From c1bc3dd97685dbda775fad4a67e7c8735bd5da26 Mon Sep 17 00:00:00 2001 From: Adam Roberts Date: Fri, 30 Mar 2018 14:46:49 -0700 Subject: [PATCH] Move data.ts to core, cleanup, and fixes. (#4) * v0.0.10 * Clean up and improve consistency across libs. * udpate ignore * udpate ignore * v0.0.11 * v0.0.12 * v0.0.13 * Move data.ts to core, cleanup, and fixes. * Fix indent * re-add yarn.lock * Switch to tfjs and move to core. * fix | * add util dep --- .vscode/settings.json | 3 +- core/package.json | 6 +- {music_vae => core/src}/checkpoint_loader.ts | 2 +- {music_vae => core/src}/data.ts | 43 +- core/src/index.ts | 8 + core/src/midi_io_test.ts | 2 +- core/yarn.lock | 171 +- music_rnn/demo/index.html | 8 + music_rnn/demo/index.ts | 98 +- music_rnn/package.json | 3 +- music_rnn/src/data.ts | 299 -- music_rnn/src/index.ts | 2 +- music_rnn/src/model.ts | 108 +- music_rnn/yarn.lock | 83 +- music_vae/.gitignore | 3 + music_vae/demo/index.ts | 12 +- music_vae/package.json | 8 +- music_vae/{ => src}/index.ts | 13 +- music_vae/{ => src}/model.ts | 254 +- music_vae/tsconfig.json | 2 +- music_vae/yarn.lock | 3451 ++++++++++++++++++ testdata/melody.mid | Bin 0 -> 129 bytes 22 files changed, 3934 insertions(+), 645 deletions(-) rename {music_vae => core/src}/checkpoint_loader.ts (99%) rename {music_vae => core/src}/data.ts (89%) delete mode 100644 music_rnn/src/data.ts create mode 100644 music_vae/.gitignore rename music_vae/{ => src}/index.ts (81%) rename music_vae/{ => src}/model.ts (79%) create mode 100644 music_vae/yarn.lock create mode 100644 testdata/melody.mid diff --git a/.vscode/settings.json b/.vscode/settings.json index d6e4e49fa..f1ae205fe 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -25,5 +25,6 @@ "editor.detectIndentation": false, "editor.wrappingIndent": "none", "typescript.tsdk": "node_modules/typescript/lib", - "clang-format.executable": "${workspaceRoot}/node_modules/.bin/clang-format" + "clang-format.executable": "${workspaceRoot}/node_modules/.bin/clang-format", + "git.ignoreLimitWarning": true } diff --git a/core/package.json b/core/package.json index 9ed27d7db..7badc334b 100644 --- a/core/package.json +++ b/core/package.json @@ -1,6 +1,6 @@ { "name": "@magenta/core", - "version": "0.0.9", + "version": "0.0.13", "description": "", "main": "es5/index.js", "types": "es5/index.d.ts", @@ -8,7 +8,9 @@ "unpkg": "dist/magenta.js", "dependencies": { "@magenta/protobuf": "^0.0.4", - "midiconvert": "^0.4.3" + "@tensorflow/tfjs": "^0.6.1", + "midiconvert": "^0.4.3", + "util": "^0.10.3" }, "devDependencies": { "@types/tape": "^4.2.31", diff --git a/music_vae/checkpoint_loader.ts b/core/src/checkpoint_loader.ts similarity index 99% rename from music_vae/checkpoint_loader.ts rename to core/src/checkpoint_loader.ts index 0bfe92998..8ecdb1810 100644 --- a/music_vae/checkpoint_loader.ts +++ b/core/src/checkpoint_loader.ts @@ -15,7 +15,7 @@ * ============================================================================= */ // Adds quantization support to dl.CheckpointLoader. -import {Tensor} from 'deeplearn'; +import {Tensor} from '@tensorflow/tfjs'; /** * @hidden diff --git a/music_vae/data.ts b/core/src/data.ts similarity index 89% rename from music_vae/data.ts rename to core/src/data.ts index 456ef1a57..21162bcbc 100644 --- a/music_vae/data.ts +++ b/core/src/data.ts @@ -14,10 +14,10 @@ * limitations under the License. * ============================================================================= */ -import * as dl from 'deeplearn'; -import {INoteSequence, NoteSequence} from '@magenta/core'; - -export {INoteSequence}; +import * as tf from '@tensorflow/tfjs'; +import {tensorflow} from '@magenta/protobuf'; +import NoteSequence = tensorflow.magenta.NoteSequence; +import INoteSequence = tensorflow.magenta.INoteSequence; const DEFAULT_DRUM_PITCH_CLASSES: number[][] = [ // bass drum @@ -80,8 +80,8 @@ export abstract class DataConverter { abstract numSteps: number; // Total length of sequences. abstract numSegments: number; // Number of steps for conductor. abstract readonly NUM_SPLITS: number; // Number of conductor splits. - abstract toTensor(noteSequence: INoteSequence): dl.Tensor2D; - abstract toNoteSequence(tensor: dl.Tensor2D): Promise; + abstract toTensor(noteSequence: INoteSequence): tf.Tensor2D; + abstract async toNoteSequence(tensor: tf.Tensor2D): Promise; } /** @@ -134,19 +134,20 @@ export class DrumsConverter extends DataConverter{ } toTensor(noteSequence: INoteSequence) { - const drumRoll = dl.buffer([this.numSteps, this.pitchClasses.length + 1]); + const numSteps = this.numSteps || noteSequence.totalQuantizedSteps; + const drumRoll = tf.buffer([numSteps, this.pitchClasses.length + 1]); // Set final values to 1 and change to 0 later if the column gets a note. - for (let i = 0; i < this.numSteps; ++i) { + for (let i = 0; i < numSteps; ++i) { drumRoll.set(1, i, -1); } noteSequence.notes.forEach((note) => { drumRoll.set(1, note.quantizedStartStep, this.pitchToClass[note.pitch]); drumRoll.set(0, note.quantizedStartStep, -1); }); - return drumRoll.toTensor() as dl.Tensor2D; + return drumRoll.toTensor() as tf.Tensor2D; } - async toNoteSequence(oh: dl.Tensor2D) { + async toNoteSequence(oh: tf.Tensor2D) { const noteSequence = NoteSequence.create(); const labelsTensor = oh.argMax(1); const labels: Int32Array = await labelsTensor.data() as Int32Array; @@ -180,7 +181,7 @@ export class DrumsConverter extends DataConverter{ * pitch class are used. */ export class DrumRollConverter extends DrumsConverter { - async toNoteSequence(roll: dl.Tensor2D) { + async toNoteSequence(roll: tf.Tensor2D) { const noteSequence = NoteSequence.create(); for (let s = 0; s < roll.shape[0]; ++s) { // step const rollSlice = roll.slice([s, 0], [1, roll.shape[1]]); @@ -236,9 +237,9 @@ export class MelodyConverter extends DataConverter{ minPitch: number; // inclusive maxPitch: number; // inclusive depth: number; - NUM_SPLITS = 0; - NOTE_OFF = 1; - FIRST_PITCH = 2; + readonly NUM_SPLITS = 0; + readonly NOTE_OFF = 1; + readonly FIRST_PITCH = 2; constructor(args: MelodyConverterArgs) { super(); @@ -246,13 +247,14 @@ export class MelodyConverter extends DataConverter{ this.numSegments = args.numSegments; this.minPitch = args.minPitch; this.maxPitch = args.maxPitch; - this.depth = args.maxPitch - args.minPitch + 3; + this.depth = args.maxPitch - args.minPitch + 1 + this.FIRST_PITCH; } toTensor(noteSequence: INoteSequence) { + const numSteps = this.numSteps || noteSequence.totalQuantizedSteps; const sortedNotes: NoteSequence.INote[] = noteSequence.notes.sort( (n1, n2) => n1.quantizedStartStep - n2.quantizedStartStep); - const mel = dl.buffer([this.numSteps]); + const mel = tf.buffer([numSteps]); let lastEnd = -1; sortedNotes.forEach(n => { if (n.quantizedStartStep < lastEnd) { @@ -260,17 +262,18 @@ export class MelodyConverter extends DataConverter{ } if (n.pitch < this.minPitch || n.pitch > this.maxPitch) { throw Error( - '`NoteSequence` has a pitch outside of the valid range: ' + n.pitch); + `\`NoteSequence\` has a pitch outside of the valid range: ${n.pitch}` + ); } mel.set(n.pitch - this.minPitch + this.FIRST_PITCH, n.quantizedStartStep); mel.set(this.NOTE_OFF, n.quantizedEndStep); lastEnd = n.quantizedEndStep; }); - return dl.oneHot( - mel.toTensor() as dl.Tensor1D, this.depth) as dl.Tensor2D; + return tf.oneHot( + mel.toTensor() as tf.Tensor1D, this.depth) as tf.Tensor2D; } - async toNoteSequence(oh: dl.Tensor2D) { + async toNoteSequence(oh: tf.Tensor2D) { const noteSequence = NoteSequence.create(); const labelsTensor = oh.argMax(1); const labels: Int32Array = await labelsTensor.data() as Int32Array; diff --git a/core/src/index.ts b/core/src/index.ts index 9d67d11ca..e6a7e031c 100644 --- a/core/src/index.ts +++ b/core/src/index.ts @@ -22,5 +22,13 @@ import INote = NoteSequence.INote; export {NoteSequence, INoteSequence, INote}; +import * as tf from '@tensorflow/tfjs'; +export {tf}; + +export { CheckpointLoader } from './checkpoint_loader'; + +import * as data from './data'; +export {data}; + export * from './midi_io'; export * from './sequences'; diff --git a/core/src/midi_io_test.ts b/core/src/midi_io_test.ts index 35081b899..4a249b6fc 100644 --- a/core/src/midi_io_test.ts +++ b/core/src/midi_io_test.ts @@ -22,7 +22,7 @@ import {MidiIO} from './midi_io'; import fs = require('fs'); test("Parse Simple MIDI", (t:test.Test) => { - const midi = fs.readFileSync('../../music/testdata/melody.mid', "binary"); + const midi = fs.readFileSync('../testdata/melody.mid', "binary"); const ns = MidiIO.midiToSequenceProto(midi); const expectedNs = NoteSequence.create({ diff --git a/core/yarn.lock b/core/yarn.lock index 6e62cce11..3dc01c20d 100644 --- a/core/yarn.lock +++ b/core/yarn.lock @@ -51,25 +51,40 @@ version "1.1.0" resolved "https://registry.yarnpkg.com/@protobufjs/utf8/-/utf8-1.1.0.tgz#a777360b5b39a1a2e5106f8e858f2fd2d060c570" -"@types/clone@^0.1.30": - version "0.1.30" - resolved "https://registry.yarnpkg.com/@types/clone/-/clone-0.1.30.tgz#e7365648c1b42136a59c7d5040637b3b5c83b614" +"@tensorflow/tfjs-core@0.6.0": + version "0.6.0" + resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-core/-/tfjs-core-0.6.0.tgz#e33d9889472b0599291ecdb7fa83579586b21e66" + dependencies: + seedrandom "~2.4.3" + +"@tensorflow/tfjs-layers@0.1.2": + version "0.1.2" + resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-layers/-/tfjs-layers-0.1.2.tgz#fead9860495a0bdb1525645ed7614675e8decaaf" + dependencies: + underscore "~1.8.3" + +"@tensorflow/tfjs@^0.6.1": + version "0.6.1" + resolved "https://registry.yarnpkg.com/@tensorflow/tfjs/-/tfjs-0.6.1.tgz#8cb0133a0f2d8d6068c0742d8ceb998ef39899b0" + dependencies: + "@tensorflow/tfjs-core" "0.6.0" + "@tensorflow/tfjs-layers" "0.1.2" "@types/long@^3.0.32": version "3.0.32" resolved "https://registry.yarnpkg.com/@types/long/-/long-3.0.32.tgz#f4e5af31e9e9b196d8e5fca8a5e2e20aa3d60b69" "@types/node@*": - version "9.4.6" - resolved "https://registry.yarnpkg.com/@types/node/-/node-9.4.6.tgz#d8176d864ee48753d053783e4e463aec86b8d82e" + version "9.6.1" + resolved "https://registry.yarnpkg.com/@types/node/-/node-9.6.1.tgz#e2d374ef15b315b48e7efc308fa1a7cd51faa06c" "@types/node@^8.9.4": - version "8.9.4" - resolved "https://registry.yarnpkg.com/@types/node/-/node-8.9.4.tgz#dfd327582a06c114eb6e0441fa3d6fab35edad48" + version "8.10.1" + resolved "https://registry.yarnpkg.com/@types/node/-/node-8.10.1.tgz#aac98b810c50568054486f2bb8c486d824713be8" "@types/tape@^4.2.31": - version "4.2.31" - resolved "https://registry.yarnpkg.com/@types/tape/-/tape-4.2.31.tgz#d2cd0f2e410d2c288a5eb54c01c04e6b8a301fd2" + version "4.2.32" + resolved "https://registry.yarnpkg.com/@types/tape/-/tape-4.2.32.tgz#1188330d22c4e822648c344faa070277737982d9" dependencies: "@types/node" "*" @@ -92,8 +107,8 @@ acorn@^4.0.3: resolved "https://registry.yarnpkg.com/acorn/-/acorn-4.0.13.tgz#105495ae5361d697bd195c825192e1ad7f253787" acorn@^5.2.1, acorn@^5.4.1: - version "5.4.1" - resolved "https://registry.yarnpkg.com/acorn/-/acorn-5.4.1.tgz#fdc58d9d17f4a4e98d102ded826a9b9759125102" + version "5.5.3" + resolved "https://registry.yarnpkg.com/acorn/-/acorn-5.5.3.tgz#f473dd47e0277a08e28e9bec5aeeb04751f0b8c9" ansi-regex@^2.0.0: version "2.1.1" @@ -103,9 +118,9 @@ ansi-styles@^2.2.1: version "2.2.1" resolved "https://registry.yarnpkg.com/ansi-styles/-/ansi-styles-2.2.1.tgz#b432dd3358b634cf75e1e4664368240533c1ddbe" -ansi-styles@^3.2.0: - version "3.2.0" - resolved "https://registry.yarnpkg.com/ansi-styles/-/ansi-styles-3.2.0.tgz#c159b8d5be0f9e5a6f346dab94f16ce022161b88" +ansi-styles@^3.2.1: + version "3.2.1" + resolved "https://registry.yarnpkg.com/ansi-styles/-/ansi-styles-3.2.1.tgz#41fbb20243e50b12be0f04b8dedbf07520ce841d" dependencies: color-convert "^1.9.0" @@ -311,6 +326,10 @@ browserify@~14.4.0: vm-browserify "~0.0.1" xtend "^4.0.0" +buffer-from@^1.0.0: + version "1.0.0" + resolved "https://registry.yarnpkg.com/buffer-from/-/buffer-from-1.0.0.tgz#4cb8832d23612589b0406e9e2956c17f06fdf531" + buffer-xor@^1.0.3: version "1.0.3" resolved "https://registry.yarnpkg.com/buffer-xor/-/buffer-xor-1.0.3.tgz#26e61ed1422fb70dd42e6e36729ed51d855fe8d9" @@ -345,12 +364,12 @@ chalk@^1.1.3: supports-color "^2.0.0" chalk@^2.3.0: - version "2.3.1" - resolved "https://registry.yarnpkg.com/chalk/-/chalk-2.3.1.tgz#523fe2678aec7b04e8041909292fe8b17059b796" + version "2.3.2" + resolved "https://registry.yarnpkg.com/chalk/-/chalk-2.3.2.tgz#250dc96b07491bfd601e648d66ddf5f60c7a5c65" dependencies: - ansi-styles "^3.2.0" + ansi-styles "^3.2.1" escape-string-regexp "^1.0.5" - supports-color "^5.2.0" + supports-color "^5.3.0" cipher-base@^1.0.0, cipher-base@^1.0.1, cipher-base@^1.0.3: version "1.0.4" @@ -360,17 +379,13 @@ cipher-base@^1.0.0, cipher-base@^1.0.1, cipher-base@^1.0.3: safe-buffer "^5.0.1" clang-format@^1.2.2: - version "1.2.2" - resolved "https://registry.yarnpkg.com/clang-format/-/clang-format-1.2.2.tgz#a7277a03fce9aa4e387ddaa83b60d99dab115737" + version "1.2.3" + resolved "https://registry.yarnpkg.com/clang-format/-/clang-format-1.2.3.tgz#2763561aa7449c43737480f8df3a2b5b66e6cf37" dependencies: async "^1.5.2" glob "^7.0.0" resolve "^1.1.6" -clone@^1.0.4: - version "1.0.4" - resolved "https://registry.yarnpkg.com/clone/-/clone-1.0.4.tgz#da309cc263df15994c688ca902179ca3c7cd7c7e" - color-convert@^1.9.0: version "1.9.1" resolved "https://registry.yarnpkg.com/color-convert/-/color-convert-1.9.1.tgz#c1261107aeb2f294ebffec9ed9ecad529a6097ed" @@ -381,16 +396,7 @@ color-name@^1.1.1: version "1.1.3" resolved "https://registry.yarnpkg.com/color-name/-/color-name-1.1.3.tgz#a7d0558bd89c42f795dd42328f740831ca53bc25" -combine-source-map@~0.7.1: - version "0.7.2" - resolved "https://registry.yarnpkg.com/combine-source-map/-/combine-source-map-0.7.2.tgz#0870312856b307a87cc4ac486f3a9a62aeccc09e" - dependencies: - convert-source-map "~1.1.0" - inline-source-map "~0.6.0" - lodash.memoize "~3.0.3" - source-map "~0.5.3" - -combine-source-map@~0.8.0: +combine-source-map@^0.8.0, combine-source-map@~0.8.0: version "0.8.0" resolved "https://registry.yarnpkg.com/combine-source-map/-/combine-source-map-0.8.0.tgz#a58d0df042c186fcf822a8e8015f5450d2d79a8b" dependencies: @@ -400,13 +406,22 @@ combine-source-map@~0.8.0: source-map "~0.5.3" commander@^2.12.1: - version "2.15.0" - resolved "https://registry.yarnpkg.com/commander/-/commander-2.15.0.tgz#ad2a23a1c3b036e392469b8012cec6b33b4c1322" + version "2.15.1" + resolved "https://registry.yarnpkg.com/commander/-/commander-2.15.1.tgz#df46e867d0fc2aec66a34662b406a9ccafff5b0f" concat-map@0.0.1: version "0.0.1" resolved "https://registry.yarnpkg.com/concat-map/-/concat-map-0.0.1.tgz#d8a96bd77fd68df7793a73036a3ba0d5405d477b" +concat-stream@^1.6.1: + version "1.6.2" + resolved "https://registry.yarnpkg.com/concat-stream/-/concat-stream-1.6.2.tgz#904bdf194cd3122fc675c77fc4ac3d4ff0fd1a34" + dependencies: + buffer-from "^1.0.0" + inherits "^2.0.3" + readable-stream "^2.2.2" + typedarray "^0.0.6" + concat-stream@~1.5.0, concat-stream@~1.5.1: version "1.5.2" resolved "https://registry.yarnpkg.com/concat-stream/-/concat-stream-1.5.2.tgz#708978624d856af41a5a741defdd261da752c266" @@ -523,8 +538,8 @@ detective@^4.0.0: defined "^1.0.0" diff@^3.1.0, diff@^3.2.0: - version "3.4.0" - resolved "https://registry.yarnpkg.com/diff/-/diff-3.4.0.tgz#b1d85507daf3964828de54b37d0d73ba67dda56c" + version "3.5.0" + resolved "https://registry.yarnpkg.com/diff/-/diff-3.5.0.tgz#800c0dd1e0a8bfbc95835c202ad220fe317e5a12" diffie-hellman@^5.0.0: version "5.0.2" @@ -563,8 +578,8 @@ error-ex@^1.2.0: is-arrayish "^0.2.1" es-abstract@^1.5.0: - version "1.10.0" - resolved "https://registry.yarnpkg.com/es-abstract/-/es-abstract-1.10.0.tgz#1ecb36c197842a00d8ee4c2dfd8646bb97d60864" + version "1.11.0" + resolved "https://registry.yarnpkg.com/es-abstract/-/es-abstract-1.11.0.tgz#cce87d518f0496893b1a30cd8461835535480681" dependencies: es-to-primitive "^1.1.1" function-bind "^1.1.1" @@ -685,8 +700,8 @@ https-browserify@^1.0.0: resolved "https://registry.yarnpkg.com/https-browserify/-/https-browserify-1.0.0.tgz#ec06c10e0a34c0f2faf199f7fd7fc78fffd03c73" ieee754@^1.1.4: - version "1.1.8" - resolved "https://registry.yarnpkg.com/ieee754/-/ieee754-1.1.8.tgz#be33d40ac10ef1926701f6f08a2d86fbfd1ad3e4" + version "1.1.11" + resolved "https://registry.yarnpkg.com/ieee754/-/ieee754-1.1.11.tgz#c16384ffe00f5b7835824e67b6f2bd44a5229455" indexof@0.0.1: version "0.0.1" @@ -714,12 +729,12 @@ inline-source-map@~0.6.0: source-map "~0.5.3" insert-module-globals@^7.0.0: - version "7.0.1" - resolved "https://registry.yarnpkg.com/insert-module-globals/-/insert-module-globals-7.0.1.tgz#c03bf4e01cb086d5b5e5ace8ad0afe7889d638c3" + version "7.0.5" + resolved "https://registry.yarnpkg.com/insert-module-globals/-/insert-module-globals-7.0.5.tgz#6d0a6f28d4a7e0eae171ad305e0f47bdfe0c258e" dependencies: JSONStream "^1.0.3" - combine-source-map "~0.7.1" - concat-stream "~1.5.1" + combine-source-map "^0.8.0" + concat-stream "^1.6.1" is-buffer "^1.1.0" lexical-scope "^1.2.0" process "~0.11.0" @@ -760,9 +775,9 @@ is-utf8@^0.2.0: version "0.2.1" resolved "https://registry.yarnpkg.com/is-utf8/-/is-utf8-0.2.1.tgz#4b0da1442104d1b336340e80797e865cf39f7d72" -isarray@~0.0.1: - version "0.0.1" - resolved "https://registry.yarnpkg.com/isarray/-/isarray-0.0.1.tgz#8a18acfca9a8f4177e09abfc6038939b05d1eedf" +isarray@^2.0.4: + version "2.0.4" + resolved "https://registry.yarnpkg.com/isarray/-/isarray-2.0.4.tgz#38e7bcbb0f3ba1b7933c86ba1894ddfc3781bbb7" isarray@~1.0.0: version "1.0.0" @@ -798,11 +813,11 @@ jsonparse@^1.2.0: resolved "https://registry.yarnpkg.com/jsonparse/-/jsonparse-1.3.1.tgz#3f4dae4a91fac315f71062f8521cc239f1366280" labeled-stream-splicer@^2.0.0: - version "2.0.0" - resolved "https://registry.yarnpkg.com/labeled-stream-splicer/-/labeled-stream-splicer-2.0.0.tgz#a52e1d138024c00b86b1c0c91f677918b8ae0a59" + version "2.0.1" + resolved "https://registry.yarnpkg.com/labeled-stream-splicer/-/labeled-stream-splicer-2.0.1.tgz#9cffa32fd99e1612fd1d86a8db962416d5292926" dependencies: inherits "^2.0.1" - isarray "~0.0.1" + isarray "^2.0.4" stream-splicer "^2.0.0" lexical-scope@^1.2.0: @@ -1045,9 +1060,9 @@ read-only-stream@^2.0.0: dependencies: readable-stream "^2.0.2" -readable-stream@^2.0.2, readable-stream@^2.1.5, readable-stream@^2.3.3: - version "2.3.4" - resolved "https://registry.yarnpkg.com/readable-stream/-/readable-stream-2.3.4.tgz#c946c3f47fa7d8eabc0b6150f4a12f69a4574071" +readable-stream@^2.0.2, readable-stream@^2.1.5, readable-stream@^2.2.2, readable-stream@^2.3.3: + version "2.3.5" + resolved "https://registry.yarnpkg.com/readable-stream/-/readable-stream-2.3.5.tgz#b4f85003a938cbb6ecbce2a124fb1012bd1a838d" dependencies: core-util-is "~1.0.0" inherits "~2.0.3" @@ -1072,7 +1087,13 @@ resolve@1.1.7: version "1.1.7" resolved "https://registry.yarnpkg.com/resolve/-/resolve-1.1.7.tgz#203114d82ad2c5ed9e8e0411b3932875e889e97b" -resolve@^1.1.3, resolve@^1.1.4, resolve@^1.1.6, resolve@^1.3.2, resolve@~1.5.0: +resolve@^1.1.3, resolve@^1.1.4, resolve@^1.1.6, resolve@^1.3.2: + version "1.6.0" + resolved "https://registry.yarnpkg.com/resolve/-/resolve-1.6.0.tgz#0fbd21278b27b4004481c395349e7aba60a9ff5c" + dependencies: + path-parse "^1.0.5" + +resolve@~1.5.0: version "1.5.0" resolved "https://registry.yarnpkg.com/resolve/-/resolve-1.5.0.tgz#1f09acce796c9a762579f31b2c1cc4c3cddf9f36" dependencies: @@ -1095,13 +1116,17 @@ safe-buffer@^5.0.1, safe-buffer@^5.1.0, safe-buffer@^5.1.1, safe-buffer@~5.1.0, version "5.1.1" resolved "https://registry.yarnpkg.com/safe-buffer/-/safe-buffer-5.1.1.tgz#893312af69b2123def71f57889001671eeb2c853" +seedrandom@~2.4.3: + version "2.4.3" + resolved "https://registry.yarnpkg.com/seedrandom/-/seedrandom-2.4.3.tgz#2438504dad33917314bff18ac4d794f16d6aaecc" + semver@^5.1.0, semver@^5.3.0: version "5.5.0" resolved "https://registry.yarnpkg.com/semver/-/semver-5.5.0.tgz#dc4bbc7a6ca9d916dee5d43516f0092b58f7b8ab" sha.js@^2.4.0, sha.js@^2.4.8, sha.js@~2.4.4: - version "2.4.10" - resolved "https://registry.yarnpkg.com/sha.js/-/sha.js-2.4.10.tgz#b1fde5cd7d11a5626638a07c604ab909cfa31f9b" + version "2.4.11" + resolved "https://registry.yarnpkg.com/sha.js/-/sha.js-2.4.11.tgz#37a5cf0b81ecbc6943de109ba2960d1b26584ae7" dependencies: inherits "^2.0.1" safe-buffer "^5.0.1" @@ -1123,8 +1148,8 @@ shell-quote@^1.6.1: jsonify "~0.0.0" source-map-support@^0.5.3: - version "0.5.3" - resolved "https://registry.yarnpkg.com/source-map-support/-/source-map-support-0.5.3.tgz#2b3d5fff298cfa4d1afd7d4352d569e9a0158e76" + version "0.5.4" + resolved "https://registry.yarnpkg.com/source-map-support/-/source-map-support-0.5.4.tgz#54456efa89caa9270af7cd624cc2f123e51fbae8" dependencies: source-map "^0.6.0" @@ -1155,8 +1180,8 @@ stream-combiner2@^1.1.1: readable-stream "^2.0.2" stream-http@^2.0.0: - version "2.8.0" - resolved "https://registry.yarnpkg.com/stream-http/-/stream-http-2.8.0.tgz#fd86546dac9b1c91aff8fc5d287b98fafb41bc10" + version "2.8.1" + resolved "https://registry.yarnpkg.com/stream-http/-/stream-http-2.8.1.tgz#d0441be1a457a73a733a8a7b53570bebd9ef66a4" dependencies: builtin-status-codes "^3.0.0" inherits "^2.0.1" @@ -1215,9 +1240,9 @@ supports-color@^2.0.0: version "2.0.0" resolved "https://registry.yarnpkg.com/supports-color/-/supports-color-2.0.0.tgz#535d045ce6b6363fa40117084629995e9df324c7" -supports-color@^5.2.0: - version "5.2.0" - resolved "https://registry.yarnpkg.com/supports-color/-/supports-color-5.2.0.tgz#b0d5333b1184dd3666cbe5aa0b45c5ac7ac17a4a" +supports-color@^5.3.0: + version "5.3.0" + resolved "https://registry.yarnpkg.com/supports-color/-/supports-color-5.3.0.tgz#5b24ac15db80fa927cf5227a4a33fd3c4c7676c0" dependencies: has-flag "^3.0.0" @@ -1321,8 +1346,8 @@ tslint@~5.9.1: tsutils "^2.12.1" tsutils@^2.12.1: - version "2.21.2" - resolved "https://registry.yarnpkg.com/tsutils/-/tsutils-2.21.2.tgz#902aa5987e1440c47673543d96752df8d44ab9c7" + version "2.25.0" + resolved "https://registry.yarnpkg.com/tsutils/-/tsutils-2.25.0.tgz#14d616ef59224a3c9fb7eb483e1c182b6665ae5e" dependencies: tslib "^1.8.1" @@ -1330,7 +1355,7 @@ tty-browserify@~0.0.0: version "0.0.1" resolved "https://registry.yarnpkg.com/tty-browserify/-/tty-browserify-0.0.1.tgz#3f05251ee17904dfd0677546670db9651682b811" -typedarray@~0.0.5: +typedarray@^0.0.6, typedarray@~0.0.5: version "0.0.6" resolved "https://registry.yarnpkg.com/typedarray/-/typedarray-0.0.6.tgz#867ac74e3864187b1d3d47d996a78ec5c8830777" @@ -1339,8 +1364,12 @@ typescript@2.7.2: resolved "https://registry.yarnpkg.com/typescript/-/typescript-2.7.2.tgz#2d615a1ef4aee4f574425cdff7026edf81919836" umd@^3.0.0: - version "3.0.1" - resolved "https://registry.yarnpkg.com/umd/-/umd-3.0.1.tgz#8ae556e11011f63c2596708a8837259f01b3d60e" + version "3.0.3" + resolved "https://registry.yarnpkg.com/umd/-/umd-3.0.3.tgz#aa9fe653c42b9097678489c01000acb69f0b26cf" + +underscore@~1.8.3: + version "1.8.3" + resolved "https://registry.yarnpkg.com/underscore/-/underscore-1.8.3.tgz#4f3fb53b106e6097fcf9cb4109f2a5e9bdfa5022" url@~0.11.0: version "0.11.0" @@ -1353,7 +1382,7 @@ util-deprecate@~1.0.1: version "1.0.2" resolved "https://registry.yarnpkg.com/util-deprecate/-/util-deprecate-1.0.2.tgz#450d4dc9fa70de732762fbd2d4a28981419a0ccf" -util@0.10.3, util@~0.10.1: +util@0.10.3, util@^0.10.3, util@~0.10.1: version "0.10.3" resolved "https://registry.yarnpkg.com/util/-/util-0.10.3.tgz#7afb1afe50805246489e3db7fe0ed379336ac0f9" dependencies: diff --git a/music_rnn/demo/index.html b/music_rnn/demo/index.html index 53fe23da6..b3e8abd02 100644 --- a/music_rnn/demo/index.html +++ b/music_rnn/demo/index.html @@ -24,6 +24,14 @@

Melody Continuation

Results:

 +

Drums Continuation

+

Time:

+
+

Inputs:

+
+

Results:

+
+ diff --git a/music_rnn/demo/index.ts b/music_rnn/demo/index.ts index a63794645..ba4f0845d 100644 --- a/music_rnn/demo/index.ts +++ b/music_rnn/demo/index.ts @@ -17,8 +17,13 @@ import * as magenta from '@magenta/core'; import INoteSequence = magenta.INoteSequence; -import { MelodyRnn } from '../src/index'; -import * as dl from 'deeplearn'; +import tf = magenta.tf; +import { MusicRNN } from '../src/index'; + +// tslint:disable:max-line-length +const MEL_CHECKPOINT = "https://storage.googleapis.com/download.magenta.tensorflow.org/models/music_rnn/dljs/basic_rnn"; +const DRUMS_CHECKPOINT = "https://storage.googleapis.com/download.magenta.tensorflow.org/models/music_rnn/dljs/drums_rnn"; +// tslint:enable:max-line-length const MELODY_NS:INoteSequence = { ticksPerQuarter: 220, @@ -76,6 +81,72 @@ const MELODY_NS:INoteSequence = { ] }; +const DRUMS_NS:INoteSequence = { + ticksPerQuarter: 220, + totalTime: 1.5, + timeSignatures: [ + { + time: 0, + numerator: 4, + denominator: 4 + } + ], + tempos: [ + { + time: 0, + qpm: 120 + } + ], + notes: [ + { + startTime: 0, + endTime: 0.5, + pitch: 34, + velocity: 100, + isDrum: true + }, + { + instrument: 0, + startTime: 0.5, + endTime: 1.0, + pitch: 39, + velocity: 100, + isDrum: true + }, + { + instrument: 0, + startTime: 0.5, + endTime: 1.0, + pitch: 43, + velocity: 100, + isDrum: true + }, + { + startTime: 1.0, + endTime: 1.5, + pitch: 34, + velocity: 100, + isDrum: true + }, + { + instrument: 0, + startTime: 1.5, + endTime: 2.0, + pitch: 39, + velocity: 100, + isDrum: true + }, + { + instrument: 0, + startTime: 1.5, + endTime: 2.0, + pitch: 43, + velocity: 100, + isDrum: true + }, + ] +}; + function writeTimer(elementId: string, startTime: number) { document.getElementById(elementId).innerHTML = ( (performance.now() - startTime) / 1000.).toString() + 's'; @@ -94,7 +165,7 @@ function writeNoteSeqs(elementId: string, seqs: INoteSequence[]) { } async function runMelodyRnn() { - const melodyRnn = new MelodyRnn(); + const melodyRnn = new MusicRNN(MEL_CHECKPOINT); await melodyRnn.initialize(); const qns = magenta.Sequences.quantizeNoteSequence(MELODY_NS, 1); @@ -105,9 +176,26 @@ async function runMelodyRnn() { writeNoteSeqs('melody-cont-results', [continuation]); melodyRnn.dispose(); - console.log(dl.getBackend()); - console.log(dl.memory()); + console.log(tf.getBackend()); + console.log(tf.memory()); +} + +async function runDrumsRnn() { + const drumsRnn = new MusicRNN(DRUMS_CHECKPOINT); + await drumsRnn.initialize(); + + const qns = magenta.Sequences.quantizeNoteSequence(DRUMS_NS, 1); + writeNoteSeqs('drums-cont-inputs', [qns]); + const start = performance.now(); + const continuation = await drumsRnn.continueSequence(qns, 20); + writeTimer('drums-cont-time', start); + writeNoteSeqs('drums-cont-results', [continuation]); + drumsRnn.dispose(); + + console.log(tf.getBackend()); + console.log(tf.memory()); } runMelodyRnn(); +runDrumsRnn(); diff --git a/music_rnn/package.json b/music_rnn/package.json index 7c8a64acb..342185cd3 100644 --- a/music_rnn/package.json +++ b/music_rnn/package.json @@ -7,7 +7,8 @@ "jsdelivr": "dist/musicrnn.js", "unpkg": "dist/musicrnn.js", "dependencies": { - "@magenta/core": "^0.0.10", + "@magenta/core": "^0.0.13", + "@magenta/protobuf": "^0.0.4", "deeplearn": "^0.6.0-alpha6" }, "devDependencies": { diff --git a/music_rnn/src/data.ts b/music_rnn/src/data.ts deleted file mode 100644 index d9fbb1d70..000000000 --- a/music_rnn/src/data.ts +++ /dev/null @@ -1,299 +0,0 @@ -/** - * @license - * Copyright 2018 Google Inc. All Rights Reserved. - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ============================================================================= - */ -import * as dl from 'deeplearn'; -import * as magenta from '@magenta/core'; -import NoteSequence = magenta.NoteSequence; -import INoteSequence = magenta.INoteSequence; - -const DEFAULT_DRUM_PITCH_CLASSES: number[][] = [ - // bass drum - [36, 35], - // snare drum - [38, 27, 28, 31, 32, 33, 34, 37, 39, 40, 56, 65, 66, 75, 85], - // closed hi-hat - [42, 44, 54, 68, 69, 70, 71, 73, 78, 80], - // open hi-hat - [46, 67, 72, 74, 79, 81], - // low tom - [45, 29, 41, 61, 64, 84], - // mid tom - [48, 47, 60, 63, 77, 86, 87], - // high tom - [50, 30, 43, 62, 76, 83], - // crash cymbal - [49, 55, 57, 58], - // ride cymbal - [51, 52, 53, 59, 82] -]; - -/** - * Interface for JSON specification of a `DataConverter`. - * - * @property type The name of the `DataConverter` class. - * @property args Map containing values for argments to the constructor of the - * `DataConverter` class specified above. - */ -export interface ConverterSpec { - type: string; - args: {[argName: string] : any}; // tslint:disable-line:no-any -} - -/** - * Builds a `DataConverter` based on the given `ConverterSpec`. - * - * @param spec Specifies the `DataConverter` to build. - * @returns A new `DataConverter` object based on `spec`. - * @throws Error if the specified type is not recognized. - */ -export function converterFromSpec(spec: ConverterSpec) { - if (spec.type === 'MelodyConverter') { - return new MelodyConverter( - spec.args.numSteps, - spec.args.minPitch, - spec.args.maxPitch); - } else if (spec.type === 'DrumsConverter') { - return new DrumsConverter( - spec.args.numSteps, - spec.args.pitchClasses); - } else if (spec.type === 'DrumRollConverter') { - return new DrumRollConverter( - spec.args.numSteps, - spec.args.pitchClasses); - } else { - throw new Error( - 'Unknown DataConverter type in spec: ' + spec.type); - } -} - -/** - * Abstract DataConverter class for converting between `Tensor` and - * `NoteSequence` objects. - */ -export abstract class DataConverter { - abstract numSteps: number; - abstract toTensor(noteSequence: INoteSequence): dl.Tensor2D; - abstract toNoteSequence(tensor: dl.Tensor2D): INoteSequence; -} - -/** - * Converts between a quantized `NoteSequence` containing a drum sequence - * and the `Tensor` objects used by `MusicVAE`. - * - * The `Tensor` output by `toTensor` is a 2D "drum roll" format. Each - * row is a time step, and each column (up to the final) is a vector of Booleans - * representing whether a drum from the associated pitch class is being hit at - * that time. The final column is a Boolean computed by taking a NOR of the - * other columns in the row. - * - * The expected `Tensor` in `toNoteSequence` is a one-hot encoding of labels - * generated by converting the bit string from the input (excluding the final - * bit) to an integer. - * - * The output `NoteSequence` uses quantized time and only the first pitch in - * pitch class are used. - */ -export class DrumsConverter extends DataConverter{ - numSteps: number; - pitchClasses: number[][]; - pitchToClass: {[pitch: number] : number}; - - /** - * `DrumsConverter` constructor. - * - * @param numSteps The length of each sequence. - * @param pitchClasses An array of arrays, grouping together MIDI pitches to - * treat as the same drum. The first pitch in each class will be used in the - * `NoteSequence` returned by `toNoteSequence`. - */ - constructor( - numSteps: number, pitchClasses?: number[][]) { - super(); - pitchClasses = (pitchClasses) ? pitchClasses : DEFAULT_DRUM_PITCH_CLASSES; - this.numSteps = numSteps; - this.pitchClasses = pitchClasses; - this.pitchToClass = {}; - for (let c = 0; c < pitchClasses.length; ++c) { // class - pitchClasses[c].forEach((p) => {this.pitchToClass[p] = c;}); - } - } - - toTensor(noteSequence: INoteSequence): dl.Tensor2D { - const drumRoll = dl.buffer([this.numSteps, this.pitchClasses.length + 1]); - // Set final values to 1 and change to 0 later if the has gets a note. - for (let i = 0; i < this.numSteps; ++i) { - drumRoll.set(1, i, -1); - } - noteSequence.notes.forEach((note) => { - drumRoll.set(1, note.quantizedStartStep, this.pitchToClass[note.pitch]); - drumRoll.set(0, note.quantizedStartStep, -1); - }); - return drumRoll.toTensor() as dl.Tensor2D; - } - - toNoteSequence(oh: dl.Tensor2D): INoteSequence { - const noteSequence = NoteSequence.create(); - const labelsTensor = oh.argMax(1); - const labels: Int32Array = labelsTensor.dataSync() as Int32Array; - labelsTensor.dispose(); - for (let s = 0; s < labels.length; ++s) { // step - for (let p = 0; p < this.pitchClasses.length; p++) { // pitch class - if (labels[s] >> p & 1) { - noteSequence.notes.push( - NoteSequence.Note.create({ - pitch: this.pitchClasses[p][0], - quantizedStartStep: s, - quantizedEndStep: s + 1})); - } - } - } - return noteSequence; - } -} - -/** - * Converts between a quantized `NoteSequence` containing a drum sequence - * and the `Tensor` objects used by `MusicVAE`. - * - * The `Tensor` output by `toTensor` is the same 2D "drum roll" as in - * `DrumsConverter`. - * - * The expected `Tensor` in `toNoteSequence` is the same as the "drum roll", - * excluding the final NOR column. - * - * The output `NoteSequence` uses quantized time and only the first pitch in - * pitch class are used. - * - */ -export class DrumRollConverter extends DrumsConverter { - toNoteSequence(roll: dl.Tensor2D) { - const noteSequence = NoteSequence.create(); - for (let s = 0; s < roll.shape[0]; ++s) { // step - const rollSlice = roll.slice([s, 0], [1, roll.shape[1]]); - const pitches = rollSlice.dataSync() as Uint8Array; - rollSlice.dispose(); - for (let p = 0; p < pitches.length; ++p) { // pitch class - if (pitches[p]) { - noteSequence.notes.push( - NoteSequence.Note.create({ - pitch: this.pitchClasses[p][0], - quantizedStartStep: s, - quantizedEndStep: s + 1})); - } - } - } - return noteSequence; - } -} - -/** - * Converts between a monophonic, quantized `NoteSequence` containing a melody - * and the `Tensor` objects used by `MusicVAE`. - * - * Melodies are represented as a sequence of categorical variables, representing - * one of three possible events: - * - A non-event, i.e. holding a note or resting. (0) - * - A note off. (1) - * - A note on with a specific pitch. (> 1) - * - * The `Tensor` output by `toTensor` is a one-hot encoding of the sequence of - * labels extracted from the `NoteSequence`. - * - * The expected `Tensor` in `toNoteSequence` is a one-hot encoding of melody - * sequence labels like those returned by `toTensor`. - */ -export class MelodyConverter extends DataConverter { - numSteps: number; - minPitch: number; // inclusive - maxPitch: number; // inclusive - depth: number; - NOTE_OFF = 1; - FIRST_PITCH = 2; - - /** - * `MelodyConverter` constructor. - * - * @param numSteps The length of each sequence. - * @param minPitch The minimum pitch to model. Those above this value will - * cause an errot to be thrown. - * @param maxPitch The maximum pitch to model. Those above this value will - * cause an error to be thrown. - */ - constructor(numSteps: number, minPitch: number, maxPitch: number) { - super(); - this.numSteps = numSteps; - this.minPitch = minPitch; - this.maxPitch = maxPitch; - this.depth = maxPitch - minPitch + 3; - } - - toTensor(noteSequence: INoteSequence): dl.Tensor2D { - const sortedNotes: NoteSequence.INote[] = noteSequence.notes.sort( - (n1, n2) => n1.quantizedStartStep - n2.quantizedStartStep); - const mel = dl.buffer([this.numSteps]); - let lastEnd = -1; - sortedNotes.forEach(n => { - if (n.quantizedStartStep < lastEnd) { - throw new Error('`NoteSequence` is not monophonic.'); - } - if (n.pitch < this.minPitch || n.pitch > this.maxPitch) { - throw Error( - `\`NoteSequence\` has a pitch outside of the valid range: ` + - `${n.pitch}`); - } - mel.set(n.pitch - this.minPitch + this.FIRST_PITCH, n.quantizedStartStep); - mel.set(this.NOTE_OFF, n.quantizedEndStep); - lastEnd = n.quantizedEndStep; - }); - return dl.oneHot( - mel.toTensor() as dl.Tensor1D, this.depth) as dl.Tensor2D; - } - - toNoteSequence(oh: dl.Tensor2D): INoteSequence { - const noteSequence = NoteSequence.create(); - const labelsTensor = oh.argMax(1); - const labels: Int32Array = labelsTensor.dataSync() as Int32Array; - labelsTensor.dispose(); - let currNote: NoteSequence.Note = null; - for (let s = 0; s < labels.length; ++s) { // step - const label = labels[s]; - switch (label) { - case 0: - break; - case 1: - if (currNote) { - currNote.quantizedEndStep = s; - noteSequence.notes.push(currNote); - currNote = null; - } - break; - default: - if (currNote) { - currNote.quantizedEndStep = s; - noteSequence.notes.push(currNote); - } - currNote = NoteSequence.Note.create({ - pitch: label - this.FIRST_PITCH + this.minPitch, - quantizedStartStep: s}); - } - } - if (currNote) { - currNote.quantizedEndStep = labels.length; - noteSequence.notes.push(currNote); - } - return noteSequence; - } -} diff --git a/music_rnn/src/index.ts b/music_rnn/src/index.ts index 943b2a922..679c9cc82 100644 --- a/music_rnn/src/index.ts +++ b/music_rnn/src/index.ts @@ -15,4 +15,4 @@ * limitations under the License. */ -export { MelodyRnn } from './model'; +export { MusicRNN } from './model'; diff --git a/music_rnn/src/model.ts b/music_rnn/src/model.ts index 5c6e00920..4bb85141a 100644 --- a/music_rnn/src/model.ts +++ b/music_rnn/src/model.ts @@ -15,42 +15,32 @@ * ============================================================================= */ -import * as dl from 'deeplearn'; import * as magenta from '@magenta/core'; -import * as data from './data'; -import { INoteSequence } from '@magenta/core'; - -// tslint:disable-next-line:max-line-length -const CHECKPOINT_URL = "https://storage.googleapis.com/download.magenta.tensorflow.org/models/music_rnn/dljs/basic_rnn/"; - -const DEFAULT_MIN_NOTE = 48; -const DEFAULT_MAX_NOTE = 83; +import tf = magenta.tf; +import { isNullOrUndefined } from 'util'; const CELL_FORMAT = "rnn/multi_rnn_cell/cell_%d/basic_lstm_cell/"; /** - * Main MusicVAE model class. + * Main MusicRNN model class. * - * A MusicVAE is a variational autoencoder made up of an `Encoder` and - * `Decoder`, along with a `DataConverter` for converting between `Tensor` - * and `NoteSequence` objects for input and output. - * - * Exposes methods for interpolation and sampling of musical sequences. + * A MusicRNN is an LSTM-based language model for musical notes. */ -export class MelodyRnn { +export class MusicRNN { checkpointURL: string; + dataConverter: magenta.data.DataConverter; - lstmCells: dl.LSTMCellFunc[]; - lstmFcB: dl.Tensor1D; - lstmFcW: dl.Tensor2D; - forgetBias: dl.Scalar; + lstmCells: tf.LSTMCellFunc[]; + lstmFcB: tf.Tensor1D; + lstmFcW: tf.Tensor2D; + forgetBias: tf.Scalar; biasShapes: number[]; - rawVars: {[varName: string]: dl.Tensor}; // Store for disposal. + rawVars: {[varName: string]: tf.Tensor}; // Store for disposal. initialized: boolean; /** - * `MusicVAE` constructor. + * `MusicRNN` constructor. * * @param checkpointURL Path to the checkpoint directory. * @param dataConverter A `DataConverter` object to use for converting between @@ -58,12 +48,13 @@ export class MelodyRnn { * file must exist within the checkpoint directory specifying the type and * args for the correct `DataConverter`. */ - constructor(checkpointURL=CHECKPOINT_URL) { + constructor(checkpointURL:string, dataConverter?:magenta.data.DataConverter) { this.checkpointURL = checkpointURL; this.initialized = false; this.rawVars = {}; this.biasShapes = []; this.lstmCells = []; + this.dataConverter = dataConverter; } /** @@ -73,10 +64,18 @@ export class MelodyRnn { async initialize() { this.dispose(); - const reader = new dl.CheckpointLoader(this.checkpointURL); + if (isNullOrUndefined(this.dataConverter)) { + fetch(this.checkpointURL + '/converter.json') + .then((response) => response.json()) + .then((converterSpec: magenta.data.ConverterSpec) => { + this.dataConverter = magenta.data.converterFromSpec(converterSpec); + }); + } + + const reader = new magenta.CheckpointLoader(this.checkpointURL); const vars = await reader.getAllVariables(); - this.forgetBias = dl.scalar(1.0); + this.forgetBias = tf.scalar(1.0); this.lstmCells.length = 0; this.biasShapes.length = 0; @@ -88,16 +87,16 @@ export class MelodyRnn { } // TODO(fjord): Support attention model. this.lstmCells.push( - (data: dl.Tensor2D, c: dl.Tensor2D, h: dl.Tensor2D) => - dl.basicLSTMCell(this.forgetBias, - vars[cellPrefix + 'kernel'] as dl.Tensor2D, - vars[cellPrefix + 'bias'] as dl.Tensor1D, data, c, h)); - this.biasShapes.push((vars[cellPrefix + 'bias'] as dl.Tensor2D).shape[0]); + (data: tf.Tensor2D, c: tf.Tensor2D, h: tf.Tensor2D) => + tf.basicLSTMCell(this.forgetBias, + vars[cellPrefix + 'kernel'] as tf.Tensor2D, + vars[cellPrefix + 'bias'] as tf.Tensor1D, data, c, h)); + this.biasShapes.push((vars[cellPrefix + 'bias'] as tf.Tensor2D).shape[0]); ++l; } - this.lstmFcW = vars['fully_connected/weights'] as dl.Tensor2D; - this.lstmFcB = vars['fully_connected/biases'] as dl.Tensor1D; + this.lstmFcW = vars['fully_connected/weights'] as tf.Tensor2D; + this.lstmFcB = vars['fully_connected/biases'] as tf.Tensor1D; this.rawVars = vars; this.initialized = true; @@ -110,6 +109,7 @@ export class MelodyRnn { this.forgetBias.dispose(); this.forgetBias = undefined; } + this.dataConverter= undefined; this.initialized = false; } @@ -125,55 +125,47 @@ export class MelodyRnn { temperature?: number): Promise { magenta.Sequences.assertIsQuantizedSequence(sequence); - let continuation: INoteSequence; - if(!this.initialized) { await this.initialize(); } - dl.tidy(() => { - const converterIn = new data.MelodyConverter( - sequence.totalQuantizedSteps, DEFAULT_MIN_NOTE, DEFAULT_MAX_NOTE); - const inputs = converterIn.toTensor(sequence); + const oh = tf.tidy(() => { + const inputs = this.dataConverter.toTensor(sequence); const length:number = inputs.shape[0]; const outputSize:number = inputs.shape[1]; - let c: dl.Tensor2D[] = []; - let h: dl.Tensor2D[] = []; + let c: tf.Tensor2D[] = []; + let h: tf.Tensor2D[] = []; for (let i = 0; i < this.biasShapes.length; i++) { - c.push(dl.zeros([1, this.biasShapes[i] / 4])); - h.push(dl.zeros([1, this.biasShapes[i] / 4])); + c.push(tf.zeros([1, this.biasShapes[i] / 4])); + h.push(tf.zeros([1, this.biasShapes[i] / 4])); } // Initialize with input. - const samples: dl.Tensor1D[] = []; + const samples: tf.Tensor1D[] = []; for (let i = 0; i < length + steps; i++) { - let nextInput: dl.Tensor2D; + let nextInput: tf.Tensor2D; if (i < length) { - nextInput = inputs.slice([ - i, 0], [1, outputSize]).as2D(1, outputSize); + nextInput = inputs.slice([i, 0], [1, outputSize]).as2D(1, outputSize); } else { - const logits = h[1].matMul(this.lstmFcW).add(this.lstmFcB); + const logits = h[h.length - 1].matMul(this.lstmFcW).add(this.lstmFcB); const sampledOutput = ( temperature ? - dl.multinomial(logits.div(dl.scalar(temperature)), 1).as1D(): + tf.multinomial(logits.div(tf.scalar(temperature)), 1).as1D(): logits.argMax(1).as1D()); - nextInput = dl.oneHot(sampledOutput, outputSize).toFloat(); + nextInput = tf.oneHot(sampledOutput, outputSize).toFloat(); // Save samples as bool to reduce data sync time. samples.push(nextInput.as1D().toBool()); } - const output = dl.multiRNNCell(this.lstmCells, nextInput, c, h); - c = output[0]; - h = output[1]; + [c, h] = tf.multiRNNCell(this.lstmCells, nextInput, c, h); } - const output = dl.stack(samples).as2D(samples.length, outputSize); - const converterOut = new data.MelodyConverter( - sequence.totalQuantizedSteps, DEFAULT_MIN_NOTE, DEFAULT_MAX_NOTE); - continuation = converterOut.toNoteSequence(output); + return tf.stack(samples).as2D(samples.length, outputSize); }); - return continuation; - } + const result = this.dataConverter.toNoteSequence(await oh); + oh.dispose(); + return result; + } } diff --git a/music_rnn/yarn.lock b/music_rnn/yarn.lock index 144703b7f..ff54e074e 100644 --- a/music_rnn/yarn.lock +++ b/music_rnn/yarn.lock @@ -2,15 +2,16 @@ # yarn lockfile v1 -"@magenta/core@^0.0.10": - version "0.0.9" - resolved "https://registry.yarnpkg.com/@magenta/core/-/core-0.0.9.tgz#6fb53301b9062a570c740fe596629d402bd35c8b" +"@magenta/core@^0.0.13": + version "0.0.13" + resolved "https://registry.yarnpkg.com/@magenta/core/-/core-0.0.13.tgz#8da227eb6ca5e546d5c162c78dbee71e93d684fa" dependencies: - "@magenta/protobuf" "^0.0.3" + "@magenta/protobuf" "^0.0.4" + midiconvert "^0.4.3" -"@magenta/protobuf@^0.0.3": - version "0.0.3" - resolved "https://registry.yarnpkg.com/@magenta/protobuf/-/protobuf-0.0.3.tgz#f8d624166638c14ee3ca8c75864f75a2ce96e3b7" +"@magenta/protobuf@^0.0.4": + version "0.0.4" + resolved "https://registry.yarnpkg.com/@magenta/protobuf/-/protobuf-0.0.4.tgz#554d7fe1e38d7849c37038771e4e4eb904cca0b6" dependencies: protobufjs "^6.8.6" @@ -62,12 +63,12 @@ resolved "https://registry.yarnpkg.com/@types/long/-/long-3.0.32.tgz#f4e5af31e9e9b196d8e5fca8a5e2e20aa3d60b69" "@types/node@*": - version "9.4.7" - resolved "https://registry.yarnpkg.com/@types/node/-/node-9.4.7.tgz#57d81cd98719df2c9de118f2d5f3b1120dcd7275" + version "9.6.1" + resolved "https://registry.yarnpkg.com/@types/node/-/node-9.6.1.tgz#e2d374ef15b315b48e7efc308fa1a7cd51faa06c" "@types/node@^8.9.4": - version "8.9.5" - resolved "https://registry.yarnpkg.com/@types/node/-/node-8.9.5.tgz#162b864bc70be077e6db212b322754917929e976" + version "8.10.1" + resolved "https://registry.yarnpkg.com/@types/node/-/node-8.10.1.tgz#aac98b810c50568054486f2bb8c486d824713be8" "@types/tape@^4.2.31": version "4.2.32" @@ -366,8 +367,8 @@ cipher-base@^1.0.0, cipher-base@^1.0.1, cipher-base@^1.0.3: safe-buffer "^5.0.1" clang-format@^1.2.2: - version "1.2.2" - resolved "https://registry.yarnpkg.com/clang-format/-/clang-format-1.2.2.tgz#a7277a03fce9aa4e387ddaa83b60d99dab115737" + version "1.2.3" + resolved "https://registry.yarnpkg.com/clang-format/-/clang-format-1.2.3.tgz#2763561aa7449c43737480f8df3a2b5b66e6cf37" dependencies: async "^1.5.2" glob "^7.0.0" @@ -387,16 +388,7 @@ colors@1.0.3: version "1.0.3" resolved "https://registry.yarnpkg.com/colors/-/colors-1.0.3.tgz#0433f44d809680fdeb60ed260f1b0c262e82a40b" -combine-source-map@~0.7.1: - version "0.7.2" - resolved "https://registry.yarnpkg.com/combine-source-map/-/combine-source-map-0.7.2.tgz#0870312856b307a87cc4ac486f3a9a62aeccc09e" - dependencies: - convert-source-map "~1.1.0" - inline-source-map "~0.6.0" - lodash.memoize "~3.0.3" - source-map "~0.5.3" - -combine-source-map@~0.8.0: +combine-source-map@^0.8.0, combine-source-map@~0.8.0: version "0.8.0" resolved "https://registry.yarnpkg.com/combine-source-map/-/combine-source-map-0.8.0.tgz#a58d0df042c186fcf822a8e8015f5450d2d79a8b" dependencies: @@ -514,8 +506,8 @@ deep-equal@~1.0.1: resolved "https://registry.yarnpkg.com/deep-equal/-/deep-equal-1.0.1.tgz#f5d260292b660e084eff4cdbc9f08ad3247448b5" deeplearn@^0.6.0-alpha6: - version "0.6.0-alpha6" - resolved "https://registry.yarnpkg.com/deeplearn/-/deeplearn-0.6.0-alpha6.tgz#5f9b3af18db3186dbdc5e05310369fbf30131921" + version "0.6.0-alpha7" + resolved "https://registry.yarnpkg.com/deeplearn/-/deeplearn-0.6.0-alpha7.tgz#ca19def5d9794dc1de223bfcad9703fd6bc1bd07" dependencies: seedrandom "~2.4.3" utf8 "~2.1.2" @@ -754,8 +746,8 @@ https-browserify@^1.0.0: resolved "https://registry.yarnpkg.com/https-browserify/-/https-browserify-1.0.0.tgz#ec06c10e0a34c0f2faf199f7fd7fc78fffd03c73" ieee754@^1.1.4: - version "1.1.10" - resolved "https://registry.yarnpkg.com/ieee754/-/ieee754-1.1.10.tgz#719a6f7b026831e64bdb838b0de1bb0029bbf716" + version "1.1.11" + resolved "https://registry.yarnpkg.com/ieee754/-/ieee754-1.1.11.tgz#c16384ffe00f5b7835824e67b6f2bd44a5229455" indexof@0.0.1: version "0.0.1" @@ -783,11 +775,11 @@ inline-source-map@~0.6.0: source-map "~0.5.3" insert-module-globals@^7.0.0: - version "7.0.4" - resolved "https://registry.yarnpkg.com/insert-module-globals/-/insert-module-globals-7.0.4.tgz#1f144b807884507894bdbbb564c0d70075460251" + version "7.0.5" + resolved "https://registry.yarnpkg.com/insert-module-globals/-/insert-module-globals-7.0.5.tgz#6d0a6f28d4a7e0eae171ad305e0f47bdfe0c258e" dependencies: JSONStream "^1.0.3" - combine-source-map "~0.7.1" + combine-source-map "^0.8.0" concat-stream "^1.6.1" is-buffer "^1.1.0" lexical-scope "^1.2.0" @@ -829,9 +821,9 @@ is-utf8@^0.2.0: version "0.2.1" resolved "https://registry.yarnpkg.com/is-utf8/-/is-utf8-0.2.1.tgz#4b0da1442104d1b336340e80797e865cf39f7d72" -isarray@~0.0.1: - version "0.0.1" - resolved "https://registry.yarnpkg.com/isarray/-/isarray-0.0.1.tgz#8a18acfca9a8f4177e09abfc6038939b05d1eedf" +isarray@^2.0.4: + version "2.0.4" + resolved "https://registry.yarnpkg.com/isarray/-/isarray-2.0.4.tgz#38e7bcbb0f3ba1b7933c86ba1894ddfc3781bbb7" isarray@~1.0.0: version "1.0.0" @@ -848,6 +840,10 @@ js-yaml@^3.7.0: argparse "^1.0.7" esprima "^4.0.0" +jsmidgen@^0.1.5: + version "0.1.5" + resolved "https://registry.yarnpkg.com/jsmidgen/-/jsmidgen-0.1.5.tgz#ad0afd6375eb12f51064025393ce3baec380585d" + json-stable-stringify@~0.0.0: version "0.0.1" resolved "https://registry.yarnpkg.com/json-stable-stringify/-/json-stable-stringify-0.0.1.tgz#611c23e814db375527df851193db59dd2af27f45" @@ -863,11 +859,11 @@ jsonparse@^1.2.0: resolved "https://registry.yarnpkg.com/jsonparse/-/jsonparse-1.3.1.tgz#3f4dae4a91fac315f71062f8521cc239f1366280" labeled-stream-splicer@^2.0.0: - version "2.0.0" - resolved "https://registry.yarnpkg.com/labeled-stream-splicer/-/labeled-stream-splicer-2.0.0.tgz#a52e1d138024c00b86b1c0c91f677918b8ae0a59" + version "2.0.1" + resolved "https://registry.yarnpkg.com/labeled-stream-splicer/-/labeled-stream-splicer-2.0.1.tgz#9cffa32fd99e1612fd1d86a8db962416d5292926" dependencies: inherits "^2.0.1" - isarray "~0.0.1" + isarray "^2.0.4" stream-splicer "^2.0.0" lexical-scope@^1.2.0: @@ -895,6 +891,17 @@ md5.js@^1.3.4: hash-base "^3.0.0" inherits "^2.0.1" +midi-file-parser@^1.0.0: + version "1.0.0" + resolved "https://registry.yarnpkg.com/midi-file-parser/-/midi-file-parser-1.0.0.tgz#175a013f277e9d26acf94cfe6c03b13abb0a1e9e" + +midiconvert@^0.4.3: + version "0.4.3" + resolved "https://registry.yarnpkg.com/midiconvert/-/midiconvert-0.4.3.tgz#97623b0ddecf39dd9d12499aef96611a83bba585" + dependencies: + jsmidgen "^0.1.5" + midi-file-parser "^1.0.0" + miller-rabin@^4.0.0: version "4.0.1" resolved "https://registry.yarnpkg.com/miller-rabin/-/miller-rabin-4.0.1.tgz#f080351c865b0dc562a8462966daa53543c78a4d" @@ -1424,8 +1431,8 @@ tslint@~5.9.1: tsutils "^2.12.1" tsutils@^2.12.1: - version "2.22.2" - resolved "https://registry.yarnpkg.com/tsutils/-/tsutils-2.22.2.tgz#0b9f3d87aa3eb95bd32d26ce2b88aa329a657951" + version "2.25.0" + resolved "https://registry.yarnpkg.com/tsutils/-/tsutils-2.25.0.tgz#14d616ef59224a3c9fb7eb483e1c182b6665ae5e" dependencies: tslib "^1.8.1" diff --git a/music_vae/.gitignore b/music_vae/.gitignore new file mode 100644 index 000000000..4793a5128 --- /dev/null +++ b/music_vae/.gitignore @@ -0,0 +1,3 @@ +node_modules/ +es5/ +yarn-error.log diff --git a/music_vae/demo/index.ts b/music_vae/demo/index.ts index 6f48d1998..433322409 100644 --- a/music_vae/demo/index.ts +++ b/music_vae/demo/index.ts @@ -1,6 +1,6 @@ // tslint:disable-next-line:max-line-length -import { MusicVAE, INoteSequence} from '../index'; -import * as dl from 'deeplearn'; +import { MusicVAE } from '../src/index'; +import { INoteSequence, tf } from '@magenta/core'; import * as clone from 'clone'; // tslint:disable:max-line-length @@ -139,7 +139,7 @@ async function runDrums(){ writeNoteSeqs('drums-samples', sample); mvae.dispose(); - console.log(dl.memory()); + console.log(tf.memory()); } async function runDrumsNade(){ @@ -159,7 +159,7 @@ async function runDrumsNade(){ writeNoteSeqs('nade-samples', sample); mvae.dispose(); - console.log(dl.memory()); + console.log(tf.memory()); } async function runMel(){ @@ -180,7 +180,7 @@ async function runMel(){ writeNoteSeqs('mel-samples', sample); mvae.dispose(); - console.log(dl.memory()); + console.log(tf.memory()); } // TODO(adarob): Switch to magenta/core function once implemented. @@ -226,7 +226,7 @@ async function runMel16(){ writeNoteSeqs('mel16-samples', sample); mvae.dispose(); - console.log(dl.memory()); + console.log(tf.memory()); } try { diff --git a/music_vae/package.json b/music_vae/package.json index 435069d59..8d56cd0ad 100644 --- a/music_vae/package.json +++ b/music_vae/package.json @@ -7,12 +7,13 @@ "jsdelivr": "dist/musicvae.js", "unpkg": "dist/musicvae.js", "scripts": { - "bundle": "browserify index.ts -p [tsify] --standalone musicvae -o dist/musicvae.js", + "lint": "tslint -p . -t verbose", + "bundle": "browserify src/index.ts -p [tsify] --standalone musicvae -o dist/musicvae.js", "build-demo": "browserify demo/index.ts -p [tsify] > demo/bundle.js", "build": "tsc", "run-demo": "yarn build && yarn build-demo && http-server ./demo", "test": "echo \"Error: no test specified\" && exit 1", - "pub": "yarn build && yarn bundle && yarn publish" + "prepublish": "yarn lint && yarn build && yarn bundle" }, "repository": { "type": "git", @@ -31,11 +32,10 @@ "typescript": "^2.6.2" }, "dependencies": { - "@magenta/core": "^0.0.9", + "@magenta/core": "^0.0.13", "@types/clone": "^0.1.30", "clang-format": "^1.2.2", "clone": "^1.0.4", - "deeplearn": "^0.6.0-alpha6", "npm": "^5.7.1", "tslint": "^5.9.1" } diff --git a/music_vae/index.ts b/music_vae/src/index.ts similarity index 81% rename from music_vae/index.ts rename to music_vae/src/index.ts index a17d12cf9..bceee162c 100644 --- a/music_vae/index.ts +++ b/music_vae/src/index.ts @@ -14,7 +14,7 @@ * limitations under the License. * ============================================================================= */ -import * as dl from 'deeplearn'; +import {tf} from '@magenta/core'; export { Encoder, @@ -22,15 +22,6 @@ export { MusicVAE } from './model'; -export { - ConverterSpec, - DrumsConverter, - DrumRollConverter, - MelodyConverter -} from './data'; - -export { INoteSequence, INote } from '@magenta/core'; - export const isDeviceSupported = isWebGLSupported(); //////////// @@ -38,5 +29,5 @@ export const isDeviceSupported = isWebGLSupported(); // ideally could be retrieved from NPM modules or internally from deeplearn via // NPM function isWebGLSupported(): boolean { - return dl.ENV.get('WEBGL_VERSION') >= 1; + return tf.ENV.get('WEBGL_VERSION') >= 1; } diff --git a/music_vae/model.ts b/music_vae/src/model.ts similarity index 79% rename from music_vae/model.ts rename to music_vae/src/model.ts index b71d3b808..18ee7532f 100644 --- a/music_vae/model.ts +++ b/music_vae/src/model.ts @@ -14,10 +14,9 @@ * limitations under the License. * ============================================================================= */ -import * as dl from 'deeplearn'; -import * as data from './data'; -// Use custom CheckpointLoader until quantization is added to dl. -import { CheckpointLoader } from './checkpoint_loader'; +// Use custom CheckpointLoader until quantization is added to tf. +import { data, tf, CheckpointLoader, INoteSequence } from '@magenta/core'; +import { isNullOrUndefined } from 'util'; /** * A class for keeping track of the parameters of an affine transformation. @@ -26,9 +25,9 @@ import { CheckpointLoader } from './checkpoint_loader'; * @param bias A 1-dimensional tensor with the bias parameters. */ class LayerVars { - kernel: dl.Tensor2D; - bias: dl.Tensor1D; - constructor(kernel: dl.Tensor2D, bias: dl.Tensor1D) { + kernel: tf.Tensor2D; + bias: tf.Tensor1D; + constructor(kernel: tf.Tensor2D, bias: tf.Tensor1D) { this.kernel = kernel; this.bias = bias; } @@ -41,8 +40,8 @@ class LayerVars { * transformation. * @param inputs A batch of input vectors to transform. */ -function dense(vars: LayerVars, inputs: dl.Tensor2D) { - return inputs.matMul(vars.kernel).add(vars.bias) as dl.Tensor2D; +function dense(vars: LayerVars, inputs: tf.Tensor2D) { + return inputs.matMul(vars.kernel).add(vars.bias) as tf.Tensor2D; } /** @@ -50,7 +49,7 @@ function dense(vars: LayerVars, inputs: dl.Tensor2D) { */ abstract class Encoder { abstract zDims: number; - abstract encode(sequence: dl.Tensor3D): dl.Tensor2D; + abstract encode(sequence: tf.Tensor3D): tf.Tensor2D; } /** @@ -86,11 +85,11 @@ class BidirectonalLstmEncoder extends Encoder { * @returns A batch of concatenated final LSTM states, or the `mu` if `muVars` * is known. */ - encode(sequence: dl.Tensor3D) { - return dl.tidy(() => { + encode(sequence: tf.Tensor3D) { + return tf.tidy(() => { const fwState = this.singleDirection(sequence, true); const bwState = this.singleDirection(sequence, false); - const finalState = dl.concat2d([fwState[1], bwState[1]], 1); + const finalState = tf.concat2d([fwState[1], bwState[1]], 1); if (this.muVars) { return dense(this.muVars, finalState); } else { @@ -99,19 +98,19 @@ class BidirectonalLstmEncoder extends Encoder { }); } - private singleDirection(inputs: dl.Tensor3D, fw: boolean) { + private singleDirection(inputs: tf.Tensor3D, fw: boolean) { const batchSize = inputs.shape[0]; const length = inputs.shape[1]; const outputSize = inputs.shape[2]; const lstmVars = fw ? this.lstmFwVars : this.lstmBwVars; - let state: [dl.Tensor2D, dl.Tensor2D] = [ - dl.zeros([batchSize, lstmVars.bias.shape[0] / 4]), - dl.zeros([batchSize, lstmVars.bias.shape[0] / 4]) + let state: [tf.Tensor2D, tf.Tensor2D] = [ + tf.zeros([batchSize, lstmVars.bias.shape[0] / 4]), + tf.zeros([batchSize, lstmVars.bias.shape[0] / 4]) ]; - const forgetBias = dl.scalar(1.0); - const lstm = (data: dl.Tensor2D, state: [dl.Tensor2D, dl.Tensor2D]) => - dl.basicLSTMCell( + const forgetBias = tf.scalar(1.0); + const lstm = (data: tf.Tensor2D, state: [tf.Tensor2D, tf.Tensor2D]) => + tf.basicLSTMCell( forgetBias, lstmVars.kernel, lstmVars.bias, data, state[0], state[1]); for (let i = 0; i < length; i++) { const index = fw ? i : length - 1 - i; @@ -157,16 +156,16 @@ class HierarchicalEncoder extends Encoder { * @param sequence The batch of sequences to be encoded. * @returns A batch of `mu` values. */ - encode(sequence: dl.Tensor3D) { - return dl.tidy(() => { + encode(sequence: tf.Tensor3D) { + return tf.tidy(() => { const batchSize = sequence.shape[0]; - let inputs: dl.Tensor3D = sequence; + let inputs: tf.Tensor3D = sequence; for (let level = 0; level < this.baseEncoders.length; ++level) { const levelSteps = this.numSteps[level]; const stepSize = inputs.shape[1] / levelSteps; const depth = inputs.shape[2]; - const embeddings: dl.Tensor2D[] = []; + const embeddings: tf.Tensor2D[] = []; for (let step = 0; step < levelSteps; ++ step) { embeddings.push(this.baseEncoders[level].encode( @@ -174,7 +173,7 @@ class HierarchicalEncoder extends Encoder { [batchSize, stepSize, depth]))); } inputs = (embeddings.length > 1) ? - dl.stack(embeddings, 1) as dl.Tensor3D : + tf.stack(embeddings, 1) as tf.Tensor3D : embeddings[0].expandDims(1); } return dense(this.muVars, inputs.squeeze([1])); @@ -192,21 +191,21 @@ class HierarchicalEncoder extends Encoder { * @returns An Object containing the LSTM cells and initial states. */ function initLstmCells( - z: dl.Tensor2D, lstmCellVars: LayerVars[], zToInitStateVars: LayerVars) { + z: tf.Tensor2D, lstmCellVars: LayerVars[], zToInitStateVars: LayerVars) { const batchSize = z.shape[0]; - const lstmCells: dl.LSTMCellFunc[] = []; - const c: dl.Tensor2D[] = []; - const h: dl.Tensor2D[] = []; + const lstmCells: tf.LSTMCellFunc[] = []; + const c: tf.Tensor2D[] = []; + const h: tf.Tensor2D[] = []; const initialStates = dense(zToInitStateVars, z).tanh(); let stateOffset = 0; for (let i = 0; i < lstmCellVars.length; ++i) { const lv = lstmCellVars[i]; const stateWidth = lv.bias.shape[0] / 4; - const forgetBias = dl.scalar(1.0); + const forgetBias = tf.scalar(1.0); lstmCells.push( - (data: dl.Tensor2D, c: dl.Tensor2D, h: dl.Tensor2D) => - dl.basicLSTMCell(forgetBias, lv.kernel, lv.bias, data, c, h)); + (data: tf.Tensor2D, c: tf.Tensor2D, h: tf.Tensor2D) => + tf.basicLSTMCell(forgetBias, lv.kernel, lv.bias, data, c, h)); c.push(initialStates.slice([0, stateOffset], [batchSize, stateWidth])); stateOffset += stateWidth; h.push(initialStates.slice([0, stateOffset], [batchSize, stateWidth])); @@ -223,8 +222,8 @@ abstract class Decoder { abstract zDims: number; abstract decode( - z: dl.Tensor2D, length: number, initialInput?: dl.Tensor2D, - temperature?: number): dl.Tensor3D; + z: tf.Tensor2D, length: number, initialInput?: tf.Tensor2D, + temperature?: number): tf.Tensor3D; } /** @@ -283,33 +282,33 @@ class BaseDecoder extends Decoder { * @returns A boolean tensor containing the decoded sequences, shaped * `[batchSize, length, depth]`. */ - decode(z: dl.Tensor2D, length: number, initialInput?: dl.Tensor2D, + decode(z: tf.Tensor2D, length: number, initialInput?: tf.Tensor2D, temperature?: number) { const batchSize = z.shape[0]; - return dl.tidy(() => { + return tf.tidy(() => { // Initialize LSTMCells. const lstmCell = initLstmCells( z, this.lstmCellVars, this.zToInitStateVars); // Generate samples. - const samples: dl.Tensor2D[] = []; + const samples: tf.Tensor2D[] = []; let nextInput = initialInput ? - initialInput : dl.zeros([batchSize, this.outputDims]) as dl.Tensor2D; + initialInput : tf.zeros([batchSize, this.outputDims]) as tf.Tensor2D; for (let i = 0; i < length; ++i) { - [lstmCell.c, lstmCell.h] = dl.multiRNNCell( - lstmCell.cell, dl.concat2d([nextInput, z], 1), lstmCell.c, + [lstmCell.c, lstmCell.h] = tf.multiRNNCell( + lstmCell.cell, tf.concat2d([nextInput, z], 1), lstmCell.c, lstmCell.h); const logits = dense( this.outputProjectVars, lstmCell.h[lstmCell.h.length - 1]); - let timeSamples: dl.Tensor2D; + let timeSamples: tf.Tensor2D; if (this.nade == null) { const timeLabels = ( temperature ? - dl.multinomial(logits.div(dl.scalar(temperature)), 1).as1D(): + tf.multinomial(logits.div(tf.scalar(temperature)), 1).as1D(): logits.argMax(1).as1D()); - nextInput = dl.oneHot(timeLabels, this.outputDims).toFloat(); + nextInput = tf.oneHot(timeLabels, this.outputDims).toFloat(); timeSamples = nextInput.toBool(); } else { const encBias = logits.slice( @@ -322,7 +321,7 @@ class BaseDecoder extends Decoder { samples.push(timeSamples); } - return dl.stack(samples, 1) as dl.Tensor3D; + return tf.stack(samples, 1) as tf.Tensor3D; }); } } @@ -375,23 +374,23 @@ class ConductorDecoder extends Decoder { * @returns A boolean tensor containing the decoded sequences, shaped * `[batchSize, length, depth]`. */ - decode(z: dl.Tensor2D, length: number, initialInput?: dl.Tensor2D, + decode(z: tf.Tensor2D, length: number, initialInput?: tf.Tensor2D, temperature?: number) { const batchSize = z.shape[0]; - return dl.tidy(() => { + return tf.tidy(() => { // Initialize LSTMCells. const lstmCell = initLstmCells( z, this.lstmCellVars, this.zToInitStateVars); // Generate embeddings. - const samples: dl.Tensor3D[] = []; - let initialInput: dl.Tensor2D[] = this.coreDecoders.map(_ => undefined); - const dummyInput: dl.Tensor2D = dl.zeros([batchSize, 1]); + const samples: tf.Tensor3D[] = []; + let initialInput: tf.Tensor2D[] = this.coreDecoders.map(_ => undefined); + const dummyInput: tf.Tensor2D = tf.zeros([batchSize, 1]); for (let i = 0; i < this.numSteps; ++i) { - [lstmCell.c, lstmCell.h] = dl.multiRNNCell( + [lstmCell.c, lstmCell.h] = tf.multiRNNCell( lstmCell.cell, dummyInput, lstmCell.c, lstmCell.h); - const currSamples: dl.Tensor3D[] = []; + const currSamples: tf.Tensor3D[] = []; for (let j = 0; j < this.coreDecoders.length; ++j) { currSamples.push( this.coreDecoders[j].decode( @@ -400,12 +399,12 @@ class ConductorDecoder extends Decoder { } samples.push( (currSamples.length > 1) ? - dl.concat(currSamples, -1) : currSamples[0]); + tf.concat(currSamples, -1) : currSamples[0]); initialInput = currSamples.map(s => s.slice( [0, -1, 0], [batchSize, 1, this.outputDims]).as2D(batchSize, -1).toFloat()); } - return dl.concat(samples, 1); + return tf.concat(samples, 1); }); } } @@ -414,8 +413,8 @@ class ConductorDecoder extends Decoder { * A Neural Autoregressive Distribution Estimator (NADE). */ class Nade { - encWeights: dl.Tensor2D; - decWeightsT: dl.Tensor2D; + encWeights: tf.Tensor2D; + decWeightsT: tf.Tensor2D; numDims: number; numHidden: number; @@ -427,7 +426,7 @@ class Nade { * @param decWeightsT The transposed decoder weights (kernel), sized * `[numDims, numHidden, 1]`. */ - constructor(encWeights: dl.Tensor3D, decWeightsT: dl.Tensor3D) { + constructor(encWeights: tf.Tensor3D, decWeightsT: tf.Tensor3D) { this.numDims = encWeights.shape[0]; this.numHidden = encWeights.shape[2]; @@ -445,33 +444,33 @@ class Nade { * @param decBias A batch of biases to use when decoding, sized * `[batchSize, numDims]`. */ - sample(encBias: dl.Tensor2D, decBias: dl.Tensor2D) { + sample(encBias: tf.Tensor2D, decBias: tf.Tensor2D) { const batchSize = encBias.shape[0]; - return dl.tidy(()=> { - const samples: dl.Tensor1D[] = []; + return tf.tidy(()=> { + const samples: tf.Tensor1D[] = []; let a = encBias.clone(); for (let i = 0; i < this.numDims; i++) { - const h = dl.sigmoid(a); + const h = tf.sigmoid(a); const encWeightsI = this.encWeights.slice( [i, 0], [1, this.numHidden]).as1D(); const decWeightsTI = this.decWeightsT.slice( [i, 0], [1, this.numHidden]); const decBiasI = decBias.slice([0, i], [batchSize, 1]); - const condLogitsI = decBiasI.add( - dl.matMul(h, decWeightsTI, false, true)); - const condProbsI = condLogitsI.sigmoid(); + const contfogitsI = decBiasI.add( + tf.matMul(h, decWeightsTI, false, true)); + const condProbsI = contfogitsI.sigmoid(); const samplesI = condProbsI.greaterEqual( - dl.scalar(0.5)).toFloat().as1D(); + tf.scalar(0.5)).toFloat().as1D(); if (i < this.numDims - 1) { a = a.add( - dl.outerProduct(samplesI.toFloat(), encWeightsI)) as dl.Tensor2D; + tf.outerProduct(samplesI.toFloat(), encWeightsI)) as tf.Tensor2D; } samples.push(samplesI); } - return dl.stack(samples, 1) as dl.Tensor2D; + return tf.stack(samples, 1) as tf.Tensor2D; }); } } @@ -490,7 +489,7 @@ class MusicVAE { private dataConverter: data.DataConverter; private encoder: Encoder; private decoder: Decoder; - private rawVars: {[varName: string]: dl.Tensor}; // Store for disposal. + private rawVars: {[varName: string]: tf.Tensor}; // Store for disposal. /** * `MusicVAE` constructor. * @@ -500,30 +499,26 @@ class MusicVAE { * file must exist within the checkpoint directory specifying the type and * args for the correct `DataConverter`. */ - constructor(checkpointURL: string, dataConverter?: data.DataConverter) { + constructor( + checkpointURL: string, dataConverter?: data.DataConverter) { this.checkpointURL = checkpointURL; - if (dataConverter) { - this.dataConverter = dataConverter; - } else { - fetch(checkpointURL + '/converter.json') - .then((response) => response.json()) - .then((converterSpec: data.ConverterSpec) => { - this.dataConverter = data.converterFromSpec(converterSpec); - }); - } + this.dataConverter = dataConverter; } /** * Disposes of any untracked `Tensors` to avoid GPU memory leaks. */ dispose() { - Object.keys(this.rawVars).forEach(name => this.rawVars[name].dispose()); - this.encoder = null; - this.decoder = null; + if (!isNullOrUndefined(this.rawVars)) { + Object.keys(this.rawVars).forEach(name => this.rawVars[name].dispose()); + } + this.encoder = undefined; + this.decoder = undefined; + this.dataConverter = undefined; } private getLstmLayers( - cellFormat: string, vars: {[varName: string]: dl.Tensor}) { + cellFormat: string, vars: {[varName: string]: tf.Tensor}) { const lstmLayers: LayerVars[] = []; let l = 0; while (true) { @@ -532,8 +527,8 @@ class MusicVAE { break; } lstmLayers.push(new LayerVars( - vars[cellPrefix + 'kernel'] as dl.Tensor2D, - vars[cellPrefix + 'bias'] as dl.Tensor1D)); + vars[cellPrefix + 'kernel'] as tf.Tensor2D, + vars[cellPrefix + 'bias'] as tf.Tensor1D)); ++l; } return lstmLayers; @@ -544,6 +539,8 @@ class MusicVAE { * `Decoder`. */ async initialize() { + this.dispose(); + const LSTM_CELL_FORMAT = 'cell_%d/lstm_cell/'; const MUTLI_LSTM_CELL_FORMAT = 'multi_rnn_cell/' + LSTM_CELL_FORMAT; const CONDUCTOR_PREFIX = 'decoder/hierarchical_level_0/'; @@ -553,13 +550,21 @@ class MusicVAE { const HIER_ENCODER_FORMAT = 'encoder/hierarchical_level_%d/' + BIDI_LSTM_CELL.replace('%d', '0'); + if (isNullOrUndefined(this.dataConverter)) { + fetch(this.checkpointURL + '/converter.json') + .then((response) => response.json()) + .then((converterSpec: data.ConverterSpec) => { + this.dataConverter = data.converterFromSpec(converterSpec); + }); + } + const reader = new CheckpointLoader(this.checkpointURL); const vars = await reader.getAllVariables(); this.rawVars= vars; // Save for disposal. // Encoder variables. const encMu = new LayerVars( - vars['encoder/mu/kernel'] as dl.Tensor2D, - vars['encoder/mu/bias'] as dl.Tensor1D); + vars['encoder/mu/kernel'] as tf.Tensor2D, + vars['encoder/mu/bias'] as tf.Tensor1D); if (this.dataConverter.numSegments) { const fwLayers = this.getLstmLayers( @@ -569,8 +574,8 @@ class MusicVAE { if (fwLayers.length !== bwLayers.length || fwLayers.length !== 2) { throw Error('Only 2 hierarchical encoder levels are supported. ' + - 'Got ' + fwLayers.length + ' forward and ' + - bwLayers.length + ' backward.'); + `Got ${fwLayers.length} forward and ${bwLayers.length} ` + + 'backward.'); } const baseEncoders: BidirectonalLstmEncoder[] = [0, 1].map( l => new BidirectonalLstmEncoder(fwLayers[l], bwLayers[l])); @@ -583,8 +588,8 @@ class MusicVAE { ENCODER_FORMAT.replace('%s', 'bw'), vars); if (fwLayers.length !== bwLayers.length || fwLayers.length !== 1) { throw Error('Only single-layer bidirectional encoders are supported. ' + - 'Got ' + fwLayers.length + ' forward and ' + - bwLayers.length + ' backward.'); + `Got ${fwLayers.length} forward and ${bwLayers.length} ` + + 'backward.'); } this.encoder = new BidirectonalLstmEncoder( fwLayers[0], bwLayers[0], encMu); @@ -597,7 +602,7 @@ class MusicVAE { const decVarPrefixes: string[] = []; if (this.dataConverter.NUM_SPLITS) { for (let i = 0; i < this.dataConverter.NUM_SPLITS; ++i) { - decVarPrefixes.push(decVarPrefix + 'core_decoder_' + i + '/decoder/'); + decVarPrefixes.push(decVarPrefix + `core_decoder_${i}/decoder/`); } } else { decVarPrefixes.push(decVarPrefix + 'decoder/'); @@ -607,35 +612,35 @@ class MusicVAE { const decLstmLayers = this.getLstmLayers( varPrefix + MUTLI_LSTM_CELL_FORMAT, vars); const decZtoInitState = new LayerVars( - vars[varPrefix + 'z_to_initial_state/kernel'] as dl.Tensor2D, - vars[varPrefix + 'z_to_initial_state/bias'] as dl.Tensor1D); + vars[varPrefix + 'z_to_initial_state/kernel'] as tf.Tensor2D, + vars[varPrefix + 'z_to_initial_state/bias'] as tf.Tensor1D); const decOutputProjection = new LayerVars( - vars[varPrefix + 'output_projection/kernel'] as dl.Tensor2D, - vars[varPrefix + 'output_projection/bias'] as dl.Tensor1D); + vars[varPrefix + 'output_projection/kernel'] as tf.Tensor2D, + vars[varPrefix + 'output_projection/bias'] as tf.Tensor1D); // Optional NADE for the BaseDecoder. const nade = ((varPrefix + 'nade/w_enc' in vars) ? new Nade( - vars[varPrefix + 'nade/w_enc'] as dl.Tensor3D, - vars[varPrefix + 'nade/w_dec_t'] as dl.Tensor3D) : null); + vars[varPrefix + 'nade/w_enc'] as tf.Tensor3D, + vars[varPrefix + 'nade/w_dec_t'] as tf.Tensor3D) : null); return new BaseDecoder( decLstmLayers, decZtoInitState, decOutputProjection, nade); }); // ConductorDecoder variables. if (this.dataConverter.numSegments) { - const condLstmLayers = this.getLstmLayers( + const contfstmLayers = this.getLstmLayers( CONDUCTOR_PREFIX + LSTM_CELL_FORMAT, vars); const condZtoInitState = new LayerVars( - vars[CONDUCTOR_PREFIX + 'initial_state/kernel'] as dl.Tensor2D, - vars[CONDUCTOR_PREFIX + 'initial_state/bias'] as dl.Tensor1D); + vars[CONDUCTOR_PREFIX + 'initial_state/kernel'] as tf.Tensor2D, + vars[CONDUCTOR_PREFIX + 'initial_state/bias'] as tf.Tensor1D); this.decoder = new ConductorDecoder( - baseDecoders, condLstmLayers, condZtoInitState, + baseDecoders, contfstmLayers, condZtoInitState, this.dataConverter.numSegments); } else if (baseDecoders.length === 1) { this.decoder = baseDecoders[0]; } else { throw Error('Unexpected number of base decoders without conductor: ' + - baseDecoders.length); + `${baseDecoders.length}`); } return this; @@ -676,15 +681,14 @@ class MusicVAE { * @returns An array of interpolation `NoteSequence` objects, as described * above. */ - async interpolate(inputSequences: data.INoteSequence[], numInterps: number) { + async interpolate(inputSequences: INoteSequence[], numInterps: number) { const numSteps = this.dataConverter.numSteps; - - const oh = dl.tidy(() => { - const inputTensors = dl.stack( + const oh = tf.tidy(() => { + const inputTensors = tf.stack( inputSequences.map( - this.dataConverter.toTensor.bind(this.dataConverter)) as dl.Tensor2D[] - ) as dl.Tensor3D; + this.dataConverter.toTensor.bind(this.dataConverter)) as tf.Tensor2D[] + ) as tf.Tensor3D; const outputTensors = this.interpolateTensors(inputTensors, numInterps); const result = []; @@ -698,7 +702,7 @@ class MusicVAE { return result; }); - const outputSequences: data.INoteSequence[] = []; + const outputSequences: INoteSequence[] = []; for (const tensor of oh) { outputSequences.push(await this.dataConverter.toNoteSequence(tensor)); tensor.dispose(); @@ -706,7 +710,7 @@ class MusicVAE { return outputSequences; } - private interpolateTensors(sequences: dl.Tensor3D, numInterps: number) { + private interpolateTensors(sequences: tf.Tensor3D, numInterps: number) { if (sequences.shape[0] !== 2 && sequences.shape[0] !== 4) { throw new Error( 'Invalid number of input sequences. Requires length 2, or 4'); @@ -716,33 +720,33 @@ class MusicVAE { const z =this.encoder.encode(sequences); // Compute the interpolations of the latent variable. - const interpolatedZs: dl.Tensor2D = dl.tidy(() => { - const rangeArray = dl.linspace(0.0, 1.0, numInterps); + const interpolatedZs: tf.Tensor2D = tf.tidy(() => { + const rangeArray = tf.linspace(0.0, 1.0, numInterps); const z0 = z.slice([0, 0], [1, z.shape[1]]).as1D(); const z1 = z.slice([1, 0], [1, z.shape[1]]).as1D(); if (sequences.shape[0] === 2) { - const zDiff = z1.sub(z0) as dl.Tensor1D; - return dl.outerProduct(rangeArray, zDiff).add(z0) as dl.Tensor2D; + const zDiff = z1.sub(z0) as tf.Tensor1D; + return tf.outerProduct(rangeArray, zDiff).add(z0) as tf.Tensor2D; } else if (sequences.shape[0] === 4) { const z2 = z.slice([2, 0], [1, z.shape[1]]).as1D(); const z3 = z.slice([3, 0], [1, z.shape[1]]).as1D(); - const revRangeArray = dl.scalar(1.0).sub(rangeArray) as dl.Tensor1D; + const revRangeArray = tf.scalar(1.0).sub(rangeArray) as tf.Tensor1D; const r = numInterps; let finalZs = z0.mul( - dl.outerProduct(revRangeArray, revRangeArray).as3D(r, r, 1)); - finalZs = dl.addStrict( + tf.outerProduct(revRangeArray, revRangeArray).as3D(r, r, 1)); + finalZs = tf.addStrict( finalZs, - z1.mul(dl.outerProduct(rangeArray, revRangeArray).as3D(r, r, 1))); - finalZs = dl.addStrict( + z1.mul(tf.outerProduct(rangeArray, revRangeArray).as3D(r, r, 1))); + finalZs = tf.addStrict( finalZs, - z2.mul(dl.outerProduct(revRangeArray, rangeArray).as3D(r, r, 1))); - finalZs = dl.addStrict( + z2.mul(tf.outerProduct(revRangeArray, rangeArray).as3D(r, r, 1))); + finalZs = tf.addStrict( finalZs, - z3.mul(dl.outerProduct(rangeArray, rangeArray).as3D(r, r, 1))); + z3.mul(tf.outerProduct(rangeArray, rangeArray).as3D(r, r, 1))); return finalZs.as2D(r * r, z.shape[1]); } else { @@ -766,7 +770,7 @@ class MusicVAE { async sample(numSamples: number, temperature=0.5) { const numSteps = this.dataConverter.numSteps; - const oh = dl.tidy(() => { + const oh = tf.tidy(() => { const outputTensors = this.sampleTensors( numSamples, numSteps, temperature); const result = []; @@ -780,7 +784,7 @@ class MusicVAE { return result; }); - const outputSequences: data.INoteSequence[] = []; + const outputSequences: INoteSequence[] = []; for (const tensor of oh) { outputSequences.push(await this.dataConverter.toNoteSequence(tensor)); tensor.dispose(); @@ -790,8 +794,8 @@ class MusicVAE { private sampleTensors( numSamples: number, numSteps: number, temperature?: number) { - return dl.tidy(() => { - const randZs: dl.Tensor2D = dl.randomNormal( + return tf.tidy(() => { + const randZs: tf.Tensor2D = tf.randomNormal( [numSamples, this.decoder.zDims]); return this.decoder.decode(randZs, numSteps, undefined, temperature); }); diff --git a/music_vae/tsconfig.json b/music_vae/tsconfig.json index db4ad978d..42c92f2a2 100644 --- a/music_vae/tsconfig.json +++ b/music_vae/tsconfig.json @@ -19,6 +19,6 @@ "allowUnreachableCode": false }, "include": [ - "index.ts" + "src/index.ts" ] } diff --git a/music_vae/yarn.lock b/music_vae/yarn.lock new file mode 100644 index 000000000..3825ff82d --- /dev/null +++ b/music_vae/yarn.lock @@ -0,0 +1,3451 @@ +# THIS IS AN AUTOGENERATED FILE. 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