with tf.name_scope("Loss_and_Detect"):
# Label
y_predict = [Y1, Y2, Y3]
# Calculate loss
loss = compute_loss(scale_total,
y_predict,
anchors,
len(classes_data),
print_loss=False)
image_shappe = tf.placeholder(tf.float32, shape=[
2,
])
boxes, scores, classes = predict(scale_total,
anchors,
len(classes_data),
image_shappe,
score_threshold=threshold,
iou_threshold=ignore_thresh)
# loss_print = compute_loss(scale_total, y_predict, anchors, len(classes_data), print_loss=False)
tf.summary.scalar("Loss", loss)
with tf.name_scope("Optimizer"):
# optimizer
# for VOC: lr:0.0001, decay:0.0003 with RMSProOp after 60 epochs
# learning_rate = tf.placeholder(tf.float32, shape=[1], name='lr')
# decay = 0.0003
# optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)
applied_learning_rate = tf.placeholder(tf.float32)
optimizer = tf.train.AdamOptimizer(
learning_rate=applied_learning_rate).minimize(
loss, global_step=global_step)
def load(self):
# Remove nodes from graph or reset entire default graph
tf.reset_default_graph()
# Generate colors for drawing bounding boxes.
hsv_tuples = [(x * 1.0 / len(self.class_names), 1., 1.)
for x in range(len(self.class_names))]
self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
self.colors = list(
map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
self.colors))
random.seed(10101) # Fixed seed for consistent colors across runs.
random.shuffle(
self.colors) # Shuffle colors to decorrelate adjacent classes.
random.seed(None) # Reset seed to default.
# Generate output tensor targets for filtered bounding boxes.
self.x = tf.placeholder(
tf.float32, shape=[None, Input_shape, Input_shape, channels])
self.image_shape = tf.placeholder(tf.float32, shape=[
2,
])
# Generate output tensor targets for filtered bounding boxes.
scale1, scale2, scale3 = YOLOv3(
self.x, len(self.class_names),
trainable=self.trainable).feature_extractor()
scale_total = [scale1, scale2, scale3]
# detect
boxes, scores, classes = predict(scale_total,
self.anchors,
len(self.class_names),
self.image_shape,
max_boxes=max_boxes,
score_threshold=self.threshold,
iou_threshold=self.ignore_thresh)
# Add ops to save and restore all the variables
saver = tf.train.Saver(
var_list=None if self.COCO == True else tf.trainable_variables())
# Allowing GPU memory growth
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
# Initialize tf session
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
# # For the case of COCO
if (self.trainable):
epoch = num_epochs if self.COCO == False else 2000
checkpoint = model_checkpoint_path + '/model.ckpt-' + str(epoch -
1)
try:
aaa = checkpoint + '.meta'
my_abs_path = Path(aaa).resolve()
except FileNotFoundError:
print("Not yet training!")
else:
saver.restore(sess, checkpoint)
print("checkpoint: ", checkpoint)
print("already training!")
return boxes, scores, classes, sess