polish weights (#2376)

* unity weights

dygraph/configs/dcn/cascade_rcnn_dcn_x101_vd_64x4d_fpn_1x_coco.yml

 _BASE_: [
   'cascade_rcnn_dcn_r50_fpn_1x_coco.yml',
 ]
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_vd_64x4d_pretrained.tar
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNeXt101_vd_64x4d_pretrained.pdparams
 weights: output/cascade_rcnn_dcn_x101_vd_64x4d_fpn_1x_coco/model_final
 
 ResNet:

dygraph/configs/dcn/faster_rcnn_dcn_r101_vd_fpn_1x_coco.yml

 _BASE_: [
   'faster_rcnn_dcn_r50_fpn_1x_coco.yml',
 ]
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_vd_pretrained.tar
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet101_vd_pretrained.pdparams
 weights: output/faster_rcnn_dcn_r101_vd_fpn_1x_coco/model_final
 
 ResNet:

dygraph/configs/dcn/faster_rcnn_dcn_r50_vd_fpn_1x_coco.yml

 _BASE_: [
   'faster_rcnn_dcn_r50_fpn_1x_coco.yml',
 ]
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_vd_pretrained.pdparams
 weights: output/faster_rcnn_dcn_r50_vd_fpn_2x_coco/model_final
 
 ResNet:

dygraph/configs/dcn/faster_rcnn_dcn_r50_vd_fpn_2x_coco.yml

 _BASE_: [
   'faster_rcnn_dcn_r50_fpn_1x_coco.yml',
 ]
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_vd_pretrained.pdparams
 weights: output/faster_rcnn_dcn_r50_vd_fpn_2x_coco/model_final
 
 ResNet:

dygraph/configs/dcn/faster_rcnn_dcn_x101_vd_64x4d_fpn_1x_coco.yml

 _BASE_: [
   'faster_rcnn_dcn_r50_fpn_1x_coco.yml',
 ]
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_vd_64x4d_pretrained.tar
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNeXt101_vd_64x4d_pretrained.pdparams
 weights: output/faster_rcnn_dcn_x101_vd_64x4d_fpn_1x_coco/model_final
 
 ResNet:

dygraph/configs/dcn/mask_rcnn_dcn_r101_vd_fpn_1x_coco.yml

 _BASE_: [
   'mask_rcnn_dcn_r50_fpn_1x_coco.yml',
 ]
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_vd_pretrained.tar
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet101_vd_pretrained.pdparams
 weights: output/mask_rcnn_dcn_r101_vd_fpn_1x_coco/model_final
 
 ResNet:

dygraph/configs/dcn/mask_rcnn_dcn_r50_vd_fpn_2x_coco.yml

 _BASE_: [
   'mask_rcnn_dcn_r50_fpn_1x_coco.yml',
 ]
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_vd_pretrained.pdparams
 weights: output/mask_rcnn_dcn_r50_vd_fpn_2x_coco/model_final
 
 ResNet:

dygraph/configs/dcn/mask_rcnn_dcn_x101_vd_64x4d_fpn_1x_coco.yml

 _BASE_: [
   'mask_rcnn_dcn_r50_fpn_1x_coco.yml',
 ]
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_vd_64x4d_pretrained.tar
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNeXt101_vd_64x4d_pretrained.pdparams
 weights: output/mask_rcnn_dcn_x101_vd_64x4d_fpn_1x_coco/model_final
 
 ResNet:

dygraph/configs/faster_rcnn/faster_rcnn_r101_1x_coco.yml

@@ -2,7 +2,7 @@ _BASE_: [
   'faster_rcnn_r50_1x_coco.yml',
 ]
 
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.tar
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet101_pretrained.pdparams
 weights: output/faster_rcnn_r101_1x_coco/model_final
 
 ResNet:

dygraph/configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.yml

@@ -2,7 +2,7 @@ _BASE_: [
   'faster_rcnn_r50_fpn_1x_coco.yml',
 ]
 
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.tar
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet101_pretrained.pdparams
 weights: output/faster_rcnn_r101_fpn_1x_coco/model_final
 
 ResNet:

dygraph/configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.yml

@@ -2,7 +2,7 @@ _BASE_: [
   'faster_rcnn_r50_fpn_1x_coco.yml',
 ]
 
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.tar
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet101_pretrained.pdparams
 weights: output/faster_rcnn_r101_fpn_2x_coco/model_final
 
 ResNet:

dygraph/configs/faster_rcnn/faster_rcnn_r101_vd_fpn_1x_coco.yml

 _BASE_: [
   'faster_rcnn_r50_fpn_1x_coco.yml',
 ]
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_vd_pretrained.tar
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet101_vd_pretrained.pdparams
 weights: output/faster_rcnn_r101_vd_fpn_1x_coco/model_final
 
 ResNet:

dygraph/configs/faster_rcnn/faster_rcnn_r101_vd_fpn_2x_coco.yml

 _BASE_: [
   'faster_rcnn_r50_fpn_1x_coco.yml',
 ]
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_vd_pretrained.tar
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet101_vd_pretrained.pdparams
 weights: output/faster_rcnn_r101_vd_fpn_2x_coco/model_final
 
 ResNet:

dygraph/configs/faster_rcnn/faster_rcnn_r34_fpn_1x_coco.yml

@@ -2,7 +2,7 @@ _BASE_: [
   'faster_rcnn_r50_fpn_1x_coco.yml',
 ]
 
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet34_pretrained.tar
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet34_pretrained.pdparams
 weights: output/faster_rcnn_r34_fpn_1x_coco/model_final
 
 ResNet:

dygraph/configs/faster_rcnn/faster_rcnn_r34_vd_fpn_1x_coco.yml

@@ -2,7 +2,7 @@ _BASE_: [
   'faster_rcnn_r50_fpn_1x_coco.yml',
 ]
 
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet34_vd_pretrained.tar
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet34_vd_pretrained.pdparams
 weights: output/faster_rcnn_r34_vd_fpn_1x_coco/model_final
 
 ResNet:

dygraph/configs/faster_rcnn/faster_rcnn_r50_vd_1x_coco.yml

 _BASE_: [
   'faster_rcnn_r50_1x_coco.yml',
 ]
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_vd_pretrained.pdparams
 weights: output/faster_rcnn_r50_vd_1x_coco/model_final
 
 ResNet:

dygraph/configs/faster_rcnn/faster_rcnn_r50_vd_fpn_1x_coco.yml

 _BASE_: [
   'faster_rcnn_r50_fpn_1x_coco.yml',
 ]
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_vd_pretrained.pdparams
 weights: output/faster_rcnn_r50_vd_fpn_1x_coco/model_final
 
 ResNet:

dygraph/configs/faster_rcnn/faster_rcnn_r50_vd_fpn_2x_coco.yml

 _BASE_: [
   'faster_rcnn_r50_fpn_1x_coco.yml',
 ]
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_vd_pretrained.pdparams
 weights: output/faster_rcnn_r50_vd_fpn_2x_coco/model_final
 
 ResNet:

dygraph/configs/faster_rcnn/faster_rcnn_x101_vd_64x4d_fpn_1x_coco.yml

@@ -2,7 +2,7 @@ _BASE_: [
   'faster_rcnn_r50_fpn_1x_coco.yml',
 ]
 
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_vd_64x4d_pretrained.tar
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNeXt101_vd_64x4d_pretrained.pdparams
 weights: output/faster_rcnn_x101_vd_64x4d_fpn_1x_coco/model_final
 
 ResNet:

dygraph/configs/faster_rcnn/faster_rcnn_x101_vd_64x4d_fpn_2x_coco.yml

@@ -2,7 +2,7 @@ _BASE_: [
   'faster_rcnn_r50_fpn_1x_coco.yml',
 ]
 
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_vd_64x4d_pretrained.tar
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNeXt101_vd_64x4d_pretrained.pdparams
 weights: output/faster_rcnn_x101_vd_64x4d_fpn_2x_coco/model_final
 
