def train():
os.makedirs(cfg.save_weights_path, exist_ok=True)
#选择网络
train_model, eval_model = cfg.select_model
#编译和打印模型
train_model.compile(optimizer=cfg.optimizer,
loss=cfg.loss,
loss_weights=cfg.loss_weighting,
metrics=cfg.metrics)
print_summary(model=train_model)
#训练数据生成器G1
G1 = imageSegmentationGenerator(cfg.train_images, cfg.train_label,
cfg.train_batch_size, cfg.n_classes,
cfg.input_shape[0], cfg.input_shape[1],
cfg.output_shape[0], cfg.output_shape[1])
#测试数据生成器G2
if cfg.validate:
G2 = imageSegmentationGenerator(cfg.val_images, cfg.train_label,
cfg.val_batch_size, cfg.n_classes,
cfg.input_shape[0], cfg.input_shape[1],
cfg.output_shape[0],
cfg.output_shape[1])
#循环训练
for ep in range(cfg.epochs):
#1、训练两种方式
if not cfg.validate: #只有G1
hisroy = train_model.fit_generator(
G1,
steps_per_epoch=cfg.train_steps_per_epoch,
workers=cfg.workers,
epochs=1,
verbose=1,
use_multiprocessing=cfg.use_multiprocessing,
class_weight='auto')
else: #有G1和G2
hisroy = train_model.fit_generator(
G1,
steps_per_epoch=cfg.train_steps_per_epoch,
workers=cfg.workers,
epochs=1,
verbose=1,
use_multiprocessing=cfg.use_multiprocessing,
validation_data=G2,
validation_steps=cfg.validate_steps_per_epoch)
# 3、保存模型
if (ep % cfg.epochs_save) == (cfg.epochs_save - 1):
print('saving model.{}.......'.format(ep))
save_weights_name = 'model.{}'.format(ep)
save_weights_path = os.path.join(cfg.save_weights_path,
save_weights_name)
train_model.save_weights(save_weights_path)
def train():
model = UNet(cfg.input_shape)
#编译和打印模型
model.compile(optimizer=cfg.optimizer, loss=cfg.loss, metrics=cfg.metrics)
print_summary(model=model)
#训练数据生成器G1
G1 = imageSegmentationGenerator(cfg.train_images, cfg.train_annotations,
cfg.train_batch_size, cfg.n_classes,
cfg.input_shape[0], cfg.input_shape[1],
cfg.output_shape[0], cfg.output_shape[1])
#测试数据生成器G2
if cfg.validate:
G2 = imageSegmentationGenerator(cfg.val_images, cfg.val_annotations,
cfg.val_batch_size, cfg.n_classes,
cfg.input_shape[0], cfg.input_shape[1],
cfg.output_shape[0],
cfg.output_shape[1])
#循环训练
save_index = 1
for ep in range(cfg.epochs):
#1、训练两种方式
if not cfg.validate: #只有G1
hisroy = model.fit_generator(
G1,
steps_per_epoch=cfg.train_steps_per_epoch,
workers=cfg.workers,
epochs=1,
verbose=1,
use_multiprocessing=cfg.use_multiprocessing)
else: #有G1和G2
hisroy = model.fit_generator(
G1,
steps_per_epoch=cfg.train_steps_per_epoch,
workers=cfg.workers,
epochs=1,
verbose=1,
use_multiprocessing=cfg.use_multiprocessing,
validation_data=G2,
validation_steps=cfg.validate_steps_per_epoch)
# 2、保存模型
if save_index == cfg.epochs_save:
save_index = 1
save_weights_name = 'model.{}'.format(ep)
save_weights_path = os.path.join(cfg.save_weights_path,
save_weights_name)
model.save_weights(save_weights_path)
save_index += 1
def use_generator_to_show(images_path, label_path, batch_size, n_classes,
input_height, input_width, output_height,
output_width):
batch_size_n = 0 #取第batch_size_n张图片观察
plt.figure()
colors = [(np.random.randint(0, 255), np.random.randint(0, 255),
np.random.randint(0, 255)) for _ in range(n_classes)]
#使用Generator，返回一个batch_size的 im_fn和seg_fn
for im_fn, seg_vec in imageSegmentationGenerator(images_path, label_path,
batch_size, n_classes,
input_height, input_width,
output_height,
output_width):
# 1、原图
print('return img shape: ', im_fn.shape)
pics_group = split_batch_to_pic_list(im_fn) # batchsize切成图片列表
pic = pics_group[batch_size_n] # 取第batch_size_n张图片观察
# 2、label图
if use_binary_label:
n_classes = 1
print('return label shape: ', seg_vec.shape)
seg_vec = split_batch_to_pic_list(seg_vec) # batchsize切成图片列表
seg_vec = seg_vec[batch_size_n] # 取第batch_size_n张图片观察
seg_img = seg_vec_to_pic(seg_vec, pic.shape, colors, n_classes)
# 5、显示img和label
plt_imshow_two_pics(pic, seg_img) # 用plt显示
time.sleep(1)
