support VGG16/19 for kunlun (#561)

configs/quick_start/VGG16_finetune_kunlun.yaml

+mode: 'train'
+ARCHITECTURE:
+    name: 'VGG16'
+    params:
+        stop_grad_layers: 5
+pretrained_model: "./pretrained/VGG16_pretrained"
+model_save_dir: "./output/"
+classes_num: 102
+total_images: 1020
+save_interval: 1
+validate: True
+valid_interval: 1
+epochs: 20
+topk: 5
+image_shape: [3, 224, 224]
+
+LEARNING_RATE:
+    function: 'Cosine'
+    params:
+        lr: 0.0005
+
+OPTIMIZER:
+    function: 'Momentum'
+    params:
+        momentum: 0.9
+    regularizer:
+        function: 'L2'
+        factor: 0.00001
+
+TRAIN:
+    batch_size: 20
+    num_workers: 0
+    file_list: "./dataset/flowers102/train_list.txt"
+    data_dir: "./dataset/flowers102/"
+    shuffle_seed: 0
+    transforms:
+        - DecodeImage:
+            to_rgb: True
+            to_np: False
+            channel_first: False
+        - RandCropImage:
+            size: 224
+        - RandFlipImage:
+            flip_code: 1
+        - NormalizeImage:
+            scale: 1./255.
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+        - ToCHWImage:
+
+VALID:
+    batch_size: 20
+    num_workers: 0
+    file_list: "./dataset/flowers102/val_list.txt"
+    data_dir: "./dataset/flowers102/"
+    shuffle_seed: 0
+    transforms:
+        - DecodeImage:
+            to_rgb: True
+            to_np: False
+            channel_first: False
+        - ResizeImage:
+            resize_short: 256
+        - CropImage:
+            size: 224
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+        - ToCHWImage:

configs/quick_start/VGG19_finetune_kunlun.yaml

+mode: 'train'
+ARCHITECTURE:
+    name: 'VGG19'
+    params:
+        stop_grad_layers: 5
+pretrained_model: "./pretrained/VGG19_pretrained"
+model_save_dir: "./output/"
+classes_num: 102
+total_images: 1020
+save_interval: 1
+validate: True
+valid_interval: 1
+epochs: 20
+topk: 5
+image_shape: [3, 224, 224]
+
+LEARNING_RATE:
+    function: 'Cosine'
+    params:
+        lr: 0.0005
+
+OPTIMIZER:
+    function: 'Momentum'
+    params:
+        momentum: 0.9
+    regularizer:
+        function: 'L2'
+        factor: 0.00001
+
+TRAIN:
+    batch_size: 20
+    num_workers: 0
+    file_list: "./dataset/flowers102/train_list.txt"
+    data_dir: "./dataset/flowers102/"
+    shuffle_seed: 0
+    transforms:
+        - DecodeImage:
+            to_rgb: True
+            to_np: False
+            channel_first: False
+        - RandCropImage:
+            size: 224
+        - RandFlipImage:
+            flip_code: 1
+        - NormalizeImage:
+            scale: 1./255.
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+        - ToCHWImage:
+
+VALID:
+    batch_size: 20
+    num_workers: 0
+    file_list: "./dataset/flowers102/val_list.txt"
+    data_dir: "./dataset/flowers102/"
+    shuffle_seed: 0
+    transforms:
+        - DecodeImage:
+            to_rgb: True
+            to_np: False
+            channel_first: False
+        - ResizeImage:
+            resize_short: 256
+        - CropImage:
+            size: 224
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+        - ToCHWImage:

docs/zh_CN/extension/train_on_xpu.md

@@ -23,3 +23,10 @@
 * 命令：
 
 ```python3.7 tools/static/train.py -c configs/quick_start/HRNet_W18_C_finetune.yaml -o is_distributed=False -o use_cpu=False  -o use_xpu=True -o use_gpu=False```
+
+
+### VGG16/19
+* 命令：
+
+```python3.7 tools/static/train.py -c configs/quick_start/VGG16_finetune_kunlun.yaml -o use_gpu=False -o use_cpu=False -o use_xpu=True -o is_distributed=False```
+```python3.7 tools/static/train.py -c configs/quick_start/VGG19_finetune_kunlun.yaml -o use_gpu=False -o use_cpu=False -o use_xpu=True -o is_distributed=False```