TensorFlow.NET/src/TensorFlowNET.Keras/Engine/Functional.cs at master · prilcool/TensorFlow.NET · GitHub

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using System;

using System.Collections.Generic;

using System.Linq;

using Tensorflow.Keras.ArgsDefinition;

using Tensorflow.Keras.Utils;

using static Tensorflow.Binding;

namespace Tensorflow.Keras.Engine

{

/// <summary>

/// A `Functional` model is a `Model` defined as a directed graph of layers.

/// </summary>

public partial class Functional : Model

{

TensorShape _build_input_shape;

bool _compute_output_and_mask_jointly;

bool _expects_training_arg;

bool _expects_mask_arg;

bool _autocast;

List<ILayer> _output_layers;

List<ILayer> _input_layers;

List<KerasHistory> _input_coordinates;

List<KerasHistory> _output_coordinates;

public string[] NetworkNodes { get; set; }

Dictionary<long, int> tensor_usage_count;

public Functional(Tensors inputs, Tensors outputs, string name = null)

: base(new ModelArgs

{

Name = name,

Inputs = inputs,

Outputs = outputs

})

{

_input_layers = new List<ILayer>();

_output_layers = new List<ILayer>();

_input_coordinates = new List<KerasHistory>();

_output_coordinates = new List<KerasHistory>();

tensor_usage_count = new Dictionary<long, int>();

if (this is Sequential)

return;

_init_graph_network(inputs, outputs);

}

protected void _init_graph_network(Tensors inputs, Tensors outputs)

{

_is_graph_network = true;

this.inputs = inputs;

this.outputs = outputs;

built = true;

_build_input_shape = inputs.shape;

_compute_output_and_mask_jointly = true;

_expects_training_arg = true;

_expects_mask_arg = true;

// A graph network does not autocast inputs, as its layers will cast them instead.

_autocast = false;

if (outputs.Any(x => x.KerasHistory == null))

base_layer_utils.create_keras_history(outputs);

// Build self._output_layers:

foreach (var x in outputs)

{

var (layer, node_index, tensor_index) = x.KerasHistory;

_output_layers.append(layer);

_output_coordinates.append(new KerasHistory(layer, node_index, tensor_index, x));

}

// Build self._input_layers:

foreach (var x in inputs)

{

var (layer, node_index, tensor_index) = x.KerasHistory;

_input_layers.append(layer);

_input_coordinates.append(new KerasHistory(layer, node_index, tensor_index, x));

}

// Keep track of the network's nodes and layers.

var (nodes, nodes_by_depth, layers, _) = MapGraphNetwork(inputs, outputs);

NetworkNodes = nodes;

NodesByDepth = nodes_by_depth;

_layers = layers;

// Build self.input_names and self.output_names.

_set_output_names();

ComputeTensorUsageCount();

}

/// <summary>

/// Assigns unique names to the Network's outputs.

/// </summary>

void _set_output_names()

{

var uniquified = new List<string>();

var output_names = new List<string>();

var prefix_count = new Dictionary<string, int>();

foreach (var layer in _output_layers)

{

var proposal = layer.Name;

while (output_names.Contains(proposal))

{

var existing_count = prefix_count.Get(layer.Name, 1);

proposal = $"{layer.Name}_{existing_count}";

prefix_count[layer.Name] = existing_count + 1;

}

output_names.add(proposal);

uniquified.append(proposal);

}

this.output_names = uniquified.ToArray();

}

void ComputeTensorUsageCount()

{

var available_tensors = inputs.Select(x => x.Id).ToList();

var depth_keys = NodesByDepth.Keys.OrderBy(x => x).Reverse().Skip(1).ToArray();

foreach (var depth in depth_keys)

{

foreach (var node in NodesByDepth[depth])

{

var input_tensors = node.KerasInputs.Select(x => x.Id).ToArray();

if (input_tensors.issubset(available_tensors))

{

foreach (var tensor in node.KerasInputs)

{

if (!tensor_usage_count.ContainsKey(tensor.Id))

tensor_usage_count[tensor.Id] = 0;

tensor_usage_count[tensor.Id] += 1;

}

foreach (var output_tensor in node.Outputs)

available_tensors.Add(output_tensor.Id);

}

foreach (var tensor in outputs)

{

if (!tensor_usage_count.ContainsKey(tensor.Id))

tensor_usage_count[tensor.Id] = 0;

tensor_usage_count[tensor.Id] += 1;

}

/// <summary>

/// Validates a network's topology and gather its layers and nodes.

/// </summary>

/// <param name="inputs"></param>

/// <param name="outputs"></param>

(string[], Dictionary<int, List<INode>>, List<ILayer>, Dictionary<int, List<ILayer>>) MapGraphNetwork(Tensors inputs, Tensors outputs)

{

var (nodes_in_decreasing_depth, layer_indices) = BuildMap(outputs);

var network_nodes = nodes_in_decreasing_depth

.Select(node => MakeNodeKey(node.Layer.Name, node.Layer.InboundNodes.IndexOf(node)))

.ToArray();

var nodes_depths = new Dictionary<INode, int>();

var layers_depths = new Dictionary<ILayer, int>();

nodes_in_decreasing_depth.Reverse();

foreach (var node in nodes_in_decreasing_depth)

{

// If the depth is not set, the node has no outbound nodes (depth 0).

int depth = nodes_depths.SetDefault(node, 0);

// Update the depth of the corresponding layer

int previous_depth = layers_depths.Get(node.Layer, 0);

// If we've seen this layer before at a higher depth,

// we should use that depth instead of the node depth.

// This is necessary for shared layers that have inputs at different

// depth levels in the graph.

depth = Math.Max(depth, previous_depth);

layers_depths[node.Layer] = depth;

nodes_depths[node] = depth;

// Update the depth of inbound nodes.

// The "depth" of a node is the max of the depths

// of all nodes it is connected to + 1.

foreach (var node_dep in node.ParentNodes)

{

previous_depth = nodes_depths.Get(node_dep, 0);

nodes_depths[node_dep] = Math.Max(depth + 1, previous_depth);

}

// Handle inputs that are not connected to outputs.

// We do not error out here because the inputs may be used to compute losses

// and metrics.

foreach (var input_t in inputs)

{

var (input_layer, _, _) = input_t.KerasHistory;

if (!layers_depths.ContainsKey(input_layer))

{

layers_depths[input_layer] = 0;

layer_indices[input_layer] = -1;

nodes_depths[input_layer.InboundNodes[0]] = 0;

network_nodes.add(MakeNodeKey(input_layer.Name, 0));

}

// Build a dict {depth: list of nodes with this depth}

var nodes_by_depth = new Dictionary<int, List<INode>>();

foreach (var (node, depth) in enumerate(nodes_depths))

{

if (!nodes_by_depth.ContainsKey(depth))

nodes_by_depth[depth] = new List<INode>();

nodes_by_depth[depth].append(node);

}

var layers_by_depth = new Dictionary<int, List<ILayer>>();

foreach (var (layer, depth) in enumerate(layers_depths))

{

if (!layers_by_depth.ContainsKey(depth))

layers_by_depth[depth] = new List<ILayer>();

layers_by_depth[depth].append(layer);

}

// Get sorted list of layer depths.

var depth_keys = layers_by_depth.Keys.OrderBy(x => x).Reverse();

// Set self.layers ordered by depth.

var layers = new List<ILayer>();

foreach (var depth in depth_keys)

{

var layers_for_depth = layers_by_depth[depth];

// Network.layers needs to have a deterministic order:

// here we order them by traversal order.

layers_for_depth = layers_for_depth.OrderBy(x => layer_indices[x]).ToList();

layers.AddRange(layers_for_depth);

}

// Get sorted list of node depths.

depth_keys = nodes_by_depth.Keys.OrderBy(x => x).Reverse();

return (network_nodes, nodes_by_depth, layers, layers_by_depth);

}

string MakeNodeKey(string layer_name, int node_index)

=> $"{layer_name}_ib-{node_index}";

/// <summary>

/// This method topologically sorts nodes in order from inputs to outputs.

/// </summary>

/// <param name="outputs"></param>

(List<INode>, Dictionary<ILayer, int>) BuildMap(Tensors outputs)

{

var finished_nodes = new List<INode>();

var nodes_in_progress = new List<INode>();

var nodes_in_decreasing_depth = new List<INode>();

var layer_indices = new Dictionary<ILayer, int>();

foreach (var output in outputs)

BuildMapHelper(output,

finished_nodes,

nodes_in_progress,

nodes_in_decreasing_depth,

layer_indices);

return (nodes_in_decreasing_depth, layer_indices);

}

void BuildMapHelper(Tensor tensor,

List<INode> finished_nodes,

List<INode> nodes_in_progress,

List<INode> nodes_in_decreasing_depth,

Dictionary<ILayer, int> layer_indices)

{

var (layer, node_index, _) = tensor.KerasHistory;

var node = layer.InboundNodes[node_index] as Node;

// Don't repeat work for shared subgraphs

if (finished_nodes.Contains(node))

return;

// Prevent cycles.

if (nodes_in_progress.Contains(node))

throw new ValueError($"The tensor {tensor.name} at layer {layer.Name} is part of a cycle.");

// Store the traversal order for layer sorting.

if (!layer_indices.ContainsKey(layer))

layer_indices[layer] = layer_indices.Count;

// Propagate to all previous tensors connected to this node.

nodes_in_progress.Add(node);

if (!node.is_input)

{

foreach (var k_tensor in node.KerasInputs)

{

BuildMapHelper(k_tensor,

finished_nodes,

nodes_in_progress,

nodes_in_decreasing_depth,

layer_indices);

}

finished_nodes.Add(node);

nodes_in_progress.Remove(node);

nodes_in_decreasing_depth.append(node);

}

protected override Tensors Call(Tensors inputs, Tensor state = null, bool is_training = false)

{

return run_internal_graph(inputs, is_training);

}

Tensors run_internal_graph(Tensors inputs, bool training = false, Tensors mask = null)

{

if (mask == null)

{

Tensor[] masks = new Tensor[inputs.Count()];

foreach (var (i, input_t) in enumerate(inputs))

input_t.KerasMask = masks[i];

}

var tensor_dict = new Dictionary<long, Queue<Tensor>>();

foreach (var (x, y) in zip(this.inputs, inputs))

{

var y1 = conform_to_reference_input(y, x);

tensor_dict[x.Id] = new Queue<Tensor>(Enumerable.Range(0, tensor_usage_count[x.Id]).Select(x => y1));

}

var depth_keys = NodesByDepth.Keys.OrderBy(x => x).Reverse().ToArray();

foreach (var depth in depth_keys)

{

var nodes = NodesByDepth[depth];

foreach (Node node in nodes)

{

// Input tensors already exist.

if (node.is_input)

continue;

var layer_inputs = node.MapArguments(tensor_dict);

tf.Logger.Debug($"Depth {depth}: {node.Layer}: {node.Layer.Name}");

var outputs = node.Layer.Apply(layer_inputs, is_training: training);

foreach (var output in outputs.Where(x => x != null))

tf.Logger.Information($"Depth {depth}: {node.Layer}: {node.Layer.Name} {output.TensorShape}");

// Update tensor_dict for next input

foreach (var (x_id, y) in zip(node.FlatOutputIds, outputs))

tensor_dict[x_id] = new Queue<Tensor>(Enumerable.Range(0, tensor_usage_count[x_id]).Select(x => y));

}

var output_tensors = new Tensors();

foreach (var x in outputs)

output_tensors.Add(tensor_dict[x.Id].Dequeue());

return output_tensors;

}

Tensor conform_to_reference_input(Tensor tensor, Tensor ref_input)

{

return tensor;

}

TensorFlow.NET/src/TensorFlowNET.Keras/Engine/Functional.cs at master · prilcool/TensorFlow.NET · GitHub

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