def test(model_path='models/model-900', video_feat_path=video_feat_path):
meta_data, train_data, val_data, test_data = get_video_data_jukin(video_data_path_train, video_data_path_val, video_data_path_test)
test_data = val_data
ixtoword = pd.Series(np.load('./data'+str(gpu_id)+'/ixtoword.npy').tolist())
model = Video_Caption_Generator(
dim_image=dim_image,
n_words=len(ixtoword),
dim_hidden=dim_hidden,
batch_size=batch_size,
n_lstm_steps=n_frame_step,
drop_out_rate = 0,
bias_init_vector=None)
video_tf, video_mask_tf, caption_tf, lstm3_variables_tf = model.build_generator()
sess = tf.InteractiveSession(config=tf.ConfigProto(allow_soft_placement=True))
with tf.device("/cpu:0"):
saver = tf.train.Saver()
saver.restore(sess, model_path)
for ind, row in enumerate(lstm3_variables_tf):
if ind % 4 == 0:
assign_op = row.assign(tf.mul(row,1-0.5))
sess.run(assign_op)
[pred_sent, gt_sent] = testing_all(sess, test_data, ixtoword,video_tf, video_mask_tf, caption_tf)
#np.savez('Att_result/'+model_path.split('/')[1],gt = gt_sent,pred=pred_sent)
scorer = COCOScorer()
total_score = scorer.score(gt_sent, pred_sent, range(len(pred_sent)))
return total_score
def test(model_path='models/model-900', video_feat_path=video_feat_path):
meta_data, train_data, test_data = get_video_data_jukin(video_data_path_train, video_data_path_test)
#test_data = train_data
ixtoword = pd.Series(np.load('./data/ixtoword.npy').tolist())
model = Video_Caption_Generator(
dim_image=dim_image,
n_words=len(ixtoword),
dim_hidden=dim_hidden,
batch_size=batch_size,
n_lstm_steps=n_frame_step,
drop_out_rate = 0,
bias_init_vector=None)
video_tf, video_mask_tf, video_len_tf, HLness_tf, caption_tf, HLness_att_mask_tf, lstmRNN_variables_tf, lstm3_variables_tf = model.build_generator()
sess = tf.InteractiveSession()
saver = tf.train.Saver()
saver.restore(sess, model_path)
for ind, row in enumerate(lstmRNN_variables_tf):
if ind % 4 == 0:
assign_op = row.assign(tf.mul(row,1-0.5))
sess.run(assign_op)
for ind, row in enumerate(lstm3_variables_tf):
if ind % 4 == 0:
assign_op = row.assign(tf.mul(row,1-0.5))
sess.run(assign_op)
[mp, pred_sent, gt_sent, HLness] = testing_all(sess, test_data, ixtoword,video_tf, video_mask_tf, video_len_tf, HLness_tf, caption_tf, HLness_att_mask_tf)
np.savez('HS_result/'+model_path.split('/')[1],gt = gt_sent,pred=pred_sent,mp=mp,HLness=HLness)
total_score = np.mean(mp)
print model_path.split('/')[1]+' mAP: ' + str(total_score)
scorer = COCOScorer()
total_score = scorer.score(gt_sent, pred_sent, range(len(pred_sent)))
return total_score
def score_with_cocoeval(samples_valid, samples_test, engine):
scorer = COCOScorer()
if samples_valid:
gts_valid = OrderedDict()
for vidID in engine.valid_ids:
gts_valid[vidID] = engine.CAP[vidID]
valid_score = scorer.score(gts_valid, samples_valid, engine.valid_ids)
else:
valid_score = None
if samples_test:
gts_test = OrderedDict()
for vidID in engine.test_ids:
gts_test[vidID] = engine.CAP[vidID]
test_score = scorer.score(gts_test, samples_test, engine.test_ids)
else:
test_score = None
return valid_score, test_score
def train():
meta_data, train_data, val_data, test_data = get_video_data_jukin(video_data_path_train, video_data_path_val, video_data_path_test)
captions = meta_data['Description'].values
captions = map(lambda x: x.replace('.', ''), captions)
captions = map(lambda x: x.replace(',', ''), captions)
wordtoix, ixtoword, bias_init_vector = preProBuildWordVocab(captions, word_count_threshold=1)
np.save('./data'+str(gpu_id)+'/ixtoword', ixtoword)
model = Video_Caption_Generator(
dim_image=dim_image,
n_words=len(wordtoix),
dim_hidden=dim_hidden,
batch_size=batch_size,
n_lstm_steps=n_frame_step,
drop_out_rate = 0.5,
bias_init_vector=None)
tf_loss, tf_video, tf_video_mask, tf_caption, tf_caption_mask= model.build_model()
sess = tf.InteractiveSession(config=tf.ConfigProto(allow_soft_placement=True))
with tf.device("/cpu:0"):
saver = tf.train.Saver(max_to_keep=100)
train_op = tf.train.AdamOptimizer(learning_rate).minimize(tf_loss)
tf.initialize_all_variables().run()
saver.restore(sess, 'models_Att_update_new/model-30')
tStart_total = time.time()
for epoch in range(n_epochs):
index = np.arange(len(train_data))
np.random.shuffle(index)
train_data = train_data[index]
tStart_epoch = time.time()
loss_epoch = np.zeros(len(train_data))
for current_batch_file_idx in xrange(len(train_data)):
tStart = time.time()
current_batch = h5py.File(train_data[current_batch_file_idx])
current_feats = np.zeros((batch_size, n_frame_step, dim_image))
current_video_masks = np.zeros((batch_size, n_frame_step))
current_video_len = np.zeros(batch_size)
for ind in xrange(batch_size):
current_feats[ind,:,:] = current_batch['data'][:,ind,:]
idx = np.where(current_batch['label'][:,ind] != -1)[0]
if len(idx) == 0:
continue
current_video_masks[ind,:idx[-1]+1] = 1
current_captions = current_batch['title']
current_caption_ind = map(lambda cap: [wordtoix[word] for word in cap.lower().split(' ') if word in wordtoix], current_captions)
current_caption_matrix = sequence.pad_sequences(current_caption_ind, padding='post', maxlen=16-1)
current_caption_matrix = np.hstack( [current_caption_matrix, np.zeros( [len(current_caption_matrix),1]) ] ).astype(int)
current_caption_masks = np.zeros((current_caption_matrix.shape[0], current_caption_matrix.shape[1]))
nonzeros = np.array( map(lambda x: (x != 0).sum()+1, current_caption_matrix ))
for ind, row in enumerate(current_caption_masks):
row[:nonzeros[ind]] = 1
_, loss_val = sess.run(
[train_op, tf_loss],
feed_dict={
tf_video: current_feats,
tf_video_mask : current_video_masks,
tf_caption: current_caption_matrix,
tf_caption_mask: current_caption_masks
})
loss_epoch[current_batch_file_idx] = loss_val
tStop = time.time()
#print "Epoch:", epoch, " Batch:", current_batch_file_idx, " Loss:", loss_val
#print "Time Cost:", round(tStop - tStart,2), "s"
print "Epoch:", epoch, " done. Loss:", np.mean(loss_epoch)
tStop_epoch = time.time()
print "Epoch Time Cost:", round(tStop_epoch - tStart_epoch,2), "s"
if np.mod(epoch, 10) == 0 or epoch == n_epochs - 1:
print "Epoch ", epoch, " is done. Saving the model ..."
with tf.device("/cpu:0"):
saver.save(sess, os.path.join(model_path, 'model'), global_step=epoch)
current_batch = h5py.File(val_data[np.random.randint(0,len(val_data))])
video_tf, video_mask_tf, caption_tf, lstm3_variables_tf = model.build_generator()
ixtoword = pd.Series(np.load('./data'+str(gpu_id)+'/ixtoword.npy').tolist())
[pred_sent, gt_sent] = testing_all(sess, train_data[-2:], ixtoword, video_tf, video_mask_tf, caption_tf)
for idx in range(len(pred_sent)):
print "GT: " + gt_sent[idx][0]['caption']
print "PD: " + pred_sent[idx][0]['caption']
print '-------'
[pred_sent, gt_sent] = testing_all(sess, val_data, ixtoword,video_tf, video_mask_tf, caption_tf)
scorer = COCOScorer()
total_score = scorer.score(gt_sent, pred_sent, range(len(pred_sent)))
sys.stdout.flush()
print "Finally, saving the model ..."
with tf.device("/cpu:0"):
saver.save(sess, os.path.join(model_path, 'model'), global_step=n_epochs)
tStop_total = time.time()
print "Total Time Cost:", round(tStop_total - tStart_total,2), "s"
def train():
assert os.path.isfile(video_data_path_train)
assert os.path.isfile(video_data_path_val)
assert os.path.isdir(model_path)
assert os.path.isfile(wordtoix_file)
assert os.path.isfile(ixtoword_file)
assert drop_strategy in ['block_video', 'block_sent', 'random', 'keep']
wordtoix = np.load(wordtoix_file).tolist()
ixtoword = pd.Series(np.load(ixtoword_file).tolist())
print 'build model and session...'
