├── LICENSE ├── README.md ├── bbox ├── coco_bbox_categorized.json ├── umbrae-v-1-4 │ ├── sub01_decoded_bbox.json │ ├── sub02_decoded_bbox.json │ ├── sub05_decoded_bbox.json │ └── sub07_decoded_bbox.json └── umbrae │ ├── sub01_decoded_bbox.json │ ├── sub02_decoded_bbox.json │ ├── sub05_decoded_bbox.json │ └── sub07_decoded_bbox.json ├── caption ├── comparison │ ├── braincap │ │ ├── sub01_decoded_caption.json │ │ ├── sub02_decoded_caption.json │ │ ├── sub05_decoded_caption.json │ │ └── sub07_decoded_caption.json │ ├── onellm │ │ └── sub01_decoded_caption.json │ ├── sdrecon │ │ ├── sub01_decoded_caption.json │ │ ├── sub02_decoded_caption.json │ │ ├── sub05_decoded_caption.json │ │ └── sub07_decoded_caption.json │ ├── umbrae-v-1-4 │ │ ├── sub01_decoded_caption.json │ │ ├── sub02_decoded_caption.json │ │ ├── sub05_decoded_caption.json │ │ └── sub07_decoded_caption.json │ └── umbrae │ │ ├── sub01_decoded_caption.json │ │ ├── sub02_decoded_caption.json │ │ ├── sub05_decoded_caption.json │ │ └── sub07_decoded_caption.json └── fmri_cococap.json ├── eval_bbox_rec.py ├── eval_caption.py ├── metrics.py ├── processing ├── coco │ ├── coco_categorized_labels.json │ └── coco_labels.txt └── decode_images.py └── run.sh /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2024 weihao 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # BrainHub: Multimodal Brain Understanding Benchmark 2 | 3 | ## Updates 4 | - [2024/07/01] UMBRAE is accepted to ECCV 2024. 5 | - [2024/05/18] Update Leaderboard results. 6 | - [2024/04/11] The brainhub benchmark has been released. 7 | 8 | ## Motivation 9 | 10 | Unlike texts, images, or audio, whose contents are intuitively aligned with human perception and judgment, we lack sufficient knowledge of the information contained in captured brain responses, as they are not directly interpretable or interoperable to humans. We could translate the brain's responses into other understandable modalities as an indirect method of ascertaining its ability to describe, recognize, and localize instances, as well as discern spatial relationships among multiple exemplars. These abilities are important for brain-machine interfaces and other brain-related research. Therefore, we construct BrainHub, a brain understanding benchmark, based on [NSD](https://naturalscenesdataset.org/) and [COCO](https://cocodataset.org). 11 | 12 | ## Tasks and Metrics 13 | 14 | The objectives are categorized into concept recognition and spatial localization, including: 15 | 16 | - brain captioning, which is to generate textual descriptions summarizing the primary content of a given brain response. To evaluate the quality of generated captions, we use five [standard metrics](https://github.com/tylin/coco-caption), BLEU, METEOR, ROUGE, CIDEr, and SPICE, in addition to [CLIP-based scores](https://github.com/jmhessel/clipscore), CLIP-S and RefCLIP-S. 17 | 18 | - brain grounding, which is the counterpart of visual grounding and seeks to recover spatial locations from brain responses by inferring coordinates. Given that identified classes might be named differently, or simply absent from ground truth labels, we evaluate boundingboxes through REC, using accuracy and IoU as metrics. 19 | 20 | ## Evaluation 21 | 22 | There are 982 test images, 80 classes, 4,913 captions, and 5,829 boundingboxes. For grounding evaluation, we further group the 80 classes of COCO into four salience categories according to their salience in images: Salient (S), Salient Creatures (SC), Salient Objects (SO), and Inconspicuous (I). The illustration shows the statistics and mapping of our categories, w.r.t. COCO classes. 23 | 24 | We provide the processed [text](https://github.com/weihaox/brainhub/caption) and [boundingbox](https://github.com/weihaox/brainhub/bbox) groundtruth. The demo evaluation script is provided [here](https://github.com/weihaox/brainhub/run.sh). If you would like to evaluate your produced results, please modify the result path accordingly. 25 | 26 | We also provide baseline results associated with BrainHub, including the captioning results from [SDRecon](https://github.com/yu-takagi/StableDiffusionReconstruction), [BrainCap](https://arxiv.org/abs/2305.11560), and [OneLLM](https://onellm.csuhan.com/), as well as the captioning and grounding results from [UMBRAE](https://weihaox.github.io/UMBRAE/). 27 | 28 | For contributing, please (a) update the leaderboard and (b) upload the results to the desired path with the required file name, such as `caption/comparison/umbrae/sub01_decoded_caption.json`. 29 | 30 | ## Leaderboard 31 | 32 | ### Captioning 33 | 34 | This is the quantitative comparison for subject 1 (S1). For results on other subjects, refer to the [paper](https://weihaox.github.io/UMBRAE/). 'UMBRAE-S1' refers to model trained with S1 only, while 'UMBRAE' denotes the model with cross-subject training. 35 | 36 | | Method | Eval | BLEU1 | BLEU4 | METEOR | ROUGE | CIDEr | SPICE | CLIPS | RefCLIPS | 37 | |-----------|------|-------|-------|--------|-------|-------|-------|-------|----------| 38 | | UMBRAE | S1 | 59.44 | 19.03 | 19.45 | 43.71 | 61.06 | 12.79 | 67.78 | 73.54 | 39 | | UMBRAE-S1 | S1 | 57.63 | 16.76 | 18.41 | 42.15 | 51.93 | 11.83 | 66.44 | 72.12 | 40 | | BrainCap | S1 | 55.96 | 14.51 | 16.68 | 40.69 | 41.30 | 9.06 | 64.31 | 69.90 | 41 | | OneLLM | S1 | 47.04 | 9.51 | 13.55 | 35.05 | 22.99 | 6.26 | 54.80 | 61.28 | 42 | | SDRecon | S1 | 36.21 | 3.43 | 10.03 | 25.13 | 13.83 | 5.02 | 61.07 | 66.36 | 43 | 44 | | Method | Eval | BLEU1 | BLEU4 | METEOR | ROUGE | CIDEr | SPICE | CLIPS | RefCLIPS | 45 | |-----------|------|-------|-------|--------|-------|-------|-------|-------|----------| 46 | | UMBRAE | S2 | 59.37 | 18.41 | 19.17 | 43.86 | 55.93 | 12.08 | 66.46 | 72.36 | 47 | | UMBRAE-S2 | S2 | 57.18 | 17.18 | 18.11 | 41.85 | 50.62 | 11.50 | 64.87 | 71.06 | 48 | | BrainCap | S2 | 53.80 | 13.03 | 15.90 | 39.96 | 35.60 | 8.47 | 62.48 | 68.19 | 49 | | SDRecon | S2 | 34.71 | 3.02 | 9.60 | 24.22 | 13.38 | 4.58 | 59.52 | 65.30 | 50 | 51 | | Method | Eval | BLEU1 | BLEU4 | METEOR | ROUGE | CIDEr | SPICE | CLIPS | RefCLIPS | 52 | |-----------|------|-------|-------|--------|-------|-------|-------|-------|----------| 53 | | UMBRAE | S5 | 60.36 | 19.03 | 20.04 | 44.81 | 61.32 | 13.19 | 68.39 | 74.11 | 54 | | UMBRAE-S5 | S5 | 58.99 | 18.73 | 19.04 | 43.30 | 57.09 | 12.70 | 66.48 | 72.69 | 55 | | BrainCap | S5 | 55.28 | 14.62 | 16.45 | 40.87 | 41.05 | 9.24 | 63.89 | 69.64 | 56 | | SDRecon | S5 | 34.96 | 3.49 | 9.93 | 24.77 | 13.85 | 5.19 | 60.83 | 66.30 | 57 | 58 | | Method | Eval | BLEU1 | BLEU4 | METEOR | ROUGE | CIDEr | SPICE | CLIPS | RefCLIPS | 59 | |-----------|------|-------|-------|--------|-------|-------|-------|-------|----------| 60 | | UMBRAE | S7 | 57.20 | 17.13 | 18.29 | 42.16 | 52.73 | 11.63 | 65.90 | 71.83 | 61 | | UMBRAE-S7 | S7 | 55.71 | 15.75 | 17.51 | 40.64 | 47.07 | 11.26 | 63.66 | 70.09 | 62 | | BrainCap | S7 | 54.25 | 14.00 | 15.94 | 40.02 | 37.49 | 8.57 | 62.52 | 68.48 | 63 | | SDRecon | S7 | 34.99 | 3.26 | 9.54 | 24.33 | 13.01 | 4.74 | 58.68 | 64.59 | 64 | 65 | ### Grounding 66 | 67 | | Method | Eval | acc@0.5 (A) | IoU (A) | acc@0.5 (S) | IoU (S) | acc@0.5 (I) | IoU (I) | 68 | |-----------|------|-------------|---------|-------------|---------|-------------|---------| 69 | | UMBRAE | S1 | 18.93 | 21.28 | 30.23 | 30.18 | 4.83 | 10.18 | 70 | | UMBRAE-S1 | S1 | 13.72 | 17.56 | 21.52 | 25.14 | 4.00 | 8.08 | 71 | | UMBRAE | S2 | 18.27 | 20.77 | 28.22 | 29.19 | 5.86 | 1025 | 72 | | UMBRAE-S2 | S2 | 15.21 | 18.68 | 23.60 | 26.59 | 4.74 | 8.81 | 73 | | UMBRAE | S5 | 18.19 | 20.85 | 28.74 | 30.02 | 5.02 | 9.41 | 74 | | UMBRAE-S5 | S5 | 14.72 | 18.45 | 22.93 | 26.34 | 4.46 | 8.60 | 75 | | UMBRAE | S7 | 16.74 | 19.63 | 25.69 | 27.90 | 5.58 | 9.31 | 76 | | UMBRAE-S7 | S7 | 13.60 | 17.83 | 21.07 | 25.19 | 4.28 | 8.64 | 77 | 78 | ## Citation 79 | 80 | ```bibtex 81 | @inproceedings{xia2024umbrae, 82 | author = {Xia, Weihao and de Charette, Raoul and Öztireli, Cengiz and Xue, Jing-Hao}, 83 | title = {UMBRAE: Unified Multimodal Brain Decoding}, 84 | booktitle = {European Conference on Computer Vision (ECCV)}, 85 | year = {2024}, 86 | } 87 | ``` -------------------------------------------------------------------------------- /caption/comparison/braincap/sub01_decoded_caption.json: -------------------------------------------------------------------------------- 1 | {"0": "a group of people eating food at a table.", "1": "a woman is serving a man.", "2": "a man riding a surfboard on top of a wave.", "3": "a person is standing on a beach with a snowboard.", "4": "a corner of a building with a train station.", "5": "a plate of food with mixed salad dressing with mixed salad.", "6": "a close up of a person's hand on a table", "7": "a group of people playing a game of frisbee.", "8": "a black and white photo of a brown and black dog.", "9": "a man riding a surfboard on top of a wave.", "10": "a jet airliner is seen from the front of the plane.", "11": "a person riding a wave on a surfboard.", "12": "a young male in a green field with a brown and white dog.", "13": "a young man sitting in a chair with a laptop.", "14": "a large passenger plane is traveling down the road.", "15": "a room with a couch, chairs, a table and a chair.", "16": "a man wearing a suit and tie sitting in front of a table.", "17": "a large group of people on a grassy field.", "18": "a young man wearing a red shirt and black pants.", "19": "a bathroom with a sink and a toilet", "20": "a young woman with a young girl holding a stuffed bear.", "21": "a group of people standing around each other.", "22": "a clock on the side of a tower.", "23": "a group of people in the woods.", "24": "a large area of grass.", "25": "a large stuffed animal.", "26": "a couple of plates of food on a table", "27": "a large white and blue jet ski in the air.", "28": "a pair of two birds are on a pole.", "29": "a young woman sitting on a chair with her feet on her lap.", "30": "a piece of food with a bite taken out of it", "31": "a room with a couch, chairs, and a table.", "32": "a room with a couch, a table and a chair.", "33": "a bathroom with a toilet and a sink", "34": "a close up of a food dish with a pizza crust", "35": "a city with a lot of cars on the road.", "36": "a city bus station with a train station in the background.", "37": "a close up of food with a slice of cheese and a slice of bread.", "38": "a young man in a black shirt and white shirt.", "39": "a long line of vehicles on a road.", "40": "a young man holding a tennis racket in his hand.", "41": "a group of trees and a zebra", "42": "a large group of buildings and a street with a lot of vehicles.", "43": "a large body of water with some birds on it", "44": "a small glass bowl with a small vase of flowers in it.", "45": "a man flying a kite while standing on a beach.", "46": "a large passenger plane on a runway near a large city.", "47": "a city street with a lot of cars and a bus.", "48": "a man standing on a field with a young woman.", "49": "a close up of a person with a ball", "50": "a person is sitting on a chair with a bag.", "51": "a view of the ocean from the beach.", "52": "a group of four - headed aircraft in the sky.", "53": "a small white and black jet of water with a small amount of water in it.", "54": "a street scene with a car and a large tree.", "55": "a long line of people on a road", "56": "a close up of a food dish", "57": "a young man holding a baseball bat.", "58": "a city street with a lot of people walking on it", "59": "a kitchen with a table and a chair", "60": "a young tennis player in action on the court.", "61": "a plate of food with vegetables and cheese.", "62": "a person is riding a skateboard on a slope.", "63": "a plate of food with a variety of toppings.", "64": "a man is riding a skateboard on a ramp.", "65": "a group of people sitting in a room.", "66": "a lone zebra is standing in the grass.", "67": "a stop sign with a large red and yellow traffic cone on top of it.", "68": "a person is riding a surfboard on a beach.", "69": "the head of a giraffe", "70": "a plate of food with a slice of cheese and a slice of pepperoni.", "71": "a large body of water with a few clouds in the sky.", "72": "a large blue and red bus driving down a street.", "73": "a person holding a kite and a kite", "74": "a man sitting at a table with a slice of pizza.", "75": "a small garden with a table, chairs, and a lamp.", "76": "a person is sitting at a table with a laptop and a remote control.", "77": "a very cute little face with a big smile.", "78": "a train is parked on the side of the road.", "79": "a tall giraffe standing in the grass.", "80": "a small plane is parked in a parking lot.", "81": "a view of a city and a large body of water.", "82": "a person on a surfboard riding a wave.", "83": "a man in a red shirt is playing a game of tennis.", "84": "a close up of a cat's face with a glass of water", "85": "a young man holding a tennis racket in his hand.", "86": "a person is holding a surfboard on a sidewalk.", "87": "a group of people on a field with some animals.", "88": "a city street with a few people on the sidewalk.", "89": "a view of a kitchen with a glass door.", "90": "a very old style clock with a fancy top.", "91": "a small pile of food with a slice of bread and a small piece of bread.", "92": "a man is surfing on a sunny day.", "93": "a close up of two different types of food.", "94": "a young man is looking at a camera.", "95": "a kitchen with a sink and a refrigerator", "96": "a woman sitting in a chair with a man sitting at her desk.", "97": "a small building with a lot of windows", "98": "a man is holding a knife and fork and a woman is holding a plate of food.", "99": "a young man wearing a red shirt and a black hat holding a young girl.", "100": "a person on a skateboard on a ramp.", "101": "a bathroom with a sink and a toilet", "102": "a close up of a person with a surfboard", "103": "a large jetliner sitting on top of a large body of water.", "104": "a person is riding a surfboard on a wave.", "105": "a train car with a large front.", "106": "a man riding a skateboard down a snow covered slope.", "107": "a kitchen with a double sink and a wall mounted light.", "108": "a man riding a wave on top of a surfboard.", "109": "a plate of sweet and sweet potato with a side of sweet and sweet.", "110": "a large passenger jetliner flying past a blue sky.", "111": "a woman sitting on a chair with a tennis racket.", "112": "a man swinging a bat at a ball with a baseball bat.", "113": "a man in a suit and a hat on a skateboard.", "114": "a plate of food with a fork and knife and fork.", "115": "a