def main(args, train_set, class_num, pre_ckpt, model_def, depth_multiplier,
is_augmenter, image_size, output_size, batch_size, rand_seed,
max_nrof_epochs, init_learning_rate, learning_rate_decay_factor,
obj_weight, noobj_weight, wh_weight, obj_thresh, iou_thresh,
vaildation_split, log_dir, is_prune, initial_sparsity, final_sparsity,
end_epoch, frequency):
# Build path
log_dir = (Path(log_dir) /
datetime.strftime(datetime.now(), '%Y%m%d-%H%M%S')
) # type: Path
ckpt_weights = log_dir / 'yolo_weights.h5'
ckpt = log_dir / 'yolo_model.h5'
if not log_dir.exists():
log_dir.mkdir(parents=True)
write_arguments_to_file(args, str(log_dir / 'args.txt'))
# Build utils
h = Helper(f'data/{train_set}_img_ann.npy', class_num,
f'data/{train_set}_anchor.npy',
np.reshape(np.array(image_size), (-1, 2)),
np.reshape(np.array(output_size), (-1, 2)), vaildation_split)
h.set_dataset(batch_size, rand_seed, is_training=(is_augmenter == 'True'))
# Build network
if False:
network = eval(model_def) # type :yolo_mobilev2
yolo_model, yolo_model_wrapper = network(
[image_size[0], image_size[1], 3],
len(h.anchors[0]),
class_num,
alpha=depth_multiplier)
else:
yolo_model, yolo_model_wrapper,output_size = \
convert.make_model(model_def, model_def + '.weights',
f'data/{train_set}_anchor.npy',
h.train_epoch_step * end_epoch,
initial_sparsity,
final_sparsity,
frequency)
tf.keras.models.save_model(yolo_model,
'pre_prun{model_def}',
include_optimizer=False)
if pre_ckpt != None and pre_ckpt != 'None' and pre_ckpt != '':
if 'h5' in pre_ckpt:
yolo_model_wrapper.load_weights(str(pre_ckpt))
print(INFO, f' Load CKPT {str(pre_ckpt)}')
else:
print(ERROR, ' Pre CKPT path is unvalid')
# prune model
pruning_params = {
'pruning_schedule':
sparsity.PolynomialDecay(initial_sparsity=.50,
final_sparsity=.90,
begin_step=0,
end_step=h.train_epoch_step * end_epoch,
frequency=frequency)
}
train_model = yolo_model_wrapper
train_model.compile(
keras.optimizers.Adam(lr=init_learning_rate,
decay=learning_rate_decay_factor),
loss=[
create_loss_fn(h, obj_thresh, iou_thresh, obj_weight, noobj_weight,
wh_weight, layer)
for layer in range(
len(train_model.output) if isinstance(train_model.output, list
) else 1)
],
metrics=[
Yolo_Precision(obj_thresh, name='p'),
Yolo_Recall(obj_thresh, name='r')
])
""" NOTE fix the dataset output shape """
shapes = (train_model.input.shape, tuple(h.output_shapes))
h.train_dataset = h.train_dataset.apply(assert_element_shape(shapes))
h.test_dataset = h.test_dataset.apply(assert_element_shape(shapes))
""" Callbacks """
if is_prune == 'True':
cbs = [
sparsity.UpdatePruningStep(),
sparsity.PruningSummaries(log_dir=str(log_dir), profile_batch=0)
]
else:
cbs = [TensorBoard(str(log_dir), update_freq='batch', profile_batch=3)]
# Training
try:
train_model.fit(h.train_dataset,
epochs=max_nrof_epochs,
steps_per_epoch=h.train_epoch_step,
callbacks=cbs,
validation_data=h.test_dataset,
validation_steps=int(h.test_epoch_step *
h.validation_split))
except KeyboardInterrupt as e:
pass
train_model.summary()
if is_prune == 'True':
final_model = tmot.sparsity.keras.strip_pruning(train_model)
final_model.summary()
model_name = 'sparse1.h5'
yolo_model = tmot.sparsity.keras.strip_pruning(yolo_model)
tf.keras.models.save_model(yolo_model,
model_name,
include_optimizer=False)
tf.keras.models.save_model(yolo_model,
'{model_def}.tf',
include_optimizer=False)
else:
keras.models.save_model(yolo_model, str(ckpt))
print()
print(INFO, f' Save Model as {str(ckpt)}')
num_train = len(lines) - int(len(lines) * val_split)
num_val = int(len(lines) * val_split)
