config_parser.py 134.1 KB
Newer Older
1
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
Z
zhangjinchao01 已提交
2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101
#
# 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.

from __future__ import print_function
'''
The following functions are available in the config file:

Bias: define bias. To be used as value of bias argument in Layer().

Data: define data provider.

Input: define input layer for a layer. To be used as element of inputs argument
       in Layer().

Conv: define a convolution operation for an input of a layer.

Norm: define a normalization operation for an input of a layer.

Pool: define a pooling operation for an input of a layer.

Layer: define a layer.

Parameter: define a parameter.

Import: import another config file. If the imported config file name is
        a relative path, then it will be searched under the directory of the
        current config file.

Inputs(layer_names...):
    Define the name of the input layers of the NeuralNetwork.
    The type of these layers must be "data".
    These layers will be provided with the DataBatch obtained
    from DataProvider. The data streams from DataProvider must
    have the same order.

Outputs(layer_names...):
    Define the name of the output layers of the NeuralNetwork.
    Usually the output is simply the cost layer.
    You can specify other layers as outputs and  calculate the
    cost (and its derivative) yourself.


default_initial_std(val)
default_initial_mean(val)
default_momentum(val):
default_decay_rate(val): Set the default value for these parameters


get_config_arg(name, type, default): Get the value for a config parameter.


*** customized extension to config_parser ***
The functionality of the config_parser can be extended.
If the config_arg_str for parse_config() contains
extension_module_name=[MODULE_NAME], then config_parser will call
MODULE_NAME.get_config_funcs(g_config)
MODULE_NAME.get_config_funcs() should return a dictionary of name to functions,
those functions will be available in the config file.
See trainer/tests/config_parser_test.py for example

To use this from paddle_trainer, paddle_trainer should be called with
--config_args=extension_module_name=[MODULE_NAME]

'''
import copy
import logging
import os
import sys
import traceback
import math
import shutil

try:
    from paddle.proto.DataConfig_pb2 import DataConfig
    from paddle.proto.ModelConfig_pb2 import ModelConfig
    from paddle.proto.ModelConfig_pb2 import LayerConfig
    from paddle.proto.ModelConfig_pb2 import LayerInputConfig
    from paddle.proto.ModelConfig_pb2 import ProjectionConfig
    from paddle.proto.ModelConfig_pb2 import OperatorConfig
    from paddle.proto.ModelConfig_pb2 import GeneratorConfig
    from paddle.proto.ModelConfig_pb2 import LinkConfig
    from paddle.proto.ParameterConfig_pb2 import ParameterConfig
    from paddle.proto.ParameterConfig_pb2 import ParameterUpdaterHookConfig
    from paddle.proto.TrainerConfig_pb2 import TrainerConfig

except Exception as e:
    traceback.print_exc()
    raise

logging.basicConfig(
Q
qijun 已提交
102
    format='[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s', )
Z
zhangjinchao01 已提交
103 104 105
logger = logging.getLogger('paddle')
logger.setLevel(logging.INFO)
__real_print__ = print
Q
qijun 已提交
106
print = logger.info
Z
zhangjinchao01 已提交
107 108 109 110

# from layer type name to layer class
g_layer_type_map = {}

Q
qijun 已提交
111

Z
zhangjinchao01 已提交
112 113 114
# Initialize global variables. We use this function so that we can
# call parse_config() multiple times
def init_config_environment(
Q
qijun 已提交
115 116 117 118 119 120 121 122 123 124 125 126 127 128
        g_default_momentum=None,
        g_default_decay_rate=None,
        g_default_initial_mean=0.,
        g_default_initial_std=0.01,
        g_default_num_batches_regularization=None,
        g_default_initial_strategy=0,
        g_default_initial_smart=False,
        g_default_gradient_clipping_threshold=None,
        g_default_device=None,
        g_default_update_hooks=None,
        g_default_compact_func=None,
        g_config=TrainerConfig(),
        g_layer_map={},
        g_parameter_map={},
X
xuwei06 已提交
129
        g_parameter_initializer_map={},
Q
qijun 已提交
130
        g_extended_config_funcs={},
Z
zhangjinchao01 已提交
131 132

        # store command args of paddle_trainer
Q
qijun 已提交
133
        g_command_config_args={},
Z
zhangjinchao01 已提交
134 135

        # Used for PyDataProvider to avoid duplicate module name
Q
qijun 已提交
136 137 138 139 140
        g_py_module_name_list=[],
        g_current_submodel=None,
        g_root_submodel=None,
        g_submodel_map={},
        g_submodel_stack=[],
141
        g_add_submodel_suffix=False, ):
Z
zhangjinchao01 已提交
142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157

    for k, v in locals().iteritems():
        globals()[k] = copy.deepcopy(v)


# Because type is widely used as a variable name in this code.
# we need a different function name for the builtin type()
def type_of(x):
    return type(x)


# Check a condition derived config file
def config_assert(b, msg):
    if not b:
        logger.fatal(msg)

Q
qijun 已提交
158

Z
zhangjinchao01 已提交
159 160
g_config_funcs = {}

Q
qijun 已提交
161

Z
zhangjinchao01 已提交
162 163 164 165 166
# decorator for indicating a function which can be used in config file
def config_func(func):
    g_config_funcs[func.func_name] = func
    return func

Q
qijun 已提交
167

Z
zhangjinchao01 已提交
168 169 170 171 172
# decorator for indicating a class which can be used in config file
def config_class(cls):
    g_config_funcs[cls.__name__] = cls
    return cls

Q
qijun 已提交
173

Z
zhangjinchao01 已提交
174 175 176 177 178 179
# decorator for indicating a class for a layer type
def config_layer(layer_type):
    def wrap(cls):
        g_config_funcs[cls.__name__] = cls
        g_layer_type_map[layer_type] = cls
        return cls
Q
qijun 已提交
180

Z
zhangjinchao01 已提交
181 182
    return wrap

Q
qijun 已提交
183

Z
zhangjinchao01 已提交
184 185 186
def gen_parameter_name(layer_name, input_index):
    return '_%s.w%d' % (layer_name, input_index)

Q
qijun 已提交
187

Z
zhangjinchao01 已提交
188 189 190
def gen_bias_parameter_name(layer_name):
    return '_%s.wbias' % layer_name

Q
qijun 已提交
191

Z
zhangjinchao01 已提交
192 193 194
def default(x, default_value):
    return default_value if x is None else x

Q
qijun 已提交
195

Z
zhangjinchao01 已提交
196 197 198 199 200 201
class Cfg(object):
    def add_keys(self, locals):
        for k, v in locals.iteritems():
            if not k.startswith('_'):
                self.__setattr__(k, v)

Q
qijun 已提交
202

Z
zhangjinchao01 已提交
203 204
# functions available in config file

Q
qijun 已提交
205

Z
zhangjinchao01 已提交
206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223
# Define the name of the input layers of the NeuralNetwork.
# The type of these layers must be "data".
# These layers will be provided with the DataBatch obtained
# from DataProvider. The data streams from DataProvider must
# have the same order.
@config_func
def Inputs(*args):
    for name in args:
        name = MakeLayerNameInSubmodel(name)
        global g_current_submodel, g_root_submodel
        if g_current_submodel.is_recurrent_layer_group:
            config_assert(False, "Do not set Inputs in recurrent layer group")
        else:
            g_current_submodel.input_layer_names.append(name)

        if g_current_submodel is g_root_submodel:
            g_config.model_config.input_layer_names.append(name)

Q
qijun 已提交
224

225 226
@config_func
def HasInputsSet():
227
    return len(g_current_submodel.input_layer_names) != 0
228

Z
zhangjinchao01 已提交
229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252

# Define the name of the output layers of the NeuralNetwork.
# Usually the output is simply the cost layer.
# You can specify other layers as outputs and calculate the
# cost (and its derivative) yourself.
@config_func
def Outputs(*args):
    for name in args:
        name = MakeLayerNameInSubmodel(name)
        global g_current_submodel, g_root_submodel
        if g_current_submodel.is_recurrent_layer_group:
            config_assert(False, "Do not set Outputs in recurrent layer group")
        else:
            g_current_submodel.output_layer_names.append(name)

        if g_current_submodel is g_root_submodel:
            g_config.model_config.output_layer_names.append(name)


@config_func
def SubModelBegin(name):
    global g_current_submodel, g_root_submodel, g_submodel_stack
    g_submodel_stack.append(g_current_submodel)

Q
qijun 已提交
253
    name = MakeLayerNameInParentSubmodel(name)  #rename in nested submodel
Z
zhangjinchao01 已提交
254 255 256 257 258 259 260 261 262

    config_assert(name not in g_submodel_map,
                  'Duplicated submodel name: %s' % name)

    sub_model = g_config.model_config.sub_models.add()
    sub_model.name = name
    g_submodel_map[name] = sub_model
    g_current_submodel = sub_model

Q
qijun 已提交
263

Z
zhangjinchao01 已提交
264
@config_func
Q
qijun 已提交
265
def SubModelEnd(name=None):
Z
zhangjinchao01 已提交
266
    global g_current_submodel, g_root_submodel, g_submodel_stack
Q
qijun 已提交
267 268
    config_assert(g_current_submodel is not g_root_submodel,
                  "submodel not begin")
Z
zhangjinchao01 已提交
269
    if name is not None:
Q
qijun 已提交
270 271 272
        config_assert(
            g_current_submodel.name == MakeLayerNameInParentSubmodel(name),
            "submodel name error")
Z
zhangjinchao01 已提交
273 274 275

    g_current_submodel = g_submodel_stack.pop()

Q
qijun 已提交
276

Z
zhangjinchao01 已提交
277 278
def MakeLayerNameInParentSubmodel(name):
    suffix = ""
279 280
    if len(g_submodel_stack) > 1:
        suffix = "@" + g_submodel_stack[-1].name
Z
zhangjinchao01 已提交
281 282
    return name + suffix

Q
qijun 已提交
283

Z
zhangjinchao01 已提交
284 285 286
def GetLayerBaseName(name):
    return name.split('@')[0]

Q
qijun 已提交
287 288

def MakeLayerNameInSubmodel(name, submodel_name=None):
Z
zhangjinchao01 已提交
289 290
    global g_current_submodel
    global g_add_submodel_suffix
Q
qijun 已提交
291 292
    if (submodel_name is None and not g_add_submodel_suffix and
            not g_current_submodel.is_recurrent_layer_group):
Z
zhangjinchao01 已提交
293 294 295 296 297
        return name
    if submodel_name is None:
        submodel_name = g_current_submodel.name
    return name + "@" + submodel_name

Q
qijun 已提交
298

Z
zhangjinchao01 已提交
299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321
# Define a recurrent layer group begin with RecurrentLayerGroupBegin
# and end with RecurrentLayerGroupEnd.
# A recurrent layer group forward/backward one frame after previous frame
# forward/backward through all layers in layer group.
# in_links are names of layer used as input layer in the layer group.
# out_links are names of layer in layer group used as outside layer's input.
#
# If generator is set, the layer group need one or more than one outlinks.
# The first outlink should always be the generated token ids.
# If generator.num_results_per_sample is not set, the output for one sample is
# a ids sequence. Else if num_results_per_sample is more than one,
# the output for one sample is up to #num_results_per_sample generated
# sequences, which are packed in one sequence in output ids vector. Each
# generated sequence has a generation probability. The probabilities for one
# sample are stored in one row of output value matrix.
# Packed generated sequences format, for each i:
#   seq_i_length: one interger, seq_i content length,
#   [seq_i content], length = seq_i_length
#   seq_i_end_mark: one interger, for format check, always -1
# You can use "seq_text_printer" to print the output of the generator.
@config_func
def RecurrentLayerGroupWithoutOutLinksBegin(name,
                                            in_links,
322 323
                                            seq_reversed=False,
                                            target_inlinkname=""):
Z
zhangjinchao01 已提交
324 325 326 327 328 329 330 331
    global g_current_submodel
    config_assert(g_config.model_config.type == "recurrent_nn",
                  "RecurrentLayerGroup should be used only in recurrent_nn")
    RecurrentLayerGroup(name=name)  # add to father model
    SubModelBegin(name)
    g_current_submodel.is_recurrent_layer_group = True
    g_current_submodel.reversed = seq_reversed
    in_links_count = 0
332
    for linkid, link in enumerate(in_links):
Z
zhangjinchao01 已提交
333 334 335 336
        if isinstance(link, basestring):
            name = link
        else:
            name = link.link_name
337

Z
zhangjinchao01 已提交
338 339 340
        in_links_count += 1
        layer_name = MakeLayerNameInParentSubmodel(name)
        layer = g_layer_map[layer_name]
341
        ScatterAgentLayer(name=name, size=layer.size)
342

Z
zhangjinchao01 已提交
343 344 345 346
        pair = g_current_submodel.in_links.add()
        pair.layer_name = layer_name
        pair.link_name = MakeLayerNameInSubmodel(name)

Q
qijun 已提交
347

Z
zhangjinchao01 已提交
348 349 350 351 352 353 354 355 356 357 358 359 360
@config_func
def RecurrentLayerGroupSetOutLink(link):
    if isinstance(link, basestring):
        name = link
    else:
        name = link.link_name
    layer_name = MakeLayerNameInParentSubmodel(name)
    pair = g_current_submodel.out_links.add()
    pair.layer_name = MakeLayerNameInSubmodel(name)
    pair.link_name = layer_name


def RecurrentLayerGroupSetGenerator(generator=None):
Q
qijun 已提交
361
    generator.eos_layer_name = MakeLayerNameInSubmodel(generator.eos_layer_name)
Z
zhangjinchao01 已提交
362 363 364 365 366 367 368 369
    g_current_submodel.generator.CopyFrom(generator)


@config_func
def RecurrentLayerGroupBegin(name,
                             in_links,
                             out_links,
                             generator=None,
370
                             target_inlinkname="",
Z
zhangjinchao01 已提交
371
                             seq_reversed=False):
372
    RecurrentLayerGroupWithoutOutLinksBegin(name, in_links, seq_reversed)
Z
zhangjinchao01 已提交
373 374 375 376 377
    for link in out_links:
        RecurrentLayerGroupSetOutLink(link)

    if generator is not None:
        RecurrentLayerGroupSetGenerator(generator)
Q
qijun 已提交
378 379 380 381 382
        config_assert(
            len(in_links) == 0, "no in_links should be passed to generator")
        config_assert(
            len(out_links) >= 1,
            "one or more than one out_links should be passed to generator")
Z
zhangjinchao01 已提交
383 384 385 386 387 388 389


@config_func
def RecurrentLayerGroupEnd(name):
    global g_current_submodel
    config_assert(g_current_submodel.is_recurrent_layer_group,
                  "RecurrentLayerGroup not begin")
Q
qijun 已提交
390
    for pair in g_current_submodel.memories:  #check exist
Z
zhangjinchao01 已提交
391
        layer = g_layer_map[pair.layer_name]
Y
Yu Yang 已提交
392 393
        config_assert(layer is not None,
                      "memory declare wrong name:%s" % pair.layer_name)
Z
zhangjinchao01 已提交
394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409
        memory_link = g_layer_map[pair.link_name]
        config_assert(layer.size == memory_link.size,
                      "memory declare wrong size:%d" % memory_link.size)

    prev_submodel = g_current_submodel
    SubModelEnd(name)

    for pair in prev_submodel.out_links:
        layer = g_layer_map[pair.layer_name]
        # add out agent to father model
        agent_name = GetLayerBaseName(pair.link_name)
        if prev_submodel.HasField("generator"):
            DataLayer(name=agent_name, size=layer.size)
        else:
            GatherAgentLayer(name=agent_name, size=layer.size)

Q
qijun 已提交
410

Z
zhangjinchao01 已提交
411 412 413 414 415 416
# Define the model type
# currently, the paddle supports "nn", "recurrent_nn", "recursive_nn" and "multi_nn"
@config_func
def model_type(name):
    g_config.model_config.type = name

Q
qijun 已提交
417

Z
zhangjinchao01 已提交
418 419
@config_class
class Bias(Cfg):
X
xuwei06 已提交
420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435
    def __init__(self,
                 parameter_name=None,
                 learning_rate=None,
                 momentum=None,
                 decay_rate=None,
                 decay_rate_l1=None,
                 initial_mean=None,
                 initial_std=None,
                 initial_strategy=None,
                 initial_smart=None,
                 num_batches_regularization=None,
                 sparse_remote_update=None,
                 gradient_clipping_threshold=None,
                 is_static=None,
                 is_shared=None,
                 initializer=None):
Z
zhangjinchao01 已提交
436 437
        self.add_keys(locals())

