def evaluation_captions(result):
test = result
datasetGTS = {'annotations': []}
datasetRES = {'annotations': []}
for i, image_id in enumerate(test):
array = []
for each in test[image_id]['Pred Sent']:
array.append(test[image_id]['Pred Sent'][each])
pred_sent = '. '.join(array)
array = []
for each in test[image_id]['Real Sent']:
sent = test[image_id]['Real Sent'][each]
if len(sent) != 0:
array.append(sent)
real_sent = '. '.join(array)
datasetGTS['annotations'].append({
'image_id': i,
'caption': real_sent
})
datasetRES['annotations'].append({
'image_id': i,
'caption': pred_sent
})
rng = range(len(test))
eva_scores = calculate_metrics(rng, datasetGTS, datasetRES)
print(eva_scores)
test_result_dir = './results/BLEUS.txt'
with open(test_result_dir, 'a') as f:
f.writelines(str(eva_scores))
f.writelines('\n')
Bleu = eva_scores['Bleu_1']
return Bleu
parser.add_argument(
'--result_path',
type=str,
default='./report_v4_models/v4/20210405-09:05/results/debug.json')
args = parser.parse_args()
test = load_json(args.result_path)
datasetGTS = {'annotations': []}
datasetRES = {'annotations': []}
for i, image_id in enumerate(test):
array = []
for each in test[image_id]['Pred Sent']:
array.append(test[image_id]['Pred Sent'][each])
pred_sent = '. '.join(array)
array = []
for each in test[image_id]['Real Sent']:
sent = test[image_id]['Real Sent'][each]
if len(sent) != 0:
array.append(sent)
real_sent = '. '.join(array)
datasetGTS['annotations'].append({'image_id': i, 'caption': real_sent})
datasetRES['annotations'].append({'image_id': i, 'caption': pred_sent})
rng = range(len(test))
result_dict = calculate_metrics(rng, datasetGTS, datasetRES)
print("\nIU-XRAY Test Dataset Evaluation Metrics & Scores:")
for key in result_dict.keys():
print(key, round(result_dict[key], 5))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--result_path',
type=str,
default='./results/clean_test.json')
args = parser.parse_args()
test = load_json(args.result_path)
datasetGTS = {'annotations': []}
datasetRES = {'annotations': []}
for i, image_id in enumerate(test):
array = []
for each in test[image_id]['Pred Sent']:
array.append(test[image_id]['Pred Sent'][each])
pred_sent = '. '.join(array)
array = []
for each in test[image_id]['Real Sent']:
sent = test[image_id]['Real Sent'][each]
if len(sent) != 0:
array.append(sent)
real_sent = '. '.join(array)
datasetGTS['annotations'].append({'image_id': i, 'caption': real_sent})
datasetRES['annotations'].append({'image_id': i, 'caption': pred_sent})
rng = range(len(test))
print calculate_metrics(rng, datasetGTS, datasetRES)
def generate_hier_with_spatial(self, epoch_id, Cider_max):
self.extractor.eval()
self.multi_rnn_model.eval()
results = {}
for k, (frontal_images, lateral_images, image_names, captions,
probs) in enumerate(self.val_data_loader):
frontal_images = self._to_var(frontal_images, requires_grad=False)
lateral_images = self._to_var(lateral_images, requires_grad=False)
V_frontal, v_g_frontal, V_lateral, v_g_lateral = self.extractor.forward(
frontal_images, lateral_images)
pred_sentences = {}
real_sentences = {}
for image_name in image_names:
image_prefix = image_name[0].split('_')[0]
pred_sentences[image_prefix] = {}
real_sentences[image_prefix] = {}
sampled_captions = self.multi_rnn_model.sample(
v_g_frontal, V_frontal, v_g_lateral, V_lateral)
for i in range(self.args.s_max):
sampled_ids = sampled_captions[:, i, :]
for id, array in zip(image_names, sampled_ids):
image_prefix = id[0].split('_')[0]
pred_sentences[image_prefix][i] = self.__vec2sent(
array.cpu().detach().numpy())
for id, array in zip(image_names, captions):
image_prefix = id[0].split('_')[0]
for i, sent in enumerate(array):
real_sentences[image_prefix][i] = self.__vec2sent(sent[1:])
for image_name in image_names:
id = image_name[0].split('_')[0]
results[id] = {
'Pred Sent': pred_sentences[id],
'Real Sent': real_sentences[id]
}
datasetGTS = {'annotations': []}
datasetRES = {'annotations': []}
for i, image_id in enumerate(results):
array = []
for each in results[image_id]['Pred Sent']:
array.append(results[image_id]['Pred Sent'][each])
pred_sent = '. '.join(array)
array = []
for each in results[image_id]['Real Sent']:
sent = results[image_id]['Real Sent'][each]
if len(sent) != 0:
array.append(sent)
real_sent = '. '.join(array)
datasetGTS['annotations'].append({
'image_id': i,
'caption': real_sent
})
datasetRES['annotations'].append({
'image_id': i,
'caption': pred_sent
})
rng = range(len(results))
evaluations = calculate_metrics(rng, datasetGTS, datasetRES)
print(type(evaluations))
print(evaluations)
if evaluations['CIDEr'] > Cider_max:
self.__save_json(results, epoch_id)
return evaluations['Bleu_1'], evaluations['Bleu_2'], evaluations[
'Bleu_3'], evaluations['Bleu_4'], evaluations[
'CIDEr'], evaluations['METEOR'], evaluations['ROUGE_L']