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Browse files- .gitattributes +5 -0
- LICENSE +21 -0
- README.md +175 -0
- __pycache__/generation_eval_utils.cpython-311.pyc +0 -0
- checksums/README.md +8 -0
- checksums/composite_flickr30k_checksum.txt +991 -0
- checksums/composite_flickr8k_checksum.txt +997 -0
- checksums/composite_mscoco_checksum.txt +0 -0
- checksums/flickr8k_checksum.txt +0 -0
- checksums/pascal_50s_checksum.txt +1000 -0
- clipscore.py +343 -0
- coco_gen_200k.json +3 -0
- coco_raw_200k.json +3 -0
- example/bad_captions.json +1 -0
- example/good_captions.json +1 -0
- example/images/image1.jpg +0 -0
- example/images/image2.jpg +0 -0
- example/refs.json +1 -0
- flickr8k_example/README.md +19 -0
- flickr8k_example/compute_metrics.py +86 -0
- flickr8k_example/download.py +145 -0
- generation_eval_utils.py +66 -0
- llava_gen_200k.json +3 -0
- llava_raw_200k.json +3 -0
- llava_raw_558k.json +3 -0
- requirements.txt +2 -0
- run.sh +4 -0
- run_coco_gen.sh +1 -0
- run_coco_raw.sh +1 -0
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llava_raw_558k.json filter=lfs diff=lfs merge=lfs -text
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LICENSE
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MIT License
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Copyright (c) 2021 Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, Yejin Choi
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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README.md
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+
# What's in here?
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| 2 |
+
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+
This repo contains the code for our EMNLP 2021 paper: [CLIPScore: A
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| 4 |
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Reference-free Evaluation Metric for Image
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+
Captioning](https://arxiv.org/abs/2104.08718). CLIPScore is a metric
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+
that you can use to evaluate the quality of an automatic image
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| 7 |
+
captioning system. In our paper, we show that CLIPScore achieves high
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correlation with human judgment on literal image captioning
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| 9 |
+
tasks. However, unlike BLEU or CIDEr, CLIPScore doesn't require
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+
reference captions.
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| 11 |
+
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+
If you find the paper or this code useful, please consider citing:
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| 13 |
+
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+
```
|
| 15 |
+
@inproceedings{hessel2021clipscore,
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+
title={{CLIPScore:} A Reference-free Evaluation Metric for Image Captioning},
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| 17 |
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author={Hessel, Jack and Holtzman, Ari and Forbes, Maxwell and Bras, Ronan Le and Choi, Yejin},
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| 18 |
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booktitle={EMNLP},
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year={2021}
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}
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```
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| 22 |
+
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# How do I run the code?
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+
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## Command Line
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+
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Example usage
|
| 28 |
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```
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> python clipscore.py example/good_captions.json example/images/
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+
...
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| 31 |
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CLIPScore: 0.8584
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+
```
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+
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+
If you include optionally some references, you will see RefCLIPScore, alongside a usual set of
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| 35 |
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caption generation evaluation metrics. The references are optional.
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| 36 |
+
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```
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+
> python clipscore.py example/good_captions.json example/images/ --references_json example/refs.json
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+
...
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+
BLEU-1: 0.6667
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BLEU-2: 0.4899
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| 42 |
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BLEU-3: 0.3469
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| 43 |
+
BLEU-4: 0.0000
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| 44 |
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METEOR: 0.3444
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| 45 |
+
ROUGE: 0.4280
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CIDER: 0.5637
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SPICE: 0.4000
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CLIPScore: 0.8584
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RefCLIPScore: 0.8450
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```
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| 51 |
+
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+
Worse captions should get lower scores:
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| 53 |
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```
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> python clipscore.py example/bad_captions.json example/images/ --references_json example/refs.json
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+
...
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| 56 |
+
BLEU-1: 0.4815
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| 57 |
+
BLEU-2: 0.2404
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| 58 |
+
BLEU-3: 0.1359
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| 59 |
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BLEU-4: 0.0000
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| 60 |
+
METEOR: 0.1861
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| 61 |
+
ROUGE: 0.3121
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| 62 |
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CIDER: 0.2790
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| 63 |
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SPICE: 0.1500
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| 64 |
+
CLIPScore: 0.7153
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+
RefCLIPScore: 0.7253
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| 66 |
+
```
|
| 67 |
+
|
| 68 |
+
You can treat/report CLIPScore and RefCLIPScore similarly to the other
|
| 69 |
+
evaluation metrics. See the paper for more details about CLIPScore and
|
| 70 |
+
RefCLIPScore. Full usage options can be given by `python clipscore.py
|
| 71 |
+
-h`. An example set of inputs, including a candidate json, image
|
| 72 |
+
directory, and references json is given this repo under `example/`
|
| 73 |
+
|
| 74 |
+
The input files are formatted as follows:
|
| 75 |
+
|
| 76 |
+
The candidates json should be a dictionary that maps from
|
| 77 |
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`{"string_image_identifier": "candidate"}`, e.g.,
|
| 78 |
+
|
| 79 |
+
```
|
| 80 |
+
{'image1': 'an orange cat and a grey cat are lying together.',
|
| 81 |
+
'image2': 'a black dog looks at the camera.'
|
| 82 |
+
...}
|
| 83 |
+
```
|
| 84 |
+
|
| 85 |
+
The image directory should be a directory containing the images that
|
| 86 |
+
act as the keys in the candidates json, e.g.,
|
| 87 |
+
|
| 88 |
+
```
|
| 89 |
+
images/
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| 90 |
+
├── image1.jpg
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| 91 |
+
└── image2.jpg
|
| 92 |
+
```
|
| 93 |
+
|
| 94 |
+
and, finally, the references json should be a dictionary that maps from
|
| 95 |
+
`{"string_image_identifier": ["list", "of", "references"]}`, e.g.,
|
| 96 |
+
|
| 97 |
+
```
|
| 98 |
+
{"image1": ["two cats are sleeping next to each other.",
|
| 99 |
+
"a grey cat is cuddling with an orange cat on a blanket.",
|
| 100 |
+
"the orange cat is happy that the black cat is close to it."],
|
| 101 |
+
"image2": ["a dog is wearing ear muffs as it lies on a carpet.",
|
| 102 |
+
"a black dog and an orange cat are looking at the photographer.",
|
| 103 |
+
"headphones are placed on a dogs ears."]}
|
| 104 |
+
```
|
| 105 |
+
|
| 106 |
+
## MSCOCO dataset in pycocoevalcap
|
| 107 |
+
|
| 108 |
+
If you're running on the MSCOCO dataset and using the standard
|
| 109 |
+
evaluation toolkit, you can use our version of
|
| 110 |
+
[pycocoevalcap](https://github.com/jmhessel/pycocoevalcap) to evaluate.
|
| 111 |
+
You won't even need to download the original MSCOCO images, thanks to
|
| 112 |
+
a bit of magic :-)
|
| 113 |
+
|
| 114 |
+
To use `pycocoevalcap` on the MSCOCO dataset in the MSCOCO format, you
|
| 115 |
+
can simply use:
|
| 116 |
+
|
| 117 |
+
```
|
| 118 |
+
pip install git+https://github.com/jmhessel/pycocoevalcap.git
|
| 119 |
+
```
|
| 120 |
+
|
| 121 |
+
there is an example evaluation in that repo under
|
| 122 |
+
`examples/eval.py`. After pip installing, if you clone the
|
| 123 |
+
`pycocoeval` repo and run
|
| 124 |
+
|
| 125 |
+
```
|
| 126 |
+
python eval.py
|
| 127 |
+
```
|
| 128 |
+
|
| 129 |
+
after a bit of time, the output should be:
|
| 130 |
+
```
|
| 131 |
+
Bleu_1: 0.579
|
| 132 |
+
Bleu_2: 0.404
|
| 133 |
+
Bleu_3: 0.279
|
| 134 |
+
Bleu_4: 0.191
|
| 135 |
+
METEOR: 0.195
|
| 136 |
+
ROUGE_L: 0.396
|
| 137 |
+
CIDEr: 0.600
|
| 138 |
+
SPICE: 0.133
|
| 139 |
+
CLIPScore: 0.528
|
| 140 |
+
RefCLIPScore: 0.605
|
| 141 |
+
```
|
| 142 |
+
|
| 143 |
+
## Reproducibility notes:
|
| 144 |
+
|
| 145 |
+
- CLIPScore can run either on CPU or GPU. But, there are slight
|
| 146 |
+
differences due to floating point precision. As discussed
|
| 147 |
+
[here](https://github.com/openai/CLIP/issues/30#issuecomment-771099118),
|
| 148 |
+
on CPU, all operations run in `float32`, but on GPU, some operations
|
| 149 |
+
run in `float16`. The differences are generally small (e.g., for the
|
| 150 |
+
example run above, with `example/good_captions.json` captions and
|
| 151 |
+
`example/images/` images, on CPU, the output is `CLIPScore: 0.8585`,
|
| 152 |
+
but on GPU, the output is `CLIPScore: 0.8584`.) *All experiments in the
|
| 153 |
+
paper were run on GPU, and this code will raise a warning if you're not
|
| 154 |
+
using a GPU.*
|
| 155 |
+
|
| 156 |
+
- Because CLIPScore depends on the images to compute, resizing,
|
| 157 |
+
compressing, etc. can all cause slight differences in computing the
|
| 158 |
+
CLIPScore. Even saving a jpg twice can result in different
|
| 159 |
+
compression, because that format is lossy! To this end, we release
|
| 160 |
+
the checksums of the images we used for the paper. see `checksums/`
|
| 161 |
+
for more info. For the pycocoevalcap repo, we have also included the
|
| 162 |
+
checksums for MSCOCO --- see
|
| 163 |
+
[here](https://github.com/jmhessel/pycocoevalcap/tree/master/clipscore)
|
| 164 |
+
for more info.
|
| 165 |
+
|
| 166 |
+
- The prompt we used for the text side of CLIP, as mentioned in the
|
| 167 |
+
paper is ``A photo depicts" This is hard-coded into this repo. Other
|
| 168 |
+
prompts will result in slightly different results, and we don't
|
| 169 |
+
recommend them for the sake of reproducibility.
|
| 170 |
+
|
| 171 |
+
## Acknowledgment
|
| 172 |
+
|
| 173 |
+
The authors would like to thank Jungo Kasai for being the first to use
|
| 174 |
+
this repo. Thanks to Jungo, we fixed a few issues, and added some
|
| 175 |
+
information about reproducibility that was missing before.
|
__pycache__/generation_eval_utils.cpython-311.pyc
ADDED
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Binary file (3.95 kB). View file
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checksums/README.md
ADDED
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### Whats in here?
|
| 2 |
+
|
| 3 |
+
We include the checksums of the images we used to compute CLIPScore
|
| 4 |
+
for the paper. Because the evaluation metric depends on the image
|
| 5 |
+
itself, things like jpg compression, resizing, etc. can impact
|
| 6 |
+
reproducability. We downloaded the datasets from the original sources
|
| 7 |
+
and didn't modify them. But if you're having trouble matching these
|
| 8 |
+
checksums, definitely get in touch!
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checksums/composite_flickr30k_checksum.txt
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3f7ff349a9d7f4935233bbfbc44fcf06 images/2008_008490.jpg
|
| 949 |
+
1ed030e091cf1c6574fa0272b4b419a0 images/2008_008501.jpg
|
| 950 |
+
0a9dffd993b436e4b9b5dc91bcd7747e images/2008_008517.jpg
|
| 951 |
+
79115bb51988bb8a95a5bf19460a15a5 images/2008_008521.jpg
|
| 952 |
+
b90e4231be6f081a91f168478694baba images/2008_008524.jpg
|
| 953 |
+
10ae473dd39724dd9f4fe96c66ec0fe5 images/2008_008525.jpg
|
| 954 |
+
5aeec4f8ebdf9c767fe1dd1712e3cb3c images/2008_008526.jpg
|
| 955 |
+
c1633e9d8fc446f8fc1c2d865d17a0d1 images/2008_008527.jpg
|
| 956 |
+
06be0c31e916b1691b855bc5b4762c00 images/2008_008528.jpg
|
| 957 |
+
d5eab3b92fc90f5f9bf63280d353ba1f images/2008_008538.jpg
|
| 958 |
+
9c03a21936e7a1c29d2e7afc4ed73a40 images/2008_008541.jpg
|
| 959 |
+
d53a3252079d508d1006ae3e8084e05b images/2008_008545.jpg
|
| 960 |
+
730a1e440515d79185d262ef4d55a6c1 images/2008_008546.jpg
|
| 961 |
+
a21f0005adab2741d19a987a2b71ce16 images/2008_008549.jpg
|
| 962 |
+
a4c3979fae3b2d215815a06175b39263 images/2008_008552.jpg
|
| 963 |
+
008abf8fb85bac35a7fccba630940c44 images/2008_008567.jpg
|
| 964 |
+
163f564587ca287ec4a80d1ddb0ee2f7 images/2008_008572.jpg
|
| 965 |
