def main(_):
# Build the inference graph.
g = tf.Graph()
with g.as_default():
model = gradcam_wrapper.GradCamWrapper()
restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
FLAGS.checkpoint_path)
#g.finalize()
save_path = osp.join(FLAGS.save_path,
osp.basename(FLAGS.model_name) + '_gt')
if FLAGS.save_path != "" and not osp.isdir(save_path):
os.makedirs(save_path)
# Create the vocabulary.
vocab = vocabulary.Vocabulary(FLAGS.vocab_file)
man_id = vocab.word_to_id('man')
woman_id = vocab.word_to_id('woman')
#person_id = vocab.word_to_id('person') # if we want to additionally process "person" words
of = open(FLAGS.img_path, 'r')
image_ids = of.read().split('\n')
if image_ids[-1] == '':
image_ids = image_ids[0:-1]
json_path = coco_dir + '/annotations/captions_val2014.json'
json_data = json.load(open(json_path, 'r'))
json_dict = {}
for entry in json_data['annotations']:
image_id = entry['image_id']
if str(image_id) not in image_ids: continue
if image_id not in json_dict:
caption = entry['caption']
caption = caption.lower()
tokens = caption.split(' ')
if '_man' in FLAGS.img_path: look_for = 'man'
elif '_woman' in FLAGS.img_path: look_for = 'woman'
else: assert (False)
if look_for in tokens:
json_dict[image_id] = caption
if len(json_dict) == 500: break
image_ids = json_dict.keys()
#assert(len(image_ids)==500)
with tf.Session(graph=g) as sess:
# Load the model from checkpoint.
restore_fn(sess)
global_index = 0
for i, image_id in enumerate(image_ids):
image_id = int(image_id)
sys.stdout.write('\r%d/%d' % (i, len(image_ids)))
filename = coco_dir + '/images/val2014/COCO_val2014_' + "%012d" % (
image_id) + '.jpg'
with tf.gfile.GFile(filename, "r") as f:
image = f.read()
caption = json_dict[image_id]
print(caption)
if caption[-1] == '.':
caption = caption[0:-1]
tokens = caption.split(' ')
tokens.insert(0, '<S>')
encoded_tokens = [vocab.word_to_id(w) for w in tokens]
man_ids = [i for i, c in enumerate(encoded_tokens) if c == man_id]
woman_ids = [
i for i, c in enumerate(encoded_tokens) if c == woman_id
]
#person_ids = [i for i, c in enumerate(encoded_tokens) if c == person_id]
if not (man_ids or woman_ids): # or person_ids):
assert (False)
else:
for wid in man_ids:
if FLAGS.save_path != "":
save_path_pre = save_path + '/' + "%06d" % (
global_index) + '_'
else:
save_path_pre = ""
model.process_image(sess,
image,
encoded_tokens,
filename,
vocab,
word_index=wid - 1,
word_id=man_id,
save_path=save_path_pre)
global_index += 1
for wid in woman_ids:
if FLAGS.save_path != "":
save_path_pre = save_path + '/' + "%06d" % (
global_index) + '_'
else:
save_path_pre = ""
model.process_image(sess,
image,
encoded_tokens,
filename,
vocab,
word_index=wid - 1,
word_id=woman_id,
save_path=save_path_pre)
global_index += 1
#for wid in person_ids:
# if FLAGS.save_path != "":
# save_path_pre = save_path + '/' + "%06d" % (global_index) + '_'
# else:
# save_path_pre = ""
# model.process_image(sess, image, encoded_tokens, filename, vocab, word_index=wid-1, word_id=person_id, save_path=save_path_pre)
# global_index += 1
import gc
gc.collect()
def main(_):
# Build the inference graph.
g = tf.Graph()
import ipdb
ipdb.set_trace()
with g.as_default():
model = gradcam_wrapper.GradCamWrapper()
restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
FLAGS.checkpoint_path)
#g.finalize()
save_path = osp.join(FLAGS.save_path, osp.basename(FLAGS.json_path)[0:-5])
if FLAGS.save_path != "" and not osp.isdir(save_path):
os.makedirs(save_path)
# Create the vocabulary.
vocab = vocabulary.Vocabulary(FLAGS.vocab_file)
man_id = vocab.word_to_id('man')
woman_id = vocab.word_to_id('woman')
person_id = vocab.word_to_id('person')
#filenames = glob.glob(FLAGS.input_files)
json_data = json.load(open(FLAGS.json_path, 'r'))
json_dict = {}
for entry in json_data:
image_id = entry['image_id']
if image_id not in json_dict:
json_dict[image_id] = entry['caption']
else:
ipdb.set_trace()
of = open(FLAGS.img_path, 'r')
image_ids = of.read().split('\n')
if image_ids[-1] == '':
image_ids = image_ids[0:-1]
with tf.Session(graph=g) as sess:
# Load the model from checkpoint.
restore_fn(sess)
global_index = 0
for i, image_id in enumerate(image_ids):
image_id = int(image_id)
sys.stdout.write('\r%d/%d' % (i, len(image_ids)))
filename = 'im2txt/data/mscoco/images/val2014/COCO_val2014_' + "%012d" % (
image_id) + '.jpg'
with tf.gfile.GFile(filename, "r") as f:
image = f.read()
if image_id not in json_dict:
continue
caption = json_dict[image_id]
print(caption)
if caption[-1] == '.':
caption = caption[0:-1]
tokens = caption.split(' ')
tokens.insert(0, '<S>')
encoded_tokens = [vocab.word_to_id(w) for w in tokens]
man_ids = [i for i, c in enumerate(encoded_tokens) if c == man_id]
woman_ids = [
i for i, c in enumerate(encoded_tokens) if c == woman_id
]
person_ids = [
i for i, c in enumerate(encoded_tokens) if c == person_id
]
if not (man_ids or woman_ids or person_ids):
# nothing to do
continue
else:
for wid in man_ids:
if FLAGS.save_path != "":
save_path_pre = save_path + '/' + "%06d" % (
global_index) + '_'
else:
save_path_pre = ""
model.process_image(sess,
image,
encoded_tokens,
filename,
vocab,
word_index=wid - 1,
word_id=man_id,
save_path=save_path_pre)
global_index += 1
for wid in woman_ids:
if FLAGS.save_path != "":
save_path_pre = save_path + '/' + "%06d" % (
global_index) + '_'
else:
save_path_pre = ""
model.process_image(sess,
image,
encoded_tokens,
filename,
vocab,
word_index=wid - 1,
word_id=woman_id,
save_path=save_path_pre)
global_index += 1
for wid in person_ids:
if FLAGS.save_path != "":
save_path_pre = save_path + '/' + "%06d" % (
global_index) + '_'
else:
save_path_pre = ""
model.process_image(sess,
image,
encoded_tokens,
filename,
vocab,
word_index=wid - 1,
word_id=person_id,
save_path=save_path_pre)
global_index += 1
import gc
gc.collect()
def main(_):
# Build the inference graph.
g = tf.Graph()
with g.as_default():
model = gradcam_wrapper.GradCamWrapper()
restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
FLAGS.checkpoint_path)
#g.finalize()
save_path = osp.join(FLAGS.save_path, osp.basename(FLAGS.json_path)[0:-5])
if FLAGS.save_path != "" and not osp.isdir(save_path):
os.makedirs(save_path)
# Create the vocabulary.
vocab = vocabulary.Vocabulary(FLAGS.vocab_file)
man_id = vocab.word_to_id(FLAGS.male_word)
woman_id = vocab.word_to_id(FLAGS.female_word)
# person_id = vocab.word_to_id(FLAGS.neutral_word) # if we want to additionally process "person" words
json_data = json.load(open(FLAGS.json_path, 'r'))
json_dict = {}
for entry in json_data:
file_id = entry['filename']
if file_id not in json_dict:
caption = entry['caption']
caption = caption.lower()
json_dict[str(file_id)] = caption
filenames = glob.glob(FLAGS.img_path)
print(json_dict)
with tf.Session(graph=g) as sess:
# Load the model from checkpoint.
restore_fn(sess)
global_index = 0
for i, filename in enumerate(filenames):
with tf.gfile.GFile(filename, "r") as f:
image = f.read()
caption = json_dict[filename]
print(caption)
if caption[-1] == '.':
caption = caption[0:-1]
tokens = caption.split(' ')
tokens.insert(0, '<S>')
encoded_tokens = [vocab.word_to_id(w) for w in tokens]
man_ids = [i for i, c in enumerate(encoded_tokens) if c == man_id]
woman_ids = [
i for i, c in enumerate(encoded_tokens) if c == woman_id
]
# person_ids = [i for i, c in enumerate(encoded_tokens) if c == person_id]
if not (man_ids or woman_ids):
# nothing to do
continue
else:
for wid in man_ids:
if FLAGS.save_path != "":
save_path_pre = save_path + '/' + "%06d" % (
global_index) + '_'
else:
save_path_pre = ""
model.process_image(sess,
image,
encoded_tokens,
filename,
vocab,
word_index=wid - 1,
word_id=man_id,
save_path=save_path_pre)
global_index += 1
for wid in woman_ids:
if FLAGS.save_path != "":
save_path_pre = save_path + '/' + "%06d" % (
global_index) + '_'
else:
save_path_pre = ""
model.process_image(sess,
image,
encoded_tokens,
filename,
vocab,
word_index=wid - 1,
word_id=woman_id,
save_path=save_path_pre)
global_index += 1
# for wid in person_ids:
# if FLAGS.save_path != "":
# save_path_pre = save_path + '/' + "%06d" % (global_index) + '_'
# else:
# save_path_pre = ""
# model.process_image(sess, image, encoded_tokens, filename, vocab, word_index=wid-1, word_id=person_id, save_path=save_path_pre)
# global_index += 1
import gc
gc.collect()