support load pretrain from url

ppcls/configs/Products/ResNet50_vd_Inshop.yaml

 # global configs
 Global:
   checkpoints: null
-# please download pretrained model via this link:
-# https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/pretrain/product_ResNet50_vd_Aliproduct_v1.0_pretrained.pdparams
-  pretrained_model: product_ResNet50_vd_Aliproduct_v1.0_pretrained
+  pretrained_model: "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/pretrain/product_ResNet50_vd_Aliproduct_v1.0_pretrained.pdparams"
   output_dir: ./output/
   device: gpu
   save_interval: 10

ppcls/configs/Products/ResNet50_vd_SOP.yaml

 # global configs
 Global:
   checkpoints: null
-# please download pretrained model via this link:
-# https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/pretrain/product_ResNet50_vd_Aliproduct_v1.0_pretrained.pdparams
-  pretrained_model: product_ResNet50_vd_Aliproduct_v1.0_pretrained
+  pretrained_model: "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/pretrain/product_ResNet50_vd_Aliproduct_v1.0_pretrained.pdparams"
   output_dir: ./output/
   device: gpu
   save_interval: 10

ppcls/engine/trainer.py

@@ -39,7 +39,7 @@ from ppcls.arch import apply_to_static
 from ppcls.loss import build_loss
 from ppcls.metric import build_metrics
 from ppcls.optimizer import build_optimizer
-from ppcls.utils.save_load import load_dygraph_pretrain
+from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 from ppcls.utils.save_load import init_model
 from ppcls.utils import save_load
 
@@ -77,8 +77,12 @@ class Trainer(object):
         apply_to_static(self.config, self.model)
 
         if self.config["Global"]["pretrained_model"] is not None:
-            load_dygraph_pretrain(self.model,
-                                  self.config["Global"]["pretrained_model"])
+            if self.config["Global"]["pretrained_model"].startswith("http"):
+                load_dygraph_pretrain_from_url(
+                    self.model, self.config["Global"]["pretrained_model"])
+            else:
+                load_dygraph_pretrain(
+                    self.model, self.config["Global"]["pretrained_model"])
 
         if self.config["Global"]["distributed"]:
             self.model = paddle.DataParallel(self.model)
@@ -362,6 +366,7 @@ class Trainer(object):
             if self.is_rec:
                 out = self.model(batch[0], batch[1])
             else:
+                self.model.eval()
                 out = self.model(batch[0])
             # calc loss
             if self.eval_loss_func is not None: