[GitHub] xiaolin-cheng opened a new issue #14106: Question on ResNet C++ example with Cifar10 dataset

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xiaolin-cheng opened a new issue #14106: Question on ResNet C++ example with Cifar10 dataset
URL: https://github.com/apache/incubator-mxnet/issues/14106
 
 
   Hello all,
   
   I modified the ResNet C++ example to be a ResNet18 model (4 levels and 2 blocks) working with Cifar10 dataset, but unfortunately it didn't work (validation accuracy always ~0.1). Only ResNet6 (1 level, 2 blocks) and ResNet10 (2 levels, 2 blocks) worked. My code is the following. Could you help me point out where I did wrong? Thank you very much!
   
   `Symbol getConv(const std::string & name, Symbol data,
                  int  num_filter,
                  Shape kernel, Shape stride, Shape pad,
                  bool with_relu,
                  mx_float bn_momentum) 
   `
   {
    Symbol conv_w(name + "_w");
     Symbol conv = ConvolutionNoBias(name, data, conv_w,
                                     kernel, num_filter, stride, Shape(1, 1),
                                     pad, 1, 512);
   
     Symbol gamma(name + "_gamma");
     Symbol beta(name + "_beta");
     Symbol mmean(name + "_mmean");
     Symbol mvar(name + "_mvar");
     Symbol bn = BatchNorm(name + "_bn", conv, gamma, beta, mmean, mvar, 2e-5, bn_momentum, false);
   
     if (with_relu) {
       return Activation(name + "_relu", bn, "relu");
     } else {
       return bn;
     }
   }
   
   `Symbol makeBlock(const std::string & name, Symbol data, int num_filter,
                    bool dim_match, mx_float bn_momentum) `
   {
     Shape stride;
     if (dim_match) {
       stride = Shape(1, 1);
     } else {
       stride = Shape(2, 2);
     }
   
     Symbol conv1 = getConv(name + "_conv1", data, num_filter,
                            Shape(3, 3), stride, Shape(1, 1),
                            true, bn_momentum);
   
     Symbol conv2 = getConv(name + "_conv2", conv1, num_filter,
                            Shape(3, 3), Shape(1, 1), Shape(1, 1),
                            false, bn_momentum);
   
     Symbol shortcut;
   
     if (dim_match) {
       shortcut = data;
     } else {
       Symbol shortcut_w(name + "_proj_w");
       shortcut = ConvolutionNoBias(name + "_proj", data, shortcut_w,
                                    Shape(2, 2), num_filter,
                                    Shape(2, 2), Shape(1, 1), Shape(0, 0),
                                    1, 512);
     }
   
     Symbol fused = shortcut + conv2;
     return Activation(name + "_relu", fused, "relu");
   }
   
   `Symbol getBody(Symbol data, int num_level, int num_block, int num_filter, mx_float bn_momentum) {
     for (int level = 0; level < num_level; level++) `
   {
       for (int block = 0; block < num_block; block++) {
         data = makeBlock("level" + std::to_string(level + 1) + "_block" + std::to_string(block + 1),
                          data, num_filter * (std::pow(2, level)),
                          (level == 0 || block > 0), bn_momentum);
       }
     }
     return data;
   }
   
   `Symbol ResNetSymbol(int num_class, int num_level = 2, int num_block = 2, int num_filter = 64, mx_float bn_momentum = 0.9)`
   {
     // data and label
     Symbol data = Symbol::Variable("data");
     Symbol data_label = Symbol::Variable("data_label");
   
   //===== top =====//
     Symbol conv = getConv("conv0", data, num_filter,
                           Shape(7, 7), Shape(2, 2), Shape(3, 3),
                           true, bn_momentum);
     Symbol max_pool = Pooling("max_pool", conv, Shape(3, 3), PoolingPoolType::kMax,
                               false, false, PoolingPoolingConvention::kValid,
                               Shape(2, 2), Shape(1, 1));
   
   //===== body =====//
     Symbol body = getBody(conv, num_level, num_block, num_filter, bn_momentum);
   
   //===== pool and fc =====//
     Symbol avg_pool = Pooling("avg_pool", body, Shape(7, 7), PoolingPoolType::kAvg,
                               true, false, PoolingPoolingConvention::kValid);
     Symbol flatten = Flatten("flatten", avg_pool);
     Symbol fc_w("fc_w"), fc_b("fc_b");
     Symbol fc = FullyConnected("fc", flatten, fc_w, fc_b, num_class);
   
     return SoftmaxOutput("softmax", fc, data_label);
   }

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