+
c3e89c4af59dceab1a3f5ff2adfd5654 images/2008_008583.jpg
|
| 966 |
+
2a7d8f161f43ead1c855d9e850254d53 images/2008_008589.jpg
|
| 967 |
+
83bf27c1d965b2fafde038828ff60d6b images/2008_008593.jpg
|
| 968 |
+
4fbdd8d2751561c8f3359fe4faad9610 images/2008_008601.jpg
|
| 969 |
+
94d9fa5de1fbd5f0b67a77f16228202e images/2008_008608.jpg
|
| 970 |
+
91779e8723cd1ee71ca23b173a28d30b images/2008_008613.jpg
|
| 971 |
+
defe9cbb35d01826d2e08ad499db1534 images/2008_008617.jpg
|
| 972 |
+
d8c349e9d9353b1e019f67b1af37f3bc images/2008_008618.jpg
|
| 973 |
+
54aacf68963ee199be3a298b37d150e4 images/2008_008619.jpg
|
| 974 |
+
9b78b88b50572c45ac94173d8017fd5e images/2008_008622.jpg
|
| 975 |
+
8d2a806ab1fc49275cdb1a8ef614393d images/2008_008623.jpg
|
| 976 |
+
8bfb498700a92b2935915ab2261e542d images/2008_008628.jpg
|
| 977 |
+
a04df6613e0abe1c0a13e2044f2c55e8 images/2008_008632.jpg
|
| 978 |
+
d5e95f0cc2a1c4d2c09e141fd41274b1 images/2008_008635.jpg
|
| 979 |
+
34709ba7c89b7ebe66a1c9db69a6789d images/2008_008641.jpg
|
| 980 |
+
74e22515280065cd713e7c0b01cb23b5 images/2008_008642.jpg
|
| 981 |
+
483ca7b5d102b033cb2488301f822c1a images/2008_008649.jpg
|
| 982 |
+
fcc4a35b6638d115c85d9b3eb0a3f3f4 images/2008_008654.jpg
|
| 983 |
+
ed7fc150a5157941a38687ded5d5f4e6 images/2008_008662.jpg
|
| 984 |
+
d91238fb6f27c801835ac23e07169c84 images/2008_008666.jpg
|
| 985 |
+
a0ec8d18011c01a11426f43e4c94b20d images/2008_008668.jpg
|
| 986 |
+
0d0fea28dedfa649911b8c90d4079293 images/2008_008671.jpg
|
| 987 |
+
68b407152a7231dee747da3cc57cbc03 images/2008_008674.jpg
|
| 988 |
+
54a83f3091005c5031f43ed5d2cb12aa images/2008_008679.jpg
|
| 989 |
+
2bb82c6475349060f8aa91a8f20ebb96 images/2008_008685.jpg
|
| 990 |
+
eec59b27aa70c10cabb6f0b402fe0ad6 images/2008_008708.jpg
|
| 991 |
+
6bc0e3ec09eb536654fe44db070154a0 images/2008_008714.jpg
|
| 992 |
+
1033301d4b066cdc98b124b3571222a5 images/2008_008718.jpg
|
| 993 |
+
187c39ebfc342fe6af18724a83e72d74 images/2008_008719.jpg
|
| 994 |
+
e34b4f9cbc2f95afd281385da7ccf3ad images/2008_008725.jpg
|
| 995 |
+
373ddb50226ce2332318c996063de35f images/2008_008739.jpg
|
| 996 |
+
6d2132ecc73b7c08739571aed6d94c65 images/2008_008744.jpg
|
| 997 |
+
502ee017596437b6f27f34ab92c392e6 images/2008_008748.jpg
|
| 998 |
+
dff691e8311464acf92a341ec99276b1 images/2008_008751.jpg
|
| 999 |
+
55d0dd4998bb07f491c844a9e001f347 images/2008_008753.jpg
|
| 1000 |
+
f67f4fc7884d6f168e6ee8dfc1b8b26d images/2008_008755.jpg
|
clipscore.py
ADDED
|
@@ -0,0 +1,343 @@
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|
| 1 |
+
"""
|
| 2 |
+
Code for CLIPScore (https://arxiv.org/abs/2104.08718)
|
| 3 |
+
@inproceedings{hessel2021clipscore,
|
| 4 |
+
title={{CLIPScore:} A Reference-free Evaluation Metric for Image Captioning},
|
| 5 |
+
author={Hessel, Jack and Holtzman, Ari and Forbes, Maxwell and Bras, Ronan Le and Choi, Yejin},
|
| 6 |
+
booktitle={EMNLP},
|
| 7 |
+
year={2021}
|
| 8 |
+
}
|
| 9 |
+
"""
|
| 10 |
+
|
| 11 |
+
import argparse
|
| 12 |
+
import clip
|
| 13 |
+
import torch
|
| 14 |
+
from PIL import Image
|
| 15 |
+
from sklearn.preprocessing import normalize
|
| 16 |
+
from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
|
| 17 |
+
import torch
|
| 18 |
+
import tqdm
|
| 19 |
+
import numpy as np
|
| 20 |
+
import sklearn.preprocessing
|
| 21 |
+
import collections
|
| 22 |
+
import os
|
| 23 |
+
import pathlib
|
| 24 |
+
import json
|
| 25 |
+
import generation_eval_utils
|
| 26 |
+
import pprint
|
| 27 |
+
import warnings
|
| 28 |
+
from packaging import version
|
| 29 |
+
|
| 30 |
+
|
| 31 |
+
def parse_args():
|
| 32 |
+
parser = argparse.ArgumentParser()
|
| 33 |
+
parser.add_argument(
|
| 34 |
+
"candidates_json",
|
| 35 |
+
type=str,
|
| 36 |
+
help="Candidates json mapping from image_id --> candidate.",
|
| 37 |
+
)
|
| 38 |
+
|
| 39 |
+
parser.add_argument(
|
| 40 |
+
"image_dir",
|
| 41 |
+
type=str,
|
| 42 |
+
help="Directory of images, with the filenames as image ids.",
|
| 43 |
+
)
|
| 44 |
+
parser.add_argument(
|
| 45 |
+
"result_file",
|
| 46 |
+
type=str,
|
| 47 |
+
help="Directory of images, with the filenames as image ids.",
|
| 48 |
+
)
|
| 49 |
+
parser.add_argument(
|
| 50 |
+
"--references_json",
|
| 51 |
+
default=None,
|
| 52 |
+
help="Optional references json mapping from image_id --> [list of references]",
|
| 53 |
+
)
|
| 54 |
+
|
| 55 |
+
parser.add_argument(
|
| 56 |
+
"--compute_other_ref_metrics",
|
| 57 |
+
default=1,
|
| 58 |
+
type=int,
|
| 59 |
+
help="If references is specified, should we compute standard reference-based metrics?",
|
| 60 |
+
)
|
| 61 |
+
|
| 62 |
+
parser.add_argument(
|
| 63 |
+
"--save_per_instance",
|
| 64 |
+
default=None,
|
| 65 |
+
help="if set, we will save per instance clipscores to this file",
|
| 66 |
+
)
|
| 67 |
+
|
| 68 |
+
args = parser.parse_args()
|
| 69 |
+
|
| 70 |
+
if isinstance(args.save_per_instance, str) and not args.save_per_instance.endswith(
|
| 71 |
+
".json"
|
| 72 |
+
):
|
| 73 |
+
print(
|
| 74 |
+
"if you're saving per-instance, please make sure the filepath ends in json."
