Pixtral: vectorize patch embeddings and enable tests (#35122)
* initial POC * - batch mix feature * fix tests * fix tests * make style * do not skip and instead fix tests * update * return back the test * correct text with the correct ckpt
This commit is contained in:
Raushan Turganbay
committed by
GitHub
GitHub
parent
8bc4c89ee9
commit
9725e5be2f
@@ -13,7 +13,6 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import random
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import time
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import unittest
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@@ -92,49 +91,47 @@ class PixtralImageProcessingTester:
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"do_convert_rgb": self.do_convert_rgb,
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}
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def expected_output_image_shape(self, image):
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if isinstance(image, Image.Image):
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width, height = image.size
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elif isinstance(image, np.ndarray):
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height, width = image.shape[:2]
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elif isinstance(image, torch.Tensor):
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height, width = image.shape[-2:]
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def expected_output_image_shape(self, images):
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if not isinstance(images, (list, tuple)):
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images = [images]
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max_height = max_width = self.size.get("longest_edge")
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batch_size = len(images)
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return_height, return_width = 0, 0
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for image in images:
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if isinstance(image, Image.Image):
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width, height = image.size
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elif isinstance(image, np.ndarray):
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height, width = image.shape[:2]
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elif isinstance(image, torch.Tensor):
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height, width = image.shape[-2:]
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ratio = max(height / max_height, width / max_width)
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if ratio > 1:
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height = int(np.ceil(height / ratio))
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width = int(np.ceil(width / ratio))
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max_height = max_width = self.size.get("longest_edge")
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patch_height, patch_width = self.patch_size["height"], self.patch_size["width"]
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num_height_tokens = (height - 1) // patch_height + 1
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num_width_tokens = (width - 1) // patch_width + 1
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ratio = max(height / max_height, width / max_width)
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if ratio > 1:
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height = int(np.ceil(height / ratio))
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width = int(np.ceil(width / ratio))
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height = num_height_tokens * patch_height
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width = num_width_tokens * patch_width
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patch_height, patch_width = self.patch_size["height"], self.patch_size["width"]
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num_height_tokens = (height - 1) // patch_height + 1
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num_width_tokens = (width - 1) // patch_width + 1
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return self.num_channels, height, width
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return_height = max(num_height_tokens * patch_height, return_height)
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return_width = max(num_width_tokens * patch_width, return_width)
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return batch_size, self.num_channels, return_height, return_width
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def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
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# Use prepare_image_inputs to make a list of list of single images
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images_list = []
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for _ in range(self.batch_size):
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images = []
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for _ in range(random.randint(1, self.max_num_images_per_sample)):
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img = prepare_image_inputs(
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batch_size=1,
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num_channels=self.num_channels,
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min_resolution=self.min_resolution,
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max_resolution=self.max_resolution,
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equal_resolution=equal_resolution,
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numpify=numpify,
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torchify=torchify,
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)[0]
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images.append(img)
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images_list.append(images)
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return images_list
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images = prepare_image_inputs(
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batch_size=self.batch_size,
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num_channels=self.num_channels,
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min_resolution=self.min_resolution,
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max_resolution=self.max_resolution,
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equal_resolution=equal_resolution,
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numpify=numpify,
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torchify=torchify,
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)
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return images
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@require_torch
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@@ -173,23 +170,18 @@ class PixtralImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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image_processing = image_processing_class(**self.image_processor_dict)
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# create random PIL images
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image_inputs_list = self.image_processor_tester.prepare_image_inputs()
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for image_inputs in image_inputs_list:
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for image in image_inputs:
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self.assertIsInstance(image, Image.Image)
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for image in image_inputs_list:
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self.assertIsInstance(image, Image.Image)
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# Test not batched input
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encoded_images = image_processing(image_inputs_list[0][0], return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(
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image_inputs_list[0][0]
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)
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self.assertEqual(tuple(encoded_images[0][0].shape), expected_output_image_shape)
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encoded_images = image_processing(image_inputs_list[0], return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs_list[0])
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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# Test batched
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batch_encoded_images = image_processing(image_inputs_list, return_tensors="pt").pixel_values
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for encoded_images, images in zip(batch_encoded_images, image_inputs_list):
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for encoded_image, image in zip(encoded_images, images):
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image)
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self.assertEqual(tuple(encoded_image.shape), expected_output_image_shape)
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encoded_images = image_processing(image_inputs_list, return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs_list)
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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def test_call_numpy(self):
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for image_processing_class in self.image_processor_list:
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@@ -197,23 +189,18 @@ class PixtralImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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image_processing = image_processing_class(**self.image_processor_dict)
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# create random numpy tensors
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image_inputs_list = self.image_processor_tester.prepare_image_inputs(numpify=True)
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for image_inputs in image_inputs_list:
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for image in image_inputs:
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self.assertIsInstance(image, np.ndarray)
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for image in image_inputs_list:
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self.assertIsInstance(image, np.ndarray)
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# Test not batched input
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encoded_images = image_processing(image_inputs_list[0][0], return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(
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image_inputs_list[0][0]
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)
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self.assertEqual(tuple(encoded_images[0][0].shape), expected_output_image_shape)
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encoded_images = image_processing(image_inputs_list[0], return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs_list[0])
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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# Test batched
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batch_encoded_images = image_processing(image_inputs_list, return_tensors="pt").pixel_values
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for encoded_images, images in zip(batch_encoded_images, image_inputs_list):
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for encoded_image, image in zip(encoded_images, images):
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image)
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self.assertEqual(tuple(encoded_image.shape), expected_output_image_shape)
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs_list)
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self.assertEqual(tuple(batch_encoded_images.shape), expected_output_image_shape)
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def test_call_pytorch(self):
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for image_processing_class in self.image_processor_list:
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@@ -221,23 +208,18 @@ class PixtralImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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image_processing = image_processing_class(**self.image_processor_dict)
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# create random PyTorch tensors
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image_inputs_list = self.image_processor_tester.prepare_image_inputs(torchify=True)
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for image_inputs in image_inputs_list:
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for image in image_inputs:
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self.assertIsInstance(image, torch.Tensor)
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for image in image_inputs_list:
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self.assertIsInstance(image, torch.Tensor)
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# Test not batched input
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encoded_images = image_processing(image_inputs_list[0][0], return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(
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image_inputs_list[0][0]
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)
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self.assertEqual(tuple(encoded_images[0][0].shape), expected_output_image_shape)
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encoded_images = image_processing(image_inputs_list[0], return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs_list[0])
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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# Test batched
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batch_encoded_images = image_processing(image_inputs_list, return_tensors="pt").pixel_values
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for encoded_images, images in zip(batch_encoded_images, image_inputs_list):
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for encoded_image, image in zip(encoded_images, images):
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image)
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self.assertEqual(tuple(encoded_image.shape), expected_output_image_shape)
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs_list)
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self.assertEqual(tuple(batch_encoded_images.shape), expected_output_image_shape)
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@require_vision
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@require_torch
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@@ -74,15 +74,17 @@ class PixtralVisionModelTester:
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self.initializer_range = initializer_range
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self.scope = scope
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# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
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num_patches = (image_size // patch_size) ** 2
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self.seq_length = num_patches + 1
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# in Pixtral, the seq length equals the number of patches * batch_size because the patches are flattened
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self.seq_length = (image_size // patch_size) ** 2 * batch_size
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def prepare_config_and_inputs(self):
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pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
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image_sizes = torch.tensor(
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[[self.image_size, self.image_size]] * self.batch_size, dtype=torch.long, device=torch_device
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)
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config = self.get_config()
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return config, pixel_values
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return config, pixel_values, image_sizes
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def get_config(self):
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return PixtralVisionConfig(
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@@ -127,8 +129,8 @@ class PixtralVisionModelTester:
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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config, pixel_values = config_and_inputs
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inputs_dict = {"pixel_values": pixel_values}
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config, pixel_values, image_sizes = config_and_inputs
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inputs_dict = {"pixel_values": pixel_values, "image_sizes": image_sizes}
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return config, inputs_dict
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@@ -142,113 +144,17 @@ class PixtralVisionModelModelTest(ModelTesterMixin, unittest.TestCase):
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test_pruning = False
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test_head_masking = False
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test_torchscript = False
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test_resize_embeddings = False
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def setUp(self):
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self.model_tester = PixtralVisionModelTester(self)
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self.config_tester = ConfigTester(self, config_class=PixtralVisionConfig, has_text_modality=False)
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@unittest.skip("model does not support input embeds")
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def test_inputs_embeds(self):
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pass
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@unittest.skip("model does not support input embeds")
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def test_inputs_embeds_matches_input_ids(self):
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pass
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@unittest.skip(
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reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
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)
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def test_training_gradient_checkpointing(self):
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pass
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@unittest.skip(
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reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
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)
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def test_training_gradient_checkpointing_use_reentrant(self):
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pass
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@unittest.skip(
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reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
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)
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def test_training_gradient_checkpointing_use_reentrant_false(self):
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pass
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@unittest.skip(reason="Compile not yet supported because in Pixtral models")
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def test_sdpa_can_compile_dynamic(self):
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pass
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@unittest.skip(reason="Compile not yet supported because in Pixtral models")
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def test_sdpa_can_dispatch_on_flash(self):
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pass
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@unittest.skip(reason="Not supported yet")
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def test_attention_outputs(self):
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pass
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@unittest.skip(reason="Not supported yet")
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def test_cpu_offload(self):
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pass
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@unittest.skip(reason="Not supported yet")
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def test_batching_equivalence(self):
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pass
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@unittest.skip(reason="Not supported yet")
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def test_disk_offload_bin(self):
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pass
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@unittest.skip(reason="Not supported yet")
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def test_retain_grad_hidden_states_attentions(self):
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pass
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@unittest.skip(reason="Not supported yet")
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def test_multi_gpu_data_parallel_forward(self):
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pass
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@unittest.skip(reason="Not supported yet")
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def test_model_parallelism(self):
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pass
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@unittest.skip(reason="Not supported yet")
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def test_model_outputs_equivalence(self):
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pass
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@unittest.skip(reason="Not supported yet")
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def test_save_load(self):
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pass
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@unittest.skip(reason="Not supported yet")
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def test_model_get_set_embeddings(self):
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pass
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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@unittest.skip(reason="Not supported yet")
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def test_resize_tokens_embeddings(self):
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pass
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@unittest.skip(reason="Not supported yet")
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def test_model_main_input_name(self):
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pass
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@unittest.skip(reason="Not supported yet")
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def test_initialization(self):
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pass
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@unittest.skip(reason="Not supported yet")
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def test_hidden_states_output(self):
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pass
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@unittest.skip(reason="Not supported yet")
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def test_gradient_checkpointing_backward_compatibility(self):
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pass
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@unittest.skip(reason="Not supported yet")
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def test_feed_forward_chunking(self):
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pass
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@unittest.skip(reason="Not supported yet")
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def test_disk_offload_safetensors(self):
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pass
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@unittest.skip(reason="Not supported yet")
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def test_determinism(self):
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pass
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for model_class in self.all_model_classes:
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model = model_class(config)
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self.assertIsInstance(model.get_input_embeddings(), (torch.nn.Module))
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x = model.get_output_embeddings()
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self.assertTrue(x is None or isinstance(x, torch.nn.Linear))
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@@ -14,7 +14,6 @@
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import shutil
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import tempfile
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import unittest
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from typing import Optional
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import requests
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import torch
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@@ -28,7 +27,7 @@ from ...test_processing_common import ProcessorTesterMixin
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if is_vision_available():
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from PIL import Image
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from transformers import AutoTokenizer, PixtralImageProcessor, PixtralProcessor
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from transformers import PixtralProcessor
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@require_vision
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@@ -46,20 +45,15 @@ class PixtralProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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def setUp(self):
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self.tmpdirname = tempfile.mkdtemp()
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# FIXME - just load the processor directly from the checkpoint
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tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/pixtral-12b")
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image_processor = PixtralImageProcessor()
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processor = PixtralProcessor(tokenizer=tokenizer, image_processor=image_processor)
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processor = PixtralProcessor.from_pretrained("mistral-community/pixtral-12b")
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processor.save_pretrained(self.tmpdirname)
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def tearDown(self):
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shutil.rmtree(self.tmpdirname)
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@unittest.skip("No chat template was set for this model (yet)")
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def test_chat_template(self):
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processor = self.processor_class.from_pretrained(self.tmpdirname)
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expected_prompt = "USER: [IMG]\nWhat is shown in this image? ASSISTANT:"
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expected_prompt = "<s>[INST][IMG]What is shown in this image?[/INST]"
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messages = [
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{
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@@ -73,13 +67,12 @@ class PixtralProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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formatted_prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
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self.assertEqual(expected_prompt, formatted_prompt)
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@unittest.skip("No chat template was set for this model (yet)")
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def test_image_token_filling(self):
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processor = self.processor_class.from_pretrained(self.tmpdirname)
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# Important to check with non square image
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image = torch.randint(0, 2, (3, 500, 316))
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expected_image_tokens = 1526
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image_token_index = 32000
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expected_image_tokens = 640
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image_token_index = 10
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messages = [
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{
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@@ -111,11 +104,8 @@ class PixtralProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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self.assertIn("input_ids", inputs_image)
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self.assertTrue(len(inputs_image["input_ids"]) == 1)
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self.assertIsInstance(inputs_image["input_ids"], torch.Tensor)
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self.assertIsInstance(inputs_image["pixel_values"], list)
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self.assertTrue(len(inputs_image["pixel_values"]) == 1)
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self.assertIsInstance(inputs_image["pixel_values"][0], list)
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self.assertTrue(len(inputs_image["pixel_values"][0]) == 1)
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self.assertIsInstance(inputs_image["pixel_values"][0][0], torch.Tensor)
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self.assertIsInstance(inputs_image["pixel_values"], torch.Tensor)
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self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([1, 3, 32, 32]))
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# fmt: off
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input_ids = inputs_image["input_ids"]
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@@ -131,11 +121,8 @@ class PixtralProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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self.assertIn("input_ids", inputs_url)
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self.assertTrue(len(inputs_url["input_ids"]) == 1)
|
||||
self.assertIsInstance(inputs_url["input_ids"], torch.Tensor)
|
||||
self.assertIsInstance(inputs_url["pixel_values"], list)
|
||||
self.assertTrue(len(inputs_url["pixel_values"]) == 1)
|
||||
self.assertIsInstance(inputs_url["pixel_values"][0], list)
|
||||
self.assertTrue(len(inputs_url["pixel_values"][0]) == 1)
|
||||
self.assertIsInstance(inputs_url["pixel_values"][0][0], torch.Tensor)
|
||||
self.assertIsInstance(inputs_image["pixel_values"], torch.Tensor)
|
||||
self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([1, 3, 32, 32]))
|
||||
|
||||
# fmt: off
|
||||
input_ids = inputs_url["input_ids"]
|
||||
@@ -146,6 +133,28 @@ class PixtralProcessorTest(ProcessorTesterMixin, unittest.TestCase):
|
||||
)
|
||||
# fmt: on
|
||||
|
||||
# Test passing inputs as a single list
|
||||
inputs_image = processor(text=prompt_string, images=[self.image_0], return_tensors="pt")
|
||||
self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([1, 3, 32, 32]))
|
||||
|
||||
# fmt: off
|
||||
self.assertEqual(
|
||||
inputs_image["input_ids"][0].tolist(),
|
||||
[21510, 1058, 1032, 10, 10, 12, 10, 10, 13, 1010, 7493, 1681, 1278, 4701, 1307, 1278, 3937, 1063, 1349, 4290, 16002, 41150, 1058]
|
||||
)
|
||||
# fmt: on
|
||||
|
||||
# Test as nested single list
|
||||
inputs_image = processor(text=prompt_string, images=[[self.image_0]], return_tensors="pt")
|
||||
self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([1, 3, 32, 32]))
|
||||
|
||||
# fmt: off
|
||||
self.assertEqual(
|
||||
inputs_image["input_ids"][0].tolist(),
|
||||
[21510, 1058, 1032, 10, 10, 12, 10, 10, 13, 1010, 7493, 1681, 1278, 4701, 1307, 1278, 3937, 1063, 1349, 4290, 16002, 41150, 1058]
|
||||
)
|
||||
# fmt: on
|
||||
|
||||
def test_processor_with_multiple_images_single_list(self):
|
||||
processor = self.processor_class.from_pretrained(self.tmpdirname)
|
||||
prompt_string = "USER: [IMG][IMG]\nWhat's the difference between these two images? ASSISTANT:"
|
||||
@@ -159,11 +168,8 @@ class PixtralProcessorTest(ProcessorTesterMixin, unittest.TestCase):
|
||||
self.assertIn("input_ids", inputs_image)
|
||||
self.assertTrue(len(inputs_image["input_ids"]) == 1)
|
||||
self.assertIsInstance(inputs_image["input_ids"], torch.Tensor)
|
||||
self.assertIsInstance(inputs_image["pixel_values"], list)
|
||||
self.assertTrue(len(inputs_image["pixel_values"]) == 1)
|
||||
self.assertIsInstance(inputs_image["pixel_values"][0], list)
|
||||
self.assertTrue(len(inputs_image["pixel_values"][0]) == 2)
|
||||
self.assertIsInstance(inputs_image["pixel_values"][0][0], torch.Tensor)
|
||||
self.assertIsInstance(inputs_image["pixel_values"], torch.Tensor)
|
||||
self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([2, 3, 32, 32]))
|
||||
|
||||
# fmt: off
|
||||
input_ids = inputs_image["input_ids"]
|
||||
@@ -179,11 +185,9 @@ class PixtralProcessorTest(ProcessorTesterMixin, unittest.TestCase):
|
||||
self.assertIn("input_ids", inputs_url)
|
||||
self.assertTrue(len(inputs_url["input_ids"]) == 1)
|
||||
self.assertIsInstance(inputs_url["input_ids"], torch.Tensor)
|
||||
self.assertIsInstance(inputs_url["pixel_values"], list)
|
||||
self.assertTrue(len(inputs_url["pixel_values"]) == 1)
|
||||
self.assertIsInstance(inputs_url["pixel_values"][0], list)
|
||||
self.assertTrue(len(inputs_url["pixel_values"][0]) == 2)
|
||||
self.assertIsInstance(inputs_url["pixel_values"][0][0], torch.Tensor)
|
||||
self.assertIsInstance(inputs_image["pixel_values"], torch.Tensor)
|
||||
self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([2, 3, 32, 32]))
|
||||
|
||||
# fmt: off
|
||||
input_ids = inputs_url["input_ids"]
|
||||
self.assertEqual(
|
||||
@@ -193,6 +197,17 @@ class PixtralProcessorTest(ProcessorTesterMixin, unittest.TestCase):
|
||||
)
|
||||
# fmt: on
|
||||
|
||||
# Test passing in as a nested list
|
||||
inputs_url = processor(text=prompt_string, images=[[self.image_0, self.image_1]], return_tensors="pt")
|
||||
self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([2, 3, 32, 32]))
|
||||
|
||||
# fmt: off
|
||||
self.assertEqual(
|
||||
inputs_url["input_ids"][0].tolist(),
|
||||
[21510, 1058, 1032, 10, 10, 12, 10, 10, 13, 10, 10, 12, 10, 10, 13, 1010, 7493, 1681, 1278, 6592, 2396, 2576, 2295, 8061, 1063, 1349, 4290, 16002, 41150, 1058]
|
||||
)
|
||||
# fmt: on
|
||||
|
||||
def test_processor_with_multiple_images_multiple_lists(self):
|
||||
processor = self.processor_class.from_pretrained(self.tmpdirname)
|
||||
prompt_string = [
|
||||
@@ -211,11 +226,8 @@ class PixtralProcessorTest(ProcessorTesterMixin, unittest.TestCase):
|
||||
self.assertIn("input_ids", inputs_image)
|
||||
self.assertTrue(len(inputs_image["input_ids"]) == 2)
|
||||
self.assertIsInstance(inputs_image["input_ids"], torch.Tensor)
|
||||
self.assertIsInstance(inputs_image["pixel_values"], list)
|
||||
self.assertTrue(len(inputs_image["pixel_values"]) == 2)
|
||||
self.assertIsInstance(inputs_image["pixel_values"][0], list)
|
||||
self.assertTrue(len(inputs_image["pixel_values"][0]) == 2)
|
||||
self.assertIsInstance(inputs_image["pixel_values"][0][0], torch.Tensor)
|
||||
self.assertIsInstance(inputs_image["pixel_values"], torch.Tensor)
|
||||
self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([3, 3, 32, 32]))
|
||||
|
||||
# fmt: off
|
||||
input_ids = inputs_image["input_ids"]
|
||||
@@ -231,11 +243,8 @@ class PixtralProcessorTest(ProcessorTesterMixin, unittest.TestCase):
|
||||
self.assertIn("input_ids", inputs_url)
|
||||
self.assertTrue(len(inputs_url["input_ids"]) == 2)
|
||||
self.assertIsInstance(inputs_url["input_ids"], torch.Tensor)
|
||||
self.assertIsInstance(inputs_url["pixel_values"], list)
|
||||
self.assertTrue(len(inputs_url["pixel_values"]) == 2)
|
||||
self.assertIsInstance(inputs_url["pixel_values"][0], list)
|
||||
self.assertTrue(len(inputs_url["pixel_values"][0]) == 2)
|
||||
self.assertIsInstance(inputs_url["pixel_values"][0][0], torch.Tensor)
|
||||
self.assertIsInstance(inputs_image["pixel_values"], torch.Tensor)
|
||||
self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([3, 3, 32, 32]))
|
||||
|
||||
# fmt: off
|
||||
input_ids = inputs_url["input_ids"]
|
||||
@@ -246,6 +255,19 @@ class PixtralProcessorTest(ProcessorTesterMixin, unittest.TestCase):
|
||||
)
|
||||
# fmt: on
|
||||
|
||||
# Test passing as a single flat list
|
||||
inputs_image = processor(
|
||||
text=prompt_string, images=[self.image_0, self.image_1, self.image_2], return_tensors="pt", padding=True
|
||||
)
|
||||
self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([3, 3, 32, 32]))
|
||||
|
||||
# fmt: off
|
||||
self.assertEqual(
|
||||
inputs_image["input_ids"][0].tolist(),
|
||||
[21510, 1058, 1032, 10, 10, 12, 10, 10, 13, 10, 10, 12, 10, 10, 13, 1010, 7493, 1681, 1278, 6592, 2396, 2576, 2295, 8061, 1063, 1349, 4290, 16002, 41150, 1058]
|
||||
)
|
||||
# fmt: on
|
||||
|
||||
def test_processor_returns_full_length_batches(self):
|
||||
# to avoid https://github.com/huggingface/transformers/issues/34204
|
||||
processor = self.processor_class.from_pretrained(self.tmpdirname)
|
||||
@@ -264,13 +286,3 @@ class PixtralProcessorTest(ProcessorTesterMixin, unittest.TestCase):
|
||||
self.assertIn("input_ids", inputs_image)
|
||||
self.assertTrue(len(inputs_image["input_ids"]) == 5)
|
||||
self.assertTrue(len(inputs_image["pixel_values"]) == 5)
|
||||
|
||||
# Override as PixtralProcessor needs nested images to work properly with batched inputs
|
||||
@require_vision
|
||||
def prepare_image_inputs(self, batch_size: Optional[int] = None):
|
||||
"""This function prepares a list of PIL images for testing"""
|
||||
if batch_size is None:
|
||||
return super().prepare_image_inputs()
|
||||
if batch_size < 1:
|
||||
raise ValueError("batch_size must be greater than 0")
|
||||
return [[super().prepare_image_inputs()]] * batch_size
|
||||
|
||||
Reference in New Issue
Block a user