Merge pull request #437 from SunAhong1993/develop

add tf optimizer

tools/README.md

@@ -6,7 +6,6 @@ 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转换后产出的路径下包括两个目录，  
 1. `model_with_code`: 包含保存的参数文件和模型python代码文件，供用户debug  

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]),

x2paddle/optimizer/tensorflow/prelu.py

+import copy
+import numpy as np
+from collections import OrderedDict
+from x2paddle.core.program import PaddleLayer
+from x2paddle.core.util import *
+
+
+class PReLUOpt:
+    def __init__(self):
+        pass
+
+    def run(self, graph):
+        print("Optimize: PReLUOpt...")
+        layers = copy.deepcopy(graph.layers)
+        for layer_id, layer in layers.items():
+            if layer.kernel != "fluid.layers.elementwise_add":
+                continue
+            axis = layer.attrs.get('axis', -1)
+            if axis != -1 and axis != 3:
+                continue
+
+            input_ids0 = graph.edges_in[layer_id]
+            relu_layer0 = graph.layers[input_ids0[0]]
+            mul_layer0 = graph.layers[input_ids0[1]]
+            
+            if relu_layer0.kernel != "fluid.layers.relu":
+                continue
+            if mul_layer0.kernel != "fluid.layers.elementwise_mul":
+                continue
+            
+            axis = mul_layer0.attrs.get('axis', -1)
+            if axis != -1 and axis != 3:
+                continue
+            if len(graph.edges_out.get(input_ids0[0], [])) != 1:
+                continue
+            if len(graph.edges_out.get(input_ids0[1], [])) != 1:
+                continue
+                
+            input_ids1_0 = graph.edges_in[input_ids0[0]]
+            input_ids1_1 = graph.edges_in[input_ids0[1]]
+            fill_layer = graph.layers[input_ids1_1[1]]
+            mul_layer1 = graph.layers[input_ids1_1[0]]
+            if fill_layer.kernel != "fluid.layers.fill_constant":
+                continue
+            if mul_layer1.kernel != "fluid.layers.elementwise_mul":
+                continue
+            axis = mul_layer1.attrs.get('axis', -1)
+            if axis != -1 and axis != 0:
+                continue
+            if len(graph.edges_out.get(input_ids1_1[1], [])) != 1:
+                continue
+            if len(graph.edges_out.get(input_ids1_0[0], [])) != 3:
+                continue     
+              
+            input_ids2 = graph.edges_in[input_ids1_1[0]]    
+            alpha = graph.layers[input_ids2[0]]
+            sub_layer = graph.layers[input_ids2[1]]
+            if alpha.kernel != "fluid.layers.create_parameter":
+                continue
+            if sub_layer.kernel != "fluid.layers.elementwise_sub":
+                continue
+            axis = sub_layer.attrs.get('axis', -1)
+            if axis != -1 and axis != 3:
+                continue
+            if len(graph.edges_out.get(input_ids2[0], [])) != 1:
+                continue
+            if len(graph.edges_out.get(input_ids2[1], [])) != 1:
+                continue
+            if alpha.outputs[0] not in graph.parameters:
+                continue
+            
+            input_ids3 = graph.edges_in[input_ids2[1]]
+            add_layer = graph.layers[input_ids3[0]]
+            abs_layer = graph.layers[input_ids3[1]]
+            if abs_layer.kernel != "fluid.layers.abs":
+                continue
+            if len(graph.edges_out.get(input_ids3[1], [])) != 1:
+                continue
+                
+
+            ids = set([
+                layer.id, relu_layer0.id, mul_layer0.id, fill_layer.id, mul_layer1.id, alpha.id,
+                sub_layer.id, abs_layer.id])
+
+            for id in ids:
+                del graph.layers[id]
+                if id in graph.edges_in:
+                    del graph.edges_in[id]
+                if id in graph.edges_out:
+                    del graph.edges_out[id]
+
+            copy_layers = copy.deepcopy(graph.layers)
+            graph.layers = OrderedDict()
+            for k, v in copy_layers.items():
+                if k != add_layer.id:
+                    graph.layers[k] = v
+                    continue
+                graph.layers[k] = v
+                transpose0 = PaddleLayer(
+                    id='{}_1'.format(k),
+                    kernel="fluid.layers.transpose",
+                    inputs={"x": v.outputs[0]},
+                    outputs=["transpose_for_prelu"],
+                    perm=[0, 3, 1, 2])
+                prelu = PaddleLayer(
+                    id='{}_2'.format(k),
+                    kernel="fluid.layers.prelu",
+                    inputs={"x": "transpose_for_prelu"},
+                    outputs=layer.outputs,
+                    mode=string("channel"),
+                    param_attr="'{}'".format(alpha.outputs[0]))
+                transpose1 = PaddleLayer(
+                    id=layer_id,
+                    kernel="fluid.layers.transpose",
+                    inputs={"x": layer.outputs[0]},
+                    outputs=layer.outputs,
+                    perm=[0, 2, 3, 1])
+                graph.layers[transpose0.id] = transpose0
+                graph.layers[prelu.id] = prelu
+                graph.layers[transpose1.id] = transpose1
+                graph.parameters[alpha.outputs[0]] = np.expand_dims(graph.parameters[alpha.outputs[0]], 0)
+        graph.build()
+