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added_tokens.json

@@ -0,0 +1,1030 @@
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+}

preprocessor_config.json

@@ -0,0 +1,33 @@
+{
+  "auto_map": {
+    "AutoProcessor": "processing_florence2.Florence2Processor"
+  },
+  "crop_size": {
+    "height": 768,
+    "width": 768
+  },
+  "do_center_crop": false,
+  "do_convert_rgb": null,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.485,
+    0.456,
+    0.406
+  ],
+  "image_processor_type": "CLIPImageProcessor",
+  "image_seq_length": 577,
+  "image_std": [
+    0.229,
+    0.224,
+    0.225
+  ],
+  "processor_class": "Florence2Processor",
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 768,
+    "width": 768
+  }
+}

processing_florence2.py

@@ -0,0 +1,393 @@
+# coding=utf-8
+# Copyright 2024 Microsoft and The HuggingFace Inc. team.
+#
+# 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 Florence-2.
+"""
+
+import re
+import logging
+from typing import List, Optional, Union
+import numpy as np
+
+import torch
+import PIL
+
+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
+import re
+
+logger = logging.getLogger(__name__)
+
+
+class Florence2Processor(ProcessorMixin):
+    attributes = ["image_processor", "tokenizer"]
+    image_processor_class = "CLIPImageProcessor"
+    tokenizer_class = ("BartTokenizer", "BartTokenizerFast")
+
+    def __init__(
+        self,
+        image_processor=None,
+        tokenizer=None,
+    ):
+        if image_processor is None:
+            raise ValueError("You need to specify an `image_processor`.")
+        if tokenizer is None:
+            raise ValueError("You need to specify a `tokenizer`.")
+        if not hasattr(image_processor, "image_seq_length"):
+            raise ValueError("Image processor is missing an `image_seq_length` attribute.")
+
+        self.image_seq_length = image_processor.image_seq_length
+
+        tokens_to_add = {
+                'additional_special_tokens': \
+                    tokenizer.additional_special_tokens + \
+                    ['<od>', '</od>', '<ocr>', '</ocr>'] + \
+                    [f'<loc_{x}>' for x in range(1000)] + \
+                    ['<cap>', '</cap>', '<ncap>', '</ncap>','<dcap>', '</dcap>', '<grounding>', '</grounding>', '<seg>', '</seg>', '<sep>', '<region_cap>', '</region_cap>', '<region_to_desciption>', '</region_to_desciption>', '<proposal>', '</proposal>', '<poly>', '</poly>', '<and>'] + \
+                    ['<panel>', '<text>', '<character>', '<tail>']
+            }
+        tokenizer.add_special_tokens(tokens_to_add)
+        self.decoder_start_token_id = 2
+
+        self.box_quantizer = BoxQuantizer(
+            mode='floor',
+            bins=(1000, 1000),
+        )
+
+        super().__init__(image_processor, tokenizer)
+    
+    def __call__(
+        self,
+        batch_input_text: List[TextInput] = None,
+        batch_input_list_of_list_of_bboxes: List[List[List[List[float]]]] = None,
+        batch_output_text: List[TextInput] = None,
+        batch_output_list_of_list_of_bboxes: List[List[List[List[float]]]] = None,
+        batch_images: ImageInput = None,
+        batch_character_cluster_labels = None,
+        batch_text_character_association_labels = None,
+        batch_text_tail_association_labels = None,
+        batch_is_essential_text_labels = None,
+        batch_tail_character_association_labels = None,
+        padding: Union[bool, str, PaddingStrategy] = None,
+        truncation: Union[bool, str, TruncationStrategy] = None,
+        max_input_length_including_image_tokens=None,
+        max_output_length=None,
+        return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
+        do_resize: bool = None,
+        do_normalize: bool = None,
+        image_mean: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, List[float]]] = None,
+        data_format: Optional["ChannelDimension"] = "channels_first",  # noqa: F821
+        input_data_format: Optional[
+            Union[str, "ChannelDimension"]  # noqa: F821
+        ] = None,
+        resample: "PILImageResampling" = None,  # noqa: F821
+        do_convert_rgb: bool = None,
+        dtype: torch.dtype = None,
+        device: torch.device = None,
+    ) -> BatchFeature:
+
+        assert batch_images is not None, "`batch_images` are expected as arguments to a `Florence2Processor` instance."
+        assert batch_input_text is not None, "`batch_input_text` are expected as arguments to a `Florence2Processor` instance."
