tests/models/sam3/test_image_processing_sam3.py - main - githubmirror/transformers

tests/models/sam3/test_image_processing_sam3.py
228 строк8 KB
guarin
Return logits from semantic segmentation post-process (#46163)
19 июн 2026, 10:38
19 июн 2026, 10:38fc5401d
# Copyright 2025 HuggingFace Inc.
#
# 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 unittest

from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import (
    ImageProcessingTestMixin,
    PostProcessSemanticSegmentationTestMixin,
    prepare_image_inputs,
)


if is_torch_available():
    import torch

    from transformers.models.sam3.modeling_sam3 import Sam3ImageSegmentationOutput

if is_vision_available():
    pass


class Sam3ImageProcessingTester:
    def __init__(
        self,
        parent,
        batch_size=7,
        num_channels=3,
        min_resolution=30,
        max_resolution=400,
        mask_size=None,
        do_resize=True,
        size=None,
        do_normalize=True,
        image_mean=[0.5, 0.5, 0.5],
        image_std=[0.5, 0.5, 0.5],
    ):
        size = size if size is not None else {"height": 20, "width": 20}
        mask_size = mask_size if mask_size is not None else {"height": 12, "width": 12}
        self.parent = parent
        self.batch_size = batch_size
        self.num_channels = num_channels
        self.min_resolution = min_resolution
        self.max_resolution = max_resolution
        self.mask_size = mask_size
        self.do_resize = do_resize
        self.size = size
        self.do_normalize = do_normalize
        self.image_mean = image_mean
        self.image_std = image_std

    def prepare_image_processor_dict(self):
        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "mask_size": self.mask_size,
        }

    def expected_output_image_shape(self, images):
        return self.num_channels, self.size["height"], self.size["width"]

    def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
        return prepare_image_inputs(
            batch_size=self.batch_size,
            num_channels=self.num_channels,
            min_resolution=self.min_resolution,
            max_resolution=self.max_resolution,
            equal_resolution=equal_resolution,
            numpify=numpify,
            torchify=torchify,
        )

    def prepare_post_process_semantic_segmentation_inputs(self):
        inputs = {
            "outputs": Sam3ImageSegmentationOutput(
                semantic_seg=torch.randn(self.batch_size, 1, self.mask_size["height"], self.mask_size["width"])
            )
        }
        expected_shape = {
            "num_labels": 1,
            "height": self.mask_size["height"],
            "width": self.mask_size["width"],
        }
        return inputs, expected_shape


def prepare_semantic_single_inputs():
    ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test")
    example = ds[0]
    return example["image"], example["map"]


def prepare_semantic_batch_inputs():
    ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test")
    return list(ds["image"][:2]), list(ds["map"][:2])


@require_torch
@require_vision
class Sam3ImageProcessingTest(ImageProcessingTestMixin, PostProcessSemanticSegmentationTestMixin, unittest.TestCase):
    def setUp(self):
        super().setUp()
        self.image_processor_tester = Sam3ImageProcessingTester(self)

    @property
    def image_processor_dict(self):
        return self.image_processor_tester.prepare_image_processor_dict()

    def test_call_segmentation_maps(self):
        for image_processing_class in self.image_processing_classes.values():
            image_processor = image_processing_class(**self.image_processor_dict)
            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
            maps = []
            for image in image_inputs:
                self.assertIsInstance(image, torch.Tensor)
                maps.append(torch.zeros(image.shape[-2:]).long())

            # Test not batched input
            encoding = image_processor(image_inputs[0], maps[0], return_tensors="pt")
            self.assertEqual(
                encoding["pixel_values"].shape,
                (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["height"],
                    self.image_processor_tester.size["width"],
                ),
            )
            self.assertEqual(
                encoding["labels"].shape,
                (
                    1,
                    self.image_processor_tester.mask_size["height"],
                    self.image_processor_tester.mask_size["width"],
                ),
            )
            self.assertEqual(encoding["labels"].dtype, torch.long)
            self.assertTrue(encoding["labels"].min().item() >= 0)
            self.assertTrue(encoding["labels"].max().item() <= 255)

            # Test batched
            encoding = image_processor(image_inputs, maps, return_tensors="pt")
            self.assertEqual(
                encoding["pixel_values"].shape,
                (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["height"],
                    self.image_processor_tester.size["width"],
                ),
            )
            self.assertEqual(
                encoding["labels"].shape,
                (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.mask_size["height"],
                    self.image_processor_tester.mask_size["width"],
                ),
            )
            self.assertEqual(encoding["labels"].dtype, torch.long)
            self.assertTrue(encoding["labels"].min().item() >= 0)
            self.assertTrue(encoding["labels"].max().item() <= 255)

            # Test not batched input (PIL images) with segmentation maps from dataset
            image, segmentation_map = prepare_semantic_single_inputs()
            encoding = image_processor(image, segmentation_map, return_tensors="pt")
            self.assertEqual(
                encoding["pixel_values"].shape,
                (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["height"],
                    self.image_processor_tester.size["width"],
                ),
            )
            self.assertEqual(
                encoding["labels"].shape,
                (
                    1,
                    self.image_processor_tester.mask_size["height"],
                    self.image_processor_tester.mask_size["width"],
                ),
            )
            self.assertEqual(encoding["labels"].dtype, torch.long)
            self.assertTrue(encoding["labels"].min().item() >= 0)
            self.assertTrue(encoding["labels"].max().item() <= 255)

            # Test batched input (PIL images)
            images, segmentation_maps = prepare_semantic_batch_inputs()
            encoding = image_processor(images, segmentation_maps, return_tensors="pt")
            self.assertEqual(
                encoding["pixel_values"].shape,
                (
                    2,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["height"],
                    self.image_processor_tester.size["width"],
                ),
            )
            self.assertEqual(
                encoding["labels"].shape,
                (
                    2,
                    self.image_processor_tester.mask_size["height"],
                    self.image_processor_tester.mask_size["width"],
                ),
            )
            self.assertEqual(encoding["labels"].dtype, torch.long)
            self.assertTrue(encoding["labels"].min().item() >= 0)
            self.assertTrue(encoding["labels"].max().item() <= 255)