Upload eval.py (#3)

- Upload eval.py (18c211549eb4153515204726c46a88c1be03f051)


Co-authored-by: Yash Butala <Yash-Butala@users.noreply.huggingface.co>

eval.py

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+import json
+from math import sqrt
+import re
+from nltk.translate.bleu_score import sentence_bleu
+
+# gold label file
+gold_fn = 'test.json'
+
+pred_fn = 'llava-v1.5-13b.json'
+gold = json.load(open(gold_fn))
+pred = json.load(open(pred_fn))
+
+sequence_match = 0
+action_score = 0
+total_click_penalty = 0
+total_press_penalty = 0
+total_write_penalty = 0
+ideal_score = 0
+max_click_penalty = 0
+max_press_penalty = 0
+max_write_penalty = 0
+
+
+
+def get_bounds(box: dict(), cx, cy):
+    for i in box:
+        tl = box[i]["top_left"]
+        br = box[i]["bottom_right"]
+        if (tl[0]+br[0])/2 == cx and (tl[1]+br[1])/2 == cy:
+            return (tl,br)
+    
+    assert False
+
+    
+def dynamic_dirichlet_l2_penalty(tl, br, px, py):
+    
+    len_x = br[0] - tl[0]
+    len_y = br[1] - tl[1]
+    
+    cx = ( br[0] - tl[0] ) / 2
+    cy = ( br[1] - tl[1] ) / 2
+    
+    dx = abs(cx - px) - (len_x * 0.5)
+    dy = abs(cy - py) - (len_y * 0.5)
+    dist = sqrt((dx * (dx > 0)) ** 2 + (dy * (dy > 0)) ** 2)
+    
+    mu = sqrt( len_x ** 2 + len_y ** 2)
+    
+    score = mu / (dist+mu)
+    penalty = 1 - score
+    return penalty
+
+for idx in gold:
+    
+    gold_script = open(gold[idx]['task']).read().strip().split('\n')[2:]
+    llm_script = pred[idx].strip().split()
+    llm_script = [x for x in llm_script if x.strip().startswith('pyautogui')]
+    #find extreme case values
+    sample_weight = (len(gold_script)-0.9)
+
+    ideal_score += sample_weight
+    for gold_line in gold_script:
+        action_type = gold_line.split("pyautogui.")[1].split("(")[0]
+        if action_type == 'click' or action_type == 'rightClick' or action_type == 'moveTo' or action_type == 'dragTo':
+            max_click_penalty += sample_weight/len(gold_script)
+        if action_type == 'press' or action_type == 'hotkey':
+            max_press_penalty += sample_weight/len(gold_script)
+        if action_type == 'write':
+            max_write_penalty += sample_weight/len(gold_script)
+       
+    seq_match_flag = 1
+    click_penalty = 0
+    press_penalty = 0
+    write_penalty = 0
+    
+    # if length doesn't seq match is 0
+    # llm_script = llm_script[:len(gold_script)]
+    if len(llm_script) != len(gold_script):
+        seq_match_flag = 0
+    if seq_match_flag == 1:
+        for i in range(len(gold_script)):
+            gold_line = gold_script[i].strip()
+            gold_action = gold_line.split('pyautogui.')[1].split('(')[0]
+            pred_line = llm_script[i]
+            if pred_line.startswith('pyautogui.') == False:
+                seq_match_flag = 0
+                break
+            pred_action = pred_line.split('pyautogui.')[1].split('(')[0]
+            if pred_action != gold_action:
+                seq_match_flag = 0
+                break
+        
+    # find penalties for correct and wrong sequences 
+    box_path = gold[idx]['box']
+    box_num = box_path.split("_")[-1].split(".json")[0]
+    box_path = "_".join(box_path.split("_")[:-1])+box_num+"_boxes.json"
+    box = json.load(open(box_path))
+
+    for i in range(len(gold_script)):
+        gold_line = gold_script[i].strip()
+        gold_action = gold_line.split('pyautogui.')[1].split('(')[0]
+        # just add the penalties
+        if seq_match_flag == 0:
+            if gold_action == 'click' or gold_action == 'rightClick' or gold_action == 'moveTo' or gold_action == 'dragTo':
+                click_penalty += 1/len(gold_script)
+            if gold_action == 'press' or gold_action == 'hotkey':
+                press_penalty += 1/len(gold_script)
+            if gold_action == 'write':
+                write_penalty += 1/len(gold_script)
+            continue   
+        pred_line = llm_script[i]
+        pred_action = pred_line.split('pyautogui.')[1].split('(')[0]
+
+        # l2 penalty for click
+        
+        if gold_action == 'click' or gold == 'rightClick':
+            # get original box bounds
+            gold_cx = gold_line.split("pyautogui.")[1].split('(')[1].split(',')[0]
+            gold_cy = gold_line.split("pyautogui.")[1].split('(')[1].split(',')[1].split(')')[0]
+            tl, br = get_bounds(box, float(gold_cx), float(gold_cy))
+            
+            # get predicted point
+            pred_cx = gold_line.split("pyautogui.")[1].split('(')[1].split(',')[0]
+            pred_cy = gold_line.split("pyautogui.")[1].split('(')[1].split(',')[1].split(')')[0]
+            
+            click_penalty += (1.0/len(gold_script)) * dynamic_dirichlet_l2_penalty(tl, br, float(pred_cx), float(pred_cy))
+            
+        # penalty for press
+        if gold_action == 'press':
+            gold_key = gold_line.split("\"")[1]
+            pred_key = (re.split("\"|'", pred_line))[1]
+            if gold_key.strip() != pred_key.strip():
+                press_penalty += 1/len(gold_script)
+            
+        # penalty for hotkey
+        if gold_action == 'hotkey':
+            gold_keys = gold_line.split("(")[1].split(")")[0].split(",")
+            pred_keys = pred_line.split("(")[1].split(")")[0].split(",")
+            
+            gold_key_set = set([x[1:-1] for x in gold_keys if len(x)>2])
+            pred_key_set = set([x[1:-1] for x in pred_keys if len(x)>2])
+            if gold_key_set != pred_key_set:
+                press_penalty += 1/len(gold_script)
+    
+    
+        if gold_action == 'write':
+            reference = [gold_line.split("\"")[1]]
+            candidate = re.split("\"|'", pred_line)[1]
+            write_penalty += (1-sentence_bleu(reference, candidate, weights=(0.5, 0.5))) / len(gold_script)
+            
+    sequence_match += (seq_match_flag) * sample_weight
+    action_score += (max(seq_match_flag - click_penalty - press_penalty - write_penalty, 0)) * sample_weight
+    if seq_match_flag:
+        total_click_penalty += click_penalty  * sample_weight
+        total_press_penalty += press_penalty * sample_weight
+        total_write_penalty += write_penalty * sample_weight
+    
+
+print(ideal_score)   
+print(f"Sequence match: {sequence_match/ideal_score}")
+print(f"Action match: {action_score/ideal_score}")
+    
+    
+print(total_click_penalty/ideal_score)
+print(total_press_penalty/ideal_score)
+print(total_write_penalty/ideal_score)
+