# Print the parameters
print "Running CIDEr with the following settings"
print "*****************************"
print "Reference File:%s" % (refName)
print "Candidate File:%s" % (candName)
print "Result File:%s" % (resultFile)
print "IDF:%s" % (df_mode)
print "*****************************"
# In[2]:
# load reference and candidate sentences
loadDat = LoadData(pathToData)
gts, res = loadDat.readJson(refName, candName)
# In[3]:
# calculate cider scores
scorer = ciderEval(gts, res, 'corpus')
# scores: dict of list with key = metric and value = score given to each
# candidate
scores = scorer.evaluate()
# In[7]:
# scores['CIDEr'] contains CIDEr scores in a list for each candidate
# scores['CIDErD'] contains CIDEr-D scores in a list for each candidate
with open(resultFile, 'w') as outfile:
json.dump(scores, outfile)
# coding: utf-8
# In[1]:
# demo script for running CIDEr
import json
from pydataformat.loadData import LoadData
from pyciderevalcap.eval import CIDErEvalCap as ciderEval
# load the configuration file
config = json.loads(open('params.json', 'r').read())
pathToData = config['pathToData']
refName = config['refName']
candName = config['candName']
resultFile = config['resultFile']
df_mode = config['idf']
# load reference and candidate sentences
loadDat = LoadData(pathToData)
gts, res = loadDat.readJson(refName, candName)
# calculate cider scores
scorer = ciderEval(gts, res, df_mode)
scores = scorer.evaluate()
with open(resultFile, 'w') as outfile:
json.dump(scores, outfile)
def generate_cidre_rewards(self,
sess,
data,
path,
keys,
captions_GT,
num_samples=10):
actions = path["actions"]
observation_image_features = path["observation_image_features"]
observation_hidden_state = path["observation_hidden_state"]
observation_cell_state = path["observation_cell_state"]
cand_list = []
for i in range(self.gen_spec.n_seq_steps):
for j in range(num_samples):
sampled_actions = np.zeros(
(self.gen_spec.batch_size, self.gen_spec.n_seq_steps),
dtype=int)
sampled_actions[:, 0:i + 1] = actions[:, 0:i + 1]
if i + 1 < self.gen_spec.n_seq_steps:
feed_dict = {
self.generator.ph_input:
actions[:, i:i + 1],
self.generator.ph_image_feat_input:
observation_image_features[:, i + 1, :],
self.generator.ph_hidden_state:
observation_hidden_state[:, i + 1, :],
self.generator.ph_cell_state:
observation_cell_state[:, i + 1, :],
self.generator.ph_initial_step:
False
}
for k in range(i + 1, self.gen_spec.n_seq_steps):
ac, rs = sess.run([
self.generator.sampled_ac, self.generator.rnn_state
],
feed_dict=feed_dict)
feed_dict[self.generator.ph_hidden_state] = rs[0]
feed_dict[self.generator.ph_cell_state] = rs[1]
feed_dict[self.generator.ph_input] = ac
sampled_actions[:, k] = ac[:, 0]
candidates = data.decode(sampled_actions)
assert (len(candidates) == len(keys))
for idx, cap in enumerate(candidates):
cand_list.append({'image_id': keys[idx], 'caption': cap})
scorer = ciderEval(captions_GT, cand_list, "coco-val-df")
scores = scorer.evaluate()
reshaped_scores = np.reshape(
scores,
(self.gen_spec.n_seq_steps, num_samples, self.gen_spec.batch_size))
rewards = np.mean(np.swapaxes(np.swapaxes(reshaped_scores, 1, 2), 0,
1),
axis=2)
return rewards
print("Result File:%s" % (resultFile))
print("IDF:%s" % (df_mode))
print("*****************************")
ref_file = os.path.join(pathToData, refName)
cand_file = os.path.join(pathToData, candName)
ref_list = json.load(open(ref_file))
cand_list = json.load(open(cand_file))
gts = {}
for ref in ref_list:
if ref['image_id'] in gts:
gts[ref['image_id']].append(ref['caption'])
else:
gts[ref['image_id']] = [ref['caption']]
# calculate cider scores
scorer = ciderEval(gts, cand_list, df_mode)
# scores: dict of list with key = metric and value = score given to each
# candidate
scores = scorer.evaluate()
# In[7]:
# scores['CIDEr'] contains CIDEr scores in a list for each candidate
# scores['CIDErD'] contains CIDEr-D scores in a list for each candidate
with open(resultFile, 'w') as outfile:
json.dump(scores, outfile)