fix import

image_processing_phi3_v.py

@@ -1,274 +0,0 @@
-# coding=utf-8
-# Copyright 2024 Microsoft and 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.
-
-"""Image processor class for Phi3-V."""
-
-from typing import List, Optional, Union
-
-import numpy as np
-
-from transformers.image_processing_utils import BaseImageProcessor, BatchFeature
-from transformers.image_transforms import (
-    convert_to_rgb,
-)
-from transformers.image_utils import (
-    OPENAI_CLIP_MEAN,
-    OPENAI_CLIP_STD,
-    ImageInput,
-    make_list_of_images,
-    valid_images,
-)
-from transformers.utils import TensorType, is_vision_available, logging
-
-from transformers import AutoImageProcessor
-
-logger = logging.get_logger(__name__)
-
-
-if is_vision_available():
-    from PIL import Image
-
-import torch
-import torchvision
-
-def padding_336(b):
-    width, height = b.size
-    tar = int(np.ceil(height / 336) * 336)
-    top_padding = int((tar - height)/2)
-    bottom_padding = tar - height - top_padding
-    left_padding = 0
-    right_padding = 0
-    b = torchvision.transforms.functional.pad(b, [left_padding, top_padding, right_padding, bottom_padding], fill=[255,255,255])
-
-    return b
-
-def calc_padded_size(width, height, padding_unit=336):  
-    target_height = int(np.ceil(height / padding_unit) * padding_unit)  
-    top_padding = int((target_height - height) / 2)  
-    bottom_padding = target_height - height - top_padding  
-    left_padding = 0  
-    right_padding = 0  
-    padded_width = width + left_padding + right_padding  
-    padded_height = height + top_padding + bottom_padding  
-    return padded_width, padded_height  
-
-def HD_transform(img, hd_num=16):
-    width, height = img.size
-    trans = False
-    if width < height:
-        img = img.transpose(Image.TRANSPOSE)
-        trans = True
-        width, height = img.size
-    ratio = (width/ height)
-    scale = 1
-    while scale*np.ceil(scale/ratio) <= hd_num:
-        scale += 1
-    scale -= 1
-    new_w = int(scale * 336)
-    new_h = int(new_w / ratio)
-
-    img = torchvision.transforms.functional.resize(img, [new_h, new_w],)
-    img = padding_336(img)
-    width, height = img.size
-    if trans:
-        img = img.transpose(Image.TRANSPOSE)
-
-    return img
-
-def calc_hd_transform_size(width, height, hd_num=16):  
-    transposed = False  
-    if width < height:  
-        width, height = height, width  
-        transposed = True  
-  
-    ratio = width / height  
-    scale = 1  
-    while scale * np.ceil(scale / ratio) <= hd_num:  
-        scale += 1  
-    scale -= 1  
-  
-    new_width = int(scale * 336)  
-    new_height = int(new_width / ratio)  
-  
-    padded_width, padded_height = calc_padded_size(new_width, new_height)  
-      
-    if transposed:  
-        padded_width, padded_height = padded_height, padded_width  
-  
-    return padded_width, padded_height  
-
-def pad_to_max_num_crops_tensor(images, max_crops=5):
-    """
-    images: B x 3 x H x W, B<=max_crops
-    """
-    B, _, H, W = images.shape
-    if B < max_crops:
-        pad = torch.zeros(max_crops - B, 3, H, W, dtype=images.dtype, device=images.device)
-        images = torch.cat([images, pad], dim=0)
-    return images
-
-
-class Phi3VImageProcessor(BaseImageProcessor):
-    r"""
-    Constructs a Phi3 image processor. Based on [`CLIPImageProcessor`] with incorporation of additional techniques
-    for processing high resolution images as explained in the [InternLM-XComposer2-4KHD](https://arxiv.org/pdf/2404.06512)
-
-    Args:
-        image_mean (`float` or `List[float]`, *optional*, defaults to `[0.48145466, 0.4578275, 0.40821073]`):
-            Mean to use if normalizing the image. This is a float or list of floats the length of the number of
-            channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
-        image_std (`float` or `List[float]`, *optional*, defaults to `[0.26862954, 0.26130258, 0.27577711]`):
-            Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
-            number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
-            Can be overridden by the `image_std` parameter in the `preprocess` method.
