fix fp16 inference

deploy/configs/inference_cls.yaml

@@ -22,6 +22,7 @@ PreProcess:
         mean: [0.485, 0.456, 0.406]
         std: [0.229, 0.224, 0.225]
         order: ''
+        channel_num: 3
     - ToCHWImage:
 PostProcess:
   main_indicator: Topk

deploy/configs/inference_cls_ch4.yaml

+Global:
+  infer_imgs: "./images/ILSVRC2012_val_00000010.jpeg"
+  inference_model_dir: "./models"
+  batch_size: 1
+  use_gpu: True
+  enable_mkldnn: True
+  cpu_num_threads: 10
+  enable_benchmark: True
+  use_fp16: False
+  ir_optim: True
+  use_tensorrt: False
+  gpu_mem: 8000
+  enable_profile: False
+PreProcess:
+  transform_ops:
+    - ResizeImage:
+        resize_short: 256
+    - CropImage:
+        size: 224
+    - NormalizeImage:
+        scale: 0.00392157
+        mean: [0.485, 0.456, 0.406]
+        std: [0.229, 0.224, 0.225]
+        order: ''
+        channel_num: 4
+    - ToCHWImage:
+PostProcess:
+  main_indicator: Topk
+  Topk:
+    topk: 5
+    class_id_map_file: "../ppcls/utils/imagenet1k_label_list.txt"
+  SavePreLabel:
+    save_dir: ./pre_label/

ppcls/configs/ImageNet/ResNet/ResNet50_fp16.yaml

@@ -28,6 +28,7 @@ Arch:
   name: ResNet50
   class_num: 1000
   input_image_channel: *image_channel
+  data_format: "NHWC"
  
 # loss function config for traing/eval process
 Loss:
@@ -41,7 +42,7 @@ Loss:
 Optimizer:
   name: Momentum
   momentum: 0.9
-  multi_precision: False # *use_pure_fp16
+  multi_precision: *use_pure_fp16
   lr:
     name: Piecewise
     learning_rate: 0.1