modify tf

x2paddle/convert.py

@@ -141,11 +141,14 @@ 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
         bias_opt = BiasOpt()
         transpose_opt = TransposeOpt()
         batch_norm_opt = BatchNormOpt()
+        prelu_opt = PReLUOpt()
         bias_opt.run(mapper.paddle_graph)
         batch_norm_opt.run(mapper.paddle_graph)
+        prelu_opt.run(mapper.paddle_graph)
         transpose_opt.run(mapper.paddle_graph)
     mapper.paddle_graph.gen_model(save_dir)
         

x2paddle/op_mapper/dygraph/tf2paddle/tf_op_mapper.py

@@ -508,14 +508,6 @@ class TFOpMapper(OpMapper):
         param = self.graph.get_node(node.layer.input[1])
 
         input_name = input.name
-        if input.dtype == 'bool':
-            cast_name = gen_name('reshape', 'cast')
-            self.paddle_graph.add_layer(
-                kernel="paddle.cast",
-                inputs={"x": input_name},
-                outputs=[cast_name],
-                dtype="'int32'")
-            input_name = cast_name
 
         if param.layer_type == "Const":
             shape = param.value.tolist()
@@ -540,13 +532,6 @@ class TFOpMapper(OpMapper):
                     outputs=[node.name],
                     shape=out_shape.tolist())
 
-        if input.dtype == 'bool':
-            self.paddle_graph.add_layer(
-                kernel="paddle.cast",
-                inputs={"x": node.name},
-                outputs=[node.name],
-                dtype="'bool'")
-
     def Pad(self, node):
         input = self.graph.get_node(node.layer.input[0])
         paddings = self.graph.get_node(node.layer.input[1])
@@ -592,14 +577,6 @@ class TFOpMapper(OpMapper):
     def Shape(self, node):
         input = self.graph.get_node(node.layer.input[0])
         input_name = input.name
-        if input.dtype == 'bool':
-            cast_name = gen_name('shape', 'cast')
-            self.paddle_graph.add_layer(
-                kernel="paddle.cast",
-                inputs={"x": input.name},
-                outputs=[cast_name],
-                dtype=string("int32"))
-            input_name = cast_name
         self.paddle_graph.add_layer(
             kernel="paddle.shape",
             inputs={"input": input_name},
@@ -791,26 +768,11 @@ class TFOpMapper(OpMapper):
             axis += len(inputs[0].out_shapes[0])
 
         input_names = [i.name for i in inputs]
-        for i, ipt in enumerate(inputs):
-            if ipt.dtype == 'bool':
-                cast_name = gen_name('concat', 'cast')
-                self.paddle_graph.add_layer(
-                    kernel="paddle.cast",
-                    inputs={"x": ipt.name},
-                    outputs=[cast_name],
-                    dtype="'int32'")
-                input_names[i] = cast_name
         self.paddle_graph.add_layer(
             kernel="paddle.concat",
             inputs={"x": input_names},
             outputs=[node.name],
             axis=axis)
-        if node.dtype == 'bool':
-            self.paddle_graph.add_layer(
-                kernel="paddle.cast",
-                inputs={"x": node.name},
-                outputs=[node.name],
-                dtype="'bool'")
 
     def StridedSlice(self, node):
         input = self.graph.get_node(node.layer.input[0])

x2paddle/optimizer/fusion/dygraph/__init__.py

@@ -28,6 +28,8 @@ from .fc_fuser import DygraphFcFuser
 from .fc_fuse_pass import DygraphFcFusePass
 from .interpolate_bilinear_fuser import DygraphInterpolateBilinearFuser
 from .interpolate_bilinear_fuse_pass import DygraphInterpolateBilinearFusePass
+from .prelu_fuser import DygraphPReLUFuser
+from .prelu_fuse_pass import DygraphPReLUFusePass
 from .reshape_fuser import DygraphReshapeFuser
 from .reshape_fuse_pass import DygraphReshapeFusePass
 from .tf_batchnorm_fuser import DygraphTFBatchNormFuser

x2paddle/optimizer/fusion/dygraph/prelu_fuse_pass.py

+#   Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from x2paddle.optimizer.pass_ import Pass
+from x2paddle.optimizer.fusion.dygraph import DygraphPReLUFuser
+from x2paddle.optimizer.pass_manager import pass_register
+
+
+@pass_register
+class DygraphPReLUFusePass(Pass):
+    name = "dygraph_prelu_fuse_pass"
+
+    def __init__(self):
+        Pass.__init__(self)
+
+    def apply(self, graph):
+        fuser = DygraphPReLUFuser()
+        fuser.operate(graph, match_kind="edge")
+
+
+# 用于注册
+dygraph_prelu_fuse_pass = DygraphPReLUFusePass()
\ No newline at end of file

