TFRecords Reader and Builder-Reader integration tests (#517)
* Use boundigBox * WIP: Adding TFRecords Reader * Read TFRecords with multiple records * change length as a property * Added TFRecords Builder-Reader integration tests * Adding Guard and resolving other pr feedback
Esse commit está contido em:
Jacopo Mangiavacchi
gerado
+5
@@ -3042,6 +3042,11 @@
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"resolved": "https://registry.npmjs.org/buffer-indexof/-/buffer-indexof-1.1.1.tgz",
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"integrity": "sha512-4/rOEg86jivtPTeOUUT61jJO1Ya1TrR/OkqCSZDyq84WJh3LuuiphBYJN+fm5xufIk4XAFcEwte/8WzC8If/1g=="
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},
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"buffer-reverse": {
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"version": "1.0.1",
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"resolved": "https://registry.npmjs.org/buffer-reverse/-/buffer-reverse-1.0.1.tgz",
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"integrity": "sha1-SSg8jvpvkBvAH6MwTQYCeXGuL2A="
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},
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"buffer-xor": {
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"version": "1.0.3",
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"resolved": "https://registry.npmjs.org/buffer-xor/-/buffer-xor-1.0.3.tgz",
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+4
-2
@@ -8,10 +8,12 @@
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"@types/snapsvg": "^0.4.35",
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"axios": "^0.18.0",
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"bootstrap": "^4.1.3",
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"buffer-reverse": "^1.0.1",
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"deepmerge": "^2.2.1",
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"google-protobuf": "^3.6.1",
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"lodash": "^4.17.11",
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"md5.js": "^1.3.5",
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"node-int64": "^0.4.0",
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"react": "^16.7.0",
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"react-dom": "^16.7.0",
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"react-keydown": "^1.9.7",
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@@ -27,8 +29,8 @@
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"redux-thunk": "^2.3.0",
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"rimraf": "^2.6.2",
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"shortid": "^2.2.14",
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"vott-ct": "^2.1.11",
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"video-react": "^0.13.2"
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"video-react": "^0.13.2",
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"vott-ct": "^2.1.11"
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},
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"scripts": {
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"start": "nf start -p 3000",
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@@ -176,19 +176,17 @@ export class TFRecordsJsonExportProvider extends ExportProvider<ITFRecordsJsonEx
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}
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private updateAssetTagArrays(element: IAssetMetadata, imageInfo: IImageInfo) {
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element.regions.filter((region) => (region.type === RegionType.Rectangle ||
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region.type === RegionType.Square) &&
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region.points.length === 2)
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element.regions.filter((region) => region.boundingBox)
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.forEach((region) => {
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region.tags.forEach((tag) => {
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const index = this.project.tags.map((pTag) => pTag.name).indexOf(tag.name);
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imageInfo.text.push(tag.name);
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imageInfo.label.push(index);
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imageInfo.xmin.push(region.points[0].x);
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imageInfo.ymin.push(region.points[0].y);
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imageInfo.xmax.push(region.points[1].x);
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imageInfo.ymax.push(region.points[1].y);
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imageInfo.xmin.push(region.boundingBox.left);
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imageInfo.ymin.push(region.boundingBox.top);
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imageInfo.xmax.push(region.boundingBox.left + region.boundingBox.width);
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imageInfo.ymax.push(region.boundingBox.top + region.boundingBox.height);
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imageInfo.difficult.push(0);
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imageInfo.truncated.push(0);
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imageInfo.view.push("Unspecified");
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@@ -20,11 +20,16 @@
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// const maskDelta uint32 = 0xa282ead8
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// mask returns a masked representation of crc.
