mirror of https://github.com/vladmandic/human
optimize model loading
Vladimir Mandic
parent
c52f1c979c
commit
8bbfb9615a
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@ -31,10 +31,10 @@ import jsonView from './helpers/jsonview.js';
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let human;
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let userConfig = {
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/*
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warmup: 'none',
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backend: 'humangl',
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debug: true,
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/*
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wasmPath: 'https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@3.9.0/dist/',
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async: false,
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cacheSensitivity: 0.75,
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@ -51,8 +51,8 @@ let userConfig = {
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},
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object: { enabled: false },
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gesture: { enabled: true },
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hand: { enabled: false },
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// hand: { enabled: true, maxDetected: 1, minConfidence: 0.5, detector: { modelPath: 'handtrack.json' } },
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// hand: { enabled: false },
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hand: { enabled: true, maxDetected: 1, minConfidence: 0.5, detector: { modelPath: 'handtrack.json' } },
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body: { enabled: false },
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// body: { enabled: true, modelPath: 'movenet-multipose.json' },
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// body: { enabled: true, modelPath: 'posenet.json' },
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@ -226,10 +226,11 @@ export const detectFace = async (parent /* instance of human */, input: Tensor):
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delete faces[i].annotations.leftEyeIris;
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delete faces[i].annotations.rightEyeIris;
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}
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const irisSize = (faces[i].annotations?.leftEyeIris && faces[i].annotations?.rightEyeIris)
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/* note: average human iris size is 11.7mm */
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const irisSize = (faces[i].annotations && faces[i].annotations.leftEyeIris && faces[i].annotations.rightEyeIris
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&& (faces[i].annotations.leftEyeIris.length > 0) && (faces[i].annotations.rightEyeIris.length > 0)
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&& (faces[i].annotations.leftEyeIris[0] !== null) && (faces[i].annotations.rightEyeIris[0] !== null))
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? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2]
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: 0;
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: 0; // note: average human iris size is 11.7mm
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// optionally return tensor
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const tensor = parent.config.face.detector.return ? tf.squeeze(faces[i].tensor) : null;
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@ -9,9 +9,11 @@ import type { GraphModel, Tensor } from '../tfjs/types';
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import type { Config } from '../config';
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import { env } from '../env';
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import * as fingerPose from '../fingerpose/fingerpose';
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import { fakeOps } from '../tfjs/backend';
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const models: [GraphModel | null, GraphModel | null] = [null, null];
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const modelOutputNodes = ['StatefulPartitionedCall/Postprocessor/Slice', 'StatefulPartitionedCall/Postprocessor/ExpandDims_1'];
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const inputSize = [[0, 0], [0, 0]];
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const classes = [
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@ -55,12 +57,12 @@ const fingerMap = {
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palm: [0],
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};
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export async function load(config: Config): Promise<[GraphModel, GraphModel]> {
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if (env.initial) {
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models[0] = null;
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models[1] = null;
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}
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export async function loadDetect(config: Config): Promise<GraphModel> {
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if (env.initial) models[0] = null;
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if (!models[0]) {
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// handtrack model has some kernel ops defined in model but those are never referenced and non-existent in tfjs
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// ideally need to prune the model itself
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fakeOps(['tensorlistreserve', 'enter', 'tensorlistfromtensor', 'merge', 'loopcond', 'switch', 'exit', 'tensorliststack', 'nextiteration', 'tensorlistsetitem', 'tensorlistgetitem', 'reciprocal', 'shape', 'split', 'where'], config);
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models[0] = await tf.loadGraphModel(join(config.modelBasePath, config.hand.detector?.modelPath || '')) as unknown as GraphModel;
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const inputs = Object.values(models[0].modelSignature['inputs']);
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inputSize[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
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@ -68,6 +70,11 @@ export async function load(config: Config): Promise<[GraphModel, GraphModel]> {
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if (!models[0] || !models[0]['modelUrl']) log('load model failed:', config.object.modelPath);
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else if (config.debug) log('load model:', models[0]['modelUrl']);
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} else if (config.debug) log('cached model:', models[0]['modelUrl']);
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return models[0];
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}
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export async function loadSkeleton(config: Config): Promise<GraphModel> {
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if (env.initial) models[1] = null;
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if (!models[1]) {
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models[1] = await tf.loadGraphModel(join(config.modelBasePath, config.hand.skeleton?.modelPath || '')) as unknown as GraphModel;
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const inputs = Object.values(models[1].modelSignature['inputs']);
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@ -76,7 +83,13 @@ export async function load(config: Config): Promise<[GraphModel, GraphModel]> {
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if (!models[1] || !models[1]['modelUrl']) log('load model failed:', config.object.modelPath);
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else if (config.debug) log('load model:', models[1]['modelUrl']);
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} else if (config.debug) log('cached model:', models[1]['modelUrl']);
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return models as [GraphModel, GraphModel];
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return models[1];
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}
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export async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {
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if (!models[0]) await loadDetect(config);
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if (!models[1]) await loadSkeleton(config);
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return models;
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}
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async function detectHands(input: Tensor, config: Config): Promise<HandDetectResult[]> {
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@ -217,4 +230,20 @@ export async function predict(input: Tensor, config: Config): Promise<HandResult
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- TFJS Port: <https://github.com/victordibia/handtrack.js/>
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- Original: <https://github.com/victordibia/handtracking>
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- Writeup: <https://medium.com/@victor.dibia/how-to-build-a-real-time-hand-detector-using-neural-networks-ssd-on-tensorflow-d6bac0e4b2ce>
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- Convert:
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tensorflowjs_converter --input_format=tf_frozen_model --output_format=tfjs_graph_model \
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--output_node_names='num_detections,detection_boxes,detection_scores,detection_classes' --saved_model_tags=serve --quantize_uint8=* \
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--strip_debug_ops=* --weight_shard_size_bytes=10000000000 --control_flow_v2=true frozen_inference_graph.pb graph
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webmodel/efficientdet512d0/base/model.json
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webmodel/centernet512fpn/base/model.json
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https://github.com/victordibia/handtrack.js/commit/70d5d9c98e69688414cddaad044bd8730bc982d1#diff-c40e819be4ec1dc29f26913f5cdeb05202261b3a1725ab259cb235ea0f0fc5d6
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git rev-list HEAD -- webmodel/*
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9ba7220fb31e9168aa248500cc70800566f4c719
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70d5d9c98e69688414cddaad044bd8730bc982d1
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git checkout 9ba7220fb31e9168aa248500cc70800566f4c719^ -- webmodel
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git checkout 70d5d9c98e69688414cddaad044bd8730bc982d1^ -- webmodel
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*/
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39
src/human.ts
39
src/human.ts
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@ -29,7 +29,7 @@ import * as backend from './tfjs/backend';
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import * as humangl from './tfjs/humangl';
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import * as app from '../package.json';
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import * as warmups from './warmup';