# Train with frozen layers first, to get a stable loss.
# Adjust num epochs to your dataset. This step is enough to obtain a not bad model.
model.compile(optimizer=Adam(lr=1e-3), loss={
# use custom yolo_loss Lambda layer.
'yolo_loss': lambda y_true, y_pred: y_pred})
print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))
train_set = create_dataset(lines[:num_train], batch_size, input_shape, anchors, num_classes)
vail_set = create_dataset(lines[num_train:], batch_size, input_shape, anchors, num_classes)
shapes = (tuple([ins.shape for ins in model.input]), tuple(tf.TensorShape([batch_size, ])))
train_set = train_set.apply(assert_element_shape(shapes))
vail_set = vail_set.apply(assert_element_shape(shapes))
try:
model.fit(train_set,
epochs=10,
validation_data=vail_set, validation_steps=40,
steps_per_epoch=max(1, num_train // batch_size),
callbacks=[logging, checkpoint],
verbose=0)
except KeyboardInterrupt:
pass
# train_set = YOLOSequence(lines[:num_train], batch_size, input_shape, anchors, num_classes)
# model.fit_generator(train_set,
# epochs=20,
def runTrainingDetection(uuid,
datasetDir,
numOfClass,
obj_thresh=0.7,
iou_thresh=0.5,
obj_weight=1.0,
noobj_weight=1.0,
wh_weight=1.0,
max_nrof_epochs=50,
batch_size=96,
vaildation_split=0.2):
config = tf.ConfigProto()
sess = tf.Session(config=config)
keras.backend.set_session(sess)
datasetList = [os.path.join(datasetDir, f) for f in os.listdir(datasetDir)]
image_list = []
#img_ann_f = open(os.path.join(datasetDir, 'dataset_img_ann.txt'), 'w+')
for fileName in datasetList:
if '.jpg' in fileName:
# print('/home/m5stack/VTrainingService/' + fileName, file=img_ann_f)
image_list.append('/home/m5stack/VTrainingService/' + fileName)
#img_ann_f.close()
image_path_list = np.array(
image_list
) #np.loadtxt(os.path.join(datasetDir, 'dataset_img_ann.txt'), dtype=str)
ann_list = list(image_path_list)
ann_list = [re.sub(r'JPEGImages', 'labels', s) for s in ann_list]
ann_list = [re.sub(r'.jpg', '.txt', s) for s in ann_list]
lines = np.array([
np.array([
image_path_list[i],
np.loadtxt(ann_list[i], dtype=float, ndmin=2),
np.array(skimage.io.imread(image_path_list[i]).shape[0:2])
]) for i in range(len(ann_list))
])
np.save(os.path.join(datasetDir, 'dataset_img_ann.npy'), lines)
#print('dataset npu>>>', os.path.join(datasetDir, 'dataset_img_ann.npy'))
h = Helper(os.path.join(datasetDir, 'dataset_img_ann.npy'), numOfClass,
'voc_anchor.npy', np.reshape(np.array((224, 320)), (-1, 2)),
np.reshape(np.array((7, 10, 14, 20)),
(-1, 2)), vaildation_split)
h.set_dataset(batch_size, 6)
network = eval('yolo_mobilev1') # type :yolo_mobilev2
yolo_model, train_model = network([224, 320, 3],
len(h.anchors[0]),
numOfClass,
alpha=0.5)
train_model.compile(
RAdam(),
loss=[
create_loss_fn(h, obj_thresh, iou_thresh, obj_weight, noobj_weight,
wh_weight, layer)
for layer in range(
len(train_model.output) if isinstance(train_model.output, list
) else 1)
],
metrics=[
Yolo_Precision(obj_thresh, name='p'),
Yolo_Recall(obj_thresh, name='r')
])
shapes = (train_model.input.shape, tuple(h.output_shapes))
h.train_dataset = h.train_dataset.apply(assert_element_shape(shapes))
h.test_dataset = h.test_dataset.apply(assert_element_shape(shapes))
#print('train', h.train_dataset, '\n\r\n\rtest', h.test_dataset)
try:
train_model.fit(h.train_dataset,
epochs=max_nrof_epochs,
steps_per_epoch=10,
validation_data=h.test_dataset,
validation_steps=1)
except Exception as e:
return (-45, 'Unexpected error found during training, err:', e)
keras.models.save_model(
yolo_model, f'{localSSDLoc}trained_h5_file/{uuid}_mbnet5_yolov3.h5')
converter = tf.lite.TFLiteConverter.from_keras_model_file(