 ResNet:

dygraph/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.yml

 _BASE_: [
   'mask_rcnn_r50_fpn_1x_coco.yml',
 ]
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.tar
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet101_pretrained.pdparams
 weights: output/mask_rcnn_r101_fpn_1x_coco/model_final
 
 ResNet:

dygraph/configs/mask_rcnn/mask_rcnn_r101_vd_fpn_1x_coco.yml

 _BASE_: [
   'mask_rcnn_r50_fpn_1x_coco.yml',
 ]
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_vd_pretrained.tar
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet101_vd_pretrained.pdparams
 weights: output/mask_rcnn_r101_vd_fpn_1x_coco/model_final
 
 ResNet:

dygraph/configs/mask_rcnn/mask_rcnn_r50_vd_fpn_1x_coco.yml

@@ -2,7 +2,7 @@ _BASE_: [
   'mask_rcnn_r50_fpn_1x_coco.yml',
 ]
 
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_vd_pretrained.pdparams
 weights: output/mask_rcnn_r50_vd_fpn_1x_coco/model_final
 
 ResNet:

dygraph/configs/mask_rcnn/mask_rcnn_r50_vd_fpn_2x_coco.yml

@@ -2,7 +2,7 @@ _BASE_: [
   'mask_rcnn_r50_fpn_1x_coco.yml',
 ]
 
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_vd_pretrained.pdparams
 weights: output/mask_rcnn_r50_vd_fpn_2x_coco/model_final
 
 ResNet:

dygraph/configs/mask_rcnn/mask_rcnn_x101_vd_64x4d_fpn_1x_coco.yml

@@ -2,7 +2,7 @@ _BASE_: [
   'mask_rcnn_r50_fpn_1x_coco.yml',
 ]
 
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_vd_64x4d_pretrained.tar
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNeXt101_vd_64x4d_pretrained.pdparams
 weights: output/mask_rcnn_x101_vd_64x4d_fpn_1x_coco/model_final
 
 ResNet:

dygraph/configs/mask_rcnn/mask_rcnn_x101_vd_64x4d_fpn_2x_coco.yml

@@ -2,7 +2,7 @@ _BASE_: [
   'mask_rcnn_r50_fpn_1x_coco.yml',
 ]
 
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_vd_64x4d_pretrained.tar
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNeXt101_vd_64x4d_pretrained.pdparams
 weights: output/mask_rcnn_x101_vd_64x4d_fpn_2x_coco/model_final
 
 ResNet:

dygraph/configs/slim/prune/yolov3_prune_fpgm.yml

 # Weights of yolov3_mobilenet_v1_voc
-pretrain_weights: https://paddlemodels.bj.bcebos.com/object_detection/dygraph/yolov3_mobilenet_v1_270e_voc.pdparams
+pretrain_weights: https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_270e_voc.pdparams
 weight_type: resume
 slim: Pruner
 

dygraph/configs/slim/prune/yolov3_prune_l1_norm.yml

 # Weights of yolov3_mobilenet_v1_voc
-pretrain_weights: https://paddlemodels.bj.bcebos.com/object_detection/dygraph/yolov3_mobilenet_v1_270e_voc.pdparams
+pretrain_weights: https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_270e_voc.pdparams
 weight_type: resume
 slim: Pruner
 

dygraph/configs/slim/quant/yolov3_mobilenet_v1_qat.yml

 # Weights of yolov3_mobilenet_v1_coco
-pretrain_weights: https://paddlemodels.bj.bcebos.com/object_detection/dygraph/yolov3_mobilenet_v1_270e_coco.pdparams
+pretrain_weights: https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_270e_coco.pdparams
 weight_type: resume
 slim: QAT
 

dygraph/configs/slim/quant/yolov3_mobilenet_v3_qat.yml

 # Weights of yolov3_mobilenet_v3_coco
-pretrain_weights: https://paddlemodels.bj.bcebos.com/object_detection/dygraph/yolov3_mobilenet_v3_large_270e_coco.pdparams
+pretrain_weights: https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v3_large_270e_coco.pdparams
 weight_type: resume
 slim: QAT
 

dygraph/configs/ssd/_base_/ssd_vgg16_300.yml

 architecture: SSD
-pretrain_weights: https://paddlemodels.bj.bcebos.com/object_detection/dygraph/VGG16_caffe_pretrained.pdparams
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/VGG16_caffe_pretrained.pdparams
 
 # Model Achitecture
 SSD:

dygraph/configs/yolov3/yolov3_mobilenet_v1_roadsign.yml

@@ -4,7 +4,7 @@ _BASE_: [
   '_base_/yolov3_mobilenet_v1.yml',
   '_base_/yolov3_reader.yml',
 ]
-pretrain_weights: https://paddlemodels.bj.bcebos.com/object_detection/dygraph/yolov3_mobilenet_v1_270e_coco.pdparams
+pretrain_weights: https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_270e_coco.pdparams
 norm_type: sync_bn
 weights: output/yolov3_mobilenet_v1_roadsign/model_final
 metric: VOC

dygraph/ppdet/engine/trainer.py

@@ -58,7 +58,7 @@ class Trainer(object):
         # model slim build
         if 'slim' in cfg and cfg.slim:
             if self.mode == 'train':
-                self.load_weights(cfg.pretrain_weights, cfg.weight_type)
+                self.load_weights(cfg.pretrain_weights)
             self.slim = create(cfg.slim)
             self.slim(self.model)
 
@@ -174,17 +174,14 @@ class Trainer(object):
                     "metrics shoule be instances of subclass of Metric"
         self._metrics.extend(metrics)
 
-    def load_weights(self, weights, weight_type='pretrain'):
-        assert weight_type in ['pretrain', 'resume', 'finetune'], \
-                "weight_type can only be 'pretrain', 'resume', 'finetune'"
-        if weight_type == 'resume':
+    def load_weights(self, weights):
+        self.start_epoch = 0
+        load_pretrain_weight(self.model, weights)
+        logger.debug("Load weights {} to start training".format(weights))
+
+    def resume_weights(self, weights):
         self.start_epoch = load_weight(self.model, weights, self.optimizer)
         logger.debug("Resume weights of epoch {}".format(self.start_epoch))
-        else:
-            self.start_epoch = 0
-            load_pretrain_weight(self.model, weights, weight_type)
-            logger.debug("Load {} weights {} to start training".format(
-                weight_type, weights))
 
     def train(self, validate=False):
         assert self.mode == 'train', "Model not in 'train' mode"

dygraph/ppdet/utils/checkpoint.py

@@ -123,8 +123,7 @@ def load_weight(model, weight, optimizer=None):
     assert incorrect_keys == 0, "Load weight {} incorrectly, \
             {} keys unmatched, please check again.".format(weight,
                                                            incorrect_keys)
-    logger.info('Finish loading model weight parameter: {}'.format(
-        pdparam_path))
+    logger.info('Finish resuming model weights: {}'.format(pdparam_path))
 
     model.set_dict(model_weight)
 
@@ -142,9 +141,7 @@ def load_weight(model, weight, optimizer=None):
     return last_epoch
 
 
-def load_pretrain_weight(model, pretrain_weight, weight_type='pretrain'):
-
-    assert weight_type in ['pretrain', 'finetune']
+def load_pretrain_weight(model, pretrain_weight):
     if is_url(pretrain_weight):
         pretrain_weight = get_weights_path_dist(pretrain_weight)
 
@@ -158,24 +155,27 @@ def load_pretrain_weight(model, pretrain_weight, weight_type='pretrain'):
 
     model_dict = model.state_dict()
 
