from config.config import Config
from utils.dataprovider import DataProvider
from utils.metrics.pycocoevalcap.eval import COCOEvalCap
print("Evaluating the model ...")
config = Config()
config.phase = 'eval'
eval_data = DataProvider(config)
eval_gt_coco = eval_data.returncoco()
# Evaluate these captions
eval_result_coco = eval_gt_coco.loadRes(config.eval_result_file)
scorer = COCOEvalCap(eval_gt_coco, eval_result_coco)
scorer.evaluate()
def main(argv):
print("Testing the model ...")
config = Config()
config.beam_size = FLAGS.beam_size
config.phase = 'test'
if not os.path.exists(config.test_result_dir):
os.mkdir(config.test_result_dir)
print("Building the vocabulary...")
vocabulary = Vocabulary(config.vocabulary_size)
vocabulary.load(config.vocabulary_file)
print("Vocabulary built.")
print("Number of words = %d" % (vocabulary.size))
test_data = DataProvider(config)
test_gt_coco = test_data.returncoco()
model = ShowAttendTell(config)
model.build()
with tf.Session() as sess:
model.setup_graph_from_checkpoint(sess, config.caption_checkpoint_dir)
tf.get_default_graph().finalize()
captiongen = CaptionGenerator(model, vocabulary, config.beam_size,
config.max_caption_length,
config.batch_size)
# Generate the captions for the images
results = []
idx = 0
for k in tqdm(list(range(test_data.num_batches)), desc='batch'):
batch, images = test_data.next_batch_and_images()
caption_data = captiongen.beam_search(sess, images, vocabulary)
fake_cnt = 0 if k<test_data.num_batches-1 \
else test_data.fake_count
for l in range(test_data.batch_size - fake_cnt):
word_idxs = caption_data[l][0].sentence
score = caption_data[l][0].score
caption = vocabulary.get_sentence(word_idxs)
results.append({
'image_id': test_data.image_ids[idx],
'caption': caption
})
idx += 1
# Save the result in an image file, if requested
if config.save_test_result_as_image:
image_file = batch[l]
image_name = image_file.split(os.sep)[-1]
image_name = os.path.splitext(image_name)[0]
img = plt.imread(image_file)
plt.switch_backend('agg')
plt.imshow(img)
plt.axis('off')
plt.title(caption)
plt.savefig(
os.path.join(config.test_result_dir,
image_name + '_result.png'))
fp = open(config.test_result_file, 'wb')
json.dump(results, fp)
fp.close()
# Evaluate these captions
test_result_coco = test_gt_coco.loadRes(config.test_result_file)
scorer = COCOEvalCap(test_gt_coco, test_result_coco)
scorer.evaluate()
print("Evaluation complete.")
from config.config import Config
from utils.dataprovider import DataProvider
from utils.metrics.pycocoevalcap.eval import COCOEvalCap
print("Evaluating the model ...")
config = Config()
config.phase = 'test'
test_data = DataProvider(config)
test_gt_coco = test_data.returncoco()
# Evaluate these captions
test_result_coco = test_gt_coco.loadRes(config.test_result_file)
scorer = COCOEvalCap(test_gt_coco, test_result_coco)
scorer.evaluate()
