Implement ONNX importing (#408)

* Impl onnx

* Prepare onnx before test

Author: ishii-norimi

.github/workflows/gh-pages.yml

@@ -25,6 +25,8 @@ jobs:
           node-version: '16.x'
       - name: Install
         run: yarn
+      - name: Prepare ONNX
+        run: bash ./onnx.sh
       - name: Test lib
         run: yarn test:lib --bail
       - name: Coveralls

.github/workflows/npm.yml

@@ -12,6 +12,8 @@ jobs:
           node-version: '16.x'
       - name: Install
         run: yarn
+      - name: Prepare ONNX
+        run: bash ./onnx.sh
       - name: Test
         run: yarn test:lib --bail
   publish:

.github/workflows/test.yml

@@ -10,6 +10,8 @@ jobs:
           node-version: '16.x'
       - name: Install
         run: yarn
+      - name: Prepare ONNX
+        run: bash ./onnx.sh
       - name: Test lib
         run: yarn test:lib --bail
       - name: Coveralls

.prettierignore

@@ -0,0 +1 @@
+lib/model/nns/onnx/onnx_pb.js

NOTICE.md

@@ -0,0 +1,249 @@
+# Third Party Licenses
+
+## ONNX
+
+Source: [https://github.com/onnx/onnx](https://github.com/onnx/onnx)
+
+### License
+
+```
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+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
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+   4. Redistribution. You may reproduce and distribute copies of the
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+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
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+
+## Protocol Buffers
+
+Source: [https://github.com/protocolbuffers/protobuf-javascript](https://github.com/protocolbuffers/protobuf-javascript)
+
+### License
+
+```
+BSD 3-Clause License
+
+Copyright (c) 2022, Google Inc.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
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lib/model/neuralnetwork.js

@@ -11,6 +11,8 @@ import { SGDOptimizer, MomentumOptimizer, RMSPropOptimizer, AdamOptimizer } from
 import InputLayer from './nns/layer/input.js'
 import OutputLayer from './nns/layer/output.js'
 
+let ONNXImporter = null
+
 /**
  * Exception for neuralnetwork class
  */
@@ -79,6 +81,19 @@ export default class NeuralNetwork {
 		return new NeuralNetwork(graph, optimizer)
 	}
 
+	/**
+	 * Load onnx model.
+	 *
+	 * @param {Uint8Array | ArrayBuffer | File} buffer File
+	 * @returns {Promise<NeuralNetwork>} Loaded NeuralNetwork
+	 */
+	static async fromONNX(buffer) {
+		if (!ONNXImporter) {
+			ONNXImporter = (await import('./nns/onnx/onnx_importer.js')).default
+		}
+		return ONNXImporter.load(buffer)
+	}
+
 	/**
 	 * @param {ComputationalGraph} graph Graph of a network
 	 * @param {'sgd' | 'adam' | 'momentum' | 'rmsprop'} [optimizer=sgd] Optimizer of the network

lib/model/nns/layer/reshape.js

@@ -14,7 +14,13 @@ export default class ReshapeLayer extends Layer {
 			const sizes = this.graph.getNode(this._size).lastOutputSize
 			this._out_size = sizes
 		} else {
-			this._out_size = [x.sizes[0], ...this._size]
+			this._out_size = this._size
+		}
+		if (
+			this._out_size.length === 1 ||
+			this._in_size.slice(1).reduce((s, v) => s * v, 1) === this._out_size.reduce((s, v) => s * v, 1)
+		) {
+			this._out_size = [x.sizes[0], ...this._out_size]
 		}
 		const o = this._out_size.length === 2 ? x.copy() : Tensor.fromArray(x.copy())
 		o.reshape(...this._out_size)

lib/model/nns/onnx/onnx_importer.js

@@ -0,0 +1,51 @@
+import onnx from './onnx_pb.js'
+export * as onnx from './onnx_pb.js'
+
+import NeuralNetwork from '../../neuralnetwork.js'
+
+import input from './operators/input.js'
+import output from './operators/output.js'
+
+const operators = {}
+
+export default class ONNXImporter {
+	/**
+	 * Load onnx model.
+	 *
+	 * @param {Uint8Array | ArrayBuffer | File} buffer File
+	 */
+	static async load(buffer) {
+		if (globalThis.File && buffer instanceof File) {
+			buffer = new Uint8Array(await buffer.arrayBuffer())
+		}
+		if (buffer instanceof ArrayBuffer) {
+			buffer = new Uint8Array(buffer)
+		}
+		const modelProto = onnx.ModelProto.deserializeBinary(buffer)
+		const model = modelProto.toObject()
+
+		const nodes = []
+		for (const node of model.graph.inputList) {
+			nodes.push(...input.import(model, node))
+		}
+		for (const node of model.graph.nodeList) {
+			const opType = node.opType
+			if (!operators[opType]) {
+				try {
+					const module = await import(`./operators/${opType.toLowerCase()}.js`)
+					operators[opType] = module.default
+				} catch (e) {
+					console.error(opType, e.name, e.message)
+					continue
+				}
+			}
+			const op = operators[opType]
+			nodes.push(...op.import(model, node))
+		}
+		for (const node of model.graph.outputList) {
+			nodes.push(...output.import(model, node))
+		}
+		const net = NeuralNetwork.fromObject(nodes)
+		return net
+	}
+}