large jetliner flying over a large jetliner on a clear day.", "116": "a man and a woman sitting on a bench.", "117": "a room with a sink, a mirror and a mirror.", "118": "a bathroom with a sink and a toilet", "119": "a man hitting a tennis ball with a tennis racket", "120": "a close up of a plate of food", "121": "a kitchen with a sink and a counter top", "122": "a kitchen with a double bed and a refrigerator.", "123": "a young man wearing a black shirt and a black shirt", "124": "a modern kitchen with a double sink and a small counter.", "125": "a large group of trees", "126": "a person is on a surfboard in the water.", "127": "a couple of people sitting in a room.", "128": "a man in a white shirt and a black and blue shirt holding a white surfboard.", "129": "a couple of animals that are standing in the grass.", "130": "a smiling man with a smile on his face.", "131": "a man riding a small boat on top of a small boat.", "132": "a large long boat with a lot of windows.", "133": "a couple of giraffes standing next to each other.", "134": "a small kitchen with a small vase and a pair of glasses.", "135": "a white and brown meal with a slice of bread and a piece of bread.", "136": "a large piece of luggage on the ground.", "137": "a modern style kitchen with a modern style.", "138": "a couple of people sitting in a room.", "139": "a room with a sink, a toilet and a sink.", "140": "a dog and a cat on a leash", "141": "a table with a plate of food and a knife", "142": "a group of animals that are standing in the grass.", "143": "a close up of a plane on a clear day", "144": "a close up of a giraffe's face.", "145": "a large passenger jet airplane with a passenger jet on it.", "146": "a meal of food and a sandwich on a plate.", "147": "a small stick with a small red bird on it.", "148": "a person on a snowboard in the air", "149": "a close up of a giraffe's face.", "150": "people playing a game of game of game with people watching.", "151": "a picture of a room with a computer and a laptop.", "152": "a close up of a person with a shirt", "153": "a close up of a person with a piece of food", "154": "a room with a table, chairs, and a table.", "155": "a young woman is sitting in a chair with a remote control.", "156": "a young man playing a game of tennis.", "157": "a man riding a horse down a street.", "158": "a room with a sink and a toilet", "159": "a bus that is parked on the side of the road.", "160": "a large passenger bus with a large passenger car on the back.", "161": "a street scene with a lot of people.", "162": "a man riding a bike down a road next to a person on a small bike.", "163": "a tall building with a clock on it.", "164": "a man swinging a bat at a ball with a tennis racquet.", "165": "a couple of animals that are walking in the grass.", "166": "a room filled with lots of clutter.", "167": "a man riding a wave on top of a surfboard.", "168": "a single bird is flying over a small lake.", "169": "a large jetliner flying in a blue sky.", "170": "a plate of food with sliced meat and vegetables", "171": "a smiling young man with a beard and a shirt.", "172": "a close up of a piece of cut up carrots", "173": "a young man hitting a ball with a young woman.", "174": "a small bird is on the water and a small bird is in the air.", "175": "a large passenger train on a track near a large train.", "176": "a bathroom with a toilet and a sink", "177": "a man in a black and white ski suit is on a hill", "178": "a close up of a person's head", "179": "a couple of people standing in front of a building", "180": "a group of people riding on the back of a large beach.", "181": "a couple of animals that are in the grass.", "182": "a close up of a person with a baby's head against a blue sky", "183": "a young elephant playing with a toy.", "184": "a small wooden bench with a cat on it.", "185": "a large area of trees.", "186": "a view of the inside of a building.", "187": "a large passenger train on the tracks near a city.", "188": "a white and brown cat is laying down", "189": "people sitting at tables eating food.", "190": "a kitchen with a sink, a table, and a chair.", "191": "a city street scene with cars and buses.", "192": "a group of zebras standing next to each other.", "193": "a view of a train track and a small town.", "194": "a group of people on a field with a kite flying in the sky.", "195": "a man is standing on a beach with a surfboard.", "196": "a group of people riding on the backs of motorcycles.", "197": "a man is walking through the snow on a sunny day.", "198": "a herd of animals grazing on a hill.", "199": "a meal of food and a meal on a plate.", "200": "a city street with a lot of traffic.", "201": "a large black and white clock on a building.", "202": "a large passenger window with a lot of windows.", "203": "a group of people sitting around a table.", "204": "a stop sign with a picture of a street sign.", "205": "a large passenger bus driving down a street.", "206": "a train station with a lot of cars on the side of it.", "207": "a kitchen with a sink, a cabinet and a sink.", "208": "a kitchen with a sink and a mirror", "209": "a large jetliner flying through the sky.", "210": "a city with a lot of trees", "211": "a surfer is riding a wave on a surfboard.", "212": "a woman with a plate of food in front of a man.", "213": "a plate of food with a slice of cheese and a slice of pizza.", "214": "a woman in a white shirt and a man in a black shirt holding a black and white shirt and a woman in a", "215": "a black and white photo of a clock tower on a beach.", "216": "two zebras are standing together with their heads together.", "217": "a hand holding a spoon with a bite taken out of it.", "218": "a group of people in the grass near trees.", "219": "a large, long, metal structure sits in the shade of a large tree.", "220": "a view of a plane flying through the air.", "221": "a young man riding a skateboard with a small black helmet on.", "222": "a close up of a pair of giraffes.", "223": "a plate of food with a variety of toppings.", "224": "a train traveling through a city.", "225": "a large passenger train on a city street.", "226": "a bathroom with a sink, a toilet and a sink.", "227": "a man is standing in front of a woman.", "228": "a large passenger train on a city street.", "229": "a young boy with a black and white cat", "230": "a person on a beach with a dog on a skateboard.", "231": "a close up of a person's face.", "232": "a man riding a bicycle down a street.", "233": "a person flying a kite with a blue sky and a white sky", "234": "a close up of a cat on a bed", "235": "a view of a large body of water with a few clouds.", "236": "a large open area with a large plane and a large plane in the sky.", "237": "a person on a snowboard on a small hill.", "238": "a young man is standing on a beach with a skateboard.", "239": "a large metal plane with a large propeller.", "240": "a bus is parked on the side of the road.", "241": "a plate of food with a salad and a side of cheese and a side of salad.", "242": "a young man in a blue shirt and red shirt playing a game of tennis.", "243": "a person is playing a game of tennis", "244": "a close up of a pair of animals", "245": "a very tall piece of paper sitting on top of a piece of furniture.", "246": "a man riding a skateboard