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import type { Tensor, GraphModel } from './tfjs/types';
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import type { Tensor } from './tfjs/types';
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import type { DrawOptions } from './draw';
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// export types
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@ -37,6 +37,7 @@ export * from './config';
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export * from './result';
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export type { DrawOptions } from './draw';
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export { env, Env } from './env';
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export { Models } from './models';
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/** Defines all possible input types for **Human** detection
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* @typedef Input Type
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@ -119,23 +120,7 @@ export class Human {
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/** Currently loaded models
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* @internal
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*/
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models: {
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face: [unknown, GraphModel | null, GraphModel | null] | null,
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posenet: GraphModel | null,
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blazepose: GraphModel | null,
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efficientpose: GraphModel | null,
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movenet: GraphModel | null,
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handpose: [GraphModel | null, GraphModel | null] | null,
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handtrack: [GraphModel | null, GraphModel | null] | null,
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age: GraphModel | null,
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gender: GraphModel | null,
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emotion: GraphModel | null,
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embedding: GraphModel | null,
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nanodet: GraphModel | null,
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centernet: GraphModel | null,
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faceres: GraphModel | null,
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segmentation: GraphModel | null,
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};
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models: models.Models;
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/** Container for events dispatched by Human
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*
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@ -187,23 +172,7 @@ export class Human {
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this.performance = { backend: 0, load: 0, image: 0, frames: 0, cached: 0, changed: 0, total: 0, draw: 0 };
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this.events = new EventTarget();
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// object that contains all initialized models
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this.models = {
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face: null, // array of models
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handpose: null, // array of models
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handtrack: null, // array of models
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posenet: null,
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blazepose: null,
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efficientpose: null,
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movenet: null,
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age: null,
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gender: null,
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emotion: null,
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embedding: null,
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nanodet: null,
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centernet: null,
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faceres: null,
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segmentation: null,
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};
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this.models = new models.Models();
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// reexport draw methods
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this.draw = {
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options: draw.options as DrawOptions,
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123
src/models.ts
123
src/models.ts
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@ -12,77 +12,72 @@ import * as movenet from './movenet/movenet';
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import * as nanodet from './object/nanodet';
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import * as centernet from './object/centernet';
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import * as segmentation from './segmentation/segmentation';
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import type { Human } from './human';
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import { env } from './env';
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// import * as agegenderrace from './gear/agegenderrace';
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import * as agegenderrace from './gear/agegenderrace';
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export function reset(instance) {
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/** Instances of all possible TFJS Graph Models used by Human
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* - loaded as needed based on configuration
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* - initialized explictly with `human.load()` method
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* - initialized implicity on first call to `human.detect()`
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* - each model can be `null` if not loaded, instance of `GraphModel` if loaded or `Promise` if loading
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*/
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export class Models {
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age: null | GraphModel | Promise<GraphModel> = null;
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agegenderrace: null | GraphModel | Promise<GraphModel> = null;
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blazepose: null | GraphModel | Promise<GraphModel> = null;
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centernet: null | GraphModel | Promise<GraphModel> = null;
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efficientpose: null | GraphModel | Promise<GraphModel> = null;
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embedding: null | GraphModel | Promise<GraphModel> = null;
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emotion: null | GraphModel | Promise<GraphModel> = null;
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facedetect: null | GraphModel | Promise<GraphModel> = null;
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faceiris: null | GraphModel | Promise<GraphModel> = null;
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facemesh: null | GraphModel | Promise<GraphModel> = null;
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faceres: null | GraphModel | Promise<GraphModel> = null;
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gender: null | GraphModel | Promise<GraphModel> = null;
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handpose: null | GraphModel | Promise<GraphModel> = null;
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handskeleton: null | GraphModel | Promise<GraphModel> = null;
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handtrack: null | GraphModel | Promise<GraphModel> = null;
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movenet: null | GraphModel | Promise<GraphModel> = null;
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nanodet: null | GraphModel | Promise<GraphModel> = null;
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posenet: null | GraphModel | Promise<GraphModel> = null;
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segmentation: null | GraphModel | Promise<GraphModel> = null;
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}
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export function reset(instance: Human) {
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// if (instance.config.debug) log('resetting loaded models');
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instance.models = {
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face: null, // array of models
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handpose: null, // array of models
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handtrack: null, // array of models
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posenet: null,
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blazepose: null,
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efficientpose: null,
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movenet: null,
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age: null,
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gender: null,
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emotion: null,
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embedding: null,
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nanodet: null,
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centernet: null,
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faceres: null,
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segmentation: null,
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};
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for (const model of Object.keys(instance.models)) instance.models[model] = null;
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}
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/** Load method preloads all instance.configured models on-demand */
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export async function load(instance) {
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export async function load(instance: Human) {
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if (env.initial) reset(instance);
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if (instance.config.async) { // load models concurrently
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[
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instance.models.face,
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instance.models.emotion,
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instance.models.handpose,
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instance.models.handtrack,
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instance.models.posenet,
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instance.models.blazepose,
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instance.models.efficientpose,
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instance.models.movenet,
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instance.models.nanodet,
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instance.models.centernet,
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instance.models.faceres,
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instance.models.segmentation,
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// instance.models.agegenderrace,
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] = await Promise.all([
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instance.models.face || (instance.config.face.enabled ? facemesh.load(instance.config) : null),
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instance.models.emotion || ((instance.config.face.enabled && instance.config.face.emotion.enabled) ? emotion.load(instance.config) : null),
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instance.models.handpose || (instance.config.hand.enabled && instance.config.hand.detector.modelPath.includes('handdetect') ? handpose.load(instance.config) : null),
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instance.models.handtrack || (instance.config.hand.enabled && instance.config.hand.detector.modelPath.includes('handtrack') ? handtrack.load(instance.config) : null),
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instance.models.posenet || (instance.config.body.enabled && instance.config.body.modelPath.includes('posenet') ? posenet.load(instance.config) : null),
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instance.models.blazepose || (instance.config.body.enabled && instance.config.body.modelPath.includes('blazepose') ? blazepose.load(instance.config) : null),
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instance.models.efficientpose || (instance.config.body.enabled && instance.config.body.modelPath.includes('efficientpose') ? efficientpose.load(instance.config) : null),
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instance.models.movenet || (instance.config.body.enabled && instance.config.body.modelPath.includes('movenet') ? movenet.load(instance.config) : null),
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instance.models.nanodet || (instance.config.object.enabled && instance.config.object.modelPath.includes('nanodet') ? nanodet.load(instance.config) : null),
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instance.models.centernet || (instance.config.object.enabled && instance.config.object.modelPath.includes('centernet') ? centernet.load(instance.config) : null),
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instance.models.faceres || ((instance.config.face.enabled && instance.config.face.description.enabled) ? faceres.load(instance.config) : null),
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instance.models.segmentation || (instance.config.segmentation.enabled ? segmentation.load(instance.config) : null),
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// instance.models.agegenderrace || ((instance.config.face.enabled && instance.config.face.agegenderrace.enabled) ? agegenderrace.load(instance.config) : null),
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]);
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} else { // load models sequentially
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if (instance.config.face.enabled && !instance.models.face) instance.models.face = await facemesh.load(instance.config);
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if (instance.config.face.enabled && instance.config.face.emotion.enabled && !instance.models.emotion) instance.models.emotion = await emotion.load(instance.config);
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if (instance.config.hand.enabled && !instance.models.handpose && instance.config.hand.detector.modelPath.includes('handdetect')) instance.models.handpose = await handpose.load(instance.config);
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if (instance.config.hand.enabled && !instance.models.handtrack && instance.config.hand.detector.modelPath.includes('handtrack')) instance.models.handtrack = await handtrack.load(instance.config);
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if (instance.config.body.enabled && !instance.models.posenet && instance.config.body.modelPath.includes('posenet')) instance.models.posenet = await posenet.load(instance.config);
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if (instance.config.body.enabled && !instance.models.blazepose && instance.config.body.modelPath.includes('blazepose')) instance.models.blazepose = await blazepose.load(instance.config);
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if (instance.config.body.enabled && !instance.models.efficientpose && instance.config.body.modelPath.includes('efficientpose')) instance.models.efficientpose = await blazepose.load(instance.config);
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if (instance.config.body.enabled && !instance.models.movenet && instance.config.body.modelPath.includes('movenet')) instance.models.movenet = await movenet.load(instance.config);
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if (instance.config.object.enabled && !instance.models.nanodet && instance.config.object.modelPath.includes('nanodet')) instance.models.nanodet = await nanodet.load(instance.config);
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if (instance.config.object.enabled && !instance.models.centernet && instance.config.object.modelPath.includes('centernet')) instance.models.centernet = await centernet.load(instance.config);
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if (instance.config.face.enabled && instance.config.face.description.enabled && !instance.models.faceres) instance.models.faceres = await faceres.load(instance.config);
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if (instance.config.segmentation.enabled && !instance.models.segmentation) instance.models.segmentation = await segmentation.load(instance.config);
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// if (instance.config.face.enabled && instance.config.face.agegenderrace.enabled && !instance.models.agegenderrace) instance.models.agegenderrace = await agegenderrace.load(instance.config);
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if (instance.config.face.enabled) { // face model is a combo that must be loaded as a whole
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if (!instance.models.facedetect) [instance.models.facedetect, instance.models.facemesh, instance.models.faceiris] = await facemesh.load(instance.config);
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if (instance.config.face.mesh?.enabled && !instance.models.facemesh) [instance.models.facedetect, instance.models.facemesh, instance.models.faceiris] = await facemesh.load(instance.config);
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if (instance.config.face.iris?.enabled && !instance.models.faceiris) [instance.models.facedetect, instance.models.facemesh, instance.models.faceiris] = await facemesh.load(instance.config);
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}
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if (instance.config.hand.enabled) { // handpose model is a combo that must be loaded as a whole
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if (!instance.models.handpose && instance.config.hand.detector?.modelPath?.includes('handdetect')) [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);
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if (!instance.models.handskeleton && instance.config.hand.landmarks && instance.config.hand.detector?.modelPath?.includes('handdetect')) [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);
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}
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if (instance.config.hand.enabled && !instance.models.handtrack && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handtrack = handtrack.loadDetect(instance.config);
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if (instance.config.hand.enabled && instance.config.hand.landmarks && !instance.models.handskeleton && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handskeleton = handtrack.loadSkeleton(instance.config);
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if (instance.config.body.enabled && !instance.models.posenet && instance.config.body?.modelPath?.includes('posenet')) instance.models.posenet = posenet.load(instance.config);
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if (instance.config.body.enabled && !instance.models.efficientpose && instance.config.body?.modelPath?.includes('efficientpose')) instance.models.efficientpose = efficientpose.load(instance.config);
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if (instance.config.body.enabled && !instance.models.blazepose && instance.config.body?.modelPath?.includes('blazepose')) instance.models.blazepose = blazepose.load(instance.config);
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if (instance.config.body.enabled && !instance.models.efficientpose && instance.config.body?.modelPath?.includes('efficientpose')) instance.models.efficientpose = blazepose.load(instance.config);
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if (instance.config.body.enabled && !instance.models.movenet && instance.config.body?.modelPath?.includes('movenet')) instance.models.movenet = movenet.load(instance.config);
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if (instance.config.object.enabled && !instance.models.nanodet && instance.config.object?.modelPath?.includes('nanodet')) instance.models.nanodet = nanodet.load(instance.config);
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if (instance.config.object.enabled && !instance.models.centernet && instance.config.object?.modelPath?.includes('centernet')) instance.models.centernet = centernet.load(instance.config);
|
||||
if (instance.config.face.enabled && instance.config.face.emotion?.enabled && !instance.models.emotion) instance.models.emotion = emotion.load(instance.config);
|
||||
if (instance.config.face.enabled && instance.config.face.description?.enabled && !instance.models.faceres) instance.models.faceres = faceres.load(instance.config);
|
||||
if (instance.config.segmentation.enabled && !instance.models.segmentation) instance.models.segmentation = segmentation.load(instance.config);
|
||||
if (instance.config.face.enabled && instance.config.face['agegenderrace']?.enabled && !instance.models.agegenderrace) instance.models.agegenderrace = agegenderrace.load(instance.config);
|
||||
|
||||
// models are loaded in parallel asynchronously so lets wait until they are actually loaded
|
||||
for await (const model of Object.keys(instance.models)) {
|
||||
if (instance.models[model] && typeof instance.models[model] !== 'undefined') instance.models[model] = await instance.models[model];
|
||||
}
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -9,6 +9,7 @@ import type { ObjectResult } from '../result';
|
|||
import type { GraphModel, Tensor } from '../tfjs/types';
|
||||
import type { Config } from '../config';
|
||||
import { env } from '../env';
|
||||
import { fakeOps } from '../tfjs/backend';
|
||||
|
||||
let model: GraphModel | null;
|
||||
let inputSize = 0;
|
||||
|
|
@ -18,6 +19,7 @@ let skipped = Number.MAX_SAFE_INTEGER;
|
|||
export async function load(config: Config): Promise<GraphModel> {
|
||||
if (env.initial) model = null;
|
||||
if (!model) {
|
||||
fakeOps(['floormod'], config);
|
||||
model = await tf.loadGraphModel(join(config.modelBasePath, config.object.modelPath || '')) as unknown as GraphModel;