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import Guard from "../../../common/guard";
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import Int64 from "node-int64";
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import reverse from "buffer-reverse";
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/**
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* @s - Buffer input
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* @buffer - Buffer input
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* @description - Calculate 32-bit CRC using the Castagnoli polynomial (0x1EDC6F41)
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*/
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export function crc32c(s: Buffer): number {
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export function crc32c(buffer: Buffer): number {
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Guard.null(buffer);
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const polynomial = 0x1EDC6F41; // 0x04C11DB7 for crc32
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const initialValue = 0xFFFFFFFF;
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const finalXORValue = 0xFFFFFFFF;
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@@ -55,8 +60,8 @@ export function crc32c(s: Buffer): number {
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table[i] = reverse(c, 32);
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}
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for (i = 0; i < s.length; i++) {
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c = s[i];
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for (i = 0; i < buffer.length; i++) {
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c = buffer[i];
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if (c > 255) {
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throw new RangeError();
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}
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@@ -68,10 +73,11 @@ export function crc32c(s: Buffer): number {
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}
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/**
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* @s - Input CRC32 value
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* @value - Input CRC32 value
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* @description - Mask an input CRC32 value according to the TensorFlow TFRecords specs
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*/
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export function maskCrc(value: number): number {
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Guard.null(value);
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const kCrc32MaskDelta = 0xa282ead8;
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const fourGb = Math.pow(2, 32);
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@@ -79,10 +85,11 @@ export function maskCrc(value: number): number {
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}
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/**
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* @s - Input number value
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* @value - Input number value
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* @description - Get a Buffer representation of a Int64 bit value
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*/
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export function getInt64Buffer(value: number): Buffer {
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Guard.null(value);
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const metadataBuffer = new ArrayBuffer(8);
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const intArray = new Uint8Array(metadataBuffer, 0, 8);
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const dataView = new DataView(metadataBuffer, 0, 8);
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@@ -94,10 +101,11 @@ export function getInt64Buffer(value: number): Buffer {
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}
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/**
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* @s - Input number value
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* @value - Input number value
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* @description - Get a Buffer representation of a Int32 bit value
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*/
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export function getInt32Buffer(value: number): Buffer {
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Guard.null(value);
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const fourGb = Math.pow(2, 32);
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const value32 = value % fourGb;
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@@ -111,10 +119,11 @@ export function getInt32Buffer(value: number): Buffer {
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}
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/**
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* @s - Input string
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* @str - Input string
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* @description - Get a Uint8Array representation of an input string value
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*/
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export function textEncode(str: string): Uint8Array {
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export function textEncode(str: string): Uint8Array {
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Guard.null(str);
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const utf8 = unescape(encodeURIComponent(str));
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const result = new Uint8Array(utf8.length);
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for (let i = 0; i < utf8.length; i++) {
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@@ -122,3 +131,16 @@ export function textEncode(str: string): Uint8Array {
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}
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return result;
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}
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/**
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* @buffer - Input buffer
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* @description - Read an Int64 value from buffer
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*/
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export function readInt64(buffer: Buffer): number {
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Guard.null(buffer);
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Guard.expression(buffer.length, (num) => num >= 8);
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buffer = reverse(buffer.slice(0, 8));
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const int64 = new Int64(buffer, 0);
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return int64.toNumber(true);
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}
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@@ -0,0 +1,77 @@
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import { TFRecordsBuilder, FeatureType } from "./tensorFlowBuilder";
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import { TFRecordsReader } from "./tensorFlowReader";
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describe("TFRecords Reader/Builder Integration test", () => {
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describe("Check Adding Single TFRecords", () => {
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let builder: TFRecordsBuilder;
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beforeEach(() => {
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builder = new TFRecordsBuilder();
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});
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it("Check single TFRecord", async () => {
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builder.addArrayFeature("feature/1", FeatureType.Int64, [1, 2]);
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builder.addArrayFeature("feature/2", FeatureType.Float, [1.0, 2.0]);
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builder.addArrayFeature("feature/3", FeatureType.String, ["1", "2"]);
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const buffer = builder.build();
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const tfrecords = TFRecordsBuilder.buildTFRecords([buffer]);
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expect(tfrecords.length).toEqual(89);
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const reader = new TFRecordsReader(tfrecords);
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expect(reader.length).toEqual(1);
