debug (#2934)

* debug: fix model name

* fix

* fix Infer.transforms.ResizeImage

docs/zh_CN/models/ImageNet1k/MixNet.md

@@ -38,7 +38,7 @@ MixNet 是谷歌出的一篇关于轻量级网络的文章，主要工作就在
 ### 1.2 模型指标
 
 | Models | Top1 | Top5 | Reference<br>top1| Reference<br>top5 | FLOPs<br>(M) | Params<br/>(M) |
-|:--:|:--:|:--:|:--:|:--:|----|
+|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
 | MixNet_S | 76.28 | 92.99 | 75.8 | - | 252.977 | 4.167 |
 | MixNet_M | 77.67 | 93.64 | 77.0 | - | 357.119 | 5.065 |
 | MixNet_L | 78.60 | 94.37 | 78.9 | - | 579.017 | 7.384 |

ppcls/configs/CLIP/CLIP_vit_base_patch16_224_finetune.yaml

@@ -133,7 +133,9 @@ Infer:
         to_rgb: True
         channel_first: False
     - ResizeImage:
-        resize_short: 256
+        resize_short: 224
+        interpolation: bicubic
+        backend: pil
     - CropImage:
         size: 224
     - NormalizeImage:

ppcls/configs/CLIP/CLIP_vit_large_patch14_224_finetune.yaml

@@ -133,7 +133,9 @@ Infer:
         to_rgb: True
         channel_first: False
     - ResizeImage:
-        resize_short: 256
+        resize_short: 224
+        interpolation: bicubic
+        backend: pil
     - CropImage:
         size: 224
     - NormalizeImage:

ppcls/loss/tripletangularmarginloss.py

@@ -59,7 +59,7 @@ class TripletAngularMarginLoss(nn.Layer):
         """
         Args:
             inputs: feature matrix with shape (batch_size, feat_dim)
-            target: ground truth labels with shape (num_classes)
+            target: ground truth labels with shape (batch_size)
         """
         inputs = input[self.feature_from]
 
@@ -160,7 +160,7 @@ class TripletAngularMarginLoss_XBM(TripletAngularMarginLoss):
         """
         Args:
             inputs: feature matrix with shape (batch_size, feat_dim)
-            target: ground truth labels with shape (num_classes)
+            target: ground truth labels with shape (batch_size)
         """
         feats = input[self.feature_from]
         if self.normalize_feature: