private NDArray _run <T>(T fetches, Dictionary <Tensor, NDArray> feed_dict = null)
{
var feed_dict_tensor = new Dictionary <Tensor, NDArray>();
if (feed_dict != null)
{
foreach (var feed in feed_dict)
{
feed_dict_tensor[feed.Key] = feed.Value;
}
}
// Create a fetch handler to take care of the structure of fetches.
var fetch_handler = new _FetchHandler <T>(_graph, fetches, feed_dict_tensor);
// Run request and get response.
// We need to keep the returned movers alive for the following _do_run().
// These movers are no longer needed when _do_run() completes, and
// are deleted when `movers` goes out of scope when this _run() ends.
var _ = _update_with_movers();
var final_fetches = fetch_handler.fetches();
var final_targets = fetch_handler.targets();
// We only want to really perform the run if fetches or targets are provided,
// or if the call is a partial run that specifies feeds.
var results = _do_run(final_targets.Select(x => (Operation)(object)x).ToList(), final_fetches, feed_dict_tensor);
return(fetch_handler.build_results(null, results));
}
private NDArray _run(object fetches, FeedItem[] feed_dict = null)
{
var feed_dict_tensor = new Dictionary <object, object>();
var feed_map = new Dictionary <object, object>();
// Validate and process feed_dict.
if (feed_dict != null)
{
foreach (var subfeed in feed_dict)
{
var subfeed_t = _graph.as_graph_element(subfeed.Key, allow_tensor: true, allow_operation: false);
var subfeed_dtype = subfeed_t.dtype.as_numpy_datatype();
switch (subfeed.Value)
{
case float floatVal:
feed_dict_tensor[subfeed_t] = (NDArray)floatVal;
break;
case int intVal:
feed_dict_tensor[subfeed_t] = (NDArray)intVal;
break;
case string str:
feed_dict_tensor[subfeed_t] = (NDArray)str;
break;
default:
throw new NotImplementedException("_run subfeed");
}
feed_map[subfeed_t.name] = (subfeed_t, subfeed.Value);
}
}
// Create a fetch handler to take care of the structure of fetches.
var fetch_handler = new _FetchHandler(_graph, fetches, feed_dict_tensor);
// Run request and get response.
// We need to keep the returned movers alive for the following _do_run().
// These movers are no longer needed when _do_run() completes, and
// are deleted when `movers` goes out of scope when this _run() ends.
var _ = _update_with_movers();
var final_fetches = fetch_handler.fetches();
var final_targets = fetch_handler.targets();
// We only want to really perform the run if fetches or targets are provided,
// or if the call is a partial run that specifies feeds.
var results = _do_run(final_targets.Select(x => (Operation)(object)x).ToList(), final_fetches, feed_dict_tensor);
return(fetch_handler.build_results(this, results));
}
private unsafe object _run(Tensor fetches, Dictionary <Tensor, object> feed_dict = null)
{
var feed_dict_tensor = new Dictionary <Tensor, NDArray>();
if (feed_dict != null)
{
NDArray np_val = null;
foreach (var feed in feed_dict)
{
switch (feed.Value)
{
case float value:
np_val = np.asarray(value);
break;
}
feed_dict_tensor[feed.Key] = np_val;
}
}
// Create a fetch handler to take care of the structure of fetches.
var fetch_handler = new _FetchHandler(_graph, fetches);
// Run request and get response.
// We need to keep the returned movers alive for the following _do_run().
// These movers are no longer needed when _do_run() completes, and
// are deleted when `movers` goes out of scope when this _run() ends.
var _ = _update_with_movers();
var final_fetches = fetch_handler.fetches();
var final_targets = fetch_handler.targets();
// We only want to really perform the run if fetches or targets are provided,
// or if the call is a partial run that specifies feeds.
var results = _do_run(final_fetches);
return(fetch_handler.build_results(null, results));
}