def main(args):
env_info = get_sys_env()
place = 'gpu' if env_info['Paddle compiled with cuda'] and env_info[
'GPUs used'] else 'cpu'
paddle.set_device(place)
if not args.cfg:
raise RuntimeError('No configuration file specified.')
cfg = Config(args.cfg)
val_dataset = cfg.val_dataset
if not val_dataset:
raise RuntimeError(
'The verification dataset is not specified in the configuration file.'
)
msg = '\n---------------Config Information---------------\n'
msg += str(cfg)
msg += '------------------------------------------------'
logger.info(msg)
model = cfg.model
transforms = val_dataset.transforms
image_list, image_dir = get_image_list(args.image_path)
predict(model,
model_path=args.model_path,
transforms=transforms,
image_list=image_list,
image_dir=image_dir,
save_dir=args.save_dir)
def main(args):
env_info = get_sys_env()
place = 'gpu' if env_info['Paddle compiled with cuda'] and env_info[
'GPUs used'] else 'cpu'
paddle.set_device(place)
if not args.cfg:
raise RuntimeError('No configuration file specified.')
cfg = Config(args.cfg)
msg = '\n---------------Config Information---------------\n'
msg += str(cfg)
msg += '------------------------------------------------'
logger.info(msg)
model = cfg.model
transforms = Compose(cfg.val_transforms)
print(transforms)
image_list, image_dir = get_image_list(args.image_path)
logger.info('Number of predict images = {}'.format(len(image_list)))
test_config = get_test_config(cfg, args)
predict(model,
model_path=args.model_path,
transforms=transforms,
image_list=image_list,
image_dir=image_dir,
save_dir=args.save_dir,
**test_config)
def main(args):
env_info = get_sys_env()
place = 'gpu' if env_info['Paddle compiled with cuda'] and env_info[
'GPUs used'] else 'cpu'
paddle.set_device(place)
if not args.cfg:
raise RuntimeError('No configuration file specified.')
cfg = Config(args.cfg)
val_dataset = cfg.val_dataset
if not val_dataset:
raise RuntimeError(
'The verification dataset is not specified in the configuration file.'
)
msg = '\n---------------Config Information---------------\n'
msg += str(cfg)
msg += '------------------------------------------------'
logger.info(msg)
model = cfg.model
transforms = val_dataset.transforms
image_list, image_dir = get_image_list(args.image_path)
logger.info('Number of predict images = {}'.format(len(image_list)))
config_check(cfg, val_dataset=val_dataset)
predict(
model,
model_path=args.model_path,
transforms=transforms,
image_list=image_list,
image_dir=image_dir,
save_dir=args.save_dir,
aug_pred=args.aug_pred,
scales=args.scales,
flip_horizontal=args.flip_horizontal,
flip_vertical=args.flip_vertical,
is_slide=args.is_slide,
crop_size=args.crop_size,
stride=args.stride,
)
transforms = T.Compose([
T.Resize(target_size=(512, 512)),
T.Normalize()
])
model = UNet(num_classes=3)
#生成图片列表
image_list = []
with open('/home/aistudio/work/newdata/test_list.txt' ,'r') as f:
for line in f.readlines():
image_list.append(line.split()[0])
predict(
model,
#换自己保存的模型文件
model_path = '/home/aistudio/my_save_model/best_model/model.pdparams',
transforms=transforms,
image_list=image_list,
save_dir='/home/aistudio/save_model/results',
)
# 9.预览分割结果
num = 6
img_list = random.sample(image_list, num)
pre_path = 'save_model/results/pseudo_color_prediction'
plt.figure(figsize=(12,num*4))
index = 1
for i in range(len(img_list)):
plt.subplot(num,3,index)
img_origin = cv2.imread(img_list[i],0)
plt.title('origin')
plt.xticks([])