del batchsize (#1532)

修复animeGan不应支持batch_size问题。

modules/thirdparty/image/Image_gan/style_transfer/animegan_v1_hayao_60/README.md

@@ -23,7 +23,6 @@ def style_transfer(
     self,
     images=None,
     paths=None,
-    batch_size=1,
     output_dir='output',
     visualization=False,
     min_size=32,
@@ -43,7 +42,6 @@ def style_transfer(
 
 * images (list\[numpy.ndarray\]): 图片数据，ndarray.shape 为 \[H, W, C\]，默认为 None；
 * paths (list\[str\]): 图片的路径，默认为 None；
-* batch\_size (int): batch 的大小，默认设为 1；
 * visualization (bool): 是否将识别结果保存为图片文件，默认设为 False；
 * output\_dir (str): 图片的保存路径，默认设为 output；
 * min\_size (int): 输入图片的短边最小尺寸，默认设为 32；

modules/thirdparty/image/Image_gan/style_transfer/animegan_v1_hayao_60/module.py

@@ -28,13 +28,12 @@ class Animegan_V1_Hayao_60(Module):
     def style_transfer(self,
                        images=None,
                        paths=None,
-                       batch_size=1,
                        output_dir='output',
                        visualization=False,
                        min_size=32,
                        max_size=1024):
         # 加载数据处理器
-        processor = Processor(images, paths, batch_size, output_dir, min_size, max_size)
+        processor = Processor(images=images, paths=paths, batch_size=1, output_dir=output_dir, min_size=min_size, max_size=max_size)
 
         # 模型预测
         outputs = self.model.predict(processor.input_datas)

modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_hayao_64/README.md

@@ -23,7 +23,6 @@ def style_transfer(
     self,
     images=None,
     paths=None,
-    batch_size=1,
     output_dir='output',
     visualization=False,
     min_size=32,
@@ -43,7 +42,6 @@ def style_transfer(
 
 * images (list\[numpy.ndarray\]): 图片数据，ndarray.shape 为 \[H, W, C\]，默认为 None；
 * paths (list\[str\]): 图片的路径，默认为 None；
-* batch\_size (int): batch 的大小，默认设为 1；
 * visualization (bool): 是否将识别结果保存为图片文件，默认设为 False；
 * output\_dir (str): 图片的保存路径，默认设为 output；
 * min\_size (int): 输入图片的短边最小尺寸，默认设为 32；

modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_hayao_64/module.py

@@ -28,13 +28,12 @@ class Animegan_V2_Hayao_64(Module):
     def style_transfer(self,
                        images=None,
                        paths=None,
-                       batch_size=1,
                        output_dir='output',
                        visualization=False,
                        min_size=32,
                        max_size=1024):
         # 加载数据处理器
-        processor = Processor(images, paths, batch_size, output_dir, min_size, max_size)
+        processor = Processor(images=images, paths=paths, batch_size=1, output_dir=output_dir, min_size=min_size, max_size=max_size)
 
         # 模型预测
         outputs = self.model.predict(processor.input_datas)

modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_hayao_99/README.md

@@ -23,7 +23,6 @@ def style_transfer(
     self,
     images=None,
     paths=None,
-    batch_size=1,
     output_dir='output',
     visualization=False,
     min_size=32,
@@ -43,7 +42,6 @@ def style_transfer(
 
 * images (list\[numpy.ndarray\]): 图片数据，ndarray.shape 为 \[H, W, C\]，默认为 None；
 * paths (list\[str\]): 图片的路径，默认为 None；
-* batch\_size (int): batch 的大小，默认设为 1；
 * visualization (bool): 是否将识别结果保存为图片文件，默认设为 False；
 * output\_dir (str): 图片的保存路径，默认设为 output；
 * min\_size (int): 输入图片的短边最小尺寸，默认设为 32；

modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_hayao_99/module.py

@@ -28,13 +28,12 @@ class Animegan_V2_Hayao_99(Module):
     def style_transfer(self,
                        images=None,
                        paths=None,
-                       batch_size=1,
                        output_dir='output',
                        visualization=False,
                        min_size=32,
                        max_size=1024):
         # 加载数据处理器
-        processor = Processor(images, paths, batch_size, output_dir, min_size, max_size)
+        processor = Processor(images=images, paths=paths, batch_size=1, output_dir=output_dir, min_size=min_size, max_size=max_size)
 
