1# Copyright (c) 2019 Guo Yejun 2# 3# This file is part of FFmpeg. 4# 5# FFmpeg is free software; you can redistribute it and/or 6# modify it under the terms of the GNU Lesser General Public 7# License as published by the Free Software Foundation; either 8# version 2.1 of the License, or (at your option) any later version. 9# 10# FFmpeg is distributed in the hope that it will be useful, 11# but WITHOUT ANY WARRANTY; without even the implied warranty of 12# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU 13# Lesser General Public License for more details. 14# 15# You should have received a copy of the GNU Lesser General Public 16# License along with FFmpeg; if not, write to the Free Software 17# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA 18# ============================================================================== 19 20import tensorflow as tf 21import numpy as np 22import sys, struct 23import convert_header as header 24 25__all__ = ['convert_from_tensorflow'] 26 27class Operand(object): 28 IOTYPE_INPUT = 1 29 IOTYPE_OUTPUT = 2 30 IOTYPE_INTERMEDIATE = IOTYPE_INPUT | IOTYPE_OUTPUT 31 DTYPE_FLOAT = 1 32 DTYPE_UINT8 = 4 33 index = 0 34 def __init__(self, name, dtype, dims): 35 self.name = name 36 self.dtype = dtype 37 self.dims = dims 38 self.iotype = 0 39 self.used_count = 0 40 self.index = Operand.index 41 Operand.index = Operand.index + 1 42 self.iotype2str = {Operand.IOTYPE_INPUT: 'in', Operand.IOTYPE_OUTPUT: 'out', Operand.IOTYPE_INTERMEDIATE: 'inout'} 43 self.dtype2str = {Operand.DTYPE_FLOAT: 'DT_FLOAT', Operand.DTYPE_UINT8: 'DT_UINT8'} 44 45 def add_iotype(self, iotype): 46 self.iotype = self.iotype | iotype 47 if iotype == Operand.IOTYPE_INPUT: 48 self.used_count = self.used_count + 1 49 50 def __str__(self): 51 return "{}: (name: {}, iotype: {}, dtype: {}, dims: {}, used_count: {})".format(self.index, 52 self.name, self.iotype2str[self.iotype], self.dtype2str[self.dtype], 53 self.dims, self.used_count) 54 55 def __lt__(self, other): 56 return self.index < other.index 57 58class TFConverter: 59 def __init__(self, graph_def, nodes, outfile, dump4tb): 60 self.graph_def = graph_def 61 self.nodes = nodes 62 self.outfile = outfile 63 self.dump4tb = dump4tb 64 self.layer_number = 0 65 self.output_names = [] 66 self.name_node_dict = {} 67 self.edges = {} 68 self.conv_activations = {'Relu':0, 'Tanh':1, 'Sigmoid':2, 'None':3, 'LeakyRelu':4} 69 self.conv_paddings = {'VALID':0, 'SAME':1} 70 self.pool_paddings = {'VALID':0, 'SAME':1} 71 self.converted_nodes = set() 72 self.conv2d_scope_names = set() 73 self.conv2d_scopename_inputname_dict = {} 74 self.dense_scope_names = set() 75 self.dense_scopename_inputname_dict = {} 76 self.op2code = {'Conv2D':1, 'DepthToSpace':2, 'MirrorPad':3, 'Maximum':4, 77 'MathBinary':5, 'MathUnary':6, 'AvgPool':7, 'MatMul':8} 78 self.mathbin2code = {'Sub':0, 'Add':1, 'Mul':2, 'RealDiv':3, 'Minimum':4, 'FloorMod':5} 79 self.mathun2code = {'Abs':0, 'Sin':1, 'Cos':2, 'Tan':3, 'Asin':4, 80 'Acos':5, 'Atan':6, 'Sinh':7, 'Cosh':8, 'Tanh':9, 'Asinh':10, 81 'Acosh':11, 'Atanh':12, 'Ceil':13, 'Floor':14, 'Round':15} 82 self.mirrorpad_mode = {'CONSTANT':0, 'REFLECT':1, 'SYMMETRIC':2} 83 self.name_operand_dict = {} 84 85 86 def add_operand(self, name, type): 87 node = self.name_node_dict[name] 