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import os
import argparse
from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix
parser = argparse.ArgumentParser()
parser.add_argument('--results_dir', default='./LaVIN', type=str)
eval_type_dict = {
"Perception": ["existence", "count", "position", "color", "posters", "celebrity", "scene", "landmark", "artwork", "OCR"],
"Cognition": ["commonsense_reasoning", "numerical_calculation", "code_reasoning"]
}
class calculate_metrics:
def divide_chunks(self, l, n=2):
# looping till length l
for i in range(0, len(l), n):
yield l[i:i + n]
return
def parse_pred_ans(self, pred_ans):
pred_label = None
if pred_ans in ["Sim", "Não"]:
pred_label = pred_ans
else:
prefix_pred_ans = pred_ans[:4]
if "Sim" in prefix_pred_ans or "sim" in prefix_pred_ans:
pred_label = "Sim"
elif "Não" in prefix_pred_ans or "não" in prefix_pred_ans or "Nao" in prefix_pred_ans or "nao" in prefix_pred_ans:
pred_label = "Não"
else:
pred_label = "other"
return pred_label
def compute_metric(self, gts, preds):
assert len(gts) == len(preds)
label_map = {
"sim": 1,
"não": 0,
"other": -1,
}
gts = [label_map[x] for x in gts]
preds = [label_map[x] for x in preds]
acc = accuracy_score(gts, preds)
clean_gts = []
clean_preds = []
other_num = 0
for gt, pred in zip(gts, preds):
if pred == -1:
other_num += 1
continue
clean_gts.append(gt)
clean_preds.append(pred)
conf_mat = confusion_matrix(clean_gts, clean_preds, labels=[1,0])
precision = precision_score(clean_gts, clean_preds, average='binary')
recall = recall_score(clean_gts, clean_preds, average='binary')
tp, fn = conf_mat[0]
fp, tn = conf_mat[1]
metric_dict = dict()
metric_dict = {
"TP": tp,
"FN": fn,
"TN": tn,
"FP": fp,
"precision": precision,
"recall": recall,
"other_num": other_num,
"acc": acc,
}
return metric_dict
def process_result(self, results_dir):
model_score_dict = dict()
for eval_type, task_name_list in eval_type_dict.items():
print("===========", eval_type, "===========")
scores = 0
task_score_dict = dict()
for task_name in task_name_list:
task_txt = os.path.join(results_dir, task_name + ".txt")
lines = open(task_txt, 'r').readlines()
chunk_lines = list(self.divide_chunks(lines)) # one image corresponds to two questions
img_num = len(chunk_lines)
task_other_ans_num = 0
task_score = 0
acc_plus_correct_num = 0
gts = []
preds = []
for img_items in chunk_lines:
assert len(img_items) == 2
img_correct_num = 0
for img_item in img_items:
img_name, question, gt_ans, pred_ans = img_item.split("\t")
gt_ans = gt_ans.lower()
pred_ans = pred_ans.lower()
assert gt_ans in ["sim", "não"] # gt can only be Sim or Não.
pred_ans = self.parse_pred_ans(pred_ans)
assert pred_ans in ["sim", "não", "other"]
gts.append(gt_ans)
preds.append(pred_ans)
if gt_ans == pred_ans:
img_correct_num += 1
if pred_ans not in ["sim", "não"]:
task_other_ans_num += 1
if img_correct_num == 2:
acc_plus_correct_num += 1
# cal TP precision acc, etc.
metric_dict = self.compute_metric(gts, preds)
acc_plus = acc_plus_correct_num / img_num
metric_dict["acc_plus"] = acc_plus
for k, v in metric_dict.items():
if k in ["acc", "acc_plus"]:
task_score += v*100
task_score_dict[task_name] = task_score
scores += task_score
print("total score:", scores, "\n")
for task_name, score in task_score_dict.items():
print("\t", task_name, " score:", score)
print("\n")
return
if __name__ == "__main__":
cal = calculate_metrics()
args = parser.parse_args()
results_dir = args.results_dir
cal.process_result(results_dir)
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