Function create_batch_norm_node

internal/tests/executor/tests/test_batch_norm.cpp:52–193

float op_fops;

Node* create_batch_norm_node(int n, int c, int h, int w)
{
    Operator* op = OpManager::CreateOp("BatchNormalization");
    BatchNorm* bn_op = dynamic_cast<BatchNorm*>(op);

    BatchNormParam* param = bn_op->GetParam();
    param->caffe_flavor = 1;
    param->rescale_factor = 1.2;
    param->eps = 1e-5;

    /* input shape */
    int input_h = h;
    int input_w = w;
    int input_c = c;
    int input_n = n;

    std::vector<int> input_dims = {input_n, input_c, input_h, input_w};
    std::vector<int> channel_dims = {input_c};

    op_fops = 1.0 * input_n * input_c * input_h * input_w * 2;

    Node* node = new Node("test_convolution");

    node->SetOp(bn_op);

    // prepare tensor: input/gmma/beta/mean/vars
    Tensor* tensor;
    int mem_size;
    void* addr;

    tensor = new Tensor("input");

    tensor->SetDataType("float32");
    tensor->SetType(kVarTensor);

    TShape* shape = &tensor->GetShape();

    shape->SetDataLayout("NCHW");
    shape->SetDim(input_dims);

    node->SetInputPort(0, tensor);

    mem_size = tensor->GetTotalSize();
    addr = std::malloc(mem_size);
    init_tensor_data(( float* )addr, mem_size / sizeof(float), -1);

    set_tensor_mem(tensor, addr, mem_size, std::free);

    tensor = new Tensor("gamma");

    tensor->SetDataType("float32");
    tensor->SetType(kVarTensor);

    shape = &tensor->GetShape();

    shape->SetDataLayout("W");
    shape->SetDim(channel_dims);