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tests/models/owlvit/test_processing_owlvit.py

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+# Copyright 2022 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 json
+import os
+import unittest
+
+import pytest
+
+from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
+from transformers.testing_utils import require_vision
+from transformers.utils import is_vision_available
+
+from ...test_processing_common import ProcessorTesterMixin
+
+
+if is_vision_available():
+    from transformers import OwlViTProcessor
+
+
+@require_vision
+class OwlViTProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = OwlViTProcessor
+
+    @classmethod
+    def _setup_image_processor(cls):
+        image_processor_class = cls._get_component_class_from_processor("image_processor")
+        image_processor_map = {
+            "do_resize": True,
+            "size": 20,
+            "do_center_crop": True,
+            "crop_size": 18,
+            "do_normalize": True,
+            "image_mean": [0.48145466, 0.4578275, 0.40821073],
+            "image_std": [0.26862954, 0.26130258, 0.27577711],
+        }
+        return image_processor_class(**image_processor_map)
+
+    @classmethod
+    def _setup_tokenizer(cls):
+        tokenizer_class = cls._get_component_class_from_processor("tokenizer")
+        vocab = ["", "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]  # fmt: skip
+        vocab_tokens = dict(zip(vocab, range(len(vocab))))
+        merges = ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
+
+        vocab_file = os.path.join(cls.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
+        merges_file = os.path.join(cls.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
+        with open(vocab_file, "w", encoding="utf-8") as fp:
+            fp.write(json.dumps(vocab_tokens) + "\n")
+        with open(merges_file, "w", encoding="utf-8") as fp:
+            fp.write("\n".join(merges))
+        return tokenizer_class.from_pretrained(cls.tmpdirname)
+
+    def test_processor_with_text_list(self):
+        model_name = "google/owlvit-base-patch32"
+        processor = OwlViTProcessor.from_pretrained(model_name)
+
+        input_text = ["cat", "nasa badge"]
+        inputs = processor(text=input_text)
+
+        seq_length = 16
+        self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask"])
+        self.assertEqual(inputs["input_ids"].shape, (2, seq_length))
+
+        # test if it raises when no input is passed
+        with pytest.raises(ValueError):
+            processor()
+
+    def test_processor_with_nested_text_list(self):
+        model_name = "google/owlvit-base-patch32"
+        processor = OwlViTProcessor.from_pretrained(model_name)
+
+        input_texts = [["cat", "nasa badge"], ["person"]]
+        inputs = processor(text=input_texts)
+
+        seq_length = 16
+        batch_size = len(input_texts)
+        num_max_text_queries = max(len(texts) for texts in input_texts)
+
+        self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask"])
+        self.assertEqual(inputs["input_ids"].shape, (batch_size * num_max_text_queries, seq_length))
+
+        # test if it raises when no input is passed
+        with pytest.raises(ValueError):
+            processor()
+
+    def test_processor_case(self):
+        model_name = "google/owlvit-base-patch32"
+        processor = OwlViTProcessor.from_pretrained(model_name)
+
+        input_texts = ["cat", "nasa badge"]
+        inputs = processor(text=input_texts)
+
+        seq_length = 16
+        input_ids = inputs["input_ids"]
+        predicted_ids = [
+            [49406, 2368, 49407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            [49406, 6841, 11301, 49407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        ]
+
+        self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask"])
+        self.assertEqual(inputs["input_ids"].shape, (2, seq_length))
+        self.assertListEqual(list(input_ids[0]), predicted_ids[0])
+        self.assertListEqual(list(input_ids[1]), predicted_ids[1])
+
+    def test_processor_case2(self):
+        processor = self.get_processor()
+
+        image_input = self.prepare_image_inputs()
+        query_input = self.prepare_image_inputs()
+
+        inputs = processor(images=image_input, query_images=query_input)
+
+        self.assertListEqual(list(inputs.keys()), ["query_pixel_values", "pixel_values"])
+
+        # test if it raises when no input is passed
+        with pytest.raises(ValueError):
+            processor()