-        do_convert_rgb (`bool`, *optional*, defaults to `True`):
-            Whether to convert the image to RGB.
-    """
-
-    model_input_names = ["pixel_values"]
-
-    def __init__(
-        self,
-        num_crops: int = 1,
-        image_mean: Optional[Union[float, List[float]]] = None,
-        image_std: Optional[Union[float, List[float]]] = None,
-        do_convert_rgb: bool = True,
-        **kwargs,
-    ) -> None:
-        super().__init__(**kwargs)
-        self.num_crops = num_crops
-        self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
-        self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD
-        self.do_convert_rgb = do_convert_rgb
-    
-    def calc_num_image_tokens(
-            self, 
-            images: ImageInput 
-    ):
-        """ Calculate the number of image tokens for each image.
-        Args:
-            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`.
-        """
-        images = make_list_of_images(images)
-
-        if not valid_images(images):
-            raise ValueError(
-                "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
-                "torch.Tensor, tf.Tensor or jax.ndarray."
-            )
-
-        images = [image.convert('RGB') for image in images]
-        # (H, W, C)
-        elems = [HD_transform(im, hd_num = self.num_crops) for im in images] 
-        shapes = [[im.size[1], im.size[0]] for im in elems]
-        num_img_tokens = [int((h//336*w//336+1)*144 + 1 + (h//336+1)*12) for h, w in shapes]
-        return num_img_tokens
-
-    def calc_num_image_tokens_from_image_size(self, width, height):
-        """
-        Calculate the number of image tokens for a given image size.
-        Args:
-            width (`int`): Width of the image.
-            height (`int`): Height of the image.
-        """
-        new_width, new_height = calc_hd_transform_size(width, height, hd_num=self.num_crops)  
-        num_img_tokens = int((new_height // 336 * new_width // 336 + 1) * 144 + 1 + (new_height // 336 + 1) * 12)  
-        return num_img_tokens
-
-    def preprocess(
-        self,
-        images: ImageInput,
-        image_mean: Optional[Union[float, List[float]]] = None,
-        image_std: Optional[Union[float, List[float]]] = None,
-        do_convert_rgb: bool = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-    ):
-        """
-        Args:
-            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`.
-            image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
-                Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
-            image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
-                Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
-                `True`.
-            do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
-                Whether to convert the image to RGB.
-            return_tensors (`str` or `TensorType`, *optional*):
-                The type of tensors to return. Can be one of:
-                - Unset: Return a list of `np.ndarray`.
-                - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
-                - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
-                - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
-                - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
-        """
-        image_mean = image_mean if image_mean is not None else self.image_mean
-        image_std = image_std if image_std is not None else self.image_std
-        do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
-
-        images = make_list_of_images(images)
-
-        if not valid_images(images):
-            raise ValueError(
-                "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
-                "torch.Tensor, tf.Tensor or jax.ndarray."