Q
qijun 已提交
438

Z
zhangjinchao01 已提交
439 440 441 442 443 444 445
# Define one input for a layer
@config_class
class Input(Cfg):
    def __init__(
            self,
            input_layer_name,
            parameter_name=None,
X
xuwei06 已提交
446
            initializer=None,
Z
zhangjinchao01 已提交
447 448 449 450 451 452 453 454 455 456 457 458 459
            learning_rate=None,
            momentum=None,
            decay_rate=None,
            decay_rate_l1=None,
            initial_mean=None,
            initial_std=None,
            initial_strategy=None,
            initial_smart=None,
            num_batches_regularization=None,
            sparse_remote_update=None,
            sparse_update=None,
            gradient_clipping_threshold=None,
            conv=None,
L
liaogang 已提交
460
            bilinear_interp=None,
Z
zhangjinchao01 已提交
461 462 463 464
            norm=None,
            pool=None,
            image=None,
            block_expand=None,
465
            maxout=None,
Q
qijun 已提交
466
            spp=None,
D
dangqingqing 已提交
467
            pad=None,
Z
zhangjinchao01 已提交
468 469 470 471 472
            format=None,
            nnz=None,
            is_static=None,
            is_shared=None,
            update_hooks=None,
473
            input_layer_argument=None,
D
dangqingqing 已提交
474 475 476 477 478
            make_layer_name_in_submodel=True, ):
        """
        @param make_layer_name_in_submodel True by defalut, you might need to
        set it carefully when adding Input in config_parser.py.
        """
Z
zhangjinchao01 已提交
479
        self.add_keys(locals())
D
dangqingqing 已提交
480 481 482
        self.input_layer_name = MakeLayerNameInSubmodel(
            input_layer_name
        ) if make_layer_name_in_submodel else input_layer_name
Z
zhangjinchao01 已提交
483

Q
qijun 已提交
484

Z
zhangjinchao01 已提交
485 486 487
# Define a projection for iexed layer
@config_class
class Projection(Input):
Q
qijun 已提交
488 489
    type = None  # subclass should set it correctly

Z
zhangjinchao01 已提交
490 491 492
    def __init__(
            self,
            input_layer_name,
Q
qijun 已提交
493
            size=0,  # projection output size
Z
zhangjinchao01 已提交
494 495 496 497 498 499 500 501 502
            parameter_name=None,
            learning_rate=None,
            momentum=None,
            decay_rate=None,
            decay_rate_l1=None,
            initial_mean=None,
            initial_std=None,
            initial_strategy=None,
            initial_smart=None,
X
xuwei06 已提交
503
            initializer=None,
Z
zhangjinchao01 已提交
504 505 506 507 508 509 510 511 512 513
            num_batches_regularization=None,
            sparse_remote_update=None,
            sparse_update=None,
            gradient_clipping_threshold=None,
            ptype=None,
            format=None,
            nnz=None,
            is_static=None,
            is_shared=None,
            update_hooks=None,
Q
qijun 已提交
514
            input_layer_argument=None, ):
Z
zhangjinchao01 已提交
515 516 517 518 519 520 521 522 523 524 525 526 527
        self.add_keys(locals())
        self.input_layer_name = MakeLayerNameInSubmodel(input_layer_name)

        self.proj_conf = ProjectionConfig()
        if ptype is not None:
            self.proj_conf.type = ptype
        else:
            self.proj_conf.type = self.type

    # calculate the output_size given input_size. return 0
    # to indicate using the size from Layer config
    def calc_output_size(self, input_layer_config):
        return self.size
Q
qijun 已提交
528

Z
zhangjinchao01 已提交
529 530
    def calc_parameter_size(self, input_size, output_size):
        raise NotimplementedError
Q
qijun 已提交
531

Z
zhangjinchao01 已提交
532 533 534 535 536 537 538 539 540 541
    def calc_parameter_dims(self, input_size, output_size):
        raise NotimplementedError


@config_class
class IdentityProjection(Projection):
    type = 'identity'

    def calc_output_size(self, input_layer_config):
        return input_layer_config.size
Q
qijun 已提交
542

Z
zhangjinchao01 已提交
543 544
    def calc_parameter_size(self, input_size, output_size):
        return 0
Q
qijun 已提交
545

Z
zhangjinchao01 已提交
546 547 548
    def calc_parameter_dims(self, input_size, output_size):
        return []

Q
qijun 已提交
549

Z
zhangjinchao01 已提交
550 551 552 553 554 555
# Like IdentityProjection, but layer size may smaller than input size,
# the projection select dimesions [offset, offset+layer_size) from input
@config_class
class IdentityOffsetProjection(Projection):
    type = 'identity_offset'

Q
qijun 已提交
556 557 558
    def __init__(self, input_layer_name, offset, **xargs):
        super(IdentityOffsetProjection, self).__init__(input_layer_name,
                                                       **xargs)
Z
zhangjinchao01 已提交
559 560 561 562
        self.proj_conf.offset = offset

    def calc_parameter_size(self, input_size, output_size):
        return 0
Q
qijun 已提交
563

Z
zhangjinchao01 已提交
564 565 566
    def calc_parameter_dims(self, input_size, output_size):
        return []

Q
qijun 已提交
567

568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596
@config_class
class SliceProjection(Projection):
    type = 'slice'

    def __init__(self, input_layer_name, slices, **xargs):
        super(SliceProjection, self).__init__(input_layer_name, **xargs)
        input = g_layer_map[input_layer_name]
        if input.type in ["exconv", "cudnn_conv"]:
            # the slice operator is for the channel dimension
            assert input.num_filters is not None
            channels = input.num_filters
            image_size = input.size / channels
            assert slices[len(slices) - 1][1] <= channels
            for i in xrange(len(slices)):
                slice = self.proj_conf.slices.add()
                slice.start = slices[i][0] * image_size
                slice.end = slices[i][1] * image_size
                self.size += slice.end - slice.start
        else:
            config_assert(False,
                          'Currently the input should be convolution layer')

    def calc_parameter_size(self, input_size, output_size):
        return 0

    def calc_parameter_dims(self, input_size, output_size):
        return []


Z
zhangjinchao01 已提交
597 598 599 600 601 602 603
# DotMulProjection performs element-wise multiplication with weight
@config_class
class DotMulProjection(Projection):
    type = 'dot_mul'

    def calc_output_size(self, input_layer_config):
        return input_layer_config.size
Q
qijun 已提交
604

Z
zhangjinchao01 已提交
605 606
    def calc_parameter_size(self, input_size, output_size):
        return output_size
Q
qijun 已提交
607

Z
zhangjinchao01 已提交
608 609 610
    def calc_parameter_dims(self, input_size, output_size):
        return [1, output_size]

L
Luo Tao 已提交
611

X
xuwei06 已提交
612 613 614 615 616 617 618 619 620 621 622 623 624 625
# ScalingProjection
@config_class
class ScalingProjection(Projection):
    type = 'scaling'

    def calc_output_size(self, input_layer_config):
        return input_layer_config.size

    def calc_parameter_size(self, input_size, output_size):
        return 1

    def calc_parameter_dims(self, input_size, output_size):
        return [1, 1]

Q
qijun 已提交
626

Z
zhangjinchao01 已提交
627 628 629 630 631 632
@config_class
class TableProjection(Projection):
    type = 'table'

    def calc_parameter_size(self, input_size, output_size):
        return input_size * output_size
Q
qijun 已提交
633

Z
zhangjinchao01 已提交
634 635 636
    def calc_parameter_dims(self, input_size, output_size):
        return [input_size, output_size]

Q
qijun 已提交
637

Z
zhangjinchao01 已提交
638 639 640 641 642 643
@config_class
class FullMatrixProjection(Projection):
    type = 'fc'

    def calc_parameter_size(self, input_size, output_size):
        return input_size * output_size
Q
qijun 已提交
644

Z
zhangjinchao01 已提交
645 646 647
    def calc_parameter_dims(self, input_size, output_size):
        return [input_size, output_size]

Q
qijun 已提交
648

Z
zhangjinchao01 已提交
649 650 651 652 653 654
@config_class
class TransposedFullMatrixProjection(Projection):
    type = 'trans_fc'

    def calc_parameter_size(self, input_size, output_size):
        return input_size * output_size
Q
qijun 已提交
655

Z
zhangjinchao01 已提交
656 657 658
    def calc_parameter_dims(self, input_size, output_size):
        return [output_size, input_size]

Q
qijun 已提交
659

Z
zhangjinchao01 已提交
660 661 662 663
@config_class
class ContextProjection(Projection):
    type = 'context'

Q
qijun 已提交
664 665
    def __init__(self, input_layer_name, context_start, context_length,
                 trainable_padding, **xargs):
Z
zhangjinchao01 已提交
666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688
        super(ContextProjection, self).__init__(input_layer_name, **xargs)
        self.proj_conf.context_start = context_start
        self.proj_conf.context_length = context_length
        self.proj_conf.trainable_padding = trainable_padding
        self._total_pad = max(0, -self.proj_conf.context_start) \
                          + max(0, self.proj_conf.context_start \
                                + self.proj_conf.context_length - 1)

    def calc_output_size(self, input_layer_config):
        return input_layer_config.size * self.proj_conf.context_length

    def calc_parameter_size(self, input_size, output_size):
        if self.proj_conf.trainable_padding == False:
            return 0
        else:
            return input_size * self._total_pad

    def calc_parameter_dims(self, input_size, output_size):
        return [self._total_pad, input_size]

    _total_pad = 0


689
@config_class
690
class ConvBaseProjection(Projection):
Q
qijun 已提交
691 692 693 694 695
    def __init__(self,
                 input_layer_name,
                 num_filters=None,
                 conv_conf=None,
                 **xargs):
696
        super(ConvBaseProjection, self).__init__(input_layer_name, **xargs)
697 698 699 700 701 702 703 704 705 706 707 708

        if num_filters is not None:
            self.proj_conf.num_filters = num_filters

    def calc_output_size(self, input_layer_config):
        return self.proj_conf.output_size

    def calc_parameter_size(self, input_size, output_size):
        co = self.proj_conf.num_filters
        ci = self.proj_conf.conv_conf.channels
        fh = self.proj_conf.conv_conf.filter_size
        fw = self.proj_conf.conv_conf.filter_size_y
709 710
        gr = self.proj_conf.conv_conf.groups
        return co * ci * fh * fw / gr
711 712 713 714 715 716 717

    def calc_bias_size(self):
        return self.proj_conf.num_filters

    def calc_parameter_dims(self, input_size, output_size):
        return None

Q
qijun 已提交
718

719 720 721 722 723 724 725 726 727
@config_class
class ConvProjection(ConvBaseProjection):
    type = 'conv'

    def __init__(self,
                 input_layer_name,
                 num_filters=None,
                 conv_conf=None,
                 **xargs):
728 729
        super(ConvProjection, self).__init__(input_layer_name, num_filters,
                                             conv_conf, **xargs)
730

731
        parse_conv(conv_conf, self.input_layer_name, self.proj_conf.conv_conf,
732 733 734 735 736 737 738 739 740 741 742 743 744 745 746
                   num_filters)
        self.proj_conf.output_size = self.proj_conf.conv_conf.output_x * \
                                     self.proj_conf.conv_conf.output_y * \
                                     num_filters


@config_class
class ConvTransProjection(ConvBaseProjection):
    type = 'convt'

    def __init__(self,
                 input_layer_name,
                 num_filters=None,
                 conv_conf=None,
                 **xargs):
747 748
        super(ConvTransProjection, self).__init__(input_layer_name, num_filters,
                                                  conv_conf, **xargs)
749 750 751

        parse_conv(
            conv_conf,
752
            self.input_layer_name,
753 754 755 756 757 758 759 760
            self.proj_conf.conv_conf,
            num_filters,
            trans=True)
        self.proj_conf.output_size = self.proj_conf.conv_conf.img_size_y * \
                                     self.proj_conf.conv_conf.img_size * \
                                     num_filters


Z
zhangjinchao01 已提交
761 762 763
# Define a operator for mixed layer
@config_class
class Operator(Cfg):
Q
qijun 已提交
764 765
    type = None  # subclass should set it correctly

Z
zhangjinchao01 已提交
766 767
    def __init__(
            self,
Q
qijun 已提交
768
            input_layer_names, ):
Z
zhangjinchao01 已提交
769 770 771 772 773 774 775 776 777 778
        self.add_keys(locals())
        self.operator_conf = OperatorConfig()
        self.operator_conf.type = self.type

    def check_dims(self):
        pass

    def calc_output_size(self, input_sizes):
        return 0

Q
qijun 已提交
779

Z
zhangjinchao01 已提交
780 781 782
@config_class
class DotMulOperator(Operator):
    type = 'dot_mul'
Q
qijun 已提交
783 784 785

    def __init__(self, input_layer_names, scale=None, **xargs):
        super(DotMulOperator, self).__init__(input_layer_names, **xargs)
Z
zhangjinchao01 已提交
786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803
        if scale is not None:
            self.operator_conf.dotmul_scale = scale

        config_assert(len(input_layer_names) == 2, "DotMul is binary operator")

    def check_dims(self):
        for i in range(2):
            config_assert(self.operator_conf.input_sizes[i] ==
                          self.operator_conf.output_size,
                          "DotMul input_size != output_size")

    def calc_output_size(self, input_sizes):
        return input_sizes[0]


@config_class
class ConvOperator(Operator):
    type = 'conv'
Q
qijun 已提交
804 805 806 807 808 809 810

    def __init__(self,
                 input_layer_names,
                 num_filters=None,
                 conv_conf=None,
                 **xargs):
        super(ConvOperator, self).__init__(input_layer_names, **xargs)
Z
zhangjinchao01 已提交
811 812 813
        if num_filters is not None:
            self.operator_conf.num_filters = num_filters

814 815
        parse_conv(conv_conf,
                   MakeLayerNameInSubmodel(input_layer_names[0]),
Q
qijun 已提交
816
                   self.operator_conf.conv_conf, num_filters)
L
Luo Tao 已提交
817 818 819
        self.operator_conf.output_size = self.operator_conf.conv_conf.output_x * \
                                         self.operator_conf.conv_conf.output_y * \
                                         num_filters
Z
zhangjinchao01 已提交
820 821 822

        config_assert(len(input_layer_names) == 2, "Conv is binary operator")

823 824
    def calc_output_size(self, input_sizes):
        return self.operator_conf.output_size
Z
zhangjinchao01 已提交
825 826


827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856
@config_class
class ConvTransOperator(Operator):
    type = 'convt'

    def __init__(self,
                 input_layer_names,
                 num_filters=None,
                 conv_conf=None,
                 **xargs):
        super(ConvTransOperator, self).__init__(input_layer_names, **xargs)
        if num_filters is not None:
            self.operator_conf.num_filters = num_filters

        parse_conv(
            conv_conf,
            MakeLayerNameInSubmodel(input_layer_names[0]),
            self.operator_conf.conv_conf,
            num_filters,
            trans=True)
        self.operator_conf.output_size = \
            self.operator_conf.conv_conf.img_size * \
            self.operator_conf.conv_conf.img_size_y * \
            num_filters

        config_assert(len(input_layer_names) == 2, "Conv is binary operator")

    def calc_output_size(self, input_sizes):
        return self.operator_conf.output_size


Z
zhangjinchao01 已提交
857 858 859
# please refer to the comments in proto/ModelConfig.proto
@config_class
class Conv(Cfg):
Q
qijun 已提交
860 861 862 863 864 865 866 867 868 869 870 871 872
    def __init__(self,
                 filter_size,
                 channels,
                 padding=None,
                 stride=None,
                 groups=None,
                 filter_channels=None,
                 output_x=None,
                 img_size=None,
                 caffe_mode=True,
                 filter_size_y=None,
                 padding_y=None,
                 stride_y=None):
Z
zhangjinchao01 已提交
873 874
        self.add_keys(locals())
        if filter_size_y is None:
Q
qijun 已提交
875
            self.filter_size_y = filter_size
Z
zhangjinchao01 已提交
876
        if padding_y is None:
Q
qijun 已提交
877
            self.padding_y = padding
Z
zhangjinchao01 已提交
878
        if stride_y is None:
Q
qijun 已提交
879
            self.stride_y = stride
Z
zhangjinchao01 已提交
880
        if output_x is not None:
Q
qijun 已提交
881 882
            config_assert(output_x <= 0)

Z
zhangjinchao01 已提交
883

L
liaogang 已提交
884 885
@config_class
class BilinearInterp(Cfg):
L
Luo Tao 已提交
886
    def __init__(self, out_size_x=None, out_size_y=None, channels=None):
L
liaogang 已提交
887 888
        self.add_keys(locals())

Q
qijun 已提交
889

Z
zhangjinchao01 已提交
890 891
@config_class
class Pool(Cfg):
D
dangqingqing 已提交
892 893 894 895 896 897 898 899 900 901 902
    def __init__(
            self,
            pool_type,
            channels,
            size_x,
            size_y=None,
            start=None,
            stride=None,  # 1 by defalut in protobuf
            stride_y=None,
            padding=None,  # 0 by defalut in protobuf
            padding_y=None):
Z
zhangjinchao01 已提交
903
        self.add_keys(locals())
Q
qijun 已提交
904 905


Q
qijun 已提交
906
@config_class
Q
qijun 已提交
907
class SpatialPyramidPool(Cfg):
L
Luo Tao 已提交
908
    def __init__(self, pool_type, pyramid_height, channels):
Q
qijun 已提交
909
        self.add_keys(locals())
Z
zhangjinchao01 已提交
910

Q
qijun 已提交
911

D
dangqingqing 已提交
912 913 914 915 916 917
@config_class
class Pad(Cfg):
    def __init__(self, channels, pad_c, pad_h, pad_w):
        self.add_keys(locals())


Z
zhangjinchao01 已提交
918 919
@config_class
class Norm(Cfg):
Q
qijun 已提交
920 921 922 923 924 925 926 927 928
    def __init__(self,
                 norm_type,
                 channels,
                 size,
                 scale,
                 pow,
                 output_x=None,
                 img_size=None,
                 blocked=None):
Z
zhangjinchao01 已提交
929 930
        self.add_keys(locals())

Q
qijun 已提交
931

Z
zhangjinchao01 已提交
932 933
@config_class
class Image(Cfg):
Q
qijun 已提交
934
    def __init__(self, channels, img_size=None):
Z
zhangjinchao01 已提交
935 936
        self.add_keys(locals())

Q
qijun 已提交
937

Z
zhangjinchao01 已提交
938 939
@config_class
class BlockExpand(Cfg):
Q
qijun 已提交
940 941 942 943 944 945 946 947 948 949 950 951
    def __init__(self,
                 channels,
                 padding_x=0,
                 padding_y=0,
                 stride_x=0,
                 stride_y=0,
                 block_x=0,
                 block_y=0,
                 img_size_x=0,
                 img_size_y=0,
                 output_x=0,
                 output_y=0):
Z
zhangjinchao01 已提交
952 953
        self.add_keys(locals())

Q
qijun 已提交
954

955 956
@config_class
class MaxOut(Cfg):
Q
qijun 已提交
957
    def __init__(self, channels, groups, img_size_x=0, img_size_y=0):
958 959
        self.add_keys(locals())

Q
qijun 已提交
960

961
def create_data_config_proto(async_load_data=False,
962
                             constant_slots=None,
王益 已提交
963 964 965
                             data_ratio=1,
                             is_main_data=True,
                             usage_ratio=None):
Z
zhangjinchao01 已提交
966 967 968 969 970 971 972 973
    # default: all sub dataproviders are treat as "main data".
    # see proto/DataConfig.proto for is_main_data
    data_config = DataConfig()

    data_config.async_load_data = async_load_data

    if constant_slots:
        data_config.constant_slots.extend(constant_slots)
Q
qijun 已提交
974 975
    data_config.data_ratio = data_ratio
    data_config.is_main_data = is_main_data
Z
zhangjinchao01 已提交
976

Q
qijun 已提交
977
    usage_ratio = default(usage_ratio, settings_deprecated["usage_ratio"])
Z
zhangjinchao01 已提交
978 979 980 981 982 983
    config_assert(usage_ratio >= 0 and usage_ratio <= 1,
                  "The range of usage_ratio is [0, 1]")
    data_config.usage_ratio = usage_ratio

    return data_config

Q
qijun 已提交
984

Z
zhangjinchao01 已提交
985
@config_func
Q
qijun 已提交
986 987 988 989 990
def SimpleData(files=None,
               feat_dim=None,
               context_len=None,
               buffer_capacity=None,
               **xargs):
991
    data_config = create_data_config_proto(**xargs)
Z
zhangjinchao01 已提交
992 993 994 995 996 997 998 999 1000
    data_config.type = 'simple'
    data_config.files = files
    data_config.feat_dim = feat_dim
    if context_len is not None:
        data_config.context_len = context_len
    if buffer_capacity:
        data_config.buffer_capacity = buffer_capacity
    return data_config

Q
qijun 已提交
1001

Z
zhangjinchao01 已提交
1002
@config_func
Q
qijun 已提交
1003 1004 1005 1006 1007 1008 1009 1010 1011 1012
def PyData(files=None,
           type=None,
           file_group_queue_capacity=None,
           load_data_module=None,
           load_data_object=None,
           load_data_args="",
           load_file_count=None,
           constant_slots=None,
           load_thread_num=None,
           **xargs):
1013
    data_config = create_data_config_proto(**xargs)
Z
zhangjinchao01 已提交
1014 1015
    data_config.type = 'py'
    if load_data_module in g_py_module_name_list:
Q
qijun 已提交
1016

Z
zhangjinchao01 已提交
1017 1018 1019
        def get_path(module):
            m = __import__(load_data_module)
            return os.path.split(os.path.realpath(m.__file__))[0]
Q
qijun 已提交
1020

Z
zhangjinchao01 已提交
1021 1022 1023
        # python C-api is not thread safe, one module can only be import once,
        # so here we nedd to copy the module with different names if it has to be
        # imported several times.
Q
qijun 已提交
1024 1025
        module_new_name = "%s_copy_%d" % (load_data_module,
                                          len(g_py_module_name_list))
Z
zhangjinchao01 已提交
1026
        g_py_module_name_list.append(module_new_name)
Q
qijun 已提交
1027 1028 1029 1030
        module_path = "%s/%s.py" % (get_path(load_data_module),
                                    load_data_module)
        new_module_path = "%s/%s.py" % (get_path(load_data_module),
                                        module_new_name)
Z
zhangjinchao01 已提交
1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054
        if os.path.isfile(module_path) == False:
            raise Exception("File %s is not exist." % module_path)
        shutil.copy2(module_path, new_module_path)
        load_data_module = module_new_name
    else:
        g_py_module_name_list.append(load_data_module)
    if load_data_module is not None and load_data_object is not None:
        data_config.load_data_module = load_data_module
        data_config.load_data_object = load_data_object
    else:
        raise ValueError('load_data_module, load_data_object is not defined.')
    data_config.load_data_args = load_data_args

    data_config.files = files or ''
    if file_group_queue_capacity is not None:
        data_config.file_group_conf.queue_capacity = file_group_queue_capacity
    if load_file_count is not None:
        data_config.file_group_conf.load_file_count = load_file_count
    if load_thread_num is not None:
        data_config.file_group_conf.load_thread_num = load_thread_num
    if constant_slots:
        data_config.constant_slots.extend(constant_slots)
    return data_config

Q
qijun 已提交
1055

Z
zhangjinchao01 已提交
1056
@config_func
Q
qijun 已提交
1057 1058 1059 1060 1061 1062 1063
def ProtoData(files=None,
              type=None,
              file_group_queue_capacity=None,
              load_file_count=None,
              constant_slots=None,
              load_thread_num=None,
              **xargs):
1064
    data_config = create_data_config_proto(**xargs)
Z
zhangjinchao01 已提交
1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083
    if type is None:
        data_config.type = 'proto'
    else:
        data_config.type = type
    data_config.files = files

    # When type="proto_group", one data provider contains at most
    # load_file_count files, and there are at most
    # (queue_capacity + load_thread_num + 1) data providers in memory
    if file_group_queue_capacity is not None:
        data_config.file_group_conf.queue_capacity = file_group_queue_capacity
    if load_file_count is not None:
        data_config.file_group_conf.load_file_count = load_file_count
    if load_thread_num is not None:
        data_config.file_group_conf.load_thread_num = load_thread_num
    if constant_slots:
        data_config.constant_slots.extend(constant_slots)
    return data_config

Q
qijun 已提交
1084

Z
zhangjinchao01 已提交
1085 1086
#real data for training is actually provided by "sub_data" data providers.
@config_func
Q
qijun 已提交
1087
def MultiData(sub_data=[]):
Z
zhangjinchao01 已提交
1088 1089 1090 1091 1092
    data_config = DataConfig()
    data_config.type = 'multi'
    data_config.sub_data_configs.extend(sub_data)
    return data_config

Q
qijun 已提交
1093

Z
zhangjinchao01 已提交
1094
@config_func
Q
qijun 已提交
1095 1096 1097 1098 1099 1100 1101
def Data(type,
         files=None,
         feat_dim=None,
         slot_dims=None,
         context_len=None,
         buffer_capacity=None,
         **xargs):
Z
zhangjinchao01 已提交
1102

1103
    data_config = create_data_config_proto(**xargs)
Z
zhangjinchao01 已提交
1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136
    data_config.type = type
    data_config.files = files
    data_config.feat_dim = feat_dim
    data_config.slot_dims.extend(slot_dims)
    if context_len is not None:
        data_config.context_len = context_len
    data_config.buffer_capacity = buffer_capacity
    return data_config


@config_func
def TrainData(data_config, async_load_data=None):
    config_assert(not g_config.HasField('data_config'),
                  'Only one TrainData definition is allowed')
    g_config.data_config.CopyFrom(data_config)
    g_config.data_config.for_test = False
    if async_load_data is not None:
        logger.warning("Deprecated: async_load_data should be used inside"
                       " Data definition")
        g_config.data_config.async_load_data = async_load_data


@config_func
def TestData(data_config, async_load_data=None):
    config_assert(not g_config.HasField('test_data_config'),
                  'Only one TestData definition is allowed')
    g_config.test_data_config.CopyFrom(data_config)
    g_config.test_data_config.for_test = True
    if async_load_data is not None:
        logger.warning("Deprecated: async_load_data should be used inside"
                       " Data definition")
        g_config.test_data_config.async_load_data = async_load_data

Q
qijun 已提交
1137

L
Luo Tao 已提交
1138 1139
#caffe_mode: compute the output size using floor instead of ceil,
#            which is consistent of caffe and CuDNN's convention.
1140 1141 1142 1143 1144 1145 1146
def cnn_output_size(img_size, filter_size, padding, stride, caffe_mode):
    output = (2 * padding + img_size - filter_size) / float(stride)
    if caffe_mode:
        return 1 + int(math.floor(output))
    else:
        return 1 + int(math.ceil(output))

Q
qijun 已提交
1147

1148
#calcualte image_size based on output_size for de-convolution (ConvTransLayer).
L
Luo Tao 已提交
1149
#It is the reverse function of cnn_output_size
1150
def cnn_image_size(output_size, filter_size, padding, stride, caffe_mode):
L
Luo Tao 已提交
1151 1152 1153
    img_size = (output_size - 1) * stride + filter_size - 2 * padding
    if not caffe_mode:
        img_size = img_size + 1
1154 1155
    return img_size

Q
qijun 已提交
1156

L
Luo Tao 已提交
1157
def get_img_size(input_layer_name, channels):
L
Luo Tao 已提交
1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175
    input = g_layer_map[input_layer_name]
    img_pixels = input.size / channels
    img_size = input.width if input.width > 0 else int(img_pixels**0.5)
    img_size_y = input.height if input.height > 0 else int(img_pixels /
                                                           img_size)
    config_assert(
        img_size * img_size_y == img_pixels,
        "Input layer %s: Incorrect input image size %d * %d for input image pixels %d"
        % (input_layer_name, img_size, img_size_y, img_pixels))
    return img_size, img_size_y


def parse_bilinear(bilinear, input_layer_name, bilinear_conf):
    parse_image(bilinear, input_layer_name, bilinear_conf.image_conf)
    bilinear_conf.out_size_x = bilinear.out_size_x
    bilinear_conf.out_size_y = bilinear.out_size_y


1176
def parse_pool(pool, input_layer_name, pool_conf, ceil_mode):
Z
zhangjinchao01 已提交
1177
    pool_conf.pool_type = pool.pool_type
Q
qijun 已提交
1178 1179 1180
    config_assert(pool.pool_type in [
        'max-projection', 'avg-projection', 'cudnn-max-pool', 'cudnn-avg-pool'
    ], "pool-type %s is not in "
Z
zhangjinchao01 已提交
1181
                  "['max-projection', 'avg-projection', "
Q
qijun 已提交
1182
                  "'cudnn-max-pool', 'cudnn-avg-pool']" % pool.pool_type)
Z
zhangjinchao01 已提交
1183 1184 1185 1186 1187 1188

    pool_conf.channels = pool.channels
    pool_conf.size_x = pool.size_x
    pool_conf.stride = pool.stride

    pool_conf.size_y = default(pool.size_y, pool_conf.size_x)
Q
qijun 已提交
1189
    pool_conf.stride_y = default(pool.stride_y, pool_conf.stride)
Z
zhangjinchao01 已提交
1190

L
Luo Tao 已提交
1191
    pool_conf.img_size, pool_conf.img_size_y = \
L
Luo Tao 已提交
1192
        get_img_size(input_layer_name, pool.channels)
Z
zhangjinchao01 已提交
1193

1194
    config_assert(not pool.start, "start is deprecated in pooling.")
Z
zhangjinchao01 已提交
1195

1196
    if pool.padding is not None:
Z
zhangjinchao01 已提交
1197
        pool_conf.padding = pool.padding
1198
    pool_conf.padding_y = default(pool.padding_y, pool_conf.padding)
D
dangqingqing 已提交
1199 1200
    pool_conf.output_x = cnn_output_size(pool_conf.img_size, pool_conf.size_x,
                                         pool_conf.padding, pool_conf.stride,
1201
                                         not ceil_mode)
D
dangqingqing 已提交
1202 1203
    pool_conf.output_y = cnn_output_size(pool_conf.img_size_y, pool_conf.size_y,
                                         pool_conf.padding_y,
1204
                                         pool_conf.stride_y, not ceil_mode)
Q
qijun 已提交
1205

Z
zhangjinchao01 已提交
1206

Q
qijun 已提交
1207
def parse_spp(spp, input_layer_name, spp_conf):
L
Luo Tao 已提交
1208
    parse_image(spp, input_layer_name, spp_conf.image_conf)
Q
qijun 已提交
1209 1210
    spp_conf.pool_type = spp.pool_type
    config_assert(spp.pool_type in ['max-projection', 'avg-projection'],
Q
qijun 已提交
1211 1212
                  "pool-type %s is not in "
                  "['max-projection', 'avg-projection']" % spp.pool_type)
Q
qijun 已提交
1213
    spp_conf.pyramid_height = spp.pyramid_height
Q
qijun 已提交
1214

Q
qijun 已提交
1215

Z
zhangjinchao01 已提交
1216 1217
def parse_image(image, input_layer_name, image_conf):
    image_conf.channels = image.channels
L
Luo Tao 已提交
1218
    image_conf.img_size, image_conf.img_size_y = \
L
Luo Tao 已提交
1219
        get_img_size(input_layer_name, image_conf.channels)
Q
qijun 已提交
1220

Z
zhangjinchao01 已提交
1221 1222 1223

def parse_norm(norm, input_layer_name, norm_conf):
    norm_conf.norm_type = norm.norm_type
1224 1225 1226 1227 1228
    config_assert(
        norm.norm_type in
        ['rnorm', 'cmrnorm-projection', 'cross-channel-norm'],
        "norm-type %s is not in [rnorm, cmrnorm-projection, cross-channel-norm]"
        % norm.norm_type)
Z
zhangjinchao01 已提交
1229 1230 1231 1232 1233 1234
    norm_conf.channels = norm.channels
    norm_conf.size = norm.size
    norm_conf.scale = norm.scale
    norm_conf.pow = norm.pow
    norm_conf.blocked = norm.blocked

L
Luo Tao 已提交
1235
    norm_conf.img_size, norm_conf.img_size_y = \
L
Luo Tao 已提交
1236
        get_img_size(input_layer_name, norm.channels)
Z
zhangjinchao01 已提交
1237
    norm_conf.output_x = norm_conf.img_size
L
Luo Tao 已提交
1238
    norm_conf.output_y = norm_conf.img_size_y
Z
zhangjinchao01 已提交
1239 1240 1241
    if norm.norm_type in ['cmrnorm-projection']:
        norm_conf.scale /= norm.size
    else:
Q
qijun 已提交
1242 1243
        norm_conf.scale /= norm.size**2

1244

L
Luo Tao 已提交
1245 1246
#caffe_mode: compute the output size using floor instead of ceil,
#            which is consistent of caffe and CuDNN's convention.
1247
def parse_conv(conv, input_layer_name, conv_conf, num_filters, trans=False):
Z
zhangjinchao01 已提交
1248 1249 1250 1251 1252 1253 1254 1255 1256
    conv_conf.filter_size = conv.filter_size
    conv_conf.filter_size_y = conv.filter_size_y
    conv_conf.channels = conv.channels
    conv_conf.padding = conv.padding
    conv_conf.padding_y = conv.padding_y
    conv_conf.stride = conv.stride
    conv_conf.stride_y = conv.stride_y
    conv_conf.groups = conv.groups
    conv_conf.caffe_mode = conv.caffe_mode
Q
qijun 已提交
1257

1258
    if not trans:
1259
        conv_conf.filter_channels = conv.channels / conv.groups
L
Luo Tao 已提交
1260
        conv_conf.img_size, conv_conf.img_size_y = \
L
Luo Tao 已提交
1261
            get_img_size(input_layer_name, conv.channels)
1262
        conv_conf.output_x = cnn_output_size(
Q
qijun 已提交
1263 1264
            conv_conf.img_size, conv_conf.filter_size, conv_conf.padding,
            conv_conf.stride, conv_conf.caffe_mode)
L
Luo Tao 已提交
1265 1266 1267
        conv_conf.output_y = cnn_output_size(
            conv_conf.img_size_y, conv_conf.filter_size_y, conv_conf.padding_y,
            conv_conf.stride_y, conv_conf.caffe_mode)
1268
    else:
1269
        conv_conf.filter_channels = num_filters / conv.groups
L
Luo Tao 已提交
1270
        conv_conf.output_x, conv_conf.output_y = \
L
Luo Tao 已提交
1271
            get_img_size(input_layer_name, conv.channels)
1272
        conv_conf.img_size = cnn_image_size(
Q
qijun 已提交
1273 1274
            conv_conf.output_x, conv_conf.filter_size, conv_conf.padding,
            conv_conf.stride, conv_conf.caffe_mode)
L
Luo Tao 已提交
1275
        conv_conf.img_size_y = cnn_image_size(
L
Luo Tao 已提交
1276 1277
            conv_conf.output_y, conv_conf.filter_size_y, conv_conf.padding_y,
            conv_conf.stride_y, conv_conf.caffe_mode)
Q
qijun 已提交
1278

1279

Z
zhangjinchao01 已提交
1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292
def parse_block_expand(block_expand, input_layer_name, block_expand_conf):
    block_expand_conf.channels = block_expand.channels
    block_expand_conf.stride_x = block_expand.stride_x
    block_expand_conf.stride_y = block_expand.stride_y
    block_expand_conf.padding_x = block_expand.padding_x
    block_expand_conf.padding_y = block_expand.padding_y
    block_expand_conf.block_x = block_expand.block_x
    block_expand_conf.block_y = block_expand.block_y
    block_expand_conf.img_size_x = block_expand.img_size_x
    block_expand_conf.img_size_y = block_expand.img_size_y
    if block_expand_conf.img_size_x == 0:
        block_expand_conf.output_x = 0
    else:
1293
        block_expand_conf.output_x = cnn_output_size(
1294
            block_expand.img_size_x, block_expand.block_x,
1295
            block_expand.padding_x, block_expand.stride_x, False)
Z
zhangjinchao01 已提交
1296 1297

    if block_expand_conf.img_size_y == 0:
1298
        block_expand_conf.output_y = 0
Z
zhangjinchao01 已提交
1299
    else:
1300
        block_expand_conf.output_y = cnn_output_size(
1301
            block_expand.img_size_y, block_expand.block_y,
1302
            block_expand.padding_y, block_expand.stride_y, False)
Z
zhangjinchao01 已提交
1303

Q
qijun 已提交
1304

1305
def parse_maxout(maxout, input_layer_name, maxout_conf):
L
Luo Tao 已提交
1306
    parse_image(maxout, input_layer_name, maxout_conf.image_conf)
1307
    maxout_conf.groups = maxout.groups
1308

Q
qijun 已提交
1309

Z
zhangjinchao01 已提交
1310 1311
# Define an evaluator
@config_func
Y
yangyaming 已提交
1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328
def Evaluator(name,
              type,
              inputs,
              chunk_scheme=None,
              num_chunk_types=None,
              classification_threshold=None,
              positive_label=None,
              dict_file=None,
              result_file=None,
              num_results=None,
              top_k=None,
              delimited=None,
              excluded_chunk_types=None,
              overlap_threshold=None,
              background_id=None,
              evaluate_difficult=None,
              ap_type=None):
Z
zhangjinchao01 已提交
1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342
    evaluator = g_config.model_config.evaluators.add()
    evaluator.type = type
    evaluator.name = MakeLayerNameInSubmodel(name)
    if type_of(inputs) == str:
        inputs = [inputs]

    evaluator.input_layers.extend(
        [MakeLayerNameInSubmodel(name) for name in inputs])

    if chunk_scheme is not None:
        evaluator.chunk_scheme = chunk_scheme
        evaluator.num_chunk_types = num_chunk_types
    g_current_submodel.evaluator_names.append(evaluator.name)

1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353
    if classification_threshold is not None:
        evaluator.classification_threshold = classification_threshold
    if positive_label is not None:
        evaluator.positive_label = positive_label
    if dict_file is not None:
        evaluator.dict_file = dict_file

    if result_file is not None:
        evaluator.result_file = result_file
    if num_results is not None:
        evaluator.num_results = num_results
L
Liang Zhao 已提交
1354 1355
    if top_k is not None:
        evaluator.top_k = top_k
1356 1357
    if delimited is not None:
        evaluator.delimited = delimited
Z
zhangjinchao01 已提交
1358

1359 1360 1361
    if excluded_chunk_types:
        evaluator.excluded_chunk_types.extend(excluded_chunk_types)

Y
yangyaming 已提交
1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373
    if overlap_threshold is not None:
        evaluator.overlap_threshold = overlap_threshold

    if background_id is not None:
        evaluator.background_id = background_id

    if evaluate_difficult is not None:
        evaluator.evaluate_difficult = evaluate_difficult

    if ap_type is not None:
        evaluator.ap_type = ap_type

Q
qijun 已提交
1374

Z
zhangjinchao01 已提交
1375 1376 1377 1378 1379
class LayerBase(object):
    def __init__(
            self,
            name,
            type,
Q
qijun 已提交
1380
            size,  # size can be 0. In this case, subclass should set it.
Z
zhangjinchao01 已提交
1381 1382 1383 1384
            inputs,
            device=None,
            active_type="",
            drop_rate=0.,
C
caoying03 已提交
1385 1386
            coeff=None,
            error_clipping_threshold=None):
Z
zhangjinchao01 已提交
1387
        config_assert('@' not in name,
Q
qijun 已提交
1388
                      "layer name: %s contain special character @" % name)
Z
zhangjinchao01 已提交
1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403
        global g_current_submodel
        name = MakeLayerNameInSubmodel(name)

        config_assert(name not in g_layer_map,
                      'Duplicated layer name: %s' % name)

        self.inputs = copy.deepcopy(inputs)
        self.operators = []

        if self.inputs is None:
            self.inputs = []
        elif type_of(self.inputs) != list:
            self.inputs = [self.inputs]

        self.config = g_config.model_config.layers.add()
1404
        assert isinstance(self.config, LayerConfig)
Z
zhangjinchao01 已提交
1405 1406 1407
        self.config.name = name
        self.config.type = type
        self.config.active_type = active_type
1408 1409
        if coeff is not None:
            self.config.coeff = float(coeff)
Z
zhangjinchao01 已提交
1410 1411 1412 1413 1414 1415 1416
        if size != 0:
            self.config.size = size
        if drop_rate != 0:
            self.config.drop_rate = drop_rate

        if device is not None:
            self.config.device = device
1417
        elif g_default_device is not None:
Z
zhangjinchao01 已提交
1418 1419
            self.config.device = g_default_device

C
caoying03 已提交
1420 1421 1422
        if error_clipping_threshold is not None:
            self.config.error_clipping_threshold = error_clipping_threshold

Z
zhangjinchao01 已提交
1423 1424 1425 1426 1427 1428 1429
        for input_index in xrange(len(self.inputs)):
            input = self.inputs[input_index]
            input_config = None
            input_layer_name = ''
            if type_of(input) == str:
                input_layer_name = input
                input_config = Input(
Q
qijun 已提交
1430 1431
                    input_layer_name=input,
                    parameter_name=gen_parameter_name(name, input_index))
Z
zhangjinchao01 已提交
1432 1433 1434 1435 1436 1437 1438 1439
                input_layer_name = input_config.input_layer_name
            elif isinstance(input, Input):
                input_layer_name = input.input_layer_name
                input_config = input
                if input_config.parameter_name is None:
                    input_config.parameter_name = \
                        gen_parameter_name(name, input_index)
            elif isinstance(input, Operator):
Q
qijun 已提交
1440
                self.operators.append(input)
Z
zhangjinchao01 已提交
1441 1442 1443 1444
                input.operator_conf.input_indices.append(input_index)
                input_config = Input(input.input_layer_names[0])
                input_layer_name = input_config.input_layer_name
            else:
Q
qijun 已提交
1445
                raise ValueError('Wrong type for inputs: %s' % type_of(input))
Z
zhangjinchao01 已提交
1446
            config_assert(input_layer_name in g_layer_map,
Q
qijun 已提交
1447 1448
                          "Unknown input layer '%s' for layer %s" %
                          (input_layer_name, name))
Z
zhangjinchao01 已提交
1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465
            self.inputs[input_index] = input_config
            layer_input = self.config.inputs.add()
            layer_input.input_layer_name = input_config.input_layer_name
            if input_config.input_layer_argument is not None:
                layer_input.input_layer_argument = \
                    input_config.input_layer_argument

        g_layer_map[name] = self.config

        g_current_submodel.layer_names.append(self.config.name)

    def get_input_layer(self, input_index):
        return g_layer_map[self.config.inputs[input_index].input_layer_name]

    # will return the bias created if not *for_self*
    def create_bias_parameter(
            self,
Q
qijun 已提交
1466
            bias,  # True/False or BiasCfg
Z
zhangjinchao01 已提交
1467
            size,
Q
qijun 已提交
1468 1469 1470
            dims=None,
            for_self=True,  # whether create bias for layer self
    ):
Z
zhangjinchao01 已提交
1471 1472 1473 1474 1475 1476

        if size == 0:
            return
        if dims is None:
            dims = [1, size]

Q
qijun 已提交
1477 1478 1479
        config_assert(
            type_of(bias) == bool or type_of(bias) == Bias,
            'Incorrect type for bias: %s' % type_of(bias))
Z
zhangjinchao01 已提交
1480 1481 1482 1483 1484 1485 1486 1487 1488

        if type_of(bias) == bool:
            if bias:
                bias = Bias()

        if type_of(bias) == Bias:
            if bias.parameter_name is None:
                bias.parameter_name = gen_bias_parameter_name(self.config.name)
            if bias.parameter_name not in g_parameter_map:
1489 1490
                assert isinstance(self.config, LayerConfig)

Z
zhangjinchao01 已提交
1491 1492 1493
                Parameter(
                    bias.parameter_name,
                    size,
Q
qijun 已提交
1494 1495
                    self.config.device
                    if self.config.HasField('device') else None,
Z
zhangjinchao01 已提交
1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506
                    dims,
                    bias.learning_rate,
                    bias.momentum,
                    decay_rate=bias.decay_rate,
                    decay_rate_l1=bias.decay_rate_l1,
                    initial_mean=bias.initial_mean,
                    initial_std=bias.initial_std,
                    initial_strategy=bias.initial_strategy,
                    initial_smart=bias.initial_smart,
                    num_batches_regularization=bias.num_batches_regularization,
                    sparse_remote_update=bias.sparse_remote_update,
Q
qijun 已提交
1507 1508
                    gradient_clipping_threshold=bias.
                    gradient_clipping_threshold,
Z
zhangjinchao01 已提交
1509
                    is_static=bias.is_static,
X
xuwei06 已提交
1510 1511
                    is_shared=bias.is_shared,
                    initializer=bias.initializer)
Z
zhangjinchao01 已提交
1512 1513 1514 1515 1516
            if for_self:
                self.config.bias_parameter_name = bias.parameter_name
            else:
                return bias.parameter_name

Q
qijun 已提交
1517 1518 1519 1520 1521 1522
    def create_input_parameter(self,
                               input_index,
                               size,
                               dims=None,
                               sparse=None,
                               format=None):
Z
zhangjinchao01 已提交
1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536
        if dims is None:
            # TODO(yuyang18): print warning and callstack here!
            dims = list()

        if size == 0:
            return

        input_config = self.inputs[input_index]

        self.config.inputs[input_index].input_parameter_name = \
            input_config.parameter_name

        if input_config.parameter_name in g_parameter_map:
            para = g_parameter_map[input_config.parameter_name]
Q
qijun 已提交
1537 1538
            config_assert(size == para.size, (
                'Shared parameter "%s" does not ' + 'have same size: %s vs. %s')
Z
zhangjinchao01 已提交
1539 1540
                          % (input_config.parameter_name, para.size, size))

Q
qijun 已提交
1541 1542
            config_assert(dims == para.dims, (
                'Shared parameter "%s" does not ' + 'have same dims: %s vs. %s')
Z
zhangjinchao01 已提交
1543 1544 1545 1546 1547 1548
                          % (input_config.parameter_name, para.dims, dims))
            return

        Parameter(
            input_config.parameter_name,
            size,
1549
            self.config.device if self.config.HasField("device") else None,
Z
zhangjinchao01 已提交
1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561
            dims,
            input_config.learning_rate,
            input_config.momentum,
            decay_rate=input_config.decay_rate,
            decay_rate_l1=input_config.decay_rate_l1,
            initial_mean=input_config.initial_mean,
            initial_std=input_config.initial_std,
            initial_strategy=input_config.initial_strategy,
            initial_smart=input_config.initial_smart,
            num_batches_regularization=input_config.num_batches_regularization,
            sparse_remote_update=input_config.sparse_remote_update,
            sparse_update=input_config.sparse_update,
Q
qijun 已提交
1562 1563
            gradient_clipping_threshold=input_config.
            gradient_clipping_threshold,
Z
zhangjinchao01 已提交
1564 1565 1566 1567
            sparse=sparse,
            format=format,
            is_static=input_config.is_static,
            is_shared=input_config.is_shared,
X
xuwei06 已提交
1568 1569
            update_hooks=input_config.update_hooks,
            initializer=input_config.initializer)
Z
zhangjinchao01 已提交
1570 1571 1572 1573 1574 1575 1576 1577 1578

    def set_layer_size(self, size):
        if self.config.size == 0:
            self.config.size = size
        else:
            config_assert(self.config.size == size,
                          'Different inputs result in' +
                          'different layer size at layer %s' % self.config.name)

L
Luo Tao 已提交
1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595
    def set_layer_height_width(self, height, width):
        self.config.height = height
        self.config.width = width

    def set_cnn_layer(self,
                      input_layer_name,
                      height,
                      width,
                      channels,
                      is_print=True):
        size = height * width * channels
        self.set_layer_size(size)
        self.set_layer_height_width(height, width)
        if is_print:
            print("output for %s: c = %d, h = %d, w = %d, size = %d" %
                  (input_layer_name, channels, height, width, size))

Q
qijun 已提交
1596

Z
zhangjinchao01 已提交
1597 1598
@config_layer('multi_class_cross_entropy_with_selfnorm')
class MultiClassCrossEntropySelfNormCostLayer(LayerBase):
Q
qijun 已提交
1599 1600 1601
    def __init__(self, name, inputs, softmax_selfnorm_alpha=0.1, **xargs):
        super(MultiClassCrossEntropySelfNormCostLayer, self).__init__(
            name, 'multi_class_cross_entropy_with_selfnorm', 0, inputs, **xargs)
Z
zhangjinchao01 已提交
1602 1603
        self.config.softmax_selfnorm_alpha = softmax_selfnorm_alpha

Q
qijun 已提交
1604

Z
zhangjinchao01 已提交
1605 1606
@config_layer('fc')
class FCLayer(LayerBase):
L
lianxiaochen 已提交
1607 1608 1609 1610 1611 1612 1613
    def __init__(self,
                 name,
                 size,
                 inputs,
                 bias=True,
                 error_clipping_threshold=None,
                 **xargs):
Z
zhangjinchao01 已提交
1614 1615 1616 1617 1618 1619 1620 1621 1622 1623
        super(FCLayer, self).__init__(name, 'fc', size, inputs=inputs, **xargs)
        for input_index in xrange(len(self.inputs)):
            input_layer = self.get_input_layer(input_index)
            psize = self.config.size * input_layer.size
            dims = [input_layer.size, self.config.size]
            format = self.inputs[input_index].format
            sparse = format == "csr" or format == "csc"

            if sparse:
                psize = self.inputs[input_index].nnz
1624 1625
            else:
                sparse = None
Z
zhangjinchao01 已提交
1626

Q
qijun 已提交
1627 1628
            self.create_input_parameter(input_index, psize, dims, sparse,
                                        format)
Z
zhangjinchao01 已提交
1629
        self.create_bias_parameter(bias, self.config.size)
L
lianxiaochen 已提交
1630 1631
        if error_clipping_threshold is not None:
            self.config.error_clipping_threshold = error_clipping_threshold
Z
zhangjinchao01 已提交
1632

Q
qijun 已提交
1633

Z
zhangjinchao01 已提交
1634 1635
@config_layer('selective_fc')
class SelectiveFCLayer(LayerBase):
Q
qijun 已提交
1636 1637 1638 1639 1640 1641 1642 1643 1644 1645
    def __init__(self,
                 name,
                 size,
                 inputs,
                 bias=True,
                 selective_fc_pass_generation=False,
                 has_selected_colums=True,
                 selective_fc_full_mul_ratio=0.02,
                 selective_fc_parallel_plain_mul_thread_num=None,
                 **xargs):
Z
zhangjinchao01 已提交
1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665
        super(SelectiveFCLayer, self).__init__(
            name, 'selective_fc', size, inputs=inputs, **xargs)
        # user MUST know if selctive fc is used in training,
        # parameter matrices saved by this layer are automatically transposed,
        # BUT bias is not.

