def generator(data, opt, batch_size=cfg.BATCH_SIZE):
num_records = len(data)
while True:
#shuffle again for good measure
random.shuffle(data)
for offset in range(0, num_records, batch_size):
batch_data = data[offset:offset + batch_size]
if len(batch_data) != batch_size:
break
b_inputs_img = []
b_vec_in = []
b_labels = []
b_vec_out = []
for seq in batch_data:
inputs_img = []
vec_in = []
labels = []
vec_out = []
num_images_target = len(seq)
iTargetOutput = -1
if opt['look_ahead']:
num_images_target = cfg.SEQUENCE_LENGTH
iTargetOutput = cfg.SEQUENCE_LENGTH - 1
for iRec, record in enumerate(seq):
#get image data if we don't already have it
if len(inputs_img) < num_images_target:
if record['img_data'] is None:
img_arr = load_scaled_image_arr(
record['image_path'], cfg)
if img_arr is None:
break
if aug:
img_arr = augment_image(img_arr)
if cfg.CACHE_IMAGES:
record['img_data'] = img_arr
else:
img_arr = record['img_data']
inputs_img.append(img_arr)
if iRec >= iTargetOutput:
vec_out.append(record['angle'])
vec_out.append(record['throttle'])
else:
vec_in.append(0.0) #record['angle'])
vec_in.append(0.0) #record['throttle'])
label_vec = seq[iTargetOutput]['target_output']
if look_ahead:
label_vec = np.array(vec_out)
labels.append(label_vec)
b_inputs_img.append(inputs_img)
b_vec_in.append(vec_in)
b_labels.append(labels)
if look_ahead:
X = [np.array(b_inputs_img).reshape(batch_size,\
cfg.TARGET_H, cfg.TARGET_W, cfg.SEQUENCE_LENGTH)]
X.append(np.array(b_vec_in))
y = np.array(b_labels).reshape(
batch_size, (cfg.SEQUENCE_LENGTH + 1) * 2)
else:
X = [np.array(b_inputs_img).reshape(batch_size,\
cfg.SEQUENCE_LENGTH, cfg.TARGET_H, cfg.TARGET_W, cfg.TARGET_D)]
y = np.array(b_labels).reshape(batch_size, 2)
yield X, y
def generator(save_best,
opts,
data,
batch_size,
isTrainSet=True,
min_records_to_train=1000):
num_records = len(data)
while True:
if isTrainSet and opts['continuous']:
'''
When continuous training, we look for new records after each epoch.
This will add new records to the train and validation set.
'''
records = gather_records(cfg, tub_names, opts)
if len(records) > num_records:
collate_records(records, gen_records, opts)
new_num_rec = len(data)
if new_num_rec > num_records:
print('picked up', new_num_rec - num_records,
'new records!')
num_records = new_num_rec
save_best.reset_best()
if num_records < min_records_to_train:
print(
"not enough records to train. need %d, have %d. waiting..."
% (min_records_to_train, num_records))
time.sleep(10)
continue
batch_data = []
keys = list(data.keys())
random.shuffle(keys)
kl = opts['keras_pilot']
if type(kl.model.output) is list:
model_out_shape = (2, 1)
else:
model_out_shape = kl.model.output.shape
if type(kl.model.input) is list:
model_in_shape = (2, 1)
else:
model_in_shape = kl.model.input.shape
has_imu = type(kl) is KerasIMU
has_bvh = type(kl) is KerasBehavioral
img_out = type(kl) is KerasLatent
if img_out:
import cv2
for key in keys:
if not key in data:
continue
_record = data[key]
if _record['train'] != isTrainSet:
continue
if continuous:
#in continuous mode we need to handle files getting deleted
filename = _record['image_path']
if not os.path.exists(filename):
data.pop(key, None)
continue
batch_data.append(_record)
if len(batch_data) == batch_size:
inputs_img = []
inputs_imu = []
inputs_bvh = []
angles = []
throttles = []
out_img = []
out = []
for record in batch_data:
#get image data if we don't already have it
if record['img_data'] is None:
filename = record['image_path']
img_arr = load_scaled_image_arr(filename, cfg)
if img_arr is None:
break
if aug:
img_arr = augment_image(img_arr)
if cfg.CACHE_IMAGES:
record['img_data'] = img_arr
else:
img_arr = record['img_data']
if img_out:
rz_img_arr = cv2.resize(img_arr,
(127, 127)) / 255.0
out_img.append(rz_img_arr[:, :, 0].reshape(
(127, 127, 1)))
if has_imu:
inputs_imu.append(record['imu_array'])
if has_bvh:
inputs_bvh.append(record['behavior_arr'])
inputs_img.append(img_arr)
angles.append(record['angle'])
throttles.append(record['throttle'])
out.append([record['angle'], record['throttle']])
if img_arr is None:
continue
img_arr = np.array(inputs_img).reshape(batch_size,\
cfg.TARGET_H, cfg.TARGET_W, cfg.TARGET_D)
if has_imu:
X = [img_arr, np.array(inputs_imu)]
elif has_bvh:
X = [img_arr, np.array(inputs_bvh)]
else:
X = [img_arr]
if img_out:
y = [out_img, np.array(angles), np.array(throttles)]
elif model_out_shape[1] == 2:
y = [np.array([out]).reshape(batch_size, 2)]
else:
y = [np.array(angles), np.array(throttles)]
yield X, y
batch_data = []