🔴 🔴 🔴 Added segmentation maps support for DPT image processor (#34345)
* Added `segmentation_maps` support for DPT image processor * Added tests for dpt image processor * Moved preprocessing into separate functions * Added # Copied from statements * Fixed # Copied from statements * Added `segmentation_maps` support for DPT image processor * Added tests for dpt image processor * Moved preprocessing into separate functions * Added # Copied from statements * Fixed # Copied from statements
This commit is contained in:
Mikhail Moskovchenko
committed by
GitHub
GitHub
parent
a50befa9b9
commit
5450e7c84a
@@ -139,6 +139,11 @@ class DPTImageProcessor(BaseImageProcessor):
|
||||
size_divisor (`int`, *optional*):
|
||||
If `do_pad` is `True`, pads the image dimensions to be divisible by this value. This was introduced in the
|
||||
DINOv2 paper, which uses the model in combination with DPT.
|
||||
do_reduce_labels (`bool`, *optional*, defaults to `False`):
|
||||
Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is
|
||||
used for background, and background itself is not included in all classes of a dataset (e.g. ADE20k). The
|
||||
background label will be replaced by 255. Can be overridden by the `do_reduce_labels` parameter in the
|
||||
`preprocess` method.
|
||||
"""
|
||||
|
||||
model_input_names = ["pixel_values"]
|
||||
@@ -157,6 +162,7 @@ class DPTImageProcessor(BaseImageProcessor):
|
||||
image_std: Optional[Union[float, List[float]]] = None,
|
||||
do_pad: bool = False,
|
||||
size_divisor: int = None,
|
||||
do_reduce_labels: bool = False,
|
||||
**kwargs,
|
||||
) -> None:
|
||||
super().__init__(**kwargs)
|
||||
@@ -174,6 +180,7 @@ class DPTImageProcessor(BaseImageProcessor):
|
||||
self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD
|
||||
self.do_pad = do_pad
|
||||
self.size_divisor = size_divisor
|
||||
self.do_reduce_labels = do_reduce_labels
|
||||
|
||||
def resize(
|
||||
self,
|
||||
@@ -275,10 +282,160 @@ class DPTImageProcessor(BaseImageProcessor):
|
||||
|
||||
return pad(image, ((pad_size_left, pad_size_right), (pad_size_top, pad_size_bottom)), data_format=data_format)
|
||||
|
||||
# Copied from transformers.models.beit.image_processing_beit.BeitImageProcessor.reduce_label
|
||||
def reduce_label(self, label: ImageInput) -> np.ndarray:
|
||||
label = to_numpy_array(label)
|
||||
# Avoid using underflow conversion
|
||||
label[label == 0] = 255
|
||||
label = label - 1
|
||||
label[label == 254] = 255
|
||||
return label
|
||||
|
||||
def _preprocess(
|
||||
self,
|
||||
image: ImageInput,
|
||||
do_reduce_labels: bool = None,
|
||||
do_resize: bool = None,
|
||||
size: Dict[str, int] = None,
|
||||
resample: PILImageResampling = None,
|
||||
keep_aspect_ratio: bool = None,
|
||||
ensure_multiple_of: int = None,
|
||||
do_rescale: bool = None,
|
||||
rescale_factor: float = None,
|
||||
do_normalize: bool = None,
|
||||
image_mean: Optional[Union[float, List[float]]] = None,
|
||||
image_std: Optional[Union[float, List[float]]] = None,
|
||||
do_pad: bool = None,
|
||||
size_divisor: int = None,
|
||||
input_data_format: Optional[Union[str, ChannelDimension]] = None,
|
||||
):
|
||||
if do_reduce_labels:
|
||||
image = self.reduce_label(image)
|
||||
|
||||
if do_resize:
|
||||
image = self.resize(
|
||||
image=image,
|
||||
size=size,
|
||||
resample=resample,
|
||||
keep_aspect_ratio=keep_aspect_ratio,
|
||||
ensure_multiple_of=ensure_multiple_of,
|
||||
input_data_format=input_data_format,
|
||||
)
|
||||
|
||||
if do_rescale:
|
||||
image = self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
|
||||
|
||||
if do_normalize:
|
||||
image = self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format)
|
||||
|
||||
if do_pad:
|
||||
image = self.pad_image(image=image, size_divisor=size_divisor, input_data_format=input_data_format)
|
||||
|
||||
return image
|
||||
|
||||
def _preprocess_image(
|
||||
self,
|
||||
image: ImageInput,
|
||||
do_resize: bool = None,
|
||||
size: Dict[str, int] = None,
|
||||
resample: PILImageResampling = None,
|
||||
keep_aspect_ratio: bool = None,
|
||||
ensure_multiple_of: int = None,
|
||||
do_rescale: bool = None,
|
||||
rescale_factor: float = None,
|
||||
do_normalize: bool = None,
|
||||
image_mean: Optional[Union[float, List[float]]] = None,
|
||||
image_std: Optional[Union[float, List[float]]] = None,
|
||||
do_pad: bool = None,
|
||||
size_divisor: int = None,
|
||||
data_format: Optional[Union[str, ChannelDimension]] = None,
|
||||
input_data_format: Optional[Union[str, ChannelDimension]] = None,
|
||||
) -> np.ndarray:
|
||||
"""Preprocesses a single image."""
|
||||
# All transformations expect numpy arrays.
|
||||
image = to_numpy_array(image)
|
||||
if do_rescale and is_scaled_image(image):
|
||||
logger.warning_once(
|
||||
"It looks like you are trying to rescale already rescaled images. If the input"
|
||||
" images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
|
||||
)
|
||||
if input_data_format is None:
|
||||
# We assume that all images have the same channel dimension format.
|
||||
input_data_format = infer_channel_dimension_format(image)
|
||||
|
||||
image = self._preprocess(
|
||||
image,
|
||||
do_reduce_labels=False,
|
||||
do_resize=do_resize,
|
||||
size=size,
|
||||
resample=resample,
|
||||
keep_aspect_ratio=keep_aspect_ratio,
|
||||
ensure_multiple_of=ensure_multiple_of,
|
||||
do_rescale=do_rescale,
|
||||
rescale_factor=rescale_factor,
|
||||
do_normalize=do_normalize,
|
||||
image_mean=image_mean,
|
||||
image_std=image_std,
|
||||
do_pad=do_pad,
|
||||
size_divisor=size_divisor,
|
||||
input_data_format=input_data_format,
|
||||
)
|
||||
if data_format is not None:
|
||||
image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
|
||||
return image
|
||||
|
||||
def _preprocess_segmentation_map(
|
||||
self,
|
||||
segmentation_map: ImageInput,
|
||||
do_resize: bool = None,
|
||||
size: Dict[str, int] = None,
|
||||
resample: PILImageResampling = None,
|
||||
keep_aspect_ratio: bool = None,
|
||||
ensure_multiple_of: int = None,
|
||||
do_reduce_labels: bool = None,
|
||||
input_data_format: Optional[Union[str, ChannelDimension]] = None,
|
||||
):
|
||||
"""Preprocesses a single segmentation map."""
|
||||
# All transformations expect numpy arrays.
|
||||
segmentation_map = to_numpy_array(segmentation_map)
|
||||
# Add an axis to the segmentation maps for transformations.
|
||||
if segmentation_map.ndim == 2:
|
||||
segmentation_map = segmentation_map[None, ...]