|
| 75 |
+
)
|
| 76 |
+
quit()
|
| 77 |
+
return args
|
| 78 |
+
|
| 79 |
+
|
| 80 |
+
class CLIPCapDataset(torch.utils.data.Dataset):
|
| 81 |
+
def __init__(self, data, prefix="A photo depicts"):
|
| 82 |
+
self.data = data
|
| 83 |
+
self.prefix = prefix
|
| 84 |
+
if self.prefix[-1] != " ":
|
| 85 |
+
self.prefix += " "
|
| 86 |
+
|
| 87 |
+
def __getitem__(self, idx):
|
| 88 |
+
c_data = self.data[idx]
|
| 89 |
+
c_data = clip.tokenize(self.prefix + c_data, truncate=True).squeeze()
|
| 90 |
+
return {"caption": c_data}
|
| 91 |
+
|
| 92 |
+
def __len__(self):
|
| 93 |
+
return len(self.data)
|
| 94 |
+
|
| 95 |
+
|
| 96 |
+
class CLIPImageDataset(torch.utils.data.Dataset):
|
| 97 |
+
def __init__(self, data):
|
| 98 |
+
self.data = data
|
| 99 |
+
# only 224x224 ViT-B/32 supported for now
|
| 100 |
+
self.preprocess = self._transform_test(224)
|
| 101 |
+
|
| 102 |
+
def _transform_test(self, n_px):
|
| 103 |
+
return Compose(
|
| 104 |
+
[
|
| 105 |
+
Resize(n_px, interpolation=Image.BICUBIC),
|
| 106 |
+
CenterCrop(n_px),
|
| 107 |
+
lambda image: image.convert("RGB"),
|
| 108 |
+
ToTensor(),
|
| 109 |
+
Normalize(
|
| 110 |
+
(0.48145466, 0.4578275, 0.40821073),
|
| 111 |
+
(0.26862954, 0.26130258, 0.27577711),
|
| 112 |
+
),
|
| 113 |
+
]
|
| 114 |
+
)
|
| 115 |
+
|
| 116 |
+
def __getitem__(self, idx):
|
| 117 |
+
c_data = self.data[idx]
|
| 118 |
+
image = Image.open(c_data)
|
| 119 |
+
image = self.preprocess(image)
|
| 120 |
+
return {"image": image}
|
| 121 |
+
|
| 122 |
+
def __len__(self):
|
| 123 |
+
return len(self.data)
|
| 124 |
+
|
| 125 |
+
|
| 126 |
+
def extract_all_captions(captions, model, device, batch_size=256, num_workers=8):
|
| 127 |
+
data = torch.utils.data.DataLoader(
|
| 128 |
+
CLIPCapDataset(captions),
|
| 129 |
+
batch_size=batch_size,
|
| 130 |
+
num_workers=num_workers,
|
| 131 |
+
shuffle=False,
|
| 132 |
+
)
|
| 133 |
+
all_text_features = []
|
| 134 |
+
with torch.no_grad():
|
| 135 |
+
for b in tqdm.tqdm(data):
|
| 136 |
+
b = b["caption"].to(device)
|
| 137 |
+
all_text_features.append(model.encode_text(b).cpu().numpy())
|
| 138 |
+
all_text_features = np.vstack(all_text_features)
|
| 139 |
+
return all_text_features
|
| 140 |
+
|
| 141 |
+
|
| 142 |
+
def extract_all_images(images, model, device, batch_size=64, num_workers=8):
|
| 143 |
+
data = torch.utils.data.DataLoader(
|
| 144 |
+
CLIPImageDataset(images),
|
| 145 |
+
batch_size=batch_size,
|
| 146 |
+
num_workers=num_workers,
|
| 147 |
+
shuffle=False,
|
| 148 |
+
)
|
| 149 |
+
all_image_features = []
|
| 150 |
+
with torch.no_grad():
|
| 151 |
+
for b in tqdm.tqdm(data):
|
| 152 |
+
b = b["image"].to(device)
|
| 153 |
+
if device == "cuda":
|
| 154 |
+
b = b.to(torch.float16)
|
| 155 |
+
all_image_features.append(model.encode_image(b).cpu().numpy())
|
| 156 |
+
all_image_features = np.vstack(all_image_features)
|
| 157 |
+
return all_image_features
|
| 158 |
+
|
| 159 |
+
|
| 160 |
+
def get_clip_score(model, images, candidates, device, w=2.5):
|
| 161 |
+
"""
|
| 162 |
+
get standard image-text clipscore.
|
| 163 |
+
images can either be:
|
| 164 |
+
- a list of strings specifying filepaths for images
|
| 165 |
+
- a precomputed, ordered matrix of image features
|
| 166 |
+
"""
|
| 167 |
+
if isinstance(images, list):
|
| 168 |
+
# need to extract image features
|
| 169 |
+
images = extract_all_images(images, model, device)
|
| 170 |
+
|
| 171 |
+
candidates = extract_all_captions(candidates, model, device)
|
| 172 |
+
|
| 173 |
+
# as of numpy 1.21, normalize doesn't work properly for float16
|
| 174 |
+
if version.parse(np.__version__) < version.parse("1.21"):
|
| 175 |
+
images = sklearn.preprocessing.normalize(images, axis=1)
|
| 176 |
+
candidates = sklearn.preprocessing.normalize(candidates, axis=1)
|
| 177 |
+
else:
|
| 178 |
+
warnings.warn(
|
| 179 |
+
"due to a numerical instability, new numpy normalization is slightly different than paper results. "
|
| 180 |
+
"to exactly replicate paper results, please use numpy version less than 1.21, e.g., 1.20.3."
|
| 181 |
+
)
|
| 182 |
+
images = images / np.sqrt(np.sum(images**2, axis=1, keepdims=True))
|
| 183 |
+
candidates = candidates / np.sqrt(np.sum(candidates**2, axis=1, keepdims=True))
|
| 184 |
+
|
| 185 |
+
per = w * np.clip(np.sum(images * candidates, axis=1), 0, None)
|
| 186 |
+
return np.mean(per), per, candidates
|
| 187 |
+
|
| 188 |
+
|
| 189 |
+
def get_refonlyclipscore(model, references, candidates, device):
|
| 190 |
+
"""
|
| 191 |
+
The text only side for refclipscore
|
| 192 |
+
"""
|
| 193 |
+
if isinstance(candidates, list):
|
| 194 |
+
candidates = extract_all_captions(candidates, model, device)
|
| 195 |
+
|
| 196 |
+
flattened_refs = []
|
| 197 |
+
flattened_refs_idxs = []
|
| 198 |
+
for idx, refs in enumerate(references):
|
| 199 |
+
flattened_refs.extend(refs)
|
| 200 |
+
flattened_refs_idxs.extend([idx for _ in refs])
|
| 201 |
+
|
| 202 |
+
flattened_refs = extract_all_captions(flattened_refs, model, device)
|
| 203 |
+
|
| 204 |
+
if version.parse(np.__version__) < version.parse("1.21"):
|
| 205 |
+
candidates = sklearn.preprocessing.normalize(candidates, axis=1)
|
| 206 |
+
flattened_refs = sklearn.preprocessing.normalize(flattened_refs, axis=1)
|
| 207 |
+
else:
|
| 208 |
+
warnings.warn(
|
| 209 |
+
"due to a numerical instability, new numpy normalization is slightly different than paper results. "
|
| 210 |
+
"to exactly replicate paper results, please use numpy version less than 1.21, e.g., 1.20.3."
|
| 211 |
+
)
|
| 212 |
+
|
| 213 |
+
candidates = candidates / np.sqrt(np.sum(candidates**2, axis=1, keepdims=True))
|
| 214 |
+
flattened_refs = flattened_refs / np.sqrt(
|
| 215 |
+
np.sum(flattened_refs**2, axis=1, keepdims=True)
|
| 216 |
+
)
|
| 217 |
+
|
| 218 |
+
cand_idx2refs = collections.defaultdict(list)
|
| 219 |
+
for ref_feats, cand_idx in zip(flattened_refs, flattened_refs_idxs):
|
| 220 |
+
cand_idx2refs[cand_idx].append(ref_feats)
|
| 221 |
+
|
| 222 |
+
assert len(cand_idx2refs) == len(candidates)
|
| 223 |
+
|
| 224 |
+
cand_idx2refs = {k: np.vstack(v) for k, v in cand_idx2refs.items()}
|
| 225 |
+
|
| 226 |
+
per = []
|
| 227 |
+
for c_idx, cand in tqdm.tqdm(enumerate(candidates)):
|
| 228 |
+
cur_refs = cand_idx2refs[c_idx]
|
| 229 |
+
all_sims = cand.dot(cur_refs.transpose())
|
| 230 |
+
per.append(np.max(all_sims))
|
| 231 |
+
|
| 232 |
+
return np.mean(per), per
|
| 233 |
+
|
| 234 |
+
|
| 235 |
+
def main():
|
| 236 |
+
args = parse_args()
|
| 237 |
+
|
| 238 |
+
# image_paths = [
|
| 239 |
+
# os.path.join(args.image_dir, path)
|
| 240 |
+
# for path in os.listdir(args.image_dir)
|
| 241 |
+
# if path.endswith((".png", ".jpg", ".jpeg", ".tiff"))
|
| 242 |
+
# ]
|
| 243 |
+
# image_ids = [pathlib.Path(path).stem for path in image_paths]
|
| 244 |
+
# print(image_ids)
|
| 245 |
+
with open(args.candidates_json) as f:
|
| 246 |
+
candidates = json.load(f)
|
| 247 |
+
|
| 248 |
+
image_paths = [os.path.join(args.image_dir, anno["image"]) for anno in candidates]
|
| 249 |
+
relative_image_paths = [anno["image"] for anno in candidates]
|
| 250 |
+
candidates = [anno["conversations"][1]["value"] for anno in candidates]
|
| 251 |
+
if args.references_json:
|
| 252 |
+
with open(args.references_json) as f:
|
| 253 |
+
references = json.load(f)
|
| 254 |
+
references = [references[cid] for cid in image_ids]
|
| 255 |
+
if isinstance(references[0], str):
|
| 256 |
+
references = [[r] for r in references]
|
| 257 |
+
|
| 258 |
+
device = "cuda" if torch.cuda.is_available() else "cpu"
|
| 259 |
+
if device == "cpu":
|
| 260 |
+
warnings.warn(
|
| 261 |
+
"CLIP runs in full float32 on CPU. Results in paper were computed on GPU, which uses float16. "
|
| 262 |
+
"If you're reporting results on CPU, please note this when you report."
|
| 263 |
+
)
|
| 264 |
+
model, transform = clip.load("ViT-B/32", device=device, jit=False)
|
| 265 |
+
model.eval()
|
| 266 |
+
|
| 267 |
+
# image_feats = extract_all_images(
|
| 268 |
+
# image_paths, model, device, batch_size=64, num_workers=32
|
| 269 |
+
# )
|
| 270 |
+
|
| 271 |
+
# get image-text clipscore
|
| 272 |
+
_, per_instance_image_text, candidate_feats = get_clip_score(
|
| 273 |
+
model, image_paths, candidates, device
|
| 274 |
+
)
|
| 275 |
+
|
| 276 |
+
if args.references_json:
|
| 277 |
+
# get text-text clipscore
|
| 278 |
+
_, per_instance_text_text = get_refonlyclipscore(
|
| 279 |
+
model, references, candidate_feats, device
|
| 280 |
+
)
|
| 281 |
+
# F-score
|
| 282 |
+
refclipscores = (
|
| 283 |
+
2
|
| 284 |
+
* per_instance_image_text
|
| 285 |
+
* per_instance_text_text
|
| 286 |
+
/ (per_instance_image_text + per_instance_text_text)
|
| 287 |
+
)
|
| 288 |
+
scores = {
|
| 289 |
+
image_id: {
|
| 290 |
+
"CLIPScore": float(clipscore),
|
| 291 |
+
"RefCLIPScore": float(refclipscore),
|
| 292 |
+
}
|
| 293 |
+
for image_id, clipscore, refclipscore in zip(
|
| 294 |
+
image_ids, per_instance_image_text, refclipscores
|
| 295 |
+
)
|
| 296 |
+
}
|
| 297 |
+
|
| 298 |
+
else:
|
| 299 |
+
scores = [
|
| 300 |
+
{"image_id": image_id, "clipscore": float(clipscore), "caption": caption}
|
| 301 |
+
for image_id, clipscore, caption in zip(
|
| 302 |
+
relative_image_paths, per_instance_image_text, candidates
|
| 303 |
+
)
|
| 304 |
+
]
|
| 305 |
+
# scores = {
|
| 306 |
+
# image_id: {"CLIPScore": float(clipscore)}
|
| 307 |
+
# for image_id, clipscore captions in zip(
|
| 308 |
+
# relative_image_paths, per_instance_image_text, candidates
|
| 309 |
+
# )
|
| 310 |
+
# }
|
| 311 |
+
with open(args.result_file, "w") as f:
|
| 312 |
+
json.dump(scores, f)
|
| 313 |
+
# print('CLIPScore: {:.4f}'.format(np.mean([s['CLIPScore'] for s in scores.values()])))
|
| 314 |
+
|
| 315 |
+
if args.references_json:
|
| 316 |
+
if args.compute_other_ref_metrics:
|
| 317 |
+
other_metrics = generation_eval_utils.get_all_metrics(
|
| 318 |
+
references, candidates
|
| 319 |
+
)
|
| 320 |
+
for k, v in other_metrics.items():
|
| 321 |
+
if k == "bleu":
|
| 322 |
+
for bidx, sc in enumerate(v):
|
| 323 |
+
print("BLEU-{}: {:.4f}".format(bidx + 1, sc))
|
| 324 |
+
else:
|
| 325 |
+
print("{}: {:.4f}".format(k.upper(), v))
|
| 326 |
+
print(
|
| 327 |
+
"CLIPScore: {:.4f}".format(
|
| 328 |
+
np.mean([s["CLIPScore"] for s in scores.values()])
|
| 329 |
+
)
|
| 330 |
+
)
|
| 331 |
+
print(
|
| 332 |
+
"RefCLIPScore: {:.4f}".format(
|
| 333 |
+
np.mean([s["RefCLIPScore"] for s in scores.values()])
|
| 334 |
+
)
|
| 335 |
+
)
|
| 336 |
+
|
| 337 |
+
if args.save_per_instance:
|
| 338 |
+
with open(args.save_per_instance, "w") as f:
|
| 339 |
+
f.write(json.dumps(scores))
|
| 340 |
+
|
| 341 |
+
|
| 342 |
+
if __name__ == "__main__":
|
| 343 |
+
main()
|
coco_gen_200k.json
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:310b244bf393850b7ebb6b1ff222886552988546cc9ff07d1427d4d29c64a8fe
|
| 3 |
+
size 33059506
|
coco_raw_200k.json
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:ea280b1f8505aa9f485baab5dff4ca5bd0c7dbec19be72d4bb964e1771ecf8e6
|
| 3 |
+
size 33063802
|
example/bad_captions.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"image1": "a calico cat and a white cat are lying together.", "image2": "a black dog wearing a hat looks at the camera as a tabby walks in the background."}
|
example/good_captions.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"image1": "an orange cat and a grey cat are lying together.", "image2": "a black dog wearing headphones looks at the camera as an orange cat walks in the background."}
|
example/images/image1.jpg
ADDED
|
example/images/image2.jpg
ADDED
|
example/refs.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"image1": ["two cats are sleeping next to each other.", "a grey cat is cuddling with an orange cat on a blanket.", "the orange cat is happy that the black cat is close to it."], "image2": ["a dog is wearing ear muffs as it lies on a carpet.", "a black dog and an orange cat are looking at the photographer.", "headphones are placed on a dogs ears."]}
