fix bugs and update docs (#187)

configs/animeganv2_pretrain.yaml

@@ -42,20 +42,21 @@ dataset:
   test:
     name: SingleDataset
     dataroot: data/animedataset/test/test_photo
-    max_dataset_size: inf
-    direction: BtoA
-    input_nc: 3
-    output_nc: 3
-    serial_batches: False
-    pool_size: 50
-    transforms:
-      - name: Resize
+    preprocess:
+      - name: LoadImageFromFile
+        key: A
+      - name: Transforms
+        input_keys: [A]
+        pipeline:
+          - name: ResizeToScale
             size: [256, 256]
-        interpolation: "bicubic" #cv2.INTER_CUBIC
+            scale: 32
+            interpolation: bilinear
           - name: Transpose
           - name: Normalize
             mean: [127.5, 127.5, 127.5]
             std: [127.5, 127.5, 127.5]
+            keys: [image, image]
 
 lr_scheduler:
   name: LinearDecay

configs/ugatit_photo2cartoon.yaml

@@ -78,7 +78,7 @@ dataset:
         key: A
       - name: LoadImageFromFile
         key: B
-      - name: Transfroms
+      - name: Transforms
         input_keys: [A, B]
         pipeline:
           - name: Resize

docs/en_US/tutorials/animegan.md

@@ -70,7 +70,7 @@ animedataset
 
   2.  After the warmup, we strat to training GAN.:
   **NOTE：** you must modify the `configs/animeganv2.yaml > pretrain_ckpt ` parameter first! ensure the GAN can reuse the warmup generator model.
-  Set the `batch size=4` and the `learning rate=0.00002`. Train 30 epochs on a GTX2060S GPU to reproduce the result. For other hyperparameters, please refer to `configs/animeganv2.yaml`.
+  Set the `batch size=4` and the `learning rate=0.0002`. Train 30 epochs on a GTX2060S GPU to reproduce the result. For other hyperparameters, please refer to `configs/animeganv2.yaml`.
   ```sh
   python tools/main.py --config-file configs/animeganv2.yaml
   ```

docs/zh_CN/tutorials/animegan.md

@@ -70,7 +70,7 @@ animedataset
 
   1.  预热模型完成后，训练风格迁移模型:
   **注意：** 必须先修改在`configs/animeganv2.yaml`中的`pretrain_ckpt`参数，确保指向正确的 **预热模型权重路径**
-  设置`batch size=4`，`learning rate=0.00002`，在一个  GTX2060S GPU上训练30个epoch即可获得较好的效果，其他超参数请参考`configs/animeganv2.yaml`。
+  设置`batch size=4`，`learning rate=0.0002`，在一个  GTX2060S GPU上训练30个epoch即可获得较好的效果，其他超参数请参考`configs/animeganv2.yaml`。
 
   ```sh
   python tools/main.py --config-file configs/animeganv2.yaml