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"""
Generate prompts for evaluation
"""
import argparse
import json
import os
import numpy as np
import yaml
# Load classnames
with open("object_names.txt") as cls_file:
classnames = [line.strip() for line in cls_file]
# Proper a vs an
def with_article(name: str):
if name[0] in "aeiou":
return f"an {name}"
return f"a {name}"
# Proper plural
def make_plural(name: str):
if name[-1] in "s":
return f"{name}es"
return f"{name}s"
# Generates single object samples
def generate_single_object_sample(rng: np.random.Generator, size: int = None):
TAG = "single_object"
if size > len(classnames):
size = len(classnames)
print(f"Not enough distinct classes, generating only {size} samples")
return_scalar = size is None
size = size or 1
idxs = rng.choice(len(classnames), size=size, replace=False)
samples = [
dict(
tag=TAG,
include=[{"class": classnames[idx], "count": 1}],
prompt=f"a photo of {with_article(classnames[idx])}",
)
for idx in idxs
]
if return_scalar:
return samples[0]
return samples
# Generate two object samples
def generate_two_object_sample(rng: np.random.Generator):
TAG = "two_object"
idx_a, idx_b = rng.choice(len(classnames), size=2, replace=False)
return dict(
tag=TAG,
include=[{"class": classnames[idx_a], "count": 1}, {"class": classnames[idx_b], "count": 1}],
prompt=f"a photo of {with_article(classnames[idx_a])} and {with_article(classnames[idx_b])}",
)
# Generate counting samples
numbers = ["zero", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine", "ten"]
def generate_counting_sample(rng: np.random.Generator, max_count=4):
TAG = "counting"
idx = rng.choice(len(classnames))
num = int(rng.integers(2, max_count, endpoint=True))
return dict(
tag=TAG,
include=[{"class": classnames[idx], "count": num}],
exclude=[{"class": classnames[idx], "count": num + 1}],
prompt=f"a photo of {numbers[num]} {make_plural(classnames[idx])}",
)
# Generate color samples
colors = ["red", "orange", "yellow", "green", "blue", "purple", "pink", "brown", "black", "white"]
def generate_color_sample(rng: np.random.Generator):
TAG = "colors"
idx = rng.choice(len(classnames) - 1) + 1
idx = (idx + classnames.index("person")) % len(classnames) # No "[COLOR] person" prompts
color = colors[rng.choice(len(colors))]
return dict(
tag=TAG,
include=[{"class": classnames[idx], "count": 1, "color": color}],
prompt=f"a photo of {with_article(color)} {classnames[idx]}",
)
# Generate position samples
positions = ["left of", "right of", "above", "below"]
def generate_position_sample(rng: np.random.Generator):
TAG = "position"
idx_a, idx_b = rng.choice(len(classnames), size=2, replace=False)
position = positions[rng.choice(len(positions))]
return dict(
tag=TAG,
include=[
{"class": classnames[idx_b], "count": 1},
{"class": classnames[idx_a], "count": 1, "position": (position, 0)},
],
prompt=f"a photo of {with_article(classnames[idx_a])} {position} {with_article(classnames[idx_b])}",
)
# Generate color attribution samples
def generate_color_attribution_sample(rng: np.random.Generator):
TAG = "color_attr"
idxs = rng.choice(len(classnames) - 1, size=2, replace=False) + 1
idx_a, idx_b = (idxs + classnames.index("person")) % len(classnames) # No "[COLOR] person" prompts
cidx_a, cidx_b = rng.choice(len(colors), size=2, replace=False)
return dict(
tag=TAG,
include=[
{"class": classnames[idx_a], "count": 1, "color": colors[cidx_a]},
{"class": classnames[idx_b], "count": 1, "color": colors[cidx_b]},
],
prompt=f"a photo of {with_article(colors[cidx_a])} {classnames[idx_a]} and {with_article(colors[cidx_b])} {classnames[idx_b]}",
)
# Generate evaluation suite
def generate_suite(rng: np.random.Generator, n: int = 100, output_path: str = ""):
samples = []
# Generate single object samples for all COCO classnames
samples.extend(generate_single_object_sample(rng, size=len(classnames)))
# Generate two object samples (~100)
for _ in range(n):
samples.append(generate_two_object_sample(rng))
# Generate counting samples
for _ in range(n):
samples.append(generate_counting_sample(rng, max_count=4))
# Generate color samples
for _ in range(n):
samples.append(generate_color_sample(rng))
# Generate position samples
for _ in range(n):
samples.append(generate_position_sample(rng))
# Generate color attribution samples
for _ in range(n):
samples.append(generate_color_attribution_sample(rng))
# De-duplicate
unique_samples, used_samples = [], set()
for sample in samples:
sample_text = yaml.safe_dump(sample)
if sample_text not in used_samples:
unique_samples.append(sample)
used_samples.add(sample_text)
# Write to files
os.makedirs(output_path, exist_ok=True)
with open(os.path.join(output_path, "generation_prompts.txt"), "w") as fp:
for sample in unique_samples:
print(sample["prompt"], file=fp)
with open(os.path.join(output_path, "evaluation_metadata.jsonl"), "w") as fp:
for sample in unique_samples:
print(json.dumps(sample), file=fp)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--seed", type=int, default=43, help="generation seed (default: 43)")
parser.add_argument("--num-prompts", "-n", type=int, default=100, help="number of prompts per task (default: 100)")
parser.add_argument(
"--output-path",
"-o",
type=str,
default="prompts",
help="output folder for prompts and metadata (default: 'prompts/')",
)
args = parser.parse_args()
rng = np.random.default_rng(args.seed)
generate_suite(rng, args.num_prompts, args.output_path)
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