|
||||
const inputs = Object.values(model.modelSignature['inputs']);
|
||||
inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
|
||||
|
|
|
|||
|
|
@ -96,3 +96,19 @@ export async function check(instance, force = false) {
|
|||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
// register fake missing tfjs ops
|
||||
export function fakeOps(kernelNames: Array<string>, config) {
|
||||
// if (config.debug) log('registerKernel:', kernelNames);
|
||||
for (const kernelName of kernelNames) {
|
||||
const kernelConfig = {
|
||||
kernelName,
|
||||
backendName: config.backend,
|
||||
kernelFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },
|
||||
// setupFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },
|
||||
// disposeFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },
|
||||
};
|
||||
tf.registerKernel(kernelConfig);
|
||||
}
|
||||
env.env.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); // re-scan registered ops
|
||||
}
|
||||
|
|
|
|||
|
|
@ -3,7 +3,6 @@ const process = require('process');
|
|||
const canvasJS = require('canvas');
|
||||
|
||||
let fetch; // fetch is dynamically imported later
|
||||
let tensors = 0;
|
||||
let config;
|
||||
|
||||
const log = (status, ...data) => {
|
||||
|
|
@ -73,10 +72,9 @@ async function testInstance(human) {
|
|||
log('info', 'tfjs version:', human.tf.version.tfjs);
|
||||
|
||||
await human.load();
|
||||
tensors = human.tf.engine().state.numTensors;
|
||||
if (config.backend === human.tf.getBackend()) log('state', 'passed: set backend:', config.backend);
|
||||
else log('error', 'failed: set backend:', config.backend);
|
||||
log('state', 'tensors', tensors);
|
||||
log('state', 'tensors', human.tf.memory().numTensors);
|
||||
|
||||
if (human.models) {
|
||||
log('state', 'passed: load models');
|
||||
|
|
@ -107,9 +105,9 @@ async function testWarmup(human, title) {
|
|||
return warmup;
|
||||
}
|
||||
|
||||
async function testDetect(human, input, title) {
|
||||
async function testDetect(human, input, title, checkLeak = true) {
|
||||
await human.load(config);
|
||||
tensors = human.tf.engine().state.numTensors;
|
||||
const tensors = human.tf.engine().state.numTensors;
|
||||
const image = input ? await getImage(human, input) : human.tf.randomNormal([1, 1024, 1024, 3]);
|
||||
if (!image) {
|
||||
log('error', 'failed: detect: input is null');
|
||||
|
|
@ -130,6 +128,11 @@ async function testDetect(human, input, title) {
|
|||
} else {
|
||||
log('error', 'failed: detect', input || 'random', title);
|
||||
}
|
||||
// check tensor leaks
|
||||
if (checkLeak) {
|
||||
const leak = human.tf.engine().state.numTensors - tensors;
|
||||
if (leak !== 0) log('error', 'failed: memory leak', leak);
|
||||
}
|
||||
return detect;
|
||||
}
|
||||
const evt = { image: 0, detect: 0, warmup: 0 };
|
||||
|
|
@ -170,8 +173,8 @@ async function test(Human, inputConfig) {
|
|||
await human.load();
|
||||
const models = Object.keys(human.models).map((model) => ({ name: model, loaded: (human.models[model] !== null) }));
|
||||
const loaded = models.filter((model) => model.loaded);
|
||||
if (models.length === 15 && loaded.length === 7) log('state', 'passed: models loaded', models.length, loaded.length);
|
||||
else log('error', 'failed: models loaded', models.length, loaded.length);
|
||||
if (models.length === 19 && loaded.length === 10) log('state', 'passed: models loaded', models);
|
||||
else log('error', 'failed: models loaded', models);
|
||||
|
||||
// test warmup sequences
|
||||
await testInstance(human);
|
||||
|
|
@ -315,15 +318,15 @@ async function test(Human, inputConfig) {
|
|||
// test async multiple instances
|
||||
log('info', 'test: concurrent');
|
||||
await Promise.all([
|
||||
testDetect(human, 'samples/ai-face.jpg', 'default'),
|
||||
testDetect(first, 'samples/ai-face.jpg', 'default'),
|
||||
testDetect(second, 'samples/ai-face.jpg', 'default'),
|
||||
testDetect(human, 'samples/ai-body.jpg', 'default'),
|
||||
testDetect(first, 'samples/ai-body.jpg', 'default'),
|
||||
testDetect(second, 'samples/ai-body.jpg', 'default'),
|
||||
testDetect(human, 'samples/ai-upper.jpg', 'default'),
|
||||
testDetect(first, 'samples/ai-upper.jpg', 'default'),
|
||||
testDetect(second, 'samples/ai-upper.jpg', 'default'),
|
||||
testDetect(human, 'samples/ai-face.jpg', 'default', false),
|
||||
testDetect(first, 'samples/ai-face.jpg', 'default', false),
|
||||
testDetect(second, 'samples/ai-face.jpg', 'default', false),
|
||||
testDetect(human, 'samples/ai-body.jpg', 'default', false),
|
||||
testDetect(first, 'samples/ai-body.jpg', 'default', false),
|
||||
testDetect(second, 'samples/ai-body.jpg', 'default', false),
|
||||
testDetect(human, 'samples/ai-upper.jpg', 'default', false),
|
||||
testDetect(first, 'samples/ai-upper.jpg', 'default', false),
|
||||
testDetect(second, 'samples/ai-upper.jpg', 'default', false),
|
||||
]);
|
||||
|
||||
// test monkey-patch
|
||||
|
|
@ -346,11 +349,6 @@ async function test(Human, inputConfig) {
|
|||
// tests end
|
||||
const t1 = process.hrtime.bigint();
|
||||
|
||||
// check tensor leaks
|
||||
const leak = human.tf.engine().state.numTensors - tensors;
|
||||
if (leak === 0) log('state', 'passeed: no memory leak');
|
||||
else log('error', 'failed: memory leak', leak);
|
||||
|
||||
// check if all instances reported same
|
||||
const tensors1 = human.tf.engine().state.numTensors;
|
||||
const tensors2 = first.tf.engine().state.numTensors;
|
||||
|
|
@ -360,6 +358,7 @@ async function test(Human, inputConfig) {
|
|||
|
||||
// report end
|
||||
log('info', 'events:', evt);
|
||||
log('info', 'tensors', human.tf.memory().numTensors);
|
||||
log('info', 'test complete:', Math.trunc(Number(t1 - t0) / 1000 / 1000), 'ms');
|
||||
}
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue