File size: 2,936 Bytes
6ef1166 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 | from collections import defaultdict
import difflib
import os
import argparse
import pdb
import random
import cssutils
from tqdm import tqdm
from utils import *
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("-f", "--file", type=str)
args = parser.parse_args()
ref_file = load_json("dataset/NSR-1K/spatial/spatial.val.json")
ref_file = {x['id']: x for x in ref_file}
fname = args.file
basename = os.path.basename(fname)
dirname = os.path.dirname(fname)
assert "raw" not in basename
responses = load_json(fname)
n_correct = defaultdict(lambda: 0)
n_miss = defaultdict(lambda: 0)
n_type = defaultdict(lambda: 0)
print(f"Evaluating {basename}")
for r in tqdm(responses):
try:
ref_sample = ref_file[int(r['query_id'])]
except:
ref_sample = ref_file[int(r['id'])]
ref_relation = ref_sample['relation']
obj1, _ = ref_sample['obj1']
obj2, _ = ref_sample['obj2']
prompt_type = ref_sample['type']
n_type[prompt_type] += 1
pred_objects = [obj for obj in r['object_list'] if obj[1] != [0]*4 and obj[0] != None]
all_objects = [pred_obj[0] for pred_obj in pred_objects]
close_obj1 = difflib.get_close_matches(obj1, all_objects)[:1]
pred_bbox1 = [obj[1] for i, obj in enumerate(pred_objects) if obj[0] in close_obj1]
if len(pred_bbox1) == 0:
n_miss[prompt_type] += 1
continue
close_obj2 = difflib.get_close_matches(obj2, all_objects)[:1]
pred_bbox2 = [obj[1] for i, obj in enumerate(pred_objects) if obj[0] in close_obj2]
if len(pred_bbox2) == 0:
n_miss[prompt_type] += 1
continue
all_relations = [eval_spatial_relation(b1, b2) for b1 in pred_bbox1 for b2 in pred_bbox2]
if ref_relation in all_relations:
n_correct[prompt_type] += 1
else:
if ref_relation == 'next to' and ('left' in all_relations or 'right' in all_relations):
n_correct[prompt_type] += 1
else:
pass
for prompt_type in n_correct.keys():
print(f'{basename} {prompt_type} (#eg: {n_type[prompt_type]})')
acc = n_correct[prompt_type]/n_type[prompt_type]
score_info = {'acc': acc, 'n_miss': n_miss[prompt_type]}
print(f'\tAcc = {acc*100:.2f} %, #miss = {n_miss[prompt_type]}')
# save output
args.output_dir = os.path.join('./eval_score/spatial/')
os.makedirs(args.output_dir, exist_ok=True)
output_filename = os.path.join(args.output_dir, 'layout_eval.'+basename)
with open(output_filename, 'w') as fout:
json.dump(score_info, fout)
print("{}, Overall, acc: {:.4f}, missing: {}".format(basename, sum(n_correct.values())/len(responses), sum(n_miss.values())))
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