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tests/models/grounding_dino/test_processing_grounding_dino.py

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+# Copyright 2024 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 os
+import unittest
+
+from transformers import GroundingDinoProcessor
+from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
+from transformers.testing_utils import require_torch, require_vision
+from transformers.utils import is_torch_available
+
+from ...test_processing_common import ProcessorTesterMixin
+
+
+if is_torch_available():
+    import torch
+
+    from transformers.models.grounding_dino.modeling_grounding_dino import GroundingDinoObjectDetectionOutput
+
+
+@require_torch
+@require_vision
+class GroundingDinoProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    model_id = "IDEA-Research/grounding-dino-base"
+    processor_class = GroundingDinoProcessor
+    batch_size = 7
+    num_queries = 5
+    embed_dim = 5
+    seq_length = 5
+
+    @classmethod
+    def _setup_image_processor(cls):
+        image_processor_class = cls._get_component_class_from_processor("image_processor")
+        return image_processor_class(
+            do_resize=True,
+            size=None,
+            do_normalize=True,
+            image_mean=[0.5, 0.5, 0.5],
+            image_std=[0.5, 0.5, 0.5],
+            do_rescale=True,
+            rescale_factor=1 / 255,
+            do_pad=True,
+        )
+
+    @classmethod
+    def _setup_tokenizer(cls):
+        tokenizer_class = cls._get_component_class_from_processor("tokenizer")
+        vocab_tokens = ["[UNK]","[CLS]","[SEP]","[PAD]","[MASK]","want","##want","##ed","wa","un","runn","##ing",",","low","lowest"]  # fmt: skip
+        vocab_file = os.path.join(cls.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
+        with open(vocab_file, "w", encoding="utf-8") as vocab_writer:
+            vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
+        return tokenizer_class.from_pretrained(cls.tmpdirname)
+
+    @unittest.skip("GroundingDinoProcessor merges candidate labels text")
+    def test_tokenizer_defaults(self):
+        pass
+
+    def prepare_text_inputs(self, batch_size: int | None = None, **kwargs):
+        labels = ["a cat", "remote control"]
+        labels_longer = ["a person", "a car", "a dog", "a cat"]
+
+        if batch_size is None:
+            return labels
+
+        if batch_size < 1:
+            raise ValueError("batch_size must be greater than 0")
+
+        if batch_size == 1:
+            return [labels]
+        return [labels, labels_longer] + [labels] * (batch_size - 2)
+
+    def get_fake_grounding_dino_output(self):
+        torch.manual_seed(42)
+        return GroundingDinoObjectDetectionOutput(
+            pred_boxes=torch.rand(self.batch_size, self.num_queries, 4),
+            logits=torch.rand(self.batch_size, self.num_queries, self.embed_dim),
+            input_ids=self.get_fake_grounding_dino_input_ids(),
+        )
+
+    def get_fake_grounding_dino_input_ids(self):
+        input_ids = torch.tensor([101, 1037, 4937, 1012, 102])
+        return torch.stack([input_ids] * self.batch_size, dim=0)
+
+    def test_post_process_grounded_object_detection(self):
+        processor = self.get_processor()
+
+        grounding_dino_output = self.get_fake_grounding_dino_output()
+
+        post_processed = processor.post_process_grounded_object_detection(grounding_dino_output)
+
+        self.assertEqual(len(post_processed), self.batch_size)
+        self.assertEqual(list(post_processed[0].keys()), ["scores", "boxes", "text_labels", "labels"])
+        self.assertEqual(post_processed[0]["boxes"].shape, (self.num_queries, 4))
+        self.assertEqual(post_processed[0]["scores"].shape, (self.num_queries,))
+
+        expected_scores = torch.tensor([0.7050, 0.7222, 0.7222, 0.6829, 0.7220])
+        torch.testing.assert_close(post_processed[0]["scores"], expected_scores, rtol=1e-4, atol=1e-4)
+
+        expected_box_slice = torch.tensor([0.6908, 0.4354, 1.0737, 1.3947])
+        torch.testing.assert_close(post_processed[0]["boxes"][0], expected_box_slice, rtol=1e-4, atol=1e-4)
+
+    def test_text_preprocessing_equivalence(self):
+        processor = self.get_processor()
+
+        # check for single input
+        formatted_labels = "a cat. a remote control."
+        labels = ["a cat", "a remote control"]
+        inputs1 = processor(text=formatted_labels, return_tensors="pt")
+        inputs2 = processor(text=labels, return_tensors="pt")
+        self.assertTrue(
+            torch.allclose(inputs1["input_ids"], inputs2["input_ids"]),
+            f"Input ids are not equal for single input: {inputs1['input_ids']} != {inputs2['input_ids']}",
+        )
+
+        # check for batched input
+        formatted_labels = ["a cat. a remote control.", "a car. a person."]
+        labels = [["a cat", "a remote control"], ["a car", "a person"]]
+        inputs1 = processor(text=formatted_labels, return_tensors="pt", padding=True)
+        inputs2 = processor(text=labels, return_tensors="pt", padding=True)
+        self.assertTrue(
+            torch.allclose(inputs1["input_ids"], inputs2["input_ids"]),
+            f"Input ids are not equal for batched input: {inputs1['input_ids']} != {inputs2['input_ids']}",
+        )
+
+    def test_processor_text_has_no_visual(self):
+        # Overwritten: text inputs have to be nested as well
+        processor = self.get_processor()
+
+        text = self.prepare_text_inputs(batch_size=3, modalities="image")
+        image_inputs = self.prepare_image_inputs(batch_size=3)
+        processing_kwargs = {"return_tensors": "pt", "padding": True}
+
+        # Call with nested list of vision inputs
+        image_inputs_nested = [[image] if not isinstance(image, list) else image for image in image_inputs]
+        inputs_dict_nested = {"text": text, "images": image_inputs_nested}
+        inputs = processor(**inputs_dict_nested, **processing_kwargs)
+        self.assertTrue(self.text_input_name in inputs)
+
+        # Call with one of the samples with no associated vision input
+        plain_text = ["lower newer"]
+        image_inputs_nested[0] = []
+        text[0] = plain_text
+        inputs_dict_no_vision = {"text": text, "images": image_inputs_nested}
+        inputs_nested = processor(**inputs_dict_no_vision, **processing_kwargs)
+
+        # Check that text samples are same and are expanded with placeholder tokens correctly. First sample
+        # has no vision input associated, so we skip it and check it has no vision
+        self.assertListEqual(
+            inputs[self.text_input_name][1:].tolist(), inputs_nested[self.text_input_name][1:].tolist()
+        )