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Add zero-shot object detection pipeline unit test for grounding dino
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@xenova
xenova committed Jan 6, 2025
1 parent 080e556 commit 9620403
Showing 1 changed file with 170 additions and 118 deletions.
288 changes: 170 additions & 118 deletions tests/pipelines/test_pipelines_zero_shot_object_detection.js
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Original file line number Diff line number Diff line change
Expand Up @@ -7,128 +7,180 @@ const PIPELINE_ID = "zero-shot-object-detection";

export default () => {
describe("Zero-shot Object Detection", () => {
const model_id = "hf-internal-testing/tiny-random-OwlViTForObjectDetection";

const candidate_labels = ["hello", "hello world"];

/** @type {ZeroShotObjectDetectionPipeline} */
let pipe;
let images;
beforeAll(async () => {
pipe = await pipeline(PIPELINE_ID, model_id, DEFAULT_MODEL_OPTIONS);
images = await Promise.all([load_cached_image("white_image"), load_cached_image("blue_image")]);
}, MAX_MODEL_LOAD_TIME);

const targets = {
white_image: [
{
score: 0.6028420329093933,
label: "hello",
box: { xmin: 47, ymin: 117, xmax: 62, ymax: 134 },
},
{
score: 0.6026064157485962,
label: "hello world",
box: { xmin: 47, ymin: 117, xmax: 62, ymax: 134 },
},
{
score: 0.5987668037414551,
label: "hello world",
box: { xmin: 145, ymin: 47, xmax: 160, ymax: 63 },
},
{
score: 0.5986272692680359,
label: "hello",
box: { xmin: 89, ymin: 131, xmax: 104, ymax: 148 },
},
{
score: 0.5985949039459229,
label: "hello world",
box: { xmin: 89, ymin: 131, xmax: 104, ymax: 148 },
},
// ... many more
],

blue_image: [
{
score: 0.6622366309165955,
label: "hello",
box: { xmin: 48, ymin: 45, xmax: 62, ymax: 61 },
},
{
score: 0.6562080383300781,
label: "hello world",
box: { xmin: 48, ymin: 45, xmax: 62, ymax: 61 },
},
{
score: 0.6493991613388062,
label: "hello world",
box: { xmin: 34, ymin: 58, xmax: 48, ymax: 74 },
},
{
score: 0.6476974487304688,
label: "hello",
box: { xmin: 34, ymin: 58, xmax: 48, ymax: 74 },
},
{
score: 0.6391685009002686,
label: "hello",
box: { xmin: 103, ymin: 59, xmax: 117, ymax: 75 },
},
// ... many more
],
};
describe("w/ post_process_object_detection", () => {
const model_id = "hf-internal-testing/tiny-random-OwlViTForObjectDetection";

it("should be an instance of ZeroShotObjectDetectionPipeline", () => {
expect(pipe).toBeInstanceOf(ZeroShotObjectDetectionPipeline);
});
const candidate_labels = ["hello", "hello world"];

describe("batch_size=1", () => {
it(
"default",
async () => {
const output = await pipe(images[0], candidate_labels);
expect(output).toHaveLength(512);

expect(output.slice(0, targets.white_image.length)).toBeCloseToNested(targets.white_image, 5);
},
MAX_TEST_EXECUTION_TIME,
);
it(
"custom (w/ top_k & threshold)",
async () => {
const top_k = 3;
const output = await pipe(images[0], candidate_labels, { top_k, threshold: 0.05 });
expect(output).toBeCloseToNested(targets.white_image.slice(0, top_k), 5);
},
MAX_TEST_EXECUTION_TIME,
);
});
/** @type {ZeroShotObjectDetectionPipeline} */
let pipe;
let images;
beforeAll(async () => {
pipe = await pipeline(PIPELINE_ID, model_id, DEFAULT_MODEL_OPTIONS);
images = await Promise.all([load_cached_image("white_image"), load_cached_image("blue_image")]);
}, MAX_MODEL_LOAD_TIME);

const targets = {
white_image: [
{
score: 0.6028420329093933,
label: "hello",
box: { xmin: 47, ymin: 117, xmax: 62, ymax: 134 },
},
{
score: 0.6026064157485962,
label: "hello world",
box: { xmin: 47, ymin: 117, xmax: 62, ymax: 134 },
},
{
score: 0.5987668037414551,
label: "hello world",
box: { xmin: 145, ymin: 47, xmax: 160, ymax: 63 },
},
{
score: 0.5986272692680359,
label: "hello",
box: { xmin: 89, ymin: 131, xmax: 104, ymax: 148 },
},
{
score: 0.5985949039459229,
label: "hello world",
box: { xmin: 89, ymin: 131, xmax: 104, ymax: 148 },
},
// ... many more
],

blue_image: [
{
score: 0.6622366309165955,
label: "hello",
box: { xmin: 48, ymin: 45, xmax: 62, ymax: 61 },
},
{
score: 0.6562080383300781,
label: "hello world",
box: { xmin: 48, ymin: 45, xmax: 62, ymax: 61 },
},
{
score: 0.6493991613388062,
label: "hello world",
box: { xmin: 34, ymin: 58, xmax: 48, ymax: 74 },
},
{
score: 0.6476974487304688,
label: "hello",
box: { xmin: 34, ymin: 58, xmax: 48, ymax: 74 },
},
{
score: 0.6391685009002686,
label: "hello",
box: { xmin: 103, ymin: 59, xmax: 117, ymax: 75 },
},
// ... many more
],
};

it("should be an instance of ZeroShotObjectDetectionPipeline", () => {
expect(pipe).toBeInstanceOf(ZeroShotObjectDetectionPipeline);
});

describe("batch_size=1", () => {
it(
"default",
async () => {
const output = await pipe(images[0], candidate_labels);
expect(output).toHaveLength(512);

expect(output.slice(0, targets.white_image.length)).toBeCloseToNested(targets.white_image, 5);
},
MAX_TEST_EXECUTION_TIME,
);
it(
"custom (w/ top_k & threshold)",
async () => {
const top_k = 3;
const output = await pipe(images[0], candidate_labels, { top_k, threshold: 0.05 });
expect(output).toBeCloseToNested(targets.white_image.slice(0, top_k), 5);
},
MAX_TEST_EXECUTION_TIME,
);
});

describe("batch_size>1", () => {
it(
"default",
async () => {
const output = await pipe(images, candidate_labels);
const target = Object.values(targets);
expect(output.map((x, i) => x.slice(0, target[i].length))).toBeCloseToNested(target, 5);
},
MAX_TEST_EXECUTION_TIME,
);
it(
"custom (w/ top_k & threshold)",
async () => {
const top_k = 3;
const output = await pipe(images, candidate_labels, { top_k, threshold: 0.05 });
const target = Object.values(targets).map((x) => x.slice(0, top_k));
expect(output).toBeCloseToNested(target, 5);
},
MAX_TEST_EXECUTION_TIME,
);
describe("batch_size>1", () => {
it(
"default",
async () => {
const output = await pipe(images, candidate_labels);
const target = Object.values(targets);
expect(output.map((x, i) => x.slice(0, target[i].length))).toBeCloseToNested(target, 5);
},
MAX_TEST_EXECUTION_TIME,
);
it(
"custom (w/ top_k & threshold)",
async () => {
const top_k = 3;
const output = await pipe(images, candidate_labels, { top_k, threshold: 0.05 });
const target = Object.values(targets).map((x) => x.slice(0, top_k));
expect(output).toBeCloseToNested(target, 5);
},
MAX_TEST_EXECUTION_TIME,
);
});

afterAll(async () => {
await pipe.dispose();
}, MAX_MODEL_DISPOSE_TIME);
});

afterAll(async () => {
await pipe.dispose();
}, MAX_MODEL_DISPOSE_TIME);
describe("w/ post_process_grounded_object_detection", () => {
const model_id = "hf-internal-testing/tiny-random-GroundingDinoForObjectDetection";

const candidate_labels = ["a cat."];

/** @type {ZeroShotObjectDetectionPipeline} */
let pipe;
let image;
beforeAll(async () => {
pipe = await pipeline(PIPELINE_ID, model_id, DEFAULT_MODEL_OPTIONS);
image = await load_cached_image("white_image");
}, MAX_MODEL_LOAD_TIME);

const target = [
{ box: { xmax: 112, xmin: -111, ymax: 0, ymin: 0 }, label: "a cat. [SEP]", score: 1 },
{ box: { xmax: 112, xmin: -111, ymax: 0, ymin: 0 }, label: "a cat. [SEP]", score: 1 },
{ box: { xmax: 112, xmin: -111, ymax: 0, ymin: 0 }, label: "a cat. [SEP]", score: 1 },
// ... many more
];

it("should be an instance of ZeroShotObjectDetectionPipeline", () => {
expect(pipe).toBeInstanceOf(ZeroShotObjectDetectionPipeline);
});

describe("batch_size=1", () => {
it(
"default",
async () => {
const output = await pipe(image, candidate_labels);
expect(output).toHaveLength(900);
expect(output.slice(0, target.length)).toBeCloseToNested(target, 5);
},
MAX_TEST_EXECUTION_TIME,
);
it(
"custom (w/ top_k & threshold)",
async () => {
const top_k = 3;
const output = await pipe(image, candidate_labels, { top_k, threshold: 0.05 });
expect(output).toBeCloseToNested(target.slice(0, top_k), 5);
},
MAX_TEST_EXECUTION_TIME,
);
});

afterAll(async () => {
await pipe.dispose();
}, MAX_MODEL_DISPOSE_TIME);
});
});
};

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