Commit
•
b5c52ec
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Parent(s):
d5c2129
Upload folder using huggingface_hub
Browse files- README.md +21 -0
- added_tokens.json +1026 -0
- config.json +236 -0
- merges.txt +0 -0
- model.safetensors +3 -0
- model.safetensors.index.json +673 -0
- preprocessor_config.json +33 -0
- processing_florence2.py +1088 -0
- processor_config.json +6 -0
- special_tokens_map.json +0 -0
- tokenizer.json +0 -0
- tokenizer_config.json +0 -0
- vocab.json +0 -0
README.md
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---
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license: mit
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license_link: https://huggingface.co/microsoft/Florence-2-base-ft/resolve/main/LICENSE
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pipeline_tag: image-text-to-text
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tags:
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- vision
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- mlx
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---
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# mlx-community/Florence-2-base-ft-bf16
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This model was converted to MLX format from [`prince-canuma/Florence-2-base-ft`]() using mlx-vlm version **0.1.0**.
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Refer to the [original model card](https://huggingface.co/prince-canuma/Florence-2-base-ft) for more details on the model.
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## Use with mlx
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```bash
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pip install -U mlx-vlm
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```
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```bash
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python -m mlx_vlm.generate --model mlx-community/Florence-2-base-ft-bf16 --max-tokens 100 --temp 0.0
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```
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added_tokens.json
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| 1 |
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{
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| 2 |
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| 3 |
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| 4 |
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| 5 |
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| 6 |
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| 7 |
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| 8 |
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| 9 |
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| 10 |
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| 11 |
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| 12 |
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| 13 |
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| 14 |
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| 15 |
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| 16 |
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| 17 |
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| 23 |
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| 24 |
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| 27 |
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| 995 |
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| 996 |
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| 997 |
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| 998 |
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| 1000 |
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| 1001 |
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|
| 1002 |
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|
| 1003 |
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| 1004 |
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| 1005 |
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| 1006 |
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| 1007 |
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| 1008 |
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| 1009 |
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| 1010 |
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| 1011 |
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|
| 1012 |
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|
| 1013 |
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|
| 1014 |
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| 1015 |
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| 1016 |
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| 1017 |
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|
| 1018 |
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|
| 1019 |
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|
| 1020 |
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|
| 1021 |
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|
| 1022 |
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|
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|
| 1024 |
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|
| 1025 |
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|
| 1026 |
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}
|
config.json
ADDED
|
@@ -0,0 +1,236 @@
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|
| 1 |
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{
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| 2 |
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|
| 3 |
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|
| 4 |
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|
| 5 |
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|
| 6 |
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|
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| 222 |
+
"tokenizer_class": null,
|
| 223 |
+
"top_k": 50,
|
| 224 |
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"top_p": 1.0,
|
| 225 |
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"torch_dtype": null,
|
| 226 |
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"torchscript": false,
|
| 227 |
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"typical_p": 1.0,
|
| 228 |
+
"use_bfloat16": false,
|
| 229 |
+
"visual_temporal_embedding": {
|
| 230 |
+
"max_temporal_embeddings": 100,
|
| 231 |
+
"type": "COSINE"
|
| 232 |
+
},
|
| 233 |
+
"window_size": 12
|
| 234 |
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},
|
| 235 |
+
"vocab_size": 51289
|
| 236 |
+
}
|
merges.txt
ADDED
|
The diff for this file is too large to render.
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|
|
|
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
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| 1 |
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version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:190da45e7d6fa70a8db4e43dd29ece864cf1f322163f8d4224d64216dbcd867e
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| 3 |
+
size 541899275
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model.safetensors.index.json
ADDED
|
@@ -0,0 +1,673 @@
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| 1 |
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{
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| 2 |
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| 3 |
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| 662 |
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| 664 |
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| 666 |
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| 668 |
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| 669 |
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| 670 |
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|
| 671 |
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|
| 672 |
+
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|
| 673 |
+
}
|
preprocessor_config.json
ADDED
|
@@ -0,0 +1,33 @@
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|
| 1 |
+
{
|
| 2 |
+
"auto_map": {
|
| 3 |
+
"AutoProcessor": "processing_florence2.Florence2Processor"
|
| 4 |
+
},
|
| 5 |
+
"crop_size": {
|
| 6 |
+
"height": 768,
|
| 7 |
+
"width": 768
|
| 8 |
+
},
|
| 9 |
+
"do_center_crop": false,
|
| 10 |
+
"do_convert_rgb": null,
|
| 11 |
+
"do_normalize": true,
|
| 12 |
+
"do_rescale": true,
|
| 13 |
+
"do_resize": true,
|
| 14 |
+
"image_mean": [
|
| 15 |
+
0.485,
|
| 16 |
+
0.456,
|
| 17 |
+
0.406
|
| 18 |
+
],
|
| 19 |
+
"image_processor_type": "CLIPImageProcessor",
|
| 20 |
+
"image_seq_length": 577,
|
| 21 |
+
"image_std": [
|
| 22 |
+
0.229,
|
| 23 |
+
0.224,
|
| 24 |
+
0.225
|
| 25 |
+
],
|
| 26 |
+
"processor_class": "Florence2Processor",
|
| 27 |
+
"resample": 3,
|
| 28 |
+
"rescale_factor": 0.00392156862745098,
|
| 29 |
+
"size": {
|
| 30 |
+
"height": 768,
|
| 31 |
+
"width": 768
|
| 32 |
+
}
|
| 33 |
+
}
|
processing_florence2.py
ADDED
|
@@ -0,0 +1,1088 @@
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|
| 1 |
+
# coding=utf-8
|
| 2 |
+
# Copyright 2024 Microsoft and The HuggingFace Inc. team.
|
| 3 |
+
#
|
| 4 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 5 |
+
# you may not use this file except in compliance with the License.
|
| 6 |
+
# You may obtain a copy of the License at
|
| 7 |
+
#
|
| 8 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
| 9 |
+
#
|
| 10 |
+
# Unless required by applicable law or agreed to in writing, software
|
| 11 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 12 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 13 |
+
# See the License for the specific language governing permissions and
|
| 14 |
+
# limitations under the License.
|
| 15 |
+
"""
|
| 16 |
+
Processor class for Florence-2.
|
| 17 |
+
"""
|
| 18 |
+
|
| 19 |
+
import re
|
| 20 |
+
import logging
|
| 21 |
+
from typing import List, Optional, Union
|
| 22 |
+
import numpy as np
|
| 23 |
+
|
| 24 |
+
import torch
|
| 25 |
+
|
| 26 |
+
from transformers.feature_extraction_utils import BatchFeature
|
| 27 |
+
from transformers.image_utils import ImageInput, is_valid_image
|
| 28 |
+
from transformers.processing_utils import ProcessorMixin
|
| 29 |
+
from transformers.tokenization_utils_base import (
|
| 30 |
+
PaddingStrategy,
|
| 31 |
+
PreTokenizedInput,
|
| 32 |
+
TextInput,
|
| 33 |
+
TruncationStrategy,
|
| 34 |
+
)
|
| 35 |
+
from transformers.utils import TensorType
|
| 36 |
+
|
| 37 |
+
|
| 38 |
+
logger = logging.getLogger(__name__)
|
| 39 |
+
|
| 40 |
+
# Copied from transformers.models.idefics2.processing_idefics2.is_url
|
| 41 |
+
def is_url(val) -> bool:
|
| 42 |
+
return isinstance(val, str) and val.startswith("http")
|
| 43 |
+
|
| 44 |
+
# Copied from transformers.models.idefics2.processing_idefics2.is_image_or_image_url
|
| 45 |
+
def is_image_or_image_url(elem):
|
| 46 |
+
return is_url(elem) or is_valid_image(elem)
|
| 47 |
+
|
| 48 |
+
|
| 49 |
+
def _is_str_or_image(elem):
|
| 50 |
+
return isinstance(elem, (str)) or is_image_or_image_url(elem)
|
| 51 |
+
|
| 52 |
+
|
| 53 |
+
class Florence2Processor(ProcessorMixin):
|
| 54 |
+
r"""
|
| 55 |
+
Constructs a Florence2 processor which wraps a Florence2 image processor and a Florence2 tokenizer into a single processor.
|
| 56 |
+
|
| 57 |
+
[`Florence2Processor`] offers all the functionalities of [`CLIPImageProcessor`] and [`BartTokenizerFast`]. See the
|
| 58 |
+
[`~Florence2Processor.__call__`] and [`~Florence2Processor.decode`] for more information.
|
| 59 |
+
|
| 60 |
+
Args:
|
| 61 |
+
image_processor ([`CLIPImageProcessor`], *optional*):
|
| 62 |
+
The image processor is a required input.
