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|
| | import torch |
| | import torch.nn as nn |
| | from clip import clip |
| |
|
| |
|
| | def article(name): |
| | return "an" if name[0] in "aeiou" else "a" |
| |
|
| |
|
| | def processed_name(name, rm_dot=False): |
| | |
| | |
| | res = name.replace("_", " ").replace("/", " or ").lower() |
| | if rm_dot: |
| | res = res.rstrip(".") |
| | return res |
| |
|
| |
|
| | single_template = ["a photo of a {}."] |
| |
|
| | multiple_templates = [ |
| | "There is {article} {} in the scene.", |
| | "There is the {} in the scene.", |
| | "a photo of {article} {} in the scene.", |
| | "a photo of the {} in the scene.", |
| | "a photo of one {} in the scene.", |
| | "itap of {article} {}.", |
| | "itap of my {}.", |
| | "itap of the {}.", |
| | "a photo of {article} {}.", |
| | "a photo of my {}.", |
| | "a photo of the {}.", |
| | "a photo of one {}.", |
| | "a photo of many {}.", |
| | "a good photo of {article} {}.", |
| | "a good photo of the {}.", |
| | "a bad photo of {article} {}.", |
| | "a bad photo of the {}.", |
| | "a photo of a nice {}.", |
| | "a photo of the nice {}.", |
| | "a photo of a cool {}.", |
| | "a photo of the cool {}.", |
| | "a photo of a weird {}.", |
| | "a photo of the weird {}.", |
| | "a photo of a small {}.", |
| | "a photo of the small {}.", |
| | "a photo of a large {}.", |
| | "a photo of the large {}.", |
| | "a photo of a clean {}.", |
| | "a photo of the clean {}.", |
| | "a photo of a dirty {}.", |
| | "a photo of the dirty {}.", |
| | "a bright photo of {article} {}.", |
| | "a bright photo of the {}.", |
| | "a dark photo of {article} {}.", |
| | "a dark photo of the {}.", |
| | "a photo of a hard to see {}.", |
| | "a photo of the hard to see {}.", |
| | "a low resolution photo of {article} {}.", |
| | "a low resolution photo of the {}.", |
| | "a cropped photo of {article} {}.", |
| | "a cropped photo of the {}.", |
| | "a close-up photo of {article} {}.", |
| | "a close-up photo of the {}.", |
| | "a jpeg corrupted photo of {article} {}.", |
| | "a jpeg corrupted photo of the {}.", |
| | "a blurry photo of {article} {}.", |
| | "a blurry photo of the {}.", |
| | "a pixelated photo of {article} {}.", |
| | "a pixelated photo of the {}.", |
| | "a black and white photo of the {}.", |
| | "a black and white photo of {article} {}.", |
| | "a plastic {}.", |
| | "the plastic {}.", |
| | "a toy {}.", |
| | "the toy {}.", |
| | "a plushie {}.", |
| | "the plushie {}.", |
| | "a cartoon {}.", |
| | "the cartoon {}.", |
| | "an embroidered {}.", |
| | "the embroidered {}.", |
| | "a painting of the {}.", |
| | "a painting of a {}.", |
| | ] |
| |
|
| |
|
| | openimages_rare_unseen = ['Aerial photography', |
| | 'Aircraft engine', |
| | 'Ale', |
| | 'Aloe', |
| | 'Amphibian', |
| | 'Angling', |
| | 'Anole', |
| | 'Antique car', |
| | 'Arcade game', |
| | 'Arthropod', |
| | 'Assault rifle', |
| | 'Athletic shoe', |
| | 'Auto racing', |
| | 'Backlighting', |
| | 'Bagpipes', |
| | 'Ball game', |
| | 'Barbecue chicken', |
| | 'Barechested', |
| | 'Barquentine', |
| | 'Beef tenderloin', |
| | 'Billiard room', |
| | 'Billiards', |
| | 'Bird of prey', |
| | 'Black swan', |
| | 'Black-and-white', |
| | 'Blond', |
| | 'Boating', |
| | 'Bonbon', |
| | 'Bottled water', |
| | 'Bouldering', |
| | 'Bovine', |
| | 'Bratwurst', |
| | 'Breadboard', |
| | 'Briefs', |
| | 'Brisket', |
| | 'Brochette', |
| | 'Calabaza', |
| | 'Camera operator', |
| | 'Canola', |
| | 'Childbirth', |
| | 'Chordophone', |
| | 'Church bell', |
| | 'Classical sculpture', |
| | 'Close-up', |
| | 'Cobblestone', |
| | 'Coca-cola', |
| | 'Combat sport', |
| | 'Comics', |
| | 'Compact car', |
| | 'Computer speaker', |
| | 'Cookies and crackers', |
| | 'Coral reef fish', |
| | 'Corn on the cob', |
| | 'Cosmetics', |
| | 'Crocodilia', |
| | 'Digital camera', |
| | 'Dishware', |
| | 'Divemaster', |
| | 'Dobermann', |
| | 'Dog walking', |
| | 'Domestic rabbit', |
| | 'Domestic short-haired cat', |
| | 'Double-decker bus', |
| | 'Drums', |
| | 'Electric guitar', |
| | 'Electric piano', |
| | 'Electronic instrument', |
| | 'Equestrianism', |
| | 'Equitation', |
| | 'Erinaceidae', |
| | 'Extreme sport', |
| | 'Falafel', |
| | 'Figure skating', |
| | 'Filling station', |
| | 'Fire apparatus', |
| | 'Firearm', |
| | 'Flatbread', |
| | 'Floristry', |
| | 'Forklift truck', |
| | 'Freight transport', |
| | 'Fried food', |
| | 'Fried noodles', |
| | 'Frigate', |
| | 'Frozen yogurt', |
| | 'Frying', |
| | 'Full moon', |
| | 'Galleon', |
| | 'Glacial landform', |
| | 'Gliding', |
| | 'Go-kart', |
| | 'Goats', |
| | 'Grappling', |
| | 'Great white shark', |
| | 'Gumbo', |
| | 'Gun turret', |
| | 'Hair coloring', |
| | 'Halter', |
| | 'Headphones', |
| | 'Heavy cruiser', |
| | 'Herding', |
| | 'High-speed rail', |
| | 'Holding hands', |
| | 'Horse and buggy', |
| | 'Horse racing', |
| | 'Hound', |
| | 'Hunting knife', |
| | 'Hurdling', |
| | 'Inflatable', |
| | 'Jackfruit', |
| | 'Jeans', |
| | 'Jiaozi', |
| | 'Junk food', |
| | 'Khinkali', |
| | 'Kitesurfing', |
| | 'Lawn game', |
| | 'Leaf vegetable', |
| | 'Lechon', |
| | 'Lifebuoy', |
| | 'Locust', |
| | 'Lumpia', |
| | 'Luxury vehicle', |
| | 'Machine tool', |
| | 'Medical imaging', |
| | 'Melee weapon', |
| | 'Microcontroller', |
| | 'Middle ages', |
| | 'Military person', |
| | 'Military vehicle', |
| | 'Milky way', |
| | 'Miniature Poodle', |
| | 'Modern dance', |
| | 'Molluscs', |
| | 'Monoplane', |
| | 'Motorcycling', |
| | 'Musical theatre', |
| | 'Narcissus', |
| | 'Nest box', |
| | 'Newsagent\'s shop', |
| | 'Nile crocodile', |
| | 'Nordic skiing', |
| | 'Nuclear power plant', |
| | 'Orator', |
| | 'Outdoor shoe', |
| | 'Parachuting', |
| | 'Pasta salad', |
| | 'Peafowl', |
| | 'Pelmeni', |
| | 'Perching bird', |
| | 'Performance car', |
| | 'Personal water craft', |
| | 'Pit bull', |
| | 'Plant stem', |
| | 'Pork chop', |
| | 'Portrait photography', |
| | 'Primate', |
| | 'Procyonidae', |
| | 'Prosciutto', |
| | 'Public speaking', |
| | 'Racewalking', |
| | 'Ramen', |
| | 'Rear-view mirror', |
| | 'Residential area', |
| | 'Ribs', |
| | 'Rice ball', |
| | 'Road cycling', |
| | 'Roller skating', |
| | 'Roman temple', |
| | 'Rowing', |
| | 'Rural area', |
| | 'Sailboat racing', |
| | 'Scaled reptile', |
| | 'Scuba diving', |
| | 'Senior citizen', |
| | 'Shallot', |
| | 'Shinto shrine', |
| | 'Shooting range', |
| | 'Siberian husky', |
| | 'Sledding', |
| | 'Soba', |
| | 'Solar energy', |
| | 'Sport climbing', |
| | 'Sport utility vehicle', |
| | 'Steamed rice', |
| | 'Stemware', |
| | 'Sumo', |
| | 'Surfing Equipment', |
| | 'Team sport', |
| | 'Touring car', |
| | 'Toy block', |
| | 'Trampolining', |
| | 'Underwater diving', |
| | 'Vegetarian food', |
| | 'Wallaby', |
| | 'Water polo', |
| | 'Watercolor paint', |
| | 'Whiskers', |
| | 'Wind wave', |
| | 'Woodwind instrument', |
| | 'Yakitori', |
| | 'Zeppelin'] |
| |
|
| |
|
| | def build_openset_label_embedding(categories=None): |
| | if categories is None: |
| | categories = openimages_rare_unseen |
| | print("Creating pretrained CLIP model") |
| | model, _ = clip.load("ViT-B/16") |
| | templates = multiple_templates |
| |
|
| | run_on_gpu = torch.cuda.is_available() |
| |
|
| | with torch.no_grad(): |
| | openset_label_embedding = [] |
| | for category in categories: |
| | texts = [ |
| | template.format( |
| | processed_name(category, rm_dot=True), article=article(category) |
| | ) |
| | for template in templates |
| | ] |
| | texts = [ |
| | "This is " + text if text.startswith("a") or text.startswith("the") else text |
| | for text in texts |
| | ] |
| | texts = clip.tokenize(texts) |
| | if run_on_gpu: |
| | texts = texts.cuda() |
| | model = model.cuda() |
| | text_embeddings = model.encode_text(texts) |
| | text_embeddings /= text_embeddings.norm(dim=-1, keepdim=True) |
| | text_embedding = text_embeddings.mean(dim=0) |
| | text_embedding /= text_embedding.norm() |
| | openset_label_embedding.append(text_embedding) |
| | openset_label_embedding = torch.stack(openset_label_embedding, dim=1) |
| | if run_on_gpu: |
| | openset_label_embedding = openset_label_embedding.cuda() |
| |
|
| | openset_label_embedding = openset_label_embedding.t() |
| | return openset_label_embedding, categories |
| |
|
| |
|
| |
|
| | import json |
| | from tqdm import tqdm |
| |
|
| | def build_openset_llm_label_embedding(llm_tag_des): |
| | print("Creating pretrained CLIP model") |
| | model, _ = clip.load("ViT-B/16") |
| | llm_tag_des = llm_tag_des |
| | categories = [] |
| |
|
| | run_on_gpu = torch.cuda.is_available() |
| |
|
| | with torch.no_grad(): |
| | openset_label_embedding = [] |
| | for item in tqdm(llm_tag_des): |
| | category = list(item.keys())[0] |
| | des = list(item.values())[0] |
| |
|
| | categories.append(category) |
| |
|
| | texts = clip.tokenize(des, truncate=True) |
| | if run_on_gpu: |
| | texts = texts.cuda() |
| | model = model.cuda() |
| | text_embeddings = model.encode_text(texts) |
| | text_embeddings /= text_embeddings.norm(dim=-1, keepdim=True) |
| | |
| | |
| | |
| | openset_label_embedding.append(text_embeddings) |
| | |
| | openset_label_embedding = torch.cat(openset_label_embedding, dim=0) |
| | if run_on_gpu: |
| | openset_label_embedding = openset_label_embedding.cuda() |
| |
|
| | |
| | return openset_label_embedding, categories |
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