f'{localSSDLoc}trained_h5_file/{uuid}_mbnet5_yolov3.h5',
custom_objects={
'RAdam':
RAdam,
'loss_softmax_cross_entropy_with_logits_v2':
loss_softmax_cross_entropy_with_logits_v2
})
tflite_model = converter.convert()
open(f'{localSSDLoc}trained_tflite_file/{uuid}_mbnet5_yolov3_quant.tflite',
"wb").write(tflite_model)
subprocess.run([
f'{nncaseLoc}/ncc',
f'{localSSDLoc}trained_tflite_file/{uuid}_mbnet5_yolov3_quant.tflite',
f'{localSSDLoc}trained_kmodel_file/{uuid}_mbnet5_yolov3.kmodel', '-i',
'tflite', '-o', 'k210model', '--dataset', datasetDir
])
if os.path.isfile(
f'{localSSDLoc}trained_kmodel_file/{uuid}_mbnet5_yolov3.kmodel'):
return (
0, f'{localSSDLoc}trained_kmodel_file/{uuid}_mbnet5_yolov3.kmodel')
else:
return (-16,
'Unexpected Error Found During generating Kendryte k210model.')
def main(annotation_path, classes_path, anchors_path, alpha, weights_path, learning_rate, epochs, augment):
# annotation_path = 'train.txt'
# classes_path = 'model_data/voc_classes.txt'
# anchors_path = 'model_data/tiny_yolo_anchors.txt'
log_dir = Path('logs')
log_dir = log_dir / datetime.strftime(datetime.now(), '%Y%m%d-%H%M%S')
class_names = get_classes(classes_path)
num_classes = len(class_names)
anchors = get_anchors(anchors_path)
input_shape = (224, 320) # multiple of 32, hw
batch_size = 16
""" Set the Model """
# model = create_tiny_model(input_shape, anchors, num_classes, weights_path='model_data/tiny_yolo_weights.h5')
# model = create_model(input_shape, anchors, num_classes, weights_path='model_data/yolo_weights.h5') # make sure you know what you freeze
model, model_body = create_mobile_yolo(input_shape, anchors, num_classes, alpha, weights_path) # make sure you know what you freeze
logging = TensorBoard(log_dir=log_dir)
checkpoint = ModelCheckpoint(str(log_dir) + 'ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5',
monitor='val_loss', save_weights_only=True, save_best_only=True, period=3)
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3, verbose=1)
early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=1)
val_split = 0.1
with open(annotation_path) as f:
lines = f.readlines()
np.random.seed(10101)
tf.set_random_seed(10101)
num_train = len(lines) - int(len(lines) * val_split)
num_val = int(len(lines) * val_split)
# Train with frozen layers first, to get a stable loss.
# Adjust num epochs to your dataset. This step is enough to obtain a not bad model.
model.compile(optimizer=Adam(lr=learning_rate), loss={
# use custom yolo_loss Lambda layer.
'yolo_loss': lambda y_true, y_pred: y_pred})
print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))
train_set = create_dataset(lines[:num_train], batch_size, input_shape, anchors, num_classes, augment == 'True')
vail_set = create_dataset(lines[num_train:], batch_size, input_shape, anchors, num_classes, random=False)
shapes = (tuple([ins.shape for ins in model.input]), tuple(tf.TensorShape([batch_size, ])))
train_set = train_set.apply(assert_element_shape(shapes))
vail_set = vail_set.apply(assert_element_shape(shapes))
try:
model.fit(train_set,
epochs=epochs,
validation_data=vail_set, validation_steps=40,
steps_per_epoch=max(1, num_train // batch_size),
callbacks=[logging, checkpoint],
verbose=1)
except KeyboardInterrupt:
pass
# train_set = YOLOSequence(lines[:num_train], batch_size, input_shape, anchors, num_classes)
# model.fit_generator(train_set,
# epochs=20,
# steps_per_epoch=max(1, num_train // batch_size),
# callbacks=[logging, checkpoint],
# use_multiprocessing=True)
save_model(model, str(log_dir / 'yolo_model.h5'))
save_model(model_body, str(log_dir / 'yolo_model_body.h5'))
