|
|
|
@ -42,34 +42,28 @@ Status UniformAugOp::Compute(const std::vector<std::shared_ptr<Tensor>> &input,
|
|
|
|
|
std::vector<std::shared_ptr<Tensor>> *output) {
|
|
|
|
|
IO_CHECK_VECTOR(input, output);
|
|
|
|
|
|
|
|
|
|
// variables to generate random number to select ops from the list
|
|
|
|
|
std::vector<int> random_indexes;
|
|
|
|
|
|
|
|
|
|
// variables to copy the result to output if it is not already
|
|
|
|
|
std::vector<std::shared_ptr<Tensor>> even_out;
|
|
|
|
|
std::vector<std::shared_ptr<Tensor>> *even_out_ptr = &even_out;
|
|
|
|
|
int count = 1;
|
|
|
|
|
|
|
|
|
|
// select random indexes for candidates to be applied
|
|
|
|
|
for (int i = 0; i < num_ops_; ++i) {
|
|
|
|
|
random_indexes.insert(random_indexes.end(),
|
|
|
|
|
std::uniform_int_distribution<int>(0, tensor_op_list_.size() - 1)(rnd_));
|
|
|
|
|
}
|
|
|
|
|
// randomly select ops to be applied
|
|
|
|
|
std::vector<std::shared_ptr<TensorOp>> selected_tensor_ops;
|
|
|
|
|
std::sample(tensor_op_list_.begin(), tensor_op_list_.end(), std::back_inserter(selected_tensor_ops), num_ops_, rnd_);
|
|
|
|
|
|
|
|
|
|
for (auto it = random_indexes.begin(); it != random_indexes.end(); ++it) {
|
|
|
|
|
for (auto tensor_op = selected_tensor_ops.begin(); tensor_op != selected_tensor_ops.end(); ++tensor_op) {
|
|
|
|
|
// Do NOT apply the op, if second random generator returned zero
|
|
|
|
|
if (std::uniform_int_distribution<int>(0, 1)(rnd_)) {
|
|
|
|
|
continue;
|
|
|
|
|
}
|
|
|
|
|
std::shared_ptr<TensorOp> tensor_op = tensor_op_list_[*it];
|
|
|
|
|
|
|
|
|
|
// apply python/C++ op
|
|
|
|
|
if (count == 1) {
|
|
|
|
|
(*tensor_op).Compute(input, output);
|
|
|
|
|
(**tensor_op).Compute(input, output);
|
|
|
|
|
} else if (count % 2 == 0) {
|
|
|
|
|
(*tensor_op).Compute(*output, even_out_ptr);
|
|
|
|
|
(**tensor_op).Compute(*output, even_out_ptr);
|
|
|
|
|
} else {
|
|
|
|
|
(*tensor_op).Compute(even_out, output);
|
|
|
|
|
(**tensor_op).Compute(even_out, output);
|
|
|
|
|
}
|
|
|
|
|
count++;
|
|
|
|
|
}
|
|
|
|
|