickly!
## How to use (Local)
-Alternatively, the you can clone this repo and use it in the following ways on
-your own computer:
+Alternatively, you can clone this repo to your computer and use it locally.
+
+### Installation
+```bash
+conda env create -f environment.yml
+conda activate nam
+pip install .
+```
-### Train a model
+### Things you can do
+Here are the primary ways this is meant to be used:
+
+#### Train a model
You'll need at least two mono wav files: the input (DI) and the amped sound (without the cab).
You can either record enough to have a training and validation set in the same file and
@@ -43,7 +61,7 @@ bin/train/inputs/config_learning.json \
bin/train/outputs/MyAmp
```
-### Run a model on an input signal ("reamping")
+#### Run a model on an input signal ("reamping")
Handy if you want to just check it out without going through the trouble of building the
plugin.
@@ -58,7 +76,7 @@ path/to/checkpoints/epoch=123_val_loss=0.000010.ckpt \
path/to/output.wav
```
-### Export a model (to use with [the plugin](https://github.com/sdatkinson/iPlug2))
+#### Export a model (to use with [the plugin](https://github.com/sdatkinson/iPlug2))
Let's get ready to rock!
@@ -73,18 +91,10 @@ You'll want the `HardCodedModel.h` to paste over into the plugin source (i.e. [h
## Advanced usage
-The model architecture in `config_model.json` should work plenty good. However, if you
-want to, you can increase the number of channels and the model will generally fit
-better (though it'll get closer to the threshold of real-time. 20 works for a "large"
-model and is still about 6x real time on my desktop).
-
-If you want to mess with the model architecture and end up with a different receptive
-field (e.g. by messing with the dilation pattern), then you need to make sure that `nx`
-is changed accordingly in the data setup.
-The default architecture has a receptive field of 8191 samples, so `nx` is `8191`.
-Generally, for the conv net architecture the receptive field is one elss than the sum of the `dilations`.
-
-You can train for shorter or longer.
-1000 gives pretty great results, but if you're impatient you can sometimes get away with
-comparable results after 500 epochs, and you might nto even be able to tell the
-difference with far fewer (maybe 200?...100?)
+The model architectures and cofigurations in `bin/train/inputs/models` should work plenty well out of the box.
+However, feel free to play around with it; sometimes some tweaks can help improve performance.
+
+Also, you can train for shorter or longer.
+1000 epochs is typically overkill, but how little you can get away with depends on the model you're using.
+I recommend watching the checkpoints and keeping an eye out for when the ESR drops below 0.01--usually it'll
+sound pretty good by that point.
diff --git a/bin/export.py b/bin/export.py
@@ -1,40 +0,0 @@
-# File: export.py
-# Created Date: Sunday February 6th 2022
-# Author: Steven Atkinson (steven@atkinson.mn)
-
-"""
-Export a model to TorchScript
-"""
-
-import json
-from argparse import ArgumentParser
-from pathlib import Path
-
-import torch
-
-from nam.models import Model
-
-
-class Dummy(torch.nn.Module):
- def forward(self, x):
- return x[8191:]
-
-
-def main(args):
- with open(args.model_config_path, "r") as fp:
- net = Model.load_from_checkpoint(
- args.checkpoint, **Model.parse_config(json.load(fp))
- ).net
- net.eval()
- outdir = Path(args.outdir)
- outdir.mkdir(parents=True, exist_ok=True)
- net.export(outdir)
- net.export_cpp_header(Path(outdir, "HardCodedModel.h"))
-
-
-if __name__ == "__main__":
- parser = ArgumentParser()
- parser.add_argument("model_config_path", type=str)
- parser.add_argument("checkpoint", type=str)
- parser.add_argument("outdir")
- main(parser.parse_args())
diff --git a/bin/export/main.py b/bin/export/main.py
@@ -0,0 +1,55 @@
+# File: export.py
+# Created Date: Sunday February 6th 2022
+# Author: Steven Atkinson (steven@atkinson.mn)
+
+"""
+Export a model to TorchScript
+"""
+
+import json
+from argparse import ArgumentParser
+from pathlib import Path
+
+import torch
+
+from nam.models import Model
+from nam.models._base import ParametricBaseNet
+from nam.models.parametric.catnets import Param
+
+
+class Dummy(torch.nn.Module):
+ def forward(self, x):
+ return x[8191:]
+
+
+def main(args):
+ outdir = Path(args.outdir)
+ with open(args.model_config_path, "r") as fp:
+ net = Model.load_from_checkpoint(
+ args.checkpoint, **Model.parse_config(json.load(fp))
+ ).net
+ if not isinstance(net, ParametricBaseNet):
+ export_args = (outdir,)
+ else:
+ if args.param_config is None:
+ raise ValueError("Require param config for parametric model")
+ with open(Path(args.param_config), "r") as fp:
+ param_config = {
+ k: Param.init_from_config(v) for k, v in json.load(fp).items()
+ }
+ export_args = (outdir, param_config)
+ net.eval()
+ outdir.mkdir(parents=True, exist_ok=True)
+ net.export(*export_args)
+ net.export_cpp_header(Path(export_args[0], "HardCodedModel.h"), *export_args[1:])
+
+
+if __name__ == "__main__":
+ parser = ArgumentParser()
+ parser.add_argument("model_config_path", type=str)
+ parser.add_argument("checkpoint", type=str)
+ parser.add_argument("outdir")
+ parser.add_argument(
+ "--param-config", type=str, help="Configuration for a parametric model"
+ )
+ main(parser.parse_args())
diff --git a/bin/train/colab.ipynb b/bin/train/colab.ipynb
@@ -49,7 +49,7 @@
"base_uri": "https://localhost:8080/"
},
"id": "vYQIpWr5EYRb",
- "outputId": "52530563-78ad-4fb4-8669-1ba0ddd351d1"
+ "outputId": "ce378c94-da2a-47f5-f805-870a6b1284cd"
},
"outputs": [],
"source": [
@@ -119,7 +119,7 @@
}
},
"id": "R_filL-5F8HR",
- "outputId": "01632616-e5bc-4fa0-f4c0-5bca42c78d87"
+ "outputId": "67289979-ca84-4b4e-b190-24a32efb892d"
},
"outputs": [],
"source": [
@@ -133,7 +133,11 @@
},
"source": [
"## Step 2: Settings\n",
- "The defaults are what I tend to start with and should usually work well (except the file names--see above), but if you'd like, you can make changes."
+ "The defaults are what I tend to start with and should usually work well (except the file names--see above), but if you'd like, you can make changes.\n",
+ "\n",
+ "🔶**Important**🔶\n",
+ "\n",
+ "If there is a **delay** between your input and output (e.g. if you reamped and your interface has latency that your DAW doesn't factor out), then change that in the `data_config` dict below to the delay, in samples. For example, if the output lags the input by 505 samples, then change it to `\"delay\": 505`. If there's too much delay (e.g. more than 10 samples), then the model may not learn. The closer you get this, the better the results will be, but don't over-compensate or else the model would have to predict the future!"
]
},
{
@@ -148,7 +152,7 @@
" \"train\": {\n",
" \"x_path\": \"x_train.wav\",\n",
" \"y_path\": \"y_train.wav\",\n",
- " \"ny\": 1024\n",
+ " \"ny\": 32768\n",
" },\n",
" \"validation\": {\n",
" \"x_path\": \"x_validation.wav\",\n",
@@ -156,75 +160,49 @@
" \"ny\": None\n",
" },\n",
" \"common\": {\n",
- " \"nx\": 8191\n",
+ " \"delay\": 0\n",
" }\n",
"}\n",
"model_config = {\n",
" \"net\": {\n",
- " \"name\": \"ConvNet\",\n",
+ " \"name\": \"LSTM\",\n",
" \"config\": {\n",
- " \"channels\": 16,\n",
- " \"dilations\": [\n",
- " 1,\n",
- " 2,\n",
- " 4,\n",
- " 8,\n",
- " 16,\n",
- " 32,\n",
- " 64,\n",
- " 128,\n",
- " 256,\n",
- " 512,\n",
- " 1024,\n",
- " 2048,\n",
- " 1,\n",
- " 2,\n",
- " 4,\n",
- " 8,\n",
- " 16,\n",
- " 32,\n",
- " 64,\n",
- " 128,\n",
- " 256,\n",
- " 512,\n",
- " 1024,\n",
- " 2048\n",
- " ],\n",
- " \"batchnorm\": True,\n",
- " \"activation\": \"Tanh\",\n",
+ " \"num_layers\": 3,\n",
+ " \"hidden_size\": 24,\n",
+ " \"train_burn_in\": 4096,\n",
+ " \"train_truncate\": 512 \n",
" }\n",
" },\n",
+ " \"loss\": {\n",
+ " \"val_loss\": \"mse\",\n",
+ " \"mask_first\": 4096, \n",
+ " \"pre_emph_weight\": 1.0,\n",
+ " \"pre_emph_coef\": 0.85\n",
+ " },\n",
" \"optimizer\": {\n",
- " \"lr\": 0.003\n",
+ " \"lr\": 0.01\n",
" },\n",
" \"lr_scheduler\": {\n",
- " \"class\": \"ReduceLROnPlateau\",\n",
+ " \"class\": \"ExponentialLR\",\n",
" \"kwargs\": {\n",
- " \"factor\": 0.7,\n",
- " \"patience\": 20,\n",
- " \"cooldown\": 20,\n",
- " \"min_lr\": 1e-05,\n",
- " \"verbose\": True\n",
- " },\n",
- " \"monitor\": \"val_loss\"\n",
+ " \"gamma\": 0.995\n",
+ " }\n",
" }\n",
"}\n",
"learning_config = {\n",
" \"train_dataloader\": {\n",
- " \"batch_size\": 16,\n",
+ " \"batch_size\": 32,\n",
" \"shuffle\": True,\n",
" \"pin_memory\": True,\n",
" \"drop_last\": True,\n",
- " \"num_workers\": 2\n",
- " },\n",
- " \"val_dataloader\": {\n",
+ " \"num_workers\": 0\n",
" },\n",
+ " \"val_dataloader\": {},\n",
" \"trainer\": {\n",
" \"gpus\": 1,\n",
" \"max_epochs\": 100\n",
" },\n",
- " \"trainer_fit_kwargs\": {\n",
- " }\n",
+ " \"trainer_fit_kwargs\": {}\n",
"}"
]
},
@@ -242,25 +220,36 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "id": "OV4gLukTMjdD"
+ "id": "UAChcygdMTF4"
},
"outputs": [],
"source": [
- "dataset_train = init_dataset(data_config, Split.TRAIN)\n",
- "dataset_validation = init_dataset(data_config, Split.VALIDATION)\n",
- "train_dataloader = DataLoader(dataset_train, **learning_config[\"train_dataloader\"])\n",
- "val_dataloader = DataLoader(dataset_validation, **learning_config[\"val_dataloader\"])"
+ "model = Model.init_from_config(model_config)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
- "id": "UAChcygdMTF4"
+ "id": "iZ1jCOh7i7Ct"
},
"outputs": [],
"source": [
- "model = Model.init_from_config(model_config)"
+ "data_config[\"common\"][\"nx\"] = model.net.receptive_field"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "OV4gLukTMjdD"
+ },
+ "outputs": [],
+ "source": [
+ "dataset_train = init_dataset(data_config, Split.TRAIN)\n",
+ "dataset_validation = init_dataset(data_config, Split.VALIDATION)\n",
+ "train_dataloader = DataLoader(dataset_train, **learning_config[\"train_dataloader\"])\n",
+ "val_dataloader = DataLoader(dataset_validation, **learning_config[\"val_dataloader\"])"
]
},
{
@@ -271,20 +260,20 @@
"base_uri": "https://localhost:8080/"
},
"id": "vyhMf0ZyM4kt",
- "outputId": "42fde986-1d31-4dd3-c6f8-585206e0c8e0"
+ "outputId": "2a4ebf5a-9bbe-4119-f1fe-2bc1d6d665f5"
},
"outputs": [],
"source": [
"trainer = pl.Trainer(\n",
" callbacks=[\n",
" pl.callbacks.model_checkpoint.ModelCheckpoint(\n",
- " filename=\"{epoch}_{val_loss:.6f}\",\n",
+ " filename=\"{epoch:04d}_{step}_{ESR:.3e}_{MSE:.3e}\",\n",
" save_top_k=3,\n",
" monitor=\"val_loss\",\n",
" every_n_epochs=1,\n",
" ),\n",
" pl.callbacks.model_checkpoint.ModelCheckpoint(\n",
- " filename=\"checkpoint_last_{epoch:04d}\", every_n_epochs=1\n",
+ " filename=\"checkpoint_last_{epoch:04d}_{step}\", every_n_epochs=1\n",
" ),\n",
" ],\n",
" **learning_config[\"trainer\"],\n",
@@ -297,122 +286,1134 @@
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
- "height": 223,
+ "height": 295,
"referenced_widgets": [
- "b85136e683cd45109dc7db1ee974280c",
- "3eff7021744b4c40afa948f9708d8078",
- "0ad6ac5a3022484ba3078628db792c6e",
- "18f077c72c67404d81aca48d0993d9cc",
- "2ded9f52bb754adfacbb23043f3d5eeb",
- "ee32d6a903944743aa4ebb368bf7695a",
- "a34f24542f44405499a43ef283d21616",
- "2964bbf4fc4744039c473c5701173950",
- "605e4d3a77e54b64bb9e8805e8b272cf",
- "7ed21d4c4638435a84cb10817d94004c",
- "818c2626640149e4a44b16c0819b4c11",
- "43bf605542b74e84a34ad018c4b11bc5",
- "a9d499f5900346da9e6d09d2346c9baa",
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"id": "a8WLIx33M7c6",
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+ "outputId": "4e5ac39e-601c-4521-d15e-b67b82d12f15"
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"source": [
@@ -422,14 +1423,25 @@
" train_dataloader,\n",
" val_dataloader,\n",
" **learning_config.get(\"trainer_fit_kwargs\", {}),\n",
- ")"
+ ")\n",
+ "# Monitor the progress in lightning_logs/version_0/checkpoints.\n",
+ "# Tthe ESR may plateau at 1 for about 20 iterations, but if it hasn't started decreasing\n",
+ "# By then, then something might be wrong e.g. mismatched data, or an incorrect delay \n",
+ "# (see above).\n",
+ "#\n",
+ "# Many models can get a good result (rule of thumb: look for ESR<0.01) in about 15 \n",
+ "# minutes of training, but if you're more patient, it'll probably keep getting better."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
- "id": "JzGltwwJNAkI"
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "JzGltwwJNAkI",
+ "outputId": "1759b50a-f855-47e1-d7e4-54287917e33a"
},
"outputs": [],
"source": [
@@ -450,7 +1462,7 @@
},
"source": [
"# Step 4: Check\n",
- "Let's look at how good our model matches the real thing"
+ "Let's look at how well our model matches the real thing."
]
},
{
@@ -504,7 +1516,12 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "id": "C_NsBdp5NQMC"
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 287
+ },
+ "id": "C_NsBdp5NQMC",
+ "outputId": "9f49bae0-f657-4140-cfde-807c6bb0550b"
},
"outputs": [],
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@@ -513,7 +1530,9 @@
" dataset_validation,\n",
" window_start=100_000, # Start of the plotting window, in samples\n",
" window_end=101_000, # End of the plotting window, in samples\n",
- ")"
+ ")\n",
+ "# Don't worry if the LSTM runs slower than 1x. Python's interpreter is slow, and the C++\n",
+ "# version is a lot faster."
