init

transformers/tests/models/owlvit/test_image_processing_owlvit.py

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+# Copyright 2022 HuggingFace Inc.
+#
+# 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 unittest
+
+from transformers.testing_utils import require_torch, require_vision
+from transformers.utils import is_torchvision_available, is_vision_available
+
+from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
+
+
+if is_vision_available():
+    from transformers import OwlViTImageProcessor
+
+    if is_torchvision_available():
+        from transformers import OwlViTImageProcessorFast
+
+
+class OwlViTImageProcessingTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        num_channels=3,
+        image_size=18,
+        min_resolution=30,
+        max_resolution=400,
+        do_resize=True,
+        size=None,
+        do_center_crop=True,
+        crop_size=None,
+        do_normalize=True,
+        image_mean=[0.48145466, 0.4578275, 0.40821073],
+        image_std=[0.26862954, 0.26130258, 0.27577711],
+        do_convert_rgb=True,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.num_channels = num_channels
+        self.image_size = image_size
+        self.min_resolution = min_resolution
+        self.max_resolution = max_resolution
+        self.do_resize = do_resize
+        self.size = size if size is not None else {"height": 18, "width": 18}
+        self.do_center_crop = do_center_crop
+        self.crop_size = crop_size if crop_size is not None else {"height": 18, "width": 18}
+        self.do_normalize = do_normalize
+        self.image_mean = image_mean
+        self.image_std = image_std
+        self.do_convert_rgb = do_convert_rgb
+
+    def prepare_image_processor_dict(self):
+        return {
+            "do_resize": self.do_resize,
+            "size": self.size,
+            "do_center_crop": self.do_center_crop,
+            "crop_size": self.crop_size,
+            "do_normalize": self.do_normalize,
+            "image_mean": self.image_mean,
+            "image_std": self.image_std,
+            "do_convert_rgb": self.do_convert_rgb,
+        }
+
+    def expected_output_image_shape(self, images):
+        return self.num_channels, self.crop_size["height"], self.crop_size["width"]
+
+    def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
+        return prepare_image_inputs(
+            batch_size=self.batch_size,
+            num_channels=self.num_channels,
+            min_resolution=self.min_resolution,
+            max_resolution=self.max_resolution,
+            equal_resolution=equal_resolution,
+            numpify=numpify,
+            torchify=torchify,
+        )
+
+
+@require_torch
+@require_vision
+class OwlViTImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
+    image_processing_class = OwlViTImageProcessor if is_vision_available() else None
+    fast_image_processing_class = OwlViTImageProcessorFast if is_torchvision_available() else None
+
+    def setUp(self):
+        super().setUp()
+        self.image_processor_tester = OwlViTImageProcessingTester(self)
+
+    @property
+    def image_processor_dict(self):
+        return self.image_processor_tester.prepare_image_processor_dict()
+
+    def test_image_processor_properties(self):
+        for image_processing_class in self.image_processor_list:
+            image_processing = image_processing_class(**self.image_processor_dict)
+            self.assertTrue(hasattr(image_processing, "do_resize"))
+            self.assertTrue(hasattr(image_processing, "size"))
+            self.assertTrue(hasattr(image_processing, "do_center_crop"))
+            self.assertTrue(hasattr(image_processing, "center_crop"))
+            self.assertTrue(hasattr(image_processing, "do_normalize"))
+            self.assertTrue(hasattr(image_processing, "image_mean"))
+            self.assertTrue(hasattr(image_processing, "image_std"))
+            self.assertTrue(hasattr(image_processing, "do_convert_rgb"))
+
+    def test_image_processor_from_dict_with_kwargs(self):
+        for image_processing_class in self.image_processor_list:
+            image_processor = image_processing_class.from_dict(self.image_processor_dict)
+            self.assertEqual(image_processor.size, {"height": 18, "width": 18})
+            self.assertEqual(image_processor.crop_size, {"height": 18, "width": 18})
+
+            image_processor = image_processing_class.from_dict(self.image_processor_dict, size=42, crop_size=84)
+            self.assertEqual(image_processor.size, {"height": 42, "width": 42})
+            self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84})