|
flickr8k_example/README.md
ADDED
|
@@ -0,0 +1,19 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
## What's in here?
|
| 2 |
+
|
| 3 |
+
This directory contains code that replicates the experiments we ran to
|
| 4 |
+
compute correlation with human judgments in the Flickr8K corpus. This
|
| 5 |
+
setup has been used in prior work, but there are a number of specific
|
| 6 |
+
settings one needs to use to replicate the original results from the
|
| 7 |
+
SPICE paper, who are the first to run in this setup. More details are
|
| 8 |
+
available in appendix A of:
|
| 9 |
+
|
| 10 |
+
CLIPScore: A Reference-free Evaluation Metric for Image Captioning
|
| 11 |
+
by Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, Yejin Choi
|
| 12 |
+
https://arxiv.org/abs/2104.08718
|
| 13 |
+
|
| 14 |
+
## How do I run the code?
|
| 15 |
+
|
| 16 |
+
There are two steps:
|
| 17 |
+
|
| 18 |
+
1. run `download.py` which downloads and preprocesses the Flickr8K corpus.
|
| 19 |
+
2. run `compute_metrics.py` which will compute the appropriate evaluation metrics and report correlations with human judgment
|
flickr8k_example/compute_metrics.py
ADDED
|
@@ -0,0 +1,86 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
'''
|
| 2 |
+
Computes the metrics for Flickr8K.
|
| 3 |
+
'''
|
| 4 |
+
import sys
|
| 5 |
+
sys.path.append('../')
|
| 6 |
+
import clipscore
|
| 7 |
+
import generation_eval_utils
|
| 8 |
+
import scipy.stats
|
| 9 |
+
import os
|
| 10 |
+
import json
|
| 11 |
+
import numpy as np
|
| 12 |
+
import torch
|
| 13 |
+
import warnings
|
| 14 |
+
import clip
|
| 15 |
+
|
| 16 |
+
|
| 17 |
+
def compute_human_correlation(input_json, image_directory, tauvariant='c'):
|
| 18 |
+
|
| 19 |
+
data = {}
|
| 20 |
+
with open(input_json) as f:
|
| 21 |
+
data.update(json.load(f))
|
| 22 |
+
print('Loaded {} images'.format(len(data)))
|
| 23 |
+
|
| 24 |
+
images = []
|
| 25 |
+
refs = []
|
| 26 |
+
candidates = []
|
| 27 |
+
human_scores = []
|
| 28 |
+
for k, v in list(data.items()):
|
| 29 |
+
for human_judgement in v['human_judgement']:
|
| 30 |
+
if np.isnan(human_judgement['rating']):
|
| 31 |
+
print('NaN')
|
| 32 |
+
continue
|
| 33 |
+
images.append(image_directory + '/' + v['image_path'])
|
| 34 |
+
refs.append([' '.join(gt.split()) for gt in v['ground_truth']])
|
| 35 |
+
candidates.append(' '.join(human_judgement['caption'].split()))
|
| 36 |
+
human_scores.append(human_judgement['rating'])
|
| 37 |
+
|
| 38 |
+
device = "cuda" if torch.cuda.is_available() else "cpu"
|
| 39 |
+
if device == 'cpu':
|
| 40 |
+
warnings.warn(
|
| 41 |
+
'CLIP runs in full float32 on CPU. Results in paper were computed on GPU, which uses float16. '
|
| 42 |
+
'If you\'re reporting results on CPU, please note this when you report.')
|
| 43 |
+
model, transform = clip.load("ViT-B/32", device=device, jit=False)
|
| 44 |
+
model.eval()
|
| 45 |
+
|
| 46 |
+
image_feats = clipscore.extract_all_images(
|
| 47 |
+
images, model, device, batch_size=64, num_workers=8)
|
| 48 |
+
|
| 49 |
+
# get image-text clipscore
|
| 50 |
+
_, per_instance_image_text, candidate_feats = clipscore.get_clip_score(
|
| 51 |
+
model, image_feats, candidates, device)
|
| 52 |
+
|
| 53 |
+
# get text-text clipscore
|
| 54 |
+
_, per_instance_text_text = clipscore.get_refonlyclipscore(
|
| 55 |
+
model, refs, candidate_feats, device)
|
| 56 |
+
|
| 57 |
+
# F-score
|
| 58 |
+
refclipscores = 2 * per_instance_image_text * per_instance_text_text / (per_instance_image_text + per_instance_text_text)
|
| 59 |
+
other_metrics = generation_eval_utils.get_all_metrics(refs, candidates, return_per_cap=True)
|
| 60 |
+
|
| 61 |
+
print('CLIPScore Tau-{}: {:.3f}'.format(tauvariant, 100*scipy.stats.kendalltau(per_instance_image_text, human_scores, variant=tauvariant)[0]))
|
| 62 |
+
print('RefCLIPScore Tau-{}: {:.3f}'.format(tauvariant, 100*scipy.stats.kendalltau(refclipscores, human_scores, variant=tauvariant)[0]))
|
| 63 |
+
|
| 64 |
+
for k, v in other_metrics.items():
|
| 65 |
+
if k == 'bleu':
|
| 66 |
+
v = v[-1] # just do BLEU-4
|
| 67 |
+
k = 'bleu-4'
|
| 68 |
+
if k == 'spice':
|
| 69 |
+
v = [float(item['All']['f']) for item in v]
|
| 70 |
+
|
| 71 |
+
print('{} Tau-{}: {:.3f}'.format(k, tauvariant, 100*scipy.stats.kendalltau(v, human_scores, variant=tauvariant)[0]))
|
| 72 |
+
|
| 73 |
+
|
| 74 |
+
def main():
|
| 75 |
+
if not os.path.exists('flickr8k/flickr8k.json'):
|
| 76 |
+
print('Please run download.py')
|
| 77 |
+
quit()
|
| 78 |
+
print('Flickr8K Expert (Tau-c)')
|
| 79 |
+
compute_human_correlation('flickr8k/flickr8k.json', 'flickr8k/', tauvariant='c')
|
| 80 |
+
|
| 81 |
+
print('Flickr8K CrowdFlower (Tau-b)')
|
| 82 |
+
compute_human_correlation('flickr8k/crowdflower_flickr8k.json', 'flickr8k/', tauvariant='b')
|
| 83 |
+
|
| 84 |
+
|
| 85 |
+
if __name__ == '__main__':
|
| 86 |
+
main()
|
flickr8k_example/download.py
ADDED
|
@@ -0,0 +1,145 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
'''Downloads and preprocesses the Flickr8K corpus.
|
| 2 |
+
|
| 3 |
+
For more details about particular choices made when computing
|
| 4 |
+
correlations, please see appendix A in:
|
| 5 |
+
|
| 6 |
+
CLIPScore:
|
| 7 |
+
A Reference-free Evaluation Metric for Image Captioning
|
| 8 |
+
by Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, Yejin Choi
|
| 9 |
+
https://arxiv.org/abs/2104.08718
|
| 10 |
+
'''
|
| 11 |
+
from google_drive_downloader import GoogleDriveDownloader as gdd
|
| 12 |
+
import os
|
| 13 |
+
import tqdm
|
| 14 |
+
import collections
|
| 15 |
+
import json
|
| 16 |
+
import numpy as np
|
| 17 |
+
|
| 18 |
+
|
| 19 |
+
def process_flickr8k():
|
| 20 |
+
if not os.path.exists('flickr8k'):
|
| 21 |
+
os.makedirs('flickr8k')
|
| 22 |
+
|
| 23 |
+
if not os.path.exists('flickr8k/Flickr8k_Dataset.zip'):
|
| 24 |
+
gdd.download_file_from_google_drive(file_id='1WNY8pV-u8xtBYBVal03qwjQs4VKurUZn',
|
| 25 |
+
dest_path='./flickr8k/Flickr8k_Dataset.zip',
|
| 26 |
+
unzip=True,
|
| 27 |
+
showsize=True)
|
| 28 |
+
if not os.path.exists('flickr8k/Flickr8k_text.zip'):
|
| 29 |
+
gdd.download_file_from_google_drive(file_id='1ljB7DR-YM-q9WKnHDW5dHjauK029B2s6',
|
| 30 |
+
dest_path='./flickr8k/Flickr8k_text.zip',
|
| 31 |
+
unzip=True,
|
| 32 |
+
showsize=True)
|
| 33 |
+
|
| 34 |
+
|
| 35 |
+
# flickr8k #
|
| 36 |
+
flickr8k_image2ann = collections.defaultdict(list)
|
| 37 |
+
captionid2caption = {}
|
| 38 |
+
|
| 39 |
+
with open('flickr8k/Flickr8k.token.txt') as f:
|
| 40 |
+
for line in f:
|
| 41 |
+
image, ann = line.strip().split('\t')
|
| 42 |
+
flickr8k_image2ann[image.split('#')[0]].append(ann)
|
| 43 |
+
captionid2caption[image] = ann
|
| 44 |
+
|
| 45 |
+
flickr8k_image2ann = {k.split('.')[0]: v for k, v in flickr8k_image2ann.items()}
|
| 46 |
+
|
| 47 |
+
all_8k_expert, all_8k_crowdflower = [], []
|
| 48 |
+
|
| 49 |
+
total_ann = 0
|
| 50 |
+
with open('flickr8k/CrowdFlowerAnnotations.txt') as f:
|
| 51 |
+
for line in f:
|
| 52 |
+
image_id, caption_id, yes_prec, n_yes, n_no = line.strip().split('\t')
|
| 53 |
+
all_8k_crowdflower.append({
|
| 54 |
+
'image_id': image_id,
|
| 55 |
+
'caption_id': caption_id,
|
| 56 |
+
'caption': captionid2caption[caption_id],
|
| 57 |
+
'n_yes': int(n_yes),
|
| 58 |
+
'n_no': int(n_no) ,
|
| 59 |
+
'yes_prec': float(yes_prec),
|
| 60 |
+
'image_filepath' : 'Flicker8k_Dataset/{}'.format(image_id)})
|
| 61 |
+
assert np.abs(float(yes_prec) - int(n_yes) / (int(n_yes) + int(n_no))) < 10E-6
|
| 62 |
+
total_ann += (int(n_yes) + int(n_no))
|
| 63 |
+
|
| 64 |
+
|
| 65 |
+
all_index = {}
|
| 66 |
+
for d in all_8k_crowdflower:
|
| 67 |
+
if d['image_id'] not in all_index:
|
| 68 |
+
all_index[d['image_id']] = {
|
| 69 |
+
'human_judgement': [],
|
| 70 |
+
'image_id': d['image_id'],
|
| 71 |
+
'image_path': d['image_filepath'],
|
| 72 |
+
'ground_truth': [x for x in flickr8k_image2ann[d['image_id'].split('.')[0]]]}
|
| 73 |
+
|
| 74 |
+
if d['caption'] in all_index[d['image_id']]['ground_truth']:
|
| 75 |
+
all_index[d['image_id']]['ground_truth'].remove(d['caption'])
|
| 76 |
+
|
| 77 |
+
all_index[d['image_id']]['human_judgement'].append(
|
| 78 |
+
{'image_id': d['image_id'],
|
| 79 |
+
'image_path': d['image_filepath'],
|
| 80 |
+
'caption': d['caption'],
|
| 81 |
+
'rating': d['yes_prec']})
|
| 82 |
+
|
| 83 |
+
print('For crowdflower, we are dumping {} judgments between {} images'.format(
|
| 84 |
+
len(all_8k_crowdflower),
|
| 85 |
+
len(all_index)))
|
| 86 |
+
|
| 87 |
+
with open('flickr8k/crowdflower_flickr8k.json', 'w') as f:
|
| 88 |
+
f.write(json.dumps(all_index))
|
| 89 |
+
|
| 90 |
+
skip = 0
|
| 91 |
+
with open('flickr8k/ExpertAnnotations.txt') as f:
|
| 92 |
+
for line in f:
|
| 93 |
+
image_id, caption_id, ex1, ex2, ex3 = line.strip().split('\t')
|
| 94 |
+
caption = captionid2caption[caption_id]
