def test(self, gen_test, output_dir=None):
if self.model is None:
raise Exception("Model is not defined.")
images, labels = gen_test.get_all()
logger.debug("Predicting... ")
pred = self.predict(images)
df_pred = self.get_pred_df(pred)
df_ans = pd.DataFrame(labels)
df_ans = df_ans.applymap(lambda x: np.argmax(x))
df_right = (df_pred == df_ans)
for num_klass, klass_name in enumerate(df_right):
logger.debug("Label {} acc: {}/{} = {:.4f}".format(klass_name, np.sum(df_right[klass_name]), len(df_right),
np.sum(df_right[klass_name]) / len(df_right)))
# save wrong prediction
for i in df_pred[klass_name].loc[df_ans[klass_name] != df_pred[klass_name]].index:
annotation = gen_test.dataset.annotations[i]
title = f"ans({self.list_class_names[num_klass][df_ans[i]]})_pred({self.list_class_names[num_klass][df_pred[i]]})"
image = image_processing.draw_annotation(annotation)
if output_dir is None:
image_processing.show(image, title=f"{annotation.filename}\n{title}")
else:
image_processing.save_image(image, os_path.join(output_dir, f"{annotation.filename}_{title}.png"))
logger.debug(
"Total acc {}/{} = {:.4f}".format(np.sum(df_right.all(axis=1)), len(df_right),
np.sum(df_right.all(axis=1)) / len(df_right)))
def __init__(self, model_dir, version=None):
super(TFServeModel, self).__init__()
self.model_dir = model_dir
if version is None:
version = os_path.list_dir(model_dir, sort=True, sort_key=os_path.getctime, full_path=False)[-1]
self.version = version
model = tf.saved_model.load(os_path.join(self.model_dir, self.version))
self.__dict__.update(model.__dict__)
def load(self, model_dir=None, model_path=None):
if model_dir is None and model_path is None:
raise Exception("No model path can be loaded")
if model_path is None:
model_path = os_path.join(model_dir, f"{self.model_name}.h5")
if not os_path.exists(model_path):
logger.warning("Pretrained model does not exist in {}.".format(model_path))
return
logger.debug('Load model {}.'.format(model_path))
model = load_model(model_path, compile=False)
self.__dict__.update(model.__dict__)
def display(image_path, bbox, pred_classes, true_classes, title):
image_org = image_processing.imread(image_path)
image = model.img_aug.aug([image_org])[0][0]
image = image_processing.draw_box(image,
bbox,
label=get_label(pred_classes))
image_path = os_path.join(
os_path.dirname(image_path, depth=2, full_path=False),
os_path.get_filename(image_path))
image_processing.show(
image,
title=f"{title} true={get_label(true_classes)}\n {image_path}")
def load_weights(self, weight_dir=None, weight_path=None, force=False, **kwargs):
if not force:
if weight_dir is None and weight_path is None:
raise Exception("No model_weight path can be loaded")
if weight_path is None:
weight_path = os_path.join(weight_dir, f"{self.model_name}.h5")
if not os_path.exists(weight_path):
logger.warning("Pretrained weights does not exist in {}.".format(weight_path))
return
logger.debug('Load weights {}.'.format(weight_path))
super(CNNModel, self).load_weights(weight_path, **kwargs)
def run_eager_fit(model, train_dataset, val_dataset, optimizer, loss):
# Eager mode is great for debugging
# Non eager graph mode is recommended for real training
avg_loss = tf.keras.metrics.Mean('loss', dtype=tf.float32)
avg_val_loss = tf.keras.metrics.Mean('val_loss', dtype=tf.float32)
for epoch in range(1, FLAGS.epochs + 1):
for batch, (images, labels) in enumerate(train_dataset):
with tf.GradientTape() as tape:
outputs = model(images, training=True)
regularization_loss = tf.reduce_sum(model.losses)
pred_loss = []
for output, label, loss_fn in zip(outputs, labels, loss):
pred_loss.append(loss_fn(label, output))