88 if name not in self.name_operand_dict: 89 dtype = node.attr['dtype'].type 90 if dtype == 0: 91 dtype = node.attr['T'].type 92 dims = [-1,-1,-1,-1] 93 if 'shape' in node.attr: 94 dims[0] = node.attr['shape'].shape.dim[0].size 95 dims[1] = node.attr['shape'].shape.dim[1].size 96 dims[2] = node.attr['shape'].shape.dim[2].size 97 dims[3] = node.attr['shape'].shape.dim[3].size 98 operand = Operand(name, dtype, dims) 99 self.name_operand_dict[name] = operand; 100 self.name_operand_dict[name].add_iotype(type) 101 return self.name_operand_dict[name].index 102 103 104 def dump_for_tensorboard(self): 105 graph = tf.get_default_graph() 106 tf.import_graph_def(self.graph_def, name="") 107 tf.summary.FileWriter('/tmp/graph', graph) 108 print('graph saved, run "tensorboard --logdir=/tmp/graph" to see it') 109 110 111 def get_conv2d_params(self, conv2d_scope_name): 112 knode = self.name_node_dict[conv2d_scope_name + '/kernel'] 113 bnode = self.name_node_dict[conv2d_scope_name + '/bias'] 114 115 if conv2d_scope_name + '/dilation_rate' in self.name_node_dict: 116 dnode = self.name_node_dict[conv2d_scope_name + '/dilation_rate'] 117 else: 118 dnode = None 119 120 # the BiasAdd name is possible be changed into the output name, 121 # if activation is None, and BiasAdd.next is the last op which is Identity 122 if conv2d_scope_name + '/BiasAdd' in self.edges: 123 anode = self.edges[conv2d_scope_name + '/BiasAdd'][0] 124 if anode.op not in self.conv_activations: 125 anode = None 126 else: 127 anode = None 128 return knode, bnode, dnode, anode 129 130 131 def get_dense_params(self, dense_scope_name): 132 knode = self.name_node_dict[dense_scope_name + '/kernel'] 133 bnode = self.name_node_dict.get(dense_scope_name + '/bias') 134 # the BiasAdd name is possible be changed into the output name, 135 # if activation is None, and BiasAdd.next is the last op which is Identity 136 anode = None 137 if bnode: 138 if dense_scope_name + '/BiasAdd' in self.edges: 139 anode = self.edges[dense_scope_name + '/BiasAdd'][0] 140 if anode.op not in self.conv_activations: 141 anode = None 142 else: 143 anode = None 144 return knode, bnode, anode 145 146 147 def dump_complex_conv2d_to_file(self, node, f): 148 assert(node.op == 'Conv2D') 149 self.layer_number = self.layer_number + 1 150 self.converted_nodes.add(node.name) 151 152 scope_name = TFConverter.get_scope_name(node.name) 153 #knode for kernel, bnode for bias, dnode for dilation, anode for activation 154 knode, bnode, dnode, anode = self.get_conv2d_params(scope_name) 155 156 if dnode is not None: 157 dilation = struct.unpack('i', dnode.attr['value'].tensor.tensor_content[0:4])[0] 158 else: 159 dilation = 1 160 161 if anode is not None: 162 activation = anode.op 163 else: 164 activation = 'None' 165 166 padding = node.attr['padding'].s.decode("utf-8") 167 # conv2d with dilation > 1 generates tens of nodes, not easy to parse them, so use this tricky method. 