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preprocessor_config.json

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+{
+ "auto_map": {
+ "AutoImageProcessor": "processing_phi3_v.Phi3VImageProcessor",
+ "AutoProcessor": "processing_phi3_v.Phi3VProcessor"
+ },
+ "do_convert_rgb": true,
+ "image_mean": [
+ 0.48145466,
+ 0.4578275,
+ 0.40821073
+ ],
+ "image_processor_type": "Phi3VImageProcessor",
+ "image_std": [
+ 0.26862954,
+ 0.26130258,
+ 0.27577711
+ ],
+ "num_crops": 4,
+ "num_img_tokens": 144,
+ "processor_class": "Phi3VProcessor"
+}

processing_phi3_v.py

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+# 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.
+
+"""
+Processor class for Phi3-V.
+"""
+import re
+from typing import List, Optional, Union
+
+import torch
+
+import transformers
+from transformers.feature_extraction_utils import BatchFeature
+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
+
+"""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):
+ r"""
+ Constructs a Phi3-V processor which wraps a Phi3-V image processor and a LLaMa tokenizer into a single processor.
+
+ [`Phi3VProcessor`] offers all the functionalities of [`Phi3VImageProcessor`] and [`LlamaTokenizerFast`]. See the
+ [`~Phi3VProcessor.__call__`] and [`~Phi3VProcessor.decode`] for more information.
+
+ Args:
+ image_processor ([`Phi3VImageProcessor`], *optional*):
+ The image processor is a required input.
+ tokenizer ([`LlamaTokenizerFast`], *optional*):
+ The tokenizer is a required input.
+ """
+
+ attributes = ["image_processor", "tokenizer"]
+ image_processor_class = "Phi3VImageProcessor"
+ tokenizer_class = ("LlamaTokenizer", "LlamaTokenizerFast")
+ special_image_token = "<|image|>"
+
+ def __init__(self, image_processor, tokenizer):
+ self.image_processor = image_processor
+ self.tokenizer = tokenizer
+ self.num_img_tokens = image_processor.num_img_tokens
+ self.img_tokens = [f"<|image_{i + 1}|>" for i in range(1000000)]
+
+ def __call__(
+ self,
+ text: Union[TextInput, List[TextInput]],
+ images: ImageInput = None,
+ padding: Union[bool, str, PaddingStrategy] = False,
+ truncation: Union[bool, str, TruncationStrategy] = None,
+ max_length=None,
+ return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
+ ) -> BatchFeature:
+ """
+ Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
+ and `kwargs` arguments to LlamaTokenizerFast's [`~LlamaTokenizerFast.__call__`] if `text` is not `None` to encode
+ the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
+ Phi3ImageProcessor's [`~Phi3ImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring
+ of the above two methods for more information.
+
+ Args:
+ text (`str`, `List[str]`, `List[List[str]]`):
+ The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
+ (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
+ `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
+ images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
+ The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
+ tensor. Both channels-first and channels-last formats are supported.
+ padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
+ Select a strategy to pad the returned sequences (according to the model's padding side and padding
+ index) among:
+ - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
+ sequence if provided).
+ - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
+ acceptable input length for the model if that argument is not provided.
+ - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
+ lengths).
+ max_length (`int`, *optional*):
+ Maximum length of the returned list and optionally padding length (see above).
+ truncation (`bool`, *optional*):
+ Activates truncation to cut input sequences longer than `max_length` to `max_length`.
+ return_tensors (`str` or [`~utils.TensorType`], *optional*):
+ If set, will return tensors of a particular framework. Acceptable values are:
+
+ - `'tf'`: Return TensorFlow `tf.constant` objects.
+ - `'pt'`: Return PyTorch `torch.Tensor` objects.
+ - `'np'`: Return NumPy `np.ndarray` objects.
+ - `'jax'`: Return JAX `jnp.ndarray` objects.
+
+ Returns:
+ [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+
+ - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
+ - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+ `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
+ `None`).
+ - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
+ """
+ if images is not None:
+ image_inputs = self.image_processor(images, return_tensors=return_tensors)
+ else:
+ image_inputs = {}
+ inputs = self._convert_images_texts_to_inputs(image_inputs, text, padding=padding, truncation=truncation,
+ max_length=max_length, return_tensors=return_tensors)
+ return inputs
+
+ 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`.
+ """
+ return self.image_processor.calc_num_image_tokens(images)
+
+ def calc_num_image_tokens_from_image_size(self, width, height):
+ """ Calculate the number of image token for an image with given width and height.
+ Args:
+ width (`int`):
+ Width of the image.
+ height (`int`):
+ Height of the image.
+ """
+ return self.image_processor.calc_num_image_tokens_from_image_size(width, height)
+
+ @property
+ def special_image_token_id(self):
+ return self.tokenizer.convert_tokens_to_ids(self.special_image_token)
+
+ def get_special_image_token_id(self):
+ return self.tokenizer.convert_tokens_to_ids(self.special_image_token)
+
+ def _convert_images_texts_to_inputs(self, images, texts, padding=False, truncation=None, max_length=None,
+ return_tensors=None):
+ if not len(images):
+ model_inputs = self.tokenizer(texts, return_tensors=return_tensors, padding=padding, truncation=truncation,
+ max_length=max_length)
+ return BatchFeature(data={**model_inputs})
+
+ pattern = r"<\|image_\d+\|>"
+ prompt_chunks = [self.tokenizer(chunk, truncation=truncation, max_length=max_length).input_ids for chunk in re.split(pattern, texts)]
+
+ if 'num_img_tokens' in images:
+ num_img_tokens = images['num_img_tokens']
+ else:
+ assert 'num_crops' in images, 'num_crops must be provided in images if num_img_tokens is not provided'
+ num_crops = images['num_crops']
+ num_img_tokens = [_num_crops * self.num_img_tokens for _num_crops in num_crops]
+
+ images, image_sizes = images['pixel_values'], images['image_sizes']
+
+ # image_tags needs to start from 1 to n
+ image_tags = re.findall(pattern, texts)
+ # image_ids = [int(s.split("|")[1].split("_")[-1]) * -1 for s in image_tags]
+ # image_ids_pad = [[iid]*num_img_tokens[i] for i, iid in enumerate(image_ids)]
+ image_ids = [int(s.split("|")[1].split("_")[-1]) for s in image_tags]
+ unique_image_ids = sorted(list(set(image_ids)))
+ # image_ids must start from 1, and must be continuous int, e.g. [1, 2, 3], cannot be [1, 4, 5]
+ # check the condition
+ assert unique_image_ids == list(range(1,
+ len(unique_image_ids) + 1)), f"image_ids must start from 1, and must be continuous int, e.g. [1, 2, 3], cannot be {unique_image_ids}"
+ # total images must be the same as the number of image tags
+ assert len(unique_image_ids) == len(
+ images), f"total images must be the same as the number of image tags, got {len(unique_image_ids)} image tags and {len(images)} images"
+
+ image_ids_pad = [[-iid] * num_img_tokens[iid - 1] for iid in image_ids]
+
+ def insert_separator(X, sep_list):
+ if len(X) > len(sep_list):
+ sep_list.append([])
+ return [ele for sublist in zip(X, sep_list) for ele in sublist]
+
+ input_ids = []
+ offset = 0
+ for x in insert_separator(prompt_chunks, image_ids_pad):
+ input_ids.extend(x[offset:])
+
+ input_ids = torch.tensor(input_ids, dtype=torch.long).unsqueeze(0)
+ attention_mask = (input_ids > -1000000).to(torch.long)
+
+ return BatchFeature(data={"input_ids": input_ids,
+ "attention_mask": attention_mask,
+ "pixel_values": images,
+ "image_sizes": image_sizes})
+
+ # Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
+ def batch_decode(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
+ refer to the docstring of this method for more information.
+ """
+ return self.tokenizer.batch_decode(*args, **kwargs)
+
+ # Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Llama
+ def decode(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
+ the docstring of this method for more information.
+ """
+ return self.tokenizer.decode(*args, **kwargs)
+
+ @property
+ # Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names
+ def model_input_names(self):
+ tokenizer_input_names = self.tokenizer.model_input_names
+ image_processor_input_names = self.image_processor.model_input_names
+ return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))

processor_config.json

@@ -0,0 +1,6 @@
+{
+ "auto_map": {
+ "AutoProcessor": "processing_phi3_v.Phi3VProcessor"
+ },
+ "processor_class": "Phi3VProcessor"
+}