# shared parameters on the GPU
with tf.device("/gpu:0"):
model = Video_Caption_Generator(dim_image=dim_image,
n_words=len(wordtoix),
dim_hidden=dim_hidden,
batch_size=batch_size,
n_caption_steps=n_caption_steps,
n_video_steps=n_video_steps,
drop_out_rate=0.5,
bias_init_vector=None)
tStart_total = time.time()
n_epoch_steps = int(n_train_samples / batch_size)
n_steps = n_epochs * n_epoch_steps
# preprocess on the CPU
with tf.device('/cpu:0'):
train_data, train_encode_data, _, _, train_video_label, train_caption_label, train_caption_id, train_caption_id_1, \
_, _, _, _, train_frame_data = read_and_decode_with_frame(video_data_path_train)
val_data, val_encode_data, val_fname, val_title, val_video_label, val_caption_label, val_caption_id, val_caption_id_1, \
_, _, _, _, val_frame_data = read_and_decode_with_frame(video_data_path_val)
# random batches
train_data, train_encode_data, train_video_label, train_caption_label, train_caption_id, train_caption_id_1, train_frame_data = \
tf.train.shuffle_batch([train_data, train_encode_data, train_video_label, train_caption_label, train_caption_id, train_caption_id_1, train_frame_data],
batch_size=batch_size, num_threads=num_threads, capacity=prefetch, min_after_dequeue=min_queue_examples)
val_data, val_video_label, val_fname, val_caption_label, val_caption_id_1, val_frame_data = \
tf.train.batch([val_data, val_video_label, val_fname, val_caption_label, val_caption_id_1, val_frame_data],
batch_size=batch_size, num_threads=1, capacity=2* batch_size)
# graph on the GPU
with tf.device("/gpu:0"):
tf_loss, tf_loss_cap, tf_loss_lat, tf_loss_vid, tf_z, tf_v_h, tf_s_h, tf_drop_type \
= model.build_model(train_data, train_frame_data, train_video_label, train_caption_id, train_caption_id_1, train_caption_label)
val_v2s_tf, _ = model.build_v2s_generator(val_data)
val_s2s_tf, _, _ = model.build_s2s_generator(val_caption_id_1)
val_s2v_tf, _, _ = model.build_s2v_generator(val_caption_id_1,
val_frame_data)
val_v2v_tf, _ = model.build_v2v_generator(val_data, val_frame_data)
sess = tf.InteractiveSession(config=tf.ConfigProto(
allow_soft_placement=True, log_device_placement=False))
# check for model file
with tf.device(cpu_device):
saver = tf.train.Saver(max_to_keep=100)
ckpt = tf.train.get_checkpoint_state(model_path)
global_step = 0
if ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path):
print("Reading model parameters from %s" % ckpt.model_checkpoint_path)
saver.restore(sess, ckpt.model_checkpoint_path)
# print_tensors_in_checkpoint_file(ckpt.model_checkpoint_path, "", True)
global_step = get_model_step(ckpt.model_checkpoint_path)
print 'global_step:', global_step
else:
print("Created model with fresh parameters.")
sess.run(tf.global_variables_initializer())
temp = set(tf.global_variables())
# train on the GPU
with tf.device("/gpu:0"):
## 1. weight decay
for var in tf.trainable_variables():
decay_loss = tf.multiply(tf.nn.l2_loss(var),
0.0004,
name='weight_loss')
tf.add_to_collection('losses', decay_loss)
tf.add_to_collection('losses', tf_loss)
tf_total_loss = tf.add_n(tf.get_collection('losses'),
name='total_loss')
## 2. gradient clip
optimizer = tf.train.AdamOptimizer(learning_rate)
gvs = optimizer.compute_gradients(tf_total_loss)
# when variable is not related to the loss, grad returned as None
clip_gvs = [(tf.clip_by_norm(grad, clip_norm), var)
for grad, var in gvs if grad is not None]
for grad, var in gvs:
if grad is not None:
tf.summary.histogram(var.name + '/grad', grad)
tf.summary.histogram(var.name + '/data', var)
train_op = optimizer.apply_gradients(clip_gvs)
## initialize variables added for optimizer
sess.run(tf.variables_initializer(set(tf.global_variables()) - temp))
# initialize epoch variable in queue reader
sess.run(tf.local_variables_initializer())
loss_epoch = 0
loss_epoch_cap = 0
loss_epoch_vid = 0
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
##### add summaries ######
tf.summary.histogram('video_h', tf_v_h)
tf.summary.histogram('sent_h', tf_s_h)
tf.summary.scalar('loss_vid', tf_loss_vid)
tf.summary.scalar('loss_lat', tf_loss_lat)
tf.summary.scalar('loss_caption', tf_loss_cap)
# for var in tf.trainable_variables():
# summaries.append(tf.histogram_summary(var.op.name, var))
summary_op = tf.summary.merge_all()
# write graph architecture to file
summary_writer = tf.summary.FileWriter(model_path + 'summary', sess.graph)
epoch = global_step
video_label = sess.run(train_video_label)
for step in xrange(1, n_steps + 1):
tStart = time.time()
if drop_strategy == 'keep':
drop_type = 0
elif drop_strategy == 'block_sentence':
drop_type = 1
elif drop_strategy == 'block_video':
drop_type = 2
else:
drop_type = random.randint(0, 3)
_, loss_val, loss_cap, loss_lat, loss_vid = sess.run(
[train_op, tf_loss, tf_loss_cap, tf_loss_lat, tf_loss_vid],
feed_dict={tf_drop_type: drop_type})
tStop = time.time()
print "step:", step, " Loss:", loss_val, "loss_cap:", loss_cap * caption_weight, "loss_latent:", loss_lat * latent_weight, "loss_vid:", loss_vid * video_weight
print "Time Cost:", round(tStop - tStart, 2), "s"
loss_epoch += loss_val
loss_epoch_cap += loss_cap
loss_epoch_vid += loss_vid
if step % n_epoch_steps == 0:
# if step % 3 == 0:
epoch += 1
loss_epoch /= n_epoch_steps
loss_epoch_cap /= n_epoch_steps
loss_epoch_vid /= n_epoch_steps
with tf.device(cpu_device):
saver.save(sess,
os.path.join(model_path, 'model'),
global_step=epoch)
# print 'z:', z[0, :10]
print 'epoch:', epoch, 'loss:', loss_epoch, "loss_cap:", loss_epoch_cap, "loss_lat:", loss_lat, "loss_vid:", loss_epoch_vid
loss_epoch = 0
loss_epoch_cap = 0
loss_epoch_vid = 0
######### test sentence generation ##########
n_val_steps = int(n_val_samples / batch_size)
# n_val_steps = 3
### TODO: sometimes COCO test show exceptions in the beginning of training ####
if test_v2s:
[pred_sent, gt_sent, id_list, gt_dict, pred_dict,
flist] = testing_all(sess, 1, ixtoword, val_v2s_tf, val_fname)
for i, key in enumerate(pred_dict.keys()):
print 'video:', flist[i]
for ele in gt_dict[key]:
print "GT: " + ele['caption']
print "PD: " + pred_dict[key][0]['caption']
print '-------'
print '############## video to sentence result #################'
print 'epoch:', epoch
[pred_sent, gt_sent, id_list, gt_dict, pred_dict,
_] = testing_all(sess, n_val_steps, ixtoword, val_v2s_tf,
val_fname)
scorer = COCOScorer()
total_score = scorer.score(gt_dict, pred_dict, id_list)
print '############## video to sentence result #################'
if test_s2s:
[pred_sent, gt_sent, id_list, gt_dict, pred_dict,
flist] = testing_all(sess, 1, ixtoword, val_s2s_tf, val_fname)
for i, key in enumerate(pred_dict.keys()):
print 'video:', flist[i]
for ele in gt_dict[key]:
print "GT: " + ele['caption']
print "PD: " + pred_dict[key][0]['caption']
print '-------'
print '############## sentence to sentence result #################'
print 'epoch:', epoch
[pred_sent, gt_sent, id_list, gt_dict, pred_dict,
_] = testing_all(sess, n_val_steps, ixtoword, val_s2s_tf,
val_fname)
scorer = COCOScorer()
total_score = scorer.score(gt_dict, pred_dict, id_list)
print '############## sentence to sentence result #################'
######### test video generation #############
if test_v2v:
mse_v2v = test_all_videos(sess, n_val_steps, val_frame_data,
val_v2v_tf, val_video_label,
pixel_scale_factor)
print 'epoch', epoch, 'video2video mse:', mse_v2v
if test_s2v:
mse_s2v = test_all_videos(sess, n_val_steps, val_frame_data,
val_s2v_tf, val_video_label,
pixel_scale_factor)
print 'epoch', epoch, 'caption2video mse:', mse_s2v
sys.stdout.flush()
###### summary ######
if epoch % 2 == 0:
summary = sess.run(summary_op)
summary_writer.add_summary(summary, epoch)
sys.stdout.flush()
coord.request_stop()
coord.join(threads)
print "Finally, saving the model ..."
