def _get_child_configer_transform(self, child_config_file):
dataset_configer = Configer(configs=child_config_file)
child_configer = self.configer.clone()
child_configer.params_root['data'].update(dataset_configer.get('data'))
if self.configer.exists(
'use_adaptive_transform') or self.dataset == 'val':
child_configer.params_root.update({
'train_trans':
dataset_configer.params_root['train_trans'],
'val_trans':
dataset_configer.params_root['val_trans'],
})
return child_configer, CV2AugCompose(split=self.dataset,
configer=child_configer)
def __init__(self, args, device='cuda'):
if torch.cuda.device_count() == 0:
device = 'cpu'
self.device = torch.device(device)
Log.info('Resuming from {}...'.format(args.model_path))
checkpoint_dict = torch.load(args.model_path)
self.configer = Configer(config_dict=checkpoint_dict['config_dict'],
args_parser=args,
valid_flag="deploy")
self.net = ModelManager(self.configer).get_deploy_model()
RunnerHelper.load_state_dict(self.net, checkpoint_dict['state_dict'],
False)
if device == 'cuda':
self.net = DataParallelModel(self.net, gather_=True)
self.net = self.net.to(self.device).eval()
self.test_loader = DataLoader(self.configer)
color_dst[label_map == i] = color_list[i % len(color_list)]
color_img_rgb = np.array(color_dst, dtype=np.uint8)
color_img_bgr = cv2.cvtColor(color_img_rgb, cv2.COLOR_RGB2BGR)
if image_canvas is not None:
image_canvas = cv2.addWeighted(image_canvas, 0.6, color_img_bgr, 0.4, 0)
return image_canvas
else:
return color_img_bgr
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--configs', default=None, type=str,
dest='configs', help='The file of the hyper parameters.')
parser.add_argument('--image_file', default=None, type=str,
dest='image_file', help='The image file of Seg Parser.')
parser.add_argument('--label_file', default=None, type=str,
dest='label_file', help='The label file of Seg Parser.')
parser.add_argument('--image_dir', default=None, type=str,
dest='image_dir', help='The image directory of Seg Parser.')
parser.add_argument('--label_dir', default=None, type=str,
dest='label_dir', help='The label directory of Seg Parser.')
args_parser = parser.parse_args()
seg_parser = SegParser(Configer(configs=args_parser.configs))
seg_parser.parse_img_seg(args_parser.image_file, args_parser.label_file)
seg_parser.parse_dir_seg(args_parser.image_dir, args_parser.label_dir)
dest='deploy.norm_type',
help='The pool type.')
parser.add_argument('--gpu_id',
default=[0, 1],
type=int,
dest='gpu_id',
help='The gpu id.')
args = parser.parse_args()
#second
model_path1 = '../../checkpoints/cls/resnet152/google_landmark_2020_resnet152_v2cluster_448_GPU8_final.pth'
model_path2 = '../../checkpoints/cls/resnest200/google_landmark_2020_resnest200_v2cluster_448_GPU8_final.pth'
checkpoint_dict1 = torch.load(model_path1)
configer1 = Configer(config_dict=checkpoint_dict1['config_dict'],
args_parser=args,
valid_flag="deploy")
net1 = ModelManager(configer1).get_deploy_model()
RunnerHelper.load_state_dict(net1, checkpoint_dict1['state_dict'], False)
checkpoint_dict2 = torch.load(model_path2)
configer2 = Configer(config_dict=checkpoint_dict2['config_dict'],
args_parser=args,
valid_flag="deploy")
net2 = ModelManager(configer2).get_deploy_model()
RunnerHelper.load_state_dict(net2, checkpoint_dict2['state_dict'], False)
net = MergeClsModel(net1, net2)
device = torch.device('cpu')
net = net.to(device).eval()
dummy_input = torch.randn(1, 3, 512, 512).to(device)
parser.add_argument('REMAIN', nargs='*')
args_parser = parser.parse_args()
from lib.utils.distributed import handle_distributed
handle_distributed(args_parser,
os.path.expanduser(os.path.abspath(__file__)))
if args_parser.seed is not None:
random.seed(args_parser.seed)
torch.manual_seed(args_parser.seed)
cudnn.enabled = True
cudnn.benchmark = args_parser.cudnn
configer = Configer(args_parser=args_parser)
data_dir = configer.get('data', 'data_dir')
if isinstance(data_dir, str):
data_dir = [data_dir]
abs_data_dir = [os.path.expanduser(x) for x in data_dir]
configer.update(['data', 'data_dir'], abs_data_dir)
project_dir = os.path.dirname(os.path.realpath(__file__))
configer.add(['project_dir'], project_dir)
if configer.get('logging', 'log_to_file'):
log_file = configer.get('logging', 'log_file')
new_log_file = '{}_{}'.format(
log_file, time.strftime("%Y-%m-%d_%X", time.localtime()))
configer.update(['logging', 'log_file'], new_log_file)
else:
Log.info('mIoU (Class-wise)')
iou_dict = self.seg_running_score.get_cls_iu()
for cid, miou in iou_dict.items():
Log.info('\t{}\t{}'.format(cid, miou))
print(' & '.join('{:.1f}'.format(x * 100)
for x in list(iou_dict.values()) +
[self.seg_running_score.get_mean_iou()]))
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--configs',
default=None,
type=str,
dest='configs',
help='The configs file of pose.')
