From 50c247a39d20ba5325ebdecc7541b06871c6105e Mon Sep 17 00:00:00 2001 From: Steven Atkinson Date: Sat, 3 Sep 2022 19:35:49 -0400 Subject: [PATCH] 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 --- .gitignore | 6 +- README.md | 62 +- bin/{export.py => export/main.py} | 21 +- bin/train/colab.ipynb | 34953 ++++++++++++++++++++++- bin/train/inputs/data/single_pair.json | 1 - bin/train/inputs/data/two_pairs.json | 1 - bin/train/inputs/learning/default.json | 2 +- bin/train/inputs/models/catlstm.json | 20 +- bin/train/inputs/models/lstm.json | 12 +- bin/train/inputs/models/wavenet.json | 40 + bin/train/main.py | 13 +- nam/_version.py | 2 +- nam/data.py | 26 +- nam/models/_activations.py | 9 + nam/models/_base.py | 35 +- nam/models/_exportable.py | 3 + nam/models/base.py | 42 +- nam/models/conv_net.py | 3 +- nam/models/parametric/catnets.py | 49 +- nam/models/recurrent.py | 263 +- nam/models/wavenet.py | 390 + 21 files changed, 34612 insertions(+), 1341 deletions(-) rename bin/{export.py => export/main.py} (53%) create mode 100644 bin/train/inputs/models/wavenet.json create mode 100644 nam/models/_activations.py create mode 100644 nam/models/wavenet.py diff --git a/.gitignore b/.gitignore index 74e838b5..bea4226c 100644 --- 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 index 9d60b82c..2543f8d6 100644 --- 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/main.py similarity index 53% rename from bin/export.py rename to bin/export/main.py index bf67f02a..f641e842 100644 --- a/bin/export.py +++ b/bin/export/main.py @@ -13,6 +13,8 @@ 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): @@ -21,15 +23,25 @@ def forward(self, x): 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 = Path(args.outdir) outdir.mkdir(parents=True, exist_ok=True) - net.export(outdir) - net.export_cpp_header(Path(outdir, "HardCodedModel.h")) + net.export(*export_args) + net.export_cpp_header(Path(export_args[0], "HardCodedModel.h"), *export_args[1:]) if __name__ == "__main__": @@ -37,4 +49,7 @@ def main(args): 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 index 556c4345..41940849 100644 --- 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", - "090861c8ddb642b7beb53f466b5a0a8d", - "0f22042e6c914df1b6d520568e05d1d9", - 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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": [], "source": [ @@ -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" }, "language_info": { - "name": "python" + "name": "python", + "version": "3.10.4" }, "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "806b1ffcfc1136d2cb83a025e53d737ae1674e4fb75a9a1fd5fb23a0ac3e6e42" + } + }, "widgets": { "application/vnd.jupyter.widget-state+json": { - "010a795c496a4a53996f418b006975e4": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": 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- ], - "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 index 2cfe68c9..fb31c324 100644 --- 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 index 03931a1a..0912929e 100644 --- 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 index afbfee4b..03d5b599 100644 --- 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 index 45b33246..787c9fb1 100644 --- 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 index 0bd8496a..63cd660f 100644 --- 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 new file mode 100644 index 00000000..028d268e --- /dev/null +++ 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 index b855ae94..47bbc099 100644 --- a/bin/train/main.py +++ b/bin/train/main.py @@ -8,6 +8,7 @@ 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 extend_savefig(i, savefig): 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 @@ def main(args): 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 index 493f7415..6a9beea8 100644 --- 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 index c0eeb597..c982a4e1 100644 --- a/nam/data.py +++ b/nam/data.py @@ -290,13 +290,11 @@ def __init__(self, datasets: Sequence[Dataset], flatten=True): 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 @@ def _flatten_datasets(self, datasets): 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 new file mode 100644 index 00000000..02461c50 --- /dev/null +++ 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 index 7fec0616..47011031 100644 --- 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 @@ def _forward(self, *args) -> torch.Tensor: """ 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 index d7dec6dc..d8320e5f 100644 --- a/nam/models/_exportable.py +++ b/nam/models/_exportable.py @@ -72,6 +72,9 @@ def _export_config(self): 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 index a44b4c3f..2bebad3f 100644 --- 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 @@ 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 @@ def parse_config(cls, config): 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 @@ def parse_config(cls, config): 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 @@ def training_step(self, batch, batch_idx): 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 @@ def _esr_loss(self, preds, targets): """ 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 index d8c4fe28..a9fd519d 100644 --- a/nam/models/conv_net.py +++ b/nam/models/conv_net.py @@ -18,6 +18,7 @@ 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 block(cin, cout, dilation): ) 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 index 29f6ca18..1ea9cf8b 100644 --- a/nam/models/parametric/catnets.py +++ b/nam/models/parametric/catnets.py @@ -11,12 +11,14 @@ 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 @@ def _export_config(self): } 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 @@ def _forward(self, params, x): ) 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 @@ def _append_default_params(self, x: torch.Tensor) -> torch.Tensor: :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 @@ 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) - 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 index 9671baae..26254b05 100644 --- a/nam/models/recurrent.py +++ b/nam/models/recurrent.py @@ -8,18 +8,108 @@ 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 @@ def __init__( 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 @@ def pad_start_default(self) -> bool: 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 \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 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 @@ def _export_config(self): "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 @@ def _get_initial_state(self, inputs=None) -> Tuple[torch.Tensor, torch.Tensor]: 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 new file mode 100644 index 00000000..10d8b43b --- /dev/null +++ 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 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 ' '{' + + ", ".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 \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 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, :]