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tests/models/dinov3_vit/test_modeling_dinov3_vit.py

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+# Copyright 2024 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 DINOv3 model."""
+
+import unittest
+from functools import cached_property
+
+from transformers import DINOv3ViTConfig
+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
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_torch_available():
+    import torch
+    from torch import nn
+
+    from transformers import DINOv3ViTBackbone, DINOv3ViTModel
+
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import AutoImageProcessor
+
+
+class DINOv3ViTModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=13,
+        image_size=30,
+        patch_size=2,
+        num_channels=3,
+        is_training=False,
+        use_labels=True,
+        hidden_size=32,
+        num_hidden_layers=2,
+        num_attention_heads=4,
+        intermediate_size=37,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.1,
+        type_sequence_label_size=10,
+        initializer_range=0.02,
+        num_register_tokens=2,
+        mask_ratio=0.5,
+        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.use_labels = use_labels
+        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.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.type_sequence_label_size = type_sequence_label_size
+        self.initializer_range = initializer_range
+        self.num_register_tokens = num_register_tokens
+        self.scope = scope
+
+        num_patches = (image_size // patch_size) ** 2
+        self.seq_length = num_patches + 1 + self.num_register_tokens
+        self.mask_ratio = mask_ratio
+        self.num_masks = int(mask_ratio * self.seq_length)
+        self.mask_length = num_patches
+
+    def prepare_config_and_inputs(self):
+        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
+
+        labels = None
+        if self.use_labels:
+            labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
+
+        config = self.get_config()
+
+        return config, pixel_values, labels
+
+    def get_config(self):
+        return DINOv3ViTConfig(
+            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,
+            hidden_act=self.hidden_act,
+            hidden_dropout_prob=self.hidden_dropout_prob,
+            attention_probs_dropout_prob=self.attention_probs_dropout_prob,
+            is_decoder=False,
+            initializer_range=self.initializer_range,
+            num_register_tokens=self.num_register_tokens,
+            stage_names=["stem"] + [f"stage{i}" for i in range(1, self.num_hidden_layers + 1)],
+            out_indices=[0, 1],
+            reshape_hidden_states=True,
+        )
+
+    def create_and_check_backbone(self, config, pixel_values, labels):
+        config.out_features = ["stage1", "stage2"]
+        config.reshape_hidden_states = True
+
+        model = DINOv3ViTBackbone(config)
+        model.to(torch_device)
+        model.eval()
+
+        with torch.no_grad():
+            outputs = model(pixel_values)
+
+        self.parent.assertEqual(len(outputs.feature_maps), 2)
+        for fm in outputs.feature_maps:
+            b, c, h, w = fm.shape
+            self.parent.assertEqual(b, self.batch_size)
+            self.parent.assertEqual(c, self.hidden_size)
+            self.parent.assertGreater(h, 0)
+            self.parent.assertGreater(w, 0)
+
+    def test_output_hidden_states(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+
+            with torch.no_grad():
+                outputs = model(**inputs_dict, output_hidden_states=True)
+
+            self.assertIsNotNone(outputs.hidden_states)
+            expected_num_hidden_states = config.num_hidden_layers + 1
+            self.assertEqual(len(outputs.hidden_states), expected_num_hidden_states)
+
+            for hidden_state in outputs.hidden_states:
+                expected_shape = (
+                    self.model_tester.batch_size,
+                    self.model_tester.seq_length,
+                    self.model_tester.hidden_size,
+                )
+                self.assertEqual(hidden_state.shape, expected_shape)
+
+    def create_and_check_model(self, config, pixel_values, labels):
+        model = DINOv3ViTModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(pixel_values)
+        self.parent.assertEqual(
+            result.last_hidden_state.shape,
+            (self.batch_size, self.seq_length, self.hidden_size),
+        )
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        (
+            config,
+            pixel_values,
+            labels,
+        ) = config_and_inputs
+        inputs_dict = {"pixel_values": pixel_values}
+        return config, inputs_dict
+
+
+@require_torch
+class Dinov3ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    """
+    Here we also overwrite some of the tests of test_modeling_common.py, as Dinov3 does not use input_ids, inputs_embeds,
+    attention_mask and seq_length.
+    """
+
+    all_model_classes = (DINOv3ViTModel, DINOv3ViTBackbone) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "image-feature-extraction": DINOv3ViTModel,
+        }
+        if is_torch_available()
+        else {}
+    )
+
+    test_resize_embeddings = False
+
+    test_attention_outputs = False
+
+    def setUp(self):
+        self.model_tester = DINOv3ViTModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=DINOv3ViTConfig, has_text_modality=False, hidden_size=32)
+
+    def test_backbone(self):
+        config, pixel_values, labels = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_backbone(config, pixel_values, labels)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    @unittest.skip(reason="Dinov3 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_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="Dinov3 does not support feedforward chunking yet")
+    def test_feed_forward_chunking(self):
+        pass
+
+    @slow
+    def test_model_from_pretrained(self):
+        model_name = "facebook/dinov3-vits16-pretrain-lvd1689m"
+        model = DINOv3ViTModel.from_pretrained(model_name)
+        self.assertIsNotNone(model)
+
+
+# We will verify our results on an image of cute cats
+def prepare_img():
+    image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
+    return image
+
+
+@require_torch
+@require_vision
+class DINOv3ViTModelIntegrationTest(unittest.TestCase):
+    @cached_property
+    def default_image_processor(self):
+        return (
+            AutoImageProcessor.from_pretrained("facebook/dinov3-vits16-pretrain-lvd1689m")
+            if is_vision_available()
+            else None
+        )
+
+    @slow
+    def test_inference_no_head(self):
+        model = DINOv3ViTModel.from_pretrained("facebook/dinov3-vits16-pretrain-lvd1689m").to(torch_device)
+
+        image_processor = self.default_image_processor
+        image = prepare_img()
+        inputs = image_processor(image, return_tensors="pt").to(torch_device)
+
+        # forward pass
+        with torch.no_grad():
+            outputs = model(**inputs)
+
+        # verify the last hidden states
+        # in DINOv3 with Registers, the seq length equals the number of patches + 1 + num_register_tokens (we add 1 for the [CLS] token)
+        _, _, height, width = inputs["pixel_values"].shape
+        num_patches = (height // model.config.patch_size) * (width // model.config.patch_size)
+        expected_seq_length = num_patches + 1 + model.config.num_register_tokens
+        expected_shape = torch.Size((1, expected_seq_length, model.config.hidden_size))
+        self.assertEqual(outputs.last_hidden_state.shape, expected_shape)
+
+        last_layer_cls_token = outputs.pooler_output
+        expected_slice = torch.tensor([0.4637, -0.4160, 0.4086, -0.1265, -0.2865], device=torch_device)
+        torch.testing.assert_close(last_layer_cls_token[0, :5], expected_slice, rtol=1e-4, atol=1e-4)
+
+        last_layer_patch_tokens = outputs.last_hidden_state[:, model.config.num_register_tokens + 1 :]
+        expected_slice = torch.tensor([-0.0386, -0.2509, -0.0161, -0.4556, 0.5716], device=torch_device)
+        torch.testing.assert_close(last_layer_patch_tokens[0, 0, :5], expected_slice, rtol=1e-4, atol=1e-4)