        # if selective_fc is used only in testing mode, and parameters for
        # this layer are trained by fully connected layers,
        # then TranposedFullMatrixProjectin MUST be used in training
        # to avoid manual transpose in testing.

        self.config.selective_fc_pass_generation = selective_fc_pass_generation
        self.config.has_selected_colums = has_selected_colums
        self.config.selective_fc_full_mul_ratio = selective_fc_full_mul_ratio
        if selective_fc_parallel_plain_mul_thread_num is not None:
            self.config.selective_fc_parallel_plain_mul_thread_num = selective_fc_parallel_plain_mul_thread_num

        input_num = len(self.inputs)
        if has_selected_colums:
            config_assert(input_num >= 2,
Q
qijun 已提交
1666 1667
                          ("if indices of selected columns are not specified, "
                           "selective_fc Layer has at least two inputs"))
Z
zhangjinchao01 已提交
1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679
            input_num -= 1

        for input_index in xrange(input_num):
            input_layer = self.get_input_layer(input_index)
            psize = self.config.size * input_layer.size
            dims = [input_layer.size, self.config.size]
            dims = dims[::-1]  # transpose the parameter
            format = self.inputs[input_index].format
            sparse = format == "csr" or format == "csc"
            if sparse:
                psize = self.inputs[input_index].nnz

Q
qijun 已提交
1680 1681
            self.create_input_parameter(input_index, psize, dims, sparse,
                                        format)
Z
zhangjinchao01 已提交
1682 1683
        self.create_bias_parameter(bias, self.config.size)

Q
qijun 已提交
1684

1685 1686
@config_layer('print')
class PrintLayer(LayerBase):
1687
    def __init__(self, name, inputs, format=None):
1688
        super(PrintLayer, self).__init__(name, 'print', 0, inputs)
1689 1690 1691 1692 1693 1694
        if format is None:
            format = "\n".join([
                "layer=" + input.input_layer_name + " %s"
                for input in self.inputs
            ])
        self.config.user_arg = format
1695

Q
qijun 已提交
1696

Y
yuan 已提交
1697 1698
@config_layer('priorbox')
class PriorBoxLayer(LayerBase):
G
gaoyuan 已提交
1699 1700
    def __init__(self, name, inputs, size, min_size, max_size, aspect_ratio,
                 variance):
Y
yuan 已提交
1701
        super(PriorBoxLayer, self).__init__(name, 'priorbox', 0, inputs)
G
gaoyuan 已提交
1702
        config_assert(len(inputs) == 2, 'PriorBoxLayer must have 2 inputs')
G
gaoyuan 已提交
1703 1704 1705 1706 1707 1708 1709
        input_layer = self.get_input_layer(1)
        config_assert(
            input_layer.type == 'data',
            'Expecting the second input layer of an priorbox layer to be '
            'a data layer')
        config_assert(input_layer.width > 0, 'The data layer must set width')
        config_assert(input_layer.height > 0, 'The data layer must set height')
G
gaoyuan 已提交
1710
        config_assert(len(variance) == 4, 'The variance must have 4 inputs')
Y
yuan 已提交
1711 1712 1713 1714 1715 1716
        self.config.inputs[0].priorbox_conf.min_size.extend(min_size)
        self.config.inputs[0].priorbox_conf.max_size.extend(max_size)
        self.config.inputs[0].priorbox_conf.aspect_ratio.extend(aspect_ratio)
        self.config.inputs[0].priorbox_conf.variance.extend(variance)
        self.config.size = size

Q
qijun 已提交
1717

1718 1719 1720
@config_layer('multibox_loss')
class MultiBoxLossLayer(LayerBase):
    def __init__(self, name, inputs, input_num, num_classes, overlap_threshold,
1721
                 neg_pos_ratio, neg_overlap, background_id, **xargs):
1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742
        super(MultiBoxLossLayer, self).__init__(name, 'multibox_loss', 0,
                                                inputs)
        config_assert(
            len(inputs) == (input_num * 2 + 2),
            'MultiBoxLossLayer does not have enough inputs')
        config_assert(num_classes > background_id,
                      'Classes number must greater than background ID')
        self.config.inputs[0].multibox_loss_conf.num_classes = num_classes
        self.config.inputs[
            0].multibox_loss_conf.overlap_threshold = overlap_threshold
        self.config.inputs[0].multibox_loss_conf.neg_pos_ratio = neg_pos_ratio
        self.config.inputs[0].multibox_loss_conf.neg_overlap = neg_overlap
        self.config.inputs[0].multibox_loss_conf.background_id = background_id
        self.config.inputs[0].multibox_loss_conf.input_num = input_num
        self.config.size = 1


@config_layer('detection_output')
class DetectionOutputLayer(LayerBase):
    def __init__(self, name, inputs, size, input_num, num_classes,
                 nms_threshold, nms_top_k, keep_top_k, confidence_threshold,
1743
                 background_id, **xargs):
1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763
        super(DetectionOutputLayer, self).__init__(name, 'detection_output', 0,
                                                   inputs)
        config_assert(
            len(inputs) == (input_num * 2 + 1),
            'DetectionOutputLayer does not have enough inputs')
        config_assert(num_classes > background_id,
                      'Classes number must greater than background ID')
        self.config.inputs[0].detection_output_conf.num_classes = num_classes
        self.config.inputs[
            0].detection_output_conf.nms_threshold = nms_threshold
        self.config.inputs[0].detection_output_conf.nms_top_k = nms_top_k
        self.config.inputs[0].detection_output_conf.keep_top_k = keep_top_k
        self.config.inputs[
            0].detection_output_conf.confidence_threshold = confidence_threshold
        self.config.inputs[
            0].detection_output_conf.background_id = background_id
        self.config.inputs[0].detection_output_conf.input_num = input_num
        self.config.size = size


Z
zhangjinchao01 已提交
1764 1765
@config_layer('data')
class DataLayer(LayerBase):
L
Luo Tao 已提交
1766
    def __init__(self, name, size, height=None, width=None, device=None):
Q
qijun 已提交
1767 1768
        super(DataLayer, self).__init__(
            name, 'data', size, inputs=[], device=device)
L
Luo Tao 已提交
1769 1770
        if height and width:
            self.set_layer_height_width(height, width)
Q
qijun 已提交
1771

Z
zhangjinchao01 已提交
1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798

'''
DataNormLayer: A layer for data normalization
Input: One and only one input layer is accepted. The input layer must
       be DataLayer with dense data type
Output: The normalization of the input data

Reference:
    LA Shalabi, Z Shaaban, B Kasasbeh. Data mining: A preprocessing engine

Example:
    Layer(
        name = "norm_input_layer",
        type = "data_norm",
        inputs = [Input("input_layer",
                        parameter_name = "_slot0.stats")],
        data_norm_strategy = "z-score",
    )

Note:
  (1) The parameter has been calculated in the preprocessing stage,
      and should be initialized by --init_model_path when training.
  (2) Three data normalization methoeds are considered
          z-score: y = (x-mean)/std
          min-max: y = (x-min)/(max-min)
          decimal-scaling: y = x/10^j, where j is the smallest integer such that max(|y|)<1
'''
Q
qijun 已提交
1799 1800


Z
zhangjinchao01 已提交
1801 1802
@config_layer('data_norm')
class DataNormLayer(LayerBase):
Q
qijun 已提交
1803
    def __init__(self, name, inputs, data_norm_strategy="z-score", device=None):
Z
zhangjinchao01 已提交
1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814
        super(DataNormLayer, self).__init__(
            name, 'data_norm', 0, inputs=inputs, device=device)
        self.config.data_norm_strategy = data_norm_strategy
        config_assert(len(inputs) == 1, 'DataNormLayer must have 1 input')
        input_layer = self.get_input_layer(0)
        self.set_layer_size(input_layer.size)
        para_size = 5 * input_layer.size
        para_dims = [5, input_layer.size]
        self.inputs[0].is_static = True
        self.create_input_parameter(0, para_size, para_dims)

Q
qijun 已提交
1815

Z
zhangjinchao01 已提交
1816 1817 1818
@config_layer('prelu')
class ParameterReluLayer(LayerBase):
    layer_type = 'prelu'
Q
qijun 已提交
1819 1820

    def __init__(self, name, inputs, partial_sum=1, **args):
Z
zhangjinchao01 已提交
1821 1822 1823
        super(ParameterReluLayer, self).__init__(
            name, self.layer_type, 0, inputs=inputs, **args)
        input_layer = self.get_input_layer(0)
1824 1825 1826
        config_assert(len(self.inputs) == 1, "prelu layer has only one input.")
        config_assert(input_layer.size % partial_sum == 0,
                      "a wrong setting for partial_sum")
Z
zhangjinchao01 已提交
1827 1828 1829
        self.set_layer_size(input_layer.size)
        self.create_input_parameter(0, input_layer.size / partial_sum)

Q
qijun 已提交
1830

Z
zhangjinchao01 已提交
1831 1832 1833
@config_layer('conv')
class ConvLayerBase(LayerBase):
    layer_type = 'conv'
Q
qijun 已提交
1834 1835 1836 1837 1838 1839 1840 1841

    def __init__(self,
                 name,
                 inputs=[],
                 bias=True,
                 num_filters=None,
                 shared_biases=False,
                 **xargs):
Z
zhangjinchao01 已提交
1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857
        super(ConvLayerBase, self).__init__(
            name, self.layer_type, 0, inputs=inputs, **xargs)

        if num_filters is not None:
            self.config.num_filters = num_filters

        use_gpu = int(g_command_config_args.get("use_gpu", 0))
        parallel_nn = int(g_command_config_args.get("parallel_nn", 0))

        # Automatically select cudnn_type for GPU and exconv for CPU
        # if set type=conv, but still reserve the way user specify
        # exconv or cudnn_conv manually.
        if self.layer_type == "cudnn_conv":
            config_assert(use_gpu, "cudnn_conv only support GPU")

        if (use_gpu == 1 and self.layer_type != "exconv" and
Q
qijun 已提交
1858
            (parallel_nn == 0 or self.config.device > -1)):
Z
zhangjinchao01 已提交
1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870
            self.layer_type = "cudnn_conv"
        else:
            self.layer_type = "exconv"
        # need to specify layer in config
        self.config.type = self.layer_type

        if shared_biases is not None:
            self.config.shared_biases = shared_biases

        for input_index in xrange(len(self.inputs)):
            input_layer = self.get_input_layer(input_index)
            conv_conf = self.config.inputs[input_index].conv_conf
L
Luo Tao 已提交
1871 1872
            parse_conv(self.inputs[input_index].conv, input_layer.name,
                       conv_conf, num_filters)
Z
zhangjinchao01 已提交
1873 1874
            psize = self.calc_parameter_size(conv_conf)
            self.create_input_parameter(input_index, psize)
L
Luo Tao 已提交
1875 1876
            self.set_cnn_layer(name, conv_conf.output_y, conv_conf.output_x,
                               self.config.num_filters)
Z
zhangjinchao01 已提交
1877 1878 1879 1880 1881 1882 1883 1884 1885 1886

        psize = self.config.size
        if shared_biases:
            psize = self.config.num_filters
        self.create_bias_parameter(bias, psize, [psize, 1])

    def calc_parameter_size(self, conv_conf):
        return self.config.num_filters * conv_conf.filter_channels \
                    * (conv_conf.filter_size * conv_conf.filter_size_y)

Q
qijun 已提交
1887

Z
zhangjinchao01 已提交
1888 1889 1890 1891
@config_layer('exconv')
class ConvLayer(ConvLayerBase):
    layer_type = 'exconv'

Q
qijun 已提交
1892

Z
zhangjinchao01 已提交
1893 1894 1895 1896
@config_layer('cudnn_conv')
class ConvLayer(ConvLayerBase):
    layer_type = 'cudnn_conv'

1897 1898 1899 1900

@config_layer('convt')
class ConvTransLayerBase(LayerBase):
    layer_type = 'convt'
Q
qijun 已提交
1901 1902 1903 1904 1905 1906 1907 1908

    def __init__(self,
                 name,
                 inputs=[],
                 bias=True,
                 num_filters=None,
                 shared_biases=False,
                 **xargs):
1909
        super(ConvTransLayerBase, self).__init__(
1910 1911 1912 1913 1914 1915 1916 1917
            name, self.layer_type, 0, inputs=inputs, **xargs)

        if num_filters is not None:
            self.config.num_filters = num_filters

        use_gpu = int(g_command_config_args.get("use_gpu", 0))
        parallel_nn = int(g_command_config_args.get("parallel_nn", 0))

1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928
        # Automatically select cudnn_type for GPU and exconvt for CPU
        # if set type=exconvt, but still reserve the way user specify
        # exconvt or cudnn_convt manually.
        if self.layer_type == "cudnn_convt":
            config_assert(use_gpu, "cudnn_convt only support GPU")

        if (use_gpu == 1 and self.layer_type != "exconvt" and
            (parallel_nn == 0 or self.config.device > -1)):
            self.layer_type = "cudnn_convt"
        else:
            self.layer_type = "exconvt"
1929 1930 1931 1932 1933 1934 1935 1936
        # need to specify layer in config
        self.config.type = self.layer_type

        if shared_biases is not None:
            self.config.shared_biases = shared_biases

        for input_index in xrange(len(self.inputs)):
            input_layer = self.get_input_layer(input_index)
1937
            parse_conv(
1938 1939
                self.inputs[input_index].conv,
                input_layer.name,
1940
                self.config.inputs[input_index].conv_conf,
1941
                num_filters,
1942
                trans=True)
1943 1944 1945
            conv_conf = self.config.inputs[input_index].conv_conf
            psize = self.calc_parameter_size(conv_conf)
            self.create_input_parameter(input_index, psize)
1946 1947
            self.set_cnn_layer(name, conv_conf.img_size_y, conv_conf.img_size,
                               self.config.num_filters)
1948 1949 1950 1951 1952 1953 1954

        psize = self.config.size
        if shared_biases:
            psize = self.config.num_filters
        self.create_bias_parameter(bias, psize, [psize, 1])

    def calc_parameter_size(self, conv_conf):
1955
        return conv_conf.channels * conv_conf.filter_channels \
1956 1957
                    * (conv_conf.filter_size * conv_conf.filter_size_y)

