TensorFlow.NET/src/TensorFlowNET.Keras/Engine/Model.cs at master · feelsyt/TensorFlow.NET · GitHub

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using System.Diagnostics;

using Tensorflow.Common.Types;

using Tensorflow.Framework.Models;

using Tensorflow.Keras.ArgsDefinition;

using Tensorflow.Keras.Losses;

using Tensorflow.Keras.Saving.SavedModel;

using Tensorflow.Keras.Utils;

using Tensorflow.Train;

using Tensorflow.Util;

namespace Tensorflow.Keras.Engine

{

/// <summary>

/// `Model` groups layers into an object with training and inference features.

/// </summary>

public partial class Model : Layer, IModel

{

#pragma warning disable CS0169 // The field 'Model._cloning' is never used

bool _cloning;

#pragma warning restore CS0169 // The field 'Model._cloning' is never used

#pragma warning disable CS0108 // Member hides inherited member; missing new keyword

#pragma warning disable CS0414 // The field 'Model._is_compiled' is assigned but its value is never used

bool _is_compiled;

#pragma warning restore CS0414 // The field 'Model._is_compiled' is assigned but its value is never used

#pragma warning restore CS0108 // Member hides inherited member; missing new keyword

ILossFunc loss;

IOptimizer optimizer;

IVariableV1 _steps_per_execution;

protected bool _is_graph_network;

public Tensors inputs;

protected Tensors outputs;

protected List<string> input_names;

public string[] output_names;

IVariableV1 _train_counter;

IVariableV1 _test_counter;

IVariableV1 _predict_counter;

bool _base_model_initialized;

bool stop_training;

TensorSpec _saved_model_inputs_spec;

public bool IsGraphNetwork => _is_graph_network;

public IOptimizer Optimizer

{

get => optimizer;

set => optimizer = value;

}

public bool Stop_training

{

get => stop_training;

set => stop_training = value;

}

public Model(ModelArgs args)

: base(args)

{

_init_batch_counters();

}

public void _set_inputs(TensorSpec inputs)

{

_set_save_spec(inputs);

}

internal void _set_save_spec(TensorSpec inputs)

{

if(_saved_model_inputs_spec is not null)

{

return;

}

var input_names = this.input_names;

if(input_names is null || input_names.Count == 0)

{

input_names = compile_utils.create_pseudo_input_names(inputs);

}

var flat_inputs = nest.flatten(inputs);

List<TensorSpec> specs = new();

foreach(var (name, tensor) in zip(input_names, flat_inputs))

{

specs.Add(tf_utils.get_tensor_spec(tensor, dynamic_batch: false, name: name));

}

var packed_specs = nest.pack_sequence_as(inputs, specs) as TensorSpec;

Debug.Assert(specs is not null);

_saved_model_inputs_spec = packed_specs;

if(this is Sequential && _buildInputShape is null)

{

_buildInputShape = nest.map_structure<TensorSpec, TensorShapeConfig>(x => x is null ? null : x.shape, packed_specs);

}

internal override void Initialize(LayerArgs args)

{

_init_batch_counters();

base.Initialize(args);

}

void _configure_steps_per_execution(int steps_per_execution)

{

_steps_per_execution = tf.Variable(steps_per_execution,

dtype: TF_DataType.TF_INT64,

aggregation: VariableAggregation.OnlyFirstReplica);

}

void _reset_compile_cache()

{

// Used to cache `trainable` attr of `Layer`s for `fit`.

_compiled_trainable_state = _get_trainable_state();

keras.backend._GRAPH = null;

}

void _init_batch_counters()

{

_train_counter = tf.Variable(0L,

dtype: TF_DataType.TF_INT64,

aggregation: VariableAggregation.OnlyFirstReplica);

_test_counter = tf.Variable(0L,

dtype: TF_DataType.TF_INT64,

aggregation: VariableAggregation.OnlyFirstReplica);

_predict_counter = tf.Variable(0L,

dtype: TF_DataType.TF_INT64,

aggregation: VariableAggregation.OnlyFirstReplica);

}

public override List<ILayer> Layers

=> _flatten_layers(recursive: false, include_self: false).ToList();

public override List<IVariableV1> TrainableWeights

{

get

{

// skip the assertion of weights created.

var variables = new List<IVariableV1>();

if (!Trainable)

{

return variables;

}

foreach (var trackable_obj in _self_tracked_trackables)

{

if (trackable_obj.Trainable)

variables.AddRange(trackable_obj.TrainableWeights);

}

variables.AddRange(_trainable_weights);

return variables.Distinct().ToList();

}

public override List<IVariableV1> NonTrainableWeights

{

get

{

// skip the assertion of weights created.

var variables = new List<IVariableV1>();

foreach (var trackable_obj in _self_tracked_trackables)

{

variables.AddRange(trackable_obj.NonTrainableWeights);

}

if (!Trainable)

{

var trainable_variables = new List<IVariableV1>();

foreach (var trackable_obj in _self_tracked_trackables)

{

variables.AddRange(trackable_obj.TrainableWeights);

}

variables.AddRange(trainable_variables);

variables.AddRange(_trainable_weights);

variables.AddRange(_non_trainable_weights);

}

return variables.Distinct().ToList();

}

public override IDictionary<string, Trackable> _trackable_children(SaveType save_type = SaveType.CHECKPOINT, IDictionary<string, IDictionary<Trackable, ISerializedAttributes>>? cache = null)

{

if(save_type == SaveType.SAVEDMODEL)

{

//TODO: deal with `train_function`, `test_function`, `predict_function`, `train_tf_function`.

}

var children = base._trackable_children(save_type, cache);

return children;

}

public override void SetAttr(string name, object value)

{

// TODO(Rinne): deal with "_self_setattr_tracking".

//if(nest.flatten(value).All(v => v is Layer or IVariableV1 || base_layer_utils.has_weights(v)))

//{

// this._base_model_initialized;

//}

base.SetAttr(name, value);

}

TensorFlow.NET/src/TensorFlowNET.Keras/Engine/Model.cs at master · feelsyt/TensorFlow.NET · GitHub

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