add tf optimizer

tools/README.md

@@ -4,6 +4,7 @@
 ```
 python tools/check_for_lite.py paddle_model/inference_model/__model__
 ```
+> 附：check_for_lite工具并不能完全判断模型是否被支持，PaddleLite详细支持的算子请参考[PaddleLite支持算子集](https://github.com/PaddlePaddle/Paddle-Lite/blob/develop/docs/introduction/support_operation_list.md)
 
 
 ### 二、模型参数合并

x2paddle/convert.py

@@ -117,6 +117,7 @@ def tf2paddle(model_path,
     from x2paddle.optimizer.tensorflow.bias import BiasOpt
     from x2paddle.optimizer.tensorflow.transpose import TransposeOpt
     from x2paddle.optimizer.tensorflow.batch_norm import BatchNormOpt
+    from x2paddle.optimizer.tensorflow.prelu import PReLUOpt
 
     print("Now translating model from tensorflow to paddle.")
     model = TFDecoder(model_path, define_input_shape=define_input_shape)
@@ -125,8 +126,10 @@ def tf2paddle(model_path,
     bias_opt = BiasOpt()
     transpose_opt = TransposeOpt()
     batch_norm_opt = BatchNormOpt()
+    prelu_opt = PReLUOpt()
     bias_opt.run(program)
     batch_norm_opt.run(program)
+    prelu_opt.run(program)
     transpose_opt.run(program)
     program.gen_model(save_dir)
 

x2paddle/op_mapper/tf_op_mapper.py

@@ -882,7 +882,7 @@ class TFOpMapper(OpMapper):
         begin = self.graph.get_node(node.layer.input[1])
         size = self.graph.get_node(node.layer.input[2])
 
-        inputs = {"x": input.name}
+        inputs = {"input": input.name}
         attrs = {}
         if begin.layer_type == "Const":
             begin = begin.value.tolist()
@@ -901,20 +901,30 @@ class TFOpMapper(OpMapper):
         if size.layer_type == "Const":
             size = size.value.tolist()
             attrs['shape'] = size
+            shape = size
         else:
             shape = size.out_shapes[0]
-            reshape_name = gen_name("slice", "reshape")
-            program.add_layer(
-                kernel="fluid.layers.reshape",
-                inputs={"x": size.name},
-                outputs=[reshape_name],
-                shape=shape)
-            inputs['shape'] = reshape_name
+#             reshape_name = gen_name("slice", "reshape")
+#             program.add_layer(
+#                 kernel="fluid.layers.reshape",
+#                 inputs={"x": size.name},
+#                 outputs=[reshape_name],
+#                 shape=shape)
+#             inputs['shape'] = reshape_name
+        
+#         inputs.pop('shape')
         program.add_layer(
-            kernel="fluid.layers.crop_tensor",
+            kernel="fluid.layers.slice",
             inputs=inputs,
             outputs=[node.name],
-            **attrs)
+            axes=list(range(len(attrs['offsets']))),
+            starts=attrs['offsets'],
+            ends=[attrs['offsets'][i] + shape[i] for i in range(len(shape))])
+#         program.add_layer(
+#             kernel="fluid.layers.crop_tensor",
+#             inputs=inputs,
+#             outputs=[node.name],
+#             **attrs)
 
     def ResizeNearestNeighbor(self, node):
         input = self.graph.get_node(node.layer.input[0])

x2paddle/optimizer/tensorflow/batch_norm.py

@@ -20,10 +20,12 @@ class BatchNormOpt:
             input_ids0 = graph.edges_in[layer_id]
             mul_layer0 = graph.layers[input_ids0[0]]
             sub_layer0 = graph.layers[input_ids0[1]]
+            
             if mul_layer0.kernel != "fluid.layers.elementwise_mul":
                 continue
             if sub_layer0.kernel != "fluid.layers.elementwise_sub":
                 continue
+            
             axis = mul_layer0.attrs.get('axis', -1)
             if axis != -1 and axis != 3:
                 continue
@@ -116,7 +118,7 @@ class BatchNormOpt:
             other = graph.layers[input_ids6[1]]
             if variance.kernel != "fluid.layers.create_parameter":
                 continue
-            if other.kernel != "fluid.layers.create_parameter":
+            if other.kernel != "fluid.layers.fill_constant":
                 continue
             if len(graph.edges_out.get(input_ids6[0], [])) != 1:
                 continue
@@ -127,10 +129,6 @@ class BatchNormOpt:
             variance_shape = graph.parameters[variance.outputs[0]].shape
             if variance_shape != beta_shape:
                 continue
-            if other.outputs[0] not in graph.parameters:
-                continue
-            if graph.parameters[other.outputs[0]].size != 1:
-                continue
 
             ids = set([
                 layer_id, mul_layer0.id, sub_layer0.id, mul_layer1.id, beta.id,
@@ -163,7 +161,7 @@ class BatchNormOpt:
                     kernel="fluid.layers.batch_norm",
                     inputs={"input": "transpose_for_bn"},
                     outputs=layer.outputs,
-                    epsilon=graph.parameters[other.outputs[0]],
+                    epsilon=other.attrs["value"],
                     param_attr="'{}'".format(gamma.outputs[0]),
                     bias_attr="'{}'".format(beta.outputs[0]),
                     moving_mean_name="'{}'".format(mean.outputs[0]),