Q
qijun 已提交
1958

1959 1960 1961 1962
@config_layer('exconvt')
class ConvTransLayer(ConvTransLayerBase):
    layer_type = 'exconvt'

Q
qijun 已提交
1963

1964 1965 1966 1967 1968
@config_layer('cudnn_convt')
class ConvTransLayer(ConvTransLayerBase):
    layer_type = 'cudnn_convt'


Z
zhangjinchao01 已提交
1969 1970
@config_layer('norm')
class NormLayer(LayerBase):
1971 1972
    def __init__(self, name, inputs, **xargs):
        super(NormLayer, self).__init__(name, 'norm', 0, inputs=inputs, **xargs)
Z
zhangjinchao01 已提交
1973 1974 1975
        for input_index in xrange(len(self.inputs)):
            input_layer = self.get_input_layer(input_index)
            norm_conf = self.config.inputs[input_index].norm_conf
L
Luo Tao 已提交
1976 1977 1978 1979
            parse_norm(self.inputs[input_index].norm, input_layer.name,
                       norm_conf)
            self.set_cnn_layer(name, norm_conf.output_y, norm_conf.output_x,
                               norm_conf.channels, False)
1980 1981 1982
            if norm_conf.norm_type == "cross-channel-norm":
                self.create_input_parameter(0, norm_conf.channels,
                                            [norm_conf.channels, 1])
Q
qijun 已提交
1983

Z
zhangjinchao01 已提交
1984 1985 1986

@config_layer('pool')
class PoolLayer(LayerBase):
1987 1988
    def __init__(self, name, inputs, ceil_mode=True, **xargs):
        super(PoolLayer, self).__init__(name, 'pool', 0, inputs=inputs, **xargs)
Z
zhangjinchao01 已提交
1989 1990 1991
        for input_index in xrange(len(self.inputs)):
            input_layer = self.get_input_layer(input_index)
            pool_conf = self.config.inputs[input_index].pool_conf
L
Luo Tao 已提交
1992
            parse_pool(self.inputs[input_index].pool, input_layer.name,
1993
                       pool_conf, ceil_mode)
L
Luo Tao 已提交
1994 1995
            self.set_cnn_layer(name, pool_conf.output_y, pool_conf.output_x,
                               pool_conf.channels)
Q
qijun 已提交
1996

Z
zhangjinchao01 已提交
1997

Q
qijun 已提交
1998 1999
@config_layer('spp')
class SpatialPyramidPoolLayer(LayerBase):
2000
    def __init__(self, name, inputs, **xargs):
Q
qijun 已提交
2001
        super(SpatialPyramidPoolLayer, self).__init__(
2002
            name, 'spp', 0, inputs=inputs, **xargs)
Q
qijun 已提交
2003 2004 2005
        for input_index in xrange(len(self.inputs)):
            input_layer = self.get_input_layer(input_index)
            spp_conf = self.config.inputs[input_index].spp_conf
L
Luo Tao 已提交
2006 2007 2008
            parse_spp(self.inputs[input_index].spp, input_layer.name, spp_conf)
            output_x = (pow(4, spp_conf.pyramid_height) - 1) / (4 - 1)
            self.set_cnn_layer(name, 1, output_x, spp_conf.image_conf.channels)
Q
qijun 已提交
2009

Q
qijun 已提交
2010

D
dangqingqing 已提交
2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029
@config_layer('pad')
class PadLayer(LayerBase):
    def __init__(self, name, inputs, **xargs):
        super(PadLayer, self).__init__(name, 'pad', 0, inputs=inputs, **xargs)
        pad = self.inputs[0].pad
        self.config.inputs[0].pad_conf.pad_c.extend(pad.pad_c)
        self.config.inputs[0].pad_conf.pad_h.extend(pad.pad_h)
        self.config.inputs[0].pad_conf.pad_w.extend(pad.pad_w)

        input_layer = self.get_input_layer(0)
        image_conf = self.config.inputs[0].pad_conf.image_conf
        parse_image(pad, input_layer.name, image_conf)
        out_ch = pad.channels + pad.pad_c[0] + pad.pad_c[1]
        out_h = image_conf.img_size_y + pad.pad_h[0] + pad.pad_h[1]
        out_w = image_conf.img_size + pad.pad_w[0] + pad.pad_w[1]
        self.set_cnn_layer(name, out_h, out_w, out_ch)
        self.config.size = out_ch * out_h * out_w


2030 2031
@config_layer('crop')
class CropLayer(LayerBase):
2032
    def __init__(self, name, inputs, axis, offset, shape, **xargs):
2033
        super(CropLayer, self).__init__(name, 'crop', 0, inputs=inputs, **xargs)
2034 2035 2036
        self.config.axis = axis
        self.config.offset.extend(offset)
        self.config.shape.extend(shape)
2037 2038 2039 2040 2041 2042 2043 2044 2045 2046

        # get channel, width and height from input_0 layer
        input_layer = self.get_input_layer(0)
        image_conf = self.config.inputs[0].image_conf
        image_conf.img_size = input_layer.width
        image_conf.img_size_y = input_layer.height
        image_conf.channels = input_layer.size / (input_layer.width *
                                                  input_layer.height)


Z
zhangjinchao01 已提交
2047 2048 2049
@config_layer('batch_norm')
class BatchNormLayer(LayerBase):
    layer_type = 'batch_norm'
Q
qijun 已提交
2050 2051 2052 2053 2054 2055 2056 2057 2058

    def __init__(self,
                 name,
                 inputs,
                 bias=True,
                 use_global_stats=True,
                 moving_average_fraction=0.9,
                 batch_norm_type=None,
                 **xargs):
Z
zhangjinchao01 已提交
2059 2060 2061 2062
        if inputs is None:
            inputs = []
        elif not isinstance(inputs, list):
            inputs = [inputs]
Q
qijun 已提交
2063 2064
        config_assert(
            len(inputs) == 1, "BatchNormLayer must have one and only one input")
Z
zhangjinchao01 已提交
2065 2066 2067 2068 2069 2070 2071 2072
        # Create Input for moving mean and std,
        # in batch normalization layer.
        # These paras no need to update, so set is_static is true.
        # If not use is_static, even set learning_rate = 0, decay_rate = 0,
        # these paras will change if set average_window in configure.
        use_gpu = bool(int(g_command_config_args.get("use_gpu", 0)))
        is_shared = True if not use_gpu else False
        for i in xrange(2):
Q
qijun 已提交
2073 2074 2075 2076 2077 2078
            inputs.append(
                Input(
                    inputs[0].input_layer_name,
                    initial_std=0.0,
                    initial_mean=0.0,
                    is_static=True,
2079
                    is_shared=is_shared,
D
dangqingqing 已提交
2080
                    make_layer_name_in_submodel=False, ))
Z
zhangjinchao01 已提交
2081 2082 2083 2084 2085 2086

        parallel_nn = bool(int(g_command_config_args.get("parallel_nn", 0)))
        cudnn_version = int(g_command_config_args.get("cudnn_version", 0))
        # Automatically select cudnn_batch_norm for GPU and batch_norm for CPU.
        # Also based on cudnn version.
        use_cudnn = use_gpu and batch_norm_type != "batch_norm" and \
2087
                ((not parallel_nn) or self.config.device > -1)
Z
zhangjinchao01 已提交
2088
        self.layer_type = "cudnn_batch_norm" if use_cudnn else "batch_norm"
Q
qijun 已提交
2089
        super(BatchNormLayer, self).__init__(
X
xuwei06 已提交
2090
            name, self.layer_type, 0, inputs=inputs, **xargs)
Z
zhangjinchao01 已提交
2091 2092 2093 2094 2095 2096

        if use_global_stats is not None:
            self.config.use_global_stats = use_global_stats
        if moving_average_fraction is not None:
            self.config.moving_average_fraction = moving_average_fraction

Q
qijun 已提交
2097
        input_layer = self.get_input_layer(0)
Z
zhangjinchao01 已提交
2098
        image_conf = self.config.inputs[0].image_conf
L
Luo Tao 已提交
2099
        parse_image(self.inputs[0].image, input_layer.name, image_conf)
2100

2101 2102
        # Only pass the width and height of input to batch_norm layer
        # when either of it is non-zero.
2103 2104
        if input_layer.width != 0 or input_layer.height != 0:
            self.set_cnn_layer(name, image_conf.img_size_y, image_conf.img_size,
D
dangqingqing 已提交
2105
                               image_conf.channels, False)
2106 2107
        else:
            self.set_layer_size(input_layer.size)
Z
zhangjinchao01 已提交
2108 2109 2110 2111 2112 2113 2114 2115 2116 2117 2118 2119

        psize = self.calc_parameter_size(image_conf)
        dims = [1, psize]
        self.create_input_parameter(0, psize)
        self.create_input_parameter(1, psize, dims)
        self.create_input_parameter(2, psize, dims)

        self.create_bias_parameter(bias, psize)

    def calc_parameter_size(self, image_conf):
        return image_conf.channels

Q
qijun 已提交
2120

Z
zhangjinchao01 已提交
2121 2122
@config_layer('trans')
class TransLayer(LayerBase):
2123
    def __init__(self, name, inputs, **xargs):
Q
qijun 已提交
2124
        super(TransLayer, self).__init__(
2125
            name, 'trans', 0, inputs=inputs, **xargs)
Q
qijun 已提交
2126 2127 2128
        config_assert(
            len(self.inputs) == 1,
            'TransLayer must have one and only one input')
Z
zhangjinchao01 已提交
2129 2130
        self.set_layer_size(self.get_input_layer(0).size)

Q
qijun 已提交
2131

Z
zhangjinchao01 已提交
2132 2133
@config_layer('resize')
class ResizeLayer(LayerBase):
2134
    def __init__(self, name, size, inputs, **xargs):
Q
qijun 已提交
2135
        super(ResizeLayer, self).__init__(
2136
            name, 'resize', size=size, inputs=inputs, **xargs)
Q
qijun 已提交
2137 2138 2139 2140
        config_assert(
            len(self.inputs) == 1,
            'ResizeLayer must have one and only one input')

Z
zhangjinchao01 已提交
2141

2142 2143
@config_layer('rotate')
class RotateLayer(LayerBase):
H
Haonan 已提交
2144
    def __init__(self, name, inputs, height, width, device=None):
2145 2146 2147 2148 2149
        super(RotateLayer, self).__init__(
            name, 'rotate', 0, inputs=inputs, device=device)
        config_assert(
            len(self.inputs) == 1,
            'RotateLayer must have one and only one input')
H
Haonan 已提交
2150
        self.set_layer_height_width(height, width)
2151 2152 2153
        self.set_layer_size(self.get_input_layer(0).size)


Z
zhangjinchao01 已提交
2154 2155
@config_layer('blockexpand')
class BlockExpandLayer(LayerBase):
2156
    def __init__(self, name, inputs, **xargs):
Q
qijun 已提交
2157
        super(BlockExpandLayer, self).__init__(
2158
            name, 'blockexpand', 0, inputs=inputs, **xargs)
Z
zhangjinchao01 已提交
2159 2160
        for input_index in xrange(len(self.inputs)):
            input_layer = self.get_input_layer(input_index)
Q
qijun 已提交
2161 2162
            parse_block_expand(
                self.inputs[input_index].block_expand, input_layer.name,
Z
zhangjinchao01 已提交
2163
                self.config.inputs[input_index].block_expand_conf)
Q
qijun 已提交
2164 2165 2166 2167 2168 2169
            block_expand_conf = self.config.inputs[
                input_index].block_expand_conf
            self.set_layer_size(block_expand_conf.block_x *
                                block_expand_conf.block_y *
                                block_expand_conf.channels)

Z
zhangjinchao01 已提交
2170

2171 2172
@config_layer('maxout')
class MaxOutLayer(LayerBase):
Q
qijun 已提交
2173 2174 2175
    def __init__(self, name, inputs, **xargs):
        super(MaxOutLayer, self).__init__(
            name, 'maxout', 0, inputs=inputs, **xargs)
2176 2177
        input_layer = self.get_input_layer(0)
        maxout_conf = self.config.inputs[0].maxout_conf
L
Luo Tao 已提交
2178
        parse_maxout(self.inputs[0].maxout, input_layer.name, maxout_conf)
L
Luo Tao 已提交
2179 2180 2181
        out_channels = maxout_conf.image_conf.channels / maxout_conf.groups
        self.set_cnn_layer(name, g_layer_map[input_layer.name].height,
                           g_layer_map[input_layer.name].width, out_channels)
Q
qijun 已提交
2182

2183

D
dangqingqing 已提交
2184 2185 2186 2187
@config_layer('row_conv')
class RowConvLayer(LayerBase):
    def __init__(self, name, inputs, context_length, **xargs):
        super(RowConvLayer, self).__init__(
2188
            name, 'row_conv', 0, inputs=inputs, **xargs)
D
dangqingqing 已提交
2189 2190
        config_assert(
            len(self.inputs) == 1,
2191
            'row convolution layer must have one and only one input.')
D
dangqingqing 已提交
2192 2193 2194 2195 2196 2197 2198 2199 2200
        input_layer = self.get_input_layer(0)
        row_conv_conf = self.config.inputs[0].row_conv_conf
        row_conv_conf.context_length = context_length
        self.set_layer_size(input_layer.size)
        psize = context_length * input_layer.size
        dims = [context_length, input_layer.size]
        self.create_input_parameter(0, psize, dims)


G
guosheng 已提交
2201 2202
@config_layer('clip')
class ClipLayer(LayerBase):
2203 2204
    def __init__(self, name, inputs, min, max, **xargs):
        super(ClipLayer, self).__init__(name, 'clip', 0, inputs=inputs, **xargs)
G
guosheng 已提交
2205 2206
        config_assert(
            len(self.inputs) == 1,
2207 2208
            'ClipLayer must have one and only one input.')
        config_assert(min < max, 'min must be less than max.')
G
guosheng 已提交
2209 2210
        input_layer = self.get_input_layer(0)
        self.set_layer_size(input_layer.size)
2211 2212
        self.config.inputs[0].clip_conf.min = min
        self.config.inputs[0].clip_conf.max = max
G
guosheng 已提交
2213 2214


Z
zhangjinchao01 已提交
2215 2216 2217 2218
# key: cost type
# value: cost class
g_cost_map = {}

Q
qijun 已提交
2219

Z
zhangjinchao01 已提交
2220 2221 2222
# define a cost layer without any parameters
def define_cost(class_name, cost_type):
    def init(cls, name, inputs, device=None, coeff=1.):
Q
qijun 已提交
2223 2224
        super(type(cls), cls).__init__(
            name, cost_type, 1, inputs, device=device, coeff=coeff)
Z
zhangjinchao01 已提交
2225

Q
qijun 已提交
2226
    cls = type(class_name, (LayerBase, ), dict(__init__=init))
Z
zhangjinchao01 已提交
2227 2228 2229
    global g_cost_map
    g_cost_map[cost_type] = cls

Q
qijun 已提交
2230

Z
zhangjinchao01 已提交
2231 2232 2233 2234 2235 2236 2237 2238
define_cost('MultiClassCrossEntropy', 'multi-class-cross-entropy')
define_cost('RankingCost', 'rank-cost')
define_cost('AucValidation', 'auc-validation')
define_cost('PnpairValidation', 'pnpair-validation')
define_cost('SumOfSquaresCostLayer', 'square_error')
define_cost('MultiBinaryLabelCrossEntropy', 'multi_binary_label_cross_entropy')
define_cost('SoftBinaryClassCrossEntropy', 'soft_binary_class_cross_entropy')
define_cost('HuberTwoClass', 'huber')
X
xuwei06 已提交
2239
define_cost('SumCost', 'sum_cost')
D
dangqingqing 已提交
2240
define_cost('SmoothL1Cost', 'smooth_l1')
Z
zhangjinchao01 已提交
2241

Q
qijun 已提交
2242

Z
zhangjinchao01 已提交
2243 2244
@config_layer('hsigmoid')
class HierarchicalSigmoidLayer(LayerBase):
Q
qijun 已提交
2245
    def __init__(self, name, num_classes, inputs, device=None, bias=True):
Z
zhangjinchao01 已提交
2246 2247
        super(HierarchicalSigmoidLayer, self).__init__(
            name, 'hsigmoid', 1, inputs=inputs, device=device)
Q
qijun 已提交
2248 2249 2250
        config_assert(
            len(self.inputs) >= 2,
            'HierarchicalSigmoidLayer must have at least 2 inputs')
Z
zhangjinchao01 已提交
2251 2252 2253 2254 2255 2256 2257 2258
        self.config.num_classes = num_classes
        for input_index in xrange(len(self.inputs) - 1):
            input_layer = self.get_input_layer(input_index)
            psize = (num_classes - 1) * input_layer.size
            dims = [num_classes - 1, input_layer.size]
            self.create_input_parameter(input_index, psize, dims)
        self.create_bias_parameter(bias, num_classes - 1)

Q
qijun 已提交
2259

Z
zhangjinchao01 已提交
2260 2261 2262 2263 2264 2265 2266 2267 2268 2269 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 2280 2281 2282 2283
'''
lambdaCost for lambdaRank LTR approach

Usage:
  Example: Layer(name = "cost", type = "lambda_cost", NDCG_num = 8,
             max_sort_size = -1, inputs = ["output", "score"])

  Input data: Samples of the same query should be loaded as a sequence,
          by ProtoDataProvider or PyDataProvider etc.. User should provide
          scores for each sample. The score slot should be the 2nd
          input of lambdaRank layer.