|
||||
added_dimension = True
|
||||
input_data_format = ChannelDimension.FIRST
|
||||
else:
|
||||
added_dimension = False
|
||||
if input_data_format is None:
|
||||
input_data_format = infer_channel_dimension_format(segmentation_map, num_channels=1)
|
||||
segmentation_map = self._preprocess(
|
||||
image=segmentation_map,
|
||||
do_reduce_labels=do_reduce_labels,
|
||||
do_resize=do_resize,
|
||||
size=size,
|
||||
resample=resample,
|
||||
keep_aspect_ratio=keep_aspect_ratio,
|
||||
ensure_multiple_of=ensure_multiple_of,
|
||||
do_normalize=False,
|
||||
do_rescale=False,
|
||||
input_data_format=input_data_format,
|
||||
)
|
||||
# Remove extra axis if added
|
||||
if added_dimension:
|
||||
segmentation_map = np.squeeze(segmentation_map, axis=0)
|
||||
segmentation_map = segmentation_map.astype(np.int64)
|
||||
return segmentation_map
|
||||
|
||||
# Copied from transformers.models.beit.image_processing_beit.BeitImageProcessor.__call__
|
||||
def __call__(self, images, segmentation_maps=None, **kwargs):
|
||||
# Overrides the `__call__` method of the `Preprocessor` class such that the images and segmentation maps can both
|
||||
# be passed in as positional arguments.
|
||||
return super().__call__(images, segmentation_maps=segmentation_maps, **kwargs)
|
||||
|
||||
@filter_out_non_signature_kwargs()
|
||||
def preprocess(
|
||||
self,
|
||||
images: ImageInput,
|
||||
segmentation_maps: Optional[ImageInput] = None,
|
||||
do_resize: bool = None,
|
||||
size: int = None,
|
||||
keep_aspect_ratio: bool = None,
|
||||
@@ -291,6 +448,7 @@ class DPTImageProcessor(BaseImageProcessor):
|
||||
image_std: Optional[Union[float, List[float]]] = None,
|
||||
do_pad: bool = None,
|
||||
size_divisor: int = None,
|
||||
do_reduce_labels: Optional[bool] = None,
|
||||
return_tensors: Optional[Union[str, TensorType]] = None,
|
||||
data_format: ChannelDimension = ChannelDimension.FIRST,
|
||||
input_data_format: Optional[Union[str, ChannelDimension]] = None,
|
||||
@@ -302,6 +460,8 @@ class DPTImageProcessor(BaseImageProcessor):
|
||||
images (`ImageInput`):
|
||||
Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
|
||||
passing in images with pixel values between 0 and 1, set `do_rescale=False`.
|
||||
segmentation_maps (`ImageInput`, *optional*):
|
||||
Segmentation map to preprocess.
|
||||
do_resize (`bool`, *optional*, defaults to `self.do_resize`):
|
||||
Whether to resize the image.
|
||||
size (`Dict[str, int]`, *optional*, defaults to `self.size`):
|
||||
@@ -326,6 +486,10 @@ class DPTImageProcessor(BaseImageProcessor):
|
||||
Image mean.
|
||||
image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
|
||||
Image standard deviation.
|
||||
do_reduce_labels (`bool`, *optional*, defaults to `self.do_reduce_labels`):
|
||||
Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0
|
||||
is used for background, and background itself is not included in all classes of a dataset (e.g.
|
||||
ADE20k). The background label will be replaced by 255.
|
||||
return_tensors (`str` or `TensorType`, *optional*):
|
||||
The type of tensors to return. Can be one of:
|
||||
- Unset: Return a list of `np.ndarray`.
|
||||
@@ -357,9 +521,13 @@ class DPTImageProcessor(BaseImageProcessor):
|
||||
image_std = image_std if image_std is not None else self.image_std
|
||||
do_pad = do_pad if do_pad is not None else self.do_pad
|
||||
size_divisor = size_divisor if size_divisor is not None else self.size_divisor
|
||||
do_reduce_labels = do_reduce_labels if do_reduce_labels is not None else self.do_reduce_labels
|
||||
|
||||
images = make_list_of_images(images)
|
||||
|
||||
if segmentation_maps is not None:
|
||||
segmentation_maps = make_list_of_images(segmentation_maps, expected_ndims=2)
|
||||
|
||||
if not valid_images(images):
|
||||
raise ValueError(
|
||||
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
|
||||
@@ -377,55 +545,47 @@ class DPTImageProcessor(BaseImageProcessor):
|
||||
size=size,
|
||||
resample=resample,
|
||||
)
