current file used to run the benchmark (returns a json you pass to assess.py)

run.py

@@ -0,0 +1,143 @@
+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()