parser.add_argument('--gt_dir',
default=None,
type=str,
dest='gt_dir',
help='The groundtruth annotations.')
parser.add_argument('--pred_dir',
default=None,
type=str,
dest='pred_dir',
help='The label dir of predict annotations.')
parser.add_argument('--file_list', default='all')
args = parser.parse_args()
ade20k_evaluator = ADE20KEvaluator(Configer(configs=args.configs))
ade20k_evaluator.evaluate(args.pred_dir, args.gt_dir)
print(filename)
pred_path = os.path.join(pred_dir, filename)
gt_path = os.path.join(gt_dir, filename)
predmap = ImageHelper.img2np(ImageHelper.read_image(pred_path, tool='pil', mode='P'))
gtmap = ImageHelper.img2np(ImageHelper.read_image(gt_path, tool='pil', mode='P'))
predmap = self.relabel(predmap)
gtmap = self.relabel(gtmap)
gtmap[gtmap == 0] = 255
self.seg_running_score.update(predmap[np.newaxis, :, :], gtmap[np.newaxis, :, :])
img_cnt += 1
Log.info('Evaluate {} images'.format(img_cnt))
Log.info('mIOU: {}'.format(self.seg_running_score.get_mean_iou()))
Log.info('Pixel ACC: {}'.format(self.seg_running_score.get_pixel_acc()))
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--configs', default=None, type=str,
dest='configs', help='The configs file of pose.')
parser.add_argument('--gt_dir', default=None, type=str,
dest='gt_dir', help='The groundtruth annotations.')
parser.add_argument('--pred_dir', default=None, type=str,
dest='pred_dir', help='The label dir of predict annotations.')
args = parser.parse_args()
cocostuff_evaluator = COCOStuffEvaluator(Configer(configs=args.configs))
cocostuff_evaluator.evaluate(args.pred_dir, args.gt_dir)
print(filename)
pred_path = os.path.join(pred_dir, filename)
gt_path = os.path.join(gt_dir, filename)
predmap = ImageHelper.img2np(ImageHelper.read_image(pred_path, tool='pil', mode='P'))
gtmap = ImageHelper.img2np(ImageHelper.read_image(gt_path, tool='pil', mode='P'))
predmap = self.relabel(predmap)
gtmap = self.relabel(gtmap)
self.seg_running_score.update(predmap[np.newaxis, :, :], gtmap[np.newaxis, :, :])
img_cnt += 1
Log.info('Evaluate {} images'.format(img_cnt))
Log.info('mIOU: {}'.format(self.seg_running_score.get_mean_iou()))
Log.info('Pixel ACC: {}'.format(self.seg_running_score.get_pixel_acc()))
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--configs', default=None, type=str,
dest='configs', help='The configs file of pose.')
parser.add_argument('--gt_dir', default=None, type=str,
dest='gt_dir', help='The groundtruth annotations.')
parser.add_argument('--pred_dir', default=None, type=str,
dest='pred_dir', help='The label dir of predict annotations.')
args = parser.parse_args()
pcontext_evaluator = PascalContextEvaluator(Configer(configs=args.configs))
pcontext_evaluator.evaluate(args.pred_dir, args.gt_dir)