|
||||
# All transformations expect numpy arrays.
|
||||
images = [to_numpy_array(image) for image in images]
|
||||
|
||||
if do_rescale and is_scaled_image(images[0]):
|
||||
logger.warning_once(
|
||||
"It looks like you are trying to rescale already rescaled images. If the input"
|
||||
" images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
|
||||
images = [
|
||||
self._preprocess_image(
|
||||
image=img,
|
||||
do_resize=do_resize,
|
||||
do_rescale=do_rescale,
|
||||
do_normalize=do_normalize,
|
||||
do_pad=do_pad,
|
||||
size=size,
|
||||
resample=resample,
|
||||
keep_aspect_ratio=keep_aspect_ratio,
|
||||
ensure_multiple_of=ensure_multiple_of,
|
||||
rescale_factor=rescale_factor,
|
||||
image_mean=image_mean,
|
||||
image_std=image_std,
|
||||
size_divisor=size_divisor,
|
||||
data_format=data_format,
|
||||
input_data_format=input_data_format,
|
||||
)
|
||||
for img in images
|
||||
]
|
||||
|
||||
if input_data_format is None:
|
||||
# We assume that all images have the same channel dimension format.
|
||||
input_data_format = infer_channel_dimension_format(images[0])
|
||||
data = {"pixel_values": images}
|
||||
|
||||
if do_resize:
|
||||
images = [
|
||||
self.resize(
|
||||
image=image,
|
||||
if segmentation_maps is not None:
|
||||
segmentation_maps = [
|
||||
self._preprocess_segmentation_map(
|
||||
segmentation_map=segmentation_map,
|
||||
do_reduce_labels=do_reduce_labels,
|
||||
do_resize=do_resize,
|
||||
size=size,
|
||||
resample=resample,
|
||||
keep_aspect_ratio=keep_aspect_ratio,
|
||||
ensure_multiple_of=ensure_multiple_of,
|
||||
input_data_format=input_data_format,
|
||||
)
|
||||
for image in images
|
||||
for segmentation_map in segmentation_maps
|
||||
]
|
||||
|
||||
if do_rescale:
|
||||
images = [
|
||||
self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
|
||||
for image in images
|
||||
]
|
||||
data["labels"] = segmentation_maps
|
||||
|
||||
if do_normalize:
|
||||
images = [
|
||||
self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format)
|
||||
for image in images
|
||||
]
|
||||
|
||||
if do_pad:
|
||||
images = [
|
||||
self.pad_image(image=image, size_divisor=size_divisor, input_data_format=input_data_format)
|
||||
for image in images
|
||||
]
|
||||
|
||||
images = [
|
||||
to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images
|
||||
]
|
||||
|
||||
data = {"pixel_values": images}
|
||||
return BatchFeature(data=data, tensor_type=return_tensors)
|
||||
|
||||
# Copied from transformers.models.beit.image_processing_beit.BeitImageProcessor.post_process_semantic_segmentation with Beit->DPT
|
||||
|
||||
@@ -17,14 +17,20 @@
|
||||
import unittest
|
||||
|
||||
import numpy as np
|
||||
from datasets import load_dataset
|
||||
|
||||
from transformers.file_utils import is_vision_available
|
||||
from transformers.file_utils import is_torch_available, is_vision_available
|
||||
from transformers.testing_utils import require_torch, require_vision
|
||||
|
||||
from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
|
||||
|
||||
|
||||
if is_torch_available():
|
||||
import torch
|
||||
|
||||
if is_vision_available():
|
||||
from PIL import Image
|
||||
|
||||
from transformers import DPTImageProcessor
|
||||
|
||||
|
||||
@@ -42,6 +48,7 @@ class DPTImageProcessingTester(unittest.TestCase):
|
||||
do_normalize=True,
|
||||
image_mean=[0.5, 0.5, 0.5],
|
||||
image_std=[0.5, 0.5, 0.5],
|
||||
do_reduce_labels=False,
|
||||
):
|
||||
super().__init__()
|
||||
size = size if size is not None else {"height": 18, "width": 18}
|
||||
@@ -56,6 +63,7 @@ class DPTImageProcessingTester(unittest.TestCase):
|
||||
self.do_normalize = do_normalize
|
||||
self.image_mean = image_mean
|
||||
self.image_std = image_std
|
||||
self.do_reduce_labels = do_reduce_labels
|
||||
|
||||
def prepare_image_processor_dict(self):
|
||||
return {
|
||||
@@ -64,6 +72,7 @@ class DPTImageProcessingTester(unittest.TestCase):
|
||||
"do_normalize": self.do_normalize,
|
||||
"do_resize": self.do_resize,
|
||||
"size": self.size,
|
||||
"do_reduce_labels": self.do_reduce_labels,
|
||||
}
|
||||
|
||||
def expected_output_image_shape(self, images):
|
||||
@@ -81,6 +90,28 @@ class DPTImageProcessingTester(unittest.TestCase):
|
||||
)
|
||||
|
||||
|
||||
# Copied from transformers.tests.models.beit.test_image_processing_beit.prepare_semantic_single_inputs
|
||||
def prepare_semantic_single_inputs():
|
||||
dataset = load_dataset("hf-internal-testing/fixtures_ade20k", split="test", trust_remote_code=True)
|
||||
|
||||
image = Image.open(dataset[0]["file"])
|
||||
map = Image.open(dataset[1]["file"])
|
||||
|
||||
return image, map
|
||||
|
||||
|
||||
# Copied from transformers.tests.models.beit.test_image_processing_beit.prepare_semantic_batch_inputs
|
||||
def prepare_semantic_batch_inputs():
|
||||
ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test", trust_remote_code=True)
|
||||
|
||||
image1 = Image.open(ds[0]["file"])
|
||||
map1 = Image.open(ds[1]["file"])
|
||||
image2 = Image.open(ds[2]["file"])
|
||||
map2 = Image.open(ds[3]["file"])
|
||||
|
||||
return [image1, image2], [map1, map2]
|
||||
|
||||
|
||||
@require_torch
|
||||
@require_vision
|
||||
class DPTImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
|
||||
@@ -105,6 +136,7 @@ class DPTImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
|
||||
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, "do_reduce_labels"))
|
||||
|
||||
def test_image_processor_from_dict_with_kwargs(self):
|
||||
image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
|
||||
@@ -138,3 +170,126 @@ class DPTImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
|
||||
pixel_values = image_processor(image, return_tensors="pt").pixel_values
|
||||
|
||||
self.assertEqual(list(pixel_values.shape), [1, 3, 512, 672])
|
||||
|
||||
# Copied from transformers.tests.models.beit.test_image_processing_beit.BeitImageProcessingTest.test_call_segmentation_maps
|
||||
def test_call_segmentation_maps(self):
|
||||
# Initialize image_processor
|
||||
image_processor = self.image_processing_class(**self.image_processor_dict)
|
||||
# create random PyTorch tensors
|
||||
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
|
||||
maps = []
|
||||
for image in image_inputs:
|
||||
self.assertIsInstance(image, torch.Tensor)
|
||||
maps.append(torch.zeros(image.shape[-2:]).long())
|
||||
|
||||
# Test not batched input
|
||||
encoding = image_processor(image_inputs[0], maps[0], return_tensors="pt")
|
||||
self.assertEqual(
|
||||
encoding["pixel_values"].shape,
|
||||
(
|
||||
1,
|
||||
self.image_processor_tester.num_channels,
|
||||
self.image_processor_tester.size["height"],
|
||||
self.image_processor_tester.size["width"],
|
||||
),
|
||||
)
|
||||
self.assertEqual(
|
||||
encoding["labels"].shape,
|
||||
(
|
||||
1,
|
||||
self.image_processor_tester.size["height"],
|
||||
self.image_processor_tester.size["width"],
|
||||
),
|
||||
)
|
||||
self.assertEqual(encoding["labels"].dtype, torch.long)
|
||||
self.assertTrue(encoding["labels"].min().item() >= 0)
|
||||
self.assertTrue(encoding["labels"].max().item() <= 255)
|
||||
|
||||
# Test batched
|
||||
encoding = image_processor(image_inputs, maps, return_tensors="pt")
|
||||
self.assertEqual(
|
||||
encoding["pixel_values"].shape,
|
||||
(
|
||||
self.image_processor_tester.batch_size,
|
||||
self.image_processor_tester.num_channels,
|
||||
self.image_processor_tester.size["height"],
|
||||
self.image_processor_tester.size["width"],
|
||||
),
|
||||
)
|
||||
self.assertEqual(
|
||||
encoding["labels"].shape,
|
||||
(
|
||||
self.image_processor_tester.batch_size,
|
||||
self.image_processor_tester.size["height"],
|
||||
self.image_processor_tester.size["width"],
|
||||
),
|
||||
)
|
||||
self.assertEqual(encoding["labels"].dtype, torch.long)
|
||||
self.assertTrue(encoding["labels"].min().item() >= 0)
|
||||
self.assertTrue(encoding["labels"].max().item() <= 255)
|
||||
|
||||
# Test not batched input (PIL images)
|
||||
image, segmentation_map = prepare_semantic_single_inputs()
|
||||
|
||||
encoding = image_processor(image, segmentation_map, return_tensors="pt")
|
||||
self.assertEqual(
|
||||
encoding["pixel_values"].shape,
|
||||
(
|
||||
1,
|
||||
self.image_processor_tester.num_channels,
|
||||
self.image_processor_tester.size["height"],
|
||||
self.image_processor_tester.size["width"],
|
||||
),
|
||||
)
|
||||
self.assertEqual(
|
||||
encoding["labels"].shape,
|
||||
(
|
||||
1,
|
||||
self.image_processor_tester.size["height"],
|
||||
self.image_processor_tester.size["width"],
|
||||
),
|
||||
)
|
||||
self.assertEqual(encoding["labels"].dtype, torch.long)
|
||||
self.assertTrue(encoding["labels"].min().item() >= 0)
|
||||
self.assertTrue(encoding["labels"].max().item() <= 255)
|
||||
|
||||
# Test batched input (PIL images)
|
||||
images, segmentation_maps = prepare_semantic_batch_inputs()
|
||||
|
||||
encoding = image_processor(images, segmentation_maps, return_tensors="pt")
|
||||
self.assertEqual(
|
||||
encoding["pixel_values"].shape,
|
||||
(
|
||||
2,
|
||||
self.image_processor_tester.num_channels,
|
||||
self.image_processor_tester.size["height"],
|
||||
self.image_processor_tester.size["width"],
|
||||
),
|
||||
)
|
||||
self.assertEqual(
|
||||
encoding["labels"].shape,
|
||||
(
|
||||
2,
|
||||
self.image_processor_tester.size["height"],
|
||||
self.image_processor_tester.size["width"],
|
||||
),
|
||||
)
|
||||
self.assertEqual(encoding["labels"].dtype, torch.long)
|
||||
self.assertTrue(encoding["labels"].min().item() >= 0)
|
||||
self.assertTrue(encoding["labels"].max().item() <= 255)
|
||||
|
||||
# Copied from transformers.tests.models.beit.test_image_processing_beit.BeitImageProcessingTest.test_reduce_labels
|
||||
def test_reduce_labels(self):
|
||||
# Initialize image_processor
|
||||
image_processor = self.image_processing_class(**self.image_processor_dict)
|
||||
|
||||
# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
|
||||
image, map = prepare_semantic_single_inputs()
|
||||
encoding = image_processor(image, map, return_tensors="pt")
|
||||
self.assertTrue(encoding["labels"].min().item() >= 0)
|
||||
self.assertTrue(encoding["labels"].max().item() <= 150)
|
||||
|
||||
image_processor.do_reduce_labels = True
|
||||
encoding = image_processor(image, map, return_tensors="pt")
|
||||
self.assertTrue(encoding["labels"].min().item() >= 0)
|
||||
self.assertTrue(encoding["labels"].max().item() <= 255)
|
||||
|
||||
Reference in New Issue
Block a user