+        if batch_input_list_of_list_of_bboxes is None:
+            batch_input_list_of_list_of_bboxes = [[] for _ in range(len(batch_input_text))]
+        assert len(batch_input_text) == len(batch_input_list_of_list_of_bboxes) == len(batch_images), "`batch_input_text`, `batch_input_list_of_list_of_bboxes` and `batch_images` have different lengths."
+        if batch_output_text is None:
+            assert batch_output_list_of_list_of_bboxes is None, "`batch_output_text` and `batch_output_list_of_list_of_bboxes` should be provided together."
+        else:
+            if batch_output_list_of_list_of_bboxes is None:
+                batch_output_list_of_list_of_bboxes = [[] for _ in range(len(batch_output_text))]
+            assert len(batch_output_text) == len(batch_output_list_of_list_of_bboxes) == len(batch_images), "`batch_output_text`, `batch_output_list_of_list_of_bboxes` and `batch_images` have different lengths."
+
+        max_input_length = max_input_length_including_image_tokens - self.image_seq_length if max_input_length_including_image_tokens is not None else None
+        batch_input_texts = [self._format_text_with_bboxes(text, list_of_list_of_bboxes, image) for text, list_of_list_of_bboxes, image in zip(batch_input_text, batch_input_list_of_list_of_bboxes, batch_images)]
+        inputs = self.tokenizer(
+            batch_input_texts,
+            return_tensors=return_tensors,
+            padding=padding,
+            truncation=False,
+        )
+        # Truncating manually because I don't want </s> token at the end of truncated sequences, which is the default behavior
+        if inputs["input_ids"].shape[1] > max_input_length:
+            inputs["input_ids"] = inputs["input_ids"][:, :max_input_length]
+            inputs["attention_mask"] = inputs["attention_mask"][:, :max_input_length]
+            
+        if batch_output_text is not None:
+            batch_output_texts = [self._format_text_with_bboxes(text, list_of_list_of_bboxes, image) for text, list_of_list_of_bboxes, image in zip(batch_output_text, batch_output_list_of_list_of_bboxes, batch_images)]
+            decoder_inputs = self.tokenizer(
+                batch_output_texts,
+                return_tensors=return_tensors,
+                padding=padding,
+                truncation=False,
+            )
+            # Truncating manually because I don't want </s> token at the end of truncated sequences, which is the default behavior
+            if decoder_inputs["input_ids"].shape[1] > max_output_length:
+                decoder_inputs["input_ids"] = decoder_inputs["input_ids"][:, :max_output_length]
+                decoder_inputs["attention_mask"] = decoder_inputs["attention_mask"][:, :max_output_length]
+        
+
+        pixel_values = self.image_processor(
+            batch_images,
+            do_resize=do_resize,
+            do_normalize=do_normalize,
+            return_tensors=return_tensors,
+            image_mean=image_mean,
+            image_std=image_std,
+            input_data_format=input_data_format,
+            data_format=data_format,
+            resample=resample,
+            do_convert_rgb=do_convert_rgb,
+        )["pixel_values"]
+
+        if dtype is not None:
+            pixel_values = pixel_values.to(dtype)
+        
+        return_data = {**inputs, "pixel_values": pixel_values}
+
+        if batch_output_text is not None:
+            labels = decoder_inputs["input_ids"]
+            decoder_input_ids = labels.new_zeros(labels.shape)
+            decoder_input_ids[:, 1:] = labels[:, :-1].clone()
+            decoder_input_ids[:, 0] = self.decoder_start_token_id
+            decoder_attention_mask = decoder_inputs["attention_mask"].new_ones(decoder_input_ids.shape)
+            decoder_attention_mask[:, 1:] = decoder_inputs["attention_mask"][:, :-1].clone()
+            # Mask fill labels to replace pad token ID with -100
+            labels.masked_fill_(labels == self.tokenizer.pad_token_id, -100)
+            return_data.update({
+                "labels": labels,
+                "decoder_input_ids": decoder_input_ids,
+                "decoder_attention_mask": decoder_attention_mask,
+            })
+        
+        if device is not None:
+            for key, value in return_data.items():
+                if isinstance(value, torch.Tensor):
+                    return_data[key] = value.to(device)
+
+        if batch_character_cluster_labels is not None:
+            return_data["character_cluster_labels"] = batch_character_cluster_labels