  NDCG_num = the size of NDCG, e.g., 5 for NDCG@5.
    Note: NDCG_num must be less than or equal to the minimum
          size of lists.

  max_sort_size = the size of partial sorting in calculating gradient.
    Note: If max_sort_size = -1, then for each list, the algorithm will
          sort the entire list to get gradient.
          In other cases, max_sort_size must be greater than or equal
          to NDCG_num.
          max_sort_size can be greater than the size of a list, in which
          case the algorithm will sort the entire list to get gradient.
'''
Q
qijun 已提交
2284 2285


Z
zhangjinchao01 已提交
2286 2287
@config_layer('lambda_cost')
class LambdaCost(LayerBase):
Q
qijun 已提交
2288
    def __init__(self, name, inputs, NDCG_num=5, max_sort_size=-1, device=None):
Z
zhangjinchao01 已提交
2289 2290
        super(LambdaCost, self).__init__(
            name, 'lambda_cost', 1, inputs=inputs, device=device)
Q
qijun 已提交
2291
        config_assert(len(self.inputs) == 2, 'lambdaCost must have 2 inputs')
Z
zhangjinchao01 已提交
2292 2293
        self.config.NDCG_num = NDCG_num
        if max_sort_size != -1:
Q
qijun 已提交
2294 2295 2296
            config_assert(
                NDCG_num <= max_sort_size,
                'NDCG_num must be less than or equal to max_sort_size')
Z
zhangjinchao01 已提交
2297 2298
        self.config.max_sort_size = max_sort_size

Q
qijun 已提交
2299

Z
zhangjinchao01 已提交
2300 2301
@config_layer('nce')
class NCELayer(LayerBase):
Q
qijun 已提交
2302 2303 2304 2305 2306 2307 2308 2309
    def __init__(self,
                 name,
                 num_classes,
                 inputs,
                 num_neg_samples=10,
                 neg_sampling_dist=None,
                 bias=True,
                 **xargs):
Z
zhangjinchao01 已提交
2310
        super(NCELayer, self).__init__(name, 'nce', 1, inputs=inputs, **xargs)
Q
qijun 已提交
2311 2312
        config_assert(
            len(self.inputs) >= 2, 'NCELayer must have at least 2 inputs')
Z
zhangjinchao01 已提交
2313 2314
        self.config.num_classes = num_classes
        if neg_sampling_dist is not None:
Q
qijun 已提交
2315 2316 2317 2318
            config_assert(
                len(neg_sampling_dist) == num_classes,
                'len(neg_sampling_dist)(%s) is not same as num_classes (%s)' %
                (len(neg_sampling_dist), num_classes))
Z
zhangjinchao01 已提交
2319
            s = sum(neg_sampling_dist)
Q
qijun 已提交
2320 2321 2322
            config_assert(
                abs(s - 1) < 1e-5,
                'The sum of neg_sampling_dist (%s) is not 1' % s)
Z
zhangjinchao01 已提交
2323 2324 2325 2326 2327

            self.config.neg_sampling_dist.extend(neg_sampling_dist)

        self.config.num_neg_samples = num_neg_samples
        num_real_inputs = len(self.inputs) - 1
Q
qijun 已提交
2328
        input_layer = self.get_input_layer(num_real_inputs)
Z
zhangjinchao01 已提交
2329 2330 2331 2332
        config_assert(input_layer.type == 'data',
                      'Expecting the last input layer of an nce layer to be '
                      'a data layer')

Q
qijun 已提交
2333 2334
        if (num_real_inputs > 1 and input_layer.size == 1 and
                self.get_input_layer(num_real_inputs - 1).type == 'data'):
Z
zhangjinchao01 已提交
2335 2336 2337 2338 2339 2340 2341 2342 2343 2344 2345 2346 2347
            # This input layer is assumed to be a sample weight layer
            num_real_inputs -= 1

        for input_index in xrange(num_real_inputs):
            input_layer = self.get_input_layer(input_index)
            psize = num_classes * input_layer.size
            dims = [num_classes, input_layer.size]
            self.create_input_parameter(input_index, psize, dims)
        self.create_bias_parameter(bias, num_classes)


@config_layer('addto')
class AddToLayer(LayerBase):
Q
qijun 已提交
2348
    def __init__(self, name, inputs, bias=True, **xargs):
Z
zhangjinchao01 已提交
2349 2350
        super(AddToLayer, self).__init__(
            name, 'addto', 0, inputs=inputs, **xargs)
Q
qijun 已提交
2351
        config_assert(len(inputs) > 0, 'inputs cannot be empty for AddToLayer')
Z
zhangjinchao01 已提交
2352 2353 2354 2355 2356
        for input_index in xrange(len(self.inputs)):
            input_layer = self.get_input_layer(input_index)
            self.set_layer_size(input_layer.size)
        self.create_bias_parameter(bias, self.config.size)

Q
qijun 已提交
2357

Z
zhangjinchao01 已提交
2358 2359
@config_layer('agent')
class AgentLayer(LayerBase):
Q
qijun 已提交
2360 2361 2362 2363
    def __init__(self, name, size, device=None):
        super(AgentLayer, self).__init__(
            name, 'agent', size, inputs=[], device=device)

Z
zhangjinchao01 已提交
2364 2365 2366

@config_layer('gather_agent')
class GatherAgentLayer(LayerBase):
Q
qijun 已提交
2367
    def __init__(self, name, size, device=None):
Z
zhangjinchao01 已提交
2368 2369 2370
        super(GatherAgentLayer, self).__init__(
            name, 'gather_agent', size, inputs=[], device=device)

Q
qijun 已提交
2371

Z
zhangjinchao01 已提交
2372 2373
@config_layer('scatter_agent')
class ScatterAgentLayer(LayerBase):
Q
qijun 已提交
2374
    def __init__(self, name, size, device=None):
Z
zhangjinchao01 已提交
2375 2376 2377
        super(ScatterAgentLayer, self).__init__(
            name, 'scatter_agent', size, inputs=[], device=device)

Q
qijun 已提交
2378

Z
zhangjinchao01 已提交
2379 2380
@config_layer('multiplex')
class MultiplexLayer(LayerBase):
Q
qijun 已提交
2381 2382 2383 2384 2385
    def __init__(self, name, inputs, size, device=None):
        super(MultiplexLayer, self).__init__(
            name, 'multiplex', size, inputs=inputs, device=device)
        config_assert(
            len(inputs) > 2, 'MultiplexLayer should have more than 2 inputs.')
Z
zhangjinchao01 已提交
2386
        for i in range(1, len(inputs)):
Q
qijun 已提交
2387 2388 2389 2390 2391
            config_assert(
                self.get_input_layer(i).size == size,
                "All the input layers except the first one should"
                "have the same size as the MultiplexLayer.")

Z
zhangjinchao01 已提交
2392 2393

@config_func
2394 2395 2396 2397
def Link(name, has_subseq=False):
    """
    Still keeping has_subseq for backward compatibility
    """
Z
zhangjinchao01 已提交
2398 2399 2400 2401
    link_config = LinkConfig()
    link_config.link_name = name
    return link_config

Q
qijun 已提交
2402

Z
zhangjinchao01 已提交
2403 2404
# memory for recurrent layer group.
# *name* and *size* are actual layer's name and size.
2405 2406 2407 2408
# If *name* is None, need to provide *memory_name* and need to use
# SetMemoryInput() later to specify the layer which this memory remembers.
#
# return the name of the memory,
Z
zhangjinchao01 已提交
2409 2410 2411 2412 2413 2414 2415 2416 2417 2418 2419
# use this name if you assign the memory as other layer's input
#
# boot frame of memory is zeroed by default,
# or initialize by boot layer output if *boot_layer* set,
# or initialize by trainable bias if *boot_bias* set,
# or initialize by a constant id if *boot_with_const_id* set
#
# Memory can be a sequence if *is_sequence* set, this type of memory
# can only be initailized by a *boot_layer* which is a sequence.
#
@config_func
2420 2421 2422 2423 2424 2425 2426 2427 2428 2429 2430 2431
def Memory(name,
           size,
           is_sequence=False,
           boot_layer=None,
           boot_bias=False,
           boot_bias_active_type="",
           boot_with_const_id=None,
           memory_name=None):
    if not memory_name:
        config_assert(name is not None, "name needs cannot be None")
        memory_name = name + "+delay1"
    agent_name = memory_name
2432
    agent_layer = AgentLayer(agent_name, size)
Z
zhangjinchao01 已提交
2433
    config_assert(g_current_submodel.is_recurrent_layer_group,
Q
qijun 已提交
2434
                  'Memory should be used in recurrent layer group only')
Z
zhangjinchao01 已提交
2435
    memory = g_current_submodel.memories.add()
2436 2437
    if name is not None:
        memory.layer_name = MakeLayerNameInSubmodel(name)
Z
zhangjinchao01 已提交
2438
    memory.link_name = MakeLayerNameInSubmodel(agent_name)
Q
qijun 已提交
2439
    options = sum((boot_layer is not None, bool(boot_bias),
Z
zhangjinchao01 已提交
2440
                   boot_with_const_id is not None))
Q
qijun 已提交
2441 2442 2443 2444
    config_assert(
        options <= 1,
        'take one option at most from boot_layer, boot_bias, or boot_with_const_id'
    )
Z
zhangjinchao01 已提交
2445 2446 2447
    if boot_layer is not None:
        boot_layer = MakeLayerNameInParentSubmodel(boot_layer)
        config_assert(boot_layer in g_layer_map,
Q
qijun 已提交
2448 2449
                      'boot_layer "%s" does not correspond to a layer name' %
                      boot_layer)
Z
zhangjinchao01 已提交
2450 2451 2452
        memory.boot_layer_name = boot_layer
    elif boot_bias:
        memory.boot_bias_parameter_name = agent_layer.create_bias_parameter(
Q
qijun 已提交
2453
            boot_bias, size, for_self=False)
Z
zhangjinchao01 已提交
2454 2455 2456 2457 2458
        memory.boot_bias_active_type = boot_bias_active_type
    elif boot_with_const_id is not None:
        memory.boot_with_const_id = boot_with_const_id
    return agent_name

Q
qijun 已提交
2459

2460 2461 2462 2463 2464 2465 2466 2467 2468 2469 2470
@config_func
def SetMemoryInput(memory_name, layer_name):
    memory_name = MakeLayerNameInSubmodel(memory_name)
    layer_name = MakeLayerNameInSubmodel(layer_name)
    for mem in g_current_submodel.memories:
        if mem.link_name == memory_name:
            mem.layer_name = layer_name
            return
    logger.fatal("Nonexistent memory name: " + memory_name)


Z
zhangjinchao01 已提交
2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481
# Generator for recurrent layer group, to use it:
#  1. define a id layer as output of layer group
#  2. define a memory of this id layer, and assign a boot id(begin of sequence)
#  3. define a eos check layer and fill its name in generator's *eos_layer_name*
# Sequence generation will stop when eos check return 1 or *max_num_frames* reached.
# If *beam_size* is greater than one, generator will use beam search.
#   in beam search, if *num_results_per_sample* set, one sample sequence can output
#   multiple results each with a probility.
@config_func
def Generator(
        max_num_frames,
Q
qijun 已提交
2482 2483 2484 2485
        eos_layer_name="eos_check",
        num_results_per_sample=1,
        beam_size=1,
        log_prob=None, ):
Z
zhangjinchao01 已提交
2486 2487 2488 2489 2490 2491 2492 2493 2494
    generator_config = GeneratorConfig()
    generator_config.max_num_frames = max_num_frames
    generator_config.eos_layer_name = eos_layer_name
    generator_config.num_results_per_sample = num_results_per_sample
    generator_config.beam_size = beam_size
    if log_prob is not None:
        generator_config.log_prob = log_prob
    return generator_config

Q
qijun 已提交
2495

Z
zhangjinchao01 已提交
2496 2497
@config_layer('expand')
class ExpandLayer(LayerBase):
2498
    def __init__(self, name, inputs, trans_type='non-seq', bias=False, **xargs):
Q
qijun 已提交
2499
        super(ExpandLayer, self).__init__(
2500
            name, 'expand', 0, inputs=inputs, **xargs)
Q
qijun 已提交
2501 2502 2503 2504 2505 2506 2507 2508
        config_assert(
            len(self.inputs) == 2, 'ExpandLayer takes 2 and only 2 inputs')
        self.config.trans_type = trans_type
        for input_index in xrange(len(self.inputs)):
            input_layer = self.get_input_layer(input_index)
        self.set_layer_size(self.get_input_layer(0).size)
        self.create_bias_parameter(bias, self.config.size)

Z
zhangjinchao01 已提交
2509 2510 2511

@config_layer('featmap_expand')
class FeatMapExpandLayer(LayerBase):
X
xuwei06 已提交
2512 2513 2514 2515 2516
    def __init__(self,
                 name,
                 inputs,
                 num_filters=None,
                 as_row_vector=True,
X
xuwei06 已提交
2517 2518
                 bias=False,
                 **xargs):
Q
qijun 已提交
2519
        super(FeatMapExpandLayer, self).__init__(
X
xuwei06 已提交
2520
            name, 'featmap_expand', 0, inputs=inputs, **xargs)
Q
qijun 已提交
2521 2522 2523
        config_assert(
            len(self.inputs) == 1, 'ExpandLayer takes 1 and only 1 inputs')
        if num_filters is not None:
Z
zhangjinchao01 已提交
2524
            self.config.num_filters = num_filters
Q
qijun 已提交
2525
        else:
Z
zhangjinchao01 已提交
2526
            logger.fatal("FeatMapExpandLayer must specify num_filters.")
X
xuwei06 已提交
2527 2528
        if not as_row_vector:
            self.config.user_arg = "as_col_vec"
Q
qijun 已提交
2529
        self.set_layer_size(self.get_input_layer(0).size * num_filters)
Z
zhangjinchao01 已提交
2530 2531 2532 2533


@config_layer('max')
class MaxLayer(LayerBase):
Q
qijun 已提交
2534 2535 2536 2537 2538
    def __init__(self,
                 name,
                 inputs,
                 trans_type='non-seq',
                 bias=False,
2539
                 output_max_index=None,
2540
                 stride=-1,
2541
                 **xargs):
2542
        super(MaxLayer, self).__init__(name, 'max', 0, inputs=inputs, **xargs)
Z
zhangjinchao01 已提交
2543
        config_assert(len(self.inputs) == 1, 'MaxLayer must have 1 input')
2544 2545
        if trans_type == 'seq':
            config_assert(stride == -1, 'subseq does not support stride window')
Q
qijun 已提交
2546
        self.config.trans_type = trans_type
2547
        self.config.seq_pool_stride = stride
Z
zhangjinchao01 已提交
2548 2549 2550 2551
        for input_index in xrange(len(self.inputs)):
            input_layer = self.get_input_layer(input_index)
            self.set_layer_size(input_layer.size)
        self.create_bias_parameter(bias, self.config.size)
2552 2553
        if output_max_index is not None:
            self.config.output_max_index = output_max_index
Z
zhangjinchao01 已提交
2554 2555 2556 2557


