def train(data_list = ['ID001_T001', 'ID001_T002', 'ID001_T003','ID001_T004','ID001_T009', 'ID001_T010'],\ data_dir = '..\..\Out',\ im_shape = [640,480,3],\ time_step = 31): sppedup_dir = os.path.join(data_dir, 'speedup') 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') sess.run(tf.global_variables_initializer()) encoder.load_model(sess) while True: evt_data, restart, evt_index = train_data.next(shuffle=True,unit='evt') event_np = os.path.join(sppedup_dir, '%d_Inception_v3_map.npy'%(evt_index)) print(evt_data[0]['mat_path']) batch_label = [] batch_features = [] for seq_data in tqdm(evt_data): label = seq_data['label'] batch_label.extend([label]) seq_features = [] for fimg_path in seq_data['gimg']: 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(feature) batch_features.append(np.stack(seq_features)) np.save(event_np,{'data':batch_features, 'label':batch_label})
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))
def feature_extraction(data_list, data_dir='..\..\Out', im_shape=[640, 480, 3], sufix='map', subset='train'): sppedup_dir = os.path.join(data_dir, 'speedup2', subset) if os.path.isdir(sppedup_dir) is False: os.makedirs(sppedup_dir) 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') if sufix == 'map': _input, _feature, _train = encoder.last_map( [None, im_shape[0], im_shape[1], im_shape[2]]) else: _input, _feature, _train = encoder.last_feature( [None, im_shape[0], im_shape[1], im_shape[2]]) sess.run(tf.global_variables_initializer()) encoder.load_model(sess) while True: evt_data, restart, evt_index, unique_id = train_data.next( shuffle=False, unit='evt') event_np = os.path.join( sppedup_dir, '%s_%s_%s.npy' % (unique_id, encoder.model_id, sufix)) print('%d: %s' % (evt_index, evt_data[0]['id'])) event_front_features = [] event_driver_features = [] event_signal = [] seq_label = [] early_time = [] early_distance = [] front_img_path = [] driver_img_path = [] event_id = [] evt_label = -1 for seq_data in tqdm(evt_data): seq_f_features = [] seq_d_features = [] # process image for fimg_path, dimg_path in zip(seq_data['fimg'], seq_data['dimg']): img_dir = os.path.dirname(fimg_path) mat_path = os.path.join( img_dir, '%s_%s' % (encoder.model_id, sufix)) if os.path.isdir(mat_path) is False: os.makedirs(mat_path) fmat_path, dmat_path = list( map( lambda x: os.path.join( mat_path, '%s.npy' % os.path.basename(x)[0:-4] ), [fimg_path, dimg_path])) feature = gene_or_load_feature(fimg_path, fmat_path, sess, _img_str, _frame, _input, _feature, _train) seq_f_features.append(feature) feature = gene_or_load_feature(dimg_path, dmat_path, sess, _img_str, _frame, _input, _feature, _train) seq_d_features.append(feature) event_front_features.append(np.stack(seq_f_features)) event_driver_features.append(np.stack(seq_d_features)) # process other information seq_label.append(seq_data['seq_label']) early_time.append(seq_data['early_time']) early_distance.append(seq_data['early_distance']) front_img_path.append(seq_data['fimg']) driver_img_path.append(seq_data['dimg']) event_id.append(seq_data['id']) event_signal.append(seq_data['signal']) if evt_label == -1: evt_label = seq_data['evt_label'] else: assert (evt_label == seq_data['evt_label']) np.save(event_np,{'id':event_id, 'front_feature':event_front_features, 'driver_features':event_driver_features,'signal':event_signal,\ 'seq_label':seq_label, 'early_time':early_time,'early_distance':early_distance,\ 'event_label': evt_label, 'front_img_path': front_img_path, 'driver_img_path': driver_img_path })