Image transforms functionality used instead

src/transformers/image_transforms.py

@@ -14,7 +14,7 @@
 # limitations under the License.
 
 import warnings
-from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
+from typing import Iterable, List, Optional, Tuple, Union
 
 import numpy as np
 
@@ -38,13 +38,14 @@
     )
 
 
-if TYPE_CHECKING:
-    if is_torch_available():
-        import torch
-    if is_tf_available():
-        import tensorflow as tf
-    if is_flax_available():
-        import jax.numpy as jnp
+if is_torch_available():
+    import torch
+
+if is_tf_available():
+    import tensorflow as tf
+
+if is_flax_available():
+    import jax.numpy as jnp
 
 
 def to_channel_dimension_format(image: np.ndarray, channel_dim: Union[ChannelDimension, str]) -> np.ndarray:

src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py

@@ -22,43 +22,22 @@
 from PIL import Image
 
 from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
-from ...image_utils import ImageFeatureExtractionMixin, is_torch_tensor
-from ...utils import TensorType, is_torch_available, logging
+from ...image_transforms import center_to_corners_format, corners_to_center_format, rgb_to_id
+from ...image_utils import ImageFeatureExtractionMixin
+from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
 
 
 if is_torch_available():
     import torch
     from torch import nn
 
+
 logger = logging.get_logger(__name__)
 
 
 ImageInput = Union[Image.Image, np.ndarray, "torch.Tensor", List[Image.Image], List[np.ndarray], List["torch.Tensor"]]
 
 
-# Copied from transformers.models.detr.feature_extraction_detr.center_to_corners_format
-def center_to_corners_format(x):
-    """
-    Converts a PyTorch tensor of bounding boxes of center format (center_x, center_y, width, height) to corners format
-    (x_0, y_0, x_1, y_1).
-    """
-    center_x, center_y, width, height = x.unbind(-1)
-    b = [(center_x - 0.5 * width), (center_y - 0.5 * height), (center_x + 0.5 * width), (center_y + 0.5 * height)]
-    return torch.stack(b, dim=-1)
-
-
-# Copied from transformers.models.detr.feature_extraction_detr.corners_to_center_format
-def corners_to_center_format(x):
-    """
-    Converts a NumPy array of bounding boxes of shape (number of bounding boxes, 4) of corners format (x_0, y_0, x_1,
-    y_1) to center format (center_x, center_y, width, height).
-    """
-    x_transposed = x.T
-    x0, y0, x1, y1 = x_transposed[0], x_transposed[1], x_transposed[2], x_transposed[3]
-    b = [(x0 + x1) / 2, (y0 + y1) / 2, (x1 - x0), (y1 - y0)]
-    return np.stack(b, axis=-1)
-
-
 # Copied from transformers.models.detr.feature_extraction_detr.masks_to_boxes
 def masks_to_boxes(masks):
     """
@@ -93,15 +72,6 @@ def masks_to_boxes(masks):
     return np.stack([x_min, y_min, x_max, y_max], 1)
 
 
-# Copied from transformers.models.detr.feature_extraction_detr.rgb_to_id
-def rgb_to_id(color):
-    if isinstance(color, np.ndarray) and len(color.shape) == 3:
-        if color.dtype == np.uint8:
-            color = color.astype(np.int32)
-        return color[:, :, 0] + 256 * color[:, :, 1] + 256 * 256 * color[:, :, 2]
-    return int(color[0] + 256 * color[1] + 256 * 256 * color[2])
-
-
 # Copied from transformers.models.detr.feature_extraction_detr.binary_mask_to_rle
 def binary_mask_to_rle(mask):
     """

src/transformers/models/conditional_detr/modeling_conditional_detr.py

@@ -33,6 +33,7 @@
     add_start_docstrings_to_model_forward,
     is_scipy_available,
     is_timm_available,
+    is_vision_available,
     logging,
     replace_return_docstrings,
     requires_backends,
@@ -46,6 +47,9 @@
 if is_timm_available():
     from timm import create_model
 
+if is_vision_available():
+    from transformers.image_transforms import center_to_corners_format
+
 logger = logging.get_logger(__name__)
 
 _CONFIG_FOR_DOC = "ConditionalDetrConfig"
@@ -2596,17 +2600,6 @@ def generalized_box_iou(boxes1, boxes2):
     return iou - (area - union) / area
 
 
-# Copied from transformers.models.detr.modeling_detr.center_to_corners_format
-def center_to_corners_format(x):
-    """
-    Converts a PyTorch tensor of bounding boxes of center format (center_x, center_y, width, height) to corners format
-    (x_0, y_0, x_1, y_1).
-    """
-    center_x, center_y, width, height = x.unbind(-1)
-    b = [(center_x - 0.5 * width), (center_y - 0.5 * height), (center_x + 0.5 * width), (center_y + 0.5 * height)]
-    return torch.stack(b, dim=-1)
-
-
 # Copied from transformers.models.detr.modeling_detr._max_by_axis
 def _max_by_axis(the_list):
     # type: (List[List[int]]) -> List[int]

src/transformers/models/deformable_detr/feature_extraction_deformable_detr.py

@@ -22,8 +22,9 @@
 from PIL import Image
 
 from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
-from ...image_utils import ImageFeatureExtractionMixin, is_torch_tensor
-from ...utils import TensorType, is_torch_available, logging
+from ...image_transforms import center_to_corners_format, corners_to_center_format, rgb_to_id
+from ...image_utils import ImageFeatureExtractionMixin
+from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
 
 
 if is_torch_available():
@@ -36,29 +37,6 @@
 ImageInput = Union[Image.Image, np.ndarray, "torch.Tensor", List[Image.Image], List[np.ndarray], List["torch.Tensor"]]
 
 
-# Copied from transformers.models.detr.feature_extraction_detr.center_to_corners_format
-def center_to_corners_format(x):
-    """
-    Converts a PyTorch tensor of bounding boxes of center format (center_x, center_y, width, height) to corners format
-    (x_0, y_0, x_1, y_1).
-    """
-    center_x, center_y, width, height = x.unbind(-1)
-    b = [(center_x - 0.5 * width), (center_y - 0.5 * height), (center_x + 0.5 * width), (center_y + 0.5 * height)]
-    return torch.stack(b, dim=-1)
-
-
-# Copied from transformers.models.detr.feature_extraction_detr.corners_to_center_format
-def corners_to_center_format(x):
-    """
-    Converts a NumPy array of bounding boxes of shape (number of bounding boxes, 4) of corners format (x_0, y_0, x_1,
-    y_1) to center format (center_x, center_y, width, height).
-    """
-    x_transposed = x.T
-    x0, y0, x1, y1 = x_transposed[0], x_transposed[1], x_transposed[2], x_transposed[3]
-    b = [(x0 + x1) / 2, (y0 + y1) / 2, (x1 - x0), (y1 - y0)]
-    return np.stack(b, axis=-1)
-
-
 # Copied from transformers.models.detr.feature_extraction_detr.masks_to_boxes
 def masks_to_boxes(masks):
     """
@@ -93,32 +71,6 @@ def masks_to_boxes(masks):
     return np.stack([x_min, y_min, x_max, y_max], 1)
 
 
-# Copied from transformers.models.detr.feature_extraction_detr.rgb_to_id
-def rgb_to_id(color):
-    if isinstance(color, np.ndarray) and len(color.shape) == 3:
-        if color.dtype == np.uint8:
-            color = color.astype(np.int32)
-        return color[:, :, 0] + 256 * color[:, :, 1] + 256 * 256 * color[:, :, 2]
-    return int(color[0] + 256 * color[1] + 256 * 256 * color[2])
-
-
-# Copied from transformers.models.detr.feature_extraction_detr.id_to_rgb
-def id_to_rgb(id_map):
-    if isinstance(id_map, np.ndarray):
-        id_map_copy = id_map.copy()
-        rgb_shape = tuple(list(id_map.shape) + [3])
-        rgb_map = np.zeros(rgb_shape, dtype=np.uint8)
-        for i in range(3):
-            rgb_map[..., i] = id_map_copy % 256
-            id_map_copy //= 256
-        return rgb_map
-    color = []
-    for _ in range(3):
-        color.append(id_map % 256)
-        id_map //= 256
-    return color
-
-
 class DeformableDetrFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtractionMixin):
     r"""
     Constructs a Deformable DETR feature extractor. Differs only in the postprocessing of object detection compared to

src/transformers/models/deformable_detr/modeling_deformable_detr.py

@@ -35,6 +35,7 @@
     is_scipy_available,
     is_timm_available,
     is_torch_cuda_available,
+    is_vision_available,
     replace_return_docstrings,
     requires_backends,
 )
@@ -58,6 +59,9 @@
 else:
     MultiScaleDeformableAttention = None
 
+if is_vision_available():
+    from transformers.image_transforms import center_to_corners_format
+
 
 class MultiScaleDeformableAttentionFunction(Function):
     @staticmethod
@@ -2417,17 +2421,6 @@ def generalized_box_iou(boxes1, boxes2):
     return iou - (area - union) / area
 
 
-# Copied from transformers.models.detr.modeling_detr.center_to_corners_format
-def center_to_corners_format(x):
-    """
-    Converts a PyTorch tensor of bounding boxes of center format (center_x, center_y, width, height) to corners format
-    (x_0, y_0, x_1, y_1).
-    """
-    center_x, center_y, width, height = x.unbind(-1)
-    b = [(center_x - 0.5 * width), (center_y - 0.5 * height), (center_x + 0.5 * width), (center_y + 0.5 * height)]
-    return torch.stack(b, dim=-1)
-
-
 # Copied from transformers.models.detr.modeling_detr._max_by_axis
 def _max_by_axis(the_list):
     # type: (List[List[int]]) -> List[int]

src/transformers/models/detr/feature_extraction_detr.py

@@ -24,8 +24,9 @@
 from PIL import Image
 
 from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
-from ...image_utils import ImageFeatureExtractionMixin, is_torch_tensor
-from ...utils import TensorType, is_torch_available, logging
+from ...image_transforms import center_to_corners_format, corners_to_center_format, id_to_rgb, rgb_to_id
+from ...image_utils import ImageFeatureExtractionMixin
+from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
 
 
 if is_torch_available():
@@ -38,28 +39,6 @@
 ImageInput = Union[Image.Image, np.ndarray, "torch.Tensor", List[Image.Image], List[np.ndarray], List["torch.Tensor"]]
 
 
-# 2 functions below inspired by https://github.com/facebookresearch/detr/blob/master/util/box_ops.py
-def center_to_corners_format(x):
-    """
-    Converts a PyTorch tensor of bounding boxes of center format (center_x, center_y, width, height) to corners format
-    (x_0, y_0, x_1, y_1).
-    """
-    center_x, center_y, width, height = x.unbind(-1)
-    b = [(center_x - 0.5 * width), (center_y - 0.5 * height), (center_x + 0.5 * width), (center_y + 0.5 * height)]
-    return torch.stack(b, dim=-1)
-
-
-def corners_to_center_format(x):
-    """
-    Converts a NumPy array of bounding boxes of shape (number of bounding boxes, 4) of corners format (x_0, y_0, x_1,
-    y_1) to center format (center_x, center_y, width, height).
-    """
-    x_transposed = x.T
-    x0, y0, x1, y1 = x_transposed[0], x_transposed[1], x_transposed[2], x_transposed[3]
-    b = [(x0 + x1) / 2, (y0 + y1) / 2, (x1 - x0), (y1 - y0)]
-    return np.stack(b, axis=-1)
-
-
 def masks_to_boxes(masks):
     """
     Compute the bounding boxes around the provided panoptic segmentation masks.
@@ -93,33 +72,6 @@ def masks_to_boxes(masks):
     return np.stack([x_min, y_min, x_max, y_max], 1)
 
 
-# 2 functions below copied from https://github.com/cocodataset/panopticapi/blob/master/panopticapi/utils.py
-# Copyright (c) 2018, Alexander Kirillov
-# All rights reserved.
-def rgb_to_id(color):
-    if isinstance(color, np.ndarray) and len(color.shape) == 3:
-        if color.dtype == np.uint8:
-            color = color.astype(np.int32)
-        return color[:, :, 0] + 256 * color[:, :, 1] + 256 * 256 * color[:, :, 2]
-    return int(color[0] + 256 * color[1] + 256 * 256 * color[2])
-
-
-def id_to_rgb(id_map):
-    if isinstance(id_map, np.ndarray):
-        id_map_copy = id_map.copy()
-        rgb_shape = tuple(list(id_map.shape) + [3])
-        rgb_map = np.zeros(rgb_shape, dtype=np.uint8)
-        for i in range(3):
-            rgb_map[..., i] = id_map_copy % 256
-            id_map_copy //= 256
-        return rgb_map
-    color = []
-    for _ in range(3):
-        color.append(id_map % 256)
-        id_map //= 256
-    return color
-
-
 def binary_mask_to_rle(mask):
     """
     Args:

src/transformers/models/detr/modeling_detr.py

@@ -33,6 +33,7 @@
     add_start_docstrings_to_model_forward,
     is_scipy_available,
     is_timm_available,
+    is_vision_available,
     logging,
     replace_return_docstrings,
     requires_backends,
@@ -46,6 +47,9 @@
 if is_timm_available():
     from timm import create_model
 
+if is_vision_available():
+    from transformers.image_transforms import center_to_corners_format
+
 logger = logging.get_logger(__name__)
 
 _CONFIG_FOR_DOC = "DetrConfig"
@@ -2284,17 +2288,6 @@ def generalized_box_iou(boxes1, boxes2):
     return iou - (area - union) / area
 
 
-# Copied from transformers.models.detr.feature_extraction_detr.center_to_corners_format
-def center_to_corners_format(x):
-    """
-    Converts a PyTorch tensor of bounding boxes of center format (center_x, center_y, width, height) to corners format
-    (x_0, y_0, x_1, y_1).
-    """
-    center_x, center_y, width, height = x.unbind(-1)
-    b = [(center_x - 0.5 * width), (center_y - 0.5 * height), (center_x + 0.5 * width), (center_y + 0.5 * height)]
-    return torch.stack(b, dim=-1)
-
-
 # below: taken from https://github.com/facebookresearch/detr/blob/master/util/misc.py#L306
 
 

src/transformers/models/owlvit/feature_extraction_owlvit.py

@@ -22,8 +22,9 @@
 from transformers.image_utils import PILImageResampling
 
 from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
-from ...image_utils import ImageFeatureExtractionMixin, is_torch_tensor
-from ...utils import TensorType, is_torch_available, logging
+from ...image_transforms import center_to_corners_format
+from ...image_utils import ImageFeatureExtractionMixin
+from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
 
 
 if is_torch_available():
@@ -32,17 +33,6 @@
 logger = logging.get_logger(__name__)
 
 
-# Copied from transformers.models.detr.feature_extraction_detr.center_to_corners_format
-def center_to_corners_format(x):
-    """
-    Converts a PyTorch tensor of bounding boxes of center format (center_x, center_y, width, height) to corners format
-    (x_0, y_0, x_1, y_1).
-    """
-    center_x, center_y, width, height = x.unbind(-1)
-    b = [(center_x - 0.5 * width), (center_y - 0.5 * height), (center_x + 0.5 * width), (center_y + 0.5 * height)]
-    return torch.stack(b, dim=-1)
-
-
 # Copied from transformers.models.detr.modeling_detr._upcast
 def _upcast(t):
     # Protects from numerical overflows in multiplications by upcasting to the equivalent higher type