test_pipelines_image_segmentation.py

# Copyright 2021 The HuggingFace Team. 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 hashlib
import unittest

import datasets
from datasets import load_dataset

from transformers import (
    MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
    MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
    MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
    AutoFeatureExtractor,
    AutoModelForImageSegmentation,
    AutoModelForInstanceSegmentation,
    DetrForSegmentation,
    ImageSegmentationPipeline,
    MaskFormerForInstanceSegmentation,
    is_vision_available,
    pipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY, PipelineTestCaseMeta


if is_vision_available():
    from PIL import Image
else:

    class Image:
        @staticmethod
        def open(*args, **kwargs):
            pass


def hashimage(image: Image) -> str:
    m = hashlib.md5(image.tobytes())
    return m.hexdigest()


@require_vision
@require_timm
@require_torch
@is_pipeline_test
class ImageSegmentationPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
    model_mapping = {
        k: v
        for k, v in (
            list(MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()) if MODEL_FOR_IMAGE_SEGMENTATION_MAPPING else []
        )
        + (MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items() if MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING else [])
        + (MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items() if MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING else [])
    }

    def get_test_pipeline(self, model, tokenizer, feature_extractor):
        image_segmenter = ImageSegmentationPipeline(model=model, feature_extractor=feature_extractor)
        return image_segmenter, [
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
        ]

    def run_pipeline_test(self, image_segmenter, examples):
        outputs = image_segmenter("./tests/fixtures/tests_samples/COCO/000000039769.png", threshold=0.0)
        self.assertIsInstance(outputs, list)
        n = len(outputs)
        if isinstance(image_segmenter.model, (MaskFormerForInstanceSegmentation)):
            # Instance segmentation (maskformer) have a slot for null class
            # and can output nothing even with a low threshold
            self.assertGreaterEqual(n, 0)
        else:
            self.assertGreaterEqual(n, 1)
        # XXX: PIL.Image implements __eq__ which bypasses ANY, so we inverse the comparison
        # to make it work
        self.assertEqual([{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * n, outputs)

        dataset = datasets.load_dataset("hf-internal-testing/fixtures_image_utils", "image", split="test")

        # RGBA
        outputs = image_segmenter(dataset[0]["file"])
        m = len(outputs)
        self.assertEqual([{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * m, outputs)
        # LA
        outputs = image_segmenter(dataset[1]["file"])
        m = len(outputs)
        self.assertEqual([{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * m, outputs)
        # L
        outputs = image_segmenter(dataset[2]["file"])
        m = len(outputs)
        self.assertEqual([{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * m, outputs)

        if isinstance(image_segmenter.model, DetrForSegmentation):
            # We need to test batch_size with images with the same size.
            # Detr doesn't normalize the size of the images, meaning we can have
            # 800x800 or 800x1200, meaning we cannot batch simply.
            # We simply bail on this
            batch_size = 1
        else:
            batch_size = 2

        # 5 times the same image so the output shape is predictable
        batch = [
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
        ]
        outputs = image_segmenter(batch, threshold=0.0, batch_size=batch_size)
        self.assertEqual(len(batch), len(outputs))
        self.assertEqual(len(outputs[0]), n)
        self.assertEqual(
            [
                [{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * n,
                [{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * n,
                [{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * n,
                [{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * n,
                [{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * n,
            ],
            outputs,
            f"Expected [{n}, {n}, {n}, {n}, {n}], got {[len(item) for item in outputs]}",
        )

    @require_tf
    @unittest.skip("Image segmentation not implemented in TF")
    def test_small_model_tf(self):
        pass

    @require_torch
    @unittest.skip("No weights found for hf-internal-testing/tiny-detr-mobilenetsv3-panoptic")
    def test_small_model_pt(self):
        model_id = "hf-internal-testing/tiny-detr-mobilenetsv3-panoptic"

        model = AutoModelForImageSegmentation.from_pretrained(model_id)
        feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
        image_segmenter = ImageSegmentationPipeline(model=model, feature_extractor=feature_extractor)

        outputs = image_segmenter(
            "http://images.cocodataset.org/val2017/000000039769.jpg",
            task="panoptic",
            threshold=0.0,
            overlap_mask_area_threshold=0.0,
        )