x2paddle/optimizer/fusion/dygraph/prelu_fuser.py

+#   Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import copy
+import numpy as np
+from collections import OrderedDict
+from x2paddle.optimizer.pattern_matcher import FuseBase
+from x2paddle.core.program import PaddleGraph, PaddleLayer
+from x2paddle.core.util import *
+
+
+class DygraphPReLUFuser(FuseBase):
+    def __init__(self):
+        self.prelu_index = 0
+        super(DygraphPReLUFuser, self).__init__(graph_type="dygraph")
+
+    def build_pattern(self):
+        """ 描述需要替换的prelu图结构。
+        prelu层模式python实现代码示例:
+            conv2_alphas = self.conv2_alphas
+            conv2_mul_1_y = paddle.full(dtype='float32', shape=[1], fill_value=0.5)
+            conv2_Relu = self.relu1(conv2_Conv2D)
+            conv2_Abs = paddle.abs(x=conv2_Conv2D)
+            conv2_sub = fluid.layers.elementwise_sub(x=conv2_Conv2D, y=conv2_Abs)
+            conv2_mul = paddle.multiply(x=conv2_alphas, y=conv2_sub, axis=1)
+            conv2_mul_1 = paddle.multiply(x=conv2_mul, y=conv2_mul_1_y, axis=1)
+            conv2_add = paddle.add(x=conv2_Relu, y=conv2_mul_1)
+        """
+
+        def gen_name(id):
+            return "x" + str(id)
+
+        self.pattern.add_layer(
+            "self.create_parameter",
+            inputs={},
+            outputs=[gen_name(0)])
+        self.pattern.add_layer(
+            "paddle.full",
+            inputs={},
+            outputs=[gen_name(1)],
+            shape=[1],
+            fill_value=0.5)
+        self.pattern.add_layer(
+            "paddle.nn.ReLU",
+            inputs={"x": "prelu-input-0"},
+            outputs=[gen_name(2)])
+        self.pattern.add_layer(
+            "paddle.abs",
+            inputs={"x": "prelu-input-0"},
+            outputs=[gen_name(3)])
+        self.pattern.add_layer(
+            "fluid.layers.elementwise_sub",
+            inputs={"x": "prelu-input-0",
+                    "y": gen_name(3)},
+            outputs=[gen_name(4)])
+        self.pattern.add_layer(
+            "paddle.multiply",
+            inputs={"x": gen_name(0),
+                    "y": gen_name(4)},
+            outputs=[gen_name(5)])
+        self.pattern.add_layer(
+            "paddle.multiply",
+            inputs={"x": gen_name(5),
+                    "y": gen_name(1)},
+            outputs=[gen_name(6)])
+        self.pattern.add_layer(
+            "paddle.add",
+            inputs={"x": gen_name(2),
+                    "y": gen_name(6)},
+            outputs=[gen_name(7)])
+        self.pattern.build(inputs={"input-0": "prelu-input-0", })
+        
+
+    def insert_new_layer(self, graph, parameters, matches):
+        new_layers, last_layer_id = self.gen_new_layer(matches, parameters, graph)
+        matches_copy = copy.deepcopy(matches)
+        for layer_id, layer in matches_copy.items():
+            for i in range(3):
+                if layer_id == new_layers[i].id:
+                    matches.pop(new_layers[i].id)
+        prefix_layers = OrderedDict()
+        mid_layers = OrderedDict()
+        suffix_layers = OrderedDict()
+        is_need_id = False
+        for layer_id, layer in graph.layers.items():
+            if is_need_id:
+                suffix_layers[layer_id] = layer
+            else:
+                if layer_id == last_layer_id:
+                    for i in range(3):
+                        mid_layers[new_layers[i].id] = new_layers[i]
+                    is_need_id = True
+                prefix_layers[layer_id] = layer
+        prefix_layers.update(mid_layers)
+        prefix_layers.update(suffix_layers)
+        graph.layers = prefix_layers
+
+    def gen_new_layer(self, matches, parameters, graph):
+        layer_id_list = list(matches.keys())
+        layer_id_list.sort(key = int)
+        for layer_id, layer in matches.items():
+            if layer.kernel == "paddle.nn.ReLU":
+                input_name = layer.inputs["x"]
+            if layer.kernel == "self.create_parameter":
+                param_name = layer.outputs[0]
+            if layer.kernel == "paddle.add":
+                output_name = layer.outputs[0]
+        transpose0 = PaddleLayer(
+            id=layer_id_list[-1] + "_1",
+            kernel="paddle.transpose",
+            inputs={"x": input_name},
+            outputs=["{}_transpose_for_prelu".format(input_name)],
+            perm=[0, 3, 1, 2])
+        prelu_name = "merge_prelu{}".format(self.prelu_index)
+        self.prelu_index += 1
+        param = parameters[param_name]
+        c = param.shape[0]
+        prelu = PaddleLayer(id=layer_id_list[-1] + "_2",
+                            kernel="paddle.nn.PReLU",
+                            inputs={"input": "{}_transpose_for_prelu".format(input_name)},
+                            outputs=[prelu_name, "{}_prelu".format(input_name)],
+                            num_parameters=c,
+                            weight_attr=string(param_name))
+        transpose1 = PaddleLayer(
+            id=layer_id_list[-1] + "_3",
+            kernel="paddle.transpose",
+            inputs={"x": "{}_prelu".format(input_name)},
+            outputs=[output_name],
+            perm=[0, 2, 3, 1])
+        return [transpose0, prelu, transpose1], layer_id_list[-1]
+

x2paddle/optimizer/optimizer.py

@@ -41,6 +41,7 @@ class GraphOptimizer(object):
             self.passes = [
                 "dygraph_conv2d_add_fuse_pass",
                 "dygraph_tf_batchnorm_fuse_pass",
+                "dygraph_prelu_fuse_pass",
                 "transpose_eliminate_pass"
             ]
         else:

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]], axis=(0, 2, 3))
+        graph.build()
\ No newline at end of file