print('loaded!')
model.summary()
model.compile(optimizer=tf.keras.optimizers.Adam(lr=cfg.TRAIN.LR_INIT),
loss={
'yolo_loss': lambda y_true, y_pred: y_pred
})
auto_lr = AutoLr(warmup_steps)
mAP = MAP()
checkpoint = SelfModelCheckPoint(model_path, 10000)
callbacks = [auto_lr, checkpoint, mAP]
if not cfg.YOLO.TrainContinueFlag:
model.fit_generator(trainset.loda_data(),
epochs=train_epochs,
steps_per_epoch=train_steps,
validation_data=testset.loda_data(),
validation_steps=val_steps,
callbacks=callbacks)
else:
test_loss_save_dir = './plot/test_loss'
test_loss_save_path = test_loss_save_dir + '/test_loss.txt'
if os.path.exists(test_loss_save_path):
with open(test_loss_save_path, 'rb') as test_loss_f:
test_loss_list = pickle.load(test_loss_f)
current_epochs = len(test_loss_list)
model.fit_generator(trainset.loda_data(),
epochs=(train_epochs),
from core.dataset import Dataset
import tensorflow as tf
import core.utils as utils
from core.config import cfg
import tqdm
import cv2
draw_dataset = Dataset('train')
data_product = draw_dataset.loda_data()
for i in tqdm.tqdm(range(10)):
get = next(data_product)
get = get[0]
label = get[1]
print(label[].shape)
assert 0
pred_bbox = list([label[0], label[1], label[2]])
image_size = get[2]
image = get[3]
pred_bbox = [tf.reshape(x, (-1, tf.shape(x)[-1])) for x in pred_bbox]
pred_bbox = tf.concat(pred_bbox, axis=0)
bboxes = utils.postprocess_boxes(
pred_bbox, image_size, 416, 0.3
)
bboxes = utils.nms(bboxes, cfg.TEST.IOU_THRESHOLD, method="nms")
image = utils.draw_bbox(image, bboxes)
cv2.imwrite(str(i)+'.jpg', image)
model = create_model()
if os.path.exists(os.path.join(model_dir, 'checkpoint')):
model.load_weights(model_path)
print('loaded!')
model.summary()
model.compile(optimizer=tf.keras.optimizers.Adam(lr=cfg.TRAIN.LR_INIT), loss={'yolo_loss': lambda y_true, y_pred: y_pred})
auto_lr = AutoLr(warmup_steps)
mAP = MAP()
checkpoint = SelfModelCheckPoint(model_path, 10000)
callbacks = [auto_lr, checkpoint, mAP]
if not cfg.YOLO.TrainContinueFlag:
model.fit(trainset.loda_data(), epochs=train_epochs, steps_per_epoch=train_steps,
validation_data=testset.loda_data(), validation_steps=val_steps,
callbacks=callbacks)
else:
test_loss_save_dir = './plot/test_loss'
test_loss_save_path = test_loss_save_dir + '/test_loss.txt'
if os.path.exists(test_loss_save_path):
with open(test_loss_save_path, 'rb') as test_loss_f:
test_loss_list = pickle.load(test_loss_f)
current_epochs = len(test_loss_list)
model.fit(trainset.loda_data(), epochs=(train_epochs),
initial_epoch=current_epochs, steps_per_epoch=train_steps,
validation_data=testset.loda_data(), validation_steps=val_steps,
callbacks=callbacks)
else:
