TensorFlow.NET/src/TensorFlowNET.Core/Graphs/Graph.cs at master · baradgur/TensorFlow.NET · GitHub

History

560 lines (483 loc) · 20.1 KB

Raw

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

192

193

194

195

196

197

198

199

200

201

202

203

204

205

206

207

208

209

210

211

212

213

214

215

216

217

218

219

220

221

222

223

224

225

226

227

228

229

230

231

232

233

234

235

236

237

238

239

240

241

242

243

244

245

246

247

248

249

250

251

252

253

254

255

256

257

258

259

260

261

262

263

264

265

266

267

268

269

270

271

272

273

274

275

276

277

278

279

280

281

282

283

284

285

286

287

288

289

290

291

292

293

294

295

296

297

298

299

300

301

302

303

304

305

306

307

308

309

310

311

312

313

314

315

316

317

318

319

320

321

322

323

324

325

326

327

328

329

330

331

332

333

334

335

336

337

338

339

340

341

342

343

344

345

346

347

348

349

350

351

352

353

354

355

356

357

358

359

360

361

362

363

364

365

366

367

368

369

370

371

372

373

374

375

376

377

378

379

380

381

382

383

384

385

386

387

388

389

390

391

392

393

394

395

396

397

398

399

400

401

402

403

404

405

406

407

408

409

410

411

412

413

414

415

416

417

418

419

420

421

422

423

424

425

426

427

428

429

430

431

432

433

434

435

436

437

438

439

440

441

442

443

444

445

446

447

448

449

450

451

452

453

454

455

456

457

458

459

460

461

462

463

464

465

466

467

468

469

470

471

472

473

474

475

476

477

478

479

480

481

482

483

484

485

486

487

488

489

490

491

492

493

494

495

496

497

498

499

500

501

502

503

504

505

506

507

508

509

510

511

512

513

514

515

516

517

518

519

520

521

522

523

524

525

526

527

528

529

530

531

532

533

534

535

536

537

538

539

540

541

542

543

544

545

546

547

548

549

550

551

552

553

554

555

556

557

558

559

560

/*****************************************************************************

Licensed under the Apache License, Version 2.0 (the "License");

you may not use this file except in compliance with the License.

You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software

distributed under the License is distributed on an "AS IS" BASIS,

WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

See the License for the specific language governing permissions and

limitations under the License.

******************************************************************************/

using System;

using System.Collections;

using System.Collections.Generic;

using System.Linq;

using System.Runtime.InteropServices;

using static Tensorflow.Binding;

namespace Tensorflow

{

A TensorFlow computation, represented as a dataflow graph.

A `Graph` contains a set of

`tf.Operation` objects,

which represent units of computation; and

`tf.Tensor` objects, which represent

the units of data that flow between operations.

A default `Graph` is always registered, and accessible by calling

`tf.get_default_graph`.

To add an operation to the default graph, simply call one of the functions

that defines a new `Operation`:

```python

c = tf.constant(4.0)

assert c.graph is tf.get_default_graph()

```

Another typical usage involves the

`tf.Graph.as_default`

context manager, which overrides the current default graph for the

lifetime of the context:

```python

g = tf.Graph()

with g.as_default():

# Define operations and tensors in `g`.

c = tf.constant(30.0)

assert c.graph is g

```

Important note: This class *is not* thread-safe for graph construction. All

operations should be created from a single thread, or external

synchronization must be provided. Unless otherwise specified, all methods

are not thread-safe.

A `Graph` instance supports an arbitrary number of "collections"

that are identified by name. For convenience when building a large

graph, collections can store groups of related objects: for

example, the `tf.Variable` uses a collection (named

`tf.GraphKeys.GLOBAL_VARIABLES`) for

all variables that are created during the construction of a graph. The caller

may define additional collections by specifying a new name.

/// <summary>

/// TensorFlow uses a dataflow graph to represent your computation in terms of the dependencies between individual operations.

/// This leads to a low-level programming model in which you first define the dataflow graph,

/// then create a TensorFlow session to run parts of the graph across a set of local and remote devices.

/// </summary>

/// <remarks>https://www.tensorflow.org/guide/graphs <br></br>https://www.tensorflow.org/api_docs/python/tf/Graph</remarks>

public partial class Graph : DisposableObject

#if !SERIALIZABLE

, IEnumerable<Operation>

#endif

{

private Dictionary<int, ITensorOrOperation> _nodes_by_id;

public Dictionary<string, ITensorOrOperation> _nodes_by_name;

private Dictionary<string, int> _names_in_use;

public int _version;

private int _next_id_counter;

private List<Operation> _unfetchable_ops = new List<Operation>();

private List<Tensor> _unfeedable_tensors = new List<Tensor>();

public string _name_stack = "";

private string _graph_key;

public string graph_key => _graph_key;

public string _last_loss_reduction;

public bool _is_loss_scaled_by_optimizer { get; set; }

/// <summary>

/// True if the graph is considered "finalized". In that case no

/// new operations can be added.

