def getLabels(imdb, model, solverProtoName):
labelFileName = meu.get_model_label_filename(solverProtoName);
labelFileName = labelFileName%(imdb.name, imdb.split);
if not os.path.exists(labelFileName):
print 'creating ground truth labels for evaluation'
imBnames, imIds = imdb.get_all_image_bnames_ids();
gtLabel = preprocess.get_vocab_counts(imIds, imdb._coco_caption_data, model['vocab']);
sg_utils.save(labelFileName, [gtLabel], ['gtLabel'], overwrite=True)
else:
gtLabel = sg_utils.load(labelFileName)['gtLabel'];
return gtLabel;
def getLabels(imdb, model, solverProtoName):
labelFileName = meu.get_model_label_filename(solverProtoName)
labelFileName = labelFileName % (imdb.name, imdb.split)
if not os.path.exists(labelFileName):
print 'creating ground truth labels for evaluation'
imBnames, imIds = imdb.get_all_image_bnames_ids()
gtLabel = preprocess.get_vocab_counts(imIds, imdb._coco_caption_data,
model['vocab'])
sg_utils.save(labelFileName, [gtLabel], ['gtLabel'], overwrite=True)
else:
gtLabel = sg_utils.load(labelFileName)['gtLabel']
return gtLabel
mapping = {'NNS': 'NN', 'NNP': 'NN', 'NNPS': 'NN', 'NN': 'NN', \
'VB': 'VB', 'VBD': 'VB', 'VBN': 'VB', 'VBZ': 'VB', 'VBP': 'VB', 'VBP': 'VB', 'VBG': 'VB', \
'JJR': 'JJ', 'JJS': 'JJ', 'JJ': 'JJ', 'DT': 'DT', 'PRP': 'PRP', 'PRP$': 'PRP', 'IN': 'IN'};
# punctuations to be removed from the sentences
punctuations = ["''", "'", "``", "`", "-LRB-", "-RRB-", "-LCB-", "-RCB-", \
".", "?", "!", ",", ":", "-", "--", "...", ";"]
vocab = preprocess.get_vocab(imset, coco_caps, punctuations, mapping);
sg_utils.save_variables('vocab_' + imset + '.pkl', \
[vocab[x] for x in vocab.keys()], \
vocab.keys(), \
overwrite = True);
##
N_WORDS = 1000;
vocab = preprocess.get_vocab_top_k(vocab, N_WORDS)
image_ids = coco_caps.getImgIds()
counts = preprocess.get_vocab_counts(image_ids, coco_caps, 5, vocab)
P = np.zeros((N_WORDS, 1), dtype = np.float);
R = np.zeros((N_WORDS, 1), dtype = np.float);
for i, w in enumerate(vv['words']):
P[i], R[i] = cap_eval_utils.human_agreement(counts[:,i], 5)
print w, P[i], R[i]
for pos in list(set(vocab['poss'])):
ind = [i for i,x in enumerate(vocab['poss']) if pos == x]
print "{:5s} [{:3d}] : {:.2f} {:.2f} ".format(pos, len(ind), 100*np.mean(P[ind]), 100*np.mean(R[ind]))
sys.exit(1)
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
print('Called with args:')
print(args)
imdb = coco_voc.coco_voc('test')
vocab = utils.load_variables(args.vocab_file)
gt_label = preprocess.get_vocab_counts(
imdb.image_index,
imdb.coco_caption_data,
5,
vocab
)
det_file = args.det_file
det_dir = os.path.dirname(det_file) # get root dir of det_file
eval_file = os.path.join(det_dir, imdb.name + '_eval.pkl')
benchmark(imdb, vocab, gt_label, 5, det_file, eval_file=eval_file)
map_file = args.map_file
gt_label_det = preprocess.get_vocab_counts_det(
imdb.image_index,
imdb.coco_instances_data,
map_file,
vocab
)
'VB': 'VB', 'VBD': 'VB', 'VBN': 'VB', 'VBZ': 'VB', 'VBP': 'VB', 'VBP': 'VB', 'VBG': 'VB', \
'JJR': 'JJ', 'JJS': 'JJ', 'JJ': 'JJ', 'DT': 'DT', 'PRP': 'PRP', 'PRP$': 'PRP', 'IN': 'IN'}
# punctuations to be removed from the sentences
punctuations = ["''", "'", "``", "`", "-LRB-", "-RRB-", "-LCB-", "-RCB-", \
".", "?", "!", ",", ":", "-", "--", "...", ";"]
vocab = preprocess.get_vocab(imset, coco_caps, punctuations, mapping)
sg_utils.save_variables('vocab_' + imset + '.pkl', \
[vocab[x] for x in vocab.keys()], \
vocab.keys(), \
overwrite = True)
##
N_WORDS = 1000
vocab = preprocess.get_vocab_top_k(vocab, N_WORDS)
image_ids = coco_caps.getImgIds()
counts = preprocess.get_vocab_counts(image_ids, coco_caps, 5, vocab)
P = np.zeros((N_WORDS, 1), dtype=np.float)
R = np.zeros((N_WORDS, 1), dtype=np.float)
for i, w in enumerate(vv['words']):
P[i], R[i] = cap_eval_utils.human_agreement(counts[:, i], 5)
print w, P[i], R[i]
for pos in list(set(vocab['poss'])):
ind = [i for i, x in enumerate(vocab['poss']) if pos == x]
print "{:5s} [{:3d}] : {:.2f} {:.2f} ".format(pos, len(ind),
100 * np.mean(P[ind]),
100 * np.mean(R[ind]))
caffe.set_device(args.gpu_id)
# Load the vocabulary
vocab = utils.load_variables(args.vocab_file)
if args.task == 'compute_targets':
imdb = []
output_dir = args.train_dir
sets = ['train', 'val']
for i, imset in enumerate([args.train_set, args.val_set]):
imdb.append(coco_voc.coco_voc(imset))
print 'Loaded dataset {:s}'.format(imdb[i].name)
# Compute targets for the file
counts = preprocess.get_vocab_counts(imdb[i].image_index, \
imdb[i].coco_caption_data, 5, vocab)
if args.write_labels:
label_file = os.path.join(output_dir, 'labels_' + sets[i] + '.h5')
print 'Writing labels to {}'.format(label_file)
with h5py.File(label_file, 'w') as f:
for j in xrange(imdb[i].num_images):
ind = imdb[i].image_index[j]
ind_str = '{:02d}/{:d}'.format(int(math.floor(ind)/1e4), ind)
l = f.create_dataset('/labels-{}'.format(ind_str), (1, 1, counts.shape[1], 1), dtype = 'f')
c = counts[j,:].copy(); c = c > 0; c = c.astype(np.float32); c = c.reshape((1, 1, c.size, 1))
l[...] = c
utils.tic_toc_print(1, 'write labels {:6d} / {:6d}'.format(j, imdb[i].num_images))
if args.write_splits:
split_file = os.path.join(output_dir, sets[i] + '.ids')