with tf.device(cpu_device):
saver.save(sess,
os.path.join(model_path, 'model'),
global_step=n_epochs)
tStop_total = time.time()
print "Total Time Cost:", round(tStop_total - tStart_total, 2), "s"
sess.close()
def test(model_path=None,
video_data_path_test=video_data_path_val,
n_test_samples=n_val_samples,
video_name=None):
# test_data = val_data # to evaluate on testing data or validation data
wordtoix = np.load(wordtoix_file).tolist()
ixtoword = pd.Series(np.load(ixtoword_file).tolist())
with tf.device("/gpu:0"):
model = Video_Caption_Generator(dim_image=dim_image,
n_words=len(wordtoix),
dim_hidden=dim_hidden,
batch_size=batch_size,
n_caption_steps=n_caption_steps,
n_video_steps=n_video_steps,
drop_out_rate=0.5,
bias_init_vector=None)
# preprocess on the CPU
with tf.device('/cpu:0'):
train_data, train_encode_data, _, _, train_video_label, train_caption_label, train_caption_id, train_caption_id_1, \
_, _, _, _ = read_and_decode(video_data_path_train)
val_data, val_encode_data, val_fname, val_title, val_video_label, val_caption_label, val_caption_id, val_caption_id_1, \
_, _, _, _ = read_and_decode(video_data_path_test)
train_data, train_encode_data, train_video_label, train_caption_label, train_caption_id, train_caption_id_1 = \
tf.train.shuffle_batch([train_data, train_encode_data, train_video_label, train_caption_label, train_caption_id, train_caption_id_1],
batch_size=batch_size, num_threads=num_threads, capacity=prefetch, min_after_dequeue=min_queue_examples)
val_data, val_video_label, val_fname, val_caption_label, val_caption_id_1 = \
tf.train.batch([val_data, val_video_label, val_fname, val_caption_label, val_caption_id_1],
batch_size=batch_size, num_threads=1, capacity=2* batch_size)
# graph on the GPU
with tf.device("/gpu:0"):
tf_loss = model.build_model(train_caption_id, train_caption_id_1,
train_caption_label)
val_s2s_tf, s2s_lstm3_vars_tf = model.build_s2s_generator(
val_caption_id_1)
sess = tf.InteractiveSession(config=tf.ConfigProto(
allow_soft_placement=True))
with tf.device(cpu_device):
saver = tf.train.Saver()
saver.restore(sess, model_path)
print 'load parameters from:', model_path
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
######### test sentence generation ##########
print 'testing...'
n_test_steps = int(n_test_samples / batch_size)
print 'n_test_steps:', n_test_steps
tstart = time.time()
### TODO: sometimes COCO test show exceptions in the beginning of training ####
if test_s2s:
# [pred_sent, gt_sent, id_list, gt_dict, pred_dict, flist] = testing_all(sess, 1, ixtoword, val_s2s_tf, val_fname)
# for i, key in enumerate(pred_dict.keys()):
# print 'video:', flist[i]
# for ele in gt_dict[key]:
# print "GT: " + ele['caption']
# print "PD: " + pred_dict[key][0]['caption']
# print '-------'
print '############## sentence to sentence result #################'
[pred_sent, gt_sent, id_list, gt_dict, pred_dict,
flist] = testing_all(sess, n_test_steps, ixtoword, val_s2s_tf,
val_fname)
if os.path.isfile('demo_s2s.txt.videos'):
video_name = pickle.load(open('demo_s2s.txt.videos', "rb"))
if video_name:
for i, key in enumerate(pred_dict.keys()):
if flist[i] in video_name:
print flist[i]
for ele in gt_dict[key]:
print "GT: " + ele['caption']
print "PD: " + pred_dict[key][0]['caption']
print '-----------'
scorer = COCOScorer()
total_score_2 = scorer.score(gt_dict, pred_dict, id_list)
print '############## sentence to sentence result #################'
if save_demo_sent_s2s:
get_demo_sentence(sess,
n_test_steps,
ixtoword,
val_s2s_tf,
val_fname,
result_file='demo_s2s.txt')
sys.stdout.flush()
coord.request_stop()
coord.join(threads)
tstop = time.time()
print "Total Time Cost:", round(tstop - tstart, 2), "s"
sess.close()
def train():
assert os.path.isdir(home_folder)
assert os.path.isfile(video_data_path_train)
assert os.path.isfile(video_data_path_val)
assert os.path.isdir(model_path)
print 'load meta data...'
wordtoix = np.load(home_folder + 'data0/msvd_wordtoix.npy').tolist()
ixtoword = pd.Series(
np.load(home_folder + 'data0/msvd_ixtoword.npy').tolist())
print 'build model and session...'
# shared parameters on the GPU
with tf.device("/gpu:0"):
model = Video_Caption_Generator(dim_image=dim_image,
n_words=len(wordtoix),
dim_hidden=dim_hidden,
batch_size=batch_size,
n_caption_steps=n_caption_steps,
n_video_steps=n_video_steps,
drop_out_rate=0.5,
bias_init_vector=None)
tStart_total = time.time()
n_epoch_steps = int(n_train_samples / batch_size)
n_steps = n_epochs * n_epoch_steps
# preprocess on the CPU
with tf.device('/cpu:0'):
train_data, train_encode_data, _, _, train_video_label, train_caption_label, train_caption_id, train_caption_id_1, \
_, _, _, _ = read_and_decode(video_data_path_train)
val_data, val_encode_data, val_fname, val_title, val_video_label, val_caption_label, val_caption_id, val_caption_id_1, \
_, _, _, _ = read_and_decode(video_data_path_val)
# random batches
train_data, train_encode_data, train_video_label, train_caption_label, train_caption_id, train_caption_id_1 = \
tf.train.shuffle_batch([train_data, train_encode_data, train_video_label, train_caption_label, train_caption_id, train_caption_id_1],
batch_size=batch_size, num_threads=num_threads, capacity=prefetch, min_after_dequeue=min_queue_examples)
val_data, val_video_label, val_fname, val_caption_label, val_caption_id_1 = \
tf.train.batch([val_data, val_video_label, val_fname, val_caption_label, val_caption_id_1],
batch_size=batch_size, num_threads=1, capacity=2* batch_size)
# graph on the GPU
with tf.device("/gpu:0"):
tf_loss = model.build_model(train_caption_id, train_caption_id_1,
train_caption_label)
val_caption_tf, val_lstm3_variables_tf = model.build_sent_generator(
val_caption_id_1)
sess = tf.InteractiveSession(config=tf.ConfigProto(
allow_soft_placement=True, log_device_placement=False))
# check for model file
with tf.device(cpu_device):
saver = tf.train.Saver(max_to_keep=100)
ckpt = tf.train.get_checkpoint_state(model_path)
global_step = 0
if ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path):
print("Reading model parameters from %s" % ckpt.model_checkpoint_path)
saver.restore(sess, ckpt.model_checkpoint_path)
# print_tensors_in_checkpoint_file(ckpt.model_checkpoint_path, "", True)
global_step = get_model_step(ckpt.model_checkpoint_path)
print 'global_step:', global_step
else:
print("Created model with fresh parameters.")
sess.run(tf.global_variables_initializer())
temp = set(tf.global_variables())
# train on the GPU
with tf.device("/gpu:0"):
# train_op = tf.train.AdamOptimizer(learning_rate).minimize(tf_loss)
## initialize variables added for optimizer
optimizer = tf.train.AdamOptimizer(learning_rate)
gvs = optimizer.compute_gradients(tf_loss)
# when variable is not related to the loss, grad returned as None
clip_gvs = [(tf.clip_by_norm(grad, clip_norm), var)
for grad, var in gvs if grad is not None]
train_op = optimizer.apply_gradients(gvs)
sess.run(tf.variables_initializer(set(tf.global_variables()) - temp))
# initialize epoch variable in queue reader
sess.run(tf.local_variables_initializer())
loss_epoch = 0
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
# write graph architecture to file
summary_writer = tf.summary.FileWriter(model_path + 'summary', sess.graph)
loss_summary = tf.summary.scalar('loss', tf_loss)
epoch = global_step
for step in xrange(1, n_steps + 1):
tStart = time.time()
_, loss_val = sess.run([train_op, tf_loss])
tStop = time.time()
print "step:", step, " Loss:", loss_val
print "Time Cost:", round(tStop - tStart, 2), "s"
loss_epoch += loss_val
if step % n_epoch_steps == 0:
epoch += 1
loss_epoch /= n_epoch_steps
with tf.device(cpu_device):
saver.save(sess,
os.path.join(model_path, 'model'),
global_step=epoch)
# print 'z:', z[0, :10]
print 'epoch:', epoch, 'loss:', loss_epoch
loss_epoch = 0
n_val_steps = int(n_val_samples / batch_size)
######### test sentence generation ##########
[pred_sent, gt_sent, id_list, gt_dict, pred_dict,
_] = testing_all(sess, 1, ixtoword, val_caption_tf, val_fname)
for key in pred_dict.keys():
for ele in gt_dict[key]:
print "GT: " + ele['caption']
print "PD: " + pred_dict[key][0]['caption']
print '-------'
[pred_sent, gt_sent, id_list, gt_dict, pred_dict,
_] = testing_all(sess, n_val_steps, ixtoword, val_caption_tf,
val_fname)
scorer = COCOScorer()
total_score = scorer.score(gt_dict, pred_dict, id_list)
#### summary #####
summary = sess.run(loss_summary)
summary_writer.add_summary(summary, epoch)
sys.stdout.flush()
sys.stdout.flush()
coord.request_stop()
coord.join(threads)
print "Finally, saving the model ..."
with tf.device(cpu_device):
saver.save(sess,
os.path.join(model_path, 'model'),
global_step=n_epochs)
tStop_total = time.time()
print "Total Time Cost:", round(tStop_total - tStart_total, 2), "s"
sess.close()
def train():
meta_data, train_data, val_data, test_data = get_video_data_jukin(video_data_path_train, video_data_path_val, video_data_path_test)
captions = meta_data['Description'].values
captions = map(lambda x: x.replace('.', ''), captions)
captions = map(lambda x: x.replace(',', ''), captions)
wordtoix, ixtoword, bias_init_vector = preProBuildWordVocab(captions, word_count_threshold=1)
# np.save('./data'+str(gpu_id)+'/ixtoword', ixtoword)
# np.save('./data'+str(gpu_id)+'/wordtoix', wordtoix)
# sys.exit()
ixtoword=pd.Series(np.load('./data_all/ixtoword.npy').tolist())
wordtoix=pd.Series(np.load('./data_all/wordtoix.npy').tolist())
model = Video_Caption_Generator(
dim_image=dim_image,
dim_tracker=dim_tracker,
n_words=len(wordtoix),
dim_hidden=dim_hidden,
batch_size=batch_size,
n_lstm_steps=n_frame_step,
tracker_cnt=tracker_cnt,
drop_out_rate = 0.5,
bias_init_vector=None)
tf_loss, tf_video, tf_video_mask, tf_tracker, tf_tracker_mask, tf_caption, tf_caption_mask= model.build_model()
#loss_summary = tf.scalar_summary("Loss",tf_loss)
sess = tf.InteractiveSession(config=tf.ConfigProto(allow_soft_placement=True))
#merged = tf.merge_all_summaries()
#writer = tf.train.SummaryWriter('/tmp/tf_log', sess.graph_def)
with tf.device("/cpu:0"):
saver = tf.train.Saver(max_to_keep=100)
train_op = tf.train.AdamOptimizer(learning_rate).minimize(tf_loss)
tf.initialize_all_variables().run()
saver.restore(sess, 'models/model-0')
tStart_total = time.time()
nr_prefetch = int(3)
for epoch in range(n_epochs):
index = np.arange(len(train_data))
np.random.shuffle(index)
train_data = train_data[index]
tStart_epoch = time.time()
loss_epoch = np.zeros(len(train_data))
## init queue
data_queue = mp.Queue(nr_prefetch)
# tracker_queue = mp.Queue(nr_prefetch)
title_queue = mp.Queue(nr_prefetch)
t1 = Thread(target=load_data_into_queue, args=(train_data, data_queue, 'data'))
# t2 = Thread(target=load_data_into_queue, args=(train_data, tracker_queue, 'tracker'))
t3 = Thread(target=load_data_into_queue, args=(train_data, title_queue, 'title'))
t1.start()
# t2.start()
t3.start()
for current_batch_file_idx in range(len(train_data)):
tStart = time.time()
current_batch = h5py.File(train_data[current_batch_file_idx])
current_feats = np.zeros((batch_size, n_frame_step, dim_image))
current_video_masks = np.zeros((batch_size, n_frame_step))
current_video_len = np.zeros(batch_size)
if 'tracker' in current_batch.keys():
current_tracker = np.array(current_batch['tracker'])
else:
current_tracker = np.zeros((batch_size, tracker_cnt, dim_tracker))
if 'tracker_mask' in current_batch.keys():
current_tracker_mask = np.array(current_batch['tracker_mask'])
else:
current_tracker_mask = np.zeros((batch_size, tracker_cnt))
# current_tracker = tracker_queue.get()
current_batch_data = data_queue.get()
current_batch_title = title_queue.get()
for ind in xrange(batch_size):
current_feats[ind,:,:] = current_batch_data[:,ind,:]
idx = np.where(current_batch['label'][:,ind] != -1)[0]
if len(idx) == 0:
continue
current_video_masks[ind,idx[-1]] = 1
current_captions = current_batch_title
current_caption_ind = map(lambda cap: [wordtoix[word] for word in cap.lower().split(' ') if word in wordtoix], current_captions)
current_caption_matrix = sequence.pad_sequences(current_caption_ind, padding='post', maxlen=35-1)
current_caption_matrix = np.hstack( [current_caption_matrix, np.zeros( [len(current_caption_matrix),1]) ] ).astype(int)
current_caption_masks = np.zeros((current_caption_matrix.shape[0], current_caption_matrix.shape[1]))
nonzeros = np.array( map(lambda x: (x != 0).sum()+1, current_caption_matrix ))
for ind, row in enumerate(current_caption_masks):
row[:nonzeros[ind]] = 1
current_batch.close()
_, loss_val= sess.run(
[train_op, tf_loss],
feed_dict={
tf_video: current_feats,
tf_video_mask : current_video_masks,
tf_tracker : current_tracker,
tf_tracker_mask : current_tracker_mask,
tf_caption: current_caption_matrix,
tf_caption_mask: current_caption_masks
})
#writer.add_summary(summary_str, epoch)
loss_epoch[current_batch_file_idx] = loss_val
tStop = time.time()
#print "Epoch:", epoch, " Batch:", current_batch_file_idx, " Loss:", loss_val
#print "Time Cost:", round(tStop - tStart,2), "s"
t1.join()
# t2.join()
t3.join()
print "Epoch:", epoch, " done. Loss:", np.mean(loss_epoch)
tStop_epoch = time.time()
print "Epoch Time Cost:", round(tStop_epoch - tStart_epoch,2), "s"
sys.stdout.flush()
if np.mod(epoch, 2) == 0:
print "Epoch ", epoch, " is done. Saving the model ..."