168 if dilation > 1 and scope_name + '/stack' in self.name_node_dict: 169 if self.name_node_dict[scope_name + '/stack'].op == "Const": 170 padding = 'SAME' 171 padding = self.conv_paddings[padding] 172 173 ktensor = knode.attr['value'].tensor 174 filter_height = ktensor.tensor_shape.dim[0].size 175 filter_width = ktensor.tensor_shape.dim[1].size 176 in_channels = ktensor.tensor_shape.dim[2].size 177 out_channels = ktensor.tensor_shape.dim[3].size 178 kernel = np.frombuffer(ktensor.tensor_content, dtype=np.float32) 179 kernel = kernel.reshape(filter_height, filter_width, in_channels, out_channels) 180 kernel = np.transpose(kernel, [3, 0, 1, 2]) 181 182 has_bias = 1 183 np.array([self.op2code[node.op], dilation, padding, self.conv_activations[activation], in_channels, out_channels, filter_height, has_bias], dtype=np.uint32).tofile(f) 184 kernel.tofile(f) 185 186 btensor = bnode.attr['value'].tensor 187 if btensor.tensor_shape.dim[0].size == 1: 188 bias = struct.pack("f", btensor.float_val[0]) 189 else: 190 bias = btensor.tensor_content 191 f.write(bias) 192 193 input_name = self.conv2d_scopename_inputname_dict[scope_name] 194 input_operand_index = self.add_operand(input_name, Operand.IOTYPE_INPUT) 195 196 if anode is not None: 197 output_operand_index = self.add_operand(anode.name, Operand.IOTYPE_OUTPUT) 198 else: 199 output_operand_index = self.add_operand(self.edges[bnode.name][0].name, Operand.IOTYPE_OUTPUT) 200 np.array([input_operand_index, output_operand_index], dtype=np.uint32).tofile(f) 201 202 def dump_dense_to_file(self, node, f): 203 assert(node.op == 'MatMul') 204 self.layer_number = self.layer_number + 1 205 self.converted_nodes.add(node.name) 206 207 scope_name = TFConverter.get_scope_name(node.name) 208 #knode for kernel, bnode for bias, anode for activation 209 knode, bnode, anode = self.get_dense_params(scope_name.split('/')[0]) 210 211 if bnode is not None: 212 has_bias = 1 213 btensor = bnode.attr['value'].tensor 214 if btensor.tensor_shape.dim[0].size == 1: 215 bias = struct.pack("f", btensor.float_val[0]) 216 else: 217 bias = btensor.tensor_content 218 else: 219 has_bias = 0 220 221 if anode is not None: 222 activation = anode.op 223 else: 224 activation = 'None' 225 226 ktensor = knode.attr['value'].tensor 227 in_channels = ktensor.tensor_shape.dim[0].size 228 out_channels = ktensor.tensor_shape.dim[1].size 229 if in_channels * out_channels == 1: 230 kernel = np.float32(ktensor.float_val[0]) 231 else: 232 kernel = np.frombuffer(ktensor.tensor_content, dtype=np.float32) 233 kernel = kernel.reshape(in_channels, out_channels) 234 kernel = np.transpose(kernel, [1, 0]) 235 236 np.array([self.op2code[node.op], self.conv_activations[activation], in_channels, out_channels, has_bias], dtype=np.uint32).tofile(f) 237 kernel.tofile(f) 238 if has_bias: 239 f.write(bias) 240 241 input_name = self.dense_scopename_inputname_dict[scope_name.split('/')[0]] 242 input_operand_index = self.add_operand(input_name, Operand.IOTYPE_INPUT) 243 244 if anode is not None: 245 output_operand_index = self.add_operand(anode.name, Operand.IOTYPE_OUTPUT) 246 else: 247 if bnode is not None: 248 output_operand_index = self.add_operand(self.edges[bnode.name][0].name, Operand.IOTYPE_OUTPUT) 249 else: 250 output_operand_index = self.add_operand(self.edges[scope_name+'/concat_1'][0].name, Operand.IOTYPE_OUTPUT) 251 np.array([input_operand_index, output_operand_index], dtype=np.uint32).tofile(f) 252 253 254 def dump_simple_conv2d_to_file(self, node, f): 255 assert(node.op == 'Conv2D') 256 self.layer_number = self.layer_number + 1 257 self.converted_nodes.add(node.name) 258 259 node0 = self.name_node_dict[node.input[0]] 260 node1 = self.name_node_dict[node.input[1]] 261 if node0.op == 'Const': 262 knode = node0 263 input_name = node.input[1] 264 else: 265 knode = node1 266 input_name = node.input[0] 267 268 ktensor = knode.attr['value'].tensor 269 filter_height = ktensor.tensor_shape.dim[0].size 270 filter_width = ktensor.tensor_shape.dim[1].size 271 in_channels = ktensor.tensor_shape.dim[2].size 272 out_channels = ktensor.tensor_shape.dim[3].size 273 if filter_height * filter_width * in_channels * out_channels == 1: 274 kernel = np.float32(ktensor.float_val[0]) 275 else: 276 kernel = np.frombuffer(ktensor.tensor_content, dtype=np.float32) 277 kernel = kernel.reshape(filter_height, filter_width, in_channels, out_channels) 278 kernel = np.transpose(kernel, [3, 0, 1, 2]) 279 280 has_bias = 0 281 dilation = 1 282 padding = node.attr['padding'].s.decode("utf-8") 283 np.array([self.op2code[node.op], dilation, self.conv_paddings[padding], self.conv_activations['None'], 284 in_channels, out_channels, filter_height, has_bias], dtype=np.uint32).tofile(f) 285 kernel.tofile(f) 286 287 input_operand_index = self.add_operand(input_name, Operand.IOTYPE_INPUT) 288 output_operand_index = self.add_operand(node.name, Operand.IOTYPE_OUTPUT) 289 np.array([input_operand_index, output_operand_index], dtype=np.uint32).tofile(f) 290 291 292 def dump_depth2space_to_file(self, node, f): 293 assert(node.op == 'DepthToSpace') 294 self.layer_number = self.layer_number + 1 295 block_size = node.attr['block_size'].i 296 np.array([self.op2code[node.op], block_size], dtype=np.uint32).tofile(f) 297 self.converted_nodes.add(node.name) 298 input_operand_index = self.add_operand(node.input[0], Operand.IOTYPE_INPUT) 299 output_operand_index = self.add_operand(node.name, Operand.IOTYPE_OUTPUT) 300 np.array([input_operand_index, output_operand_index], dtype=np.uint32).tofile(f) 301 302 303 def dump_mirrorpad_to_file(self, node, f): 304 assert(node.op == 'MirrorPad') 305 self.layer_number = self.layer_number + 1 306 mode = node.attr['mode'].s 307 mode = self.mirrorpad_mode[mode.decode("utf-8")] 308 np.array([self.op2code[node.op], mode], dtype=np.uint32).tofile(f) 309 pnode = self.name_node_dict[node.input[1]] 310 self.converted_nodes.add(pnode.name) 311 paddings = pnode.attr['value'].tensor.tensor_content 312 f.write(paddings) 313 self.converted_nodes.add(node.name) 314 input_operand_index = self.add_operand(node.input[0], Operand.IOTYPE_INPUT) 315 output_operand_index = self.add_operand(node.name, Operand.IOTYPE_OUTPUT) 316 np.array([input_operand_index, output_operand_index], dtype=np.uint32).tofile(f) 317 318 319 def dump_maximum_to_file(self, node, f): 320 assert(node.op == 'Maximum') 321 self.layer_number = self.layer_number + 1 322 ynode = self.name_node_dict[node.input[1]] 323 y = ynode.attr['value'].tensor.float_val[0] 324 np.array([self.op2code[node.op]], dtype=np.uint32).tofile(f) 325 np.array([y], dtype=np.float32).tofile(f) 326 self.converted_nodes.add(node.name) 327 input_operand_index = self.add_operand(node.input[0], Operand.IOTYPE_INPUT) 328 output_operand_index = self.add_operand(node.name, Operand.IOTYPE_OUTPUT) 329 np.array([input_operand_index, output_operand_index], dtype=np.uint32).tofile(f) 330 331 332 def dump_mathbinary_to_file(self, node, f): 333 self.layer_number = self.layer_number + 1 334 self.converted_nodes.add(node.name) 335 i0_node = self.name_node_dict[node.input[0]] 336 i1_node = self.name_node_dict[node.input[1]] 337 np.array([self.op2code['MathBinary'], self.mathbin2code[node.op]], dtype=np.uint32).tofile(f) 338 if i0_node.op == 'Const': 339 scalar = i0_node.attr['value'].tensor.float_val[0] 340 np.array([1], dtype=np.uint32).tofile(f) # broadcast: 1 341 np.array([scalar], dtype=np.float32).tofile(f) 342 np.array([0], dtype=np.uint32).tofile(f) # broadcast: 0 343 input_operand_index = self.add_operand(i1_node.name, Operand.IOTYPE_INPUT) 344 np.array([input_operand_index], dtype=np.uint32).tofile(f) 345 elif i1_node.op == 'Const': 346 scalar = i1_node.attr['value'].tensor.float_val[0] 347 np.array([0], dtype=np.uint32).tofile(f) 348 input_operand_index = self.add_operand(i0_node.name, Operand.IOTYPE_INPUT) 349 np.array([input_operand_index], dtype=np.uint32).tofile(f) 350 np.array([1], dtype=np.uint32).tofile(f) 351 np.array([scalar], dtype=np.float32).tofile(f) 352 else: 353 np.array([0], dtype=np.uint32).tofile(f) 354 input_operand_index = self.add_operand(i0_node.name, Operand.IOTYPE_INPUT) 355 np.array([input_operand_index], dtype=np.uint32).tofile(f) 356 np.array([0], dtype=np.uint32).tofile(f) 357 input_operand_index = self.add_operand(i1_node.name, Operand.IOTYPE_INPUT) 358 np.array([input_operand_index], dtype=np.uint32).tofile(f) 359 output_operand_index = self.add_operand(node.name, Operand.IOTYPE_OUTPUT) 360 np.array([output_operand_index], dtype=np.uint32).tofile(f) 361 362 363 def dump_mathunary_to_file(self, node, f): 364 self.layer_number = self.layer_number + 1 365 self.converted_nodes.add(node.name) 366 i0_node = self.name_node_dict[node.input[0]] 367 np.array([self.op2code['MathUnary'], self.mathun2code[node.op]], dtype=np.uint32).tofile(f) 368 input_operand_index = self.add_operand(i0_node.name, Operand.IOTYPE_INPUT) 369 np.array([input_operand_index], dtype=np.uint32).tofile(f) 370 output_operand_index = self.add_operand(node.name, Operand.IOTYPE_OUTPUT) 371 np.array([output_operand_index],dtype=np.uint32).tofile(f) 372 373 374 def dump_avg_pool_to_file(self, node, f): 375 assert(node.op == 'AvgPool') 376 self.layer_number = self.layer_number + 1 377 self.converted_nodes.add(node.name) 378 node0 = self.name_node_dict[node.input[0]] 379 strides = node.attr['strides'] 380 381 # Tensorflow do not support pooling strides in batch dimension and 382 # current native NN do not support pooling strides in channel dimension, added assert() here. 383 assert(strides.list.i[1]==strides.list.i[2]) 384 assert(strides.list.i[0]==1) 385 assert(strides.list.i[3]==1) 386 strides = strides.list.i[1] 387 filter_node = node.attr['ksize'] 388 input_name = node.input[0] 389 390 # Tensorflow do not support pooling ksize in batch dimension and channel dimension. 