-            )
-
-        if do_convert_rgb:
-            images = [convert_to_rgb(image) for image in images]
-
-        image_sizes = []
-        img_processor = torchvision.transforms.Compose([
-            torchvision.transforms.ToTensor(),
-            torchvision.transforms.Normalize(image_mean, image_std)
-        ])
-
-        # PIL images
-        # HD_transform pad images to size of multiiply of 336, 336
-        # convert to RGB first
-        images = [image.convert('RGB') for image in images]
-        elems = [HD_transform(im, hd_num = self.num_crops) for im in images] 
-        # tensor transform and normalize
-        hd_images = [img_processor(im) for im in elems]
-        # create global image 
-        global_image = [torch.nn.functional.interpolate(im.unsqueeze(0).float(), size=(336, 336), mode='bicubic',).to(im.dtype) for im in hd_images]
-
-        # [(3, h, w)], where h, w is multiple of 336
-        shapes = [[im.size(1), im.size(2)] for im in hd_images]
-        num_img_tokens = [int(((h//336)*(w//336)+1)*144 + 1 + (h//336+1)*12) for h, w in shapes]
-        # reshape to channel dimension -> (num_images, num_crops, 3, 336, 336)
-        # (1, 3, h//336, 336, w//336, 336) -> (1, h//336, w//336, 3, 336, 336) -> (h//336*w//336, 3, 336, 336)
-        hd_images_reshape = [im.reshape(1, 3, h//336, 336, w//336, 336).permute(0,2,4,1,3,5).reshape(-1, 3, 336, 336).contiguous() for im, (h, w) in zip(hd_images, shapes)]
-        # concat global image and local image
-        hd_images_reshape = [torch.cat([_global_image] + [_im], dim=0) for _global_image, _im in zip(global_image, hd_images_reshape)]
-
-        # pad to max_num_crops
-        image_transformed = [pad_to_max_num_crops_tensor(im, self.num_crops+1) for im in hd_images_reshape]
-        image_transformed = torch.stack(image_transformed, dim=0)
-        image_sizes = [torch.LongTensor(_shapes) for _shapes in shapes]
-        padded_images = image_transformed
-        image_sizes = shapes
-
-        data = {"pixel_values": padded_images, 
-                "image_sizes": image_sizes,
-                "num_img_tokens": num_img_tokens
-                }
-
-        return BatchFeature(data=data, tensor_type=return_tensors)
-
-AutoImageProcessor.register("Phi3VImageProcessor", Phi3VImageProcessor)

processing_phi3_v.py

@@ -27,7 +27,268 @@ from transformers.image_utils import ImageInput
 from transformers.processing_utils import ProcessorMixin
 from transformers.tokenization_utils_base import PaddingStrategy, TextInput, TruncationStrategy
 from transformers.utils import TensorType
-from .image_processing_phi3_v import Phi3VImageProcessor 
+
+
+"""Image processor class for Phi3-V."""
+
+from typing import List, Optional, Union
+
+import numpy as np
+
+from transformers.image_processing_utils import BaseImageProcessor, BatchFeature
+from transformers.image_transforms import (
+    convert_to_rgb,
+)
+from transformers.image_utils import (
+    OPENAI_CLIP_MEAN,
+    OPENAI_CLIP_STD,
+    ImageInput,
+    make_list_of_images,
+    valid_images,
+)
+from transformers.utils import TensorType, is_vision_available, logging
+
+from transformers import AutoImageProcessor
+
+logger = logging.get_logger(__name__)
+
+
+if is_vision_available():
+    from PIL import Image
+
+import torch
+import torchvision
+
+def padding_336(b):
+    width, height = b.size
+    tar = int(np.ceil(height / 336) * 336)
+    top_padding = int((tar - height)/2)
+    bottom_padding = tar - height - top_padding
+    left_padding = 0
+    right_padding = 0
+    b = torchvision.transforms.functional.pad(b, [left_padding, top_padding, right_padding, bottom_padding], fill=[255,255,255])
+
+    return b
+
+def calc_padded_size(width, height, padding_unit=336):  
+    target_height = int(np.ceil(height / padding_unit) * padding_unit)  
+    top_padding = int((target_height - height) / 2)  
+    bottom_padding = target_height - height - top_padding  
+    left_padding = 0  
+    right_padding = 0  
+    padded_width = width + left_padding + right_padding  
+    padded_height = height + top_padding + bottom_padding  
+    return padded_width, padded_height  
+
+def HD_transform(img, hd_num=16):
