SUMIN/HuggingFace_transformer
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Small sam patch (#22920)

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* patch

* add test

* move tests

* cover more cases (will fail nw update the code)

* style

* fix

* Update src/transformers/models/sam/image_processing_sam.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/sam/image_processing_sam.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* add better check

---------

Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: younesbelkada <younesbelkada@gmail.com>
This commit is contained in:
Arthur
2023-04-21 21:41:18 +02:00
committed by GitHub
parent 5166c30e29
commit 7579a52b55
2 changed files with 47 additions and 8 deletions
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35
tests/models/sam/test_processor_sam.py
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@@ -17,8 +17,8 @@ import unittest
import numpy as np
from transformers.testing_utils import require_torchvision, require_vision
from transformers.utils import is_vision_available
from transformers.testing_utils import require_torch, require_torchvision, require_vision
from transformers.utils import is_torch_available, is_vision_available
if is_vision_available():
@@ -26,6 +26,9 @@ if is_vision_available():
from transformers import AutoProcessor, SamImageProcessor, SamProcessor
if is_torch_available():
import torch
@require_vision
@require_torchvision
@@ -79,3 +82,31 @@ class SamProcessorTest(unittest.TestCase):
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
@require_torch
def test_post_process_masks(self):
image_processor = self.get_image_processor()
processor = SamProcessor(image_processor=image_processor)
dummy_masks = [torch.ones((1, 3, 5, 5))]
original_sizes = [[1764, 2646]]
reshaped_input_size = [[683, 1024]]
masks = processor.post_process_masks(dummy_masks, original_sizes, reshaped_input_size)
self.assertEqual(masks[0].shape, (1, 3, 1764, 2646))
masks = processor.post_process_masks(
dummy_masks, torch.tensor(original_sizes), torch.tensor(reshaped_input_size)
)
self.assertEqual(masks[0].shape, (1, 3, 1764, 2646))
# should also work with np
dummy_masks = [np.ones((1, 3, 5, 5))]
masks = processor.post_process_masks(dummy_masks, np.array(original_sizes), np.array(reshaped_input_size))
self.assertEqual(masks[0].shape, (1, 3, 1764, 2646))
dummy_masks = [[1, 0], [0, 1]]
with self.assertRaises(ValueError):
masks = processor.post_process_masks(dummy_masks, np.array(original_sizes), np.array(reshaped_input_size))