]
},
{
@@ -561,123 +1580,25 @@
"name": "colab.ipynb",
"provenance": []
},
+ "gpuClass": "standard",
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3.10.4 ('nam-dev')",
+ "language": "python",
"name": "python3"
},
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- "name": "python"
+ "name": "python",
+ "version": "3.10.4"
},
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+ "vscode": {
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+ "fd542f65637c4d1fb210a51247c4aedb": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
- "model_name": "FloatProgressModel",
+ "model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
- "_model_name": "FloatProgressModel",
+ "_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
- "_view_name": "ProgressView",
- "bar_style": "",
+ "_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
- "layout": "IPY_MODEL_e83a04c5ea0f4fbb86b3214f8e1396db",
- "max": 1,
- "min": 0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_54dcc73532f04380a78c13ee96ec8304",
- "value": 1
+ "layout": "IPY_MODEL_78b8c18f9cd840ef9978414358cd69cd",
+ "placeholder": "​",
+ "style": "IPY_MODEL_2a4efdf9f214461e93f7643baf59981d",
+ "value": " 1/1 [00:00<00:00, 2.43it/s]"
}
},
- "ee32d6a903944743aa4ebb368bf7695a": {
+ "fd7deb2660464a54bc2106513cb5906c": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -3704,9 +36194,9 @@
"align_self": null,
"border": null,
"bottom": null,
- "display": null,
+ "display": "inline-flex",
"flex": null,
- "flex_flow": null,
+ "flex_flow": "row wrap",
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
@@ -3736,32 +36226,25 @@
"right": null,
"top": null,
"visibility": null,
- "width": null
+ "width": "100%"
}
},
- "f1b99ee8c6674cebbcf3ed99093f6130": {
+ "fe04cf834a6a4a84bbe6fca98fb091c0": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
- "model_name": "HBoxModel",
+ "model_name": "DescriptionStyleModel",
"state": {
- "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
- "_model_name": "HBoxModel",
+ "_model_name": "DescriptionStyleModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_d1fcb9aa6f994eb5bab3b3b3791a5664",
- "IPY_MODEL_8620607984104341adbf6ec36bc63660",
- "IPY_MODEL_a3a7cc8a1ec340748f0a7cc50ffe5dc1"
- ],
- "layout": "IPY_MODEL_5c72d475e6e54608b205d7c13d7d8372"
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
}
},
- "f4de186eb07d4e6eb060e4c46226aa58": {
+ "fe147359562f488eaf5b26ae43ac5d3a": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -3779,7 +36262,7 @@
"border": null,
"bottom": null,
"display": null,
- "flex": "2",
+ "flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
@@ -3813,7 +36296,22 @@
"width": null
}
},
- "f96de37a5b8f4c108c52d8e1329e2cd3": {
+ "fe18f28bec7a44a2b0973fb4fc1c2ceb": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "DescriptionStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ },
+ "fe32e3e34c5042d6a676a8bc010d2b46": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -3831,7 +36329,7 @@
"border": null,
"bottom": null,
"display": null,
- "flex": "2",
+ "flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
@@ -3865,7 +36363,7 @@
"width": null
}
},
- "fa6159f37588452698306786e50e6b5e": {
+ "fe3e87d968b74e719725fdaf69dddee7": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -3917,7 +36415,7 @@
"width": null
}
},
- "fbaaf1cd5c704ae1b70678bf05f4cd6a": {
+ "ff64f810b8e74ffa9b7b5d453ad26c36": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -3934,9 +36432,9 @@
"align_self": null,
"border": null,
"bottom": null,
- "display": "inline-flex",
+ "display": null,
"flex": null,
- "flex_flow": "row wrap",
+ "flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
@@ -3966,29 +36464,22 @@
"right": null,
"top": null,
"visibility": null,
- "width": "100%"
+ "width": null
}
},
- "fe68c6d0e7ca4c189538d0745567c03f": {
+ "fff4539dbeb345c0964c0b716458407d": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
- "model_name": "HBoxModel",
+ "model_name": "DescriptionStyleModel",
"state": {
- "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
- "_model_name": "HBoxModel",
+ "_model_name": "DescriptionStyleModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_d172c973a7ea4c839b13f62b39cb5849",
- "IPY_MODEL_a1130f67226543599b74d04852856bf4",
- "IPY_MODEL_647decf9a6b84c3d87979bfe321ab306"
- ],
- "layout": "IPY_MODEL_fbaaf1cd5c704ae1b70678bf05f4cd6a"
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
}
}
}
diff --git a/bin/train/inputs/data/single_pair.json b/bin/train/inputs/data/single_pair.json
@@ -12,7 +12,6 @@
"common": {
"x_path": "C:\\path\\to\\source.wav",
"y_path": "C:\\path\\to\\target.wav",
- "nx": 8191,
"delay": 0
}
}
\ No newline at end of file
diff --git a/bin/train/inputs/data/two_pairs.json b/bin/train/inputs/data/two_pairs.json
@@ -10,7 +10,6 @@
"ny": null
},
"common": {
- "nx": 8191,
"delay": 0
}
}
\ No newline at end of file
diff --git a/bin/train/inputs/learning/default.json b/bin/train/inputs/learning/default.json
@@ -4,7 +4,7 @@
"shuffle": true,
"pin_memory": true,
"drop_last": true,
- "num_workers": 4
+ "num_workers": 0
},
"val_dataloader": {
},
diff --git a/bin/train/inputs/models/catlstm.json b/bin/train/inputs/models/catlstm.json
@@ -3,32 +3,30 @@
"Parametric extension of the LSTM model. All LSTM tips apply plus:",
" * Make sure that `input_size` is the number of knobs plus one. I've set it",
" up like we're modeling a tube screamer (drive/tone/level), so 1+3=4.",
- " * I've messed around with weight decay, but I don't think it's actually",
- " helpful. Still, it's in there so you can see how to use it if you're",
- " curious.",
" * Doesn't seem like the model needs to be all that bigger than the",
" non-parametric version, even if you're modeling a fair number of knobs.",
" * You'll probably have a much larger dataset, so validating every so often ",
" in steps instead of epochs helps. Make sure to also set val_check_interval",
- " under the trainer dict in your learning.json."
+ " under the trainer dict in your learning config JSON."
],
"net": {
"name": "CatLSTM",
"config": {
- "num_layers": 3,
- "hidden_size": 24,
- "train_truncate": 1024,
+ "num_layers": 4,
+ "hidden_size": 32,
"train_burn_in": 4096,
+ "train_truncate": 512,
"input_size": 4
}
},
"loss": {
"val_loss": "mse",
- "mask_first": 4096
+ "mask_first": 4096,
+ "pre_emph_weight": 1.0,
+ "pre_emph_coef": 0.85
},
"optimizer": {
- "lr": 0.01,
- "weight_decay": 1e-09
+ "lr": 0.01
},
"lr_scheduler": {
"class": "ExponentialLR",
@@ -36,6 +34,6 @@
"gamma": 0.995
},
"interval": "step",
- "frequency": 200
+ "frequency": 100
}
}
\ No newline at end of file
diff --git a/bin/train/inputs/models/lstm.json b/bin/train/inputs/models/lstm.json
@@ -1,7 +1,7 @@
{
"_comments": [
"Reminders and tips:",
- " * For your data, use nx=1, and use a long ny like 32768.",
+ " * For your data, use a long ny like 32768.",
" * For this model, it really helps if you have the delay in your data set",
" correctly. I've seen improvements fixing a delay that was off by 10",
" samples.",
@@ -14,12 +14,18 @@
"net": {
"name": "LSTM",
"config": {
+ "num_layers": 3,
"hidden_size": 24,
"train_burn_in": 4096,
- "train_truncate": 1024,
- "num_layers": 3
+ "train_truncate": 512
}
},
+ "loss": {
+ "val_loss": "mse",
+ "mask_first": 4096,
+ "pre_emph_weight": 1.0,
+ "pre_emph_coef": 0.85
+ },
"optimizer": {
"lr": 0.01
},
diff --git a/bin/train/inputs/models/wavenet.json b/bin/train/inputs/models/wavenet.json
@@ -0,0 +1,40 @@
+{
+ "net": {
+ "name": "WaveNet",
+ "config": {
+ "layers_configs": [
+ {
+ "input_size": 1,
+ "condition_size": 1,
+ "head_size": 8,
+ "channels": 16,
+ "kernel_size": 3,
+ "dilations": [1,2,4,8,16,32,64,128,256,512],
+ "activation": "Tanh",
+ "gated": false,
+ "head_bias": false
+ },
+ {
+ "input_size": 16,
+ "condition_size": 1,
+ "head_size": 1,
+ "channels": 8,
+ "kernel_size": 3,
+ "dilations": [1,2,4,8,16,32,64,128,256,512],
+ "activation": "Tanh",
+ "gated": false,
+ "head_bias": true
+ }
+ ]
+ }
+ },
+ "optimizer": {
+ "lr": 0.003
+ },
+ "lr_scheduler": {
+ "class": "ExponentialLR",
+ "kwargs": {
+ "gamma": 0.993
+ }
+ }
+}
+\ No newline at end of file
diff --git a/bin/train/main.py b/bin/train/main.py
@@ -8,6 +8,7 @@ from datetime import datetime
from pathlib import Path
from time import time
from typing import Optional, Union
+from warnings import warn
import matplotlib.pyplot as plt
import numpy as np
@@ -76,7 +77,7 @@ def plot(
args = (ds.vals, ds.x) if isinstance(ds, ParametricDataset) else (ds.x,)
output = model(*args).flatten().cpu().numpy()
t1 = time()
- print(f"Took {t1 - t0} ({tx / (t1 - t0):.2f}x)")
+ print(f"Took {t1 - t0:.2f} ({tx / (t1 - t0):.2f}x)")
plt.figure(figsize=(16, 5))
# plt.plot(ds.x[window_start:window_end], label="Input")
@@ -104,7 +105,7 @@ def _create_callbacks(learning_config):
"every_n_train_steps": learning_config["trainer"]["val_check_interval"]
}
else:
- kwargs = {"every_n_epochs": 1}
+ kwargs = {"every_n_epochs": learning_config["trainer"].get("check_val_every_n_epoch", 1)}
checkpoint_best = pl.callbacks.model_checkpoint.ModelCheckpoint(
filename="{epoch:04d}_{step}_{ESR:.3e}_{MSE:.3e}",
@@ -137,6 +138,11 @@ def main(args):
json.dump(config, fp, indent=4)
model = Model.init_from_config(model_config)
+ # Add receptive field to data config:
+ data_config["common"] = data_config.get("common", {})
+ if "nx" in data_config["common"]:
+ warn(f"Overriding data nx={data_config['common']['nx']} with model requried {model.net.receptive_field}")
+ data_config["common"]["nx"] = model.net.receptive_field
dataset_train = init_dataset(data_config, Split.TRAIN)
dataset_validation = init_dataset(data_config, Split.VALIDATION)
@@ -172,7 +178,7 @@ def main(args):
window_end=110_000,
show=False,
)
- plot(model, dataset_validation)
+ plot(model, dataset_validation, show=not args.no_show)
if __name__ == "__main__":
@@ -181,4 +187,5 @@ if __name__ == "__main__":
parser.add_argument("model_config_path", type=str)
parser.add_argument("learning_config_path", type=str)
parser.add_argument("outdir")
+ parser.add_argument("--no-show", action="store_true", help="Don't show plots")
main(parser.parse_args())
diff --git a/nam/_version.py b/nam/_version.py
@@ -1 +1 @@
-__version__ = "0.3.0"
+__version__ = "0.4.0"
diff --git a/nam/data.py b/nam/data.py
@@ -290,13 +290,11 @@ class ConcatDataset(AbstractDataset, InitializableFromConfig):
datasets = self._flatten_datasets(datasets)
self._validate_datasets(datasets)
self._datasets = datasets
+ self._lookup = self._make_lookup()
def __getitem__(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor]:
- for d in self._datasets:
- if idx < len(d):
- return d[idx]
- else:
- idx = idx - len(d)
+ i, j = self._lookup[idx]
+ return self.datasets[i][j]
def __len__(self) -> int:
return sum(len(d) for d in self._datasets)
@@ -330,6 +328,23 @@ class ConcatDataset(AbstractDataset, InitializableFromConfig):
flattened.append(d)
return flattened
+ def _make_lookup(self):
+ """
+ For faster __getitem__
+ """
+ lookup = {}
+ offset = 0
+ j = 0 # Dataset index
+ for i in range(len(self)):
+ if offset == len(self.datasets[j]):
+ offset -= len(self.datasets[j])
+ j += 1
+ lookup[i] = (j, offset)
+ offset += 1
+ assert j == len(self.datasets)-1
+ assert offset == len(self.datasets[-1])
+ return lookup
+
@classmethod
def _validate_datasets(cls, datasets: Sequence[Dataset]):
Reference = namedtuple("Reference", ("index", "val"))
@@ -369,4 +384,3 @@ def init_dataset(config, split: Split) -> AbstractDataset:
"dataset_configs": [{**common, **c} for c in base_config],
}
)
-
diff --git a/nam/models/_activations.py b/nam/models/_activations.py
@@ -0,0 +1,9 @@
+# File: _activations.py
+# Created Date: Friday July 29th 2022
+# Author: Steven Atkinson (steven@atkinson.mn)
+
+import torch.nn as nn
+
+
+def get_activation(name: str) -> nn.Module:
+ return getattr(nn, name)()
+\ No newline at end of file
diff --git a/nam/models/_base.py b/nam/models/_base.py
@@ -3,13 +3,15 @@
# Author: Steven Atkinson (steven@atkinson.mn)
import abc
-from typing import Optional, Tuple
+import math
+from typing import Any, Optional, Tuple
import numpy as np
import torch
import torch.nn as nn
from .._core import InitializableFromConfig
+from ..data import REQUIRED_RATE
from ._exportable import Exportable
@@ -39,15 +41,30 @@ class _Base(nn.Module, InitializableFromConfig, Exportable):
"""
pass
- def _export_input_output(
- self, seed=0, extra_length=13
- ) -> Tuple[torch.Tensor, torch.Tensor]:
- x = torch.Tensor(
- np.random.default_rng(seed).normal(
- size=(self.receptive_field + extra_length,)
- )
+ def _export_input_output_args(self) -> Tuple[Any]:
+ """
+ Create any other args necessesary (e.g. params to eval at)
+ """
+ return ()
+
+ def _export_input_output(self) -> Tuple[np.ndarray, np.ndarray]:
+ args = self._export_input_output_args()
+ rate = REQUIRED_RATE
+ x = torch.cat(
+ [
+ torch.zeros((rate,)),
+ 0.5
+ * torch.sin(
+ 2.0 * math.pi * 220.0 * torch.linspace(0.0, 1.0, rate + 1)[:-1]
+ ),
+ torch.zeros((rate,)),
+ ]
+ )
+ # 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()
)
- return x, self(x, pad_start=False)
class BaseNet(_Base):
diff --git a/nam/models/_exportable.py b/nam/models/_exportable.py
@@ -72,6 +72,9 @@ class Exportable(abc.ABC):
def _export_input_output(self) -> Tuple[np.ndarray, np.ndarray]:
"""
Create an input and corresponding output signal to verify its behavior.
+
+ They should be the same length, but the start of the output might have transient
+ effects. Up to you to interpret.
"""
pass
diff --git a/nam/models/base.py b/nam/models/base.py
@@ -3,7 +3,11 @@
# Author: Steven Atkinson (steven@atkinson.mn)
"""
-Lightning stuff
+Implements the base PyTorch Lightning model.
+This is meant to combine an acutal model (subclassed from `._base.BaseNet` or
+`._base.ParametricBaseNet`) along with loss function boilerplate.
+
+For the base *PyTorch* model containing the actual architecture, see `._base`.
"""
from dataclasses import dataclass
@@ -17,9 +21,10 @@ import torch.nn as nn
from .._core import InitializableFromConfig
from .conv_net import ConvNet
from .linear import Linear
-from .parametric.catnets import CatLSTM
+from .parametric.catnets import CatLSTM, CatWaveNet
from .parametric.hyper_net import HyperConvNet
from .recurrent import LSTM
+from .wavenet import WaveNet
class ValidationLoss(Enum):
@@ -44,11 +49,15 @@ class LossConfig(InitializableFromConfig):
:param mask_first: How many of the first samples to ignore when comptuing the loss.
:param dc_weight: Weight for the DC loss term. If 0, ignored.
:params val_loss: Which loss to track for the best model checkpoint.
+ :param pre_emph_coef: Coefficient of 1st-order pre-emphasis filter from
+ https://www.mdpi.com/2076-3417/10/3/766. Paper value: 0.95.
"""
mask_first: int = 0
dc_weight: float = 0.0
val_loss: ValidationLoss = ValidationLoss.MSE
+ pre_emph_weight: Optional[float] = None
+ pre_emph_coef: Optional[float] = None
@classmethod
def parse_config(cls, config):
@@ -56,7 +65,15 @@ class LossConfig(InitializableFromConfig):
dc_weight = config.get("dc_weight", 0.0)
val_loss = ValidationLoss(config.get("val_loss", "mse"))
mask_first = config.get("mask_first", 0)
- return {"mask_first": mask_first, "dc_weight": dc_weight, "val_loss": val_loss}
+ pre_emph_coef = config.get("pre_emph_coef")
+ pre_emph_weight = config.get("pre_emph_weight")
+ return {
+ "mask_first": mask_first,
+ "dc_weight": dc_weight,
+ "val_loss": val_loss,
+ "pre_emph_coef": pre_emph_coef,
+ "pre_emph_weight": pre_emph_weight
+ }
def apply_mask(self, *args):
"""
@@ -133,10 +150,12 @@ class Model(pl.LightningModule, InitializableFromConfig):
net_config = config["net"]
net = {
"CatLSTM": CatLSTM.init_from_config,
+ "CatWaveNet": CatWaveNet.init_from_config,
"ConvNet": ConvNet.init_from_config,
"HyperConvNet": HyperConvNet.init_from_config,
"Linear": Linear.init_from_config,
"LSTM": LSTM.init_from_config,
+ "WaveNet": WaveNet.init_from_config,
}[net_config["name"]](net_config["config"])
loss_config = LossConfig.init_from_config(config.get("loss", {}))
return {
@@ -177,8 +196,17 @@ class Model(pl.LightningModule, InitializableFromConfig):
preds, targets = self._shared_step(batch)
loss = 0.0
- # Prediction aka MSE aka "ESR" loss
+ # Prediction aka MSE loss
loss = loss + self._mse_loss(preds, targets)
+ # Pre-emphasized MSE
+ if self._loss_config.pre_emph_weight is not None:
+ if (self._loss_config.pre_emph_coef is None) != (
+ self._loss_config.pre_emph_weight is None
+ ):
+ raise ValueError("Invalid pre-emph")
+ loss = loss + self._loss_config.pre_emph_weight * self._mse_loss(
+ preds, targets, pre_emph_coef=self._loss_config.pre_emph_coef
+ )
# DC loss
dc_weight = self._loss_config.dc_weight
@@ -211,5 +239,9 @@ class Model(pl.LightningModule, InitializableFromConfig):
"""
return nn.MSELoss()(preds, targets) / nn.MSELoss()(targets, 0.0 * targets)
- def _mse_loss(self, preds, targets):
+ def _mse_loss(self, preds, targets, pre_emph_coef: Optional[float]=None):
+ if pre_emph_coef is not None:
+ preds, targets = [
+ z[..., 1:] - pre_emph_coef * z[..., :-1] for z in (preds, targets)
+ ]
return nn.MSELoss()(preds, targets)
diff --git a/nam/models/conv_net.py b/nam/models/conv_net.py
@@ -18,6 +18,7 @@ import torch.nn.functional as F
from .. import __version__
from ..data import REQUIRED_RATE, wav_to_tensor
+from ._activations import get_activation
from ._base import BaseNet
from ._names import ACTIVATION_NAME, BATCHNORM_NAME, CONV_NAME
@@ -73,7 +74,7 @@ def _conv_net(
)
if batchnorm:
net.add_module(BATCHNORM_NAME, nn.BatchNorm1d(cout))
- net.add_module(ACTIVATION_NAME, getattr(nn, activation)())
+ net.add_module(ACTIVATION_NAME, get_activation(activation))
return net
def check_and_expand(n, x):
diff --git a/nam/models/parametric/catnets.py b/nam/models/parametric/catnets.py
@@ -11,12 +11,14 @@ import abc
from enum import Enum
from contextlib import contextmanager
from pathlib import Path
-from typing import Dict, Tuple
+from typing import Any, Dict, Tuple
+import numpy as np
import torch
from .._base import ParametricBaseNet
from ..recurrent import LSTM
+from ..wavenet import WaveNet
from .params import Param
@@ -94,6 +96,26 @@ class _CatMixin(ParametricBaseNet):
}
return config
+ 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.append(f' "{key}": ' "{\n")
+ for j, (k2, v2) in enumerate(val.items(), 1):
+ v_str = f'"{v2}"' if isinstance(v2, str) else str(v2)
+ s_parametric.append(
+ f' "{k2}": {v_str}' + (",\n" if j < len(val) else "\n")
+ )
+ s_parametric.append(" }" f"{',' if i < len(config) else ''}\n")
+ s_parametric.append(" }\n")
+ s_parametric.append(')");\n')
+ return tuple(s_parametric)
+
+
+ def _export_input_output_args(self) -> Tuple[torch.Tensor]:
+ return (self._sidedoor_params_to_tensor(),)
+
def _forward(self, params, x):
"""
:param params: (N,D)
@@ -118,6 +140,13 @@ class _CatMixin(ParametricBaseNet):
)
return self._single_class._forward(self, x_augmented)
+ def _sidedoor_params_to_tensor(self) -> torch.Tensor:
+ param_names = sorted([k for k in self._sidedoor_parametric_config.keys()])
+ params = torch.Tensor(
+ [self._sidedoor_parametric_config[k].default_value for k in param_names]
+ )
+ return params
+
@contextmanager
def _use_parametric_config(self, c):
"""
@@ -147,10 +176,7 @@ class CatLSTM(_CatMixin, LSTM):
:return: (B,L,1+D)
"""
assert x.ndim == 2
- param_names = sorted([k for k in self._sidedoor_parametric_config.keys()])
- params = torch.Tensor(
- [self._sidedoor_parametric_config[k].default_value for k in param_names]
- )
+ params = self._sidedoor_params_to_tensor()
sequence_length = x.shape[1]
return torch.cat(
[
@@ -164,6 +190,13 @@ class CatLSTM(_CatMixin, LSTM):
inputs = self._append_default_params(torch.zeros((1, 48_000)))
return super()._get_initial_state(inputs=inputs)
- def _export_input_output(self):
- x = self._append_default_params(self._export_input_signal()[None])
- return super()._export_input_output(x=x)
+
+class CatWaveNet(_CatMixin, WaveNet):
+ @property
+ def _shape_type(self) -> _ShapeType:
+ return _ShapeType.CONV
+
+ @property
+ def _single_class(self):
+ return WaveNet
+
+\ No newline at end of file
diff --git a/nam/models/recurrent.py b/nam/models/recurrent.py
@@ -8,18 +8,108 @@ Recurrent models (LSTM)
TODO batch_first=False (I get it...)
