tests/models/clipseg/test_modeling_clipseg.py - main - githubmirror/transformers

tests/models/clipseg/test_modeling_clipseg.py
610 строк23 KB
Raushan Turganbay
Refactor CLIP-like models (#44431)
09 апр 2026, 18:20
09 апр 2026, 18:20353a9dc
# Copyright 2022 The HuggingFace Inc. 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.
"""Testing suite for the PyTorch CLIPSeg model."""

import inspect
import tempfile
import unittest

import numpy as np
import pytest
import requests

from transformers import CLIPSegConfig, CLIPSegProcessor, CLIPSegTextConfig, CLIPSegVisionConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import (
    ModelTesterMixin,
    floats_tensor,
    ids_tensor,
    random_attention_mask,
)
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import CLIPSegForImageSegmentation, CLIPSegModel, CLIPSegTextModel, CLIPSegVisionModel
    from transformers.models.auto.modeling_auto import MODEL_MAPPING_NAMES


if is_vision_available():
    from PIL import Image


class CLIPSegVisionModelTester:
    def __init__(
        self,
        parent,
        batch_size=12,
        image_size=30,
        patch_size=2,
        num_channels=3,
        is_training=True,
        hidden_size=32,
        num_hidden_layers=2,
        num_attention_heads=4,
        intermediate_size=37,
        dropout=0.1,
        attention_dropout=0.1,
        initializer_range=0.02,
        scope=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.is_training = is_training
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.dropout = dropout
        self.attention_dropout = attention_dropout
        self.initializer_range = initializer_range
        self.scope = scope

        # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
        num_patches = (image_size // patch_size) ** 2
        self.seq_length = num_patches + 1

    def prepare_config_and_inputs(self):
        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
        config = self.get_config()

        return config, pixel_values

    def get_config(self):
        return CLIPSegVisionConfig(
            image_size=self.image_size,
            patch_size=self.patch_size,
            num_channels=self.num_channels,
            hidden_size=self.hidden_size,
            num_hidden_layers=self.num_hidden_layers,
            num_attention_heads=self.num_attention_heads,
            intermediate_size=self.intermediate_size,
            dropout=self.dropout,
            attention_dropout=self.attention_dropout,
            initializer_range=self.initializer_range,
        )

    def create_and_check_model(self, config, pixel_values):
        model = CLIPSegVisionModel(config=config)
        model.to(torch_device)
        model.eval()
        with torch.no_grad():
            result = model(pixel_values)
        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
        image_size = (self.image_size, self.image_size)
        patch_size = (self.patch_size, self.patch_size)
        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size))
        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        config, pixel_values = config_and_inputs
        inputs_dict = {"pixel_values": pixel_values}
        return config, inputs_dict


@require_torch
class CLIPSegVisionModelTest(ModelTesterMixin, unittest.TestCase):
    """
    Here we also overwrite some of the tests of test_modeling_common.py, as CLIPSeg does not use input_ids, inputs_embeds,
    attention_mask and seq_length.
    """

    all_model_classes = (CLIPSegVisionModel,) if is_torch_available() else ()

    test_resize_embeddings = False

    def setUp(self):
        self.model_tester = CLIPSegVisionModelTester(self)
        self.config_tester = ConfigTester(
            self, config_class=CLIPSegVisionConfig, has_text_modality=False, hidden_size=36
        )

    def test_config(self):
        self.config_tester.run_common_tests()

    @unittest.skip(reason="CLIPSeg does not use inputs_embeds")
    def test_inputs_embeds(self):
        pass

    def test_model_get_set_embeddings(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
            x = model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(x, nn.Linear))

    def test_forward_signature(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            signature = inspect.signature(model.forward)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            arg_names = [*signature.parameters.keys()]

            expected_arg_names = ["pixel_values"]
            self.assertListEqual(arg_names[:1], expected_arg_names)

    def test_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*config_and_inputs)

    @unittest.skip(reason="This module does not support standalone training")
    def test_training(self):
        pass

    @unittest.skip(reason="This module does not support standalone training")
    def test_training_gradient_checkpointing(self):
        pass

    @unittest.skip(reason="This module does not support standalone training")
    def test_training_gradient_checkpointing_use_reentrant_false(self):
        pass

    @unittest.skip(reason="This module does not support standalone training")
    def test_training_gradient_checkpointing_use_reentrant_true(self):
        pass

    @slow
    def test_model_from_pretrained(self):
        model_name = "CIDAS/clipseg-rd64-refined"
        model = CLIPSegVisionModel.from_pretrained(model_name)
        self.assertIsNotNone(model)


class CLIPSegTextModelTester:
    def __init__(
        self,
        parent,
        batch_size=12,
        seq_length=7,
        is_training=True,
        use_input_mask=True,
        use_labels=True,
        vocab_size=99,
        hidden_size=32,
        num_hidden_layers=2,
        num_attention_heads=4,
        intermediate_size=37,
        dropout=0.1,
        attention_dropout=0.1,
        max_position_embeddings=512,
        initializer_range=0.02,
        scope=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.seq_length = seq_length
        self.is_training = is_training
        self.use_input_mask = use_input_mask
        self.use_labels = use_labels
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.dropout = dropout
        self.attention_dropout = attention_dropout
        self.max_position_embeddings = max_position_embeddings
        self.initializer_range = initializer_range
        self.scope = scope

    def prepare_config_and_inputs(self):
        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)

        input_mask = None
        if self.use_input_mask:
            input_mask = random_attention_mask([self.batch_size, self.seq_length])

        if input_mask is not None:
            batch_size, seq_length = input_mask.shape
            rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,))
            for batch_idx, start_index in enumerate(rnd_start_indices):
                input_mask[batch_idx, :start_index] = 1
                input_mask[batch_idx, start_index:] = 0

        config = self.get_config()

        return config, input_ids, input_mask

    def get_config(self):
        return CLIPSegTextConfig(
            vocab_size=self.vocab_size,
            hidden_size=self.hidden_size,
            num_hidden_layers=self.num_hidden_layers,
            num_attention_heads=self.num_attention_heads,
            intermediate_size=self.intermediate_size,
            dropout=self.dropout,
            attention_dropout=self.attention_dropout,
            max_position_embeddings=self.max_position_embeddings,
            initializer_range=self.initializer_range,
        )

    def create_and_check_model(self, config, input_ids, input_mask):
        model = CLIPSegTextModel(config=config)
        model.to(torch_device)
        model.eval()
        with torch.no_grad():
            result = model(input_ids, attention_mask=input_mask)
            result = model(input_ids)
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        config, input_ids, input_mask = config_and_inputs
        inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
        return config, inputs_dict


@require_torch
class CLIPSegTextModelTest(ModelTesterMixin, unittest.TestCase):
    all_model_classes = (CLIPSegTextModel,) if is_torch_available() else ()

    model_split_percents = [0.5, 0.8, 0.9]

    def setUp(self):
        self.model_tester = CLIPSegTextModelTester(self)
        self.config_tester = ConfigTester(self, config_class=CLIPSegTextConfig, hidden_size=32)

    def test_config(self):
        self.config_tester.run_common_tests()

    def test_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*config_and_inputs)

    @unittest.skip(reason="This module does not support standalone training")
    def test_training(self):
        pass

    @unittest.skip(reason="This module does not support standalone training")
    def test_training_gradient_checkpointing(self):
        pass

    @unittest.skip(reason="This module does not support standalone training")
    def test_training_gradient_checkpointing_use_reentrant_false(self):
        pass

    @unittest.skip(reason="This module does not support standalone training")
    def test_training_gradient_checkpointing_use_reentrant_true(self):
        pass

    @unittest.skip(reason="CLIPSeg does not use inputs_embeds")
    def test_inputs_embeds(self):
        pass

    @slow
    def test_model_from_pretrained(self):
        model_name = "CIDAS/clipseg-rd64-refined"
        model = CLIPSegTextModel.from_pretrained(model_name)
        self.assertIsNotNone(model)


class CLIPSegModelTester:
    def __init__(
        self,
        parent,
        text_kwargs=None,
        vision_kwargs=None,
        is_training=True,
        # This should respect the `num_hidden_layers` in `CLIPSegVisionModelTester`
        extract_layers=(1,),
    ):
        if text_kwargs is None:
            text_kwargs = {}
        if vision_kwargs is None:
            vision_kwargs = {}

        self.parent = parent
        self.text_model_tester = CLIPSegTextModelTester(parent, **text_kwargs)
        self.vision_model_tester = CLIPSegVisionModelTester(parent, **vision_kwargs)
        self.batch_size = self.text_model_tester.batch_size  # need bs for batching_equivalence test
        self.is_training = is_training
        self.extract_layers = extract_layers

    def prepare_config_and_inputs(self):
        text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()
        vision_config, pixel_values = self.vision_model_tester.prepare_config_and_inputs()

        config = self.get_config()

        return config, input_ids, attention_mask, pixel_values

    def get_config(self):
        return CLIPSegConfig(
            text_config=self.text_model_tester.get_config().to_dict(),
            vision_config=self.vision_model_tester.get_config().to_dict(),
            projection_dim=64,
            reduce_dim=32,
            extract_layers=self.extract_layers,
        )

    def create_and_check_model(self, config, input_ids, attention_mask, pixel_values):
        model = CLIPSegModel(config).to(torch_device).eval()
        with torch.no_grad():
            result = model(input_ids, pixel_values, attention_mask)
        self.parent.assertEqual(
            result.logits_per_image.shape, (self.vision_model_tester.batch_size, self.text_model_tester.batch_size)
        )
        self.parent.assertEqual(
            result.logits_per_text.shape, (self.text_model_tester.batch_size, self.vision_model_tester.batch_size)
        )

    def create_and_check_model_for_image_segmentation(self, config, input_ids, attention_mask, pixel_values):
        model = CLIPSegForImageSegmentation(config).to(torch_device).eval()
        with torch.no_grad():
            result = model(input_ids, pixel_values)
        self.parent.assertEqual(
            result.logits.shape,
            (
                self.vision_model_tester.batch_size,
                self.vision_model_tester.image_size,
                self.vision_model_tester.image_size,
            ),
        )
        self.parent.assertEqual(
            result.conditional_embeddings.shape, (self.text_model_tester.batch_size, config.projection_dim)
        )

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        config, input_ids, attention_mask, pixel_values = config_and_inputs
        inputs_dict = {
            "input_ids": input_ids,
            "attention_mask": attention_mask,
            "pixel_values": pixel_values,
        }
        return config, inputs_dict


@require_torch
class CLIPSegModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (CLIPSegModel, CLIPSegForImageSegmentation) if is_torch_available() else ()
    pipeline_model_mapping = {"feature-extraction": CLIPSegModel} if is_torch_available() else {}

    test_resize_embeddings = False
    test_attention_outputs = False
    additional_model_inputs = ["pixel_values"]

    def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
        # CLIPSegForImageSegmentation requires special treatment
        if return_labels:
            if model_class.__name__ == "CLIPSegForImageSegmentation":
                batch_size, _, height, width = inputs_dict["pixel_values"].shape
                inputs_dict["labels"] = torch.zeros(
                    [batch_size, height, width], device=torch_device, dtype=torch.float
                )

        return inputs_dict

    def setUp(self):
        self.model_tester = CLIPSegModelTester(self)
        common_properties = ["projection_dim", "logit_scale_init_value"]
        self.config_tester = ConfigTester(
            self, config_class=CLIPSegConfig, has_text_modality=False, common_properties=common_properties
        )

    def test_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*config_and_inputs)

    def test_config(self):
        self.config_tester.run_common_tests()

    def test_model_for_image_segmentation(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model_for_image_segmentation(*config_and_inputs)

    @unittest.skip(reason="ClipSeg can;t compile due to the decoder module")
    def test_sdpa_can_compile_dynamic(self):
        pass

    @unittest.skip(reason="Hidden_states is tested in individual model tests")
    def test_hidden_states_output(self):
        pass

    @unittest.skip(reason="Inputs_embeds is tested in individual model tests")
    def test_inputs_embeds(self):
        pass

    @unittest.skip(reason="Retain_grad is tested in individual model tests")
    def test_retain_grad_hidden_states_attentions(self):
        pass

    @unittest.skip(reason="CLIPSegModel does not have input/output embeddings")
    def test_model_get_set_embeddings(self):
        pass

    @pytest.mark.xfail(
        reason="CLIPSegForImageSegmentation does not expose input embeddings. Gradients cannot flow back to the token embeddings when using gradient checkpointing."
    )
    def test_training_gradient_checkpointing(self):
        super().test_training_gradient_checkpointing()

    @pytest.mark.xfail(
        reason="CLIPSegForImageSegmentation does not expose input embeddings. Gradients cannot flow back to the token embeddings when using gradient checkpointing."
    )
    def test_training_gradient_checkpointing_use_reentrant_false(self):
        super().test_training_gradient_checkpointing_use_reentrant_false()

    @pytest.mark.xfail(
        reason="CLIPSegForImageSegmentation does not expose input embeddings. Gradients cannot flow back to the token embeddings when using gradient checkpointing."
    )
    def test_training_gradient_checkpointing_use_reentrant_true(self):
        super().test_training_gradient_checkpointing_use_reentrant_true()

    def test_load_vision_text_config(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        # Save CLIPSegConfig and check if we can load CLIPSegVisionConfig from it
        with tempfile.TemporaryDirectory() as tmp_dir_name:
            config.save_pretrained(tmp_dir_name)
            vision_config = CLIPSegVisionConfig.from_pretrained(tmp_dir_name)
            self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict())

        # Save CLIPSegConfig and check if we can load CLIPSegTextConfig from it
        with tempfile.TemporaryDirectory() as tmp_dir_name:
            config.save_pretrained(tmp_dir_name)
            text_config = CLIPSegTextConfig.from_pretrained(tmp_dir_name)
            self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict())

    def test_training(self):
        if not self.model_tester.is_training:
            self.skipTest(reason="Training test is skipped as the model was not trained")

        for model_class in self.all_model_classes:
            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
            config.return_dict = True

            if model_class.__name__ in MODEL_MAPPING_NAMES.values():
                continue

            print("Model class:", model_class)

            model = model_class(config)
            model.to(torch_device)
            model.train()
            inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            for k, v in inputs.items():
                print(k, v.shape)
            loss = model(**inputs).loss
            loss.backward()

    @slow
    def test_model_from_pretrained(self):
        model_name = "CIDAS/clipseg-rd64-refined"
        model = CLIPSegModel.from_pretrained(model_name)
        self.assertIsNotNone(model)


# We will verify our results on an image of cute cats
def prepare_img():
    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    image = Image.open(requests.get(url, stream=True).raw)
    return image


@require_vision
@require_torch
class CLIPSegModelIntegrationTest(unittest.TestCase):
    @slow
    def test_inference_image_segmentation(self):
        model_name = "CIDAS/clipseg-rd64-refined"
        processor = CLIPSegProcessor.from_pretrained(model_name)
        model = CLIPSegForImageSegmentation.from_pretrained(model_name).to(torch_device)

        image = prepare_img()
        texts = ["a cat", "a remote", "a blanket"]
        inputs = processor(text=texts, images=[image] * len(texts), padding=True, return_tensors="pt").to(torch_device)

        # forward pass
        with torch.no_grad():
            outputs = model(**inputs)

        # verify the predicted masks
        self.assertEqual(
            outputs.logits.shape,
            torch.Size((3, 352, 352)),
        )
        expected_masks_slice = torch.tensor(
            [[-7.4613, -7.4785, -7.3628], [-7.3268, -7.0899, -7.1333], [-6.9838, -6.7900, -6.8913]]
        ).to(torch_device)

        torch.testing.assert_close(outputs.logits[0, :3, :3], expected_masks_slice, rtol=1e-3, atol=1e-3)

        # verify conditional and pooled output
        expected_conditional = torch.tensor([0.5601, -0.0314, 0.1980]).to(torch_device)
        expected_pooled_output = torch.tensor([0.4986, -0.2698, -0.2631]).to(torch_device)
        torch.testing.assert_close(outputs.conditional_embeddings[0, :3], expected_conditional, rtol=1e-3, atol=1e-3)
        torch.testing.assert_close(outputs.pooled_output[0, :3], expected_pooled_output, rtol=1e-3, atol=1e-3)

    @slow
    def test_inference_interpolate_pos_encoding(self):
        # ViT models have an `interpolate_pos_encoding` argument in their forward method,
        # allowing to interpolate the pre-trained position embeddings in order to use
        # the model on higher resolutions. The DINO model by Facebook AI leverages this
        # to visualize self-attention on higher resolution images.
        model = CLIPSegModel.from_pretrained("openai/clip-vit-base-patch32").to(torch_device)

        processor = CLIPSegProcessor.from_pretrained(
            "openai/clip-vit-base-patch32", size={"height": 180, "width": 180}, crop_size={"height": 180, "width": 180}
        )

        image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
        inputs = processor(text="what's in the image", images=image, return_tensors="pt").to(torch_device)

        # interpolate_pos_encodiung false should return value error
        with self.assertRaises(ValueError, msg="doesn't match model"):
            with torch.no_grad():
                model(**inputs, interpolate_pos_encoding=False)

        # forward pass
        with torch.no_grad():
            outputs = model(**inputs, interpolate_pos_encoding=True)

        # verify the logits
        expected_shape = torch.Size((1, 26, 768))

        self.assertEqual(outputs.vision_model_output.last_hidden_state.shape, expected_shape)

        expected_slice = torch.tensor(
            [
                [-0.1552, 0.0314, -0.3233],
                [0.2886, 0.1141, -0.5706],
                [0.0468, 0.1569, -0.6028],
            ]
        ).to(torch_device)

        torch.testing.assert_close(
            outputs.vision_model_output.last_hidden_state[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4
        )