391 assert(filter_node.list.i[0]==1) 392 assert(filter_node.list.i[3]==1) 393 filter_height = filter_node.list.i[1] 394 filter_width = filter_node.list.i[2] 395 396 padding = node.attr['padding'].s.decode("utf-8") 397 np.array([self.op2code[node.op], strides, self.pool_paddings[padding], filter_height], 398 dtype=np.uint32).tofile(f) 399 400 input_operand_index = self.add_operand(input_name, Operand.IOTYPE_INPUT) 401 output_operand_index = self.add_operand(node.name, Operand.IOTYPE_OUTPUT) 402 np.array([input_operand_index, output_operand_index],dtype=np.uint32).tofile(f) 403 404 405 def dump_layers_to_file(self, f): 406 for node in self.nodes: 407 if node.name in self.converted_nodes: 408 continue 409 410 # conv2d with dilation generates very complex nodes, so handle it in special 411 if self.in_conv2d_scope(node.name): 412 if node.op == 'Conv2D': 413 self.dump_complex_conv2d_to_file(node, f) 414 continue 415 if self.in_dense_scope(node.name): 416 if node.op == 'MatMul': 417 self.dump_dense_to_file(node, f) 418 continue 419 420 421 if node.op == 'Conv2D': 422 self.dump_simple_conv2d_to_file(node, f) 423 continue 424 if node.name in self.output_names: 425 input_name = self.id_different_scope_dict[node.name] 426 if TFConverter.get_scope_name(input_name)!=TFConverter.get_scope_name(node.name): 427 continue 428 if node.op == 'AvgPool': 429 self.dump_avg_pool_to_file(node, f) 430 elif node.op == 'DepthToSpace': 431 self.dump_depth2space_to_file(node, f) 432 elif node.op == 'MirrorPad': 433 self.dump_mirrorpad_to_file(node, f) 434 elif node.op == 'Maximum': 435 self.dump_maximum_to_file(node, f) 436 elif node.op in self.mathbin2code: 437 self.dump_mathbinary_to_file(node, f) 438 elif node.op in self.mathun2code: 439 self.dump_mathunary_to_file(node, f) 440 441 442 def dump_operands_to_file(self, f): 443 operands = sorted(self.name_operand_dict.values()) 444 for operand in operands: 445 #print('{}'.format(operand)) 446 np.array([operand.index, len(operand.name)], dtype=np.uint32).tofile(f) 447 f.write(operand.name.encode('utf-8')) 448 np.array([operand.iotype, operand.dtype], dtype=np.uint32).tofile(f) 449 np.array(operand.dims, dtype=np.uint32).tofile(f) 450 451 452 def dump_to_file(self): 453 with open(self.outfile, 'wb') as f: 454 f.write(header.str.encode('utf-8')) 455 np.array([header.major, header.minor], dtype=np.uint32).tofile(f) 456 self.dump_layers_to_file(f) 457 self.dump_operands_to_file(f) 458 np.array([self.layer_number, len(self.name_operand_dict)], dtype=np.uint32).tofile(f) 459 460 461 def generate_name_node_dict(self): 462 for node in self.nodes: 463 self.name_node_dict[node.name] = node 464 465 466 def generate_output_names(self): 467 used_names = [] 468 for node in self.nodes: 469 for input in node.input: 470 used_names.append(input) 471 472 for node in self.nodes: 473 if node.name not in used_names: 474 self.output_names.append(node.name) 475 476 477 def remove_identity(self): 478 self.id_different_scope_dict = {} 479 id_nodes = [] 480 id_dict = {} 481 for node in self.nodes: 482 if node.op == 'Identity': 483 name = node.name 484 input = node.input[0] 485 id_nodes.append(node) 486 # do not change the output name 487 if name in self.output_names: 488 self.name_node_dict[input].name = name 489 self.name_node_dict[name] = self.name_node_dict[input] 490 del self.name_node_dict[input] 491 self.id_different_scope_dict[name] = input 492 else: 493 id_dict[name] = input 494 495 for idnode in id_nodes: 496 self.nodes.remove(idnode) 497 498 for node in self.nodes: 499 for i in range(len(node.input)): 500 input = node.input[i] 501 if input in id_dict: 502 node.input[i] = id_dict[input] 