morgolock
commented
1 year ago Hi @GGGGxxxxxxxxr
Normally the first iteration is much slower than the other iterations. Make sure you run multiple iterations and measure the time for each one.
for (int j=0; j < num_iterations; ++j)
{
gettimeofday(&start, NULL);
conv_1.run();
Nact_1.run();
Npool_1.run();
LRN_1.run();
//conv_2
// conv_2.run();
// Nact_2.run();
conv_2.run();
Nact_2.run();
Npool_2.run();
LRN_2.run();
//conv_3
conv_3.run();
Nact_3.run();
//conv_4
conv_4.run();
Nact_4.run();
//conv_5
// conv_5.run();
// Nact_5.run();
conv_5.run();
Nact_5.run();
Npool_5.run();
//fc_8
fc_8.run();
softmax.run();
gettimeofday(&end, NULL);
std::cout << "Iteration " << j << compute_elapsed(start,end) << std::endl;
}Hope this helps.
GGGGxxxxxxxxr
Hi!
I have tried to implement neural networks on Android based on Arm Compute Library. The case is that: When I tried to use direct function call to build a network such as Alexnet, the inference time is much longer than the speed performance of examples/graph_alexnet.cpp.
Here is part of the code how I implement AlexNet with direct function call:
// Tensor Definition constexpr unsigned int input_width = 227; constexpr unsigned int input_height = 227; constexpr unsigned int input_fm = 3;
....
....
//Run the model gettimeofday(&start, NULL); conv_1.run(); Nact_1.run(); Npool_1.run(); LRN_1.run();
I have implemented this on my Realm X5Pro and with my personal function call method, it took 70ms on average for one inference but the graph_alexnet benchmark is much faster.
I have made the comments on the source codes in src/cpu/operators and I have noticed that both methods have called the same backend sources, but why there is a huge difference in the speed performance?
Thank!