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Zero
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# Copyright (c) 2025 Ye Liu. Licensed under the BSD-3-Clause License.
import argparse
import nncore
import torch
from nncore.ops import temporal_area, temporal_intersection, temporal_iof, temporal_iou
from tabulate import tabulate
class SafeInt(int):
def __truediv__(self, other):
try:
return SafeInt(super().__truediv__(other))
except ZeroDivisionError:
return SafeInt(0)
def check_ans(options, ans, response):
a = ans.lower()
b = response.lower().split(' ')[0].replace('(', '').replace(')', '').replace('.', '')
if len(b) != 1:
b = b[0]
nncore.log(f'WARNING: {response} -> {b}')
if b not in [chr(ord('a') + i) for i in range(len(options))]:
nncore.log(f'ERROR: {response} -> {b}')
return
return a == b
def compute_iou(pred, span, conf, cgbench_mode, conf_thr):
pred_tensor = torch.Tensor(pred)
span_tensor = torch.Tensor(span)
if cgbench_mode:
if conf_thr > 0:
conf_tensor = torch.Tensor(conf)
keep = torch.cat((torch.LongTensor([0]), torch.where(conf_tensor > conf_thr)[0])).unique()
pred_tensor = pred_tensor[keep]
else:
pred_tensor = pred_tensor[:1]
pred_area = temporal_area(pred_tensor).sum()
span_area = temporal_area(span_tensor).sum()
inter = temporal_intersection(pred_tensor, span_tensor).sum()
iou = (inter / (pred_area + span_area - inter)).unsqueeze(0)
assert iou.numel() == 1
else:
iou = temporal_iou(pred_tensor, span_tensor)
iou = torch.where(iou.isfinite(), iou, 0)
return iou
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('pred_path')
parser.add_argument('--dataset')
parser.add_argument('--out_name', default='metrics.log')
parser.add_argument('--conf_thr', type=float, default=-1)
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
assert nncore.is_dir(args.pred_path)
log_file = nncore.join(args.pred_path, args.out_name)
nncore.set_default_logger(logger='eval', fmt=None, log_file=log_file)
if args.dataset is not None:
cgbench_mode = args.dataset == 'cgbench'
nncore.log(f'CG-Bench mode: {cgbench_mode}')
else:
cgbench_mode = False
nncore.log('Dataset is unknown, using default mode', log_level='WARNING')
pred_paths = nncore.ls(args.pred_path, ext=['json', 'jsonl'], join_path=True)
nncore.log(f'Total number of files: {len(pred_paths)}')
if cgbench_mode:
top_k = [1]
thres = [0.1, 0.2, 0.3, 0.4, 0.5]
else:
top_k = [1, 3, 5]
thres = [0.3, 0.5, 0.7]
tab_iou, tab_iop, tab_ans = dict(), dict(), dict()
iou_raise, iou_lower, iop_raise, iop_lower = SafeInt(0), SafeInt(0), SafeInt(0), SafeInt(0)
tab_iou_all = [SafeInt(0) for _ in range(len(top_k) * len(thres) + 3)]
tab_iop_all = [SafeInt(0) for _ in range(len(top_k) * len(thres) + 3)]
tab_ans_all = [SafeInt(0) for _ in range(len(thres) + 5)]
for path in pred_paths:
data = nncore.load(path)
for sample in data:
task = sample.get('task', 'unknown')
# samples in lvbench might have multiple tasks
if isinstance(task, str):
task = [task]
for t in task:
if t not in tab_iou:
tab_iou[t] = [SafeInt(0) for _ in range(len(top_k) * len(thres) + 3)]
if t not in tab_iop:
tab_iop[t] = [SafeInt(0) for _ in range(len(top_k) * len(thres) + 3)]
if t not in tab_ans:
tab_ans[t] = [SafeInt(0) for _ in range(len(thres) + 5)]
iou_hit = [False for _ in range(len(thres) + 1)]
iop_hit = False
if 'pred' in sample and 'conf' in sample and 'span' in sample:
for t in task:
tab_iou[t][0] += 1
tab_iop[t][0] += 1
tab_iou_all[0] += 1
tab_iop_all[0] += 1
iou = compute_iou(sample['pred'], sample['span'], sample['conf'], cgbench_mode, args.conf_thr)
top = iou[0].max().item()
for t in task:
tab_iou[t][-1] += top
tab_iou_all[-1] += top
for i, k in enumerate(top_k):
for j, h in enumerate(thres):
if iou[:k].max() >= h:
for t in task:
tab_iou[t][i * len(thres) + j + 2] += 1
tab_iou_all[i * len(thres) + j + 2] += 1
if k == 1:
iou_hit[j + 1] = True
if h == 0.5:
iou_hit[0] = True
if sample.get('pred_ori') is not None:
iou = compute_iou(sample['pred_ori'], sample['span'], sample['conf_ori'], cgbench_mode,
args.conf_thr)
iou = iou[0].max().item()
if iou < top:
iou_raise += 1
if iou > top:
iou_lower += 1
iop = temporal_iof(torch.Tensor(sample['pred']), torch.Tensor(sample['span']))
iop = torch.where(iop.isfinite(), iop, 0)
top = iop[0].max().item()
for t in task:
tab_iop[t][-1] += top
tab_iop_all[-1] += top
for i, k in enumerate(top_k):
for j, h in enumerate(thres):
if iop[:k].max() >= h:
for t in task:
tab_iop[t][i * len(thres) + j + 2] += 1
tab_iop_all[i * len(thres) + j + 2] += 1
if k == 1 and h == 0.5:
iop_hit = True
if sample.get('pred_ori') is not None:
iop = temporal_iof(torch.Tensor(sample['pred_ori']), torch.Tensor(sample['span']))
iop = torch.where(iop.isfinite(), iop, 0)
iop = iop[0].max().item()
if iop < top:
iop_raise += 1
if iop > top:
iop_lower += 1
if not sample.get('grounder_success', True):
for t in task:
tab_iou[t][1] += 1
tab_iop[t][1] += 1
tab_iou_all[1] += 1
tab_iop_all[1] += 1
if 'question' in sample and 'response' in sample:
for t in task:
tab_ans[t][0] += 1
tab_ans_all[0] += 1
correct = check_ans(sample['options'], sample['ans'], sample['response'])
if correct:
for t in task:
tab_ans[t][2] += 1
tab_ans_all[2] += 1
if iou_hit[0]:
for t in task:
tab_ans[t][3] += 1
tab_ans_all[3] += 1
if iop_hit:
for t in task:
tab_ans[t][4] += 1
tab_ans_all[4] += 1
for i in range(1, len(iou_hit)):
if iou_hit[i]:
for t in task:
tab_ans[t][i + 4] += 1
tab_ans_all[i + 4] += 1
elif correct is None:
for t in task:
tab_ans[t][1] += 1
tab_ans_all[1] += 1
tasks = sorted(list(set(list(tab_iou.keys()) + list(tab_iop.keys()) + list(tab_ans.keys()))))
if cgbench_mode:
nncore.log('\nGrounding (IoU):')
tab = tabulate(
[[task, tab_iou[task][0], tab_iou[task][1]] +
[f'{tab_iou[task][i] / tab_iou[task][0] * 100:.2f}' for i in range(2, len(tab_iou[task]))] +
[f'{sum(tab_iou[task][i] / tab_iou[task][0] for i in range(2, 2 + len(thres))) / len(thres) * 100:.2f}']
for task in tasks if task in tab_iou] +
[['all', tab_iou_all[0], tab_iou_all[1]] +
[f'{tab_iou_all[i] / tab_iou_all[0] * 100:.2f}' for i in range(2, len(tab_iou_all))] +
[f'{sum(tab_iou_all[i] / tab_iou_all[0] for i in range(2, 2 + len(thres))) / len(thres) * 100:.2f}']],
headers=['Task', '#Samples', 'Failed'] + [f'R{k}@{t}' for k in top_k for t in thres] + ['mIoU', 'rec.@IoU'],
tablefmt='pretty',
stralign='left')
nncore.log(tab)
nncore.log(f'\nIoU Raise ({tab_iou_all[0]} Samples): {iou_raise} ({iou_raise / tab_iou_all[0] * 100:.2f}%)')
nncore.log(f'IoU Lower ({tab_iou_all[0]} Samples): {iou_lower} ({iou_lower / tab_iou_all[0] * 100:.2f}%)')
nncore.log('\nQA:')
tab = tabulate(
[[task, tab_ans[task][0], tab_ans[task][1], f'{tab_ans[task][2] / tab_ans[task][0] * 100:.2f}'] +
[f'{sum(tab_ans[task][i] / tab_ans[task][0] for i in range(5, 5 + len(thres))) / len(thres) * 100:.2f}']
for task in tasks if task in tab_ans] +
[['all', tab_ans_all[0], tab_ans_all[1], f'{tab_ans_all[2] / tab_ans_all[0] * 100:.2f}'] +
[f'{sum(tab_ans_all[i] / tab_ans_all[0] for i in range(5, 5 + len(thres))) / len(thres) * 100:.2f}']],
headers=['Task', '#Samples', 'Failed', 'long-acc.', 'acc.@IoU'],
tablefmt='pretty',
stralign='left')
nncore.log(tab)
else:
nncore.log('\nGrounding (IoU):')
tab = tabulate(
[[task, tab_iou[task][0], tab_iou[task][1]] +
[f'{tab_iou[task][i] / tab_iou[task][0] * 100:.2f}' for i in range(2, len(tab_iou[task]))]
for task in tasks if task in tab_iou] +
[['all', tab_iou_all[0], tab_iou_all[1]] +
[f'{tab_iou_all[i] / tab_iou_all[0] * 100:.2f}' for i in range(2, len(tab_iou_all))]],
headers=['Task', '#Samples', 'Failed'] + [f'R{k}@{t}' for k in top_k for t in thres] + ['mIoU'],
tablefmt='pretty',
stralign='left')
nncore.log(tab)
nncore.log(f'\nIoU Raise ({tab_iou_all[0]} Samples): {iou_raise} ({iou_raise / tab_iou_all[0] * 100:.2f}%)')
nncore.log(f'IoU Lower ({tab_iou_all[0]} Samples): {iou_lower} ({iou_lower / tab_iou_all[0] * 100:.2f}%)')
nncore.log('\nGrounding (IoP):')
tab = tabulate(
[[task, tab_iop[task][0], tab_iop[task][1]] +
[f'{tab_iop[task][i] / tab_iop[task][0] * 100:.2f}' for i in range(2, len(tab_iop[task]))]
for task in tasks if task in tab_iop] +
[['all', tab_iop_all[0], tab_iop_all[1]] +
[f'{tab_iop_all[i] / tab_iop_all[0] * 100:.2f}' for i in range(2, len(tab_iop_all))]],
headers=['Task', '#Samples', 'Failed'] + [f'R{k}@{t}' for k in top_k for t in thres] + ['mIoP'],
tablefmt='pretty',
stralign='left')
nncore.log(tab)
nncore.log(f'\nIoP Raise ({tab_iop_all[0]} Samples): {iop_raise} ({iop_raise / tab_iop_all[0] * 100:.2f}%)')
nncore.log(f'IoP Lower ({tab_iop_all[0]} Samples): {iop_lower} ({iop_lower / tab_iop_all[0] * 100:.2f}%)')
nncore.log('\nQA:')
tab = tabulate(
[[task, tab_ans[task][0], tab_ans[task][1]] +
[f'{tab_ans[task][i] / tab_ans[task][0] * 100:.2f}' for i in range(2, 5)]
for task in tasks if task in tab_ans] +
[['all', tab_ans_all[0], tab_ans_all[1]] +
[f'{tab_ans_all[i] / tab_ans_all[0] * 100:.2f}' for i in range(2, 5)]],
headers=['Task', '#Samples', 'Failed', 'Acc', 'Acc (IoU >= 0.5)', 'Acc (IoP >= 0.5)'],
tablefmt='pretty',
stralign='left')
nncore.log(tab)
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