first commit

tests/models/prompt_depth_anything/test_image_processing_prompt_depth_anything.py

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+# Copyright 2024 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 ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
+
+
+class PromptDepthAnythingImageProcessingTester(unittest.TestCase):
+    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=[0.5, 0.5, 0.5],
+        image_std=[0.5, 0.5, 0.5],
+    ):
+        super().__init__()
+        size = size if size is not None else {"height": 18, "width": 18}
+        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
+        self.do_normalize = do_normalize
+        self.image_mean = image_mean
+        self.image_std = image_std
+
+    def prepare_image_processor_dict(self):
+        return {
+            "image_mean": self.image_mean,
+            "image_std": self.image_std,
+            "do_normalize": self.do_normalize,
+            "do_resize": self.do_resize,
+            "size": self.size,
+        }
+
+    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 PromptDepthAnythingImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
+    def setUp(self):
+        super().setUp()
+        self.image_processor_tester = PromptDepthAnythingImageProcessingTester(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_processing_classes.values():
+            image_processing = image_processing_class(**self.image_processor_dict)
+            self.assertTrue(hasattr(image_processing, "image_mean"))
+            self.assertTrue(hasattr(image_processing, "image_std"))
+            self.assertTrue(hasattr(image_processing, "do_normalize"))
+            self.assertTrue(hasattr(image_processing, "do_resize"))
+            self.assertTrue(hasattr(image_processing, "size"))
+            self.assertTrue(hasattr(image_processing, "do_rescale"))
+            self.assertTrue(hasattr(image_processing, "rescale_factor"))
+            self.assertTrue(hasattr(image_processing, "do_pad"))
+            self.assertTrue(hasattr(image_processing, "size_divisor"))
+            self.assertTrue(hasattr(image_processing, "prompt_scale_to_meter"))
+
+    def test_image_processor_from_dict_with_kwargs(self):
+        for image_processing_class in self.image_processing_classes.values():
+            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=42)
+            self.assertEqual(image_processor.size, {"height": 42, "width": 42})
+
+    def test_keep_aspect_ratio(self):
+        size = {"height": 512, "width": 512}
+        for image_processing_class in self.image_processing_classes.values():
+            image_processor = image_processing_class(size=size, keep_aspect_ratio=True, ensure_multiple_of=32)
+
+            image = np.zeros((489, 640, 3))
+
+            pixel_values = image_processor(image, return_tensors="pt").pixel_values
+
+            self.assertEqual(list(pixel_values.shape), [1, 3, 512, 672])
+
+    def test_prompt_depth_processing(self):
+        size = {"height": 756, "width": 756}
+        for image_processing_class in self.image_processing_classes.values():
+            image_processor = image_processing_class(size=size, keep_aspect_ratio=True, ensure_multiple_of=32)
+
+            image = np.zeros((756, 1008, 3))
+            prompt_depth = np.random.random((192, 256))
+
+            outputs = image_processor(image, prompt_depth=prompt_depth, return_tensors="pt")
+            pixel_values = outputs.pixel_values
+            prompt_depth_values = outputs.prompt_depth
+
+            self.assertEqual(list(pixel_values.shape), [1, 3, 768, 1024])
+            self.assertEqual(list(prompt_depth_values.shape), [1, 1, 192, 256])
+
+    def test_backends_equivalence(self):
+        """Override base class test to also compare prompt_depth."""
+        if len(self.image_processing_classes) < 2:
+            self.skipTest(reason="Skipping backends equivalence test as there are less than 2 backends")
+
+        image = np.zeros((756, 1008, 3))
+        prompt_depth = np.random.random((192, 256))
+
+        size = {"height": 756, "width": 756}
+        encodings = {}
+        for backend_name, image_processing_class in self.image_processing_classes.items():
+            image_processor = image_processing_class(
+                size=size, keep_aspect_ratio=True, ensure_multiple_of=32, do_pad=True, size_divisor=51
+            )
+            encodings[backend_name] = image_processor(image, prompt_depth=prompt_depth, return_tensors="pt")
+
+        backend_names = list(encodings.keys())
+        reference_backend = backend_names[0]
+        for backend_name in backend_names[1:]:
+            self._assert_tensors_equivalence(
+                encodings[reference_backend].pixel_values, encodings[backend_name].pixel_values
+            )
+            self.assertEqual(
+                encodings[reference_backend].prompt_depth.dtype, encodings[backend_name].prompt_depth.dtype
+            )
+            self._assert_tensors_equivalence(
+                encodings[reference_backend].prompt_depth, encodings[backend_name].prompt_depth
+            )
+
+    def test_slow_fast_equivalence_batched(self):
+        """Override base class test to also compare prompt_depth."""
+        if len(self.image_processing_classes) < 2:
+            self.skipTest(reason="Skipping backends equivalence test as there are less than 2 backends")
+
+        batch_size = self.image_processor_tester.batch_size
+        images = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True)
+        prompt_depths = [np.random.random((192, 256)) for _ in range(batch_size)]
+
+        size = {"height": 756, "width": 756}
+        encodings = {}
+        for backend_name, image_processing_class in self.image_processing_classes.items():
+            image_processor = image_processing_class(size=size, keep_aspect_ratio=False, ensure_multiple_of=32)
+            encodings[backend_name] = image_processor(images, prompt_depth=prompt_depths, return_tensors="pt")
+
+        backend_names = list(encodings.keys())
+        reference_backend = backend_names[0]
+        for backend_name in backend_names[1:]:
+            self._assert_tensors_equivalence(
+                encodings[reference_backend].pixel_values, encodings[backend_name].pixel_values
+            )
+            self.assertEqual(
+                encodings[reference_backend].prompt_depth.dtype, encodings[backend_name].prompt_depth.dtype
+            )
+            self._assert_tensors_equivalence(
+                encodings[reference_backend].prompt_depth, encodings[backend_name].prompt_depth
+            )

tests/models/prompt_depth_anything/test_modeling_prompt_depth_anything.py

@@ -0,0 +1,279 @@
+# Copyright 2024 The HuggingFace Inc. 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.
+"""Testing suite for the PyTorch Prompt Depth Anything model."""
+
+import unittest
+
+import requests
+
+from transformers import Dinov2Config, PromptDepthAnythingConfig
+from transformers.file_utils import is_torch_available, is_vision_available
+from transformers.testing_utils import require_torch, require_vision, slow, torch_device
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import PromptDepthAnythingForDepthEstimation
+
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import AutoImageProcessor
+
+
+class PromptDepthAnythingModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=2,
+        num_channels=3,
+        image_size=32,
+        patch_size=16,
+        use_labels=True,
+        num_labels=3,
+        is_training=True,
+        hidden_size=4,
+        num_hidden_layers=2,
+        num_attention_heads=2,
+        intermediate_size=8,
+        out_features=["stage1", "stage2"],
+        apply_layernorm=False,
+        reshape_hidden_states=False,
+        neck_hidden_sizes=[2, 2],
+        fusion_hidden_size=6,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.num_channels = num_channels
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.out_features = out_features
+        self.apply_layernorm = apply_layernorm
+        self.reshape_hidden_states = reshape_hidden_states
+        self.use_labels = use_labels
+        self.num_labels = num_labels
+        self.is_training = is_training
+        self.neck_hidden_sizes = neck_hidden_sizes
+        self.fusion_hidden_size = fusion_hidden_size
+        self.seq_length = (self.image_size // self.patch_size) ** 2 + 1
+
+    def prepare_config_and_inputs(self):
+        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
+
+        labels = None
+        if self.use_labels:
+            labels = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels)
+
+        prompt_depth = floats_tensor([self.batch_size, 1, self.image_size // 4, self.image_size // 4])
+
+        config = self.get_config()
+
+        return config, pixel_values, labels, prompt_depth
+
+    def get_config(self):
+        return PromptDepthAnythingConfig(
+            backbone_config=self.get_backbone_config(),
+            reassemble_hidden_size=self.hidden_size,
+            patch_size=self.patch_size,
+            neck_hidden_sizes=self.neck_hidden_sizes,
+            fusion_hidden_size=self.fusion_hidden_size,
+        )
+
+    def get_backbone_config(self):
+        return Dinov2Config(
+            image_size=self.image_size,
+            patch_size=self.patch_size,
+            num_channels=self.num_channels,
+            hidden_size=self.hidden_size,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            intermediate_size=self.intermediate_size,
+            is_training=self.is_training,
+            out_features=self.out_features,
+            reshape_hidden_states=self.reshape_hidden_states,
+        )
+
+    def create_and_check_for_depth_estimation(self, config, pixel_values, labels, prompt_depth):
+        config.num_labels = self.num_labels
+        model = PromptDepthAnythingForDepthEstimation(config)
+        model.to(torch_device)
+        model.eval()
+        result = model(pixel_values, prompt_depth=prompt_depth)
+        self.parent.assertEqual(result.predicted_depth.shape, (self.batch_size, self.image_size, self.image_size))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, pixel_values, labels, prompt_depth = config_and_inputs
+        inputs_dict = {"pixel_values": pixel_values, "prompt_depth": prompt_depth}
+        return config, inputs_dict
+
+
+@require_torch
+class PromptDepthAnythingModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    """
+    Here we also overwrite some of the tests of test_modeling_common.py, as Prompt Depth Anything does not use input_ids, inputs_embeds,
+    attention_mask and seq_length.
