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from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig
from PIL import Image
import requests
import torch
import json
import re
import os
from torch.nn.utils.rnn import pad_sequence
from datasets import load_dataset
import re
dataset = load_dataset('moondream/analog-clock-benchmark', split='test', streaming=True)
result_location = './molmo-answers.json'
model_path = 'moondream/Molmo-72B-0924'
processor = AutoProcessor.from_pretrained(
model_path,
trust_remote_code=True,
torch_dtype='auto',
device_map='auto'
)
model = AutoModelForCausalLM.from_pretrained(
model_path,
trust_remote_code=True,
torch_dtype='auto',
device_map='auto'
)
def extract_points_and_text(input_string):
# match the points attributes with single or double quotes
points_pattern = r'(?:x|y)(\d*)=["\']?([\d.]+)["\']?'
matches = re.findall(points_pattern, input_string)
# Group matches by their index (or empty string if no index)
point_dict = {}
for index, value in matches:
point_dict.setdefault(index, []).append(float(value))
# Convert matches to the desired format
points = [point for point in point_dict.values() if len(point) == 2]
text_pattern = r'<(?:point|points)[^>]*>(.*?)</(?:point|points)>'
text_match = re.search(text_pattern, input_string)
text = text_match.group(1) if text_match else ""
cleaned_string = re.sub(text_pattern, text, input_string)
# Find all integers in the cleaned string
answers = [int(num) for num in re.findall(r'\b\d+\b', cleaned_string)]
result = {
"points": points,
"cleaned_string": cleaned_string,
"answers": answers
}
return result
def do_inference(image, text):
inputs = processor.process(
images=[image],
text=text
)
# move inputs to the correct device and make a batch of size 1
inputs = {k: v.to(model.device).unsqueeze(0) for k, v in inputs.items()}
# generate output; maximum 200 new tokens; stop generation when <|endoftext|> is generated
inputs["images"] = inputs["images"].to(torch.float16)
output = model.generate_from_batch(
inputs,
GenerationConfig(max_new_tokens=200, stop_strings="<|endoftext|>", attention_type='flash'),
tokenizer=processor.tokenizer
)
# only get generated tokens; decode them to text
generated_tokens = output[0,inputs['input_ids'].size(1):]
generated_text = processor.tokenizer.decode(generated_tokens, skip_special_tokens=True)
return generated_text
# cleaned_obj = extract_points_and_text(generated_text)
# return cleaned_obj
# create results file if it doesn't exist
if not os.path.exists(result_location):
os.makedirs(os.path.dirname(result_location), exist_ok=True)
with open(result_location, 'w') as file:
json.dump([], file)
def has_one_clock(image):
molmo_res_text = do_inference(image, 'How many clocks are in this image?')
print('Clock count -> ', molmo_res_text)
if molmo_res_text.strip().endswith("1.") and molmo_res_text.strip().startswith(" Counting the"):
return True
else:
return False
def get_time(image):
return do_inference(image, '''Look at this image with a readable clock and do the following:
1. Report the hour hand location
2. Report the minute hand location
3. Report the time based on these locations''')
# Create progress bar
datapoint_index = 0
for datapoint in dataset:
molmo_time_string = get_time(datapoint['image'])
print(molmo_time_string)
print('\n\n\n\n\n')
#skip if the string is empty or function returned false (molmo could not tell the time)
# Check if molmo_time_string contains exactly one time formatted as HH:MM
time_pattern = r'\b\d{1,2}:\d{2}\b'
times_found = re.findall(time_pattern, molmo_time_string)
if len(times_found) == 1 and times_found[0] != "10:10":
obj_to_save = {
'datapoint_index': datapoint_index,
'correct_answer': molmo_time_string,
'reported_times': times_found,
'llm_string': molmo_time_string
}
with open(result_location, 'r+') as file:
data = json.load(file)
data.append(obj_to_save)
file.seek(0)
json.dump(data, file, indent=4)
file.truncate() |