"""
-import math
+import json
from pathlib import Path
+from tempfile import TemporaryDirectory
from typing import Optional, Tuple
import numpy as np
import torch
import torch.nn as nn
-from ..data import REQUIRED_RATE
from ._base import BaseNet
+# TODO merge LSTMCore into LSTM
+
+
+class _L(nn.LSTM):
+ """
+ Tweaks to LSTM
+ * Up the remembering
+ """
+ def reset_parameters(self) -> None:
+ super().reset_parameters()
+ # https://danijar.com/tips-for-training-recurrent-neural-networks/
+ # forget += 1
+ # ifgo
+ value = 2.0
+ idx_input = slice(0, self.hidden_size)
+ idx_forget = slice(self.hidden_size, 2 * self.hidden_size)
+ for layer in range(self.num_layers):
+ for input in ("i", "h"):
+ # Balance out the scale of the cell w/ a -=1
+ getattr(self, f"bias_{input}h_l{layer}").data[
+ idx_input
+ ] -= value
+ getattr(self, f"bias_{input}h_l{layer}").data[
+ idx_forget
+ ] += value
+
+
+class LSTMCore(_L):
+ def __init__(
+ self,
+ *args,
+ train_burn_in: Optional[int] = None,
+ train_truncate: Optional[int] = None,
+ **kwargs
+ ):
+ super().__init__(*args, **kwargs)
+ if not self.batch_first:
+ raise NotImplementedError("Need batch first")
+ self._train_burn_in = train_burn_in
+ self._train_truncate = train_truncate
+ assert len(args) < 3, "Provide as kwargs"
+ self._initial_cell = nn.Parameter(
+ torch.zeros((self.num_layers, self.hidden_size))
+ )
+ self._initial_hidden = nn.Parameter(
+ torch.zeros((self.num_layers, self.hidden_size))
+ )
+
+ def forward(self, x, hidden_state=None):
+ """
+ Same as nn.LSTM.forward except:
+ * Learned inital state
+ * truncated BPTT when .training
+ """
+ if x.ndim != 3:
+ raise NotImplementedError("Need (B,L,D)")
+ last_hidden_state = (
+ self._initial_state(None if x.ndim == 2 else len(x))
+ if hidden_state is None else hidden_state
+ )
+ if not self.training or self._train_truncate is None:
+ output_features = super().forward(x, last_hidden_state)[0]
+ else:
+ output_features_list = []
+ if self._train_burn_in is not None:
+ last_output_features, last_hidden_state = super().forward(
+ x[:, : self._train_burn_in, :], last_hidden_state
+ )
+ output_features_list.append(last_output_features.detach())
+ burn_in_offset = 0 if self._train_burn_in is None else self._train_burn_in
+ for i in range(burn_in_offset, x.shape[1], self._train_truncate):
+ if i > burn_in_offset:
+ # Don't detach the burn-in state so that we can learn it.
+ last_hidden_state = tuple(z.detach() for z in last_hidden_state)
+ last_output_features, last_hidden_state = super().forward(
+ x[:, i : i + self._train_truncate, :,],
+ last_hidden_state,
+ )
+ output_features_list.append(last_output_features)
+ output_features = torch.cat(output_features_list, dim=1)
+ return output_features
+
+ def _initial_state(self, n: Optional[int]) -> Tuple[torch.Tensor, torch.Tensor]:
+ return (self._initial_hidden, self._initial_cell) if n is None else (
+ torch.tile(self._initial_hidden[:, None], (1, n, 1)),
+ torch.tile(self._initial_cell[:, None], (1, n, 1))
+ )
+
+
+
class LSTM(BaseNet):
"""
ABC for recurrent architectures
@@ -48,12 +138,18 @@ class LSTM(BaseNet):
if "batch_first" in lstm_kwargs:
raise ValueError("batch_first cannot be set.")
self._input_size = input_size
- self._core = nn.LSTM(
+ self._core = _L(
self._input_size, hidden_size, batch_first=True, **lstm_kwargs
)
self._head = nn.Linear(hidden_size, 1)
self._train_burn_in = train_burn_in
self._train_truncate = train_truncate
+ self._initial_cell = nn.Parameter(
+ torch.zeros((lstm_kwargs.get("num_layers", 1), hidden_size))
+ )
+ self._initial_hidden = nn.Parameter(
+ torch.zeros((lstm_kwargs.get("num_layers", 1), hidden_size))
+ )
@property
def receptive_field(self) -> int:
@@ -65,32 +161,62 @@ class LSTM(BaseNet):
return True
def export_cpp_header(self, filename: Path):
- raise NotImplementedError()
+ with TemporaryDirectory() as tmpdir:
+ tmpdir = Path(tmpdir)
+ LSTM.export(self, Path(tmpdir)) # Hacky...need to work w/ CatLSTM
+ with open(Path(tmpdir, "config.json"), "r") as fp:
+ _c = json.load(fp)
+ version = _c["version"]
+ config = _c["config"]
+ s_parametric = self._export_cpp_header_parametric(config.get("parametric"))
+ with open(filename, "w") as f:
+ f.writelines(
+ (
+ "#pragma once\n",
+ "// Automatically-generated model file\n",
+ "#include <vector>\n",
+ '#include "json.hpp"\n',
+ '#include "lstm.h"\n',
+ f'#define PYTHON_MODEL_VERSION "{version}"\n',
+ f'const int NUM_LAYERS = {config["num_layers"]};\n',
+ f'const int INPUT_SIZE = {config["input_size"]};\n',
+ f'const int HIDDEN_SIZE = {config["hidden_size"]};\n',
+ )
+ + s_parametric
+ + (
+ "std::vector<float> PARAMS{"
+ + ", ".join(
+ [f"{w:.16f}f" for w in np.load(Path(tmpdir, "weights.npy"))]
+ )
+ + "};\n",
+ )
+ )
def _forward(self, x):
"""
:param x: (B,L) or (B,L,D)
:return: (B,L)
"""
+ last_hidden_state = self._initial_state(len(x))
if x.ndim==2:
x = x[:, :, None]
if not self.training or self._train_truncate is None:
- output_features = self._core(x)[0]
+ output_features = self._core(x, last_hidden_state)[0]
else:
- last_hidden_state = None
output_features_list = []
if self._train_burn_in is not None:
last_output_features, last_hidden_state = self._core(
- x[:, : self._train_burn_in, :]
+ x[:, : self._train_burn_in, :], last_hidden_state
)
output_features_list.append(last_output_features.detach())
burn_in_offset = 0 if self._train_burn_in is None else self._train_burn_in
for i in range(burn_in_offset, x.shape[1], self._train_truncate):
+ if i > burn_in_offset:
+ # Don't detach the burn-in state so that we can learn it.
+ last_hidden_state = tuple(z.detach() for z in last_hidden_state)
last_output_features, last_hidden_state = self._core(
x[:, i : i + self._train_truncate, :,],
- None
- if last_hidden_state is None
- else tuple(z.detach() for z in last_hidden_state),
+ last_hidden_state,
)
output_features_list.append(last_output_features)
output_features = torch.cat(output_features_list, dim=1)
@@ -128,23 +254,11 @@ class LSTM(BaseNet):
"num_layers": self._core.num_layers,
}
- def _export_input_output(self, x=None):
- x = self._export_input_signal()[None, :, None] if x is None else x
- y = self._head(self._core(x, self._get_initial_state())[0][0])[:, 0]
- return x[0, :, 0].detach().cpu().numpy(), y.detach().cpu().numpy().flatten()
-
- def _export_input_signal(self):
- rate = REQUIRED_RATE
- return torch.cat(
- [
- torch.zeros((rate,)),
- 0.5
- * torch.sin(
- 2.0 * math.pi * 220.0 * torch.linspace(0.0, 1.0, rate + 1)[:-1]
- ),
- torch.zeros((rate,)),
- ]
- )
+ def _export_cpp_header_parametric(self, config):
+ # TODO refactor to merge w/ WaveNet implementation
+ if config is not None:
+ raise ValueError("Got non-None parametric config")
+ return ("nlohmann::json PARAMETRIC {};\n",)
def _export_weights(self):
"""
@@ -181,3 +295,98 @@ class LSTM(BaseNet):
inputs = torch.zeros((1, 48_000, 1)) if inputs is None else inputs
_, (h, c) = self._core(inputs)
return h, c
+
+ def _initial_state(self, n: Optional[int]) -> Tuple[torch.Tensor, torch.Tensor]:
+ """
+ Literally what the forward pass starts with.
+ Default is zeroes; this should be better since it can be learned.
+ """
+ return (self._initial_hidden, self._initial_cell) if n is None else (
+ torch.tile(self._initial_hidden[:, None], (1, n, 1)),
+ torch.tile(self._initial_cell[:, None], (1, n, 1))
+ )
+
+
+# TODO refactor together
+
+class _SkippyLSTM(nn.Module):
+ def __init__(self, input_size, hidden_size, skip_in: bool=False, num_layers=1, **kwargs):
+ super().__init__()
+ layers_per_lstm = 1
+ self._skip_in = skip_in
+ self._lstms = nn.ModuleList(
+ [
+ _L(
+ self._layer_input_size(input_size, hidden_size, i),
+ hidden_size,
+ layers_per_lstm,
+ batch_first=True
+ )
+ for i in range(num_layers)
+ ]
+ )
+ self._initial_hidden = nn.Parameter(
+ torch.zeros((self.num_layers, layers_per_lstm, self.hidden_size))
+ )
+ self._initial_cell = nn.Parameter(
+ torch.zeros((self.num_layers, layers_per_lstm , self.hidden_size))
+ )
+
+ @property
+ def hidden_size(self):
+ return self._lstms[0].hidden_size
+
+ @property
+ def input_size(self):
+ return self._lstms[0].input_size
+
+ @property
+ def num_layers(self):
+ return len(self._lstms)
+
+ @property
+ def output_size(self):
+ return self.num_layers * self.hidden_size
+
+ def forward(self, input, state=None):
+ """
+ :param input: (N,L,DX)
+ :param state: ((L,Li,N,DH), (L,Li,N,DH))
+
+ :return: (N,L,L*DH), ((L,Li,N,DH), (L,Li,N,DH))
+ """
+ h0, c0 = self.initial_state(input) if state is None else state
+ hiddens, h_arr, c_arr, hidden = [], [], [], None
+ for layer, h0i, c0i in zip(self._lstms, h0, c0):
+ if self._skip_in:
+ # TODO dense-block
+ layer_input = input if hidden is None else torch.cat([input, hidden], dim=2)
+ else:
+ layer_input = input if hidden is None else hidden
+ hidden, (hi, ci) = layer(layer_input, (h0i, c0i))
+ hiddens.append(hidden)
+ h_arr.append(hi)
+ c_arr.append(ci)
+ return (
+ torch.cat(hiddens, dim=2), (torch.stack(h_arr), torch.stack(c_arr))
+ )
+
+ def initial_state(self, input: torch.Tensor):
+ """
+ Initial states for all the layers
+
+ :return: (L,B,Li,DH)
+ """
+ assert input.ndim==3, "Batch only for now"
+ batch_size = len(input) # Assume batch_first
+ return (
+ torch.tile(self._initial_hidden[:, :, None], (1, 1, batch_size, 1)),
+ torch.tile(self._initial_cell[:, :, None], (1, 1, batch_size, 1))
+ )
+
+ def _layer_input_size(self, input_size, hidden_size, i) -> int:
+ # TODO dense-block
+ if self._skip_in:
+ return input_size + (0 if i == 0 else hidden_size)
+ else:
+ return input_size if i == 0 else hidden_size
diff --git a/nam/models/wavenet.py b/nam/models/wavenet.py
@@ -0,0 +1,390 @@
+# File: wavenet.py
+# Created Date: Friday July 29th 2022
+# Author: Steven Atkinson (steven@atkinson.mn)
+
+"""
+WaveNet implementation
+https://arxiv.org/abs/1609.03499
+"""
+
+import json
+from copy import deepcopy
+from pathlib import Path
+from tempfile import TemporaryDirectory
+from typing import Dict, Optional, Sequence, Tuple
+
+import numpy as np
+import torch
+import torch.nn as nn
+
+from ._activations import get_activation
+from ._base import BaseNet
+from ._names import ACTIVATION_NAME, CONV_NAME
+
+
+class Conv1d(nn.Conv1d):
+ def export_weights(self) -> torch.Tensor:
+ tensors = []
+ if self.weight is not None:
+ tensors.append(self.weight.data.flatten())
+ if self.bias is not None:
+ tensors.append(self.bias.data.flatten())
+ if len(tensors) == 0:
+ return torch.zeros((0,))
+ else:
+ return torch.cat(tensors)
+
+
+class _Layer(nn.Module):
+ def __init__(
+ self,
+ condition_size: int,
+ channels: int,
+ kernel_size: int,
+ dilation: int,
+ activation: str,
+ gated: bool
+ ):
+ super().__init__()
+ # Input mixer takes care of the bias
+ mid_channels = 2 * channels if gated else channels
+ self._conv = Conv1d(channels, mid_channels, kernel_size, dilation=dilation)
+ # Custom init: favors direct input-output
+ # self._conv.weight.data.zero_()
+ self._input_mixer = Conv1d(
+ condition_size, mid_channels, 1, bias=False
+ )
+ self._activation = get_activation(activation)
+ self._activation_name = activation
+ self._1x1 = Conv1d(channels, channels, 1)
+ self._gated = gated
+
+ @property
+ def activation_name(self) -> str:
+ return self._activation_name
+
+ @property
+ def conv(self) -> Conv1d:
+ return self._conv
+
+ @property
+ def gated(self) -> bool:
+ return self._gated
+
+ @property
+ def kernel_size(self) -> int:
+ return self._conv.kernel_size[0]
+
+ def export_weights(self) -> torch.Tensor:
+ return torch.cat(
+ [
+ self.conv.export_weights(),
+ self._input_mixer.export_weights(),
+ self._1x1.export_weights()
+ ]
+ )
+
+ def forward(self, x: torch.Tensor, h: Optional[torch.Tensor], out_length: int
+ ) -> Tuple[Optional[torch.Tensor], torch.Tensor]:
+ """
+ :param x: (B,C,L1) From last layer
+ :param h: (B,DX,L2) Conditioning. If first, ignored.
+
+ :return:
+ If not final:
+ (B,C,L1-d) to next layer
+ (B,C,L1-d) to mixer
+ If final, next layer is None
+ """
+ zconv = self.conv(x)
+ z1 = zconv + self._input_mixer(h)[:, :, -zconv.shape[2]:]
+ post_activation = self._activation(z1) if not self._gated else (
+ self._activation(z1[:, :self._channels])
+ * torch.sigmoid(z1[:, self._channels:])
+ )
+ return (
+ x[:, :, -post_activation.shape[2]:] + self._1x1(post_activation),
+ post_activation[:, :, -out_length:]
+ )
+
+ @property
+ def _channels(self) -> int:
+ return self._1x1.in_channels
+
+
+class _Layers(nn.Module):
+ """
+ Takes in the input and condition (and maybe the head input so far); outputs the
+ layer output and head input.
+
+ The original WaveNet only uses one of these, but you can stack multiple of this
+ module to vary the channels throughout with minimal extra channel-changing conv
+ layers.