-    param_state_dict = paddle.load(path + '.pdparams')
-    if weight_type == 'pretrain':
-        model.backbone.set_dict(param_state_dict)
-    else:
+    weights_path = path + '.pdparams'
+    param_state_dict = paddle.load(weights_path)
     ignore_set = set()
+    lack_modules = set()
     for name, weight in model_dict.items():
         if name in param_state_dict.keys():
             if weight.shape != list(param_state_dict[name].shape):
                 logger.info(
-                        '{} not used, shape {} unmatched with {} in model.'.
-                        format(name,
-                               list(param_state_dict[name].shape),
-                               weight.shape))
+                    '{} not used, shape {} unmatched with {} in model.'.format(
+                        name, list(param_state_dict[name].shape), weight.shape))
                 param_state_dict.pop(name, None)
         else:
-                logger.info('Lack weight: {}'.format(name))
+            lack_modules.add(name.split('.')[0])
+            logger.debug('Lack weights: {}'.format(name))
+
+    if len(lack_modules) > 0:
+        logger.info('Lack weights of modules: {}'.format(', '.join(
+            list(lack_modules))))
+
     model.set_dict(param_state_dict)
-    return
+    logger.info('Finish loading model weights: {}'.format(weights_path))
 
 
 def save_model(model, optimizer, save_dir, save_name, last_epoch):

dygraph/ppdet/utils/download.py

@@ -92,7 +92,7 @@ DATASETS = {
 
 DOWNLOAD_RETRY_LIMIT = 3
 
-PPDET_WEIGHTS_DOWNLOAD_URL_PREFIX = 'https://paddlemodels.bj.bcebos.com/object_detection/'
+PPDET_WEIGHTS_DOWNLOAD_URL_PREFIX = 'https://paddledet.bj.bcebos.com/'
 
 
 def parse_url(url):

dygraph/tools/eval.py

@@ -93,7 +93,7 @@ def run(FLAGS, cfg):
     trainer = Trainer(cfg, mode='eval')
 
     # load weights
-    trainer.load_weights(cfg.weights, 'resume')
+    trainer.load_weights(cfg.weights)
 
     # training
     trainer.evaluate()

dygraph/tools/export_model.py

@@ -62,7 +62,7 @@ def run(FLAGS, cfg):
     trainer = Trainer(cfg, mode='test')
 
     # load weights
-    trainer.load_weights(cfg.weights, 'resume')
+    trainer.load_weights(cfg.weights)
 
     # export model
     trainer.export(FLAGS.output_dir)

dygraph/tools/infer.py

@@ -114,7 +114,7 @@ def run(FLAGS, cfg):
     trainer = Trainer(cfg, mode='test')
 
     # load weights
-    trainer.load_weights(cfg.weights, 'resume')
+    trainer.load_weights(cfg.weights)
 
     # get inference images
     images = get_test_images(FLAGS.infer_dir, FLAGS.infer_img)

dygraph/tools/train.py

@@ -43,17 +43,13 @@ logger = setup_logger('train')
 
 def parse_args():
     parser = cli.ArgsParser()
-    parser.add_argument(
-        "--weight_type",
-        default='pretrain',
-        type=str,
-        help="Loading Checkpoints only support 'pretrain', 'finetune', 'resume'."
-    )
     parser.add_argument(
         "--eval",
         action='store_true',
         default=False,
         help="Whether to perform evaluation in train")
+    parser.add_argument(
+        "-r", "--resume", default=None, help="weights path for resume")
     parser.add_argument(
         "--slim_config",
         default=None,
@@ -101,8 +97,10 @@ def run(FLAGS, cfg):
     trainer = Trainer(cfg, mode='train')
 
     # load weights
-    if not FLAGS.slim_config and 'pretrain_weights' in cfg and cfg.pretrain_weights:
-        trainer.load_weights(cfg.pretrain_weights, FLAGS.weight_type)
+    if FLAGS.resume is not None:
+        trainer.resume_weights(FLAGS.resume)
+    elif not FLAGS.slim_config and 'pretrain_weights' in cfg and cfg.pretrain_weights:
+        trainer.load_weights(cfg.pretrain_weights)
 
     # training
     trainer.train(FLAGS.eval)
@@ -120,8 +118,6 @@ def main():
     if FLAGS.slim_config:
         slim_cfg = load_config(FLAGS.slim_config)
         merge_config(slim_cfg)
-        if 'weight_type' not in cfg:
-            cfg.weight_type = FLAGS.weight_type
     check.check_config(cfg)
     check.check_gpu(cfg.use_gpu)
     check.check_version()