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Fix image post-processing for OWLv2 (#30686)

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* 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
2024-05-09 09:02:03 -07:00
committed by GitHub
parent df53c6e5d9
commit 218f44135f
3 changed files with 53 additions and 50 deletions
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26
tests/models/owlv2/test_image_processor_owlv2.py
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@@ -17,7 +17,7 @@
import unittest
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_vision_available
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
@@ -25,7 +25,10 @@ from ...test_image_processing_common import ImageProcessingTestMixin, prepare_im
if is_vision_available():
from PIL import Image
from transformers import Owlv2ImageProcessor
from transformers import AutoProcessor, Owlv2ForObjectDetection, Owlv2ImageProcessor
if is_torch_available():
import torch
class Owlv2ImageProcessingTester(unittest.TestCase):
@@ -120,6 +123,25 @@ class Owlv2ImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
mean_value = round(pixel_values.mean().item(), 4)
self.assertEqual(mean_value, 0.2353)
@slow
def test_image_processor_integration_test_resize(self):
checkpoint = "google/owlv2-base-patch16-ensemble"
processor = AutoProcessor.from_pretrained(checkpoint)
model = Owlv2ForObjectDetection.from_pretrained(checkpoint)
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
inputs = processor(text=["cat"], images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
target_sizes = torch.tensor([image.size[::-1]])
results = processor.post_process_object_detection(outputs, threshold=0.2, target_sizes=target_sizes)[0]
boxes = results["boxes"].tolist()
self.assertEqual(boxes[0], [341.66656494140625, 23.38756561279297, 642.321044921875, 371.3482971191406])
self.assertEqual(boxes[1], [6.753320693969727, 51.96149826049805, 326.61810302734375, 473.12982177734375])
@unittest.skip("OWLv2 doesn't treat 4 channel PIL and numpy consistently yet") # FIXME Amy
def test_call_numpy_4_channels(self):
pass