Update mobilenet_v3.py

ppcls/modeling/architectures/mobilenet_v3.py

@@ -21,7 +21,7 @@ import paddle
 from paddle import ParamAttr
 import paddle.nn as nn
 import paddle.nn.functional as F
-from paddle.nn.functional.activation import hard_sigmoid, hard_swish
+from paddle.nn.functional import hardswish, hardsigmoid
 from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
 from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
 from paddle.regularizer import L2Decay
@@ -64,15 +64,15 @@ class MobileNetV3(nn.Layer):
                 [5, 72, 40, True, "relu", 2],
                 [5, 120, 40, True, "relu", 1],
                 [5, 120, 40, True, "relu", 1],
-                [3, 240, 80, False, "hard_swish", 2],
-                [3, 200, 80, False, "hard_swish", 1],
-                [3, 184, 80, False, "hard_swish", 1],
-                [3, 184, 80, False, "hard_swish", 1],
-                [3, 480, 112, True, "hard_swish", 1],
-                [3, 672, 112, True, "hard_swish", 1],
-                [5, 672, 160, True, "hard_swish", 2],
-                [5, 960, 160, True, "hard_swish", 1],
-                [5, 960, 160, True, "hard_swish", 1],
+                [3, 240, 80, False, "hardswish", 2],
+                [3, 200, 80, False, "hardswish", 1],
+                [3, 184, 80, False, "hardswish", 1],
+                [3, 184, 80, False, "hardswish", 1],
+                [3, 480, 112, True, "hardswish", 1],
+                [3, 672, 112, True, "hardswish", 1],
+                [5, 672, 160, True, "hardswish", 2],
+                [5, 960, 160, True, "hardswish", 1],
+                [5, 960, 160, True, "hardswish", 1],
             ]
             self.cls_ch_squeeze = 960
             self.cls_ch_expand = 1280
@@ -82,14 +82,14 @@ class MobileNetV3(nn.Layer):
                 [3, 16, 16, True, "relu", 2],
                 [3, 72, 24, False, "relu", 2],
                 [3, 88, 24, False, "relu", 1],
-                [5, 96, 40, True, "hard_swish", 2],
-                [5, 240, 40, True, "hard_swish", 1],
-                [5, 240, 40, True, "hard_swish", 1],
-                [5, 120, 48, True, "hard_swish", 1],
-                [5, 144, 48, True, "hard_swish", 1],
-                [5, 288, 96, True, "hard_swish", 2],
-                [5, 576, 96, True, "hard_swish", 1],
-                [5, 576, 96, True, "hard_swish", 1],
+                [5, 96, 40, True, "hardswish", 2],
+                [5, 240, 40, True, "hardswish", 1],
+                [5, 240, 40, True, "hardswish", 1],
+                [5, 120, 48, True, "hardswish", 1],
+                [5, 144, 48, True, "hardswish", 1],
+                [5, 288, 96, True, "hardswish", 2],
+                [5, 576, 96, True, "hardswish", 1],
+                [5, 576, 96, True, "hardswish", 1],
             ]
             self.cls_ch_squeeze = 576
             self.cls_ch_expand = 1280
@@ -105,7 +105,7 @@ class MobileNetV3(nn.Layer):
             padding=1,
             num_groups=1,
             if_act=True,
-            act="hard_swish",
+            act="hardswish",
             name="conv1")
 
         self.block_list = []
@@ -135,7 +135,7 @@ class MobileNetV3(nn.Layer):
             padding=0,
             num_groups=1,
             if_act=True,
-            act="hard_swish",
+            act="hardswish",
             name="conv_last")
 
         self.pool = AdaptiveAvgPool2D(1)
@@ -167,9 +167,9 @@ class MobileNetV3(nn.Layer):
         x = self.pool(x)
 
         x = self.last_conv(x)
-        x = hard_swish(x)
+        x = hardswish(x)
         x = self.dropout(x)
-        x = paddle.reshape(x, shape=[x.shape[0], x.shape[1]])
+        x = paddle.flatten(x, start_axis=1, stop_axis=-1)
         x = self.out(x)
 
         return x
@@ -215,8 +215,8 @@ class ConvBNLayer(nn.Layer):
         if self.if_act:
             if self.act == "relu":
                 x = F.relu(x)
-            elif self.act == "hard_swish":
-                x = hard_swish(x)
+            elif self.act == "hardswish":
+                x = hardswish(x)
             else:
                 print("The activation function is selected incorrectly.")
                 exit()
@@ -305,7 +305,7 @@ class SEModule(nn.Layer):
         outputs = self.conv1(outputs)
         outputs = F.relu(outputs)
         outputs = self.conv2(outputs)
-        outputs = hard_sigmoid(outputs)
+        outputs = hardsigmoid(outputs, slope=0.2, offset=0.5)
         return paddle.multiply(x=inputs, y=outputs)