From f1d4dc6d3acdb81f1bf01c645055ecd40944e2ae Mon Sep 17 00:00:00 2001
From: haoyuying <18844182690@163.com>
Date: Sun, 27 Sep 2020 17:36:07 +0800
Subject: [PATCH] revise style transfer second time

---
 .../modules/image/style_transfer/msgnet/module.py      | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

diff --git a/hub_module/modules/image/style_transfer/msgnet/module.py b/hub_module/modules/image/style_transfer/msgnet/module.py
index 590b432f..3bb5cb62 100644
--- a/hub_module/modules/image/style_transfer/msgnet/module.py
+++ b/hub_module/modules/image/style_transfer/msgnet/module.py
@@ -187,8 +187,7 @@ class Inspiration(nn.Layer):
         return x
 
     def __repr__(self):
-        return self.__class__.__name__ + '(' \
-               + 'N x ' + str(self.C) + ')'
+        return self.__class__.__name__ + '(' + 'N x ' + str(self.C) + ')'
 
 
 class Vgg16(nn.Layer):
@@ -282,6 +281,7 @@ class MSGNet(nn.Layer):
         block = Bottleneck
         upblock = UpBottleneck
         expansion = 4
+        model = []
 
         model1 = [
             ConvLayer(input_nc, 64, kernel_size=7, stride=1),
@@ -290,14 +290,12 @@ class MSGNet(nn.Layer):
             block(64, 32, 2, 1, norm_layer),
             block(32 * expansion, ngf, 2, 1, norm_layer)
         ]
-
         self.model1 = nn.Sequential(*tuple(model1))
-
-        model = []
         model += model1
 
         self.ins = Inspiration(ngf * expansion)
         model.append(self.ins)
+
         for i in range(n_blocks):
             model += [block(ngf * expansion, ngf, 1, None, norm_layer)]
 
@@ -308,6 +306,7 @@ class MSGNet(nn.Layer):
             nn.ReLU(),
             ConvLayer(16 * expansion, output_nc, kernel_size=7, stride=1)
         ]
+
         model = tuple(model)
         self.model = nn.Sequential(*model)
 
@@ -330,7 +329,6 @@ class MSGNet(nn.Layer):
                     model_dict[key] = paddle.ones(shape=model_dict[key].shape, dtype='float32')
             self.set_dict(model_dict)
             print("load pretrained checkpoint success")
-
         self._vgg = None
 
     def transform(self, path: str):
-- 
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