fix resnest typo (#380)

fix resnest, ghostnet typo

configs/ShuffleNet/ShuffleNetV2_x1_0.yaml

@@ -1,6 +1,6 @@
 mode: 'train'
 ARCHITECTURE:
-    name: "ShuffleNetV2"
+    name: "ShuffleNetV2_x1_0"
 
 pretrained_model: ""
 model_save_dir: "./output/"

ppcls/modeling/architectures/__init__.py

@@ -32,14 +32,13 @@ from .ghostnet import GhostNet_x0_5, GhostNet_x1_0, GhostNet_x1_3
 from .mobilenet_v1 import MobileNetV1_x0_25, MobileNetV1_x0_5, MobileNetV1_x0_75, MobileNetV1
 from .mobilenet_v2 import MobileNetV2_x0_25, MobileNetV2_x0_5, MobileNetV2_x0_75, MobileNetV2, MobileNetV2_x1_5, MobileNetV2_x2_0
 from .mobilenet_v3 import MobileNetV3_small_x0_35, MobileNetV3_small_x0_5, MobileNetV3_small_x0_75, MobileNetV3_small_x1_0, MobileNetV3_small_x1_25, MobileNetV3_large_x0_35, MobileNetV3_large_x0_5, MobileNetV3_large_x0_75, MobileNetV3_large_x1_0, MobileNetV3_large_x1_25
-from .shufflenet_v2 import ShuffleNetV2_x0_25, ShuffleNetV2_x0_33, ShuffleNetV2_x0_5, ShuffleNetV2, ShuffleNetV2_x1_5, ShuffleNetV2_x2_0, ShuffleNetV2_swish
+from .shufflenet_v2 import ShuffleNetV2_x0_25, ShuffleNetV2_x0_33, ShuffleNetV2_x0_5, ShuffleNetV2_x1_0, ShuffleNetV2_x1_5, ShuffleNetV2_x2_0, ShuffleNetV2_swish
 from .alexnet import AlexNet
 from .inception_v3 import InceptionV3
 from .inception_v4 import InceptionV4
 from .xception import Xception41, Xception65, Xception71
 from .xception_deeplab import Xception41_deeplab, Xception65_deeplab, Xception71_deeplab
 from .resnext101_wsl import ResNeXt101_32x8d_wsl, ResNeXt101_32x16d_wsl, ResNeXt101_32x32d_wsl, ResNeXt101_32x48d_wsl
-from .shufflenet_v2 import ShuffleNetV2_x0_25, ShuffleNetV2_x0_33, ShuffleNetV2_x0_5, ShuffleNetV2, ShuffleNetV2_x1_5, ShuffleNetV2_x2_0, ShuffleNetV2_swish
 from .squeezenet import SqueezeNet1_0, SqueezeNet1_1
 from .vgg import VGG11, VGG13, VGG16, VGG19
 from .darknet import DarkNet53

ppcls/modeling/architectures/ghostnet.py

@@ -44,7 +44,6 @@ class ConvBNLayer(nn.Layer):
             bias_attr=False)
         bn_name = name + "_bn"
 
-        # In the old version, moving_variance_name was name + "_variance"
         self._batch_norm = BatchNorm(
             num_channels=out_channels,
             act=act,
@@ -53,9 +52,7 @@ class ConvBNLayer(nn.Layer):
             bias_attr=ParamAttr(
                 name=bn_name + "_offset", regularizer=L2Decay(0.0)),
             moving_mean_name=bn_name + "_mean",
-            moving_variance_name=name +
-            "_variance"  # wrong due to an old typo, will be fixed later.
-        )
+            moving_variance_name=bn_name + "_variance")
 
     def forward(self, inputs):
         y = self._conv(inputs)

ppcls/modeling/architectures/resnest.py

@@ -120,7 +120,7 @@ class SplatConv(nn.Layer):
             stride=stride,
             groups=groups * radix,
             act="relu",
-            name=name + "_splat1")
+            name=name + "_1_weights")
 
         self.avg_pool2d = AdaptiveAvgPool2D(1)
 
@@ -134,7 +134,7 @@ class SplatConv(nn.Layer):
             stride=1,
             groups=groups,
             act="relu",
-            name=name + "_splat2")
+            name=name + "_2_weights")
 
         # to calc atten
         self.conv3 = Conv2D(
@@ -145,7 +145,7 @@ class SplatConv(nn.Layer):
             padding=0,
             groups=groups,
             weight_attr=ParamAttr(
-                name=name + "_splat_weights", initializer=KaimingNormal()),
+                name=name + "_weights", initializer=KaimingNormal()),
             bias_attr=False)
 
         self.rsoftmax = rSoftmax(radix=radix, cardinality=groups)
@@ -233,7 +233,7 @@ class BottleneckBlock(nn.Layer):
                 bias=False,
                 radix=radix,
                 rectify_avg=rectify_avg,
-                name=name + "_splatconv")
+                name=name + "_splat")
         else:
             self.conv2 = ConvBNLayer(
                 num_channels=group_width,
@@ -403,10 +403,10 @@ class ResNeStLayer(nn.Layer):
         self.inplanes = planes * 4
         self.bottleneck_block_list = [bottleneck_func]
         for i in range(1, blocks):
-            name = name + "_bottleneck_" + str(i)
+            curr_name = name + "_bottleneck_" + str(i)
 
             bottleneck_func = self.add_sublayer(
-                name,
+                curr_name,
                 BottleneckBlock(
                     inplanes=self.inplanes,
                     planes=planes,
@@ -419,7 +419,7 @@ class ResNeStLayer(nn.Layer):
                     dilation=dilation,
                     rectify_avg=rectify_avg,
                     last_gamma=last_gamma,
-                    name=name))
+                    name=curr_name))
             self.bottleneck_block_list.append(bottleneck_func)
 
     def forward(self, x):

ppcls/modeling/architectures/shufflenet_v2.py

@@ -23,7 +23,7 @@ from paddle.nn.functional import swish
 
 __all__ = [
     "ShuffleNetV2_x0_25", "ShuffleNetV2_x0_33", "ShuffleNetV2_x0_5",
-    "ShuffleNetV2", "ShuffleNetV2_x1_5", "ShuffleNetV2_x2_0",
+    "ShuffleNetV2_x1_0", "ShuffleNetV2_x1_5", "ShuffleNetV2_x2_0",
     "ShuffleNetV2_swish"
 ]
 
@@ -299,7 +299,7 @@ def ShuffleNetV2_x0_5(**args):
     return model
 
 
-def ShuffleNetV2(**args):
+def ShuffleNetV2_x1_0(**args):
     model = ShuffleNet(scale=1.0, **args)
     return model