/// </summary>

private bool _finalized = false;

/// <summary>

/// Arbitrary collections of objects.

/// </summary>

private Dictionary<string, object> _collections = new Dictionary<string, object>();

public bool building_function;

int _seed;

public int seed

{

get => _seed;

set

{

_seed = value;

}

public Graph()

{

_handle = c_api.TF_NewGraph();

_nodes_by_id = new Dictionary<int, ITensorOrOperation>();

_nodes_by_name = new Dictionary<string, ITensorOrOperation>();

_names_in_use = new Dictionary<string, int>();

_graph_key = $"grap-key-{ops.uid()}/";

}

public Graph(IntPtr handle)

{

_handle = handle;

_nodes_by_id = new Dictionary<int, ITensorOrOperation>();

_nodes_by_name = new Dictionary<string, ITensorOrOperation>();

_names_in_use = new Dictionary<string, int>();

_graph_key = $"grap-key-{ops.uid()}/";

}

public ITensorOrOperation as_graph_element(object obj, bool allow_tensor = true, bool allow_operation = true)

{

return _as_graph_element_locked(obj, allow_tensor, allow_operation);

}

/// <summary>

/// Returns a context manager that makes this `Graph` the default graph.

/// </summary>

/// <returns></returns>

public Graph as_default()

{

return ops.set_default_graph(this);

}

private Tensor _as_graph_element(object obj)

{

if (obj is RefVariable var)

return var._as_graph_element();

return null;

}

private ITensorOrOperation _as_graph_element_locked(object obj, bool allow_tensor = true, bool allow_operation = true)

{

string types_str = "";

if (allow_tensor && allow_operation)

{

types_str = "Tensor or Operation";

}

else if (allow_tensor)

{

types_str = "Tensor";

}

else if (allow_operation)

{

types_str = "Operation";

}

var temp_obj = _as_graph_element(obj);

if (temp_obj != null)

obj = temp_obj;

// If obj appears to be a name...

if (obj is string name)

{

if (name.Contains(":") && allow_tensor)

{

string op_name = name.Split(':')[0];

int out_n = int.Parse(name.Split(':')[1]);

if (_nodes_by_name.ContainsKey(op_name))

return _nodes_by_name[op_name].outputs[out_n];

else

throw new KeyError($"The name {name} refers to a Tensor which does not " +

$"exist. The operation, {op_name}, does not exist in the " +

"graph.");

}

else if (!name.Contains(":") & allow_operation)

{

if (!_nodes_by_name.ContainsKey(name))

throw new KeyError($"The name {name} refers to an Operation not in the graph.");

return _nodes_by_name[name];

}

else if (!name.Contains(":") & !allow_operation)

{

// Looks like an Operation name but can't be an Operation.

if (_nodes_by_name.ContainsKey(name))

// Yep, it's an Operation name

throw new ValueError($"The name {name} refers to an Operation, not a {types_str}.");

else

throw new ValueError(

$"The name {name} looks like an (invalid) Operation name, not a {types_str}" +

" Tensor names must be of the form \"<op_name>:<output_index>\".");

}

if (obj is Tensor tensor && allow_tensor)

{

if (tensor.graph.Equals(this))

{

return tensor;

}

else

{

throw new Exception($"Tensor {obj} is not an element of this graph.");

}

else if (obj is Operation op && allow_operation)

{

if (op.graph.Equals(this))

{

return op;

}

else

{

throw new Exception($"Operation {obj} is not an element of this graph.");

}

throw new Exception($"Can not convert a {obj.GetType().Name} into a {types_str}.");

}

public void add_to_collection<T>(string name, T value)

{

_check_not_finalized();

if (_collections.ContainsKey(name))

(_collections[name] as List<T>).Add(value);

else

_collections[name] = new List<T> { value };

}

public void add_to_collections<T>(List<string> names, T value)

{

foreach (string name in names)

add_to_collection(name, value);

}

private void _check_not_finalized()

{

if (_finalized)

throw new RuntimeError("Graph is finalized and cannot be modified.");

}

public Operation create_op(string op_type, Tensor[] inputs, TF_DataType[] dtypes,

TF_DataType[] input_types = null, string name = null,

Dictionary<string, AttrValue> attrs = null, OpDef op_def = null)

{

if (inputs == null)

inputs = new Tensor[0];

if (string.IsNullOrEmpty(name))

name = op_type;

// If a names ends with a '/' it is a "name scope" and we use it as-is,

// after removing the trailing '/'.

name = name.EndsWith("/") ? ops.name_from_scope_name(name) : unique_name(name);

var node_def = ops._NodeDef(op_type, name, device: "", attrs: attrs);

var input_ops = inputs.Select(x => x.op).ToArray();

var control_inputs = _control_dependencies_for_inputs(input_ops);

var op = new Operation(node_def,

this,

inputs: inputs,

output_types: dtypes,

control_inputs: control_inputs,

input_types: input_types,

original_op: null,

op_def: op_def);

