minor fix init (#384)

* add densent init

* fix export model

ppcls/modeling/architectures/__init__.py

@@ -23,7 +23,7 @@ from .se_resnet_vd import SE_ResNet18_vd, SE_ResNet34_vd, SE_ResNet50_vd, SE_Res
 from .se_resnext_vd import SE_ResNeXt50_vd_32x4d, SE_ResNeXt50_vd_32x4d, SENet154_vd
 from .se_resnext import SE_ResNeXt50_32x4d, SE_ResNeXt101_32x4d, SE_ResNeXt152_64x4d
 from .dpn import DPN68, DPN92, DPN98, DPN107, DPN131
-from .densenet import DenseNet121
+from .densenet import DenseNet121, DenseNet161, DenseNet169, DenseNet201, DenseNet264
 from .hrnet import HRNet_W18_C, HRNet_W30_C, HRNet_W32_C, HRNet_W40_C, HRNet_W44_C, HRNet_W48_C, HRNet_W60_C, HRNet_W64_C, SE_HRNet_W18_C, SE_HRNet_W30_C, SE_HRNet_W32_C, SE_HRNet_W40_C, SE_HRNet_W44_C, SE_HRNet_W48_C, SE_HRNet_W60_C, SE_HRNet_W64_C
 from .efficientnet import EfficientNetB0, EfficientNetB1, EfficientNetB2, EfficientNetB3, EfficientNetB4, EfficientNetB5, EfficientNetB6, EfficientNetB7
 from .resnest import ResNeSt50_fast_1s1x64d, ResNeSt50

ppcls/modeling/architectures/efficientnet.py

@@ -243,11 +243,12 @@ inp_shape = {
 def _drop_connect(inputs, prob, is_test):
     if is_test:
         return inputs
+
     keep_prob = 1.0 - prob
     inputs_shape = paddle.shape(inputs)
     random_tensor = keep_prob + paddle.rand(shape=[inputs_shape[0], 1, 1, 1])
     binary_tensor = paddle.floor(random_tensor)
-    output = inputs / keep_prob * binary_tensor
+    output = paddle.multiply(inputs, binary_tensor) / keep_prob
     return output
 
 
@@ -507,7 +508,8 @@ class SEBlock(nn.Layer):
         x = self._pool(inputs)
         x = self._conv1(x)
         x = self._conv2(x)
-        return paddle.multiply(inputs, x)
+        out = paddle.multiply(inputs, x)
+        return out
 
 
 class MbConvBlock(nn.Layer):
@@ -572,11 +574,13 @@ class MbConvBlock(nn.Layer):
         if self.expand_ratio != 1:
             x = self._ecn(x)
             x = F.swish(x)
+
         x = self._dcn(x)
         x = F.swish(x)
         if self.has_se:
             x = self._se(x)
         x = self._pcn(x)
+
         if self.id_skip and \
                 self.block_args.stride == 1 and \
                 self.block_args.input_filters == self.block_args.output_filters:

tools/export_model.py

@@ -65,11 +65,12 @@ def main():
     net = architectures.__dict__[args.model]
 
     model = Net(net, to_static, args.class_dim)
-
     load_dygraph_pretrain(
         model.pre_net,
         path=args.pretrained_model,
         load_static_weights=args.load_static_weights)
+
+    model.eval()
     paddle.jit.save(model, args.output_path)