down a snow covered slope.", "247": "a person on a beach jumping a wave in the air.", "248": "a close up of a person wearing a shirt", "249": "a modern style kitchen with a double bed and a double bed.", "250": "a man standing in the water next to a field.", "251": "a man standing in a room with a woman.", "252": "a couple of animals that are standing in the dirt.", "253": "a man wearing a helmet and holding a skateboard.", "254": "a cat is sitting on the seat of a chair.", "255": "a plate of food with a side of cheese and vegetables.", "256": "a man riding a wave on a surfboard on a sunny day.", "257": "a passenger bus is parked in a lot.", "258": "a person on a surfboard in the water.", "259": "a man riding a wave on top of a surfboard.", "260": "a man sitting next to a woman in a chair.", "261": "a man on a skateboard in a park.", "262": "a tall giraffe standing in the grass.", "263": "a table topped with plates of food and a variety of items.", "264": "a man is skiing down a hill in the middle of a park.", "265": "a close up of a person on a field of grass", "266": "a traffic sign on a pole", "267": "a plate of food with a side of vegetables.", "268": "a man sitting at a table with a chair and a table with a glass of wine.", "269": "a room with a chair, a table and a chair.", "270": "a woman sitting in a chair with a man in a chair.", "271": "a man holding a snowboard next to a woman.", "272": "a cat is laying on a bed with a cat.", "273": "a man riding a wave board on a blue sky", "274": "a person is walking on a path", "275": "a large building with a clock on it", "276": "a group of people riding on the back of a parked vehicle.", "277": "a clock tower on a building.", "278": "a close up of a 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rest.", "968": "a group of people standing next to each other.", "969": "a bus that is parked on the side of the road.", "970": "a group of animals that are in the grass.", "971": "a man in a black and white photo with a black and white photo.", "972": "a large metal structure with a small plane on top of it.", "973": "a group of people on a road.", "974": "a man is standing on a small piece of wood.", "975": "a hand holding a spoon with a bite taken out of it.", "976": "a small elephant with a small elephant in the background.", "977": "a man is skiing down a snowy hill.", "978": "a view of a train station and a train station.", "979": "a large brown and white giraffe standing in the middle of a field.", "980": "a tiled bathroom with a sink and a toilet.", "981": "a room with a large window and a sink."} -------------------------------------------------------------------------------- /caption/comparison/braincap/sub05_decoded_caption.json: -------------------------------------------------------------------------------- 1 | {"0": "a group of people eating at a table.", "1": "a man is sitting down and holding a tray of food.", "2": "a man in a black and white shirt is on a surfboard.", "3": "a man is out in the sunshine on the beach.", "4": "a city with a lot of windows and a lot of windows.", "5": "a plate of food with a variety of toppings", "6": "a meal with a meal on the table.", "7": "a young man playing a game of tennis.", "8": "a small brown and white dog", "9": "a man riding a skateboard on a sunny day.", "10": "a passenger jet airliner is on the tarmac.", "11": "a person is playing in the water on a surfboard.", "12": "a small black and white dog is walking on the grass.", "13": "a young man sitting on a couch with a shirt on.", "14": "a large body of water with a few buildings and a large body of water.", "15": "a room with a couch, a table and a chair.", "16": "a smiling young man with a smile and a smile on his 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food with a pile of food.", "786": "a close up of a young zebra with a nose", "787": "a group of animals that are standing in the dirt.", "788": "a young male with a brown and white face.", "789": "a plate of food with a variety of vegetables and a side of bread.", "790": "a city street with a lot of cars and a small car.", "791": "a young man is standing up", "792": "a room with a couch, a table and a chair.", "793": "a street scene with a bus and a car.", "794": "a group of people flying kites in a field.", "795": "two young zebras are eating grass.", "796": "a couple of animals that are standing in the grass.", "797": "a train station with a train on the tracks.", "798": "a group of people sitting around a table.", "799": "a small, uninted, uninted, uninted, unintended, uninted, un", "800": "a young man wearing a black shirt and a white shirt.", "801": "a person is playing a game of tennis.", "802": "a person is playing.", "803": "a small kitchen with a small bowl and a small bowl with a small bowl of food.", "804": "a woman with a smile on her face.", "805": "a large group of four different colored animal sculptures.", "806": "a group of people standing on top of a field.", "807": "a small brown animal with a large black head.", "808": "a lot of people are out in the sunshine", "809": "a room with a sink and a toilet", "810": "a bathroom with a sink and a toilet.", "811": "a train is parked at the end of the road.", "812": "a man holding a tennis racquet while standing on a tennis court.", "813": "a close up of a plate of food", "814": "a view of a small island with a lot of water and a few trees.", "815": "a man riding a horse on a dirt road.", "816": "a picture of a very tall building with a clock on it.", "817": "a man is walking in a city with a bus.", "818": "a small bathroom with a toilet and a sink", "819": "a room with a table and chairs", "820": "a black and white photo of a dog", "821": "a young woman with a remote in her hand.", "822": "a brown and white animal.", "823": "a herd of animals standing on top of a lush green field.", "824": "a view of a garden from the balcony.", "825": "a room with a chair and a chair", "826": "a person riding a wave on a surfboard.", "827": "a man riding a skateboard down a ramp.", "828": "a young man wearing a hat and holding a cell phone.", "829": "a restaurant with a variety of dishes and plates.", "830": "a young man wearing a black and white jacket and a blue and red jacket.", "831": "a person is sitting on a computer chair.", "832": "a smiling young man with a smile on his face.", "833": "a group of people on a beach with a bunch of water.", "834": "a plate of sliced mushrooms with a slice of cheese and other vegetables.", "835": "a tall metal pole with a clock on it", "836": "a piece of food with a piece of bread.", "837": "a city street with a lot of cars and a building.", "838": "a room with a glass door and a red and white sink.", "839": "a room with a toilet and a sink", "840": "a group of vehicles driving down a road next to a large airport.", "841": "a close up of a plate of food", "842": "a brown and white animal is in the water.", "843": "a couple of people that are sitting in a car.", "844": "a room in a building", "845": "a man riding a wave on top of a surfboard.", "846": "a room with a table, a chair, a table and a chair.", "847": "a large, old, broken, and old fashioned fire hydrant.", "848": "a man riding a wave on top of a large ocean wave.", "849": "a plate of food with a serving of salad dressing.", "850": "a decorative piece of a building with a clock on it.", "851": "a room with a toilet and a sink", "852": "a person is surfing on a wave in the ocean.", "853": "a person on a snowboard in the air.", "854": "a wall with a window", "855": "a small town on a river", "856": "a long bus is parked on the side of the road.", "857": "a man is standing outside a building.", "858": "a black and white cat is on the edge of a pool.", "859": "a man sitting on a rock with a dog.", "860": "a young tennis player in action.", "861": "a long line of food at a restaurant", "862": "a surfer is riding a wave as it passes.", "863": "two zebras are eating together.", "864": "a view of a city with a few people.", "865": "a man wearing a hat and holding a skateboard.", "866": "a room with a sink and a window", "867": "a man is playing a game of tennis on the beach.", "868": "a room with a sink, a table and a sink.", "869": "a young man wearing a blue shirt and a green shirt.", "870": "a room with a table and a shelf with a glass door.", "871": "a delicious and delicious meal with a fresh and fresh salad dressing.", "872": "a man in a black hat is standing in front of a doorway.", "873": "a man standing on a sidewalk with a young girl.", "874": "a passenger train is pulling into a station.", "875": "a large stone structure with a large tree in the background.", "876": "a kitchen with a sink, a toilet, and a sink.", "877": "a small garden with a small table and a small wooden bench.", "878": "a close up of a plate of food with a piece of broccoli", "879": "a small sidewalk with a dog on it", "880": "a close up of a very cute little cat.", "881": "a large long necked giraffe standing next to a large tree.", "882": "a passenger jet airplane with a large engine on the front", "883": "a large passenger jetliner flying over a runway.", "884": "a modern kitchen with a double sink and a small refrigerator.", "885": "a person riding a wave on their surfboard.", "886": "a bathroom with a sink and a toilet.", "887": "a man riding a surfboard on a wave in the ocean.", "888": "a close up of a sliced up piece of food.", "889": "a person is standing on a surfboard.", "890": "a bus drives through a city.", "891": "a close up of a zebra's face.", "892": "two zebras are standing next to each other.", "893": "a close up of a giraffes face.", "894": "a large long and narrow plane flying through the air.", "895": "a group of people standing on top of a hill.", "896": "a table with a variety of dishes on it", "897": "a group of people sitting around a tree.", "898": "a tennis player in action on the court.", "899": "a dog is in the air with a dog in the background.", "900": "a room with a table, chairs, and a table.", "901": "a close up of a person's face", "902": "a room with a table, a table, a chair, a table and a tv.", "903": "a young man holding a tennis racquet on a sunny day.", "904": "a jet flies over a body of water with a jet flying in the sky.", "905": "a person is sitting down on a white and blue board.", "906": "a group of people sitting on the side of a road.", "907": "a bathroom with a sink, a toilet, and a cabinet.", "908": "a train station in the distance.", "909": "a group of animals that are standing in the grass.", "910": "a small room with a double door and a small window.", "911": "a plate with a meal on it.", "912": "a white and red dish with a bite taken out of it.", "913": "a player in action on the court.", "914": "a table with a bunch of different types of food on it", "915": "a young man wearing a red shirt and a black shirt", "916": "a man riding a snowboard on a snow covered slope.", "917": "a young man in a black jacket is on a snowboard.", "918": "a bathroom with a sink, a toilet, and a shower.", "919": "a young male in a field of grass.", "920": "a close up of a plate of food", "921": "a picture of a plate of food.", "922": "a close up of a bed with a piece of wood", "923": "a group of people sitting on chairs and standing around.", "924": "a small giraffe standing next to a tree.", "925": "a man is flying a kite over the water.", "926": "a plate of food with a small piece of food.", "927": "a passenger bus is parked on the side of the road.", "928": "a man riding a wave on a surfboard.", "929": "a woman is sitting down with a man and a woman.", "930": "a person is riding a surfboard on a beach.", "931": "a group of three different colored animals.", "932": "a man on a field playing a game of tennis.", "933": "a plate of food with a tray of food on it.", "934": "a long stretch of water with a long train on the tracks.", "935": "a room with a sink, a table and a chair.", "936": "a couple of people standing next to a building.", "937": "a young man wearing a black and white outfit.", "938": "a person is playing.", "939": "a lone lonesome of a lonesome.", "940": "a herd of zebras grazing on a lush green field.", "941": "a large body of water with a small boat in the water.", "942": "a young man playing a game of tennis.", "943": "a table with a variety of items on it.", "944": "a group of people standing in the street.", "945": "a bathroom with a toilet and a person sitting on a chair.", "946": "a man in a ski suit is skiing down a hill.", "947": "a person is holding a plate of food", "948": "a person holding a surfboard on a sunny day.", "949": "a man wearing a hat and holding a tennis racquet.", "950": "a group of people are gathered around a table.", "951": "two giraffes and two giraffes are standing together.", "952": "a bathroom with a sink and a toilet", "953": "a person is playing with a ball", "954": "a man on a field with a baseball bat.", "955": "a young man holding a bowl of food.", "956": "a room with a toilet, a sink, a toilet and a chair.", "957": "a large herd of animals walking through a field.", "958": "a group of people sitting on a bench in front of a large group of people.", "959": "a bathroom with a double sink and a small bathroom", "960": "a group of kites flying over a field of people.", "961": "a group of people on a road in the snow.", "962": "a herd of animals standing next to each other.", "963": "a slice of a piece of bread with a bite taken out of it.", "964": "a large airplane with a large wings on it's side.", "965": "a group of people riding bikes through a city.", "966": "a room with a sink, toilet, and a window.", "967": "a pair of new seats", "968": "a group of people on a field with people on skis.", "969": "a bus is parked in front of a bus.", "970": "a herd of wild animals grazing on the grass.", "971": "a young man wearing a black shirt and a white shirt.", "972": "a small wooden boat sitting on top of a wooden structure.", "973": "a group of people walking around a parking lot.", "974": "a small group of animals that are in the air.", "975": "a hand holding a piece of food with a bite taken out of it", "976": "a young male is standing with his head down.", "977": "a man is riding a surfboard on a beach.", "978": "a train is parked on the tracks in the middle of the city.", "979": "a tall giraffe standing on top of a lush green field.", "980": "a public transit system is displayed in a building.", "981": "a room with a large bed and a table"} -------------------------------------------------------------------------------- /eval_bbox_rec.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | ''' 4 | @File : eval_bbox_rec.py 5 | @Time : 2024/01/23 12:34:41 6 | @Author : Weihao Xia 7 | @Version : 1.0 8 | @Desc : modified from https://github.com/shikras/shikra/blob/main/single_image_dataset/rec.py 9 | usage: 10 | for sub in 1 2 5 7 11 | do 12 | python eval_bbox_rec.py --path_out "../umbrae/evaluation/bbox_results/brainx/sub0${sub}_dim1024" 13 | done 14 | ''' 15 | 16 | import os 17 | import json 18 | import argparse 19 | from typing import Dict, Any, Sequence 20 | import torch 21 | from torchvision.ops import box_iou #, generalized_box_iou, distance_box_iou, complete_box_iou 22 | 23 | def calculate_iou(box1, box2, box_iou_type=box_iou, threshold=0.5): 24 | ious = box_iou_type(box1 * 1000, box2 * 1000) 25 | ious = torch.einsum('i i -> i', ious) # take diag elem 26 | iou = ious.mean().item() 27 | correct = (ious > threshold).sum().item() 28 | accuracy = 1.0 * correct / len(box1) 29 | return iou, accuracy 30 | 31 | def calculate_metric(preds: Sequence[str], targets: Sequence[str], threshold: float = 0.5) -> Dict[str, Any]: 32 | failed = 0 33 | target_failed = 0 34 | 35 | pred_boxes, target_boxes = [], [] 36 | for pred, target in zip(preds, targets): 37 | extract_pred = pred 38 | extract_target = target 39 | 40 | if extract_target is None: 41 | target_failed += 1 42 | # print(f"failed to extract ans for target: {target}") 43 | continue 44 | if extract_pred is None: 45 | failed += 1 46 | # print(f"failed to extract ans for pred: {pred}") 47 | extract_pred = [0, 0, 0, 0] 48 | target_boxes.append(extract_target) 49 | pred_boxes.append(extract_pred) 50 | 51 | with torch.no_grad(): 52 | target_boxes = torch.tensor(target_boxes) 53 | pred_boxes = torch.tensor(pred_boxes) 54 | # iou, iou_accuracy = calculate_iou(pred_boxes, target_boxes, box_iou_type=box_iou) 55 | # 56 | # normalized box value is too small, so that the area is 0. 57 | ious = box_iou(pred_boxes * 1000, target_boxes * 1000) 58 | ious = torch.einsum('i i -> i', ious) # take diag elem 59 | # NOTE: please note iou only calculate for success target 60 | iou = ious.mean().item() 61 | correct = (ious > threshold).sum().item() 62 | return { 63 | 'accuracy': 1.0 * correct / len(targets), 64 | 'iou': iou, 65 | 'target_failed': target_failed, 66 | 'failed': failed 67 | } 68 | 69 | if __name__ == "__main__": 70 | 71 | parser = argparse.ArgumentParser() 72 | parser.add_argument('--path_coco_gt', type=str, default='bbox/coco_bbox_categorized.json') 73 | parser.add_argument('--path_coco_categorized_label', type=str, default='processing/coco/coco_categorized_labels.json') 74 | parser.add_argument('--path_out', type=str, default='rec_results/brainx/sub01_dim1024') 75 | parser.add_argument('--threshold', type=float, default=0.5) 76 | args = parser.parse_args() 77 | 78 | # create global variables without the args prefix 79 | for attribute_name in vars(args).keys(): 80 | globals()[attribute_name] = getattr(args, attribute_name) 81 | 82 | with open(path_coco_categorized_label, 'r') as file: 83 | coco_cats = json.load(file) 84 | 85 | path_model_out = os.path.join(path_out, 'rec_response.json') 86 | 87 | all_results = {} 88 | for inc_class in coco_cats.keys(): 89 | print("calculating for categories: ", inc_class) 90 | 91 | inc_cats = coco_cats[inc_class] 92 | 93 | with open(path_coco_gt, 'r') as file: 94 | coco_gt = json.load(file) 95 | # save coco_gt in a list, only one box for each category 96 | bbox_gt = [] 97 | for image_idx in coco_gt.keys(): 98 | bbox = [] 99 | for category in coco_gt[image_idx].keys() & inc_cats: 100 | # print(category) 101 | bbox.append(coco_gt[image_idx][category][0]) 102 | bbox_gt.append(bbox) 103 | 104 | # process converted model response 105 | with open(path_model_out, 'r') as file: 106 | model_out = json.load(file) 107 | # save model_out in a list, only one box for each category 108 | bbox_pd = [] 109 | for out_id in model_out.keys(): 110 | bbox = [] 111 | for category in model_out[out_id].keys() & inc_cats: 112 | if len(model_out[out_id][category]) == 0: 113 | bbox_o = None 114 | else: 115 | bbox_o = model_out[out_id][category][0][0] 116 | bbox.append(bbox_o) 117 | bbox_pd.append(bbox) 118 | 119 | assert len(bbox_gt) == len(bbox_pd) 120 | 121 | f_bbox_gt = [item for sublist in bbox_gt for item in sublist] 122 | f_bbox_pd = [item for sublist in bbox_pd for item in sublist] 123 | 124 | result_dict = calculate_metric(f_bbox_pd, f_bbox_gt, threshold) 125 | 126 | # print and save output evaluation scores 127 | for metric, score in result_dict.items(): 128 | print(f'{metric}: {score}') 129 | 130 | save_path = os.path.join(path_out, f'evaluation_scores_{threshold}.txt') 131 | with open(save_path, 'a') as file: 132 | file.write(f'categories ({inc_class}): {inc_cats}\n') 133 | for metric, score in result_dict.items(): 134 | file.write(f'{metric}: {score}\n') 135 | print(f'evaluation scores saved to {save_path}') 136 | 137 | # save results 138 | all_results[inc_class] = result_dict 139 | 140 | markdown_table = "| acc@0.5 (A) | IoU (A) | acc@0.5 (S) | IoU (S) | acc@0.5 (SC) | IoU (SC) | acc@0.5 (SO) | IoU (SO) | acc@0.5 (IO) | IoU (IO) |\n" 141 | markdown_table += "|-------------|---------|-------------|---------|--------------|----------|--------------|----------|--------------|----------|\n" 142 | row = "|" 143 | for metrics in all_results.values(): 144 | row += f" {metrics['accuracy']:.4f} | {metrics['iou']:.4f} |" 145 | markdown_table += row 146 | markdown_table 147 | print(markdown_table) -------------------------------------------------------------------------------- /eval_caption.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | ''' 4 | @File : eval_caption.py 5 | @Time : 2024/01/27 11:27:48 6 | @Author : Weihao Xia 7 | @Version : 1.0 8 | @Desc : modified from [CLIPScore](https://arxiv.org/abs/2104.08718) (EMNLP'21) 9 | usage: 10 | for sub in 1 2 5 7 11 | do 12 | python eval_caption.py ../umbrae/evaluation/caption_results/brainx/sub0${sub}_dim1024/fmricap.json \ 13 | caption/images --references_json caption/fmri_cococap.json 14 | done 15 | ''' 16 | 17 | import os 18 | import tqdm 19 | import json 20 | import clip 21 | import warnings 22 | import argparse 23 | import collections 24 | import pathlib 25 | import numpy as np 26 | from PIL import Image 27 | from packaging import version 28 | import sklearn.preprocessing 29 | import metrics 30 | 31 | import torch 32 | from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize 33 | 34 | def parse_args(): 35 | parser = argparse.ArgumentParser() 36 | parser.add_argument( 37 | 'candidates_json', 38 | type=str, 39 | help='Candidates json mapping from image_id --> candidate.') 40 | 41 | parser.add_argument( 42 | 'image_dir', 43 | type=str, 44 | help='Directory of images, with the filenames as image ids.') 45 | 46 | parser.add_argument( 47 | '--references_json', 48 | default=None, 49 | help='Optional references json mapping from image_id --> [list of references]') 50 | 51 | parser.add_argument( 52 | '--compute_other_ref_metrics', 53 | default=1, 54 | type=int, 55 | help='If references is specified, should we compute standard reference-based metrics?') 