this is not network precision. It's precision of input layer..

well, I remembered Maxim provided some code like:
auto nGraphFunc = network.getFunction();
for (auto &node : nGraphFunc->get_ordered_ops()) {
// auto type0 = node->input_value(0).get_element_type(); //input
auto type1 = node->input_value(1).get_element_type(); //weights
}
so is that expected? should we use typ1 or type0 as the network precision? there are lots of ops, should we use only the first one? thanks!

int8 network - if we have FakeQuantize operations in the graph
fp16 network - if we have convolution weights stored in fp16
fp32 network - if we have convolution weights stored in fp32

I'm a bit confused, as this PR overlaps heavily with (much larger) #5545, let's focus on 5545, OR change the 5545 to be merged into the branch from THIS requets.
When both requests are trying to merege to the master, we are reviewing the overlapping delta twice

ok, I will change the 5545 to be merged to this branch. the reason I create 5545 and cherry-pick this commit is because I want to test jenkins CI, it seems 5545 CI is OK, I will merge it to this. thanks!

Let's focus on #5545 , which already contains this PR's commit, I will close this PR later.