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import os
import json
import collections
from pathlib import Path
import numpy as np
import torch
from data.data_utils import Hypo3DAnswer
from evaluator.build import EVALUATOR_REGISTRY
@EVALUATOR_REGISTRY.register()
class Hypo3DEval():
# 0: what, 1: is, 2: how, 3: can, 4: which, 5: others
def __init__(self, cfg, task_name):
self.eval_dict = {
'target_metric': [], 'ans1_acc': [], 'ans10_acc': [],'non_color_ans1_acc': [], 'non_color_ans10_acc': [],
'type0_acc': [], 'type1_acc': [], 'type2_acc': [],
'type0_acc': [], 'type1_acc': [], 'type2_acc': [],
'type3_acc': [], 'type4_acc': [], 'type5_acc': []
}
# run
self.total_count = 0
self.non_color_total_count = 0
self.type_count = {
'type0_count': 1e-10, 'type1_count': 1e-10, 'type2_count': 1e-10,
'type3_count': 1e-10, 'type4_count': 1e-10, 'type5_count': 1e-10
}
self.best_result = -np.inf
self.base_dir = cfg.data.scan_family_base
answer_data = json.load(
open(os.path.join(self.base_dir,
'annotations/hypo3d_task/answer_dict.json'), encoding='utf-8')
)[0]
answer_counter = []
for data in answer_data.keys():
answer_counter.append(data)
answer_counter = collections.Counter(sorted(answer_counter))
answer_cands = answer_counter.keys()
self.answer_vocab = Hypo3DAnswer(answer_cands)
color_terms = [
"black",
"blue",
"brown",
"cyan",
"gray",
"green",
"grey",
"orange",
"pink",
"purple",
"red",
"tan",
"white",
"yellow"
]
self.color_ids = [answer_data[name] for name in color_terms]
self.save = cfg.eval.save
if self.save:
self.eval_results = []
self.save_dir = Path(cfg.exp_dir) / "eval_results" / task_name
self.save_dir.mkdir(parents=True, exist_ok=True)
def update(self, data_dict):
metrics = self.batch_metrics(data_dict)
batch_count = metrics['total_count']
self.total_count += batch_count
non_color_batch_count = metrics['non_color_total_count']
self.non_color_total_count += non_color_batch_count
for key in metrics:
if 'type' in key and 'count' in key:
self.type_count[key] += metrics[key]
if self.save:
for i in range(metrics["total_count"]):
self.eval_results.append({
# vision
"source": data_dict['source'][i],
"scan_id": data_dict['scan_id'][i],
"anchor": data_dict['anchor_locs'][i],
'anchor_ort': data_dict['anchor_orientation'][i],
# language
"instruction": data_dict['prompt_after_obj'][i],
"response_gt": data_dict['answer_list'][i].split('[answer_seq]'),
"response_pred": data_dict['output_text'][i]
})
# save eval dict
for key in self.eval_dict.keys():
if 'type' in key:
# self.eval_dict[key].append(float(metrics[key]) * metrics['type' + key[4] + '_count'])
continue
elif 'non_color' not in key:
self.eval_dict[key].append(float(metrics[key]) * batch_count)
else:
self.eval_dict[key].append(float(metrics[key]) * non_color_batch_count)
def batch_metrics(self, data_dict):
metrics = {}
# ans
choice_1 = data_dict['answer_scores'].argmax(dim=-1)
choice_10 = torch.topk(data_dict['answer_scores'].detach(), 10, -1)[1]
correct1 = 0
correct10 = 0
non_color_correct1 = 0
non_color_correct10 = 0
total_non_color = 0
# correct_type = {0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0}
count_type = {0: 1e-10, 1: 1e-10, 2: 1e-10, 3: 1e-10, 4: 1e-10, 5: 1e-10}
for i in range(data_dict['answer_label'].shape[0]):
gt_id = data_dict['answer_label'][i].argmax().item()
if gt_id not in self.color_ids:
total_non_color += 1
# count_type[data_dict['hypo3d_type'][i].item()] += 1
if data_dict['answer_label'][i, choice_1[i]] == 1:
if choice_1[i] not in self.color_ids:
non_color_correct1 += 1
correct1 += 1
# correct_type[data_dict['hypo3d_type'][i].item()] += 1
for j in range(10):
if data_dict['answer_label'][i, choice_10[i, j]] == 1:
if choice_10[i,j] not in self.color_ids:
non_color_correct10 += 1
correct10 += 1
break
metrics['ans1_acc'] = correct1 / float(len(choice_1))
metrics['ans10_acc'] = correct10 / float(len(choice_1))
metrics['non_color_ans1_acc'] = non_color_correct1 / float(total_non_color)
metrics['non_color_ans10_acc'] = non_color_correct10 / float(total_non_color)
# question type acc
# for key in count_type.keys():
# # metrics['type' + str(key) + '_acc'] = correct_type[key] / count_type[key]
# metrics['type' + str(key) + '_count'] = count_type[key]
metrics['target_metric'] = metrics['ans1_acc']
metrics["total_count"] = data_dict["answer_scores"].shape[0]
metrics["non_color_total_count"] = total_non_color
return metrics
def reset(self):
for key in self.eval_dict.keys():
self.eval_dict[key] = []
self.total_count = 0
self.non_color_total_count = 0
self.type_count = {
'type0_count': 1e-10, 'type1_count': 1e-10, 'type2_count': 1e-10,
'type3_count': 1e-10, 'type4_count': 1e-10, 'type5_count': 1e-10
}
if self.save:
self.eval_results = []
def record(self, split='val'):
# record
for k, v in self.eval_dict.items():
if k == "answer_top10":
continue
if 'type' in k:
self.eval_dict[k] = sum(v) / self.type_count['type' + k[4] + '_count']
elif 'non_color' not in k:
self.eval_dict[k] = sum(v) / self.total_count
print(k, 'overall', sum(v), self.total_count)
elif 'non_color' in k:
self.eval_dict[k] = sum(v) / self.non_color_total_count
print(k, 'non_color', sum(v), self.non_color_total_count)
if self.eval_dict["target_metric"] > self.best_result:
is_best = True
self.best_result = self.eval_dict["target_metric"]
else:
is_best = False
if self.save and (is_best or split == 'test'):
torch.save(self.eval_results, str(self.save_dir / 'results.pt'))
return is_best, self.eval_dict
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