503 504 505 def generate_edges(self): 506 for node in self.nodes: 507 for input in node.input: 508 if input in self.edges: 509 self.edges[input].append(node) 510 else: 511 self.edges[input] = [node] 512 513 514 @staticmethod 515 def get_scope_name(name): 516 index = name.rfind('/') 517 if index == -1: 518 return "" 519 return name[0:index] 520 521 522 def in_conv2d_scope(self, name): 523 inner_scope = TFConverter.get_scope_name(name) 524 if inner_scope == "": 525 return False; 526 for scope in self.conv2d_scope_names: 527 index = inner_scope.find(scope) 528 if index == 0: 529 return True 530 return False 531 532 533 def in_dense_scope(self, name): 534 inner_scope = TFConverter.get_scope_name(name) 535 if inner_scope == "": 536 return False; 537 for scope in self.dense_scope_names: 538 index = inner_scope.find(scope) 539 if index == 0: 540 return True 541 return False 542 543 def generate_sub_block_op_scope_info(self): 544 # mostly, conv2d/dense is a sub block in graph, get the scope name 545 for node in self.nodes: 546 if node.op == 'Conv2D': 547 scope = TFConverter.get_scope_name(node.name) 548 # for the case tf.nn.conv2d is called directly 549 if scope == '': 550 continue 551 # for the case tf.nn.conv2d is called within a scope 552 if scope + '/kernel' not in self.name_node_dict: 553 continue 554 self.conv2d_scope_names.add(scope) 555 elif node.op == 'MatMul': 556 scope = TFConverter.get_scope_name(node.name) 557 # for the case tf.nn.dense is called directly 558 if scope == '': 559 continue 560 # for the case tf.nn.dense is called within a scope 561 if scope + '/kernel' not in self.name_node_dict and scope.split('/Tensordot')[0] + '/kernel' not in self.name_node_dict: 562 continue 563 self.dense_scope_names.add(scope.split('/Tensordot')[0]) 564 565 # get the input name to the conv2d/dense sub block 566 for node in self.nodes: 567 scope = TFConverter.get_scope_name(node.name) 568 if scope in self.conv2d_scope_names: 569 if node.op == 'Conv2D' or node.op == 'Shape': 570 for inp in node.input: 571 if TFConverter.get_scope_name(inp) != scope: 572 self.conv2d_scopename_inputname_dict[scope] = inp 573 elif scope in self.dense_scope_names: 574 if node.op == 'MatMul' or node.op == 'Shape': 575 for inp in node.input: 576 if TFConverter.get_scope_name(inp) != scope: 577 self.dense_scopename_inputname_dict[scope] = inp 578 elif scope.split('/Tensordot')[0] in self.dense_scope_names: 579 if node.op == 'Transpose': 580 for inp in node.input: 581 if TFConverter.get_scope_name(inp).find(scope)<0 and TFConverter.get_scope_name(inp).find(scope.split('/')[0])<0: 582 self.dense_scopename_inputname_dict[scope.split('/Tensordot')[0]] = inp 583 584 585 def run(self): 586 self.generate_name_node_dict() 587 self.generate_output_names() 588 self.remove_identity() 589 self.generate_edges() 590 self.generate_sub_block_op_scope_info() 591 592 if self.dump4tb: 593 self.dump_for_tensorboard() 594 595 self.dump_to_file() 596 597 598def convert_from_tensorflow(infile, outfile, dump4tb): 599 with open(infile, 'rb') as f: 600 # read the file in .proto format 601 graph_def = tf.GraphDef() 602 graph_def.ParseFromString(f.read()) 603 nodes = graph_def.node 604 605 converter = TFConverter(graph_def, nodes, outfile, dump4tb) 606 converter.run() 607