total_loss = tf.reduce_sum(pred_loss) + regularization_loss
grads = tape.gradient(total_loss, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
logger.info(
f"[Epoch{epoch}/{FLAGS.epochs}][Train][Batch {batch + 1}] "
f"Loss: {total_loss.numpy()}, {list(map(lambda x: np.sum(x.numpy()), pred_loss))}"
)
avg_loss.update_state(total_loss)
for batch, (images, labels) in enumerate(val_dataset):
outputs = model(images)
regularization_loss = tf.reduce_sum(model.losses)
pred_loss = []
for output, label, loss_fn in zip(outputs, labels, loss):
pred_loss.append(loss_fn(label, output))
total_loss = tf.reduce_sum(pred_loss) + regularization_loss
logger.info(
f"[Epoch{epoch}/{FLAGS.epochs}][Val][Batch {batch}] "
f"Loss: {total_loss.numpy()}, {list(map(lambda x: np.sum(x.numpy()), pred_loss))}"
)
avg_val_loss.update_state(total_loss)
logger.info(
f"[Epoch {epoch}][Summary] train: {avg_loss.result().numpy()}, val: {avg_val_loss.result().numpy()}"
)
model.save_weights(
os_path.join(
os_path.dirname(FLAGS.weights), "checkpoints",
f'ep{epoch:03d}-loss{avg_loss.result().numpy():.3f}'
f'-val_loss{avg_val_loss.result().numpy():.3f}.h5'), )
avg_loss.reset_states()
avg_val_loss.reset_states()
def train(model, train_dataset, val_dataset):
flag_utils.log_flag()
"""Configure the model for transfer learning"""
if FLAGS.transfer == 'none':
pass # Nothing to do
elif FLAGS.transfer in ['darknet', 'no_output']:
if FLAGS.tiny:
model_pretrained = YoloV3Tiny()
else:
model_pretrained = YoloV3()
model_pretrained.load_weights(FLAGS.weights, by_name=True)
if FLAGS.transfer == 'darknet':
model.get_layer('yolo_darknet').set_weights(
model_pretrained.get_layer('yolo_darknet').get_weights())
yolo_utils.freeze_all(model.get_layer('yolo_darknet'),
until_layer=FLAGS.freeze)
elif FLAGS.transfer == 'no_output':
for l in model.layers:
if not l.name.startswith('yolo_output'):
l.set_weights(
model_pretrained.get_layer(l.name).get_weights())
yolo_utils.freeze_all(l, until_layer=FLAGS.freeze)
else:
# All other transfer require matching classes
model.load_weights(weight_path=FLAGS.weights)
if FLAGS.transfer == 'fine_tune':
# freeze darknet and fine tune other layers
darknet = model.get_layer('yolo_darknet')
yolo_utils.freeze_all(darknet, until_layer=FLAGS.freeze)
elif FLAGS.transfer == 'frozen':
# freeze everything
yolo_utils.freeze_all(model, until_layer=FLAGS.freeze)
lr_scheduler = CosineDecay(initial_learning_rate=FLAGS.learning_rate,
decay_steps=FLAGS.decay_steps,
alpha=FLAGS.lr_alpha)
optimizer = tf.keras.optimizers.Adam(learning_rate=lr_scheduler)
loss = [
YoloLoss(model.anchors[mask], classes=model.num_classes)
for mask in model.anchor_masks
]
"""Start training"""
if FLAGS.fit == 'eager_tf':
run_eager_fit(model, train_dataset, val_dataset, optimizer, loss)
else:
model.compile(optimizer=optimizer,
loss=loss,
run_eagerly=(FLAGS.mode == 'eager_fit'))
checkpoint_path = os_path.join(
os_path.dirname(FLAGS.weights), "checkpoints",
'ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5')
os_path.make_dir(os_path.dirname(checkpoint_path))
plt_his = PlotHistory()
callbacks = [
# ReduceLROnPlateau(verbose=1, patience=FLAGS.lr_patience),
EarlyStopping(patience=FLAGS.early_stop, verbose=1),
ModelCheckpoint(checkpoint_path,
monitor='val_loss',
verbose=0,
save_best_only=False,
save_weights_only=True,
mode='auto',
save_freq='epoch'),
# TensorBoard(log_dir='../logs'),
LogHistory(),
plt_his
]
try:
history = model.fit(train_dataset,
epochs=FLAGS.epochs,
callbacks=callbacks,
validation_data=val_dataset)
except Exception as e:
raise e
finally:
plt_his.plot(save_path=os_path.join(
os_path.dirname(FLAGS.weights),
f"{datetime.datetime.now().strftime('%Y%m%d-%H%M')}.png"))