Fixed topology build only in validation mode

Author: Laicheng0830

tensorlayerx/nn/core/core_mindspore.py

@@ -300,6 +300,7 @@ def build_graph(self, *inputs, **kwargs):
         for layer_name, layer in layers:
             if isinstance(layer, Module):
                 layer._build_graph = True
+        self.set_eval()
 
         outputs = self.forward(*inputs, **kwargs)
         self.inputs = inputs

tensorlayerx/nn/core/core_tensorflow.py

@@ -588,6 +588,7 @@ def build_graph(self, *inputs, **kwargs):
         for layer_name, layer in layers:
             if isinstance(layer, Module):
                 layer._build_graph = True
+        self.set_eval()
 
         outputs = self.forward(*inputs, **kwargs)
         self.inputs = inputs

tensorlayerx/nn/core/core_torch.py

@@ -186,6 +186,7 @@ def build_graph(self, *inputs, **kwargs):
         for name, layer in self.named_modules():
             if isinstance(layer, Module):
                 layer._build_graph = True
+        self.set_eval()
 
         outputs = self.forward(*inputs, **kwargs)
         self.inputs = inputs

tensorlayerx/nn/layers/lambda_layers.py

@@ -1,9 +1,7 @@
 #! /usr/bin/python
 # -*- coding: utf-8 -*-
 
-import tensorflow as tf
 from tensorlayerx import logging
-from tensorlayerx.files import utils
 from tensorlayerx.nn.core import Module
 
 __all__ = [
@@ -150,19 +148,6 @@ def forward(self, inputs, **kwargs):
             self._nodes_fixed = True
         return outputs
 
-    def get_args(self):
-        init_args = {}
-        if isinstance(self.fn, tf.keras.layers.Layer) or isinstance(self.fn, tf.keras.Model):
-            init_args.update({"layer_type": "keraslayer"})
-            init_args["fn"] = utils.save_keras_model(self.fn)
-            init_args["fn_weights"] = None
-            if len(self._nodes) == 0:
-                init_args["keras_input_shape"] = []
-            else:
-                init_args["keras_input_shape"] = self._nodes[0].in_tensors[0].get_shape().as_list()
-        else:
-            init_args = {"layer_type": "normal"}
-        return init_args
 
 
 class ElementwiseLambda(Module):
@@ -264,7 +249,6 @@ def build(self, inputs_shape=None):
         # the weights of the function are provided when the Lambda layer is constructed
         pass
 
-    # @tf.function
     def forward(self, inputs, **kwargs):
 
         if not isinstance(inputs, list):