56 | 57 | parser.add_argument( 58 | '--save_per_instance', 59 | default=None, 60 | help='if set, we will save per instance clipscores to this file') 61 | 62 | args = parser.parse_args() 63 | 64 | if isinstance(args.save_per_instance, str) and not args.save_per_instance.endswith('.json'): 65 | print('if you\'re saving per-instance, please make sure the filepath ends in json.') 66 | quit() 67 | return args 68 | 69 | 70 | class CLIPCapDataset(torch.utils.data.Dataset): 71 | def __init__(self, data, prefix='A photo depicts'): 72 | self.data = data 73 | self.prefix = prefix 74 | if self.prefix[-1] != ' ': 75 | self.prefix += ' ' 76 | 77 | def __getitem__(self, idx): 78 | c_data = self.data[idx] 79 | c_data = clip.tokenize(self.prefix + c_data, truncate=True).squeeze() 80 | return {'caption': c_data} 81 | 82 | def __len__(self): 83 | return len(self.data) 84 | 85 | 86 | class CLIPImageDataset(torch.utils.data.Dataset): 87 | def __init__(self, data): 88 | self.data = data 89 | # only 224x224 ViT-B/32 supported for now 90 | self.preprocess = self._transform_test(224) 91 | 92 | def _transform_test(self, n_px): 93 | return Compose([ 94 | Resize(n_px, interpolation=Image.BICUBIC), 95 | CenterCrop(n_px), 96 | lambda image: image.convert("RGB"), 97 | ToTensor(), 98 | Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)), 99 | ]) 100 | 101 | def __getitem__(self, idx): 102 | c_data = self.data[idx] 103 | image = Image.open(c_data) 104 | image = self.preprocess(image) 105 | return {'image':image} 106 | 107 | def __len__(self): 108 | return len(self.data) 109 | 110 | 111 | def extract_all_captions(captions, model, device, batch_size=256, num_workers=8): 112 | data = torch.utils.data.DataLoader( 113 | CLIPCapDataset(captions), 114 | batch_size=batch_size, num_workers=num_workers, shuffle=False) 115 | all_text_features = [] 116 | with torch.no_grad(): 117 | for b in tqdm.tqdm(data): 118 | b = b['caption'].to(device) 119 | all_text_features.append(model.encode_text(b).cpu().numpy()) 120 | all_text_features = np.vstack(all_text_features) 121 | return all_text_features 122 | 123 | 124 | def extract_all_images(images, model, device, batch_size=64, num_workers=8): 125 | data = torch.utils.data.DataLoader( 126 | CLIPImageDataset(images), 127 | batch_size=batch_size, num_workers=num_workers, shuffle=False) 128 | all_image_features = [] 129 | with torch.no_grad(): 130 | for b in tqdm.tqdm(data): 131 | b = b['image'].to(device) 132 | if device == 'cuda': 133 | b = b.to(torch.float16) 134 | all_image_features.append(model.encode_image(b).cpu().numpy()) 135 | all_image_features = np.vstack(all_image_features) 136 | return all_image_features 137 | 138 | 139 | def get_clip_score(model, images, candidates, device, w=2.5): 140 | ''' 141 | get standard image-text clipscore. 142 | images can either be: 143 | - a list of strings specifying filepaths for images 144 | - a precomputed, ordered matrix of image features 145 | ''' 146 | if isinstance(images, list): 147 | # need to extract image features 148 | images = extract_all_images(images, model, device) 149 | 150 | candidates = extract_all_captions(candidates, model, device) 151 | 152 | #as of numpy 1.21, normalize doesn't work properly for float16 153 | if version.parse(np.__version__) < version.parse('1.21'): 154 | images = sklearn.preprocessing.normalize(images, axis=1) 155 | candidates = sklearn.preprocessing.normalize(candidates, axis=1) 156 | else: 157 | warnings.warn( 158 | 'due to a numerical instability, new numpy normalization is slightly different than paper results. ' 159 | 'to exactly replicate paper results, please use numpy version less than 1.21, e.g., 1.20.3.') 160 | images = images / np.sqrt(np.sum(images**2, axis=1, keepdims=True)) 161 | candidates = candidates / np.sqrt(np.sum(candidates**2, axis=1, keepdims=True)) 162 | 163 | per = w*np.clip(np.sum(images * candidates, axis=1), 0, None) 164 | return np.mean(per), per, candidates 165 | 166 | 167 | def get_refonlyclipscore(model, references, candidates, device): 168 | ''' 169 | The text only side for refclipscore 170 | ''' 171 | if isinstance(candidates, list): 172 | candidates = extract_all_captions(candidates, model, device) 173 | 174 | flattened_refs = [] 175 | flattened_refs_idxs = [] 176 | for idx, refs in enumerate(references): 177 | flattened_refs.extend(refs) 178 | flattened_refs_idxs.extend([idx for _ in refs]) 179 | 180 | flattened_refs = extract_all_captions(flattened_refs, model, device) 181 | 182 | if version.parse(np.__version__) < version.parse('1.21'): 183 | candidates = sklearn.preprocessing.normalize(candidates, axis=1) 184 | flattened_refs = sklearn.preprocessing.normalize(flattened_refs, axis=1) 185 | else: 186 | warnings.warn( 187 | 'due to a numerical instability, new numpy normalization is slightly different than paper results. ' 188 | 'to exactly replicate paper results, please use numpy version less than 1.21, e.g., 1.20.3.') 189 | 190 | candidates = candidates / np.sqrt(np.sum(candidates**2, axis=1, keepdims=True)) 191 | flattened_refs = flattened_refs / np.sqrt(np.sum(flattened_refs**2, axis=1, keepdims=True)) 192 | 193 | cand_idx2refs = collections.defaultdict(list) 194 | for ref_feats, cand_idx in zip(flattened_refs, flattened_refs_idxs): 195 | cand_idx2refs[cand_idx].append(ref_feats) 196 | 197 | assert len(cand_idx2refs) == len(candidates) 198 | 199 | cand_idx2refs = {k: np.vstack(v) for k, v in cand_idx2refs.items()} 200 | 201 | per = [] 202 | for c_idx, cand in tqdm.tqdm(enumerate(candidates)): 203 | cur_refs = cand_idx2refs[c_idx] 204 | all_sims = cand.dot(cur_refs.transpose()) 205 | per.append(np.max(all_sims)) 206 | 207 | return np.mean(per), per 208 | 209 | 210 | def main(): 211 | args = parse_args() 212 | 213 | image_paths = [os.path.join(args.image_dir, path) for path in os.listdir(args.image_dir) 214 | if path.endswith(('.png', '.jpg', '.jpeg', '.tiff'))] 215 | image_ids = [pathlib.Path(path).stem for path in image_paths] 216 | 217 | with open(args.candidates_json) as f: 218 | candidates = json.load(f) 219 | candidates = [candidates[cid] for cid in image_ids] 220 | 221 | if args.references_json: 222 | with open(args.references_json) as f: 223 | references = json.load(f) 224 | references = [references[cid] for cid in image_ids] 225 | if isinstance(references[0], str): 226 | references = [[r] for r in references] 227 | 228 | device = "cuda" if torch.cuda.is_available() else "cpu" 229 | if device == 'cpu': 230 | warnings.warn( 231 | 'CLIP runs in full float32 on CPU. Results in paper were computed on GPU, which uses float16. ' 232 | 'If you\'re reporting results on CPU, please note this when you report.') 233 | model, transform = clip.load("ViT-B/32", device=device, jit=False) 234 | model.eval() 235 | 236 | image_feats = extract_all_images( 237 | image_paths, model, device, batch_size=64, num_workers=8) 238 | 239 | # get image-text clipscore 240 | _, per_instance_image_text, candidate_feats = get_clip_score( 241 | model, image_feats, candidates, device) 242 | 243 | if args.references_json: 244 | # get text-text clipscore 245 | _, per_instance_text_text = get_refonlyclipscore( 246 | model, references, candidate_feats, device) 247 | # F-score 248 | refclipscores = 2 * per_instance_image_text * per_instance_text_text / (per_instance_image_text + per_instance_text_text) 249 | scores = {image_id: {'CLIPScore': float(clipscore), 'RefCLIPScore': float(refclipscore)} 250 | for image_id, clipscore, refclipscore in 251 | zip(image_ids, per_instance_image_text, refclipscores)} 252 | 253 | else: 254 | scores = {image_id: {'CLIPScore': float(clipscore)} 255 | for image_id, clipscore in 256 | zip(image_ids, per_instance_image_text)} 257 | print('CLIPScore: {:.4f}'.format(np.mean([s['CLIPScore'] for s in scores.values()]))) 258 | 259 | if args.references_json: 260 | if args.compute_other_ref_metrics: 261 | other_metrics = metrics.get_all_metrics(references, candidates) 262 | for k, v in other_metrics.items(): 263 | if k == 'bleu': 264 | for bidx, sc in enumerate(v): 265 | print('BLEU-{}: {:.4f}'.format(bidx+1, sc)) 266 | else: 267 | print('{}: {:.4f}'.format(k.upper(), v)) 268 | print('CLIPScore: {:.4f}'.format(np.mean([s['CLIPScore'] for s in scores.values()]))) 269 | print('RefCLIPScore: {:.4f}'.format(np.mean([s['RefCLIPScore'] for s in scores.values()]))) 270 | 271 | if args.save_per_instance: 272 | with open(args.save_per_instance, 'w') as f: 273 | f.write(json.dumps(scores)) 274 | 275 | 276 | if __name__ == '__main__': 277 | main() 278 | -------------------------------------------------------------------------------- /metrics.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | ''' 4 | @File : metrics.py 5 | @Time : 2024/01/27 15:49:48 6 | @Author : Weihao Xia 7 | @Version : 1.0 8 | @Desc : modified from [CLIPScore](https://arxiv.org/abs/2104.08718) (EMNLP'21) 9 | ''' 10 | 11 | from pycocoevalcap.tokenizer.ptbtokenizer import PTBTokenizer 12 | from pycocoevalcap.spice.spice import Spice 13 | from pycocoevalcap.meteor.meteor import Meteor 14 | from pycocoevalcap.bleu.bleu import Bleu 15 | from pycocoevalcap.cider.cider import Cider 16 | from pycocoevalcap.rouge.rouge import Rouge 17 | from pycocoevalcap.spice.spice import Spice 18 | 19 | def get_all_metrics(refs, cands, return_per_cap=False): 20 | metrics = [] 21 | names = [] 22 | 23 | pycoco_eval_cap_scorers = [(Bleu(4), 'bleu'), 24 | (Meteor(), 'meteor'), 25 | (Rouge(), 'rouge'), 26 | (Cider(), 'cider'), 27 | (Spice(), 'spice')] 28 | 29 | for scorer, name in pycoco_eval_cap_scorers: 30 | overall, per_cap = pycoco_eval(scorer, refs, cands) 31 | if return_per_cap: 32 | metrics.append(per_cap) 33 | else: 34 | metrics.append(overall) 35 | names.append(name) 36 | 37 | metrics = dict(zip(names, metrics)) 38 | return metrics 39 | 40 | def tokenize(refs, cands, no_op=False): 41 | # no_op is a debug option to see how significantly not using the PTB tokenizer 42 | # affects things 43 | tokenizer = PTBTokenizer() 44 | 45 | if no_op: 46 | refs = {idx: [r for r in c_refs] for idx, c_refs in enumerate(refs)} 47 | cands = {idx: [c] for idx, c in enumerate(cands)} 48 | 49 | else: 50 | refs = {idx: [{'caption':r} for r in c_refs] for idx, c_refs in enumerate(refs)} 51 | cands = {idx: [{'caption':c}] for idx, c in enumerate(cands)} 52 | 53 | refs = tokenizer.tokenize(refs) 54 | cands = tokenizer.tokenize(cands) 55 | 56 | return refs, cands 57 | 58 | 59 | def pycoco_eval(scorer, refs, cands): 60 | ''' 61 | scorer is assumed to have a compute_score function. 62 | refs is a list of lists of strings 63 | cands is a list of predictions 64 | ''' 65 | refs, cands = tokenize(refs, cands) 66 | average_score, scores = scorer.compute_score(refs, cands) 67 | return average_score, scores 68 | -------------------------------------------------------------------------------- /processing/coco/coco_categorized_labels.json: -------------------------------------------------------------------------------- 1 | {"coco_cats": ["person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "salient": ["person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "chair", "couch", "bed", "dining table", "toilet", "sink", "refrigerator", "clock"], "salient_creatures": ["person", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe"], "salient_objects": ["bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "bench", "chair", "couch", "bed", "dining table", "toilet", "sink", "refrigerator", "clock"], "inconspicuous_objects": ["traffic light", "fire hydrant", "stop sign", "parking meter", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "potted plant", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "book", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"]} -------------------------------------------------------------------------------- /processing/coco/coco_labels.txt: -------------------------------------------------------------------------------- 1 | person 2 | bicycle 3 | car 4 | motorcycle 5 | airplane 6 | bus 7 | train 8 | truck 9 | boat 10 | traffic light 11 | fire hydrant 12 | stop sign 13 | parking meter 14 | bench 15 | bird 16 | cat 17 | dog 18 | horse 19 | sheep 20 | cow 21 | elephant 22 | bear 23 | zebra 24 | giraffe 25 | backpack 26 | umbrella 27 | handbag 28 | tie 29 | suitcase 30 | frisbee 31 | skis 32 | snowboard 33 | sports ball 34 | kite 35 | baseball bat 36 | baseball glove 37 | skateboard 38 | surfboard 39 | tennis racket 40 | bottle 41 | wine glass 42 | cup 43 | fork 44 | knife 45 | spoon 46 | bowl 47 | banana 48 | apple 49 | sandwich 50 | orange 51 | broccoli 52 | carrot 53 | hot dog 54 | pizza 55 | donut 56 | cake 57 | chair 58 | couch 59 | potted plant 60 | bed 61 | dining table 62 | toilet 63 | tv 64 | laptop 65 | mouse 66 | remote 67 | keyboard 68 | cell phone 69 | microwave 70 | oven 71 | toaster 72 | sink 73 | refrigerator 74 | book 75 | clock 76 | vase 77 | scissors 78 | teddy bear 79 | hair drier 80 | toothbrush -------------------------------------------------------------------------------- /processing/decode_images.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | ''' 4 | @File : decode_images.py 5 | @Time : 2024/03/13 15:52:38 6 | @Author : Weihao Xia 7 | @Version : 1.0 8 | @Desc : None 9 | ''' 10 | 11 | import os 12 | import torch 13 | import torchvision.transforms.functional as F 14 | 15 | data_path = 'caption/all_images.pt' 16 | save_path = 'caption/test_images' 17 | 18 | if not os.path.exists(save_path): 19 | os.makedirs(save_path) 20 | 21 | images = torch.load(data_path) 22 | 23 | for idx, image in enumerate(images): 24 | print (f'Processing image {idx}') 25 | image_pil = F.to_pil_image(image.squeeze(0)) 26 | image_pil.save(os.path.join(save_path, f'{idx}.png')) -------------------------------------------------------------------------------- /run.sh: -------------------------------------------------------------------------------- 1 | # sdrecon 2 | for sub in 1 2 5 7 3 | do 4 | python eval_caption.py caption/comparison/sdrecon/sub0${sub}_decoded_caption.json \ 5 | caption/images --references_json caption/fmri_cococap.json 6 | done 7 | 8 | # braincap 9 | for sub in 1 2 5 7 10 | do 11 | python eval_caption.py caption/comparison/braincap/sub0${sub}_decoded_caption.json \ 12 | caption/images --references_json caption/fmri_cococap.json 13 | done 14 | 15 | # onellm 16 | python eval_caption.py caption/comparison/onellm/sub01_decoded_caption.json \ 17 | caption/images --references_json caption/fmri_cococap.json 18 | 19 | # umbrae captioning 20 | for sub in 1 2 5 7 21 | do 22 | python eval_caption.py caption/comparison/umbrae/sub0${sub}_decoded_caption.json \ 23 | caption/images --references_json caption/fmri_cococap.json 24 | done 25 | 26 | # umbrae grounding 27 | for sub in 1 2 5 7 28 | do 29 | python eval_bbox_rec.py --path_out "bbox/umbrae/sub0${sub}_dim1024" 30 | done --------------------------------------------------------------------------------