public Dictionary <string, float> Score(DataIter eval_data, EvalMetric eval_metric, int?num_batch = null,
IBatchEndCallback[] batch_end_callback = null,
IScoreEndCallback[] score_end_callback = null, bool reset = true, int epoch = 0,
Func <DataBatch, NDArrayDict> sparse_row_id_fn = null)
{
if (!Binded && !ParamsInitialized)
{
throw new Exception("Module not binded and param initialized");
}
eval_metric.Reset();
var actual_num_batch = 0;
while (eval_data.End())
{
if (num_batch.HasValue && eval_data.Cursor == num_batch.Value)
{
break;
}
var eval_batch = eval_data.Next();
Prepare(eval_batch, sparse_row_id_fn);
Forward(eval_batch, false);
UpdateMetric(eval_metric, eval_batch.Label, true);
if (batch_end_callback != null)
{
foreach (var callback in batch_end_callback)
{
callback.Invoke(epoch, eval_data.Cursor, eval_metric);
}
}
actual_num_batch++;
}
if (score_end_callback != null)
{
foreach (var callback in score_end_callback)
{
callback.Invoke(epoch, actual_num_batch, eval_metric, new FuncArgs());
}
}
return(eval_metric.GetNameValue());
}
public IEnumerable <(NDArrayList, int, DataBatch)> IterPredict(DataIter eval_data, int?num_batch = null,
bool reset = true, int epoch = 0, Func <DataBatch, NDArrayDict> sparse_row_id_fn = null)
{
if (!Binded && !ParamsInitialized)
{
throw new Exception("Module not binded and param initialized");
}
if (reset)
{
eval_data.Reset();
}
while (eval_data.End())
{
if (num_batch.HasValue && eval_data.Cursor == num_batch.Value)
{
break;
}
var eval_batch = eval_data.Next();
Prepare(eval_batch, sparse_row_id_fn);
Forward(eval_batch, false);
var pad = eval_batch.Pad.Value;
var outputs = new NDArrayList();
foreach (var list in GetOutputs())
{
foreach (var @out in list)
{
outputs.Add(@out[$"0:{@out.Shape[0] - pad}"]);
}
}
yield return(outputs.ToArray(), eval_data.Cursor, eval_batch);
}
}
internal static void TrainMultiDevice(Symbol symbol, Context[] ctx, string[] arg_names, string[] param_names, string[] aux_names, NDArrayDict arg_params, NDArrayDict aux_params, int begin_epoch, int end_epoch, int?epoch_size, Optimizer optimizer, KVStore kvstore, bool update_on_kvstore, DataIter train_data, DataIter eval_data = null, EvalMetric eval_metric = null, IEpochEndCallback epoch_end_callback = null, IBatchEndCallback batch_end_callback = null, int[] work_load_list = null, Monitor monitor = null, IEvalEndCallback eval_end_callback = null, IEvalBatchEndCallback eval_batch_end_callback = null, Func <int, Symbol> sym_gen = null)
{
var executor_manager = new DataParallelExecutorManager(symbol: symbol,
ctx: ctx,
train_data: train_data,
arg_names: arg_names,
param_names: param_names,
aux_names: aux_names,
work_load_list: work_load_list,
sym_gen: sym_gen);
if (monitor != null)
{
executor_manager.InstallMonitor(monitor);
}
executor_manager.SetParams(arg_params, aux_params);
Updater updater = null;
if (!update_on_kvstore)
{
updater = Optimizer.GetUpdater(optimizer);
}
else
{
kvstore.SetOptimizer(optimizer);
}
if (kvstore != null)
{
InitializeKVStore(kvstore: kvstore,
param_arrays: new List <NDArrayList>()
{
executor_manager.ParamArrays
},
arg_params: arg_params,
param_names: executor_manager.param_names,
update_on_kvstore: update_on_kvstore);
}
train_data.Reset();
for (int epoch = begin_epoch; epoch < end_epoch; epoch++)
{
var tic = DateTime.Now;
eval_metric.Reset();
int nbatch = 0;
while (true)
{
bool do_reset = true;
while (!train_data.End())
{
var data_batch = train_data.Next();
executor_manager.LoadDataBatch(data_batch);
if (monitor != null)
{
monitor.Tic();
}
executor_manager.Forward(true);
executor_manager.Backward();
if (update_on_kvstore)
{
if (kvstore.Type.Contains("nccl"))
{
UpdateParamsOnKVStoreNCCL(new List <NDArrayList>()
{
executor_manager.ParamArrays
}, new List <NDArrayList>()
{
executor_manager.GradArrays
}, kvstore, executor_manager.param_names);
}
else
{
UpdateParamsOnKVStore(new List <NDArrayList>()
{
executor_manager.ParamArrays
}, new List <NDArrayList>()
{
executor_manager.GradArrays
}, kvstore, executor_manager.param_names);
}
}
else
{
UpdateParams(new List <NDArrayList>()
{
executor_manager.ParamArrays
}, new List <NDArrayList>()
{
executor_manager.GradArrays
}, updater, ctx.Length, kvstore, executor_manager.param_names);
}
if (monitor != null)
{
monitor.TocPrint();
}
executor_manager.UpdateMetric(eval_metric, data_batch.Label);
nbatch++;
if (batch_end_callback != null)
{
MultipleCallbacks(batch_end_callback, epoch, nbatch, eval_metric);
}
if (epoch_size.HasValue && nbatch >= epoch_size.Value)
{
do_reset = false;
break;
}
}
if (do_reset)
{
Logger.Info($"Epoch[{epoch}] Resetting Data Iterator");
train_data.Reset();
}
if (epoch_size.HasValue)
{
if (nbatch >= epoch_size.Value)
{
break;
}
else
{
break;
}
}
}
var toc = DateTime.Now;
Logger.Info($"Epoch[{epoch}] Time cost={(toc - tic).TotalSeconds}");
if (epoch_end_callback != null || epoch + 1 == end_epoch)
{
executor_manager.CopyTo(arg_params, aux_params);
}
MultipleCallbacks(epoch_end_callback, epoch, symbol, arg_params, aux_params);
if (eval_data != null)
{
eval_metric.Reset();
eval_data.Reset();
int total_num_batch = 0;
int i = 0;
while (!eval_data.End())
{
var eval_batch = eval_data.Next();
executor_manager.LoadDataBatch(eval_batch);
executor_manager.Forward();
executor_manager.UpdateMetric(eval_metric, eval_batch.Label);
if (eval_batch_end_callback != null)
{
MultipleCallbacks(eval_batch_end_callback, epoch, i, eval_metric);
}
total_num_batch++;
}
if (eval_end_callback != null)
{
MultipleCallbacks(eval_end_callback, epoch, eval_metric);
}
}
}
}
public List <NDArrayList> Predict(DataIter eval_data, int?num_batch = null, bool merge_batches = true,
bool reset = true, bool always_output_list = true, Func <DataBatch, NDArrayDict> sparse_row_id_fn = null)
{
if (!Binded && !ParamsInitialized)
{
throw new Exception("Module not binded and param initialized");
}
if (reset)
{
eval_data.Reset();
}
var output_list = new List <NDArrayList>();
var output_list2 = new NDArrayList();
while (eval_data.End())
{
if (num_batch.HasValue && eval_data.Cursor == num_batch.Value)
{
break;
}
var eval_batch = eval_data.Next();
Prepare(eval_batch, sparse_row_id_fn);
Forward(eval_batch, false);
var pad = eval_batch.Pad.Value;
var outputs = new NDArrayList();
foreach (var list in GetOutputs())
{
foreach (var @out in list)
{
outputs.Add(@out[$"0:{@out.Shape[0] - pad}"].Copy());
}
}
output_list.Add(outputs.ToArray());
}
if (output_list.Count == 0)
{
return(output_list);
}
if (merge_batches)
{
var num_outputs = output_list[0].Length;
foreach (var @out in output_list)
{
if (@out.Length != num_outputs)
{
throw new Exception("Cannot merge batches, as num of outputs is not the same " +
"in mini-batches. Maybe bucketing is used?");
}
output_list2.Add(nd.Concat(@out));
}
return(new List <NDArrayList> {
output_list2.ToArray()
});
}
return(output_list);
}
public void Fit(DataIter train, uint epochs = 1, uint batchSize = 32, DataIter validation = null, bool shuffle = false)
{
string labelName = "label";
var label = Symbol.Variable(labelName);
List <uint> inputShape = new List <uint>();
inputShape.Add(batchSize);
inputShape.AddRange(InputShape);
args["X"] = new NDArray(new Shape(inputShape.ToArray()));
args[labelName] = new NDArray(new Shape(batchSize));
Model.InferArgsMap(mx.Device, args, args);
var defaultInitializer = new Initializers.GlorotUniform();
foreach (var arg in args)
{
if (ParamInitializers.ContainsKey(arg.Key))
{
ParamInitializers[arg.Key].Generate(arg.Value);
}
else
{
defaultInitializer.Generate(arg.Value);
}
}
using (var exec = Model.SimpleBind(mx.Device, args))
{
var argNames = Model.ListArguments();
// Start training
var sw = new Stopwatch();
for (var iter = 1; iter <= epochs; iter++)
{
uint samples = 0;
train.BatchSize = batchSize;
train.Reset();
Metric.Reset();
TrainMetric.Reset();
sw.Restart();
while (train.IterNext())
{
samples += batchSize;
var dataBatch = train.Next();
// Set data and label
dataBatch.Data[0].CopyTo(args["X"]);
dataBatch.Label[0].CopyTo(args[labelName]);
// Compute gradients
exec.Forward(true);
exec.Backward();
TrainMetric.Update(args[labelName], exec.Output);
// Update parameters
for (var i = 0; i < argNames.Count; ++i)
{
if (argNames[i] == "X" || argNames[i] == labelName)
{
continue;
}
ModelOptimizer.Update(i, exec.ArgmentArrays[i], exec.GradientArrays[i], null);
}
}
sw.Stop();
if (validation != null)
{
validation.BatchSize = batchSize;
validation.Reset();
while (validation.IterNext())
{
var dataBatch = validation.Next();
dataBatch.Data[0].CopyTo(args["X"]);
dataBatch.Label[0].CopyTo(args[labelName]);
NDArray.WaitAll();
// Forward pass is enough as no gradient is needed when evaluating
exec.Forward(false);
Metric.Update(args[labelName], exec.Output);
}
}
var duration = sw.ElapsedMilliseconds == 0 ? 1 : sw.ElapsedMilliseconds;
if (validation == null)
{
Logging.LG($"Epoch: {iter} {Convert.ToInt32(samples * 1000 / duration)} samples/sec Train_Metric: {TrainMetric.Get()}");
}
else
{
Logging.LG($"Epoch: {iter} {Convert.ToInt32(samples * 1000 / duration)} samples/sec, Train_Metric: {TrainMetric.Get()}, Val_Metric: {Metric.Get()}");
}
}
}
//MXNet.MXNotifyShutdown();
}
public void Fit(DataIter train, uint epochs = 1, uint batchSize = 32, DataIter validation = null, bool shuffle = false)
{
var args = new SortedDictionary <string, NDArray>();
string labelName = "label";
var label = Symbol.Variable(labelName);
args["X"] = new NDArray(new Shape(batchSize, (uint)InputShape[0]));
args[labelName] = new NDArray(new Shape(batchSize, (uint)OutputShape.Size));
CompiledModel.InferArgsMap(GlobalParam.Device, args, args);
var initializer = new SiaDNN.Initializers.GlorotUniform();
foreach (var arg in args)
{
initializer.Operator(arg.Key, arg.Value);
}
ModelOptimizer.SetParam("rescale_grad", 1.0 / batchSize);
using (var exec = CompiledModel.SimpleBind(GlobalParam.Device, args))
{
var argNames = CompiledModel.ListArguments();
// Start training
var sw = new Stopwatch();
for (var iter = 0; iter < epochs; ++iter)
{
uint samples = 0;
train.BatchSize = batchSize;
train.Reset();
sw.Restart();
while (train.Next())
{
samples += batchSize;
var dataBatch = train.GetDataBatch();
// Set data and label
dataBatch.Data.CopyTo(args["X"]);
dataBatch.Label.CopyTo(args[labelName]);
// Compute gradients
exec.Forward(true);
exec.Backward();
// Update parameters
for (var i = 0; i < argNames.Count; ++i)
{
if (argNames[i] == "X" || argNames[i] == labelName)
{
continue;
}
ModelOptimizer.Update(i, exec.ArgmentArrays[i], exec.GradientArrays[i]);
}
Metric.Update(dataBatch.Label, exec.Outputs[0]);
}
sw.Stop();
if (validation != null)
{
validation.BatchSize = batchSize;
validation.Reset();
while (validation.Next())
{
var dataBatch = validation.GetDataBatch();
dataBatch.Data.CopyTo(args["X"]);
dataBatch.Label.CopyTo(args[labelName]);
// Forward pass is enough as no gradient is needed when evaluating
exec.Forward(false);
Metric.Update(dataBatch.Label, exec.Outputs[0]);
}
}
var duration = sw.ElapsedMilliseconds / 1000.0;
if (validation == null)
{
Logging.LG($"Epoch: {iter} {samples / duration} samples/sec Train_Metric: {Metric.Get()}");
}
else
{
Logging.LG($"Epoch: {iter} {samples / duration} samples/sec, Train_Metric: {Metric.Get()}, Val_Metric: {Metric.Get()}");
}
}
}
MXNet.MXNotifyShutdown();
}