with tf.device('/cpu:0'):
saver.save(sess, os.path.join(model_path, 'model'), global_step=epoch)
if np.mod(epoch, 10) == 0:
current_batch = h5py.File(val_data[np.random.randint(0,len(val_data))])
video_tf, video_mask_tf, tracker_tf, tracker_mask_tf, caption_tf, lstm1_variables_tf, lstm2_variables_tf = model.build_generator()
ixtoword = pd.Series(np.load('./data_all/ixtoword.npy').tolist())
# [pred_sent, gt_sent, id_list, gt_dict, pred_dict, fnamelist] = testing_all_multi_gt(sess, train_data[-2:], ixtoword,video_tf, video_mask_tf, tracker_tf, tracker_mask_tf, caption_tf)
# for key in pred_dict.keys():
# for ele in gt_dict[key]:
# print "GT: " + ele['caption']
# print "PD: " + pred_dict[key][0]['caption']
# print '-------'
[pred_sent, gt_sent, id_list, gt_dict, pred_dict, fnamelist] = testing_all_multi_gt(sess, val_data, ixtoword,video_tf, video_mask_tf, tracker_tf, tracker_mask_tf, caption_tf)
scorer = COCOScorer()
total_score = scorer.score(gt_dict, pred_dict, id_list)
print "Finally, saving the model ..."
with tf.device('/cpu:0'):
saver.save(sess, os.path.join(model_path, 'model'), global_step=n_epochs)
tStop_total = time.time()
print "Total Time Cost:", round(tStop_total - tStart_total,2), "s"
def test(model_path=None,
video_data_path_test='/home/shenxu/data/msvd_feat_vgg_c3d_frame/test.tfrecords',
n_test_samples=27020):
# test_data = val_data # to evaluate on testing data or validation data
wordtoix = np.load(wordtoix_file).tolist()
ixtoword = pd.Series(np.load(ixtoword_file).tolist())
with tf.device("/gpu:0"):
model = Video_Caption_Generator(
dim_image=dim_image,
n_words=len(wordtoix),
dim_hidden=dim_hidden,
batch_size=batch_size,
n_caption_steps=n_caption_steps,
n_video_steps=n_video_steps,
drop_out_rate = 0.5,
bias_init_vector=None)
# preprocess on the CPU
with tf.device('/cpu:0'):
train_data, train_encode_data, _, _, train_video_label, train_caption_label, train_caption_id, train_caption_id_1, \
_, _, _, _, train_frame_data = read_and_decode_with_frame(video_data_path_train)
val_data, val_encode_data, val_fname, val_title, val_video_label, val_caption_label, val_caption_id, val_caption_id_1, \
_, _, _, _, val_frame_data = read_and_decode_with_frame(video_data_path_test)
train_data, train_encode_data, train_video_label, train_caption_label, train_caption_id, train_caption_id_1, train_frame_data = \
tf.train.shuffle_batch([train_data, train_encode_data, train_video_label, train_caption_label, train_caption_id, train_caption_id_1, train_frame_data],
batch_size=batch_size, num_threads=num_threads, capacity=prefetch, min_after_dequeue=min_queue_examples)
val_data, val_video_label, val_fname, val_caption_label, val_caption_id_1, val_frame_data = \
tf.train.batch([val_data, val_video_label, val_fname, val_caption_label, val_caption_id_1, val_frame_data],
batch_size=batch_size, num_threads=1, capacity=2* batch_size)
# graph on the GPU
with tf.device("/gpu:0"):
tf_loss, tf_loss_cap, tf_loss_lat, tf_loss_vid, tf_z, tf_v_h, tf_s_h, tf_drop_type \
= model.build_model(train_data, train_frame_data, train_video_label, train_caption_id, train_caption_id_1, train_caption_label)
val_v2s_tf,v2s_lstm3_vars_tf = model.build_v2s_generator(val_data)
val_s2s_tf, s2s_lstm3_vars_tf = model.build_s2s_generator(val_caption_id_1)
val_s2v_tf, s2v_lstm4_vars_tf = model.build_s2v_generator(val_caption_id_1, val_frame_data)
val_v2v_tf, v2v_lstm4_vars_tf = model.build_v2v_generator(val_data, val_frame_data)
sess = tf.InteractiveSession(config=tf.ConfigProto(allow_soft_placement=True))
with tf.device(cpu_device):
saver = tf.train.Saver()
saver.restore(sess, model_path)
print 'load parameters from:', model_path
# print 'halve the dropout weights..'
# for ind, row in enumerate(v2s_lstm3_vars_tf):
# if ind % 4 == 0:
# assign_op = row.assign(tf.multiply(row,1-0.5))
# sess.run(assign_op)
# for ind, row in enumerate(s2s_lstm2_vars_tf):
# if ind % 4 == 0:
# assign_op = row.assign(tf.multiply(row,1-0.5))
# sess.run(assign_op)
# for ind, row in enumerate(s2v_lstm4_vars_tf):
# if ind % 4 == 0:
# assign_op = row.assign(tf.multiply(row,1-0.5))
# sess.run(assign_op)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
######### test sentence generation ##########
print 'testing...'
n_test_steps = int(n_test_samples / batch_size)
print 'n_test_steps:', n_test_steps
tstart = time.time()
### TODO: sometimes COCO test show exceptions in the beginning of training ####
if test_v2s:
try:
[pred_sent, gt_sent, id_list, gt_dict, pred_dict, flist] = testing_all(sess, 1, ixtoword, val_v2s_tf, val_fname)
for i, key in enumerate(pred_dict.keys()):
print 'video:', flist[i]
for ele in gt_dict[key]:
print "GT: " + ele['caption']
print "PD: " + pred_dict[key][0]['caption']
print '-------'
print '############## video to sentence result #################'
[pred_sent, gt_sent, id_list, gt_dict, pred_dict, _] = testing_all(sess, n_test_steps, ixtoword, val_v2s_tf, val_fname)
scorer = COCOScorer()
total_score_1 = scorer.score(gt_dict, pred_dict, id_list)
print '############## video to sentence result #################'
except Exception, e:
print 'v2s bleu test exception'
except Exception, e:
print 'v2s bleu test exception'
if test_s2s:
try:
[pred_sent, gt_sent, id_list, gt_dict, pred_dict, flist] = testing_all(sess, 1, ixtoword, val_s2s_tf, val_fname)
for i, key in enumerate(pred_dict.keys()):
print 'video:', flist[i]
for ele in gt_dict[key]:
print "GT: " + ele['caption']
print "PD: " + pred_dict[key][0]['caption']
print '-------'
print '############## sentence to sentence result #################'
[pred_sent, gt_sent, id_list, gt_dict, pred_dict, _] = testing_all(sess, n_test_steps, ixtoword, val_s2s_tf, val_fname)
scorer = COCOScorer()
total_score_2 = scorer.score(gt_dict, pred_dict, id_list)
print '############## sentence to sentence result #################'
except Exception, e:
print 'v2s bleu test exception'
######### test video generation #############
if test_v2v:
mse_v2v = test_all_videos(sess, n_test_steps, val_data, val_v2v_tf, val_video_label, pixel_scale_factor)
print 'video2video mse:', mse_v2v
if test_s2v:
mse_s2v = test_all_videos(sess, n_test_steps, val_data, val_s2v_tf, val_video_label, pixel_scale_factor)
print 'caption2video mse:', mse_s2v
if save_demo_sent_v2s:
get_demo_sentence(sess, n_test_steps, ixtoword, val_v2s_tf, val_fname, result_file='demo_v2s.txt')
if save_demo_sent_s2s:
get_demo_sentence(sess, n_test_steps, ixtoword, val_s2s_tf, val_fname, result_file='demo_s2s.txt')
def train():
meta_data, train_data, test_data = get_video_data_jukin(video_data_path_train, video_data_path_test)
captions = meta_data['Description'].values
captions = map(lambda x: x.replace('.', ''), captions)
captions = map(lambda x: x.replace(',', ''), captions)
wordtoix, ixtoword, bias_init_vector = preProBuildWordVocab(captions, word_count_threshold=1)
np.save('./data/ixtoword', ixtoword)
model = Video_Caption_Generator(
dim_image=dim_image,
n_words=len(wordtoix),
dim_hidden=dim_hidden,
batch_size=batch_size,
n_lstm_steps=n_frame_step,
drop_out_rate = 0.5,
bias_init_vector=None)
tf_loss, tf_video, tf_video_mask, tf_video_len, tf_caption, tf_caption_mask, tf_HLness, tf_HLness_mask, tf_HLness_att_mask= model.build_model()
loss_summary = tf.scalar_summary("Loss",tf_loss)
sess = tf.InteractiveSession()
merged = tf.merge_all_summaries()
writer = tf.train.SummaryWriter('/tmp/tf_log', sess.graph_def)
saver = tf.train.Saver(max_to_keep=100)
train_op = tf.train.AdamOptimizer(learning_rate).minimize(tf_loss)
tf.initialize_all_variables().run()
tStart_total = time.time()
for epoch in range(n_epochs):
index = np.arange(len(train_data))
np.random.shuffle(index)
train_data = train_data[index]
tStart_epoch = time.time()
loss_epoch = np.zeros(len(train_data))
for current_batch_file_idx in xrange(len(train_data)):
tStart = time.time()
current_batch = h5py.File(train_data[current_batch_file_idx])
current_feats = np.zeros((batch_size, n_frame_step, dim_image))
current_HLness = np.zeros((batch_size, n_frame_step))
current_HLness_masks = np.zeros((batch_size, n_frame_step))
current_HLness_att_masks = np.zeros((batch_size, n_frame_step))
current_video_masks = np.zeros((batch_size, n_frame_step))
current_video_len = np.zeros(batch_size)
for ind in xrange(batch_size):
current_feats[ind,:,:] = current_batch['data'][:,ind,:]
idx = np.where(current_batch['label'][:,ind] != -1)[0]
if len(idx) == 0:
continue
idy = np.where(current_batch['label'][:,ind] == 1)[0]
if len(idy) == 0:
continue
current_HLness[ind,idx] = current_batch['label'][idx,ind]
current_HLness_masks[ind,idx] = 1
current_video_masks[ind,idy[-1]] = 1
current_video_len[ind] = idx[-1] + 1
current_HLness_att_masks[ind,idy] = 1
if(idy[0] > 4):
current_HLness_att_masks[ind,idy[0]-5:idy[0]] = 1
else:
current_HLness_att_masks[ind,0:idy[0]] = 1
current_captions = current_batch['title']
current_caption_ind = map(lambda cap: [wordtoix[word] for word in cap.lower().split(' ') if word in wordtoix], current_captions)
current_caption_matrix = sequence.pad_sequences(current_caption_ind, padding='post', maxlen=15-1)
current_caption_matrix = np.hstack( [current_caption_matrix, np.zeros( [len(current_caption_matrix),1]) ] ).astype(int)
current_caption_masks = np.zeros((current_caption_matrix.shape[0], current_caption_matrix.shape[1]))
nonzeros = np.array( map(lambda x: (x != 0).sum()+1, current_caption_matrix ))
for ind, row in enumerate(current_caption_masks):
row[:nonzeros[ind]] = 1
_, loss_val, summary_str= sess.run(
[train_op, tf_loss, merged],
feed_dict={
tf_video: current_feats,
tf_video_mask : current_video_masks,
tf_caption: current_caption_matrix,
tf_caption_mask: current_caption_masks,
tf_HLness: current_HLness,
tf_HLness_mask: current_HLness_masks,
tf_HLness_att_mask: current_HLness_att_masks
})
writer.add_summary(summary_str, epoch)
loss_epoch[current_batch_file_idx] = loss_val
tStop = time.time()
#print "Epoch:", epoch, " Batch:", current_batch_file_idx, " Loss:", loss_val
#print "Time Cost:", round(tStop - tStart,2), "s"
print "Epoch:", epoch, " done. Loss:", np.mean(loss_epoch)
tStop_epoch = time.time()
print "Epoch Time Cost:", round(tStop_epoch - tStart_epoch,2), "s"
if np.mod(epoch, 20) == 0:
print "Epoch ", epoch, " is done. Saving the model ..."
saver.save(sess, os.path.join(model_path, 'model'), global_step=epoch)
current_batch = h5py.File(test_data[np.random.randint(0,len(test_data))])
video_tf, video_mask_tf, video_len_tf, HLness_tf, caption_tf, HLness_att_mask_tf, lstmRNN_variables_tf, lstm3_variables_tf = model.build_generator()
ixtoword = pd.Series(np.load('./data/ixtoword.npy').tolist())
#[mp, pred_sent, gt_sent, HLness] = testing_one(sess, current_batch, ixtoword,video_tf, video_len_tf, HLness_tf, caption_tf, HLness_att_mask_tf)
[mp, pred_sent, gt_sent, HLness] = testing_all(sess, test_data, ixtoword,video_tf, video_mask_tf, video_len_tf, HLness_tf, caption_tf, HLness_att_mask_tf)
#for xxx in xrange(current_batch['label'].shape[1]):
# print gt_sent[xxx]
# print pred_sent[xxx]
total_score = np.mean(mp)
print total_score
scorer = COCOScorer()
total_score = scorer.score(gt_sent, pred_sent, range(len(pred_sent)))
print "Finally, saving the model ..."