+        if batch_text_character_association_labels is not None:
+            return_data["text_character_association_labels"] = batch_text_character_association_labels
+        if batch_text_tail_association_labels is not None:
+            return_data["text_tail_association_labels"] = batch_text_tail_association_labels
+        if batch_is_essential_text_labels is not None:
+            return_data["is_essential_text_labels"] = batch_is_essential_text_labels
+        if batch_tail_character_association_labels is not None:
+            return_data["tail_character_association_labels"] = batch_tail_character_association_labels
+
+        return_data["tokenizer"] = self.tokenizer
+        return BatchFeature(data=return_data)
+
+    def cleanup_generated_text(self, generated_text):
+        return generated_text.replace("<s>", "").replace("</s>", "").replace("<pad>", "")
+
+    def postprocess_output(self, generated_ids, images):
+        generated_ids.masked_fill_(generated_ids == -100, self.tokenizer.pad_token_id) # only for some testing purposes
+        batch_decoded_texts = self.batch_decode(generated_ids, skip_special_tokens=False)
+        batch_decoded_texts = [self.cleanup_generated_text(text) for text in batch_decoded_texts]
+        batch_list_of_list_of_bboxes = []
+        batch_indices_of_bboxes_in_new_string = []
+        batch_new_texts = []
+        for text, image in zip(batch_decoded_texts, images):
+            size_wh = self._get_image_size_wh(image)
+            parsed_text, list_of_stringified_bboxes, start_end_in_new_string = self._parse_text_with_bboxes(text)
+            list_of_list_of_bboxes = [self.box_quantizer.dequantize_from_stringified_bboxes(stringified_bbox, size_wh) for stringified_bbox in list_of_stringified_bboxes]
+            batch_list_of_list_of_bboxes.append(list_of_list_of_bboxes)
+            batch_indices_of_bboxes_in_new_string.append(start_end_in_new_string)
+            batch_new_texts.append(parsed_text)
+        return batch_new_texts, batch_list_of_list_of_bboxes, batch_indices_of_bboxes_in_new_string
+
+    def _parse_text_with_bboxes(self, text):
+        loc_pattern = r'((?:<loc_\d+>){4}(?:,(?:<loc_\d+>){4})*)'
+        grounding_pattern = r'<grounding>(.*?)</grounding>' + loc_pattern
+        
+        list_of_stringified_bboxes = []
+        start_end_in_new_string = []
+        new_text = ""
+        original_pos = 0
+        new_pos = 0
+
+        for match in re.finditer(grounding_pattern + '|' + loc_pattern, text):
+            # Add text before the match
+            new_text += text[original_pos:match.start()]
+            new_pos += match.start() - original_pos
+
+            if match.group(0).startswith('<grounding>'):
+                # Handle grounding pattern
+                grounding_text = match.group(1)
+                locs = match.group(2)
+                new_text += grounding_text
+                list_of_stringified_bboxes.append(locs)
+                start_end_in_new_string.append((new_pos, new_pos + len(grounding_text)))
+                new_pos += len(grounding_text)
+            else:
+                # Handle loc pattern
+                locs = match.group(0)
+                replacement = ""
+                new_text += replacement
+                list_of_stringified_bboxes.append(locs)
+                start_end_in_new_string.append((new_pos, new_pos + len(replacement)))
+                new_pos += len(replacement)
+
+            original_pos = match.end()
+
+        # Add any remaining text
+        new_text += text[original_pos:]
+
+        return new_text, list_of_stringified_bboxes, start_end_in_new_string
+    
+    def _format_text_with_bboxes(self, text, list_of_list_of_bboxes, image):
+        size_wh = self._get_image_size_wh(image)
+        quantized_bbox_lists = []
+        for list_of_bboxes in list_of_list_of_bboxes:            
+            quantized_bboxes = self.box_quantizer.quantize(list_of_bboxes, size_wh=size_wh)
+            stringified_bboxes = [f"<loc_{x1}><loc_{y1}><loc_{x2}><loc_{y2}>" for x1, y1, x2, y2 in quantized_bboxes]
+            stringified_bboxes = ",".join(stringified_bboxes)
+            quantized_bbox_lists.append(stringified_bboxes)
+        return text.format(*quantized_bbox_lists)
+
+    def _get_image_size_wh(self, image):
+         # Get size_wh from image based on its type
+        if isinstance(image, torch.Tensor):
+            # For PyTorch tensor
+            if image.dim() == 3:
+                size_wh = (image.shape[2], image.shape[1])  # (width, height)
+            elif image.dim() == 4:
+                size_wh = (image.shape[3], image.shape[2])  # (width, height)
+            else:
+                raise ValueError("Unsupported tensor dimensions")
+        elif isinstance(image, np.ndarray):
+            # For NumPy array
+            if image.ndim == 2:
+                size_wh = (image.shape[1], image.shape[0])  # (width, height)
+            elif image.ndim == 3:
+                size_wh = (image.shape[1], image.shape[0])  # (width, height)
+            else:
+                raise ValueError("Unsupported array dimensions")
+        elif isinstance(image, PIL.Image.Image):
+            # For PIL Image
+            size_wh = image.size  # Already in (width, height) format
+        else:
+            raise TypeError("Unsupported image type")
+        return size_wh
+
+    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Florence2
+    def batch_decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to BartTokenizerFast'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->Florence2
+    def decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to BartTokenizerFast'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 with CLIP->Florence2
+    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))
+
+class BoxQuantizer(object):
+    def __init__(self, mode, bins):
+        self.mode = mode
+        self.bins = bins
+
+    def quantize(self, boxes, size_wh):
+        if not isinstance(boxes, torch.Tensor):
+            boxes = torch.tensor(boxes)
+        bins_w, bins_h = self.bins  # Quantization bins.
+        size_w, size_h = size_wh       # Original image size.
+        size_per_bin_w = size_w / bins_w
+        size_per_bin_h = size_h / bins_h
+        xmin, ymin, xmax, ymax = boxes.split(1, dim=-1)  # Shape: 4 * [N, 1].
+
+        if self.mode == 'floor':
+            quantized_xmin = (
+                xmin / size_per_bin_w).floor().clamp(0, bins_w - 1)
+            quantized_ymin = (
+                ymin / size_per_bin_h).floor().clamp(0, bins_h - 1)
+            quantized_xmax = (
+                xmax / size_per_bin_w).floor().clamp(0, bins_w - 1)
+            quantized_ymax = (
+                ymax / size_per_bin_h).floor().clamp(0, bins_h - 1)
+
+        elif self.mode == 'round':
+            raise NotImplementedError()
+
+        else:
+            raise ValueError('Incorrect quantization type.')
+
+        quantized_boxes = torch.cat(
+            (quantized_xmin, quantized_ymin, quantized_xmax, quantized_ymax), dim=-1
+        ).int()
+
+        return quantized_boxes.tolist()
+
+    def dequantize_from_stringified_bboxes(self, stringified_bboxes, size_wh):
+        bboxes = stringified_bboxes.split(',')
+
+        def parse_bbox(bbox_string):
+            pattern = r'<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>'
+            match = re.match(pattern, bbox_string)
+            if match:
+                return [int(match.group(i)) for i in range(1, 5)]
+            else:
+                raise ValueError(f"Invalid bbox string format: {bbox_string}")
+
+        parsed_bboxes = [parse_bbox(bbox) for bbox in bboxes]
+        return self.dequantize(parsed_bboxes, size_wh).tolist()
+
+    def dequantize(self, boxes: torch.Tensor, size):
+        if not isinstance(boxes, torch.Tensor):
+            boxes = torch.tensor(boxes)
+        bins_w, bins_h = self.bins  # Quantization bins.
+        size_w, size_h = size       # Original image size.
+        size_per_bin_w = size_w / bins_w
+        size_per_bin_h = size_h / bins_h
+        xmin, ymin, xmax, ymax = boxes.split(1, dim=-1)  # Shape: 4 * [N, 1].
+
+        if self.mode == 'floor':
+            # Add 0.5 to use the center position of the bin as the coordinate.
+            dequantized_xmin = (xmin + 0.5) * size_per_bin_w
+            dequantized_ymin = (ymin + 0.5) * size_per_bin_h
+            dequantized_xmax = (xmax + 0.5) * size_per_bin_w
+            dequantized_ymax = (ymax + 0.5) * size_per_bin_h
+
+        elif self.mode == 'round':
+            raise NotImplementedError()
+
+        else:
+            raise ValueError('Incorrect quantization type.')
+
+        dequantized_boxes = torch.cat(
+            (dequantized_xmin, dequantized_ymin,
+             dequantized_xmax, dequantized_ymax), dim=-1
+        )
+
+        return dequantized_boxes

processor_config.json

@@ -0,0 +1,6 @@
+{
+  "auto_map": {
+    "AutoProcessor": "processing_florence2.Florence2Processor"
+  },
+  "processor_class": "Florence2Processor"
+}