init

transformers/tests/models/owlv2/test_image_processing_owlv2.py

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+# Copyright 2023 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, slow
+from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available
+
+from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
+
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import AutoProcessor, Owlv2ForObjectDetection, Owlv2ImageProcessor
+
+if is_torch_available():
+    import torch
+
+    from transformers import Owlv2ImageProcessorFast
+
+
+class Owlv2ImageProcessingTester:
+    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_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_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_normalize": self.do_normalize,
+            "image_mean": self.image_mean,
+            "image_std": self.image_std,
+        }
+
+    def expected_output_image_shape(self, images):
+        return self.num_channels, self.size["height"], self.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 Owlv2ImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
+    image_processing_class = Owlv2ImageProcessor if is_vision_available() else None
+    fast_image_processing_class = Owlv2ImageProcessorFast if is_torchvision_available() else None
+
+    def setUp(self):
+        super().setUp()
+        self.image_processor_tester = Owlv2ImageProcessingTester(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_normalize"))
+            self.assertTrue(hasattr(image_processing, "image_mean"))
+            self.assertTrue(hasattr(image_processing, "image_std"))
+
+    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})
+
+            image_processor = image_processing_class.from_dict(
+                self.image_processor_dict, size={"height": 42, "width": 42}
+            )
+            self.assertEqual(image_processor.size, {"height": 42, "width": 42})
+
+    @slow
+    def test_image_processor_integration_test(self):
+        for image_processing_class in self.image_processor_list:
+            processor = image_processing_class()
+
+            image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
+            pixel_values = processor(image, return_tensors="pt").pixel_values
+
+            mean_value = round(pixel_values.mean().item(), 4)
+            self.assertEqual(mean_value, 0.2353)
+
+    @slow
+    def test_image_processor_integration_test_resize(self):
+        for use_fast in [False, True]:
+            checkpoint = "google/owlv2-base-patch16-ensemble"
+            processor = AutoProcessor.from_pretrained(checkpoint, use_fast=use_fast)
+            model = Owlv2ForObjectDetection.from_pretrained(checkpoint)
+
+            image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
+            text = ["cat"]
+            target_size = image.size[::-1]
+            expected_boxes = torch.tensor(
+                [
+                    [341.66656494140625, 23.38756561279297, 642.321044921875, 371.3482971191406],
+                    [6.753320693969727, 51.96149826049805, 326.61810302734375, 473.12982177734375],
+                ]
+            )
+
+            # single image
+            inputs = processor(text=[text], images=[image], return_tensors="pt")
+            with torch.no_grad():
+                outputs = model(**inputs)
+
+            results = processor.post_process_object_detection(outputs, threshold=0.2, target_sizes=[target_size])[0]
+
+            boxes = results["boxes"]
+            torch.testing.assert_close(boxes, expected_boxes, atol=1e-1, rtol=1e-1)
+
+            # batch of images
+            inputs = processor(text=[text, text], images=[image, image], return_tensors="pt")
+            with torch.no_grad():
+                outputs = model(**inputs)
+            results = processor.post_process_object_detection(
+                outputs, threshold=0.2, target_sizes=[target_size, target_size]
+            )
+
+            for result in results:
+                boxes = result["boxes"]
+                torch.testing.assert_close(boxes, expected_boxes, atol=1e-1, rtol=1e-1)
+
+    @unittest.skip(reason="OWLv2 doesn't treat 4 channel PIL and numpy consistently yet")  # FIXME Amy
+    def test_call_numpy_4_channels(self):
+        pass