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const jsonImage = reader.toArray();
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expect(jsonImage.length).toEqual(1);
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expect(jsonImage[0]["context"].featureMap.length).toEqual(3);
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expect(jsonImage[0]["context"].featureMap[0][0]).toEqual("feature/1");
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expect(jsonImage[0]["context"].featureMap[1][0]).toEqual("feature/2");
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expect(jsonImage[0]["context"].featureMap[2][0]).toEqual("feature/3");
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expect(jsonImage[0]["context"].featureMap[0][1]["int64List"]["valueList"].length).toEqual(2);
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expect(jsonImage[0]["context"].featureMap[1][1]["floatList"]["valueList"].length).toEqual(2);
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expect(jsonImage[0]["context"].featureMap[2][1]["bytesList"]["valueList"].length).toEqual(2);
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});
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it("Check multiple TFRecords", async () => {
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builder.addArrayFeature("feature/1", FeatureType.Int64, [1, 2]);
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builder.addArrayFeature("feature/2", FeatureType.Float, [1.0, 2.0]);
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builder.addArrayFeature("feature/3", FeatureType.String, ["1", "2"]);
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const buffer = builder.build();
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const tfrecords = TFRecordsBuilder.buildTFRecords([buffer, buffer]);
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expect(tfrecords.length).toEqual(178);
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const reader = new TFRecordsReader(tfrecords);
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expect(reader.length).toEqual(2);
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const jsonImage = reader.toArray();
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expect(jsonImage.length).toEqual(2);
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// Check First TFRecord
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expect(jsonImage[0]["context"].featureMap.length).toEqual(3);
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expect(jsonImage[0]["context"].featureMap[0][0]).toEqual("feature/1");
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expect(jsonImage[0]["context"].featureMap[1][0]).toEqual("feature/2");
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expect(jsonImage[0]["context"].featureMap[2][0]).toEqual("feature/3");
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expect(jsonImage[0]["context"].featureMap[0][1]["int64List"]["valueList"].length).toEqual(2);
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expect(jsonImage[0]["context"].featureMap[1][1]["floatList"]["valueList"].length).toEqual(2);
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expect(jsonImage[0]["context"].featureMap[2][1]["bytesList"]["valueList"].length).toEqual(2);
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// Check Second TFRecord
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expect(jsonImage[1]["context"].featureMap.length).toEqual(3);
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expect(jsonImage[1]["context"].featureMap[0][0]).toEqual("feature/1");
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expect(jsonImage[1]["context"].featureMap[1][0]).toEqual("feature/2");
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expect(jsonImage[1]["context"].featureMap[2][0]).toEqual("feature/3");
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expect(jsonImage[1]["context"].featureMap[0][1]["int64List"]["valueList"].length).toEqual(2);
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expect(jsonImage[1]["context"].featureMap[1][1]["floatList"]["valueList"].length).toEqual(2);
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expect(jsonImage[1]["context"].featureMap[2][1]["bytesList"]["valueList"].length).toEqual(2);
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});
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});
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});
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@@ -0,0 +1,65 @@
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import Guard from "../../../common/guard";
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import { TFRecordsImageMessage, Features, Feature, FeatureList,
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BytesList, Int64List, FloatList } from "./tensorFlowRecordsProtoBuf_pb";
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import { crc32c, maskCrc, getInt64Buffer, getInt32Buffer, textEncode, readInt64 } from "./tensorFlowHelpers";
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/**
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* @name - TFRecords Read Class
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* @description - Read a TFRecords object
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*/
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export class TFRecordsReader {
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private imageMessages: TFRecordsImageMessage[];
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constructor(tfrecords: Buffer) {
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Guard.null(tfrecords);
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this.imageMessages = [];
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let position = 0;
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while (position < tfrecords.length) {
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const lengthBuffer = tfrecords.slice(position, position + 8);
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const dataLength = readInt64(lengthBuffer);
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const lengthCrc = maskCrc(crc32c(lengthBuffer));
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position += 8;
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const expectedLengthCrc = tfrecords.readUInt32LE(position);
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position += 4;
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if (lengthCrc !== expectedLengthCrc) {
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console.log("Wrong Length CRC");
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break;
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}
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const dataBuffer = tfrecords.slice(position, position + dataLength);
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const dataCrc = maskCrc(crc32c(dataBuffer));
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position += dataLength;
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const expectedDataCrc = tfrecords.readUInt32LE(position);
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position += 4;
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if (dataCrc !== expectedDataCrc) {
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console.log("Wrong Data CRC");
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break;
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}
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// Deserialize TFRecord from dataBuffer
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const imageMessage: TFRecordsImageMessage = TFRecordsImageMessage.deserializeBinary(dataBuffer);
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this.imageMessages.push(imageMessage);
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}
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}
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/**
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* @description - Return the number of TFRecords read
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*/
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get length(): number {
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return this.imageMessages.length;
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}
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/**
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* @description - Return the TFRecords in a JSON Object Array format
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*/
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public toArray(): object[] {
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return this.imageMessages.map((imageMessage) => imageMessage.toObject());
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}
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}
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