+    width, height = img.size
+    trans = False
+    if width < height:
+        img = img.transpose(Image.TRANSPOSE)
+        trans = True
+        width, height = img.size
+    ratio = (width/ height)
+    scale = 1
+    while scale*np.ceil(scale/ratio) <= hd_num:
+        scale += 1
+    scale -= 1
+    new_w = int(scale * 336)
+    new_h = int(new_w / ratio)
+
+    img = torchvision.transforms.functional.resize(img, [new_h, new_w],)
+    img = padding_336(img)
+    width, height = img.size
+    if trans:
+        img = img.transpose(Image.TRANSPOSE)
+
+    return img
+
+def calc_hd_transform_size(width, height, hd_num=16):  
+    transposed = False  
+    if width < height:  
+        width, height = height, width  
+        transposed = True  
+  
+    ratio = width / height  
+    scale = 1  
+    while scale * np.ceil(scale / ratio) <= hd_num:  
+        scale += 1  
+    scale -= 1  
+  
+    new_width = int(scale * 336)  
+    new_height = int(new_width / ratio)  
+  
+    padded_width, padded_height = calc_padded_size(new_width, new_height)  
+      
+    if transposed:  
+        padded_width, padded_height = padded_height, padded_width  
+  
+    return padded_width, padded_height  
+
+def pad_to_max_num_crops_tensor(images, max_crops=5):
+    """
+    images: B x 3 x H x W, B<=max_crops
+    """
+    B, _, H, W = images.shape
+    if B < max_crops:
+        pad = torch.zeros(max_crops - B, 3, H, W, dtype=images.dtype, device=images.device)
+        images = torch.cat([images, pad], dim=0)
+    return images
+
+
+class Phi3VImageProcessor(BaseImageProcessor):
+    r"""
+    Constructs a Phi3 image processor. Based on [`CLIPImageProcessor`] with incorporation of additional techniques
+    for processing high resolution images as explained in the [InternLM-XComposer2-4KHD](https://arxiv.org/pdf/2404.06512)
+
+    Args:
+        image_mean (`float` or `List[float]`, *optional*, defaults to `[0.48145466, 0.4578275, 0.40821073]`):
+            Mean to use if normalizing the image. This is a float or list of floats the length of the number of
+            channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
+        image_std (`float` or `List[float]`, *optional*, defaults to `[0.26862954, 0.26130258, 0.27577711]`):
+            Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
+            number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
+            Can be overridden by the `image_std` parameter in the `preprocess` method.
+        do_convert_rgb (`bool`, *optional*, defaults to `True`):
+            Whether to convert the image to RGB.
+    """
+
+    model_input_names = ["pixel_values"]
+
+    def __init__(
+        self,
+        num_crops: int = 1,
+        image_mean: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, List[float]]] = None,
+        do_convert_rgb: bool = True,
+        **kwargs,
+    ) -> None:
+        super().__init__(**kwargs)
+        self.num_crops = num_crops
+        self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
+        self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD
+        self.do_convert_rgb = do_convert_rgb
+    
+    def calc_num_image_tokens(
+            self, 
+            images: ImageInput 
+    ):
+        """ Calculate the number of image tokens for each image.
+        Args:
+            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`.
+        """
+        images = make_list_of_images(images)
+
+        if not valid_images(images):
+            raise ValueError(
+                "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
+                "torch.Tensor, tf.Tensor or jax.ndarray."
+            )
+
+        images = [image.convert('RGB') for image in images]
+        # (H, W, C)
+        elems = [HD_transform(im, hd_num = self.num_crops) for im in images] 
+        shapes = [[im.size[1], im.size[0]] for im in elems]
+        num_img_tokens = [int((h//336*w//336+1)*144 + 1 + (h//336+1)*12) for h, w in shapes]
+        return num_img_tokens
+
+    def calc_num_image_tokens_from_image_size(self, width, height):
+        """
+        Calculate the number of image tokens for a given image size.
+        Args:
+            width (`int`): Width of the image.
+            height (`int`): Height of the image.
+        """
+        new_width, new_height = calc_hd_transform_size(width, height, hd_num=self.num_crops)  
+        num_img_tokens = int((new_height // 336 * new_width // 336 + 1) * 144 + 1 + (new_height // 336 + 1) * 12)  
+        return num_img_tokens
+
+    def preprocess(
+        self,
+        images: ImageInput,
+        image_mean: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, List[float]]] = None,
+        do_convert_rgb: bool = None,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+    ):
+        """
+        Args:
+            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`.
+            image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
+                Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
+            image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
+                Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
+                `True`.
+            do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
+                Whether to convert the image to RGB.
+            return_tensors (`str` or `TensorType`, *optional*):
+                The type of tensors to return. Can be one of:
+                - Unset: Return a list of `np.ndarray`.
+                - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
+                - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
+                - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
+                - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
+        """
+        image_mean = image_mean if image_mean is not None else self.image_mean
+        image_std = image_std if image_std is not None else self.image_std
+        do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
+
+        images = make_list_of_images(images)
+
+        if not valid_images(images):
+            raise ValueError(
+                "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
+                "torch.Tensor, tf.Tensor or jax.ndarray."
+            )
+
+        if do_convert_rgb:
+            images = [convert_to_rgb(image) for image in images]
+
+        image_sizes = []
+        img_processor = torchvision.transforms.Compose([
+            torchvision.transforms.ToTensor(),
+            torchvision.transforms.Normalize(image_mean, image_std)
+        ])
+
+        # PIL images
+        # HD_transform pad images to size of multiiply of 336, 336
+        # convert to RGB first
+        images = [image.convert('RGB') for image in images]
+        elems = [HD_transform(im, hd_num = self.num_crops) for im in images] 
+        # tensor transform and normalize
+        hd_images = [img_processor(im) for im in elems]
+        # create global image 
+        global_image = [torch.nn.functional.interpolate(im.unsqueeze(0).float(), size=(336, 336), mode='bicubic',).to(im.dtype) for im in hd_images]
+
+        # [(3, h, w)], where h, w is multiple of 336
+        shapes = [[im.size(1), im.size(2)] for im in hd_images]
+        num_img_tokens = [int(((h//336)*(w//336)+1)*144 + 1 + (h//336+1)*12) for h, w in shapes]
+        # reshape to channel dimension -> (num_images, num_crops, 3, 336, 336)
+        # (1, 3, h//336, 336, w//336, 336) -> (1, h//336, w//336, 3, 336, 336) -> (h//336*w//336, 3, 336, 336)
+        hd_images_reshape = [im.reshape(1, 3, h//336, 336, w//336, 336).permute(0,2,4,1,3,5).reshape(-1, 3, 336, 336).contiguous() for im, (h, w) in zip(hd_images, shapes)]
+        # concat global image and local image
+        hd_images_reshape = [torch.cat([_global_image] + [_im], dim=0) for _global_image, _im in zip(global_image, hd_images_reshape)]
+
+        # pad to max_num_crops
+        image_transformed = [pad_to_max_num_crops_tensor(im, self.num_crops+1) for im in hd_images_reshape]
+        image_transformed = torch.stack(image_transformed, dim=0)
+        image_sizes = [torch.LongTensor(_shapes) for _shapes in shapes]
+        padded_images = image_transformed
+        image_sizes = shapes
+
+        data = {"pixel_values": padded_images, 
+                "image_sizes": image_sizes,
+                "num_img_tokens": num_img_tokens
+                }
+
+        return BatchFeature(data=data, tensor_type=return_tensors)
+
+AutoImageProcessor.register("Phi3VImageProcessor", Phi3VImageProcessor)
+
 transformers.Phi3VImageProcessor = Phi3VImageProcessor 
 
 class Phi3VProcessor(ProcessorMixin):