#pragma once
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#include <unordered_set>
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#include "caffe2/core/net.h"
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#include "caffe2/core/tensor.h"
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#include "caffe2/predictor/predictor_config.h"
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namespace caffe2 {
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class CAFFE2_API Predictor {
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public:
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using TensorList = std::vector<TensorCPU>;
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using TensorMap = std::unordered_map<std::string, TensorCPU>;
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Predictor(
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const NetDef& init_net,
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const NetDef& run_net,
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Workspace* parent = nullptr,
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bool run_init = true,
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int optimization = 1);
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Predictor(PredictorConfig config);
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virtual ~Predictor() {}
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// Executes `run_net` on the inputs.
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// The first `inputs.size()` inputs from run_net::external_inputs
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// are shared with the data in `inputs`.
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// Precondition:
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// inputs.size() <= run_net_.external_inputs.size()
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// Postcondition:
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// outputs->size() == run_net.external_inputs.size()
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// NOTE: output is a part of thread local workspace
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// and is only valid until the next predictor execution.
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// Returns true on success
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virtual bool operator()(const TensorList& inputs, TensorList* outputs);
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// Similar to run, but consumes a map of name to tensor as input
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bool operator()(const TensorMap& inputs, TensorList* outputs);
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// Similar to the other run fns, except inputs and outputs are both maps of
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// string name to tensor.
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bool operator()(const TensorMap& inputs, TensorMap* outputs);
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const NetDef& def() const {
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return *config_.predict_net;
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};
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Workspace* ws() {
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return config_.ws.get();
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};
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const std::vector<std::string>& input_names() const {
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return config_.input_names;
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}
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const std::vector<std::string>& output_names() const {
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return config_.output_names;
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}
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private:
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bool run_map_workspace(const TensorMap& inputs);
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protected:
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PredictorConfig config_;
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};
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} // namespace caffe2
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