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train.py
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train.py
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'''
authored by hunglc007/tensorflow-yolov4-tflite
https://github.com/hunglc007/tensorflow-yolov4-tflite
'''
from absl import app, flags, logging
from absl.flags import FLAGS
import os
import shutil
import tensorflow as tf
from core.dataset import Dataset
from core.config import cfg
import numpy as np
from core import utils
from core.utils import freeze_all, unfreeze_all
flags.DEFINE_string('model', 'yolov4', 'yolov4, yolov3')
flags.DEFINE_string('weights', './data/yolov4.weights', 'pretrained weights')
flags.DEFINE_boolean('tiny', False, 'yolo or yolo-tiny')
def main(_argv):
physical_devices = tf.config.experimental.list_physical_devices('GPU')
if len(physical_devices) > 0:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
from core.yolov4 import YOLO, decode, compute_loss, decode_train
trainset = Dataset(FLAGS, is_training=True)
testset = Dataset(FLAGS, is_training=False)
logdir = "./data/log"
isfreeze = False
steps_per_epoch = len(trainset)
first_stage_epochs = cfg.TRAIN.FISRT_STAGE_EPOCHS
second_stage_epochs = cfg.TRAIN.SECOND_STAGE_EPOCHS
global_steps = tf.Variable(1, trainable=False, dtype=tf.int64)
warmup_steps = cfg.TRAIN.WARMUP_EPOCHS * steps_per_epoch
total_steps = (first_stage_epochs + second_stage_epochs) * steps_per_epoch
# train_steps = (first_stage_epochs + second_stage_epochs) * steps_per_period
input_layer = tf.keras.layers.Input([cfg.TRAIN.INPUT_SIZE, cfg.TRAIN.INPUT_SIZE, 3])
STRIDES, ANCHORS, NUM_CLASS, XYSCALE = utils.load_config(FLAGS)
IOU_LOSS_THRESH = cfg.YOLO.IOU_LOSS_THRESH
freeze_layers = utils.load_freeze_layer(FLAGS.model, FLAGS.tiny)
feature_maps = YOLO(input_layer, NUM_CLASS, FLAGS.model, FLAGS.tiny)
if FLAGS.tiny:
bbox_tensors = []
for i, fm in enumerate(feature_maps):
if i == 0:
bbox_tensor = decode_train(fm, cfg.TRAIN.INPUT_SIZE // 16, NUM_CLASS, STRIDES, ANCHORS, i, XYSCALE)
else:
bbox_tensor = decode_train(fm, cfg.TRAIN.INPUT_SIZE // 32, NUM_CLASS, STRIDES, ANCHORS, i, XYSCALE)
bbox_tensors.append(fm)
bbox_tensors.append(bbox_tensor)
else:
bbox_tensors = []
for i, fm in enumerate(feature_maps):
if i == 0:
bbox_tensor = decode_train(fm, cfg.TRAIN.INPUT_SIZE // 8, NUM_CLASS, STRIDES, ANCHORS, i, XYSCALE)
elif i == 1:
bbox_tensor = decode_train(fm, cfg.TRAIN.INPUT_SIZE // 16, NUM_CLASS, STRIDES, ANCHORS, i, XYSCALE)
else:
bbox_tensor = decode_train(fm, cfg.TRAIN.INPUT_SIZE // 32, NUM_CLASS, STRIDES, ANCHORS, i, XYSCALE)
bbox_tensors.append(fm)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
for l in model.layers:
if not isinstance(l, tf.keras.layers.BatchNormalization):
l.trainable = False
for name in freeze_layers:
freeze = model.get_layer(name)
unfreeze_all(freeze)
model.summary()
if FLAGS.weights == None:
print("Training from scratch")
else:
if FLAGS.weights.split(".")[len(FLAGS.weights.split(".")) - 1] == "weights":
utils.load_weights(model, FLAGS.weights, FLAGS.model, FLAGS.tiny)
else:
model.load_weights(FLAGS.weights)