+ """
+ def __init__(
+ self,
+ input_size: int,
+ condition_size: int,
+ head_size,
+ channels: int,
+ kernel_size: int,
+ dilations: Sequence[int],
+ activation: str="Tanh",
+ gated: bool=True,
+ head_bias: bool=True
+ ):
+ super().__init__()
+ self._rechannel = Conv1d(input_size, channels, 1, bias=False)
+ self._layers = nn.ModuleList(
+ [
+ _Layer(
+ condition_size,
+ channels,
+ kernel_size,
+ dilation,
+ activation,
+ gated
+ )
+ for dilation in dilations
+ ]
+ )
+ # Convert the head input from channels to head_size
+ self._head_rechannel = Conv1d(channels, head_size, 1, bias=head_bias)
+
+ self._config = {
+ "input_size": input_size,
+ "condition_size": condition_size,
+ "head_size": head_size,
+ "channels": channels,
+ "kernel_size": kernel_size,
+ "dilations": dilations,
+ "activation": activation,
+ "gated": gated,
+ "head_bias": head_bias
+ }
+
+ @property
+ def receptive_field(self) -> int:
+ return 1 + (self._kernel_size - 1) * sum(self._dilations)
+
+ def export_config(self):
+ return deepcopy(self._config)
+
+ def export_weights(self) -> torch.Tensor:
+ return torch.cat(
+ [self._rechannel.export_weights()]
+ + [layer.export_weights() for layer in self._layers]
+ + [self._head_rechannel.export_weights()]
+ )
+
+ def forward(self, x: torch.Tensor, c: torch.Tensor, head_input: Optional[torch.Tensor]=None) -> Tuple[torch.Tensor, torch.Tensor]:
+ """
+ :param x: (B,Dx,L) layer input
+ :param c: (B,Dc,L) condition
+
+ :return:
+ (B,Dc,L-R+1) head input
+ (B,Dc,L-R+1) layer output
+ """
+ out_length = x.shape[2] - (self.receptive_field - 1)
+ x = self._rechannel(x)
+ for layer in self._layers:
+ x, head_term = layer(x, c, out_length) # Ensures head_term sample length
+ head_input = (
+ head_term if head_input is None
+ else head_input[:, :, -out_length:] + head_term
+ )
+ return self._head_rechannel(head_input), x
+
+ @property
+ def _dilations(self) -> Sequence[int]:
+ return self._config["dilations"]
+
+ @property
+ def _kernel_size(self) -> int:
+ return self._layers[0].kernel_size
+
+
+class _Head(nn.Module):
+ def __init__(
+ self,
+ in_channels: int,
+ channels: int,
+ activation: str,
+ num_layers: int,
+ out_channels: int
+ ):
+ super().__init__()
+
+ def block(cx, cy):
+ net = nn.Sequential()
+ net.add_module(ACTIVATION_NAME, get_activation(activation))
+ net.add_module(CONV_NAME, Conv1d(cx, cy, 1))
+ return net
+
+ assert num_layers > 0
+
+ layers = nn.Sequential()
+ cin = in_channels
+ for i in range(num_layers):
+ layers.add_module(
+ f"layer_{i}",
+ block(cin, channels if i != num_layers-1 else out_channels)
+ )
+ cin = channels
+ self._layers = layers
+
+ self._config = {
+ "channels": channels,
+ "activation": activation,
+ "num_layers": num_layers,
+ "out_channels": out_channels
+ }
+
+ def export_config(self):
+ return deepcopy(self._config)
+
+ def export_weights(self) -> torch.Tensor:
+ return torch.cat([layer[1].export_weights() for layer in self._layers])
+
+ def forward(self, *args, **kwargs):
+ return self._layers(*args, **kwargs)
+
+
+class _WaveNet(nn.Module):
+ def __init__(
+ self,
+ layers_configs: Sequence[Dict],
+ head_config: Optional[Dict]=None,
+ head_scale: float=1.0
+ ):
+ super().__init__()
+
+ self._layers = nn.ModuleList([_Layers(**lc) for lc in layers_configs])
+ self._head = None if head_config is None else _Head(**head_config)
+ self._head_scale = head_scale
+
+ @property
+ def receptive_field(self) -> int:
+ return 1 + sum([(layer.receptive_field - 1) for layer in self._layers])
+
+ def export_config(self):
+ return {
+ "layers": [layers.export_config() for layers in self._layers],
+ "head": None if self._head is None else self._head.export_config(),
+ "head_scale": self._head_scale
+ }
+
+ def export_weights(self) -> np.ndarray:
+ """
+ :return: 1D array
+ """
+ weights = torch.cat([layer.export_weights() for layer in self._layers])
+ if self._head is not None:
+ weights = torch.cat([weights, self._head.export_weights()])
+ weights = torch.cat([weights, torch.Tensor([self._head_scale])])
+ return weights.detach().cpu().numpy()
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ """
+ :param x: (B,Cx,L)
+ :return: (B,Cy,L-R)
+ """
+ y, head_input = x, None
+ for layer in self._layers:
+ head_input, y = layer(y, x, head_input=head_input)
+ head_input = self._head_scale * head_input
+ return head_input if self._head is None else self._head(head_input)
+
+
+class WaveNet(BaseNet):
+ def __init__(self, *args, **kwargs):
+ super().__init__()
+ self._net = _WaveNet(*args, **kwargs)
+
+ @property
+ def pad_start_default(self) -> bool:
+ return True
+
+ @property
+ def receptive_field(self) -> int:
+ return self._net.receptive_field
+
+ def export_cpp_header(self, filename: Path):
+ with TemporaryDirectory() as tmpdir:
+ tmpdir = Path(tmpdir)
+ WaveNet.export(self, Path(tmpdir)) # Hacky...need to work w/ CatWaveNet
+ with open(Path(tmpdir, "config.json"), "r") as fp:
+ _c = json.load(fp)
+ version = _c["version"]
+ config = _c["config"]
+
+ if config["head"] is not None:
+ raise NotImplementedError("No heads yet")
+ # head_scale
+ # with_head
+ # parametric
+
+ # String for layer array params:
+ s_lap = (
+ "const std::vector<wavenet::LayerArrayParams> LAYER_ARRAY_PARAMS{\n",
+ )
+ for i, lc in enumerate(config["layers"], 1):
+ s_lap_line = (
+ f' wavenet::LayerArrayParams({lc["input_size"]}, '
+ f'{lc["condition_size"]}, {lc["head_size"]}, {lc["channels"]}, '
+ f'{lc["kernel_size"]}, std::vector<int> ' '{'
+ + ", ".join([str(d) for d in lc["dilations"]]) + "}, "
+ + (
+ f'\"{lc["activation"]}\", {str(lc["gated"]).lower()}, '
+ f'{str(lc["head_bias"]).lower()})'
+ )
+ )
+ if i < len(config["layers"]):
+ s_lap_line += ","
+ s_lap_line += "\n"
+ s_lap += (s_lap_line,)
+ s_lap += ("};\n",)
+ s_parametric = self._export_cpp_header_parametric(config.get("parametric"))
+ with open(filename, "w") as f:
+ f.writelines(
+ (
+ "#pragma once\n",
+ "// Automatically-generated model file\n",
+ "#include <vector>\n",
+ '#include "json.hpp"\n',
+ '#include "wavenet.h"\n',
+ f'#define PYTHON_MODEL_VERSION "{version}"\n',
+ )
+ + s_lap
+ + (
+ f'const float HEAD_SCALE = {config["head_scale"]};\n',
+ "const bool WITH_HEAD = false;\n",
+ )
+ + s_parametric
+ + (
+ "std::vector<float> PARAMS{"
+ + ", ".join(
+ [f"{w:.16f}f" for w in np.load(Path(tmpdir, "weights.npy"))]
+ )
+ + "};\n",
+ )
+ )
+
+ def _export_config(self):
+ return self._net.export_config()
+
+ def _export_cpp_header_parametric(self, config):
+ if config is not None:
+ raise ValueError("Got non-None parametric config")
+ return ("nlohmann::json PARAMETRIC {};\n",)
+
+ def _export_weights(self) -> np.ndarray:
+ return self._net.export_weights()
+
+ def _forward(self, x):
+ if x.ndim == 2:
+ x = x[:, None, :]
+ y = self._net(x)
+ assert y.shape[1] == 1
+ return y[:, 0, :]