        # Shortening by hashing
        for o in outputs:
            o["mask"] = hashimage(o["mask"])

        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                {
                    "score": 0.004,
                    "label": "LABEL_215",
                    "mask": "34eecd16bbfb0f476083ef947d81bf66",
                },
                {
                    "score": 0.004,
                    "label": "LABEL_215",
                    "mask": "34eecd16bbfb0f476083ef947d81bf66",
                },
            ],
        )

        outputs = image_segmenter(
            [
                "http://images.cocodataset.org/val2017/000000039769.jpg",
                "http://images.cocodataset.org/val2017/000000039769.jpg",
            ],
            threshold=0.0,
        )
        for output in outputs:
            for o in output:
                o["mask"] = hashimage(o["mask"])

        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                [
                    {
                        "score": 0.004,
                        "label": "LABEL_215",
                        "mask": "34eecd16bbfb0f476083ef947d81bf66",
                    },
                    {
                        "score": 0.004,
                        "label": "LABEL_215",
                        "mask": "34eecd16bbfb0f476083ef947d81bf66",
                    },
                ],
                [
                    {
                        "score": 0.004,
                        "label": "LABEL_215",
                        "mask": "34eecd16bbfb0f476083ef947d81bf66",
                    },
                    {
                        "score": 0.004,
                        "label": "LABEL_215",
                        "mask": "34eecd16bbfb0f476083ef947d81bf66",
                    },
                ],
            ],
        )

    @require_torch
    def test_small_model_pt_semantic(self):
        model_id = "hf-internal-testing/tiny-random-beit-pipeline"
        image_segmenter = pipeline(model=model_id)
        outputs = image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg")
        for o in outputs:
            # shortening by hashing
            o["mask"] = hashimage(o["mask"])

        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                {
                    "score": None,
                    "label": "LABEL_0",
                    "mask": "775518a7ed09eea888752176c6ba8f38",
                },
                {
                    "score": None,
                    "label": "LABEL_1",
                    "mask": "a12da23a46848128af68c63aa8ba7a02",
                },
            ],
        )

    @require_torch
    @slow
    def test_integration_torch_image_segmentation(self):
        model_id = "facebook/detr-resnet-50-panoptic"
        image_segmenter = pipeline("image-segmentation", model=model_id)

        outputs = image_segmenter(
            "http://images.cocodataset.org/val2017/000000039769.jpg",
            task="panoptic",
            threshold=0,
            overlap_mask_area_threshold=0.0,
        )

        # Shortening by hashing
        for o in outputs:
            o["mask"] = hashimage(o["mask"])

        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                {"score": 0.9094, "label": "blanket", "mask": "dcff19a97abd8bd555e21186ae7c066a"},
                {"score": 0.9941, "label": "cat", "mask": "9c0af87bd00f9d3a4e0c8888e34e70e2"},
                {"score": 0.9987, "label": "remote", "mask": "c7870600d6c02a1f6d96470fc7220e8e"},
                {"score": 0.9995, "label": "remote", "mask": "ef899a25fd44ec056c653f0ca2954fdd"},
                {"score": 0.9722, "label": "couch", "mask": "37b8446ac578a17108aa2b7fccc33114"},
                {"score": 0.9994, "label": "cat", "mask": "6a09d3655efd8a388ab4511e4cbbb797"},
            ],
        )

        outputs = image_segmenter(
            [
                "http://images.cocodataset.org/val2017/000000039769.jpg",
                "http://images.cocodataset.org/val2017/000000039769.jpg",
            ],
            task="panoptic",
            threshold=0.0,
            overlap_mask_area_threshold=0.0,
        )

        # Shortening by hashing
        for output in outputs:
            for o in output:
                o["mask"] = hashimage(o["mask"])

        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                [
                    {"score": 0.9094, "label": "blanket", "mask": "dcff19a97abd8bd555e21186ae7c066a"},
                    {"score": 0.9941, "label": "cat", "mask": "9c0af87bd00f9d3a4e0c8888e34e70e2"},
                    {"score": 0.9987, "label": "remote", "mask": "c7870600d6c02a1f6d96470fc7220e8e"},
                    {"score": 0.9995, "label": "remote", "mask": "ef899a25fd44ec056c653f0ca2954fdd"},
                    {"score": 0.9722, "label": "couch", "mask": "37b8446ac578a17108aa2b7fccc33114"},
                    {"score": 0.9994, "label": "cat", "mask": "6a09d3655efd8a388ab4511e4cbbb797"},
                ],
                [
                    {"score": 0.9094, "label": "blanket", "mask": "dcff19a97abd8bd555e21186ae7c066a"},
                    {"score": 0.9941, "label": "cat", "mask": "9c0af87bd00f9d3a4e0c8888e34e70e2"},
                    {"score": 0.9987, "label": "remote", "mask": "c7870600d6c02a1f6d96470fc7220e8e"},
                    {"score": 0.9995, "label": "remote", "mask": "ef899a25fd44ec056c653f0ca2954fdd"},
                    {"score": 0.9722, "label": "couch", "mask": "37b8446ac578a17108aa2b7fccc33114"},
                    {"score": 0.9994, "label": "cat", "mask": "6a09d3655efd8a388ab4511e4cbbb797"},
                ],
            ],
        )

    @require_torch
    @slow
    def test_threshold(self):
        model_id = "facebook/detr-resnet-50-panoptic"
        image_segmenter = pipeline("image-segmentation", model=model_id)

        outputs = image_segmenter(
            "http://images.cocodataset.org/val2017/000000039769.jpg", task="panoptic", threshold=0.999
        )
        # Shortening by hashing
        for o in outputs:
            o["mask"] = hashimage(o["mask"])

        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                {"score": 0.9995, "label": "remote", "mask": "d02404f5789f075e3b3174adbc3fd5b8"},
                {"score": 0.9994, "label": "cat", "mask": "eaa115b40c96d3a6f4fe498963a7e470"},
            ],
        )

        outputs = image_segmenter(
            "http://images.cocodataset.org/val2017/000000039769.jpg", task="panoptic", threshold=0.5
        )

        for o in outputs:
            o["mask"] = hashimage(o["mask"])

        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                {"score": 0.9941, "label": "cat", "mask": "9c0af87bd00f9d3a4e0c8888e34e70e2"},
                {"score": 0.9987, "label": "remote", "mask": "c7870600d6c02a1f6d96470fc7220e8e"},
                {"score": 0.9995, "label": "remote", "mask": "ef899a25fd44ec056c653f0ca2954fdd"},
                {"score": 0.9722, "label": "couch", "mask": "37b8446ac578a17108aa2b7fccc33114"},
                {"score": 0.9994, "label": "cat", "mask": "6a09d3655efd8a388ab4511e4cbbb797"},
            ],
        )

    @require_torch
    @slow
    def test_maskformer(self):
        threshold = 0.8
        model_id = "facebook/maskformer-swin-base-ade"

        model = AutoModelForInstanceSegmentation.from_pretrained(model_id)
        feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)

        image_segmenter = pipeline("image-segmentation", model=model, feature_extractor=feature_extractor)

        image = load_dataset("hf-internal-testing/fixtures_ade20k", split="test")
        file = image[0]["file"]
        outputs = image_segmenter(file, task="panoptic", threshold=threshold)

        # Shortening by hashing
        for o in outputs:
            o["mask"] = hashimage(o["mask"])

        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                {"score": 0.9974, "label": "wall", "mask": "a547b7c062917f4f3e36501827ad3cd6"},
                {"score": 0.949, "label": "house", "mask": "0da9b7b38feac47bd2528a63e5ea7b19"},
                {"score": 0.9995, "label": "grass", "mask": "1d07ea0a263dcf38ca8ae1a15fdceda1"},
                {"score": 0.9976, "label": "tree", "mask": "6cdc97c7daf1dc596fa181f461ddd2ba"},
                {"score": 0.8239, "label": "plant", "mask": "1ab4ce378f6ceff57d428055cfbd742f"},
                {"score": 0.9942, "label": "road, route", "mask": "39c5d17be53b2d1b0f46aad8ebb15813"},
                {"score": 1.0, "label": "sky", "mask": "a3756324a692981510c39b1a59510a36"},
            ],
        )