print('Restoring weights from: %s ... ' % FLAGS.weights)
optimizer = tf.keras.optimizers.Adam()
if os.path.exists(logdir): shutil.rmtree(logdir)
writer = tf.summary.create_file_writer(logdir)
# define training step function
# @tf.function
def train_step(image_data, target):
with tf.GradientTape() as tape:
pred_result = model(image_data, training=True)
giou_loss = conf_loss = prob_loss = 0
# optimizing process
for i in range(len(freeze_layers)):
conv, pred = pred_result[i * 2], pred_result[i * 2 + 1]
loss_items = compute_loss(pred, conv, target[i][0], target[i][1], STRIDES=STRIDES, NUM_CLASS=NUM_CLASS, IOU_LOSS_THRESH=IOU_LOSS_THRESH, i=i)
giou_loss += loss_items[0]
conf_loss += loss_items[1]
prob_loss += loss_items[2]
total_loss = giou_loss + conf_loss + prob_loss
gradients = tape.gradient(total_loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
tf.print("=> STEP %4d/%4d lr: %.6f giou_loss: %4.2f conf_loss: %4.2f "
"prob_loss: %4.2f total_loss: %4.2f" % (global_steps, total_steps, optimizer.lr.numpy(),
giou_loss, conf_loss,
prob_loss, total_loss))
# update learning rate
global_steps.assign_add(1)
if global_steps < warmup_steps:
lr = global_steps / warmup_steps * cfg.TRAIN.LR_INIT
else:
lr = cfg.TRAIN.LR_END + 0.5 * (cfg.TRAIN.LR_INIT - cfg.TRAIN.LR_END) * (
(1 + tf.cos((global_steps - warmup_steps) / (total_steps - warmup_steps) * np.pi))
)
optimizer.lr.assign(lr.numpy())
# writing summary data
with writer.as_default():
tf.summary.scalar("lr", optimizer.lr, step=global_steps)
tf.summary.scalar("loss/total_loss", total_loss, step=global_steps)
tf.summary.scalar("loss/giou_loss", giou_loss, step=global_steps)
tf.summary.scalar("loss/conf_loss", conf_loss, step=global_steps)
tf.summary.scalar("loss/prob_loss", prob_loss, step=global_steps)
writer.flush()
def test_step(image_data, target):
with tf.GradientTape() as tape:
pred_result = model(image_data, training=True)
giou_loss = conf_loss = prob_loss = 0
# optimizing process
for i in range(len(freeze_layers)):
conv, pred = pred_result[i * 2], pred_result[i * 2 + 1]
loss_items = compute_loss(pred, conv, target[i][0], target[i][1], STRIDES=STRIDES, NUM_CLASS=NUM_CLASS, IOU_LOSS_THRESH=IOU_LOSS_THRESH, i=i)
giou_loss += loss_items[0]
conf_loss += loss_items[1]
prob_loss += loss_items[2]
total_loss = giou_loss + conf_loss + prob_loss
tf.print("=> TEST STEP %4d giou_loss: %4.2f conf_loss: %4.2f "
"prob_loss: %4.2f total_loss: %4.2f" % (global_steps, giou_loss, conf_loss,
prob_loss, total_loss))
for epoch in range(first_stage_epochs + second_stage_epochs):
# if epoch < first_stage_epochs:
# if not isfreeze:
# isfreeze = True
# for name in freeze_layers:
# freeze = model.get_layer(name)
# freeze_all(freeze)
# elif epoch >= first_stage_epochs:
# if isfreeze:
# isfreeze = False
# for name in freeze_layers:
# freeze = model.get_layer(name)
# unfreeze_all(freeze)
for image_data, target in trainset:
train_step(image_data, target)
for image_data, target in testset:
test_step(image_data, target)
model.save_weights("./yolo_bridge/bridge")
if __name__ == '__main__':
try:
app.run(main)
except SystemExit:
pass