transformers/tests/models/owlvit/test_processing_owlvit.py

@@ -0,0 +1,246 @@
+# 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 shutil
+import tempfile
+import unittest
+
+import pytest
+
+from transformers import CLIPTokenizer, CLIPTokenizerFast
+from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
+from transformers.testing_utils import require_vision
+from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
+
+from ...test_processing_common import ProcessorTesterMixin
+
+
+if is_vision_available():
+    from transformers import OwlViTImageProcessor, OwlViTProcessor
+
+
+@require_vision
+class OwlViTProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = OwlViTProcessor
+
+    def setUp(self):
+        self.tmpdirname = tempfile.mkdtemp()
+
+        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>", ""]
+        self.special_tokens_map = {"unk_token": "<unk>"}
+
+        self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
+        self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
+        with open(self.vocab_file, "w", encoding="utf-8") as fp:
+            fp.write(json.dumps(vocab_tokens) + "\n")
+        with open(self.merges_file, "w", encoding="utf-8") as fp:
+            fp.write("\n".join(merges))
+
+        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],
+        }
+        self.image_processor_file = os.path.join(self.tmpdirname, IMAGE_PROCESSOR_NAME)
+        with open(self.image_processor_file, "w", encoding="utf-8") as fp:
+            json.dump(image_processor_map, fp)
+
+    def get_tokenizer(self, **kwargs):
+        return CLIPTokenizer.from_pretrained(self.tmpdirname, pad_token="!", **kwargs)
+
+    def get_rust_tokenizer(self, **kwargs):
+        return CLIPTokenizerFast.from_pretrained(self.tmpdirname, pad_token="!", **kwargs)
+
+    def get_image_processor(self, **kwargs):
+        return OwlViTImageProcessor.from_pretrained(self.tmpdirname, **kwargs)
+
+    def tearDown(self):
+        shutil.rmtree(self.tmpdirname)
+
+    def test_save_load_pretrained_default(self):
+        tokenizer_slow = self.get_tokenizer()
+        tokenizer_fast = self.get_rust_tokenizer()
+        image_processor = self.get_image_processor()
+
+        processor_slow = OwlViTProcessor(tokenizer=tokenizer_slow, image_processor=image_processor)
+        processor_slow.save_pretrained(self.tmpdirname)
+        processor_slow = OwlViTProcessor.from_pretrained(self.tmpdirname, use_fast=False)
+
+        processor_fast = OwlViTProcessor(tokenizer=tokenizer_fast, image_processor=image_processor)
+        processor_fast.save_pretrained(self.tmpdirname)
+        processor_fast = OwlViTProcessor.from_pretrained(self.tmpdirname)
+
+        self.assertEqual(processor_slow.tokenizer.get_vocab(), tokenizer_slow.get_vocab())
+        self.assertEqual(processor_fast.tokenizer.get_vocab(), tokenizer_fast.get_vocab())
+        self.assertEqual(tokenizer_slow.get_vocab(), tokenizer_fast.get_vocab())
+        self.assertIsInstance(processor_slow.tokenizer, CLIPTokenizer)
+        self.assertIsInstance(processor_fast.tokenizer, CLIPTokenizerFast)
+
+        self.assertEqual(processor_slow.image_processor.to_json_string(), image_processor.to_json_string())
+        self.assertEqual(processor_fast.image_processor.to_json_string(), image_processor.to_json_string())
+        self.assertIsInstance(processor_slow.image_processor, OwlViTImageProcessor)
+        self.assertIsInstance(processor_fast.image_processor, OwlViTImageProcessor)
+
+    def test_save_load_pretrained_additional_features(self):
+        processor = OwlViTProcessor(tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor())
+        processor.save_pretrained(self.tmpdirname)
+
+        tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
+        image_processor_add_kwargs = self.get_image_processor(do_normalize=False)
+
+        processor = OwlViTProcessor.from_pretrained(
+            self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", pad_token="!", do_normalize=False
+        )
+
+        self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
+        self.assertIsInstance(processor.tokenizer, CLIPTokenizerFast)
+
+        self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string())
+        self.assertIsInstance(processor.image_processor, OwlViTImageProcessor)
+
+    def test_image_processor(self):
+        image_processor = self.get_image_processor()
+        tokenizer = self.get_tokenizer()
+
+        processor = OwlViTProcessor(tokenizer=tokenizer, image_processor=image_processor)
+
+        image_input = self.prepare_image_inputs()
+
+        input_image_proc = image_processor(image_input, return_tensors="np")
+        input_processor = processor(images=image_input, return_tensors="np")
+
+        for key in input_image_proc:
+            self.assertAlmostEqual(input_image_proc[key].sum(), input_processor[key].sum(), delta=1e-2)
+
+    def test_tokenizer(self):
+        image_processor = self.get_image_processor()
+        tokenizer = self.get_tokenizer()
+
+        processor = OwlViTProcessor(tokenizer=tokenizer, image_processor=image_processor)
+
+        input_str = "lower newer"
+
+        encoded_processor = processor(text=input_str, return_tensors="np")
+
+        encoded_tok = tokenizer(input_str, return_tensors="np")
+
+        for key in encoded_tok:
+            self.assertListEqual(encoded_tok[key][0].tolist(), encoded_processor[key][0].tolist())
+
+    def test_processor(self):
+        image_processor = self.get_image_processor()
+        tokenizer = self.get_tokenizer()
+
+        processor = OwlViTProcessor(tokenizer=tokenizer, image_processor=image_processor)
+
+        input_str = "lower newer"
+        image_input = self.prepare_image_inputs()
+
+        inputs = processor(text=input_str, images=image_input)
+
+        self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask", "pixel_values"])
+
+        # test if it raises when no input is passed
+        with pytest.raises(ValueError):
+            processor()
+
+    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):
+        image_processor = self.get_image_processor()
+        tokenizer = self.get_tokenizer()
+
+        processor = OwlViTProcessor(tokenizer=tokenizer, image_processor=image_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()
+
+    def test_tokenizer_decode(self):
+        image_processor = self.get_image_processor()
+        tokenizer = self.get_tokenizer()
+
+        processor = OwlViTProcessor(tokenizer=tokenizer, image_processor=image_processor)
+
+        predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
+
+        decoded_processor = processor.batch_decode(predicted_ids)
+        decoded_tok = tokenizer.batch_decode(predicted_ids)
+
+        self.assertListEqual(decoded_tok, decoded_processor)