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processing_spec_vision.py

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+# 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.
+
+"""
+Processor class for Spec-Vision.
+"""
+
+import re
+from typing import List, Optional, Union
+
+import numpy as np
+import torch
+import torchvision
+from PIL import Image
+from transformers import AutoImageProcessor
+from transformers.feature_extraction_utils import BatchFeature
+from transformers.image_processing_utils import BaseImageProcessor
+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.processing_utils import ProcessorMixin
+from transformers.tokenization_utils_base import (PaddingStrategy, TextInput,
+                                                  TruncationStrategy)
+from transformers.utils import TensorType, is_vision_available, logging
+
+logger = logging.get_logger(__name__)
+
+def padding_336(image):
+    """Apply padding to make height a multiple of 336 while preserving aspect ratio."""
+    width, height = image.size
+    target_height = int(np.ceil(height / 336) * 336)
+    top_padding = int((target_height - height) / 2)
+    bottom_padding = target_height - height - top_padding
+    padded_image = torchvision.transforms.functional.pad(
+        image, 
+        [0, top_padding, 0, bottom_padding],
+        fill=[255, 255, 255]
+    )
+    return padded_image
+
+def calc_padded_size(width, height, padding_unit=336):
+    """Calculate the padded dimensions for an image."""
+    target_height = int(np.ceil(height / padding_unit) * padding_unit)
+    padded_width = width
+    padded_height = target_height
+    return padded_width, padded_height
+
+def hd_transform(img, hd_num=16):
+    """Apply HD transformation with support for Spec-Vision's requirements."""
+    width, height = img.size
+    transposed = False
+    
+    # Handle portrait images by transposing
+    if width < height:
+        img = img.transpose(Image.TRANSPOSE)
+        width, height = img.size
+        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)
+    
+    # Resize and pad
+    img = torchvision.transforms.functional.resize(img, [new_height, new_width])
+    img = padding_336(img)
+    
+    # Restore original orientation if needed
+    if transposed:
+        img = img.transpose(Image.TRANSPOSE)
+        
+    return img
+
+def pad_to_max_crops(images, max_crops=5):
+    """Pad batch of images to have consistent number of crops."""
+    B, _, H, W = images.shape
+    if B < max_crops:
+        padding = torch.zeros(max_crops - B, 3, H, W, dtype=images.dtype, device=images.device)
+        images = torch.cat([images, padding], dim=0)
+    return images
+
+class SpecVisionImageProcessor(BaseImageProcessor):
+    """
+    Image processor for Spec-Vision model.
+    
+    This processor handles the preparation of images for the Spec-Vision model, including:
+    - HD transformation for high-resolution image processing
+    - Multi-crop processing with configurable number of crops
+    - Normalization and padding
+    """
+
+    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,
+        hd_transform_order: str = "sub_glb",
+        **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
+        self.hd_transform_order = hd_transform_order
+
+    def calc_num_image_tokens(self, images: ImageInput) -> List[int]:
+        """Calculate number of image tokens needed for each image."""
+        images = make_list_of_images(images)
+        if not valid_images(images):
+            raise ValueError("Invalid image type provided")
+
+        images = [image.convert('RGB') for image in images]
+        transformed_images = [hd_transform(im, hd_num=self.num_crops) for im in images]
+        shapes = [[im.size[1], im.size[0]] for im in transformed_images]
+        
+        # Calculate tokens based on Spec-Vision's architecture
+        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 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,
+    ) -> BatchFeature:
+        """
+        Preprocess images for the Spec-Vision model.
+        
+        Handles HD transformation, normalization, and proper formatting of images
+        according to Spec-Vision's requirements.
+        """
+        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
+
+        # Validate and prepare images
+        images = make_list_of_images(images)
+        if not valid_images(images):
+            raise ValueError("Invalid image type provided")
+
+        if do_convert_rgb:
+            images = [convert_to_rgb(image) for image in images]
+
+        # Create image processor pipeline
+        img_processor = torchvision.transforms.Compose([
+            torchvision.transforms.ToTensor(),
+            torchvision.transforms.Normalize(image_mean, image_std)
+        ])
+
+        # Process images according to Spec-Vision's HD transform requirements
+        images = [image.convert('RGB') for image in images]
+        transformed_images = [hd_transform(im, hd_num=self.num_crops) for im in images]
+        
+        # Convert to tensors and normalize
+        hd_images = [img_processor(im) for im in transformed_images]
+        
+        # Create global views
+        global_images = [
+            torch.nn.functional.interpolate(
+                im.unsqueeze(0).float(),
+                size=(336, 336),
+                mode='bicubic'
+            ).to(im.dtype)
+            for im in hd_images
+        ]
+
+        # Process shapes and calculate tokens
+        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 images according to Spec-Vision's requirements
+        hd_images_reshaped = [
+            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)
+        ]
+
+        # Combine global and local views based on transform order
+        if self.hd_transform_order == "sub_glb":
+            processed_images = [
+                torch.cat([_im, _global_image], dim=0)
+                for _global_image, _im in zip(global_images, hd_images_reshaped)
+            ]
+        else:  # glb_sub
+            processed_images = [
+                torch.cat([_global_image, _im], dim=0)
+                for _global_image, _im in zip(global_images, hd_images_reshaped)
+            ]
+
+        # Pad to consistent number of crops
+        image_batch = [
+            pad_to_max_crops(im, self.num_crops + 1)
+            for im in processed_images
+        ]
+        image_batch = torch.stack(image_batch, dim=0)
+
+        return BatchFeature(
+            data={
+                "pixel_values": image_batch,
+                "image_sizes": shapes,
+                "num_img_tokens": num_img_tokens
+            },
+            tensor_type=return_tensors
+        )
+
+class SpecVisionProcessor(ProcessorMixin):
+    """
+    Combined processor for Spec-Vision model, handling both image and text inputs.
+    
+    Combines SpecVisionImageProcessor for images and a tokenizer for text,
+    coordinating their interaction for multi-modal inputs.
+    """
+
+    attributes = ["image_processor", "tokenizer"]
+    image_processor_class = "SpecVisionImageProcessor"
+    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_crops
+        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:
+        """Process both text and image inputs for the model."""
+        if images is not None:
+            image_features = self.image_processor(images, return_tensors=return_tensors)
+        else:
+            image_features = {}
+
+        # Process combined inputs
+        inputs = self._process_multimodal_inputs(
+            image_features,
+            text,
+            padding=padding,
+            truncation=truncation,
+            max_length=max_length,
+            return_tensors=return_tensors
+        )
+        
+        return inputs
+
+    def _process_multimodal_inputs(self, images, texts, **kwargs):
+        """Process and combine image and text inputs."""
+        if not images:
+            return BatchFeature(data=self.tokenizer(
+                texts,
+                return_tensors=kwargs.get('return_tensors'),
+                padding=kwargs.get('padding'),
+                truncation=kwargs.get('truncation'),
+                max_length=kwargs.get('max_length')
+            ))
+
+        # Process text chunks and image tags
+        pattern = r"<\|image_\d+\|>"
+        text_chunks = [
+            self.tokenizer(chunk).input_ids
+            for chunk in re.split(pattern, texts)
+        ]
+
+        # Handle image tokens
+        num_img_tokens = (
+            images['num_img_tokens']
+            if 'num_img_tokens' in images
+            else [self.num_img_tokens] * len(images['pixel_values'])
+        )
+
+        image_tags = re.findall(pattern, texts)
+        image_ids = [int(tag.split("|")[1].split("_")[-1]) for tag in image_tags]
+        
+        # Validate image IDs
+        unique_ids = sorted(set(image_ids))
+        if unique_ids != list(range(1, len(unique_ids) + 1)):
+            raise ValueError(
+                f"Image IDs must be consecutive integers starting from 1, got {unique_ids}"
+            )
+        if len(unique_ids) != len(images['pixel_values']):
+            raise ValueError(
+                f"Number of image tags ({len(unique_ids)}) doesn't match "
+                f"number of images ({len(images['pixel_values'])})"
+            )
+
+        # Create padded image IDs
+        image_ids_padded = [
+            [-iid] * num_img_tokens[iid-1]
+            for iid in image_ids
+        ]
+
+        # Combine text and image tokens
+        input_ids = []
+        for x in self._interleave_sequences(text_chunks, image_ids_padded):
+            input_ids.extend(x)
+
+        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['pixel_values'],
+            "image_sizes": images['image_sizes']
+        })
+
+    def _interleave_sequences(self, seq1, seq2):
+        """Interleave two sequences, padding second sequence if needed."""
+        if len(seq1) > len(seq2):
+            seq2.append([])
+        return [item for pair in zip(seq1, seq2) for item in pair]
+
+    def batch_decode(self, *args, **kwargs):
+        """Decode a batch of token IDs to text."""
+        return self.tokenizer.batch_decode(*args, **kwargs)
+
+    def decode(self, *args, **kwargs):
+        """Decode token IDs to text."""
+        return self.tokenizer.decode(*args, **kwargs)
+
+    @property
+    def model_input_names(self):
+        """Get combined input names from both processors."""
+        return list(dict.fromkeys(
+            self.tokenizer.model_input_names +
+            self.image_processor.model_input_names
+        ))
+
+# Register the processor with AutoImageProcessor
+AutoImageProcessor.register("SpecVisionImageProcessor", SpecVisionImageProcessor)