# 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() )