def evaluate_on_coco_caption(res_file, label_file, outfile=None):
"""
res_tsv: TSV file, each row is [image_key, json format list of captions].
Each caption is a dict, with fields "caption", "conf".
label_file: JSON file of ground truth captions in COCO format.
"""
assert label_file.endswith('.json')
if res_file.endswith('.tsv'):
res_file_coco = op.splitext(res_file)[0] + '_coco_format.json'
convert_tsv_to_coco_format(res_file, res_file_coco)
else:
raise ValueError(
'unknown prediction result file format: {}'.format(res_file))
coco = COCO(label_file)
cocoRes = coco.loadRes(res_file_coco)
cocoEval = COCOEvalCap(coco, cocoRes, 'corpus')
# evaluate on a subset of images by setting
# cocoEval.params['image_id'] = cocoRes.getImgIds()
# please remove this line when evaluating the full validation set
cocoEval.params['image_id'] = cocoRes.getImgIds()
# evaluate results
# SPICE will take a few minutes the first time, but speeds up due to caching
cocoEval.evaluate()
result = cocoEval.eval
if not outfile:
print(result)
else:
with open(outfile, 'w') as fp:
json.dump(result, fp, indent=4)
return result
def evaluate_metrics_from_files(pred_file: Union[Path, str],
ref_file: Union[Path, str]) \
-> Tuple[Dict[str, float], Dict[int, Dict[str, float]]]:
""" Evaluate the translation metrics from annotation files with the coco lib
Follows the example in the repo.
:param pred_file: File with predicted captions
:type pred_file: Path | str
:param ref_file: File with reference captions
:type ref_file: Path | str
:return: Tuple with metrics for the whole dataset and per-file metrics
:rtype: tuple[dict[str, float], dict[int, dict[str, float]]]
"""
# Load annotations from files
coco = COCO(str(ref_file))
cocoRes = coco.loadRes(str(pred_file))
# Create evaluation object and evaluate metrics
cocoEval = COCOEvalCap(coco, cocoRes)
cocoEval.params['audio_id'] = cocoRes.getAudioIds()
cocoEval.evaluate()
# Make dict from metrics
metrics = dict(
(m, s) for m, s in cocoEval.eval.items()
)
return metrics, cocoEval.audioToEval
def evaluate(self, count):
samples = []
samples_index = []
image_feature, image_id, test_annotation = self.dataset.get_test_for_eval()
num_samples = self.dataset.num_test_images
samples_index = np.full([self.batch_size*(num_samples//self.batch_size), self.max_words], self.NOT)
for i in range(num_samples//self.batch_size):
image_feature_test = image_feature[i*self.batch_size:(i+1)*self.batch_size]
feed_dict = {self.images: image_feature_test}
predict_words = self.sess.run(self.predict_words_argmax, feed_dict)
for j in range(self.batch_size):
samples.append([self.dataset.decode(predict_words[j, :], type='string', remove_END=True)[0]])
sample_index = self.dataset.decode(predict_words[j, :], type='index', remove_END=False)[0]
samples_index[i*self.batch_size+j][:len(sample_index)] = sample_index
# predict from samples
samples = np.asarray(samples)
samples_index = np.asarray(samples_index)
print '[%] Sentence:', samples[0]
meteor_pd = {}
meteor_id = []
for j in range(len(samples)):
if image_id[j] == 0:
break
meteor_pd[str(int(image_id[j]))] = [{'image_id':str(int(image_id[j])), 'caption':samples[j][0]}]
meteor_id.append(str(int(image_id[j])))
scorer = COCOEvalCap(test_annotation, meteor_pd, meteor_id)
scorer.evaluate(verbose=True)
sample_dir = os.path.join("./SeqGAN_samples_sample", self.model_name)
if not os.path.exists(sample_dir):
os.makedirs(sample_dir)
file_name = "%s_%s" % (self.dataset.dataset_name, str(count))
np.savez(os.path.join(sample_dir, file_name), string=samples, index=samples_index, id=meteor_id)
def get_metric(args_dict, results_file, ann_file):
coco = COCO(ann_file)
cocoRes = coco.loadRes(results_file)
cocoEval = COCOEvalCap(coco, cocoRes)
cocoEval.evaluate()
return cocoEval.eval[args_dict.es_metric]
def evaluate(config, corpus, data_loader, decoder, search_method):
total_vids = []
total_pd_captions = []
pd_vid_caption_dict = defaultdict(lambda: [])
for batch in iter(data_loader):
vids, encoder_outputs = batch
encoder_outputs = encoder_outputs.to(C.device)
input = torch.LongTensor([[
corpus.vocab.word2idx['<SOS>'] for _ in range(config.batch_size)
]])
input = input.to(C.device)
if config.decoder_model == "LSTM":
hidden = (
torch.zeros(config.decoder_n_layers, config.batch_size,
config.decoder_hidden_size).to(C.device),
torch.zeros(config.decoder_n_layers, config.batch_size,
