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Posted to dev@singa.apache.org by GitBox <gi...@apache.org> on 2019/03/08 05:21:04 UTC
[GitHub] [incubator-singa] ShichengChen commented on a change in pull
request #416: singa-onnx
ShichengChen commented on a change in pull request #416: singa-onnx
URL: https://github.com/apache/incubator-singa/pull/416#discussion_r263668103
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File path: python/singa/sonnx.py
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@@ -0,0 +1,289 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you 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 __future__ import division
+from singa import tensor
+from singa import autograd
+from onnx import helper,checker
+from onnx import AttributeProto, TensorProto, GraphProto
+from onnx import numpy_helper
+from onnx.backend.base import BackendRep as backendRep
+from onnx.backend.base import Backend as backend
+from collections import Counter, deque
+
+from . import singa_wrap as singa
+from autograd import *
+from autograd import _Conv2d,_Pooling2d,_BatchNorm2d
+#if not import, there will be an error
+from singa.tensor import to_numpy
+
+
+class BackendRep(backendRep):
+ def __init__(self,model,device):
+ self.model, self.modeldic = Backend.onnx_model_init(model,device)
+ self.handledic={}
+ def run(self,inputs):
+ self.y,self.modeldic=Backend.run(self.model, self.modeldic,inputs,self.handledic)
+ return self.y
+
+
+
+class Backend(backend):
+
+ @staticmethod
+ def convhandle(name,handledic,x,model):
+ if(name in handledic):return handledic
+ i = Backend.find_name(model,name)
+
+ shape = Backend.find_shape(model,i.input[1])
+ cin,cout,k=shape[1], shape[0], (shape[2],shape[2])
+ padding=(int(i.attribute[1].ints[0]),int(i.attribute[1].ints[0]))
+ stride=(int(i.attribute[2].ints[0]),int(i.attribute[2].ints[0]))
+
+ handledic[name] = singa.CudnnConvHandle(x.data, k, stride,padding, cin, cout, True)
+ handledic[name].device_id = x.device.id()
+ return handledic
+
+
+ @staticmethod
+ def MaxPool2dhandle(name,handledic,x,model):
+ if(name in handledic):return handledic
+ i = Backend.find_name(model,name)
+ k = (int(i.attribute[0].ints[0]),int(i.attribute[0].ints[0]))
+ padding=(int(i.attribute[1].ints[0]),int(i.attribute[1].ints[0]))
+ stride=(int(i.attribute[2].ints[0]),int(i.attribute[2].ints[0]))
+
+ handledic[name] = singa.CudnnPoolingHandle(x.data, k, stride, padding, True)
+ handledic[name].device_id = x.device.id()
+ return handledic
+
+ @staticmethod
+ def AveragePoolhandle(name,handledic,x,model):
+ if(name in handledic):return handledic
+ i = Backend.find_name(model,name)
+ k = (int(i.attribute[0].ints[0]),int(i.attribute[0].ints[0]))
+ padding=(int(i.attribute[1].ints[0]),int(i.attribute[1].ints[0]))
+ stride=(int(i.attribute[2].ints[0]),int(i.attribute[2].ints[0]))
+
+ handledic[name] = singa.CudnnPoolingHandle(x.data, k, stride, padding, False)
+ handledic[name].device_id = x.device.id()
+ return handledic
+
+ @staticmethod
+ def BatchNormalizationhandle(name,handledic,x,model):
+ if(name in handledic):return handledic
+ handledic[name] = singa.CudnnBatchNormHandle(0.9, x.data)
+ handledic[name].device_id = x.device.id()
+ return handledic
+
+
+
+
+
+
+ @staticmethod
+ def onnx_model_init(model,device):
+ '''
+ input model
+
+ return: model and model dictionary
+ '''
+
+ modeldic = {}
+ for i in model.graph.node:
+ if (i.op_type == 'Constant'):
+ modeldic[str(i.output[0])] = tensor.Tensor(device=device,data=numpy_helper.to_array(i.attribute[0].t),requires_grad=True, stores_grad=True)
+
+ return model,modeldic
+
+
+ @staticmethod
+ def find_name(model,name):
+ for i in model.graph.node:
+ if (i.name == name):
+ return i
+
+
+ @staticmethod
+ def find_shape(model,input):
+ '''
+ # find weight shape for layers
+ '''
+ for i in model.graph.node:
+ if (i.op_type == 'Constant' and i.output[0] == input):
+ return numpy_helper.to_array(i.attribute[0].t).shape
+
+
+ @staticmethod
+ def run_model(model,inputs,device):
+ model, modeldic = Backend.onnx_model_init(model,device)
+ return Backend.run(model, modeldic,inputs)[0]
+
+ @staticmethod
+ def run(model, modeldic,inputs,handledic={}):
+ '''
+ input: input for singa model
+ load other nodes of onnx
+ '''
+ supportLayer = ['Conv','MaxPool','AveragePool','BatchNormalization']
+ oper=modeldic
+ autograd.training = True
+ for counter,i in enumerate(model.graph.input):
+ oper[i.name] = inputs[counter]
+ for i in model.graph.node:
+ if (i.op_type == 'Relu'):
+ oper[str(i.output[0])] = autograd.relu(oper[str(i.input[0])])
+ elif (i.op_type == 'Softmax'):
+ oper[str(i.output[0])] = autograd.softmax(oper[str(i.input[0])])
+ elif (i.op_type == 'Add'):
+ oper[str(i.output[0])] = autograd.add(oper[str(i.input[0])], oper[str(i.input[1])])
+ elif (i.op_type == 'MatMul'):
+ oper[str(i.output[0])] = autograd.matmul(oper[str(i.input[0])], oper[str(i.input[1])])
+ elif (i.op_type == 'Flatten'):
+ oper[str(i.output[0])] = autograd.flatten(oper[str(i.input[0])])
+ elif(i.op_type == 'Concat'):
+ oper[str(i.output[0])] = autograd.cat((oper[str(i.input[0])], oper[str(i.input[1])]),int(i.attribute[0].i))
+ elif(i.op_type == 'Tanh'):
+ oper[str(i.output[0])] = autograd.tanh(oper[str(i.input[0])])
+ elif (i.op_type == 'Sigmoid'):
+ oper[str(i.output[0])] = autograd.sigmoid(oper[str(i.input[0])])
+ elif (i.op_type == 'Mul'):
+ oper[str(i.output[0])] = autograd.mul(oper[str(i.input[0])],oper[str(i.input[1])])
+ elif (i.op_type == 'Conv'):
+ handledic = Backend.convhandle(i.name,handledic,oper[str(i.input[0])],model)
Review comment:
I had written a if check :
` if(name in handledic):return handledic`
if I create handle before, will not create handle again
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