Fix some minor bugs to adapt to paddle2.0rc. (#431)

* Update mobilenet_v3.py

* Update densenet.py

* Update resnest.py

* Update hrnet.py

* Update vgg.py

* Update xception.py

ppcls/modeling/architectures/densenet.py

@@ -278,7 +278,7 @@ class DenseNet(nn.Layer):
 
         conv = self.batch_norm(conv)
         y = self.pool2d_avg(conv)
-        y = paddle.reshape(y, shape=[0, -1])
+        y = paddle.reshape(y, shape=[-1, y.shape[1]])
         y = self.out(y)
         return y
 

ppcls/modeling/architectures/hrnet.py

@@ -657,7 +657,7 @@ class HRNet(nn.Layer):
 
         y = self.conv_last(y)
         y = self.pool2d_avg(y)
-        y = paddle.reshape(y, shape=[0, -1])
+        y = paddle.reshape(y, shape=[-1, y.shape[1]])
         y = self.out(y)
         return y
 

ppcls/modeling/architectures/mobilenet_v3.py

@@ -306,7 +306,7 @@ class SEModule(nn.Layer):
         outputs = F.relu(outputs)
         outputs = self.conv2(outputs)
         outputs = hard_sigmoid(outputs)
-        return paddle.multiply(x=inputs, y=outputs, axis=0)
+        return paddle.multiply(x=inputs, y=outputs)
 
 
 def MobileNetV3_small_x0_35(**args):

ppcls/modeling/architectures/resnest.py

@@ -85,11 +85,11 @@ class rSoftmax(nn.Layer):
             x = paddle.reshape(
                 x=x,
                 shape=[
-                    0, cardinality, radix, int(r * h * w / cardinality / radix)
+                    batch, cardinality, radix, int(r * h * w / cardinality / radix)
                 ])
             x = paddle.transpose(x=x, perm=[0, 2, 1, 3])
             x = nn.functional.softmax(x, axis=1)
-            x = paddle.reshape(x=x, shape=[0, r * h * w])
+            x = paddle.reshape(x=x, shape=[batch, r * h * w])
         else:
             x = nn.functional.sigmoid(x)
         return x

ppcls/modeling/architectures/vgg.py

@@ -113,7 +113,7 @@ class VGGNet(nn.Layer):
         x = self._conv_block_4(x)
         x = self._conv_block_5(x)
 
-        x = paddle.reshape(x, [0, -1])
+        x = paddle.reshape(x, [-1, x.shape[1]*x.shape[2]*x.shape[3]])
         x = self._fc1(x)
         x = F.relu(x)
         x = self._drop(x)

ppcls/modeling/architectures/xception.py

@@ -305,7 +305,7 @@ class ExitFlow(nn.Layer):
         conv2 = self._conv_2(conv1)
         conv2 = F.relu(conv2)
         pool = self._pool(conv2)
-        pool = paddle.reshape(pool, [0, -1])
+        pool = paddle.reshape(pool, [-1, pool.shape[1]])
         out = self._out(pool)
         return out