config.decoder_hidden_size).to(C.device),
)
else:
hidden = torch.zeros(config.decoder_n_layers, config.batch_size,
config.decoder_hidden_size)
hidden = hidden.to(C.device)
if isinstance(search_method, str) and search_method == "greedy":
output_indices = greedy_search(config, decoder, input, hidden,
encoder_outputs)
elif isinstance(search_method, tuple) and search_method[0] == "beam":
beam_width = search_method[1]
output_indices = beam_search(config, beam_width, corpus.vocab,
decoder, input, hidden,
encoder_outputs)
output_indices = np.asarray(output_indices)
output_indices = output_indices.T
else:
raise NotImplementedError("Unknown search method: {}".format(
config.search_method))
total_vids += vids
total_pd_captions += convert_idxs_to_sentences(
output_indices, corpus.vocab.idx2word,
corpus.vocab.word2idx['<EOS>'])
total_vids = total_vids[:config.n_test]
total_pd_captions = total_pd_captions[:config.n_test]
with open("predictions.txt", 'w') as fout:
for vid, caption in zip(total_vids, total_pd_captions):
fout.write("{}\t\t{}\n".format(vid, caption))
for vid, caption in zip(total_vids, total_pd_captions):
pd_vid_caption_dict[vid].append(caption)
gts = COCOMSVD(corpus.test_dataset.video_caption_pairs)
res = load_res(pd_vid_caption_dict)
cocoEval = COCOEvalCap(gts, res)
cocoEval.params['image_id'] = gts.getImgIds()
cocoEval.evaluate()
return cocoEval.eval
def eval_coco_metrics(results_file):
coco = COCO(ANNOTATION_FILE_PATH)
cocoRes = coco.loadRes(results_file)
cocoEval = COCOEvalCap(coco, cocoRes)
# evaluate on a subset of images by setting
# cocoEval.params['image_id'] = cocoRes.getImgIds()
# please remove this line when evaluating the full validation set
cocoEval.params["image_id"] = cocoRes.getImgIds()
# evaluate results
# SPICE will take a few minutes the first time, but speeds up due to caching
cocoEval.evaluate()
def coco_eval_specific(results,eval_caption_path,entry_limit=500):
eval_json_output_dir = './coco/results/'
os.makedirs(eval_json_output_dir,exist_ok=True)
resFile = eval_json_output_dir + 'captions-generate.json'
json.dump(results,open(resFile,'w'))
annFile = eval_caption_path
coco = COCO(annFile)
cocoRes = coco.loadRes(resFile)
cocoEval = COCOEvalCap(coco,cocoRes)
cocoEval.params['image_id'] = cocoRes.getImgIds()
cocoEval.evaluate()
ans = [{'img_id':eva['image_id'],'CIDEr':eva['CIDEr']} for eva in cocoEval.evalImgs]
os.makedirs('./Data/Eval_Statics/',exist_ok=True)
with open("./Data/Eval_Statics/CIDEr_Result.txt",'w') as f:
entry = "img_id" + " " + "CIDEr" + "\n"
f.writelines(entry)
entry_num = 0
for ans_entry in ans:
entry = str(ans_entry['img_id']) + " " + str(np.round(ans_entry['CIDEr'],2)) + "\n"
f.writelines(entry)
entry_num += 1
if entry_num >= entry_limit: break
cider_list = [eva['CIDEr'] for eva in cocoEval.evalImgs]
cider_list_npy = np.array(cider_list)
indices = np.argsort(cider_list_npy)[::-1]
f.writelines('best samples:\n')
for idx in indices[:50]:
entry = str(ans[idx]['img_id']) + " " + str(np.round(ans[idx]['CIDEr'],2)) + "\n"
f.writelines(entry)
indices = indices[::-1]
f.writelines('worst samples:\n')
for idx in indices[:50]:
entry = str(ans[idx]['img_id']) + " " + str(np.round(ans[idx]['CIDEr'],2)) + "\n"
f.writelines(entry)
f.close()
ciderScores = [eva['CIDEr'] for eva in cocoEval.evalImgs]
x = plt.hist(ciderScores,bins=[0,1,2,3,4,5,6,7,8,9,10])
print(x)
plt.title('Histogram of CIDEr Scores', fontsize=20)
plt.xlabel('CIDEr score', fontsize=20)
plt.ylabel('result counts', fontsize=20)
plt.savefig('ciderHist.png',dpi=500)
plt.show()
def language_eval(preds, test_coco_ids, cache_path):
import sys
sys.path.insert(0, "coco_caption")
# generate target file
annFile = transform_annos(test_coco_ids)
from coco_caption.pycocotools.coco import COCO
from coco_caption.pycocoevalcap.eval import COCOEvalCap
encoder.FLOAT_REPR = lambda o: format(o, '.3f')
coco = COCO(annFile)
valids = coco.getImgIds()
# filter results to only those in MSCOCO validation set (will be about a third)
#preds_filt = [p for p in preds if p['image_id'] in valids]
preds_filt = []
image_id_filt = []
for p in preds:
if p['image_id'] in valids and p['image_id'] not in image_id_filt:
preds_filt.append(p)
image_id_filt.append(p['image_id'])
print('using %d/%d predictions' % (len(preds_filt), len(preds)))
json.dump(preds_filt,
open(cache_path,
'w')) # serialize to temporary json file. Sigh, COCO API...
cocoRes = coco.loadRes(cache_path)
cocoEval = COCOEvalCap(coco, cocoRes)
cocoEval.params['image_id'] = cocoRes.getImgIds()
cocoEval.evaluate()
# create output dictionary
out = {}
for metric, score in cocoEval.eval.items():
out[metric] = score
imgToEval = cocoEval.imgToEval
for p in preds_filt:
image_id, caption = p['image_id'], p['caption']
imgToEval[image_id]['caption'] = caption
with open(cache_path, 'w') as outfile:
json.dump({'overall': out, 'imgToEval': imgToEval}, outfile)
return out
def language_eval(preds, model_id, split):
annFile = 'coco_caption/annotations/captions_val2014.json'
from coco_caption.pycocotools.coco import COCO
from coco_caption.pycocoevalcap.eval import COCOEvalCap
# encoder.FLOAT_REPR = lambda o: format(o, '.3f')
if not os.path.isdir('eval_results'):
os.mkdir('eval_results')
cache_path = os.path.join('eval_results/',
model_id + '_' + split + '.json')
coco = COCO(annFile)
valids = coco.getImgIds()
# filter results to only those in MSCOCO validation set (will be about a third)
preds_filt = [p for p in preds if p['image_id'] in valids]
print(len(preds_filt))
print('using %d/%d predictions' % (len(preds_filt), len(preds)))
json.dump(preds_filt,
open(cache_path,
'w')) # serialize to temporary json file. Sigh, COCO API...
cocoRes = coco.loadRes(cache_path)
cocoEval = COCOEvalCap(coco, cocoRes)
cocoEval.params['image_id'] = cocoRes.getImgIds()
cocoEval.evaluate()
# create output dictionary
out = {}
for metric, score in cocoEval.eval.items():
out[metric] = score
imgToEval = cocoEval.imgToEval
for p in preds_filt:
image_id, caption = p['image_id'], p['caption']
imgToEval[image_id]['caption'] = caption
with open(cache_path, 'w') as outfile:
json.dump({'overall': out, 'imgToEval': imgToEval}, outfile)
return out
def coco_eval(results,eval_caption_path):
eval_json_output_dir = './coco_caption/results/'
os.makedirs(eval_json_output_dir,exist_ok=True)
resFile = eval_json_output_dir + 'captions-generate.json'
json.dump(results,open(resFile,'w'))
annFile = eval_caption_path
coco = COCO(annFile)
cocoRes = coco.loadRes(resFile)
cocoEval = COCOEvalCap(coco,cocoRes)
cocoEval.params['image_id'] = cocoRes.getImgIds()
cocoEval.evaluate()
cider = 0
print('---------------Evaluation performance-----------------')
for metric,score in cocoEval.eval.items():
print('%s: %.3f'%(metric,score))
if metric == 'CIDEr':
cider = score
return cider
def coco_metrics(generated_captions_fn, annotations_dir, split):
# Read generated captions
# resFile = '/home/plz563/projects/syncap/experiments/coco_karpathy/butd/results_best_beam_5_test.json'
# annotations_dir = '/home/plz563/data/coco2014/captions/annotations_trainval2014'
ann_fn = "{}/annotations/captions_{}.json".format(annotations_dir, split)
coco = COCO(ann_fn)
cocoRes = coco.loadRes(generated_captions_fn)
# create cocoEval object by taking coco and cocoRes
cocoEval = COCOEvalCap(coco, cocoRes)
# evaluate on a subset of images by setting
# cocoEval.params['image_id'] = cocoRes.getImgIds()
# please remove this line when evaluating the full validation set
cocoEval.params['image_id'] = cocoRes.getImgIds()
# evaluate results
# SPICE will take a few minutes the first time, but speeds up due to caching
cocoEval.evaluate()
# print output evaluation scores
for metric, score in cocoEval.eval.items():
print('%s: %.3f' % (metric, 100 * score))
return cocoEval.eval
#!/usr/bin/python
from pycocotools.coco import COCO
from coco_caption.pycocoevalcap.eval import COCOEvalCap
import sys
# create coco object and cocoRes object
coco = COCO(sys.argv[1])
cocoRes = coco.loadRes(sys.argv[2])
# create cocoEval object by taking coco and cocoRes
cocoEval = COCOEvalCap(coco, cocoRes)
# evaluate on a subset of images by setting
cocoEval.params['image_id'] = cocoRes.getImgIds()
# evaluate results
cocoEval.evaluate()
# print output evaluation scores
for metric, score in cocoEval.eval.items():
print('%s: %.3f' % (metric, score))
for key, value in cocoEval.imgToEval.iteritems():
print(key, value)