|
| 63 |
+
tokenizer ([`BartTokenizerFast`], *optional*):
|
| 64 |
+
The tokenizer is a required input.
|
| 65 |
+
"""
|
| 66 |
+
|
| 67 |
+
attributes = ["image_processor", "tokenizer"]
|
| 68 |
+
image_processor_class = "CLIPImageProcessor"
|
| 69 |
+
tokenizer_class = ("BartTokenizer", "BartTokenizerFast")
|
| 70 |
+
|
| 71 |
+
def __init__(
|
| 72 |
+
self,
|
| 73 |
+
image_processor=None,
|
| 74 |
+
tokenizer=None,
|
| 75 |
+
):
|
| 76 |
+
if image_processor is None:
|
| 77 |
+
raise ValueError("You need to specify an `image_processor`.")
|
| 78 |
+
if tokenizer is None:
|
| 79 |
+
raise ValueError("You need to specify a `tokenizer`.")
|
| 80 |
+
if not hasattr(image_processor, "image_seq_length"):
|
| 81 |
+
raise ValueError("Image processor is missing an `image_seq_length` attribute.")
|
| 82 |
+
|
| 83 |
+
self.image_seq_length = image_processor.image_seq_length
|
| 84 |
+
|
| 85 |
+
tokens_to_add = {
|
| 86 |
+
'additional_special_tokens': \
|
| 87 |
+
tokenizer.additional_special_tokens + \
|
| 88 |
+
['<od>', '</od>', '<ocr>', '</ocr>'] + \
|
| 89 |
+
[f'<loc_{x}>' for x in range(1000)] + \
|
| 90 |
+
['<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>']
|
| 91 |
+
}
|
| 92 |
+
tokenizer.add_special_tokens(tokens_to_add)
|
| 93 |
+
|
| 94 |
+
self.tasks_answer_post_processing_type = {
|
| 95 |
+
'<OCR>': 'pure_text',
|
| 96 |
+
'<OCR_WITH_REGION>': 'ocr',
|
| 97 |
+
'<CAPTION>': 'pure_text',
|
| 98 |
+
'<DETAILED_CAPTION>': 'pure_text',
|
| 99 |
+
'<MORE_DETAILED_CAPTION>': 'pure_text',
|
| 100 |
+
'<OD>': 'description_with_bboxes',
|
| 101 |
+
'<DENSE_REGION_CAPTION>': 'description_with_bboxes',
|
| 102 |
+
'<CAPTION_TO_PHRASE_GROUNDING>': "phrase_grounding",
|
| 103 |
+
'<REFERRING_EXPRESSION_SEGMENTATION>': 'polygons',
|
| 104 |
+
'<REGION_TO_SEGMENTATION>': 'polygons',
|
| 105 |
+
'<OPEN_VOCABULARY_DETECTION>': 'description_with_bboxes_or_polygons',
|
| 106 |
+
'<REGION_TO_CATEGORY>': 'pure_text',
|
| 107 |
+
'<REGION_TO_DESCRIPTION>': 'pure_text',
|
| 108 |
+
'<REGION_TO_OCR>': 'pure_text',
|
| 109 |
+
'<REGION_PROPOSAL>': 'bboxes'
|
| 110 |
+
}
|
| 111 |
+
|
| 112 |
+
self.task_prompts_without_inputs = {
|
| 113 |
+
'<OCR>': 'What is the text in the image?',
|
| 114 |
+
'<OCR_WITH_REGION>': 'What is the text in the image, with regions?',
|
| 115 |
+
'<CAPTION>': 'What does the image describe?',
|
| 116 |
+
'<DETAILED_CAPTION>': 'Describe in detail what is shown in the image.',
|
| 117 |
+
'<MORE_DETAILED_CAPTION>': 'Describe with a paragraph what is shown in the image.',
|
| 118 |
+
'<OD>': 'Locate the objects with category name in the image.',
|
| 119 |
+
'<DENSE_REGION_CAPTION>': 'Locate the objects in the image, with their descriptions.',
|
| 120 |
+
'<REGION_PROPOSAL>': 'Locate the region proposals in the image.'
|
| 121 |
+
}
|
| 122 |
+
|
| 123 |
+
self.task_prompts_with_input = {
|
| 124 |
+
'<CAPTION_TO_PHRASE_GROUNDING>': "Locate the phrases in the caption: {input}",
|
| 125 |
+
'<REFERRING_EXPRESSION_SEGMENTATION>': 'Locate {input} in the image with mask',
|
| 126 |
+
'<REGION_TO_SEGMENTATION>': 'What is the polygon mask of region {input}',
|
| 127 |
+
'<OPEN_VOCABULARY_DETECTION>': 'Locate {input} in the image.',
|
| 128 |
+
'<REGION_TO_CATEGORY>': 'What is the region {input}?',
|
| 129 |
+
'<REGION_TO_DESCRIPTION>': 'What does the region {input} describe?',
|
| 130 |
+
'<REGION_TO_OCR>': 'What text is in the region {input}?',
|
| 131 |
+
}
|
| 132 |
+
|
| 133 |
+
self.post_processor = Florence2PostProcesser(tokenizer=tokenizer)
|
| 134 |
+
|
| 135 |
+
|
| 136 |
+
super().__init__(image_processor, tokenizer)
|
| 137 |
+
|
| 138 |
+
def _construct_prompts(self, text):
|
| 139 |
+
# replace the task tokens with the task prompts if task token is in the text
|
| 140 |
+
prompts = []
|
| 141 |
+
for _text in text:
|
| 142 |
+
# 1. fixed task prompts without additional inputs
|
| 143 |
+
for task_token, task_prompt in self.task_prompts_without_inputs.items():
|
| 144 |
+
if task_token in _text:
|
| 145 |
+
assert _text == task_token, f"Task token {task_token} should be the only token in the text."
|
| 146 |
+
_text = task_prompt
|
| 147 |
+
break
|
| 148 |
+
# 2. task prompts with additional inputs
|
| 149 |
+
for task_token, task_prompt in self.task_prompts_with_input.items():
|
| 150 |
+
if task_token in _text:
|
| 151 |
+
_text = task_prompt.format(input=_text.replace(task_token, ''))
|
| 152 |
+
break
|
| 153 |
+
prompts.append(_text)
|
| 154 |
+
return prompts
|
| 155 |
+
|
| 156 |
+
def __call__(
|
| 157 |
+
self,
|
| 158 |
+
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
|
| 159 |
+
images: ImageInput = None,
|
| 160 |
+
tokenize_newline_separately: bool = True,
|
| 161 |
+
padding: Union[bool, str, PaddingStrategy] = False,
|
| 162 |
+
truncation: Union[bool, str, TruncationStrategy] = None,
|
| 163 |
+
max_length=None,
|
| 164 |
+
return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
|
| 165 |
+
do_resize: bool = None,
|
| 166 |
+
do_normalize: bool = None,
|
| 167 |
+
image_mean: Optional[Union[float, List[float]]] = None,
|
| 168 |
+
image_std: Optional[Union[float, List[float]]] = None,
|
| 169 |
+
data_format: Optional["ChannelDimension"] = "channels_first", # noqa: F821
|
| 170 |
+
input_data_format: Optional[
|
| 171 |
+
Union[str, "ChannelDimension"] # noqa: F821
|
| 172 |
+
] = None,
|
| 173 |
+
resample: "PILImageResampling" = None, # noqa: F821
|
| 174 |
+
do_convert_rgb: bool = None,
|
| 175 |
+
do_thumbnail: bool = None,
|
| 176 |
+
do_align_long_axis: bool = None,
|
| 177 |
+
do_rescale: bool = None,
|
| 178 |
+
) -> BatchFeature:
|
| 179 |
+
"""
|
| 180 |
+
Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
|
| 181 |
+
and `kwargs` arguments to BartTokenizerFast's [`~BartTokenizerFast.__call__`] if `text` is not `None` to encode
|
| 182 |
+
the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
|
| 183 |
+
CLIPImageProcessor's [`~CLIPImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring
|
| 184 |
+
of the above two methods for more information.
|
| 185 |
+
|
| 186 |
+
Args:
|
| 187 |
+
text (`str`, `List[str]`, `List[List[str]]`):
|
| 188 |
+
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
|
| 189 |
+
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
|
| 190 |
+
`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
|
| 191 |
+
images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
|
| 192 |
+
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
|
| 193 |
+
tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
|
| 194 |
+
number of channels, H and W are image height and width.
|
| 195 |
+
tokenize_newline_separately (`bool`, defaults to `True`):
|
| 196 |
+
Adds a separately tokenized '\n' at the end of the prompt.
|
| 197 |
+
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
|
| 198 |
+
Select a strategy to pad the returned sequences (according to the model's padding side and padding
|
| 199 |
+
index) among:
|
| 200 |
+
- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
|
| 201 |
+
sequence if provided).
|
| 202 |
+
- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
|
| 203 |
+
acceptable input length for the model if that argument is not provided.
|
| 204 |
+
- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
|
| 205 |
+
lengths).
|
| 206 |
+
max_length (`int`, *optional*):
|
| 207 |
+
Maximum length of the returned list and optionally padding length (see above).
|
| 208 |
+
truncation (`bool`, *optional*):
|
| 209 |
+
Activates truncation to cut input sequences longer than `max_length` to `max_length`.
|
| 210 |
+
return_tensors (`str` or [`~utils.TensorType`], *optional*):
|
| 211 |
+
If set, will return tensors of a particular framework. Acceptable values are:
|
| 212 |
+
|
| 213 |
+
- `'tf'`: Return TensorFlow `tf.constant` objects.
|
| 214 |
+
- `'pt'`: Return PyTorch `torch.Tensor` objects.
|
| 215 |
+
- `'np'`: Return NumPy `np.ndarray` objects.
|
| 216 |
+
- `'jax'`: Return JAX `jnp.ndarray` objects.
|
| 217 |
+
|
| 218 |
+
Returns:
|
| 219 |
+
[`BatchFeature`]: A [`BatchFeature`] with the following fields:
|
| 220 |
+
|
| 221 |
+
- **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. If `suffix`
|
| 222 |
+
is provided, the `input_ids` will also contain the suffix input ids.
|
| 223 |
+
- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
|
| 224 |
+
`return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
|
| 225 |
+
`None`).
|
| 226 |
+
- **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
|
| 227 |
+
- **labels** -- Labels compatible with training if `suffix` is not None
|
| 228 |
+
"""
|
| 229 |
+
|
| 230 |
+
return_token_type_ids = False
|
| 231 |
+
|
| 232 |
+
if images is None:
|
| 233 |
+
raise ValueError("`images` are expected as arguments to a `Florence2Processor` instance.")
|
| 234 |
+
if text is None:
|
| 235 |
+
logger.warning_once(
|
| 236 |
+
"You are using Florence-2 without a text prompt."
|
| 237 |
+
)
|
| 238 |
+
text = ""
|
| 239 |
+
|
| 240 |
+
if isinstance(text, List) and isinstance(images, List):
|
| 241 |
+
if len(images) < len(text):
|
| 242 |
+
raise ValueError(
|
| 243 |
+
f"Received {len(images)} images for {len(text)} prompts. Each prompt should be associated with an image."
|
| 244 |
+
)
|
| 245 |
+
if _is_str_or_image(text):
|
| 246 |
+
text = [text]
|
| 247 |
+
elif isinstance(text, list) and _is_str_or_image(text[0]):
|
| 248 |
+
pass
|
| 249 |
+
|
| 250 |
+
pixel_values = self.image_processor(
|
| 251 |
+
images,
|
| 252 |
+
do_resize=do_resize,
|
| 253 |
+
do_normalize=do_normalize,
|
| 254 |
+
return_tensors=return_tensors,
|
| 255 |
+
image_mean=image_mean,
|
| 256 |
+
image_std=image_std,
|
| 257 |
+
input_data_format=input_data_format,
|
| 258 |
+
data_format=data_format,
|
| 259 |
+
resample=resample,
|
| 260 |
+
do_convert_rgb=do_convert_rgb,
|
| 261 |
+
)["pixel_values"]
|
| 262 |
+
|
| 263 |
+
if max_length is not None:
|
| 264 |
+
max_length -= self.image_seq_length # max_length has to account for the image tokens
|
| 265 |
+
|
| 266 |
+
text = self._construct_prompts(text)
|
| 267 |
+
|
| 268 |
+
inputs = self.tokenizer(
|
| 269 |
+
text,
|
| 270 |
+
return_tensors=return_tensors,
|
| 271 |
+
padding=padding,
|
| 272 |
+
max_length=max_length,
|
| 273 |
+
truncation=truncation,
|
| 274 |
+
return_token_type_ids=return_token_type_ids,
|
| 275 |
+
)
|
| 276 |
+
|
| 277 |
+
return_data = {**inputs, "pixel_values": pixel_values}
|
| 278 |
+
|
| 279 |
+
if return_token_type_ids:
|
| 280 |
+
labels = inputs["input_ids"].masked_fill(inputs["token_type_ids"] == 0, -100)
|
| 281 |
+
return_data.update({"labels": labels})
|
| 282 |
+
return BatchFeature(data=return_data)
|
| 283 |
+
|
| 284 |
+
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Florence2
|
| 285 |
+
def batch_decode(self, *args, **kwargs):
|
| 286 |
+
"""
|
| 287 |
+
This method forwards all its arguments to BartTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
|
| 288 |
+
refer to the docstring of this method for more information.
|
| 289 |
+
"""
|
| 290 |
+
return self.tokenizer.batch_decode(*args, **kwargs)
|
| 291 |
+
|
| 292 |
+
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Florence2
|
| 293 |
+
def decode(self, *args, **kwargs):
|
| 294 |
+
"""
|
| 295 |
+
This method forwards all its arguments to BartTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
|
| 296 |
+
the docstring of this method for more information.
|
| 297 |
+
"""
|
| 298 |
+
return self.tokenizer.decode(*args, **kwargs)
|
| 299 |
+
|
| 300 |
+
@property
|
| 301 |
+
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names with CLIP->Florence2
|
| 302 |
+
def model_input_names(self):
|
| 303 |
+
tokenizer_input_names = self.tokenizer.model_input_names
|
| 304 |
+
image_processor_input_names = self.image_processor.model_input_names
|
| 305 |
+
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
|
| 306 |
+
|
| 307 |
+
def post_process_generation(self, text, task, image_size):
|
| 308 |
+
"""
|
| 309 |
+
Post-process the output of the model to each of the task outputs.
|
| 310 |
+
|
| 311 |
+
Args:
|
| 312 |
+
text (`str`): The text to post-process.
|
| 313 |
+
task (`str`): The task to post-process the text for.
|
| 314 |
+
image_size (`Tuple[int, int]`): The size of the image. height x width.
|
| 315 |
+
"""
|
| 316 |
+
|
| 317 |
+
task_answer_post_processing_type = self.tasks_answer_post_processing_type.get(task, 'pure_text')
|
| 318 |
+
task_answer = self.post_processor(
|
| 319 |
+
text=text,
|
| 320 |
+
image_size=image_size,
|
| 321 |
+
parse_tasks=task_answer_post_processing_type,
|
| 322 |
+
)[task_answer_post_processing_type]
|
| 323 |
+
|
| 324 |
+
if task_answer_post_processing_type == 'pure_text':
|
| 325 |
+
final_answer = task_answer
|
| 326 |
+
# remove the special tokens
|
| 327 |
+
final_answer = final_answer.replace('<s>', '').replace('</s>', '')
|
| 328 |
+
elif task_answer_post_processing_type in ['od', 'description_with_bboxes', 'bboxes']:
|
| 329 |
+
od_instances = task_answer
|
| 330 |
+
bboxes_od = [_od_instance['bbox'] for _od_instance in od_instances]
|
| 331 |
+
labels_od = [str(_od_instance['cat_name']) for _od_instance in od_instances]
|
| 332 |
+
final_answer = {'bboxes': bboxes_od, 'labels': labels_od}
|
| 333 |
+
elif task_answer_post_processing_type in ['ocr']:
|
| 334 |
+
bboxes = [_od_instance['quad_box'] for _od_instance in task_answer]
|
| 335 |
+
labels = [str(_od_instance['text']) for _od_instance in task_answer]
|
| 336 |
+
final_answer = {'quad_boxes': bboxes, 'labels': labels}
|
| 337 |
+
elif task_answer_post_processing_type in ['phrase_grounding']:
|
| 338 |
+
bboxes = []
|
| 339 |
+
labels = []
|
| 340 |
+
for _grounded_phrase in task_answer:
|
| 341 |
+
for _bbox in _grounded_phrase['bbox']:
|
| 342 |
+
bboxes.append(_bbox)
|
| 343 |
+
labels.append(_grounded_phrase['cat_name'])
|
| 344 |
+
final_answer = {'bboxes': bboxes, 'labels': labels}
|
| 345 |
+
elif task_answer_post_processing_type in ['description_with_polygons', 'polygons']:
|
| 346 |
+
labels = []
|
| 347 |
+
polygons = []
|
| 348 |
+
for result in task_answer:
|
| 349 |
+
label = result['cat_name']
|
| 350 |
+
_polygons = result['polygons']
|
| 351 |
+
labels.append(label)
|
| 352 |
+
polygons.append(_polygons)
|
| 353 |
+
final_answer = {'polygons': polygons, 'labels': labels}
|
| 354 |
+
elif task_answer_post_processing_type in ['description_with_bboxes_or_polygons']:
|
| 355 |
+
bboxes = []
|
| 356 |
+
bboxes_labels = []
|
| 357 |
+
polygons = []
|
| 358 |
+
polygons_labels = []
|
| 359 |
+
for result in task_answer:
|
| 360 |
+
label = result['cat_name']
|
| 361 |
+
if 'polygons' in result:
|
| 362 |
+
_polygons = result['polygons']
|
| 363 |
+
polygons.append(_polygons)
|
| 364 |
+
polygons_labels.append(label)
|
| 365 |
+
else:
|
| 366 |
+
_bbox = result['bbox']
|
| 367 |
+
bboxes.append(_bbox)
|
| 368 |
+
bboxes_labels.append(label)
|
| 369 |
+
final_answer = {'bboxes': bboxes, 'bboxes_labels': bboxes_labels, 'polygons': polygons, 'polygons_labels': polygons_labels}
|
| 370 |
+
else:
|
| 371 |
+
raise ValueError('Unknown task answer post processing type: {}'.format(task_answer_post_processing_type))
|
| 372 |
+
|
| 373 |
+
final_answer = {
|
| 374 |
+
task: final_answer}
|
| 375 |
+
return final_answer
|
| 376 |
+
|
| 377 |
+
class BoxQuantizer(object):
|
| 378 |
+
def __init__(self, mode, bins):
|
| 379 |
+
self.mode = mode
|
| 380 |
+
self.bins = bins
|
| 381 |
+
|
| 382 |
+
def quantize(self, boxes: torch.Tensor, size):
|
| 383 |
+
bins_w, bins_h = self.bins # Quantization bins.
|
| 384 |
+
size_w, size_h = size # Original image size.
|
| 385 |
+
size_per_bin_w = size_w / bins_w
|
| 386 |
+
size_per_bin_h = size_h / bins_h
|
| 387 |
+
xmin, ymin, xmax, ymax = boxes.split(1, dim=-1) # Shape: 4 * [N, 1].
|
| 388 |
+
|
| 389 |
+
if self.mode == 'floor':
|
| 390 |
+
quantized_xmin = (
|
| 391 |
+
xmin / size_per_bin_w).floor().clamp(0, bins_w - 1)
|
| 392 |
+
quantized_ymin = (
|
| 393 |
+
ymin / size_per_bin_h).floor().clamp(0, bins_h - 1)
|
| 394 |
+
quantized_xmax = (
|
| 395 |
+
xmax / size_per_bin_w).floor().clamp(0, bins_w - 1)
|
| 396 |
+
quantized_ymax = (
|
| 397 |
+
ymax / size_per_bin_h).floor().clamp(0, bins_h - 1)
|
| 398 |
+
|
| 399 |
+
elif self.mode == 'round':
|
| 400 |
+
raise NotImplementedError()
|
| 401 |
+
|
| 402 |
+
else:
|
| 403 |
+
raise ValueError('Incorrect quantization type.')
|
| 404 |
+
|
| 405 |
+
quantized_boxes = torch.cat(
|
| 406 |
+
(quantized_xmin, quantized_ymin, quantized_xmax, quantized_ymax), dim=-1
|
| 407 |
+
).int()
|
| 408 |
+
|
| 409 |
+
return quantized_boxes
|
| 410 |
+
|
| 411 |
+
def dequantize(self, boxes: torch.Tensor, size):
|
| 412 |
+
bins_w, bins_h = self.bins # Quantization bins.
|
| 413 |
+
size_w, size_h = size # Original image size.
|
| 414 |
+
size_per_bin_w = size_w / bins_w
|
| 415 |
+
size_per_bin_h = size_h / bins_h
|
| 416 |
+
xmin, ymin, xmax, ymax = boxes.split(1, dim=-1) # Shape: 4 * [N, 1].
|
| 417 |
+
|
| 418 |
+
if self.mode == 'floor':
|
| 419 |
+
# Add 0.5 to use the center position of the bin as the coordinate.
|
| 420 |
+
dequantized_xmin = (xmin + 0.5) * size_per_bin_w
|
| 421 |
+
dequantized_ymin = (ymin + 0.5) * size_per_bin_h
|
| 422 |
+
dequantized_xmax = (xmax + 0.5) * size_per_bin_w
|
| 423 |
+
dequantized_ymax = (ymax + 0.5) * size_per_bin_h
|
| 424 |
+
|
| 425 |
+
elif self.mode == 'round':
|
| 426 |
+
raise NotImplementedError()
|
| 427 |
+
|
| 428 |
+
else:
|
| 429 |
+
raise ValueError('Incorrect quantization type.')
|
| 430 |
+
|
| 431 |
+
dequantized_boxes = torch.cat(
|
| 432 |
+
(dequantized_xmin, dequantized_ymin,
|
| 433 |
+
dequantized_xmax, dequantized_ymax), dim=-1
|
| 434 |
+
)
|
| 435 |
+
|
| 436 |
+
return dequantized_boxes
|
| 437 |
+
|
| 438 |
+
|
| 439 |
+
class CoordinatesQuantizer(object):
|
| 440 |
+
"""
|
| 441 |
+
Quantize coornidates (Nx2)
|
| 442 |
+
"""
|
| 443 |
+
|
| 444 |
+
def __init__(self, mode, bins):
|
| 445 |
+
self.mode = mode
|
| 446 |
+
self.bins = bins
|
| 447 |
+
|
| 448 |
+
def quantize(self, coordinates: torch.Tensor, size):
|
| 449 |
+
bins_w, bins_h = self.bins # Quantization bins.
|
| 450 |
+
size_w, size_h = size # Original image size.
|
| 451 |
+
size_per_bin_w = size_w / bins_w
|
| 452 |
+
size_per_bin_h = size_h / bins_h
|
| 453 |
+
assert coordinates.shape[-1] == 2, 'coordinates should be shape (N, 2)'
|
| 454 |
+
x, y = coordinates.split(1, dim=-1) # Shape: 4 * [N, 1].
|
| 455 |
+
|
| 456 |
+
if self.mode == 'floor':
|
| 457 |
+
quantized_x = (x / size_per_bin_w).floor().clamp(0, bins_w - 1)
|
| 458 |
+
quantized_y = (y / size_per_bin_h).floor().clamp(0, bins_h - 1)
|
| 459 |
+
|
| 460 |
+
elif self.mode == 'round':
|
| 461 |
+
raise NotImplementedError()
|
| 462 |
+
|
| 463 |
+
else:
|
| 464 |
+
raise ValueError('Incorrect quantization type.')
|
| 465 |
+
|
| 466 |
+
quantized_coordinates = torch.cat(
|
| 467 |
+
(quantized_x, quantized_y), dim=-1
|
| 468 |
+
).int()
|
| 469 |
+
|
| 470 |
+
return quantized_coordinates
|
| 471 |
+
|
| 472 |
+
def dequantize(self, coordinates: torch.Tensor, size):
|
| 473 |
+
bins_w, bins_h = self.bins # Quantization bins.
|
| 474 |
+
size_w, size_h = size # Original image size.
|
| 475 |
+
size_per_bin_w = size_w / bins_w
|
| 476 |
+
size_per_bin_h = size_h / bins_h
|
| 477 |
+
assert coordinates.shape[-1] == 2, 'coordinates should be shape (N, 2)'
|
| 478 |
+
x, y = coordinates.split(1, dim=-1) # Shape: 4 * [N, 1].
|
| 479 |
+
|
| 480 |
+
if self.mode == 'floor':
|
| 481 |
+
# Add 0.5 to use the center position of the bin as the coordinate.
|
| 482 |
+
dequantized_x = (x + 0.5) * size_per_bin_w
|
| 483 |
+
dequantized_y = (y + 0.5) * size_per_bin_h
|
| 484 |
+
|
| 485 |
+
elif self.mode == 'round':
|
| 486 |
+
raise NotImplementedError()
|
| 487 |
+
|
| 488 |
+
else:
|
| 489 |
+
raise ValueError('Incorrect quantization type.')
|
| 490 |
+
|
| 491 |
+
dequantized_coordinates = torch.cat(
|
| 492 |
+
(dequantized_x, dequantized_y), dim=-1
|
| 493 |
+
)
|
| 494 |
+
|
| 495 |
+
return dequantized_coordinates
|
| 496 |
+
|
| 497 |
+
|
| 498 |
+
class Florence2PostProcesser(object):
|
| 499 |
+
"""
|
| 500 |
+
Florence-2 post process for converting text prediction to various tasks results.
|
| 501 |
+
|
| 502 |
+
Args:
|
| 503 |
+
config: A dict of configs.
|
| 504 |
+
tokenizer: A tokenizer for decoding text to spans.
