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tests/models/glpn/test_image_processing_glpn.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
+
+import numpy as np
+
+from transformers.testing_utils import require_torch, require_vision
+from transformers.utils import is_torch_available, is_vision_available
+
+from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
+
+
+if is_torch_available():
+    import torch
+
+if is_vision_available():
+    from PIL import Image
+
+
+class GLPNImageProcessingTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        num_channels=3,
+        image_size=18,
+        min_resolution=30,
+        max_resolution=400,
+        do_resize=True,
+        size_divisor=32,
+        do_rescale=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_divisor = size_divisor
+        self.do_rescale = do_rescale
+
+    def prepare_image_processor_dict(self):
+        return {
+            "do_resize": self.do_resize,
+            "size_divisor": self.size_divisor,
+            "do_rescale": self.do_rescale,
+        }
+
+    def expected_output_image_shape(self, images):
+        if isinstance(images[0], Image.Image):
+            width, height = images[0].size
+        elif isinstance(images[0], np.ndarray):
+            height, width = images[0].shape[0], images[0].shape[1]
+        else:
+            height, width = images[0].shape[1], images[0].shape[2]
+
+        height = height // self.size_divisor * self.size_divisor
+        width = width // self.size_divisor * self.size_divisor
+
+        return self.num_channels, height, 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,
+            size_divisor=self.size_divisor,
+            equal_resolution=equal_resolution,
+            numpify=numpify,
+            torchify=torchify,
+        )
+
+    def prepare_depth_outputs(self):
+        if not is_torch_available():
+            return None
+        depth_tensors = prepare_image_inputs(
+            batch_size=self.batch_size,
+            num_channels=1,
+            min_resolution=self.min_resolution,
+            max_resolution=self.max_resolution,
+            equal_resolution=True,
+            torchify=True,
+        )
+        depth_tensors = [depth_tensor.squeeze(0) for depth_tensor in depth_tensors]
+        stacked_depth_tensors = torch.stack(depth_tensors, dim=0)
+        return type("DepthOutput", (), {"predicted_depth": stacked_depth_tensors})
+
+
+@require_torch
+@require_vision
+class GLPNImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
+    def setUp(self):
+        super().setUp()
+        self.image_processor_tester = GLPNImageProcessingTester(self)
+        self.image_processor_dict = self.image_processor_tester.prepare_image_processor_dict()
+
+    def test_image_processor_properties(self):
+        for image_processing_class in self.image_processing_classes.values():
+            image_processing = image_processing_class(**self.image_processor_dict)
+            self.assertTrue(hasattr(image_processing, "do_resize"))
+            self.assertTrue(hasattr(image_processing, "size_divisor"))
+            self.assertTrue(hasattr(image_processing, "resample"))
+            self.assertTrue(hasattr(image_processing, "do_rescale"))
+
+    def test_call_pil(self):
+        # Initialize image_processing
+        for image_processing_class in self.image_processing_classes.values():
+            image_processing = image_processing_class(**self.image_processor_dict)
+            # create random PIL images
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
+            for image in image_inputs:
+                self.assertIsInstance(image, Image.Image)
+            # Test not batched input (GLPNImageProcessor doesn't support batching)
+            encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
+            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
+            self.assertTrue(tuple(encoded_images.shape) == (1, *expected_output_image_shape))
+
+    def test_call_numpy(self):
+        # Initialize image_processing
+        for image_processing_class in self.image_processing_classes.values():
+            image_processing = image_processing_class(**self.image_processor_dict)
+            # create random numpy tensors
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
+            for image in image_inputs:
+                self.assertIsInstance(image, np.ndarray)
+
+            # Test not batched input (GLPNImageProcessor doesn't support batching)
+            encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
+            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
+            self.assertTrue(tuple(encoded_images.shape) == (1, *expected_output_image_shape))
+
+    def test_call_pytorch(self):
+        # Initialize image_processing
+        for image_processing_class in self.image_processing_classes.values():
+            image_processing = image_processing_class(**self.image_processor_dict)
+            # create random PyTorch tensors
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
+            for image in image_inputs:
+                self.assertIsInstance(image, torch.Tensor)
+
+            # Test not batched input (GLPNImageProcessor doesn't support batching)
+            encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
+            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
+            self.assertTrue(tuple(encoded_images.shape) == (1, *expected_output_image_shape))
+
+    def test_call_numpy_4_channels(self):
+        for image_processing_class in self.image_processing_classes.values():
+            # Initialize image_processing
+            image_processing = image_processing_class(**self.image_processor_dict)
+            # create random numpy tensors
+            image_processing_class.num_channels = 4
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
+            for image in image_inputs:
+                self.assertIsInstance(image, np.ndarray)
+
+            # Test not batched input (GLPNImageProcessor doesn't support batching)
+            encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
+            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
+            self.assertTrue(tuple(encoded_images.shape) == (1, *expected_output_image_shape))
+            image_processing_class.num_channels = 3
+
+    # Override as GLPN image processors don't support heterogeneous batching (use equal_resolution=True)
+    @require_vision
+    @require_torch
+    def test_backends_equivalence_batched(self):
+        if len(self.image_processing_classes) < 2:
+            self.skipTest(reason="Skipping backends equivalence test as there are less than 2 backends")
+
+        dummy_images = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True)
+
+        # Create processors for each backend
+        encodings = {}
+        for backend_name, image_processing_class in self.image_processing_classes.items():
+            image_processor = image_processing_class(**self.image_processor_dict)
+            encodings[backend_name] = image_processor(dummy_images, return_tensors="pt")
+
+        # Compare all backends to the first one (reference backend)
+        backend_names = list(encodings.keys())
+        reference_backend = backend_names[0]
+        reference_encoding = encodings[reference_backend].pixel_values
+        for backend_name in backend_names[1:]:
+            self._assert_tensors_equivalence(reference_encoding, encodings[backend_name].pixel_values)
+
+    @require_vision
+    @require_torch
+    def test_backends_equivalence_post_process_depth(self):
+        """Check that all backends produce equivalent post-processed depth maps."""
+        if len(self.image_processing_classes) < 2:
+            self.skipTest(reason="Skipping backends equivalence test as there are less than 2 backends")
+
+        outputs = self.image_processor_tester.prepare_depth_outputs()
+        target_sizes = [(240, 320)] * self.image_processor_tester.batch_size
+
+        # Create processors and run post-processing for each backend
+        processed = {}
+        for backend_name, image_processing_class in self.image_processing_classes.items():
+            image_processor = image_processing_class(**self.image_processor_dict)
+            processed[backend_name] = image_processor.post_process_depth_estimation(outputs, target_sizes=target_sizes)
+
+        # Compare all backends to the first one (reference backend)
+        backend_names = list(processed.keys())
+        reference_backend = backend_names[0]
+        for backend_name in backend_names[1:]:
+            for pred_ref, pred_other in zip(processed[reference_backend], processed[backend_name]):
+                depth_ref = pred_ref["predicted_depth"].float()
+                depth_other = pred_other["predicted_depth"].float()
+                self._assert_tensors_equivalence(depth_ref, depth_other)