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coco_classes.txt

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+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

create_prompts.py

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+
+from itertools import combinations, permutations
+from pathlib import Path
+from typing import List
+import random
+
+SAVE_PATH = "prompts.txt"
+MINI_SAVE_PATH = "mini_prompts.txt"
+
+# COCO Classes, from https://docs.ultralytics.com/datasets/detect/coco/#dataset-yaml
+OBJECT_PATH = Path(__file__).parent / "coco_classes.txt"
+
+MAX_IMG_OBJECTS = 3
+MAX_OBJ_QUANTITY = 5
+
+
+
+def get_objects() -> List[str]:
+    with open(OBJECT_PATH) as f:
+        objects = [o.strip('\n') for o in f.readlines()]
+    return objects
+
+
+def get_plural_suffix(object: str) -> str:
+    suffix = ""
+    if object == "person":
+        object = "people"
+
+    elif object.endswith("s") or object.endswith("sh") or object.endswith("ch"):
+        suffix = "es"
+
+    elif object.endswith("y"):
+        suffix = "ies"
+        object = object[:-1]
+            
+    else:
+        suffix = "s"
+    
+    object += suffix
+    return object
+
+    
+
+def gen_prompt_quantifier_helper(object: str, n: int) -> str:
+    """
+    object:  str, ex: 'person', 'car', 'dog', etc.
+    returns: list of object strings with quantifier prefix and pluralization
+    """
+    if n > 1:
+        object = get_plural_suffix(object)
+        
+    return f"{n} {object}"
+
+
+
+def gen_single_object_prompts(objects: List[str]) -> List[str]:
+    prompts = []
+    for obj in objects:
+        prompts.extend(gen_prompt_quantifier_helper(obj))
+    return prompts  
+
+def generate_prompts(objects):
+    """
+    Generates a list of prompts and saves them for model evaluation
+    e.g. 
+        1 person
+        2 people
+        3 people
+        1 person and 1 car
+        2 people and 1 car
+        3 people and 1 car
+        1 person and 2 cars
+        2 people and 2 cars
+        3 people and 2 cars
+        ...
+    """
+    prompts = set()
+    for num_img_objects in range(1, MAX_IMG_OBJECTS + 1):
+        for combo in combinations(objects, num_img_objects):
+            for count_permute in permutations(list(range(1, MAX_OBJ_QUANTITY + 1)) * num_img_objects, num_img_objects):
+                prompt = ""
+                for i, obj in enumerate(combo):
+                    if i > 0:
+                        prompt += " and "
+                    prompt += gen_prompt_quantifier_helper(obj, count_permute[i])
+
+                prompts.add(prompt)
+                
+    return prompts
+
+
+def save_prompts(prompts: List[str], save_path: str = SAVE_PATH):
+    with open(save_path, 'w') as f:
+        for prompt in prompts:
+            f.write(prompt + '\n')
+    
+
+
+    
+
+if __name__ == "__main__":
+    objects = get_objects()
+    prompts = sorted(list(generate_prompts(objects)))
+    save_prompts(prompts)
+
+    # mini_objects = random.sample(objects, 10)
+    # prompts = sorted(list(generate_prompts(mini_objects)))
+    # save_prompts(prompts, MINI_SAVE_PATH)
+