|
| 505 |
+
sample config:
|
| 506 |
+
UNIFIED_POST_PROCESS:
|
| 507 |
+
# commom configs
|
| 508 |
+
NUM_BBOX_HEIGHT_BINS: 1000
|
| 509 |
+
NUM_BBOX_WIDTH_BINS: 1000
|
| 510 |
+
COORDINATES_HEIGHT_BINS: 1000
|
| 511 |
+
COORDINATES_WIDTH_BINS: 1000
|
| 512 |
+
# task specific configs, override the common configs
|
| 513 |
+
PRASE_TASKS:
|
| 514 |
+
- TASK_NAME: 'video_dense_caption'
|
| 515 |
+
PATTERN: 'r<time_(\d+)><time_(\d+)>([a-zA-Z0-9 ]+)'
|
| 516 |
+
SCORE_MODE: 'avg_cat_name_scores'
|
| 517 |
+
NUM_BINS: 100
|
| 518 |
+
- TASK_NAME: 'od'
|
| 519 |
+
PATTERN: 'r<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>([a-zA-Z0-9 ]+)'
|
| 520 |
+
SCORE_MODE: 'avg_cat_name_scores'
|
| 521 |
+
|
| 522 |
+
Returns:
|
| 523 |
+
parsed_dict (dict): A dict of parsed results.
|
| 524 |
+
"""
|
| 525 |
+
def __init__(
|
| 526 |
+
self,
|
| 527 |
+
tokenizer=None
|
| 528 |
+
):
|
| 529 |
+
parse_tasks = []
|
| 530 |
+
parse_task_configs = {}
|
| 531 |
+
config = self._create_default_config()
|
| 532 |
+
for task in config['PARSE_TASKS']:
|
| 533 |
+
parse_tasks.append(task['TASK_NAME'])
|
| 534 |
+
parse_task_configs[task['TASK_NAME']] = task
|
| 535 |
+
|
| 536 |
+
self.config = config
|
| 537 |
+
self.parse_tasks = parse_tasks
|
| 538 |
+
self.parse_tasks_configs = parse_task_configs
|
| 539 |
+
|
| 540 |
+
self.tokenizer = tokenizer
|
| 541 |
+
if self.tokenizer is not None:
|
| 542 |
+
self.all_special_tokens = set(self.tokenizer.all_special_tokens)
|
| 543 |
+
|
| 544 |
+
self.init_quantizers()
|
| 545 |
+
self.black_list_of_phrase_grounding = self._create_black_list_of_phrase_grounding()
|
| 546 |
+
|
| 547 |
+
def _create_black_list_of_phrase_grounding(self):
|
| 548 |
+
black_list = {}
|
| 549 |
+
|
| 550 |
+
if 'phrase_grounding' in self.parse_tasks and self.parse_tasks_configs['phrase_grounding']['FILTER_BY_BLACK_LIST']:
|
| 551 |
+
black_list = set(
|
| 552 |
+
['it', 'I', 'me', 'mine',
|
| 553 |
+
'you', 'your', 'yours',
|
| 554 |
+
'he', 'him', 'his',
|
| 555 |
+
'she', 'her', 'hers',
|
| 556 |
+
'they', 'them', 'their', 'theirs',
|
| 557 |
+
'one', 'oneself',
|
| 558 |
+
'we', 'us', 'our', 'ours',
|
| 559 |
+
'you', 'your', 'yours',
|
| 560 |
+
'they', 'them', 'their', 'theirs',
|
| 561 |
+
'mine', 'yours', 'his', 'hers', 'its',
|
| 562 |
+
'ours', 'yours', 'theirs',
|
| 563 |
+
'myself', 'yourself', 'himself', 'herself', 'itself',
|
| 564 |
+
'ourselves', 'yourselves', 'themselves',
|
| 565 |
+
'this', 'that',
|
| 566 |
+
'these', 'those',
|
| 567 |
+
'who', 'whom', 'whose', 'which', 'what',
|
| 568 |
+
'who', 'whom', 'whose', 'which', 'that',
|
| 569 |
+
'all', 'another', 'any', 'anybody', 'anyone', 'anything',
|
| 570 |
+
'each', 'everybody', 'everyone', 'everything',
|
| 571 |
+
'few', 'many', 'nobody', 'none', 'one', 'several',
|
| 572 |
+
'some', 'somebody', 'someone', 'something',
|
| 573 |
+
'each other', 'one another',
|
| 574 |
+
'myself', 'yourself', 'himself', 'herself', 'itself',
|
| 575 |
+
'ourselves', 'yourselves', 'themselves',
|
| 576 |
+
'the image', 'image', 'images', 'the', 'a', 'an', 'a group',
|
| 577 |
+
'other objects', 'lots', 'a set',
|
| 578 |
+
]
|
| 579 |
+
)
|
| 580 |
+
|
| 581 |
+
return black_list
|
| 582 |
+
|
| 583 |
+
def _create_default_config(self):
|
| 584 |
+
config = {
|
| 585 |
+
'NUM_BBOX_HEIGHT_BINS': 1000,
|
| 586 |
+
'NUM_BBOX_WIDTH_BINS': 1000,
|
| 587 |
+
'BOX_QUANTIZATION_MODE': 'floor',
|
| 588 |
+
'COORDINATES_HEIGHT_BINS': 1000,
|
| 589 |
+
'COORDINATES_WIDTH_BINS': 1000,
|
| 590 |
+
'COORDINATES_QUANTIZATION_MODE': 'floor',
|
| 591 |
+
'PARSE_TASKS': [
|
| 592 |
+
{
|
| 593 |
+
'TASK_NAME': 'od',
|
| 594 |
+
'PATTERN': r'([a-zA-Z0-9 ]+)<loc_(\\d+)><loc_(\\d+)><loc_(\\d+)><loc_(\\d+)>'
|
| 595 |
+
},
|
| 596 |
+
{
|
| 597 |
+
'TASK_NAME': 'ocr',
|
| 598 |
+
'PATTERN': r'(.+?)<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>',
|
| 599 |
+
'AREA_THRESHOLD': 0.00
|
| 600 |
+
},
|
| 601 |
+
{
|
| 602 |
+
'TASK_NAME': 'phrase_grounding',
|
| 603 |
+
'FILTER_BY_BLACK_LIST': True
|
| 604 |
+
},
|
| 605 |
+
{
|
| 606 |
+
'TASK_NAME': 'pure_text',
|
| 607 |
+
},
|
| 608 |
+
{
|
| 609 |
+
'TASK_NAME': 'description_with_bboxes',
|
| 610 |
+
},
|
| 611 |
+
{
|
| 612 |
+
'TASK_NAME': 'description_with_polygons',
|
| 613 |
+
},
|
| 614 |
+
{
|
| 615 |
+
'TASK_NAME': 'polygons',
|
| 616 |
+
},
|
| 617 |
+
{
|
| 618 |
+
'TASK_NAME': 'bboxes',
|
| 619 |
+
},
|
| 620 |
+
{
|
| 621 |
+
'TASK_NAME': 'description_with_bboxes_or_polygons',
|
| 622 |
+
}
|
| 623 |
+
]
|
| 624 |
+
}
|
| 625 |
+
|
| 626 |
+
return config
|
| 627 |
+
|
| 628 |
+
def init_quantizers(self):
|
| 629 |
+
# we have box_quantizer (od, grounding) and coordinates_quantizer (ocr, referring_segmentation)
|
| 630 |
+
num_bbox_height_bins = self.config.get('NUM_BBOX_HEIGHT_BINS', 1000)
|
| 631 |
+
num_bbox_width_bins = self.config.get('NUM_BBOX_WIDTH_BINS', 1000)
|
| 632 |
+
box_quantization_mode = self.config.get('BOX_QUANTIZATION_MODE', 'floor')
|
| 633 |
+
self.box_quantizer = BoxQuantizer(
|
| 634 |
+
box_quantization_mode,
|
| 635 |
+
(num_bbox_width_bins, num_bbox_height_bins),
|
| 636 |
+
)
|
| 637 |
+
|
| 638 |
+
num_bbox_height_bins = self.config['COORDINATES_HEIGHT_BINS'] if 'COORDINATES_HEIGHT_BINS' in self.config else self.config.get('NUM_BBOX_HEIGHT_BINS', 1000)
|
| 639 |
+
num_bbox_width_bins = self.config['COORDINATES_WIDTH_BINS'] if 'COORDINATES_WIDTH_BINS' in self.config else self.config.get('NUM_BBOX_WIDTH_BINS', 1000)
|
| 640 |
+
box_quantization_mode = self.config.get('COORDINATES_QUANTIZATION_MODE') if 'COORDINATES_QUANTIZATION_MODE' in self.config else self.config.get('BOX_QUANTIZATION_MODE', 'floor')
|
| 641 |
+
self.coordinates_quantizer = CoordinatesQuantizer(
|
| 642 |
+
box_quantization_mode,
|
| 643 |
+
(num_bbox_width_bins, num_bbox_height_bins),
|
| 644 |
+
)
|
| 645 |
+
|
| 646 |
+
def decode_with_spans(self, tokenizer, token_ids):
|
| 647 |
+
filtered_tokens = tokenizer.convert_ids_to_tokens(
|
| 648 |
+
token_ids, skip_special_tokens=False)
|
| 649 |
+
assert len(filtered_tokens) == len(token_ids)
|
| 650 |
+
|
| 651 |
+
# To avoid mixing byte-level and unicode for byte-level BPT
|
| 652 |
+
# we need to build string separately for added tokens and byte-level tokens
|
| 653 |
+
# cf. https://github.com/huggingface/transformers/issues/1133
|
| 654 |
+
sub_texts = []
|
| 655 |
+
for token in filtered_tokens:
|
| 656 |
+
if token in self.all_special_tokens:
|
| 657 |
+
sub_texts.append(token)
|
| 658 |
+
else:
|
| 659 |
+
if isinstance(tokenizer, (BartTokenizer, BartTokenizerFast)):
|
| 660 |
+
sub_text = tokenizer.convert_tokens_to_string([token])
|
| 661 |
+
elif isinstance(tokenizer, (T5Tokenizer, T5TokenizerFast)):
|
| 662 |
+
# Ref: https://github.com/google/sentencepiece#whitespace-is-treated-as-a-basic-symbol
|
| 663 |
+
# Note: Do not strip sub_text as it may have functional whitespace
|
| 664 |
+
sub_text = token.replace('▁', ' ')
|
| 665 |
+
else:
|
| 666 |
+
raise ValueError(f'type {type(tokenizer)} not supported')
|
| 667 |
+
sub_texts.append(sub_text)
|
| 668 |
+
|
| 669 |
+
text = ''
|
| 670 |
+
spans = []
|
| 671 |
+
for sub_text in sub_texts:
|
| 672 |
+
span = (len(text), len(text) + len(sub_text)) # [start index, end index).
|
| 673 |
+
text += sub_text
|
| 674 |
+
spans.append(span)
|
| 675 |
+
|
| 676 |
+
# Text format:
|
| 677 |
+
# 1. T5Tokenizer/T5TokenizerFast:
|
| 678 |
+
# "<loc_1><loc_2><loc_3><loc_4> transplanting dog<loc_1><loc_2><loc_3><loc_4> cat</s>"
|
| 679 |
+
# Equivalent to t5_tokenizer.decode(input_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False, spaces_between_special_tokens=False)
|
| 680 |
+
# 2. BartTokenizer (need to double check):
|
| 681 |
+
# "<s><loc_1><loc_2><loc_3><loc_4>transplanting dog<loc_1><loc_2><loc_3><loc_4>cat</s>"
|
| 682 |
+
# Equivalent to bart_tokenizer.decode(input_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False, spaces_between_special_tokens=False)
|
| 683 |
+
return text, spans
|
| 684 |
+
|
| 685 |
+
def parse_od_from_text_and_spans(
|
| 686 |
+
self,
|
| 687 |
+
text,
|
| 688 |
+
pattern,
|
| 689 |
+
image_size,
|
| 690 |
+
phrase_centric=False
|
| 691 |
+
):
|
| 692 |
+
parsed = list(re.finditer(pattern, text))
|
| 693 |
+
|
| 694 |
+
instances = []
|
| 695 |
+
for i in range(len(parsed)):
|
| 696 |
+
# Prepare instance.
|
| 697 |
+
instance = {}
|
| 698 |
+
|
| 699 |
+
if phrase_centric:
|
| 700 |
+
bbox_bins = [int(parsed[i].group(j)) for j in range(2, 6)]
|
| 701 |
+
else:
|
| 702 |
+
bbox_bins = [int(parsed[i].group(j)) for j in range(1, 5)]
|
| 703 |
+
instance['bbox'] = self.box_quantizer.dequantize(
|
| 704 |
+
boxes=torch.tensor(bbox_bins),
|
| 705 |
+
size=image_size
|
| 706 |
+
).tolist()
|
| 707 |
+
|
| 708 |
+
if phrase_centric:
|
| 709 |
+
instance['cat_name'] = parsed[i].group(1).lower().strip()
|
| 710 |
+
else:
|
| 711 |
+
instance['cat_name'] = parsed[i].group(5).lower().strip()
|
| 712 |
+
instances.append(instance)
|
| 713 |
+
|
| 714 |
+
return instances
|
| 715 |
+
|
| 716 |
+
def parse_ocr_from_text_and_spans(self,
|
| 717 |
+
text,
|
| 718 |
+
pattern,
|
| 719 |
+
image_size,
|
| 720 |
+
area_threshold=-1.0,
|
| 721 |
+
):
|
| 722 |
+
bboxes = []
|
| 723 |
+
labels = []
|
| 724 |
+
text = text.replace('<s>', '')
|
| 725 |
+
# ocr with regions
|
| 726 |
+
parsed = re.findall(pattern, text)
|
| 727 |
+
instances = []
|
| 728 |
+
image_width, image_height = image_size
|
| 729 |
+
|
| 730 |
+
for ocr_line in parsed:
|
| 731 |
+
ocr_content = ocr_line[0]
|
| 732 |
+
quad_box = ocr_line[1:]
|
| 733 |
+
quad_box = [int(i) for i in quad_box]
|
| 734 |
+
quad_box = self.coordinates_quantizer.dequantize(
|
| 735 |
+
torch.tensor(np.array(quad_box).reshape(-1, 2)),
|
| 736 |
+
size=image_size
|
| 737 |
+
).reshape(-1).tolist()
|
| 738 |
+
|
| 739 |
+
if area_threshold > 0:
|
| 740 |
+
x_coords = [i for i in quad_box[0::2]]
|
| 741 |
+
y_coords = [i for i in quad_box[1::2]]
|
| 742 |
+
|
| 743 |
+
# apply the Shoelace formula
|
| 744 |
+
area = 0.5 * abs(sum(x_coords[i] * y_coords[i + 1] - x_coords[i + 1] * y_coords[i] for i in range(4 - 1)))
|
| 745 |
+
|
| 746 |
+
if area < (image_width * image_height) * area_threshold:
|
| 747 |
+
continue
|
| 748 |
+
|
| 749 |
+
bboxes.append(quad_box)
|
| 750 |
+
labels.append(ocr_content)
|
| 751 |
+
instances.append({
|
| 752 |
+
'quad_box': quad_box,
|
| 753 |
+
'text': ocr_content,
|
| 754 |
+
})
|
| 755 |
+
return instances
|
| 756 |
+
|
| 757 |
+
def parse_phrase_grounding_from_text_and_spans(self, text, pattern, image_size):
|
| 758 |
+
# ignore <s> </s> and <pad>
|
| 759 |
+
cur_span = 0
|
| 760 |
+
if text.startswith('<s>'):
|
| 761 |
+
cur_span += 3
|
| 762 |
+
|
| 763 |
+
text = text.replace('<s>', '')
|
| 764 |
+
text = text.replace('</s>', '')
|
| 765 |
+
text = text.replace('<pad>', '')
|
| 766 |
+
|
| 767 |
+
pattern = r"([^<]+(?:<loc_\d+>){4,})"
|
| 768 |
+
phrases = re.findall(pattern, text)
|
| 769 |
+
|
| 770 |
+
# pattern should be text pattern and od pattern
|
| 771 |
+
pattern = r'^\s*(.*?)(?=<od>|</od>|<box>|</box>|<bbox>|</bbox>|<loc_)'
|
| 772 |
+
box_pattern = r'<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>'
|
| 773 |
+
|
| 774 |
+
instances = []
|
| 775 |
+
for pharse_text in phrases:
|
| 776 |
+
phrase_text_strip = pharse_text.replace('<ground>', '', 1)
|
| 777 |
+
phrase_text_strip = pharse_text.replace('<obj>', '', 1)
|
| 778 |
+
|
| 779 |
+
if phrase_text_strip == '':
|
| 780 |
+
cur_span += len(pharse_text)
|
| 781 |
+
continue
|
| 782 |
+
|
| 783 |
+
# Prepare instance.
|
| 784 |
+
instance = {}
|
| 785 |
+
|
| 786 |
+
# parse phrase, get string
|
| 787 |
+
phrase = re.search(pattern, phrase_text_strip)
|
| 788 |
+
if phrase is None:
|
| 789 |
+
cur_span += len(pharse_text)
|
| 790 |
+
continue
|
| 791 |
+
|
| 792 |
+
# parse bboxes by box_pattern
|
| 793 |
+
bboxes_parsed = list(re.finditer(box_pattern, pharse_text))
|
| 794 |
+
if len(bboxes_parsed) == 0:
|
| 795 |
+
cur_span += len(pharse_text)
|
| 796 |
+
continue
|
| 797 |
+
|
| 798 |
+
phrase = phrase.group()
|
| 799 |
+
# remove leading and trailing spaces
|
| 800 |
+
phrase = phrase.strip()
|
| 801 |
+
|
| 802 |
+
if phrase in self.black_list_of_phrase_grounding:
|
| 803 |
+
cur_span += len(pharse_text)
|
| 804 |
+
continue
|
| 805 |
+
|
| 806 |
+
# a list of list
|
| 807 |
+
bbox_bins = [[int(_bboxes_parsed.group(j)) for j in range(1, 5)] for _bboxes_parsed in bboxes_parsed]
|
| 808 |
+
instance['bbox'] = self.box_quantizer.dequantize(
|
| 809 |
+
boxes=torch.tensor(bbox_bins),
|
| 810 |
+
size=image_size
|
| 811 |
+
).tolist()
|
| 812 |
+
|
| 813 |
+
# exclude non-ascii characters
|
| 814 |
+
phrase = phrase.encode('ascii',errors='ignore').decode('ascii')
|
| 815 |
+
instance['cat_name'] = phrase
|
| 816 |
+
|
| 817 |
+
instances.append(instance)
|
| 818 |
+
|
| 819 |
+
return instances
|
| 820 |
+
|
| 821 |
+
def parse_description_with_bboxes_from_text_and_spans(self, text, pattern, image_size, allow_empty_phrase=False):
|
| 822 |
+
# temporary parse solution, split by '.'
|
| 823 |
+
# ignore <s> </s> and <pad>
|
| 824 |
+
|
| 825 |
+
text = text.replace('<s>', '')
|
| 826 |
+
text = text.replace('</s>', '')
|
| 827 |
+
text = text.replace('<pad>', '')
|
| 828 |
+
|
| 829 |
+
if allow_empty_phrase:
|
| 830 |
+
pattern = rf"(?:(?:<loc_\d+>){{4,}})"
|
| 831 |
+
else:
|
| 832 |
+
pattern = r"([^<]+(?:<loc_\d+>){4,})"
|
| 833 |
+
phrases = re.findall(pattern, text)
|
| 834 |
+
|
| 835 |
+
# pattern should be text pattern and od pattern
|
| 836 |
+
pattern = r'^\s*(.*?)(?=<od>|</od>|<box>|</box>|<bbox>|</bbox>|<loc_)'
|
| 837 |
+
box_pattern = r'<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>'
|
| 838 |
+
|
| 839 |
+
instances = []
|
| 840 |
+
for pharse_text in phrases:
|
| 841 |
+
phrase_text_strip = pharse_text.replace('<ground>', '', 1)
|
| 842 |
+
phrase_text_strip = pharse_text.replace('<obj>', '', 1)
|
| 843 |
+
|
| 844 |
+
if phrase_text_strip == '' and not allow_empty_phrase:
|
| 845 |
+
continue
|
| 846 |
+
|
| 847 |
+
# parse phrase, get string
|
| 848 |
+
phrase = re.search(pattern, phrase_text_strip)
|
| 849 |
+
if phrase is None:
|
| 850 |
+
continue
|
| 851 |
+
|
| 852 |
+
phrase = phrase.group()
|
| 853 |
+
# remove leading and trailing spaces
|
| 854 |
+
phrase = phrase.strip()
|
| 855 |
+
|
| 856 |
+
# parse bboxes by box_pattern
|
| 857 |
+
bboxes_parsed = list(re.finditer(box_pattern, pharse_text))
|
| 858 |
+
if len(bboxes_parsed) == 0:
|
| 859 |
+
continue
|
| 860 |
+
|
| 861 |
+
# a list of list
|
| 862 |
+
bbox_bins = [[int(_bboxes_parsed.group(j)) for j in range(1, 5)] for _bboxes_parsed in bboxes_parsed]
|
| 863 |
+
|
| 864 |
+
bboxes = self.box_quantizer.dequantize(
|
| 865 |
+
boxes=torch.tensor(bbox_bins),
|
| 866 |
+
size=image_size
|
| 867 |
+
).tolist()
|
| 868 |
+
|
| 869 |
+
phrase = phrase.encode('ascii',errors='ignore').decode('ascii')
|
| 870 |
+
for _bboxes in bboxes:
|
| 871 |
+
# Prepare instance.
|
| 872 |
+
instance = {}
|
| 873 |
+
instance['bbox'] = _bboxes
|
| 874 |
+
# exclude non-ascii characters
|
| 875 |
+
instance['cat_name'] = phrase
|
| 876 |
+
instances.append(instance)
|
| 877 |
+
|
| 878 |
+
return instances
|
| 879 |
+
|
| 880 |
+
def parse_description_with_polygons_from_text_and_spans(self, text, pattern, image_size,
|
| 881 |
+
allow_empty_phrase=False,
|
| 882 |
+
polygon_sep_token='<sep>',
|
| 883 |
+
polygon_start_token='<poly>',
|
| 884 |
+
polygon_end_token='</poly>',
|
| 885 |
+
with_box_at_start=False,
|
| 886 |
+
):
|
| 887 |
+
|
| 888 |
+
# ref_seg format: '<expression><x1><y1><x2><y2><><><sep><><><><>'
|
| 889 |
+
# ignore <s> </s> and <pad>
|
| 890 |
+
|
| 891 |
+
text = text.replace('<s>', '')
|
| 892 |
+
text = text.replace('</s>', '')
|
| 893 |
+
text = text.replace('<pad>', '')
|
| 894 |
+
|
| 895 |
+
if allow_empty_phrase:
|
| 896 |
+
pattern = rf"(?:(?:<loc_\d+>|{re.escape(polygon_sep_token)}|{re.escape(polygon_start_token)}|{re.escape(polygon_end_token)}){{4,}})"
|
| 897 |
+
else:
|
| 898 |
+
# [^<]+: This part matches one or more characters that are not the < symbol.
|
| 899 |
+
# The ^ inside the square brackets [] is a negation, meaning it matches anything except <.
|
| 900 |
+
#
|
| 901 |
+
pattern = rf"([^<]+(?:<loc_\d+>|{re.escape(polygon_sep_token)}|{re.escape(polygon_start_token)}|{re.escape(polygon_end_token)}){{4,}})"
|
| 902 |
+
phrases = re.findall(pattern, text)
|
| 903 |
+
|
| 904 |
+
phrase_string_pattern = r'^\s*(.*?)(?=<od>|</od>|<box>|</box>|<bbox>|</bbox>|<loc_|<poly>)'
|
| 905 |
+
box_pattern = rf'((?:<loc_\d+>)+)(?:{re.escape(polygon_sep_token)}|$)'
|
| 906 |
+
|
| 907 |
+
# one polygons instance is separated by polygon_start_token and polygon_end_token
|
| 908 |
+
polygons_instance_pattern = rf'{re.escape(polygon_start_token)}(.*?){re.escape(polygon_end_token)}'
|
| 909 |
+
|
| 910 |
+
instances = []
|
| 911 |
+
for phrase_text in phrases:
|
| 912 |
+
|
| 913 |
+
# exclude loc_\d+>
|
| 914 |
+
# need to get span if want to include category score
|
| 915 |
+
phrase_text_strip = re.sub(r'^loc_\d+>', '', phrase_text, count=1)
|
| 916 |
+
|
| 917 |
+
# phrase = phrase.replace('<poly>', '')
|
| 918 |
+
# phrase = phrase.replace('poly>', '')
|
| 919 |
+
|
| 920 |
+
if phrase_text_strip == '' and not allow_empty_phrase:
|
| 921 |
+
continue
|
| 922 |
+
|
| 923 |
+
|
| 924 |
+
# parse phrase, get string
|
| 925 |
+
phrase = re.search(phrase_string_pattern, phrase_text_strip)
|
| 926 |
+
if phrase is None:
|
| 927 |
+
continue
|
| 928 |
+
phrase = phrase.group()
|
| 929 |
+
# remove leading and trailing spaces
|
| 930 |
+
phrase = phrase.strip()
|
| 931 |
+
|
| 932 |
+
# parse bboxes by box_pattern
|
| 933 |
+
|
| 934 |
+
# split by polygon_start_token and polygon_end_token first using polygons_instance_pattern
|
| 935 |
+
if polygon_start_token in phrase_text and polygon_end_token in phrase_text:
|
| 936 |
+
polygons_instances_parsed = list(re.finditer(polygons_instance_pattern, phrase_text))
|
| 937 |
+
else:
|
| 938 |
+
polygons_instances_parsed = [phrase_text]
|
| 939 |
+
|
| 940 |
+
for _polygons_instances_parsed in polygons_instances_parsed:
|
| 941 |
+
# Prepare instance.
|
| 942 |
+
instance = {}
|
| 943 |
+
|
| 944 |
+
# polygons_parsed= list(re.finditer(box_pattern, phrase_text))
|
| 945 |
+
if isinstance(_polygons_instances_parsed, str):
|
| 946 |
+
polygons_parsed= list(re.finditer(box_pattern, _polygons_instances_parsed))
|
| 947 |
+
else:
|
| 948 |
+
polygons_parsed= list(re.finditer(box_pattern, _polygons_instances_parsed.group(1)))
|
| 949 |
+
if len(polygons_parsed) == 0:
|
| 950 |
+
continue
|
| 951 |
+
|
| 952 |
+
# a list of list (polygon)
|
| 953 |
+
bbox = []
|
| 954 |
+
polygons = []
|
| 955 |
+
for _polygon_parsed in polygons_parsed:
|
| 956 |
+
# group 1: whole <loc_\d+>...</loc_\d+>
|
| 957 |
+
_polygon = _polygon_parsed.group(1)
|
| 958 |
+
# parse into list of int
|
| 959 |
+
_polygon = [int(_loc_parsed.group(1)) for _loc_parsed in re.finditer(r'<loc_(\d+)>', _polygon)]
|
| 960 |
+
if with_box_at_start and len(bbox) == 0:
|
| 961 |
+
if len(_polygon) > 4:
|
| 962 |
+
# no valid bbox prediction
|
| 963 |
+
bbox = _polygon[:4]
|
| 964 |
+
_polygon = _polygon[4:]
|
| 965 |
+
else:
|
| 966 |
+
bbox = [0, 0, 0, 0]
|
| 967 |
+
# abandon last element if is not paired
|
| 968 |
+
if len(_polygon) % 2 == 1:
|
| 969 |
+
_polygon = _polygon[:-1]
|
| 970 |
+
|
| 971 |
+
# reshape into (n, 2)
|
| 972 |
+
_polygon = self.coordinates_quantizer.dequantize(
|
| 973 |
+
torch.tensor(np.array(_polygon).reshape(-1, 2)),
|
| 974 |
+
size=image_size
|
| 975 |
+
).reshape(-1).tolist()
|
| 976 |
+
# reshape back
|
| 977 |
+
polygons.append(_polygon)
|
| 978 |
+
|
| 979 |
+
instance['cat_name'] = phrase
|
| 980 |
+
instance['polygons'] = polygons
|
| 981 |
+
if len(bbox) != 0:
|
| 982 |
+
instance['bbox'] = self.box_quantizer.dequantize(
|
| 983 |
+
boxes=torch.tensor([bbox]),
|
| 984 |
+
size=image_size
|
| 985 |
+
).tolist()[0]
|
| 986 |
+
|
| 987 |
+
instances.append(instance)
|
| 988 |
+
|
| 989 |
+
return instances
|
| 990 |
+
|
| 991 |
+
def __call__(
|
| 992 |
+
self,
|
| 993 |
+
text=None,
|
| 994 |
+
image_size=None,
|
| 995 |
+
parse_tasks=None,
|
| 996 |
+
):
|
| 997 |
+
"""
|
| 998 |
+
Args:
|
| 999 |
+
text: model outputs
|
| 1000 |
+
image_size: (width, height)
|
| 1001 |
+
parse_tasks: a list of tasks to parse, if None, parse all tasks.
|
| 1002 |
+
|
| 1003 |
+
"""
|
| 1004 |
+
if parse_tasks is not None:
|
| 1005 |
+
if isinstance(parse_tasks, str):
|
| 1006 |
+
parse_tasks = [parse_tasks]
|
| 1007 |
+
for _parse_task in parse_tasks:
|
| 1008 |
+
assert _parse_task in self.parse_tasks, f'parse task {_parse_task} not supported'
|
| 1009 |
+
|
| 1010 |
+
# sequence or text should be provided
|
| 1011 |
+
assert text is not None, 'text should be provided'
|
| 1012 |
+
|
| 1013 |
+
parsed_dict = {
|
| 1014 |
+
'text': text
|
| 1015 |
+
}
|
| 1016 |
+
|
| 1017 |
+
for task in self.parse_tasks:
|
| 1018 |
+
if parse_tasks is not None and task not in parse_tasks:
|
| 1019 |
+
continue
|
| 1020 |
+
|
| 1021 |
+
pattern = self.parse_tasks_configs[task].get('PATTERN', None)
|
| 1022 |
+
|
| 1023 |
+
if task == 'ocr':
|
| 1024 |
+
instances = self.parse_ocr_from_text_and_spans(
|
| 1025 |
+
text,
|
| 1026 |
+
pattern=pattern,
|
| 1027 |
+
image_size=image_size,
|
| 1028 |
+
area_threshold=self.parse_tasks_configs[task].get('AREA_THRESHOLD', 0.0),
|
| 1029 |
+
)
|
| 1030 |
+
parsed_dict['ocr'] = instances
|
| 1031 |
+
elif task == 'phrase_grounding':
|
| 1032 |
+
instances = self.parse_phrase_grounding_from_text_and_spans(
|
| 1033 |
+
text,
|
| 1034 |
+
pattern=pattern,
|
| 1035 |
+
image_size=image_size,
|
| 1036 |
+
)
|
| 1037 |
+
parsed_dict['phrase_grounding'] = instances
|
| 1038 |
+
elif task == 'pure_text':
|
| 1039 |
+
parsed_dict['pure_text'] = text
|
| 1040 |
+
elif task == 'description_with_bboxes':
|
| 1041 |
+
instances = self.parse_description_with_bboxes_from_text_and_spans(
|
| 1042 |
+
text,
|
| 1043 |
+
pattern=pattern,
|
| 1044 |
+
image_size=image_size,
|
| 1045 |
+
)
|
| 1046 |
+
parsed_dict['description_with_bboxes'] = instances
|
| 1047 |
+
elif task == 'description_with_polygons':
|
| 1048 |
+
instances = self.parse_description_with_polygons_from_text_and_spans(
|
| 1049 |
+
text,
|
| 1050 |
+
pattern=pattern,
|
| 1051 |
+
image_size=image_size,
|
| 1052 |
+
)
|
| 1053 |
+
parsed_dict['description_with_polygons'] = instances
|
| 1054 |
+
elif task == 'polygons':
|
| 1055 |
+
instances = self.parse_description_with_polygons_from_text_and_spans(
|
| 1056 |
+
text,
|
| 1057 |
+
pattern=pattern,
|
| 1058 |
+
image_size=image_size,
|
| 1059 |
+
allow_empty_phrase=True,
|
| 1060 |
+
)
|
| 1061 |
+
parsed_dict['polygons'] = instances
|
| 1062 |
+
elif task == 'bboxes':
|
| 1063 |
+
instances = self.parse_description_with_bboxes_from_text_and_spans(
|
| 1064 |
+
text,
|
| 1065 |
+
pattern=pattern,
|
| 1066 |
+
image_size=image_size,
|
| 1067 |
+
allow_empty_phrase=True,
|
| 1068 |
+
)
|
| 1069 |
+
parsed_dict['bboxes'] = instances
|
| 1070 |
+
elif task == 'description_with_bboxes_or_polygons':
|
| 1071 |
+
if '<poly>' in text:
|
| 1072 |
+
# only support either polygons or bboxes, not both at the same time
|
| 1073 |
+
instances = self.parse_description_with_polygons_from_text_and_spans(
|
| 1074 |
+
text,
|
| 1075 |
+
pattern=pattern,
|
| 1076 |
+
image_size=image_size,
|
| 1077 |
+
)
|
| 1078 |
+
else:
|
| 1079 |
+
instances = self.parse_description_with_bboxes_from_text_and_spans(
|
| 1080 |
+
text,
|
| 1081 |
+
pattern=pattern,
|
| 1082 |
+
image_size=image_size,
|
| 1083 |
+
)
|
| 1084 |
+
parsed_dict['description_with_bboxes_or_polygons'] = instances
|
| 1085 |
+
else:
|
| 1086 |
+
raise ValueError("task {} is not supported".format(task))
|
| 1087 |
+
|
| 1088 |
+
return parsed_dict
|
processor_config.json
ADDED
|
@@ -0,0 +1,6 @@
|
|
|
|
|
|
|
|
|
|
|
|
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{
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"auto_map": {
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"AutoProcessor": "processing_florence2.Florence2Processor"
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},
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"processor_class": "Florence2Processor"
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}
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special_tokens_map.json
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tokenizer.json
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tokenizer_config.json
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vocab.json
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