def easy_train(data_list = ['ID001_T001', 'ID001_T002', 'ID001_T003','ID001_T004','ID001_T009', 'ID001_T010'],\
data_dir = '..\..\Out',\
im_shape = [299,299,3],\
time_step = 31):
train_data = data_factory('TRI', data_dir=data_dir , data_list= data_list, quiet=False)
with tf.Session() as sess:
img_str, frame = data_process(im_shape)
encoder= encoder_factory('Inception_v3')
decoder = decoder_factory('ATT_LSTM')
_input, _feature, _train = encoder.last_feature([None, im_shape[0],im_shape[1],im_shape[2]])
_decoder_input = tf.placeholder(dtype = tf.float32, name = 'decoder_input', shape=[None, time_step, 2048])
_truth, _loss, _train_op, _pred = decoder( _decoder_input, n_hidden = 50, n_class=5, learning_rate=1e-3)
sess.run(tf.global_variables_initializer())
encoder.load_model(sess)
saver = tf.train.Saver()
for epoch in range(2000):
saver.save(sess, '..\..\Backup\M81ckpt',global_step = epoch)
batch_features = []
batch_label = []
for i in range(100):
evt_data, restart, evt_index = train_data.next(shuffle = True, unit = 'evt')
event_np = os.path.join(data_dir, '%d.npy'%evt_index)
if (evt_index in [71,96]):
continue
if os.path.isfile(event_np):
batch_data = np.load(event_np).item()
batch_features.extend( batch_data['data'])
batch_label.extend(batch_data['label'])
loss,pred, _ = sess.run(( _loss, _pred, _train_op), feed_dict={_decoder_input: batch_features, _truth: batch_label})
total_correct = len([x for x, y in zip(pred, batch_label) if x==y])
print('%d: mean loss : %f, accuracy: %f'%(epoch, loss, total_correct/len(batch_label)))
def train(data_list = ['ID001_T012','ID001_T013','ID001_T014','ID001_T015','ID001_T016','ID001_T017','ID001_T018','ID001_T019'],\
data_dir = '..\..\Out',\
im_shape = [299,299,3],\
time_step = 31):
train_data = data_factory('TRI', data_dir=data_dir , data_list= data_list, quiet=False)
speedup_dir = os.path.join(data_dir,'speedup')
if os.path.isdir(speedup_dir) is False:
os.makedirs(speedup_dir)
with tf.Session() as sess:
img_str, frame = data_process(im_shape)
encoder= encoder_factory('Inception_v3')
decoder = decoder_factory('LSTM')
_input, _feature, _train = encoder.last_feature([None, im_shape[0],im_shape[1],im_shape[2]])
_decoder_input = tf.placeholder(dtype = tf.float32, name = 'decoder_input', shape=[None, time_step, 2048])
_truth, _loss, _train_op, _pred = decoder( _decoder_input, n_hidden = 100, n_class=5, learning_rate=1e-2)
sess.run(tf.global_variables_initializer())
encoder.load_model(sess)
saver = tf.train.Saver()
for epoch in range(100):
saver.save(sess, '..\..\Backup\M802.ckpt',global_step = epoch)
total_loss = 0.0
total_num = 0.0
total_correct = 0.0
total_seq = 0.0
while True:
evt_data, restart, evt_index = train_data.next(shuffle=True,unit='evt')
batch_features = []
batch_label = []
event_np = os.path.join(speedup_dir, '%d_%s_vec.npy'%(evt_index, encoder.model_id))
if os.path.isfile(event_np):
batch_data = np.load(event_np).item()
batch_features = batch_data['data']
batch_label = batch_data['label']
else:
print(evt_data[0]['mat_path'])
for seq_data in tqdm(evt_data):
label = seq_data['label']
batch_label.extend([label])
seq_features = []
for fimg_path in seq_data['fimg']:
fimg = open(fimg_path,'rb').read()
fimg = sess.run(frame, feed_dict={img_str: fimg})
feature = sess.run(_feature, feed_dict={_input: [fimg], _train: False})
seq_features.append(np.squeeze(feature))
batch_features.append(np.stack(seq_features))
np.save(event_np,{'data':batch_features, 'label':batch_label})
# batch_features = np.asanyarray(batch_features).reshape(batch, time_step, -1)
loss,pred, _ = sess.run(( _loss, _pred, _train_op), feed_dict={_decoder_input: batch_features, _truth: batch_label})
total_loss = total_loss + loss
total_num = total_num + 1
total_seq = total_seq + len(batch_label)
total_correct = total_correct + len([x for x, y in zip(pred, batch_label) if x==y])
if restart:
break
print('%d: mean loss : %f, accuracy: %f'%(epoch, total_loss/total_num, total_correct/total_seq))
def test(data_list = ['ID001_T001', 'ID001_T002', 'ID001_T003','ID001_T004','ID001_T009', 'ID001_T010'],\
data_dir = '..\..\Out',\
im_shape = [299,299,3],\
time_step = 31):
train_data = data_factory('TRI', data_dir=data_dir , data_list= data_list, quiet=True)
with tf.Session() as sess:
img_str, frame = data_process(im_shape)
encoder= encoder_factory('Inception_v3')
decoder = decoder_factory('LSTM')
_input, _feature, _train = encoder.last_feature([None, im_shape[0],im_shape[1],im_shape[2]])
_decoder_input = tf.placeholder(dtype = tf.float32, name = 'decoder_input', shape=[None, time_step, 2048])
_truth, _loss, _train_op, _pred = decoder( _decoder_input, n_hidden = 100, n_class=5)
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(sess, '..\..\Backup\M802.ckpt-24')
total_correct = 0.0
total_num = 0.0
while True:
evt_data, restart, evt_index = train_data.next(shuffle=False,unit='evt')
batch_features = []
batch_label = []
event_np = os.path.join(data_dir, '%d.npy'%evt_index)
# print(evt_index)
if (evt_index in [71,96]):
continue
if os.path.isfile(event_np):
batch_data = np.load(event_np).item()
batch_features = batch_data['data']
batch_label = batch_data['label']
else:
print(evt_data[0]['mat_path'])
for seq_data in tqdm(evt_data):
label = seq_data['label']
batch_label.extend([label])
seq_features = []
for fimg_path in seq_data['fimg']:
fimg = open(fimg_path,'rb').read()
fimg = sess.run(frame, feed_dict={img_str: fimg})
feature = sess.run(_feature, feed_dict={_input: [fimg], _train: False})
seq_features.append(np.squeeze(feature))
batch_features.append(np.stack(seq_features))
np.save(event_np,{'data':batch_features, 'label':batch_label})
pred = sess.run(( _pred), feed_dict={_decoder_input: batch_features})
correct_num = len([x for x, y in zip(pred, batch_label) if x==y])
total_correct = total_correct + correct_num
total_num = total_num + len(batch_label)
if restart:
break
print('accurate : %f'%( total_correct/total_num))