upload fast image processor

Author: geetu040

src/transformers/models/deepseek_vl/image_processing_deepseek_vl_fast.py

@@ -0,0 +1,107 @@
+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Fast Image processor class for DeepseekVL."""
+
+from ...image_processing_utils_fast import BASE_IMAGE_PROCESSOR_FAST_DOCSTRING, BaseImageProcessorFast
+from ...image_transforms import get_size_with_aspect_ratio
+from ...image_utils import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD, PILImageResampling, SizeDict, get_image_size
+from ...utils import (
+	add_start_docstrings,
+    is_torch_available,
+    is_torchvision_available,
+    is_torchvision_v2_available,
+)
+
+
+if is_torch_available():
+    import torch
+
+
+if is_torchvision_available():
+    if is_torchvision_v2_available():
+        from torchvision.transforms.v2 import functional as F
+    else:
+        from torchvision.transforms import functional as F
+
+
+@add_start_docstrings(
+    "Constructs a fast DeepseekVL image processor.",
+    BASE_IMAGE_PROCESSOR_FAST_DOCSTRING,
+)
+class DeepseekVLImageProcessorFast(BaseImageProcessorFast):
+    resample = PILImageResampling.BICUBIC
+    image_mean = OPENAI_CLIP_MEAN
+    image_std = OPENAI_CLIP_STD
+    size = 1024
+    do_resize = True
+    do_rescale = True
+    do_normalize = False
+    background_color = [122, 116, 104]
+
+    def pad_to_square(
+        self,
+        image: "torch.Tensor",
+    ) -> "torch.Tensor":
+        """
+        Pads an image to a square based on the longest edge.
+
+        Args:
+            image (`torch.Tensor`):
+                Image to pad.
+        Returns:
+            `torch.Tensor`: The padded image.
+        """
+        height, width = image.shape[-2:]
+        max_dim = max(height, width)
+        paste_x_left = (max_dim - width) // 2
+        paste_y_left = (max_dim - height) // 2
+        paste_x_right = max_dim - width - paste_x_left
+        paste_y_right = max_dim - height - paste_y_left
+        return F.pad(
+            image, padding=[paste_x_left, paste_y_left, paste_x_right, paste_y_right], fill=self.background_color
+        )
+
+    def resize(
+        self,
+        image: "torch.Tensor",
+        size: SizeDict,
+        interpolation: "F.InterpolationMode" = None,
+        antialias: bool = True,
+        **kwargs,
+    ) -> "torch.Tensor":
+        """
+        Resize an image to `(size["height"], size["width"])`.
+
+        Args:
+            image (`torch.Tensor`):
+                Image to resize.
+            size (`SizeDict`):
+                Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image.
+            resample (`InterpolationMode`, *optional*, defaults to `InterpolationMode.BILINEAR`):
+                `InterpolationMode` filter to use when resizing the image e.g. `InterpolationMode.BICUBIC`.
+
+        Returns:
+            `torch.Tensor`: The resized image.
+        """
+        interpolation = interpolation if interpolation is not None else F.InterpolationMode.BILINEAR
+        size = get_size_with_aspect_ratio(image.shape[-2:], size["height"], size["height"])
+
+        image =  F.resize(image, size, interpolation=interpolation, antialias=antialias)
+        image = self.pad_to_square(image)
+
+        return image
+
+
+__all__ = ["DeepseekVLImageProcessorFast"]