         # 模型预测
         outputs = self.model.predict(processor.input_datas)

modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_54/README.md

@@ -23,7 +23,6 @@ def style_transfer(
     self,
     images=None,
     paths=None,
-    batch_size=1,
     output_dir='output',
     visualization=False,
     min_size=32,
@@ -43,7 +42,6 @@ def style_transfer(
 
 * images (list\[numpy.ndarray\]): 图片数据，ndarray.shape 为 \[H, W, C\]，默认为 None；
 * paths (list\[str\]): 图片的路径，默认为 None；
-* batch\_size (int): batch 的大小，默认设为 1；
 * visualization (bool): 是否将识别结果保存为图片文件，默认设为 False；
 * output\_dir (str): 图片的保存路径，默认设为 output；
 * min\_size (int): 输入图片的短边最小尺寸，默认设为 32；

modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_54/module.py

@@ -28,13 +28,12 @@ class Animegan_V2_Paprika_54(Module):
     def style_transfer(self,
                        images=None,
                        paths=None,
-                       batch_size=1,
                        output_dir='output',
                        visualization=False,
                        min_size=32,
                        max_size=1024):
         # 加载数据处理器
-        processor = Processor(images, paths, batch_size, output_dir, min_size, max_size)
+        processor = Processor(images=images, paths=paths, batch_size=1, output_dir=output_dir, min_size=min_size, max_size=max_size)
 
         # 模型预测
         outputs = self.model.predict(processor.input_datas)

modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_74/README.md

@@ -23,7 +23,6 @@ def style_transfer(
     self,
     images=None,
     paths=None,
-    batch_size=1,
     output_dir='output',
     visualization=False,
     min_size=32,
@@ -43,7 +42,6 @@ def style_transfer(
 
 * images (list\[numpy.ndarray\]): 图片数据，ndarray.shape 为 \[H, W, C\]，默认为 None；
 * paths (list\[str\]): 图片的路径，默认为 None；
-* batch\_size (int): batch 的大小，默认设为 1；
 * visualization (bool): 是否将识别结果保存为图片文件，默认设为 False；
 * output\_dir (str): 图片的保存路径，默认设为 output；
 * min\_size (int): 输入图片的短边最小尺寸，默认设为 32；

modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_74/module.py

@@ -28,13 +28,12 @@ class Animegan_V2_Paprika_74(Module):
     def style_transfer(self,
                        images=None,
                        paths=None,
-                       batch_size=1,
                        output_dir='output',
                        visualization=False,
                        min_size=32,
                        max_size=1024):
         # 加载数据处理器
-        processor = Processor(images, paths, batch_size, output_dir, min_size, max_size)
+        processor = Processor(images=images, paths=paths, batch_size=1, output_dir=output_dir, min_size=min_size, max_size=max_size)
 
         # 模型预测
         outputs = self.model.predict(processor.input_datas)

modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_97/README.md

@@ -23,7 +23,6 @@ def style_transfer(
     self,
     images=None,
     paths=None,
-    batch_size=1,
     output_dir='output',
     visualization=False,
     min_size=32,
@@ -43,7 +42,6 @@ def style_transfer(
 
 * images (list\[numpy.ndarray\]): 图片数据，ndarray.shape 为 \[H, W, C\]，默认为 None；
 * paths (list\[str\]): 图片的路径，默认为 None；
-* batch\_size (int): batch 的大小，默认设为 1；
 * visualization (bool): 是否将识别结果保存为图片文件，默认设为 False；
 * output\_dir (str): 图片的保存路径，默认设为 output；
 * min\_size (int): 输入图片的短边最小尺寸，默认设为 32；

modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_97/module.py

@@ -28,13 +28,12 @@ class Animegan_V2_Paprika_97(Module):
     def style_transfer(self,
                        images=None,
                        paths=None,
-                       batch_size=1,
                        output_dir='output',
                        visualization=False,
                        min_size=32,
                        max_size=1024):
         # 加载数据处理器
-        processor = Processor(images, paths, batch_size, output_dir, min_size, max_size)
+        processor = Processor(images=images, paths=paths, batch_size=1, output_dir=output_dir, min_size=min_size, max_size=max_size)
 
         # 模型预测
         outputs = self.model.predict(processor.input_datas)

modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_98/README.md

@@ -23,7 +23,6 @@ def style_transfer(
     self,
     images=None,
     paths=None,
-    batch_size=1,
     output_dir='output',
     visualization=False,
     min_size=32,
@@ -43,7 +42,6 @@ def style_transfer(
 
 * images (list\[numpy.ndarray\]): 图片数据，ndarray.shape 为 \[H, W, C\]，默认为 None；
 * paths (list\[str\]): 图片的路径，默认为 None；
-* batch\_size (int): batch 的大小，默认设为 1；
 * visualization (bool): 是否将识别结果保存为图片文件，默认设为 False；
 * output\_dir (str): 图片的保存路径，默认设为 output；
 * min\_size (int): 输入图片的短边最小尺寸，默认设为 32；

modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_paprika_98/module.py

@@ -28,13 +28,12 @@ class Animegan_V2_Paprika_98(Module):
     def style_transfer(self,
                        images=None,
                        paths=None,
-                       batch_size=1,
                        output_dir='output',
                        visualization=False,
                        min_size=32,
                        max_size=1024):
         # 加载数据处理器
-        processor = Processor(images, paths, batch_size, output_dir, min_size, max_size)
+        processor = Processor(images=images, paths=paths, batch_size=1, output_dir=output_dir, min_size=min_size, max_size=max_size)
 
         # 模型预测
         outputs = self.model.predict(processor.input_datas)

modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_shinkai_33/README.md

@@ -23,7 +23,6 @@ def style_transfer(
     self,
     images=None,
     paths=None,
-    batch_size=1,
     output_dir='output',
     visualization=False,
     min_size=32,
@@ -43,7 +42,6 @@ def style_transfer(
 
 * images (list\[numpy.ndarray\]): 图片数据，ndarray.shape 为 \[H, W, C\]，默认为 None；
 * paths (list\[str\]): 图片的路径，默认为 None；
-* batch\_size (int): batch 的大小，默认设为 1；
 * visualization (bool): 是否将识别结果保存为图片文件，默认设为 False；
 * output\_dir (str): 图片的保存路径，默认设为 output；
 * min\_size (int): 输入图片的短边最小尺寸，默认设为 32；

modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_shinkai_33/module.py

@@ -28,13 +28,12 @@ class Animegan_V2_Shinkai_33(Module):
     def style_transfer(self,
                        images=None,
                        paths=None,
-                       batch_size=1,
                        output_dir='output',
                        visualization=False,
                        min_size=32,
                        max_size=1024):
         # 加载数据处理器
-        processor = Processor(images, paths, batch_size, output_dir, min_size, max_size)
+        processor = Processor(images=images, paths=paths, batch_size=1, output_dir=output_dir, min_size=min_size, max_size=max_size)
 
         # 模型预测
         outputs = self.model.predict(processor.input_datas)

modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_shinkai_53/README.md

@@ -23,7 +23,6 @@ def style_transfer(
     self,
     images=None,
     paths=None,
-    batch_size=1,
     output_dir='output',
     visualization=False,
     min_size=32,
@@ -43,7 +42,6 @@ def style_transfer(
 
 * images (list\[numpy.ndarray\]): 图片数据，ndarray.shape 为 \[H, W, C\]，默认为 None；
 * paths (list\[str\]): 图片的路径，默认为 None；
-* batch\_size (int): batch 的大小，默认设为 1；
 * visualization (bool): 是否将识别结果保存为图片文件，默认设为 False；
 * output\_dir (str): 图片的保存路径，默认设为 output；
 * min\_size (int): 输入图片的短边最小尺寸，默认设为 32；

modules/thirdparty/image/Image_gan/style_transfer/animegan_v2_shinkai_53/module.py

@@ -28,13 +28,12 @@ class Animegan_V2_Shinkai_53(Module):
     def style_transfer(self,
                        images=None,
                        paths=None,
-                       batch_size=1,
                        output_dir='output',
                        visualization=False,
                        min_size=32,
                        max_size=1024):
         # 加载数据处理器
-        processor = Processor(images, paths, batch_size, output_dir, min_size, max_size)
+        processor = Processor(images=images, paths=paths, batch_size=1, output_dir=output_dir, min_size=min_size, max_size=max_size)
 
         # 模型预测
         outputs = self.model.predict(processor.input_datas)