saver.save(sess, os.path.join(model_path, 'model'), global_step=n_epochs)
tStop_total = time.time()
print "Total Time Cost:", round(tStop_total - tStart_total,2), "s"
def train():
meta_data, train_data, val_data, test_data = get_video_data_jukin(video_data_path_train, video_data_path_val, video_data_path_test)
captions = meta_data['Description'].values
captions = map(lambda x: x.replace('.', ''), captions)
captions = map(lambda x: x.replace(',', ''), captions)
wordtoix, ixtoword, bias_init_vector = preProBuildWordVocab(captions, word_count_threshold=1)
# np.save('./data'+str(gpu_id)+'/ixtoword', ixtoword)
# np.save('./data'+str(gpu_id)+'/wordtoix', wordtoix)
# sys.exit()
ixtoword=pd.Series(np.load('./data_all/ixtoword.npy').tolist())
wordtoix=pd.Series(np.load('./data_all/wordtoix.npy').tolist())
model = Video_Caption_Generator(
dim_image=dim_image,
dim_tracker=dim_tracker,
n_words=len(wordtoix),
dim_hidden=dim_hidden,
batch_size=batch_size,
n_lstm_steps=n_frame_step,
tracker_cnt=tracker_cnt,
drop_out_rate = 0.5,
bias_init_vector=None)
tf_loss, tf_video, tf_video_mask, tf_tracker, tf_tracker_mask, tf_caption, tf_caption_mask= model.build_model()
#loss_summary = tf.scalar_summary("Loss",tf_loss)
sess = tf.InteractiveSession(config=tf.ConfigProto(allow_soft_placement=True))
#merged = tf.merge_all_summaries()
#writer = tf.train.SummaryWriter('/tmp/tf_log', sess.graph_def)
with tf.device("/cpu:0"):
saver = tf.train.Saver(max_to_keep=100)
train_op = tf.train.AdamOptimizer(learning_rate).minimize(tf_loss)
tf.initialize_all_variables().run()
saver.restore(sess, 'models/model-0')
tStart_total = time.time()
nr_prefetch = int(3)
for epoch in range(n_epochs):
index = np.arange(len(train_data))
np.random.shuffle(index)
train_data = train_data[index]
tStart_epoch = time.time()
loss_epoch = np.zeros(len(train_data))
## init queue
data_queue = mp.Queue(nr_prefetch)
# tracker_queue = mp.Queue(nr_prefetch)
title_queue = mp.Queue(nr_prefetch)
t1 = Thread(target=load_data_into_queue, args=(train_data, data_queue, 'data'))
# t2 = Thread(target=load_data_into_queue, args=(train_data, tracker_queue, 'tracker'))
t3 = Thread(target=load_data_into_queue, args=(train_data, title_queue, 'title'))
t1.start()
# t2.start()
t3.start()
for current_batch_file_idx in range(len(train_data)):
tStart = time.time()
current_batch = h5py.File(train_data[current_batch_file_idx])
current_feats = np.zeros((batch_size, n_frame_step, dim_image))
current_video_masks = np.zeros((batch_size, n_frame_step))
current_video_len = np.zeros(batch_size)
if 'tracker' in current_batch.keys():
current_tracker = np.array(current_batch['tracker'])
else:
current_tracker = np.zeros((batch_size, tracker_cnt, dim_tracker))
if 'tracker_mask' in current_batch.keys():
current_tracker_mask = np.array(current_batch['tracker_mask'])
else:
current_tracker_mask = np.zeros((batch_size, tracker_cnt))
# current_tracker = tracker_queue.get()
current_batch_data = data_queue.get()
current_batch_title = title_queue.get()
for ind in range(batch_size):
current_feats[ind,:,:] = current_batch_data[:,ind,:]
idx = np.where(current_batch['label'][:,ind] != -1)[0]
if len(idx) == 0:
continue
current_video_masks[ind,idx[-1]] = 1
current_captions = current_batch_title
current_caption_ind = map(lambda cap: [wordtoix[word] for word in cap.lower().split(' ') if word in wordtoix], current_captions)
current_caption_matrix = sequence.pad_sequences(current_caption_ind, padding='post', maxlen=35-1)
current_caption_matrix = np.hstack( [current_caption_matrix, np.zeros( [len(current_caption_matrix),1]) ] ).astype(int)
current_caption_masks = np.zeros((current_caption_matrix.shape[0], current_caption_matrix.shape[1]))
nonzeros = np.array( map(lambda x: (x != 0).sum()+1, current_caption_matrix ))
for ind, row in enumerate(current_caption_masks):
row[:nonzeros[ind]] = 1
current_batch.close()
_, loss_val= sess.run(
[train_op, tf_loss],
feed_dict={
tf_video: current_feats,
tf_video_mask : current_video_masks,
tf_tracker : current_tracker,
tf_tracker_mask : current_tracker_mask,
tf_caption: current_caption_matrix,
tf_caption_mask: current_caption_masks
})
#writer.add_summary(summary_str, epoch)
loss_epoch[current_batch_file_idx] = loss_val
tStop = time.time()
#print "Epoch:", epoch, " Batch:", current_batch_file_idx, " Loss:", loss_val
#print "Time Cost:", round(tStop - tStart,2), "s"
t1.join()
# t2.join()
t3.join()
print ("Epoch:", epoch, " done. Loss:", np.mean(loss_epoch))
tStop_epoch = time.time()
print ("Epoch Time Cost:", round(tStop_epoch - tStart_epoch,2), "s")
sys.stdout.flush()
if np.mod(epoch, 2) == 0:
print ("Epoch ", epoch, " is done. Saving the model ...")
with tf.device('/cpu:0'):
saver.save(sess, os.path.join(model_path, 'model'), global_step=epoch)
if np.mod(epoch, 10) == 0:
current_batch = h5py.File(val_data[np.random.randint(0,len(val_data))])
video_tf, video_mask_tf, tracker_tf, tracker_mask_tf, caption_tf, lstm1_variables_tf, lstm2_variables_tf = model.build_generator()
ixtoword = pd.Series(np.load('./data_all/ixtoword.npy').tolist())
# [pred_sent, gt_sent, id_list, gt_dict, pred_dict, fnamelist] = testing_all_multi_gt(sess, train_data[-2:], ixtoword,video_tf, video_mask_tf, tracker_tf, tracker_mask_tf, caption_tf)
# for key in pred_dict.keys():
# for ele in gt_dict[key]:
# print "GT: " + ele['caption']
# print "PD: " + pred_dict[key][0]['caption']
# print '-------'
[pred_sent, gt_sent, id_list, gt_dict, pred_dict, fnamelist] = testing_all_multi_gt(sess, val_data, ixtoword,video_tf, video_mask_tf, tracker_tf, tracker_mask_tf, caption_tf)
scorer = COCOScorer()
total_score = scorer.score(gt_dict, pred_dict, id_list)
print ("Finally, saving the model ...")
with tf.device('/cpu:0'):
saver.save(sess, os.path.join(model_path, 'model'), global_step=n_epochs)
tStop_total = time.time()
print ("Total Time Cost:", round(tStop_total - tStart_total,2), "s")
def train():
meta_data, train_data, val_data, test_data = get_video_data_jukin(
video_data_path_train, video_data_path_val, video_data_path_test)
captions = meta_data['Description'].values
captions = map(lambda x: x.replace('.', ''), captions)
captions = map(lambda x: x.replace(',', ''), captions)
wordtoix, ixtoword, bias_init_vector = preProBuildWordVocab(
captions, word_count_threshold=1)
np.save('./data0/ixtoword', ixtoword)
model = Video_Caption_Generator(dim_image=dim_image,
n_words=len(wordtoix),
dim_hidden=dim_hidden,
batch_size=batch_size,
n_lstm_steps=n_frame_step,
drop_out_rate=0.5,
bias_init_vector=None)
tf_loss, tf_video, tf_video_mask, tf_caption, tf_caption_mask = model.build_model(
)
sess = tf.InteractiveSession(config=tf.ConfigProto(
allow_soft_placement=True))
with tf.device("/cpu:0"):
saver = tf.train.Saver(max_to_keep=100)
train_op = tf.train.AdamOptimizer(learning_rate).minimize(tf_loss)
tf.initialize_all_variables().run()
tStart_total = time.time()
for epoch in range(n_epochs):
index = np.arange(len(train_data))
np.random.shuffle(index)
train_data = train_data[index]
tStart_epoch = time.time()
loss_epoch = np.zeros(len(train_data))
for current_batch_file_idx in xrange(len(train_data)):
tStart = time.time()
current_batch = h5py.File(train_data[current_batch_file_idx])
current_feats = np.zeros((batch_size, n_frame_step, dim_image))
current_video_masks = np.zeros((batch_size, n_frame_step))
current_video_len = np.zeros(batch_size)
for ind in xrange(batch_size):
current_feats[ind, :, :] = current_batch['data'][:n_frame_step,
ind, :]
idx = np.where(current_batch['label'][:, ind] != -1)[0]
if len(idx) == 0:
continue
current_video_masks[ind, :idx[-1] + 1] = 1
current_captions = current_batch['title']
current_caption_ind = map(
lambda cap: [
wordtoix[word] for word in cap.lower().split(' ')
if word in wordtoix
], current_captions)
current_caption_matrix = sequence.pad_sequences(
current_caption_ind, padding='post', maxlen=n_caption_step - 1)
current_caption_matrix = np.hstack([
current_caption_matrix,
np.zeros([len(current_caption_matrix), 1])
]).astype(int)
current_caption_masks = np.zeros((current_caption_matrix.shape[0],
current_caption_matrix.shape[1]))
nonzeros = np.array(
map(lambda x: (x != 0).sum() + 1, current_caption_matrix))
for ind, row in enumerate(current_caption_masks):
row[:nonzeros[ind]] = 1
_, loss_val = sess.run(
[train_op, tf_loss],
feed_dict={
tf_video: current_feats,
tf_video_mask: current_video_masks,
tf_caption: current_caption_matrix,
tf_caption_mask: current_caption_masks
})
loss_epoch[current_batch_file_idx] = loss_val
tStop = time.time()
#print "Epoch:", epoch, " Batch:", current_batch_file_idx, " Loss:", loss_val
#print "Time Cost:", round(tStop - tStart,2), "s"
print "Epoch:", epoch, " done. Loss:", np.mean(loss_epoch)
tStop_epoch = time.time()
print "Epoch Time Cost:", round(tStop_epoch - tStart_epoch, 2), "s"
if np.mod(epoch, 10) == 0 or epoch == n_epochs - 1:
print "Epoch ", epoch, " is done. Saving the model ..."
with tf.device("/cpu:0"):
saver.save(sess,
os.path.join(model_path, 'model'),
global_step=epoch)
current_batch = h5py.File(val_data[np.random.randint(
0, len(val_data))])
video_tf, video_mask_tf, caption_tf, lstm3_variables_tf = model.build_generator(
)
ixtoword = pd.Series(np.load('./data0/ixtoword.npy').tolist())
[pred_sent, gt_sent, id_list, gt_dict,
pred_dict] = testing_all(sess, train_data[-2:], ixtoword,
video_tf, video_mask_tf, caption_tf)
for key in pred_dict.keys():
for ele in gt_dict[key]:
print "GT: " + ele['caption']
print "PD: " + pred_dict[key][0]['caption']
print '-------'
[pred_sent, gt_sent, id_list, gt_dict,
pred_dict] = testing_all(sess, val_data, ixtoword, video_tf,
video_mask_tf, caption_tf)
scorer = COCOScorer()
total_score = scorer.score(gt_dict, pred_dict, id_list)
sys.stdout.flush()
print "Finally, saving the model ..."
with tf.device("/cpu:0"):
saver.save(sess,
os.path.join(model_path, 'model'),
global_step=n_epochs)
tStop_total = time.time()
print "Total Time Cost:", round(tStop_total - tStart_total, 2), "s"
def train():
meta_data, train_data, val_data, test_data = get_video_data_jukin(
video_data_path_train, video_data_path_val, video_data_path_test
)
captions = meta_data["Description"].values
captions = map(lambda x: x.replace(".", ""), captions)
captions = map(lambda x: x.replace(",", ""), captions)
wordtoix, ixtoword, bias_init_vector = preProBuildWordVocab(captions, word_count_threshold=1)
np.save("./data" + str(gpu_id) + "/ixtoword", ixtoword)
model = Video_Caption_Generator(
dim_image=dim_image,
n_words=len(wordtoix),
dim_hidden=dim_hidden,
batch_size=batch_size,
n_lstm_steps=n_frame_step,
drop_out_rate=0.5,
bias_init_vector=None,
)
tf_loss, tf_video, tf_video_mask, tf_caption, tf_caption_mask = model.build_model()
loss_summary = tf.scalar_summary("Loss", tf_loss)
sess = tf.InteractiveSession(config=tf.ConfigProto(allow_soft_placement=True))
merged = tf.merge_all_summaries()
writer = tf.train.SummaryWriter("/tmp/tf_log", sess.graph_def)
saver = tf.train.Saver(max_to_keep=100)
train_op = tf.train.AdamOptimizer(learning_rate).minimize(tf_loss)
tf.initialize_all_variables().run()
saver.restore(sess, "models_SS_youtube_notest_dummy/model-20")
tStart_total = time.time()
for epoch in range(n_epochs):
index = np.arange(len(train_data))
np.random.shuffle(index)
train_data = train_data[index]
tStart_epoch = time.time()
loss_epoch = np.zeros(len(train_data))
for current_batch_file_idx in xrange(len(train_data)):
tStart = time.time()
current_batch = h5py.File(train_data[current_batch_file_idx])
current_feats = np.zeros((batch_size, n_frame_step, dim_image))
current_video_masks = np.zeros((batch_size, n_frame_step))
current_video_len = np.zeros(batch_size)
for ind in xrange(batch_size):
current_feats[ind, :, :] = current_batch["data"][:, ind, :]
idx = np.where(current_batch["label"][:, ind] != -1)[0]
if len(idx) == 0:
continue
current_video_masks[ind, idx[-1]] = 1
current_captions = current_batch["title"]
current_caption_ind = map(
lambda cap: [wordtoix[word] for word in cap.lower().split(" ") if word in wordtoix], current_captions
)
current_caption_matrix = sequence.pad_sequences(current_caption_ind, padding="post", maxlen=16 - 1)
current_caption_matrix = np.hstack(
[current_caption_matrix, np.zeros([len(current_caption_matrix), 1])]
).astype(int)
current_caption_masks = np.zeros((current_caption_matrix.shape[0], current_caption_matrix.shape[1]))
nonzeros = np.array(map(lambda x: (x != 0).sum() + 1, current_caption_matrix))
for ind, row in enumerate(current_caption_masks):
row[: nonzeros[ind]] = 1
_, loss_val, summary_str = sess.run(
[train_op, tf_loss, merged],
feed_dict={
tf_video: current_feats,
tf_video_mask: current_video_masks,
tf_caption: current_caption_matrix,
tf_caption_mask: current_caption_masks,
},
)
writer.add_summary(summary_str, epoch)
loss_epoch[current_batch_file_idx] = loss_val
tStop = time.time()
# print "Epoch:", epoch, " Batch:", current_batch_file_idx, " Loss:", loss_val
# print "Time Cost:", round(tStop - tStart,2), "s"
print "Epoch:", epoch, " done. Loss:", np.mean(loss_epoch)
tStop_epoch = time.time()
print "Epoch Time Cost:", round(tStop_epoch - tStart_epoch, 2), "s"
sys.stdout.flush()
if np.mod(epoch, 10) == 0:
print "Epoch ", epoch, " is done. Saving the model ..."
saver.save(sess, os.path.join(model_path, "model"), global_step=epoch)
current_batch = h5py.File(val_data[np.random.randint(0, len(val_data))])
video_tf, video_mask_tf, caption_tf, lstm1_variables_tf, lstm2_variables_tf = model.build_generator()
ixtoword = pd.Series(np.load("./data" + str(gpu_id) + "/ixtoword.npy").tolist())
[pred_sent, gt_sent] = testing_all(sess, train_data[-2:], ixtoword, video_tf, video_mask_tf, caption_tf)
for idx in range(len(pred_sent)):
print "GT: " + gt_sent[idx][0]["caption"]
print "PD: " + pred_sent[idx][0]["caption"]
print "-------"
[pred_sent, gt_sent] = testing_all(sess, val_data, ixtoword, video_tf, video_mask_tf, caption_tf)
scorer = COCOScorer()
total_score = scorer.score(gt_sent, pred_sent, range(len(pred_sent)))
print "Finally, saving the model ..."
saver.save(sess, os.path.join(model_path, "model"), global_step=n_epochs)
tStop_total = time.time()
print "Total Time Cost:", round(tStop_total - tStart_total, 2), "s"