|
| 95 |
+
# we will skip the ones in the refs following the SPICE paper.
|
| 96 |
+
if caption in flickr8k_image2ann[image_id.split('.')[0]]:
|
| 97 |
+
skip += 1
|
| 98 |
+
continue
|
| 99 |
+
all_8k_expert.append({
|
| 100 |
+
'image_id': image_id.split('.')[0],
|
| 101 |
+
'image_filepath' : 'Flicker8k_Dataset/{}'.format(image_id),
|
| 102 |
+
'caption': caption,
|
| 103 |
+
'expert1': float(ex1),
|
| 104 |
+
'expert2': float(ex2),
|
| 105 |
+
'expert3': float(ex3)})
|
| 106 |
+
|
| 107 |
+
all_index = {}
|
| 108 |
+
for d in all_8k_expert:
|
| 109 |
+
if d['image_id'] not in all_index:
|
| 110 |
+
all_index[d['image_id']] = {
|
| 111 |
+
'human_judgement': [],
|
| 112 |
+
'image_id': d['image_id'],
|
| 113 |
+
'image_path': d['image_filepath'],
|
| 114 |
+
'ground_truth': flickr8k_image2ann[d['image_id']]}
|
| 115 |
+
|
| 116 |
+
all_index[d['image_id']]['human_judgement'].append(
|
| 117 |
+
{'image_id': d['image_id'],
|
| 118 |
+
'image_path': d['image_filepath'],
|
| 119 |
+
'caption': d['caption'],
|
| 120 |
+
'rating': d['expert1']})
|
| 121 |
+
all_index[d['image_id']]['human_judgement'].append(
|
| 122 |
+
{'image_id': d['image_id'],
|
| 123 |
+
'image_path': d['image_filepath'],
|
| 124 |
+
'caption': d['caption'],
|
| 125 |
+
'rating': d['expert2']})
|
| 126 |
+
all_index[d['image_id']]['human_judgement'].append(
|
| 127 |
+
{'image_id': d['image_id'],
|
| 128 |
+
'image_path': d['image_filepath'],
|
| 129 |
+
'caption': d['caption'],
|
| 130 |
+
'rating': d['expert3']})
|
| 131 |
+
|
| 132 |
+
print('For expert, we are dumping {} judgments between {} images'.format(
|
| 133 |
+
len(all_8k_expert)*3,
|
| 134 |
+
len(all_index)))
|
| 135 |
+
|
| 136 |
+
with open('flickr8k/flickr8k.json', 'w') as f:
|
| 137 |
+
f.write(json.dumps(all_index))
|
| 138 |
+
|
| 139 |
+
|
| 140 |
+
def main():
|
| 141 |
+
if not os.path.exists('flickr8k/flickr8k.json'):
|
| 142 |
+
process_flickr8k()
|
| 143 |
+
|
| 144 |
+
if __name__ == '__main__':
|
| 145 |
+
main()
|
generation_eval_utils.py
ADDED
|
@@ -0,0 +1,66 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
'''
|
| 2 |
+
Automatic generation evaluation metrics wrapper
|
| 3 |
+
|
| 4 |
+
The most useful function here is
|
| 5 |
+
|
| 6 |
+
get_all_metrics(refs, cands)
|
| 7 |
+
'''
|
| 8 |
+
from pycocoevalcap.tokenizer.ptbtokenizer import PTBTokenizer
|
| 9 |
+
from pycocoevalcap.spice.spice import Spice
|
| 10 |
+
from pycocoevalcap.meteor.meteor import Meteor
|
| 11 |
+
from pycocoevalcap.bleu.bleu import Bleu
|
| 12 |
+
from pycocoevalcap.cider.cider import Cider
|
| 13 |
+
from pycocoevalcap.rouge.rouge import Rouge
|
| 14 |
+
from pycocoevalcap.spice.spice import Spice
|
| 15 |
+
|
| 16 |
+
|
| 17 |
+
def get_all_metrics(refs, cands, return_per_cap=False):
|
| 18 |
+
metrics = []
|
| 19 |
+
names = []
|
| 20 |
+
|
| 21 |
+
pycoco_eval_cap_scorers = [(Bleu(4), 'bleu'),
|
| 22 |
+
(Meteor(), 'meteor'),
|
| 23 |
+
(Rouge(), 'rouge'),
|
| 24 |
+
(Cider(), 'cider'),
|
| 25 |
+
(Spice(), 'spice')]
|
| 26 |
+
|
| 27 |
+
for scorer, name in pycoco_eval_cap_scorers:
|
| 28 |
+
overall, per_cap = pycoco_eval(scorer, refs, cands)
|
| 29 |
+
if return_per_cap:
|
| 30 |
+
metrics.append(per_cap)
|
| 31 |
+
else:
|
| 32 |
+
metrics.append(overall)
|
| 33 |
+
names.append(name)
|
| 34 |
+
|
| 35 |
+
metrics = dict(zip(names, metrics))
|
| 36 |
+
return metrics
|
| 37 |
+
|
| 38 |
+
|
| 39 |
+
def tokenize(refs, cands, no_op=False):
|
| 40 |
+
# no_op is a debug option to see how significantly not using the PTB tokenizer
|
| 41 |
+
# affects things
|
| 42 |
+
tokenizer = PTBTokenizer()
|
| 43 |
+
|
| 44 |
+
if no_op:
|
| 45 |
+
refs = {idx: [r for r in c_refs] for idx, c_refs in enumerate(refs)}
|
| 46 |
+
cands = {idx: [c] for idx, c in enumerate(cands)}
|
| 47 |
+
|
| 48 |
+
else:
|
| 49 |
+
refs = {idx: [{'caption':r} for r in c_refs] for idx, c_refs in enumerate(refs)}
|
| 50 |
+
cands = {idx: [{'caption':c}] for idx, c in enumerate(cands)}
|
| 51 |
+
|
| 52 |
+
refs = tokenizer.tokenize(refs)
|
| 53 |
+
cands = tokenizer.tokenize(cands)
|
| 54 |
+
|
| 55 |
+
return refs, cands
|
| 56 |
+
|
| 57 |
+
|
| 58 |
+
def pycoco_eval(scorer, refs, cands):
|
| 59 |
+
'''
|
| 60 |
+
scorer is assumed to have a compute_score function.
|
| 61 |
+
refs is a list of lists of strings
|
| 62 |
+
cands is a list of predictions
|
| 63 |
+
'''
|
| 64 |
+
refs, cands = tokenize(refs, cands)
|
| 65 |
+
average_score, scores = scorer.compute_score(refs, cands)
|
| 66 |
+
return average_score, scores
|
llava_gen_200k.json
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:4064434dec8f08ab97679eb9803c733cdb74747fd86e4bb75f7a8159ee90abd3
|
| 3 |
+
size 33446444
|
llava_raw_200k.json
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:db011185d70c6715abbd7ec322e7ce19b586532835c8a7360c3dbe3772bf1dab
|
| 3 |
+
size 33447566
|
llava_raw_558k.json
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:8099a09193e0dbfb16d776e68e4032330390f33fd3da6624dbd1be94f32b7784
|
| 3 |
+
size 74361707
|
requirements.txt
ADDED
|
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
|
|
|
| 1 |
+
git+https://github.com/openai/CLIP.git
|
| 2 |
+
git+https://github.com/jmhessel/pycocoevalcap.git
|
run.sh
ADDED
|
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
python clipscore.py /mnt/petrelfs/zhuchenglin/LLaVA/playground/data/LLaVA-Pretrain/llava_gen_200k.json /mnt/petrelfs/zhuchenglin/LLaVA/playground/data/LLaVA-Pretrain/images llava_gen_200k.json
|
| 2 |
+
python clipscore.py /mnt/petrelfs/zhuchenglin/LLaVA/playground/data/LLaVA-Pretrain/llava_raw_200k.json /mnt/petrelfs/zhuchenglin/LLaVA/playground/data/LLaVA-Pretrain/images llava_raw_200k.json
|
| 3 |
+
python clipscore.py /mnt/petrelfs/zhuchenglin/LLaVA/playground/data/LLaVA-Pretrain/coco_gen_200k.json /mnt/petrelfs/zhuchenglin/LLaVA/playground/data/LLaVA-Pretrain/images coco_gen_200k.json
|
| 4 |
+
python clipscore.py /mnt/petrelfs/zhuchenglin/LLaVA/playground/data/LLaVA-Pretrain/coco_raw_200k.json /mnt/petrelfs/zhuchenglin/LLaVA/playground/data/LLaVA-Pretrain/images coco_raw_200k.json
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run_coco_gen.sh
ADDED
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@@ -0,0 +1 @@
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| 1 |
+
srun -p s2_bigdata --gres=gpu:4 --kill-on-bad-exit=1 python clipscore.py /mnt/petrelfs/zhuchenglin/LLaVA/playground/data/LLaVA-Pretrain/llava_gen_200k.json /mnt/petrelfs/zhuchenglin/LLaVA/playground/data/LLaVA-Pretrain/images llava_gen_200k.json
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run_coco_raw.sh
ADDED
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@@ -0,0 +1 @@
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| 1 |
+
python clipscore.py /mnt/petrelfs/zhuchenglin/LLaVA/playground/data/LLaVA-Pretrain/llava_raw_200k.json /mnt/petrelfs/zhuchenglin/LLaVA/playground/data/LLaVA-Pretrain/images llava_raw_558k.json
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