1  # -*- coding: utf-8 -*-
  2   import mxnet as mx
  3   import numpy as np
  4   import logging
  5   ​
  6   class NDArrayReorg(mx.operator.NDArrayOp):
  7       def __init__(self, stride=2):
  8           super(NDArrayReorg, self).__init__(True)
  9           self.stride = stride
 10           self.fwd_kernel = None
 
 11           self.bwd_kernel = None
 12           
 13       def list_arguments(self):
 14           return ['data']
 15       
 16       def list_outputs(self):
 17           return ['output']
 18       
 19       def infer_shape(self, in_shape):
 20           data_shape = in_shape[0]
 21           output_shape = [in_shape[0][0], in_shape[0][1]*4
 22
 
                           , in_shape[0][2]/self.stride, in_shape[0][3]/self.stride]
 23                           
 24           return [data_shape], [output_shape]
 25           
 26       def forward(self, in_data, out_data):
 27           x = in_data[0]
 28           y = out_data[0]
 29           if self.fwd_kernel is
 
 None:
 30               self.fwd_kernel = mx.rtc('reorg',[('x',x)],[('y',y)],"""
 31               int i = threadIdx.x + blockIdx.x*blockDim.x ;
 32               int yw=y_dims[3];
 33               int yh = y_dims[2];
 34               int N = yw*yh;
 35               int xw=x_dims[3];
 36               int xh = x_dims[2];
 37               int len_block = x_dims[2]*x_dims[3];
 38               for(int j =0; j<xh; j+=2)
 39                   for(int k=0; k<xw; k+=2)
 40                   {   int t=j/2;
 41                       y[i*len_block+t*yw+k/2] = x[i*len_block+j*xw+k];
 42                       y[i*len_block+t*yw+k/2+N] = x[i*len_block + j*xw+k+1];
 43                       y[i*len_block+t*yw+k/2+2*N] = x[i*len_block +(j+1)*xw+k];
 44                       y[i*len_block+t*yw+k/2+3*N] = x[i*len_block +(j+1)*xw+k+1];                 
 45                   }       
 46               """)
 47           self.fwd_kernel.push([x],[y],(x.shape[0]*x.shape[1],1,1),(1,1,1))
 48       
 49       def backward(self, out_grad, in_data, out_data, in_grad):
 50           y = out_grad[0]
 51           dx = in_grad[0]
 52           if self.bwd_kernel is None:
 53               self.bwd_kernel = mx.rtc('reorg_grad',[('y',y)],[('dx', dx)],"""
 54               int i = threadIdx.x + blockIdx.x * blockDim.x;
 55               int yh = y_dims[2];
 56               int yw = y_dims[3];
 57               int N = yw*yh;
 58               int old_block = dx_dims[2]*dx_dims[3];
 59               for(int k=0;k<4;++k)
 60                   for(int j=0; j<yw; ++j)
 61                       for(int t=0; t<yh; ++t){
 62                           dx[i*old_block+2*j*yw+t*2+k]=y[i*old_block+k*N+j*yw+t];     
 63                   }           
 64               """)
 65           self.bwd_kernel.push([y],[dx],(y.shape[0]*y.shape[1]/4,1,1),(1,1,1))
 66           
 67   mnist = mx.test_utils.get_mnist()
 68   batch_size = 100
 69   train_iter = mx.io.NDArrayIter(mnist['train_data'], mnist['train_label'], batch_size, shuffle=True)
 70   val_iter = mx.io.NDArrayIter(mnist['test_data'], mnist['test_label'], batch_size)
 71   ​
 72   ​
 73   data = mx.sym.var('data')
 74   conv1 = mx.sym.Convolution(data=data, kernel=(5,5), num_filter=20)
 75   tanh1 = mx.sym.Activation(data=conv1, act_type="tanh")
 76   # pool1 = mx.sym.Pooling(data=tanh1, pool_type="max", kernel=(2,2), stride=(2,2))
 77   ​
 78   reorg = NDArrayReorg(stride=2)
 79   reg = reorg(data=tanh1, name='reorg')  
 80 conv2 = mx.sym.Convolution(data=reg, kernel=(5,5), num_filter=20)  
 81 tanh2 = mx.sym.Activation(data=conv2, act_type="tanh") # 80x8x8  
 82 ​  
 83 conv2 = mx.sym.Convolution(data=tanh2, kernel=(5,5), num_filter=50)  
 84 tanh2 = mx.sym.Activation(data=conv2, act_type="tanh")  
 85 # pool2 = mx.sym.Pooling(data=tanh2, pool_type="max", kernel=(2,2), stride=(2,2))  
 86 ​  
 87 flatten = mx.sym.flatten(data=tanh2)  
 88 fc1 = mx.sym.FullyConnected(data=flatten,num_hidden=500)  
 89 tanh3 = mx.sym.Activation(data=fc1, act_type="tanh")  
 90 ​  
 91 fc2 = mx.sym.FullyConnected(data=tanh3, num_hidden=10)  
 92 ​  
 93 mynet = mx.sym.SoftmaxOutput(data=fc2, name='softmax')  
 94 ​  
 95 print(mynet.infer_shape(data=(100,1,28,28)))  
 96 mynet_model = mx.mod.Module(symbol=mynet, context=mx.gpu())  
 97 ​  
 98 mynet_model.fit(train_iter,  
 99 eval_data=val_iter,  
100 optimizer='sgd',  
101 optimizer_params = {'learning_rate':0.1},  
102 eval_metric='acc',  
103 batch_end_callback=mx.callback.Speedometer(100,100),  
104 num_epoch=10)  
105 
106 test_iter = mx.io.NDArrayIter(mnist['test_data'], None, batch_size)  
107 prob = mynet_model.predict(test_iter)  
108 test_iter = mx.io.NDArrayIter(mnist['test_data'], mnist['test_label'], batch_size)  
109 # predict accuracy for lenet  
110 acc = mx.metric.Accuracy()  
111 mynet_model.score(test_iter, acc)  
112 print(acc) # 網路是隨便構建的，引數也是隨便選的，所以出來的值並沒有什麼參考價值，只是為了驗證呼叫mx.rtc建立cuda的kernel