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#include "caffe2/operators/weighted_sample_op.h"
namespace caffe2 {
template <>
bool WeightedSampleOp<float, CPUContext>::RunOnDevice() {
CAFFE_ENFORCE_EQ(
InputSize(),
OutputSize(),
"The number of tensors of the input and the output must be the same.");
auto& weights = Input(0);
int batch_size = weights.size(0);
int weights_dim = weights.size(1);
if (batch_size > 0 && weights_dim > 0) {
cum_mass_.resize(weights_dim);
const float* mat_weights = weights.template data<float>();
const float* mat_values = nullptr;
auto* out_idx = Output(0, {batch_size, 1}, at::dtype<int>());
int* output_indices = out_idx->template mutable_data<int>();
float* output_values = nullptr;
if (InputSize() == 2) {
auto& values = Input(1);
CAFFE_ENFORCE_EQ(
weights.sizes(),
values.sizes(),
"The sampling weights tensor and the sampling values tensor must have the same dimensions.");
mat_values = values.template data<float>();
auto* out_value = Output(1, {batch_size, 1}, at::dtype<float>());
output_values = out_value->template mutable_data<float>();
}
for (int i = 0; i < batch_size; i++) {
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
float r;
int offset = i * weights_dim;
cum_mass_[0] = mat_weights[offset];
for (int j = 1; j < weights_dim; j++) {
cum_mass_[j] = cum_mass_[j - 1] + mat_weights[offset + j];
}
math::RandUniform<float, CPUContext>(
1, 0.0f, cum_mass_[cum_mass_.size() - 1], &r, &context_);
// Makes the element in cum_mass_ slightly bigger
// to compensate inaccuracy introduced due to rounding,
cum_mass_[cum_mass_.size() - 1] += 0.01f;
auto lb = lower_bound(cum_mass_.begin(), cum_mass_.end(), r);
CAFFE_ENFORCE(lb != cum_mass_.end(), "Cannot find ", r, " in cum_mass_.");
output_indices[i] = static_cast<int>(lb - cum_mass_.begin());
if (output_values) {
output_values[i] =
static_cast<float>(mat_values[offset + (lb - cum_mass_.begin())]);
}
}
} else {
C10_UNUSED auto* out_idx = Output(0, {0}, at::dtype<int>());
if (OutputSize() == 2) {
auto* out_value = Output(1, {0}, at::dtype<float>());
out_value->template mutable_data<float>();
}
}
return true;
}
REGISTER_CPU_OPERATOR(WeightedSample, WeightedSampleOp<float, CPUContext>);
OPERATOR_SCHEMA(WeightedSample)
.NumInputs(1, 2)
.NumOutputs(1, 2)
.TensorInferenceFunction([](const OperatorDef& def,
const vector<TensorShape>& in) {
vector<TensorShape> out(2);
int batch_size = in[0].dims(0);
out[0] = CreateTensorShape(vector<int>{batch_size}, TensorProto::INT32);
out[1] = CreateTensorShape(vector<int>{batch_size}, TensorProto::FLOAT);
return out;
})
.SetDoc(R"DOC(
The operator performs sampling based on the input sampling weights for
each batch. All weights must be non-negative numbers.
The input is a 2-D tensor (Tensor) of size (batch_size x weights_dim).
For each batch, an index is randomly sampled from the distribution given by
the weights of the corresponding batch.
The output is a 1-D tensor (Tensor) of size (batch_size x 1) and
contains the index(es) of the sampled output.
)DOC")
.Input(
0,
"sampling_weights",
"A 2-D Tensor of size (batch_size x weights_dim)."
"All weights must be non-negative numbers.")
.Input(
1,
"sampling_values",
"An optional 2-D Tensor of size (batch_size x weights_dim)."
"Its values correspond to the sampling weights.")
.Output(
0,
"sampled_indexes",
"The output tensor contains index(es) sampled from distribution given"
"by the weight vector(s) in the input tensor"
"The output is a 1-D Tensor of size (batch_size x 1)")
.Output(
1,
"sampled_values",
"The output tensor contains value(s) selected by the sampled index(es)"
"It is a 1-D Tensor of size (batch_size x 1)");
SHOULD_NOT_DO_GRADIENT(WeightedSample);
} // namespace caffe2
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