_create_op_helper(op, true);

/*Console.Write($"create_op: {op_type} '{node_def.Name}'");

Console.Write($", inputs: {(inputs.Length == 0 ? "empty" : String.Join(", ", inputs.Select(x => x.name)))}");

Console.Write($", control_inputs: {(control_inputs.Length == 0 ? "empty" : String.Join(", ", control_inputs.Select(x => x.name)))}");

Console.Write($", outputs: {(op.outputs.Length == 0 ? "empty" : String.Join(", ", op.outputs.Select(x => x.name)))}");

Console.WriteLine();*/

return op;

}

public void device(string device_name)

{

throw new NotImplementedException("");

}

private void _create_op_helper(Operation op, bool compute_device = true)

{

_record_op_seen_by_control_dependencies(op);

}

public void _add_op(Operation op)

{

op._id_value = _next_id();

_nodes_by_id[op._id] = op;

_nodes_by_name[op.name] = op;

_version = Math.Max(_version, op._id);

}

public int _next_id()

{

return ++_next_id_counter;

}

public bool is_fetchable<T>(T tensor_or_op)

{

if (tensor_or_op is Tensor tensor)

{

return !_unfetchable_ops.Contains(tensor); ;

}

else if (tensor_or_op is Operation op)

{

return !_unfetchable_ops.Contains(op);

}

return false;

}

public string get_name_scope()

{

return _name_stack;

}

public string name_scope(string name)

{

string new_stack = "";

if (string.IsNullOrEmpty(name))

new_stack = "";

else if (name.EndsWith("/"))

new_stack = ops.name_from_scope_name(name);

else

new_stack = unique_name(name);

_name_stack = new_stack;

return String.IsNullOrEmpty(new_stack) ? "" : new_stack + "/";

}

/// <summary>

/// Return a unique operation name for `name`.

///

/// Note: You rarely need to call `unique_name()` directly.Most of

/// the time you just need to create `with g.name_scope()` blocks to

/// generate structured names.

///

/// `unique_name` is used to generate structured names, separated by

/// `"/"`, to help identify operations when debugging a graph.

/// Operation names are displayed in error messages reported by the

/// TensorFlow runtime, and in various visualization tools such as

/// TensorBoard.

///

/// If `mark_as_used` is set to `True`, which is the default, a new

/// unique name is created and marked as in use.If it's set to `False`,

/// the unique name is returned without actually being marked as used.

/// This is useful when the caller simply wants to know what the name

/// to be created will be.

/// </summary>

/// <param name="name">The name for an operation.</param>

/// <param name="mark_as_used"> Whether to mark this name as being used.</param>

/// <returns>A string to be passed to `create_op()` that will be used

/// to name the operation being created.</returns>

public string unique_name(string name, bool mark_as_used = true)

{

if (name.EndsWith("basic_r_n_n_cell"))

{

}

if (!String.IsNullOrEmpty(_name_stack))

name = _name_stack + "/" + name;

// For the sake of checking for names in use, we treat names as case

// insensitive (e.g. foo = Foo).

var name_key = name.ToLower();

int i = 0;

if (_names_in_use.ContainsKey(name_key))

i = _names_in_use[name_key];

// Increment the number for "name_key".

if (mark_as_used)

_names_in_use[name_key] = i + 1;

if (i > 0)

{

// Make sure the composed name key is not already used.

var base_name_key = name_key;

while (_names_in_use.ContainsKey(name_key))

{

name_key = $"{base_name_key}_{i}";

i += 1;

}

// Mark the composed name_key as used in case someone wants

// to call unique_name("name_1").

if (mark_as_used)

_names_in_use[name_key] = 1;

// Return the new name with the original capitalization of the given name.

name = $"{name}_{i - 1}";

}

return name;

}

public TF_Output[] ReturnOutputs(IntPtr results)

{

IntPtr return_output_handle = IntPtr.Zero;

int num_return_outputs = 0;

c_api.TF_ImportGraphDefResultsReturnOutputs(results, ref num_return_outputs, ref return_output_handle);

TF_Output[] return_outputs = new TF_Output[num_return_outputs];

unsafe

{

var tf_output_ptr = (TF_Output*)return_output_handle;

for (int i = 0; i < num_return_outputs; i++)

return_outputs[i] = *(tf_output_ptr + i);

return return_outputs;

}

public string[] get_all_collection_keys()

{

return _collections.Keys.Where(x => !x.StartsWith("__")).ToArray();

}

public object get_collection(string name, string scope = null)

{

return _collections.ContainsKey(name) ? _collections[name] : null;

}

public List<T> get_collection<T>(string name, string scope = null)

{

List<T> t = default;

var collection = _collections.ContainsKey(name) ? _collections[name] : new List<T>();

switch (collection)

{

case List<VariableV1> list: