Fix image post-processing for OWLv2 (#30686)
* feat: add note about owlv2 * fix: post processing coordinates * remove: workaround document * fix: extra quotes * update: owlv2 docstrings * fix: copies check * feat: add unit test for resize * Update tests/models/owlv2/test_image_processor_owlv2.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> --------- Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
This commit is contained in:
Jacky Lee
GitHub
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commit
218f44135f
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@ -481,7 +481,6 @@ class Owlv2ImageProcessor(BaseImageProcessor):
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data = {"pixel_values": images}
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return BatchFeature(data=data, tensor_type=return_tensors)
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# Copied from transformers.models.owlvit.image_processing_owlvit.OwlViTImageProcessor.post_process_object_detection
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def post_process_object_detection(
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self, outputs, threshold: float = 0.1, target_sizes: Union[TensorType, List[Tuple]] = None
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):
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@ -525,6 +524,18 @@ class Owlv2ImageProcessor(BaseImageProcessor):
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else:
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img_h, img_w = target_sizes.unbind(1)
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# rescale coordinates
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width_ratio = 1
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height_ratio = 1
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if img_w < img_h:
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width_ratio = img_w / img_h
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elif img_h < img_w:
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height_ratio = img_h / img_w
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img_w = img_w / width_ratio
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img_h = img_h / height_ratio
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scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(boxes.device)
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boxes = boxes * scale_fct[:, None, :]
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@ -1540,9 +1540,7 @@ class Owlv2ForObjectDetection(Owlv2PreTrainedModel):
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>>> import requests
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>>> from PIL import Image
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>>> import torch
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>>> import numpy as np
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>>> from transformers import AutoProcessor, Owlv2ForObjectDetection
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>>> from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
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>>> processor = AutoProcessor.from_pretrained("google/owlv2-base-patch16-ensemble")
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>>> model = Owlv2ForObjectDetection.from_pretrained("google/owlv2-base-patch16-ensemble")
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@ -1557,20 +1555,7 @@ class Owlv2ForObjectDetection(Owlv2PreTrainedModel):
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>>> with torch.no_grad():
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... outputs = model.image_guided_detection(**inputs)
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>>> # Note: boxes need to be visualized on the padded, unnormalized image
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>>> # hence we'll set the target image sizes (height, width) based on that
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>>> def get_preprocessed_image(pixel_values):
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... pixel_values = pixel_values.squeeze().numpy()
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... unnormalized_image = (pixel_values * np.array(OPENAI_CLIP_STD)[:, None, None]) + np.array(OPENAI_CLIP_MEAN)[:, None, None]
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... unnormalized_image = (unnormalized_image * 255).astype(np.uint8)
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... unnormalized_image = np.moveaxis(unnormalized_image, 0, -1)
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... unnormalized_image = Image.fromarray(unnormalized_image)
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... return unnormalized_image
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>>> unnormalized_image = get_preprocessed_image(inputs.pixel_values)
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>>> target_sizes = torch.Tensor([unnormalized_image.size[::-1]])
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>>> target_sizes = torch.Tensor([image.size[::-1]])
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>>> # Convert outputs (bounding boxes and class logits) to Pascal VOC format (xmin, ymin, xmax, ymax)
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>>> results = processor.post_process_image_guided_detection(
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@ -1581,19 +1566,19 @@ class Owlv2ForObjectDetection(Owlv2PreTrainedModel):
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>>> for box, score in zip(boxes, scores):
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... box = [round(i, 2) for i in box.tolist()]
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... print(f"Detected similar object with confidence {round(score.item(), 3)} at location {box}")
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Detected similar object with confidence 0.938 at location [490.96, 109.89, 821.09, 536.11]
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Detected similar object with confidence 0.959 at location [8.67, 721.29, 928.68, 732.78]
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Detected similar object with confidence 0.902 at location [4.27, 720.02, 941.45, 761.59]
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Detected similar object with confidence 0.985 at location [265.46, -58.9, 1009.04, 365.66]
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Detected similar object with confidence 1.0 at location [9.79, 28.69, 937.31, 941.64]
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Detected similar object with confidence 0.998 at location [869.97, 58.28, 923.23, 978.1]
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Detected similar object with confidence 0.985 at location [309.23, 21.07, 371.61, 932.02]
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Detected similar object with confidence 0.947 at location [27.93, 859.45, 969.75, 915.44]
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Detected similar object with confidence 0.996 at location [785.82, 41.38, 880.26, 966.37]
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Detected similar object with confidence 0.998 at location [5.08, 721.17, 925.93, 998.41]
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Detected similar object with confidence 0.969 at location [6.7, 898.1, 921.75, 949.51]
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Detected similar object with confidence 0.966 at location [47.16, 927.29, 981.99, 942.14]
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Detected similar object with confidence 0.924 at location [46.4, 936.13, 953.02, 950.78]
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Detected similar object with confidence 0.938 at location [327.31, 54.94, 547.39, 268.06]
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Detected similar object with confidence 0.959 at location [5.78, 360.65, 619.12, 366.39]
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Detected similar object with confidence 0.902 at location [2.85, 360.01, 627.63, 380.8]
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Detected similar object with confidence 0.985 at location [176.98, -29.45, 672.69, 182.83]
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Detected similar object with confidence 1.0 at location [6.53, 14.35, 624.87, 470.82]
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Detected similar object with confidence 0.998 at location [579.98, 29.14, 615.49, 489.05]
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Detected similar object with confidence 0.985 at location [206.15, 10.53, 247.74, 466.01]
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Detected similar object with confidence 0.947 at location [18.62, 429.72, 646.5, 457.72]
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Detected similar object with confidence 0.996 at location [523.88, 20.69, 586.84, 483.18]
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Detected similar object with confidence 0.998 at location [3.39, 360.59, 617.29, 499.21]
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Detected similar object with confidence 0.969 at location [4.47, 449.05, 614.5, 474.76]
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Detected similar object with confidence 0.966 at location [31.44, 463.65, 654.66, 471.07]
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Detected similar object with confidence 0.924 at location [30.93, 468.07, 635.35, 475.39]
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```"""
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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output_hidden_states = (
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@ -1665,10 +1650,8 @@ class Owlv2ForObjectDetection(Owlv2PreTrainedModel):
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```python
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>>> import requests
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>>> from PIL import Image
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>>> import numpy as np
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>>> import torch
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>>> from transformers import AutoProcessor, Owlv2ForObjectDetection
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>>> from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
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>>> processor = AutoProcessor.from_pretrained("google/owlv2-base-patch16-ensemble")
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>>> model = Owlv2ForObjectDetection.from_pretrained("google/owlv2-base-patch16-ensemble")
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@ -1682,20 +1665,7 @@ class Owlv2ForObjectDetection(Owlv2PreTrainedModel):
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>>> with torch.no_grad():
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... outputs = model(**inputs)
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>>> # Note: boxes need to be visualized on the padded, unnormalized image
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>>> # hence we'll set the target image sizes (height, width) based on that
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>>> def get_preprocessed_image(pixel_values):
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... pixel_values = pixel_values.squeeze().numpy()
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... unnormalized_image = (pixel_values * np.array(OPENAI_CLIP_STD)[:, None, None]) + np.array(OPENAI_CLIP_MEAN)[:, None, None]
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... unnormalized_image = (unnormalized_image * 255).astype(np.uint8)
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... unnormalized_image = np.moveaxis(unnormalized_image, 0, -1)
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... unnormalized_image = Image.fromarray(unnormalized_image)
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... return unnormalized_image
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>>> unnormalized_image = get_preprocessed_image(inputs.pixel_values)
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>>> target_sizes = torch.Tensor([unnormalized_image.size[::-1]])
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>>> target_sizes = torch.Tensor([image.size[::-1]])
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>>> # Convert outputs (bounding boxes and class logits) to final bounding boxes and scores
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>>> results = processor.post_process_object_detection(
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... outputs=outputs, threshold=0.2, target_sizes=target_sizes
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@ -1708,8 +1678,8 @@ class Owlv2ForObjectDetection(Owlv2PreTrainedModel):
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>>> for box, score, label in zip(boxes, scores, labels):
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... box = [round(i, 2) for i in box.tolist()]
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... print(f"Detected {text[label]} with confidence {round(score.item(), 3)} at location {box}")
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Detected a photo of a cat with confidence 0.614 at location [512.5, 35.08, 963.48, 557.02]
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Detected a photo of a cat with confidence 0.665 at location [10.13, 77.94, 489.93, 709.69]
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Detected a photo of a cat with confidence 0.614 at location [341.67, 23.39, 642.32, 371.35]
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Detected a photo of a cat with confidence 0.665 at location [6.75, 51.96, 326.62, 473.13]
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```"""
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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output_hidden_states = (
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@ -17,7 +17,7 @@
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import unittest
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from transformers.testing_utils import require_torch, require_vision, slow
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from transformers.utils import is_vision_available
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from transformers.utils import is_torch_available, is_vision_available
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from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
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@ -25,7 +25,10 @@ from ...test_image_processing_common import ImageProcessingTestMixin, prepare_im
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if is_vision_available():
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from PIL import Image
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from transformers import Owlv2ImageProcessor
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from transformers import AutoProcessor, Owlv2ForObjectDetection, Owlv2ImageProcessor
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if is_torch_available():
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import torch
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class Owlv2ImageProcessingTester(unittest.TestCase):
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@ -120,6 +123,25 @@ class Owlv2ImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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mean_value = round(pixel_values.mean().item(), 4)
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self.assertEqual(mean_value, 0.2353)
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@slow
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def test_image_processor_integration_test_resize(self):
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checkpoint = "google/owlv2-base-patch16-ensemble"
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processor = AutoProcessor.from_pretrained(checkpoint)
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model = Owlv2ForObjectDetection.from_pretrained(checkpoint)
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image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
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inputs = processor(text=["cat"], images=image, return_tensors="pt")
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with torch.no_grad():
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outputs = model(**inputs)
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target_sizes = torch.tensor([image.size[::-1]])
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results = processor.post_process_object_detection(outputs, threshold=0.2, target_sizes=target_sizes)[0]
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boxes = results["boxes"].tolist()
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self.assertEqual(boxes[0], [341.66656494140625, 23.38756561279297, 642.321044921875, 371.3482971191406])
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self.assertEqual(boxes[1], [6.753320693969727, 51.96149826049805, 326.61810302734375, 473.12982177734375])
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@unittest.skip("OWLv2 doesn't treat 4 channel PIL and numpy consistently yet") # FIXME Amy
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def test_call_numpy_4_channels(self):
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pass
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