+    """
+
+    all_model_classes = (PromptDepthAnythingForDepthEstimation,) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {"depth-estimation": PromptDepthAnythingForDepthEstimation} if is_torch_available() else {}
+    )
+
+    test_resize_embeddings = False
+
+    def setUp(self):
+        self.model_tester = PromptDepthAnythingModelTester(self)
+        self.config_tester = ConfigTester(
+            self,
+            config_class=PromptDepthAnythingConfig,
+            has_text_modality=False,
+            hidden_size=32,
+            common_properties=["patch_size"],
+        )
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    @unittest.skip(
+        reason="Prompt Depth Anything with AutoBackbone does not have a base model and hence no input_embeddings"
+    )
+    def test_inputs_embeds(self):
+        pass
+
+    def test_for_depth_estimation(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_depth_estimation(*config_and_inputs)
+
+    @unittest.skip(reason="This module does not support standalone training")
+    def test_training(self):
+        pass
+
+    @unittest.skip(reason="This module does not support standalone training")
+    def test_training_gradient_checkpointing(self):
+        pass
+
+    @unittest.skip(reason="This module does not support standalone training")
+    def test_training_gradient_checkpointing_use_reentrant_false(self):
+        pass
+
+    @unittest.skip(reason="This module does not support standalone training")
+    def test_training_gradient_checkpointing_use_reentrant_true(self):
+        pass
+
+    @unittest.skip(
+        reason="Prompt Depth Anything with AutoBackbone does not have a base model and hence no input_embeddings"
+    )
+    def test_model_get_set_embeddings(self):
+        pass
+
+    @slow
+    def test_model_from_pretrained(self):
+        model_name = "depth-anything/prompt-depth-anything-vits-hf"
+        model = PromptDepthAnythingForDepthEstimation.from_pretrained(model_name)
+        self.assertIsNotNone(model)
+
+    def test_backbone_selection(self):
+        def _validate_backbone_init():
+            for model_class in self.all_model_classes:
+                model = model_class(config)
+                model.to(torch_device)
+                model.eval()
+
+                self.assertEqual(len(model.backbone.out_indices), 2)
+
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        config_dict = config.to_dict()
+        config_dict["backbone"] = "facebook/dinov2-small"
+        config_dict["use_pretrained_backbone"] = True
+        config_dict["use_timm_backbone"] = False
+        config_dict["backbone_config"] = None
+        config_dict["backbone_kwargs"] = {"out_indices": [-2, -1]}
+        config = config.__class__(**config_dict)
+        _validate_backbone_init()
+
+
+def prepare_img():
+    url = "https://raw.githubusercontent.com/DepthAnything/PromptDA/main/assets/example_images/image.jpg"
+    image = Image.open(requests.get(url, stream=True).raw)
+    return image
+
+
+def prepare_prompt_depth():
+    prompt_depth_url = (
+        "https://raw.githubusercontent.com/DepthAnything/PromptDA/main/assets/example_images/arkit_depth.png"
+    )
+    prompt_depth = Image.open(requests.get(prompt_depth_url, stream=True).raw)
+    return prompt_depth
+
+
+@require_torch
+@require_vision
+@slow
+class PromptDepthAnythingModelIntegrationTest(unittest.TestCase):
+    def test_inference_wo_prompt_depth(self):
+        image_processor = AutoImageProcessor.from_pretrained("depth-anything/prompt-depth-anything-vits-hf")
+        model = PromptDepthAnythingForDepthEstimation.from_pretrained(
+            "depth-anything/prompt-depth-anything-vits-hf"
+        ).to(torch_device)
+
+        image = prepare_img()
+        inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
+
+        with torch.no_grad():
+            outputs = model(**inputs)
+            predicted_depth = outputs.predicted_depth
+
+        expected_shape = torch.Size([1, 756, 1008])
+        self.assertEqual(predicted_depth.shape, expected_shape)
+
+        expected_slice = torch.tensor(
+            [[0.5029, 0.5120, 0.5176], [0.4998, 0.5147, 0.5197], [0.4973, 0.5201, 0.5241]]
+        ).to(torch_device)
+
+        self.assertTrue(torch.allclose(predicted_depth[0, :3, :3], expected_slice, atol=1e-3))
+
+    def test_inference(self):
+        image_processor = AutoImageProcessor.from_pretrained("depth-anything/prompt-depth-anything-vits-hf")
+        model = PromptDepthAnythingForDepthEstimation.from_pretrained(
+            "depth-anything/prompt-depth-anything-vits-hf"
+        ).to(torch_device)
+
+        image = prepare_img()
+        prompt_depth = prepare_prompt_depth()
+        inputs = image_processor(images=image, return_tensors="pt", prompt_depth=prompt_depth).to(torch_device)
+
+        with torch.no_grad():
+            outputs = model(**inputs)
+            predicted_depth = outputs.predicted_depth
+
+        expected_shape = torch.Size([1, 756, 1008])
+        self.assertEqual(predicted_depth.shape, expected_shape)
+
+        expected_slice = torch.tensor(
+            [[3.0100, 3.0016, 3.0219], [3.0046, 3.0137, 3.0275], [3.0083, 3.0191, 3.0292]]
+        ).to(torch_device)
+
+        self.assertTrue(torch.allclose(predicted_depth[0, :3, :3], expected_slice, atol=1e-3))