@config_layer('maxid')
class MaxIdLayer(LayerBase):
Q
qijun 已提交
2558
    def __init__(self, name, inputs, beam_size=None, device=None):
Z
zhangjinchao01 已提交
2559 2560 2561 2562 2563 2564 2565 2566 2567 2568 2569 2570 2571 2572 2573 2574 2575
        super(MaxIdLayer, self).__init__(
            name, 'maxid', 0, inputs=inputs, device=device)
        config_assert(len(self.inputs) == 1, 'MaxIdLayer must have 1 input')
        for input_index in xrange(len(self.inputs)):
            input_layer = self.get_input_layer(input_index)
            self.set_layer_size(input_layer.size)

        if beam_size is None:
            global g_current_submodel
            if g_current_submodel.HasField("generator"):
                self.config.beam_size = g_current_submodel.generator.beam_size
        else:
            self.config.beam_size = beam_size


@config_layer('eos_id')
class EosIdLayer(LayerBase):
Q
qijun 已提交
2576
    def __init__(self, name, inputs, eos_id, device=None):
Z
zhangjinchao01 已提交
2577 2578 2579
        super(EosIdLayer, self).__init__(
            name, 'eos_id', 0, inputs=inputs, device=device)
        config_assert(len(self.inputs) == 1, 'EosIdLayer must have 1 input')
Q
qijun 已提交
2580
        self.set_layer_size(2)  # boolean output
Z
zhangjinchao01 已提交
2581 2582
        self.config.eos_id = eos_id

Q
qijun 已提交
2583

Z
zhangjinchao01 已提交
2584 2585
@config_layer('seqlastins')
class SequenceLastInstanceLayer(LayerBase):
Q
qijun 已提交
2586 2587 2588 2589
    def __init__(self,
                 name,
                 inputs,
                 trans_type='non-seq',
2590
                 bias=False,
2591
                 stride=-1,
2592
                 **xargs):
Q
qijun 已提交
2593
        super(SequenceLastInstanceLayer, self).__init__(
X
xuwei06 已提交
2594
            name, 'seqlastins', 0, inputs=inputs, **xargs)
Q
qijun 已提交
2595 2596
        config_assert(
            len(inputs) == 1, 'SequenceLastInstanceLayer must have 1 input')
2597
        if trans_type == 'seq':
L
Luo Tao 已提交
2598
            config_assert(stride == -1, 'subseq does not support stride window')
Q
qijun 已提交
2599
        self.config.trans_type = trans_type
2600 2601
        self.config.seq_pool_stride = stride
        self.set_layer_size(self.get_input_layer(0).size)
Z
zhangjinchao01 已提交
2602 2603
        self.create_bias_parameter(bias, self.config.size)

Q
qijun 已提交
2604

Z
zhangjinchao01 已提交
2605 2606
@config_layer('seqfirstins')
class SequenceFirstInstanceLayer(SequenceLastInstanceLayer):
2607 2608 2609 2610 2611
    def __init__(self,
                 name,
                 inputs,
                 trans_type='non-seq',
                 bias=False,
2612
                 stride=-1,
2613
                 **xargs):
Q
qijun 已提交
2614
        super(SequenceFirstInstanceLayer, self).__init__(
2615 2616 2617 2618 2619 2620
            name,
            inputs=inputs,
            trans_type=trans_type,
            bias=bias,
            stride=stride,
            **xargs)
Z
zhangjinchao01 已提交
2621 2622
        self.config.select_first = True

Q
qijun 已提交
2623

Z
zhangjinchao01 已提交
2624 2625
@config_layer('seqconcat')
class SequenceConcatLayer(LayerBase):
X
xuwei06 已提交
2626
    def __init__(self, name, inputs, bias=False, **xargs):
Q
qijun 已提交
2627
        super(SequenceConcatLayer, self).__init__(
X
xuwei06 已提交
2628
            name, 'seqconcat', 0, inputs=inputs, **xargs)
Q
qijun 已提交
2629 2630
        config_assert(
            len(inputs) == 2, 'SequenceConcatLayer must have 2 inputs')
Z
zhangjinchao01 已提交
2631 2632 2633 2634 2635
        for input_index in xrange(len(self.inputs)):
            input_layer = self.get_input_layer(input_index)
            self.set_layer_size(input_layer.size)
        self.create_bias_parameter(bias, self.config.size)

Q
qijun 已提交
2636

Z
zhangjinchao01 已提交
2637 2638
@config_layer('seqreshape')
class SequenceReshapeLayer(LayerBase):
X
xuwei06 已提交
2639
    def __init__(self, name, size, inputs, bias=False, **xargs):
Q
qijun 已提交
2640
        super(SequenceReshapeLayer, self).__init__(
X
xuwei06 已提交
2641
            name, 'seqreshape', size, inputs=inputs, **xargs)
Q
qijun 已提交
2642 2643
        config_assert(
            len(inputs) == 1, 'SequenceReshapeLayer must have 1 inputs')
Z
zhangjinchao01 已提交
2644 2645 2646
        self.set_layer_size(size)
        self.create_bias_parameter(bias, size)

Q
qijun 已提交
2647

Z
zhangjinchao01 已提交
2648 2649
@config_layer('subseq')
class SubSequenceLayer(LayerBase):
X
xuwei06 已提交
2650
    def __init__(self, name, inputs, bias=False, **xargs):
Q
qijun 已提交
2651
        super(SubSequenceLayer, self).__init__(
X
xuwei06 已提交
2652
            name, 'subseq', 0, inputs=inputs, **xargs)
Z
zhangjinchao01 已提交
2653 2654 2655 2656 2657 2658
        config_assert(len(inputs) == 3, 'SubSequenceLayer must have 3 inputs')
        input_layer0 = self.get_input_layer(0)
        size = input_layer0.size
        self.set_layer_size(size)
        self.create_bias_parameter(bias, size)

Q
qijun 已提交
2659

C
caoying03 已提交
2660 2661
@config_layer('sub_nested_seq')
class SubNestedSequenceLayer(LayerBase):
2662 2663 2664 2665 2666 2667 2668 2669 2670 2671 2672 2673
    def __init__(self, name, inputs, selected_indices, bias=False, **xargs):
        if isinstance(inputs, list):
            assert len(inputs) == 1, ('the first input of sub_nested_seq '
                                      'layer is a single nested sequence.')
            inputs = inputs[0]
        if isinstance(selected_indices, list):
            assert len(selected_indices) == 1, (
                'the second input of '
                'sub_nested_seq layer is a single layer which is a '
                'set of selected indices.')
            selected_indices = selected_indices[0]

C
caoying03 已提交
2674
        super(SubNestedSequenceLayer, self).__init__(
2675 2676 2677 2678 2679
            name,
            'sub_nested_seq',
            0,
            inputs=[inputs, selected_indices],
            **xargs)
C
caoying03 已提交
2680 2681 2682 2683 2684
        input_layer0 = self.get_input_layer(0)
        size = input_layer0.size
        self.set_layer_size(size)


Z
zhangjinchao01 已提交
2685 2686
@config_layer('out_prod')
class OuterProdLayer(LayerBase):
Q
qijun 已提交
2687 2688 2689
    def __init__(self, name, inputs, device=None):
        super(OuterProdLayer, self).__init__(
            name, 'out_prod', 0, inputs=inputs, device=device)
Z
zhangjinchao01 已提交
2690 2691 2692 2693 2694
        config_assert(len(inputs) == 2, 'OuterProdLayer must have 2 inputs')
        input_layer0 = self.get_input_layer(0)
        input_layer1 = self.get_input_layer(1)
        self.set_layer_size(input_layer0.size * input_layer1.size)

Q
qijun 已提交
2695

Z
zhangjinchao01 已提交
2696 2697
@config_layer('power')
class PowerLayer(LayerBase):
Q
qijun 已提交
2698 2699 2700
    def __init__(self, name, inputs, device=None):
        super(PowerLayer, self).__init__(
            name, 'power', 0, inputs=inputs, device=device)
Z
zhangjinchao01 已提交
2701 2702 2703 2704
        config_assert(len(inputs) == 2, 'PowerLayer must have 2 inputs')
        input_layer1 = self.get_input_layer(1)
        self.set_layer_size(input_layer1.size)
        input_layer0 = self.get_input_layer(0)
Q
qijun 已提交
2705 2706 2707
        config_assert(1 == input_layer0.size,
                      'The left input is the exponent and should be of size 1')

Z
zhangjinchao01 已提交
2708 2709 2710

@config_layer('slope_intercept')
class SlopeInterceptLayer(LayerBase):
Q
qijun 已提交
2711 2712 2713
    def __init__(self, name, inputs, slope=1.0, intercept=0.0, device=None):
        super(SlopeInterceptLayer, self).__init__(
            name, 'slope_intercept', 0, inputs=inputs, device=device)
Z
zhangjinchao01 已提交
2714 2715 2716 2717 2718 2719
        self.config.slope = slope
        self.config.intercept = intercept
        config_assert(len(inputs) == 1, 'SlopeInterceptLayer must have 1 input')
        input_layer0 = self.get_input_layer(0)
        self.set_layer_size(input_layer0.size)

Q
qijun 已提交
2720

Z
zhangjinchao01 已提交
2721 2722
@config_layer('scaling')
class ScalingLayer(LayerBase):
Q
qijun 已提交
2723 2724 2725
    def __init__(self, name, inputs, device=None):
        super(ScalingLayer, self).__init__(
            name, 'scaling', 0, inputs=inputs, device=device)
Z
zhangjinchao01 已提交
2726 2727 2728 2729
        config_assert(len(inputs) == 2, 'ScalingLayer must have 2 inputs')
        input_layer1 = self.get_input_layer(1)
        self.set_layer_size(input_layer1.size)
        input_layer0 = self.get_input_layer(0)
Q
qijun 已提交
2730 2731 2732
        config_assert(1 == input_layer0.size,
                      'The left input should be of size 1')

Z
zhangjinchao01 已提交
2733 2734 2735

@config_layer('conv_shift')
class ConvShiftLayer(LayerBase):
Q
qijun 已提交
2736 2737 2738
    def __init__(self, name, inputs, device=None):
        super(ConvShiftLayer, self).__init__(
            name, 'conv_shift', 0, inputs=inputs, device=device)
Z
zhangjinchao01 已提交
2739 2740 2741 2742
        config_assert(len(inputs) == 2, 'ConvShiftLayer must have 2 inputs')
        input_layer0 = self.get_input_layer(0)
        self.set_layer_size(input_layer0.size)

Q
qijun 已提交
2743

Z
zhangjinchao01 已提交
2744 2745
@config_layer('convex_comb')
class ConvexCombinationLayer(LayerBase):
Q
qijun 已提交
2746
    def __init__(self, name, size, inputs, device=None):
Z
zhangjinchao01 已提交
2747
        super(ConvexCombinationLayer, self).__init__(
Q
qijun 已提交
2748 2749 2750
            name, 'convex_comb', size, inputs=inputs, device=device)
        config_assert(
            len(self.inputs) == 2, 'ConvexCombinationLayer must have 2 inputs')
2751 2752 2753
        config_assert(
            size * self.get_input_layer(0).size == self.get_input_layer(1).size,
            'Wrong input size for ConvexCombinationLayer')
Z
zhangjinchao01 已提交
2754 2755
        self.set_layer_size(size)

Q
qijun 已提交
2756

Z
zhangjinchao01 已提交
2757 2758
@config_layer('interpolation')
class InterpolationLayer(LayerBase):
Q
qijun 已提交
2759
    def __init__(self, name, inputs, device=None):
Z
zhangjinchao01 已提交
2760 2761
        super(InterpolationLayer, self).__init__(
            name, 'interpolation', 0, inputs=inputs, device=device)
Q
qijun 已提交
2762 2763
        config_assert(
            len(self.inputs) == 3, 'InterpolationLayer must have 3 inputs')
Z
zhangjinchao01 已提交
2764 2765 2766 2767 2768 2769 2770 2771
        input_layer0 = self.get_input_layer(0)
        input_layer1 = self.get_input_layer(1)
        input_layer2 = self.get_input_layer(2)
        self.set_layer_size(input_layer1.size)
        config_assert(input_layer0.size == 1, 'weight should be of size 1')
        config_assert(input_layer1.size == input_layer2.size,
                      'the two vector inputs should be of the same size')

Q
qijun 已提交
2772

L
liaogang 已提交
2773 2774
@config_layer('bilinear_interp')
class BilinearInterpLayer(LayerBase):
Q
qijun 已提交
2775
    def __init__(self, name, inputs, **xargs):
L
liaogang 已提交
2776
        super(BilinearInterpLayer, self).__init__(
L
liaogang 已提交
2777
            name, 'bilinear_interp', 0, inputs=inputs, **xargs)
L
liaogang 已提交
2778
        input_layer = self.get_input_layer(0)
L
Luo Tao 已提交
2779 2780 2781 2782
        conf = self.config.inputs[0].bilinear_interp_conf
        parse_bilinear(self.inputs[0].bilinear_interp, input_layer.name, conf)
        self.set_cnn_layer(name, conf.out_size_y, conf.out_size_x,
                           conf.image_conf.channels)
Q
qijun 已提交
2783

L
liaogang 已提交
2784

Z
zhangjinchao01 已提交
2785 2786
@config_layer('sum_to_one_norm')
class SumToOneNormLayer(LayerBase):
Q
qijun 已提交
2787
    def __init__(self, name, inputs, device=None):
Z
zhangjinchao01 已提交
2788
        super(SumToOneNormLayer, self).__init__(
Q
qijun 已提交
2789 2790 2791
            name, 'sum_to_one_norm', 0, inputs=inputs, device=device)
        config_assert(
            len(self.inputs) == 1, 'SumToOneNormLayer must have 1 input')
Z
zhangjinchao01 已提交
2792 2793 2794
        input_layer0 = self.get_input_layer(0)
        self.set_layer_size(input_layer0.size)

Q
qijun 已提交
2795

G
guosheng 已提交
2796 2797
@config_layer('row_l2_norm')
class RowL2NormLayer(LayerBase):
2798
    def __init__(self, name, inputs, **xargs):
G
guosheng 已提交
2799
        super(RowL2NormLayer, self).__init__(
2800
            name, 'row_l2_norm', 0, inputs=inputs, **xargs)
G
guosheng 已提交
2801
        config_assert(len(self.inputs) == 1, 'RowL2NormLayer must have 1 input')
2802 2803
        input_layer = self.get_input_layer(0)
        self.set_layer_size(input_layer.size)
G
guosheng 已提交
2804 2805


Z
zhangjinchao01 已提交
2806 2807
@config_layer('cos_vm')
class CosSimVecMatLayer(LayerBase):
Q
qijun 已提交
2808
    def __init__(self, name, size, inputs, cos_scale=1.0, device=None):
Z
zhangjinchao01 已提交
2809
        super(CosSimVecMatLayer, self).__init__(
Q
qijun 已提交
2810
            name, 'cos_vm', size, inputs=inputs, device=device)
Z
zhangjinchao01 已提交
2811
        self.config.cos_scale = cos_scale
Q
qijun 已提交
2812 2813
        config_assert(
            len(self.inputs) == 2, 'CosSimVecMatLayer must have 2 inputs')
2814 2815 2816
        config_assert(
            size * self.get_input_layer(0).size == self.get_input_layer(1).size,
            'Wrong input size for CosSimVecMatLayer')
Z
zhangjinchao01 已提交
2817

Q
qijun 已提交
2818

Z
zhangjinchao01 已提交
2819 2820
@config_layer('sampling_id')
class SamplingIdLayer(LayerBase):
Q
qijun 已提交
2821
    def __init__(self, name, inputs, device=None):
Z
zhangjinchao01 已提交
2822 2823
        super(SamplingIdLayer, self).__init__(
            name, 'sampling_id', 0, inputs=inputs, device=device)
Q
qijun 已提交
2824 2825
        config_assert(
            len(self.inputs) == 1, 'SamplingIdLayer must have 1 input')
Z
zhangjinchao01 已提交
2826 2827 2828 2829 2830 2831 2832 2833 2834 2835 2836 2837
        for input_index in xrange(len(self.inputs)):
            input_layer = self.get_input_layer(input_index)
            self.set_layer_size(input_layer.size)


# AverageLayer: "average" for each sample within a sequence.
# average_stratrgy: set to one of the following:
# 'average': plain average.
# 'sum': sum each sample instead of average (which is divide by sample_num).
# 'squarerootn': sum each sample, but divide by sqrt(sample_num).
@config_layer('average')
class AverageLayer(LayerBase):
Q
qijun 已提交
2838 2839 2840 2841 2842
    def __init__(self,
                 name,
                 inputs,
                 average_strategy='average',
                 trans_type='non-seq',
2843
                 bias=False,
2844
                 stride=-1,
2845
                 **xargs):
Q
qijun 已提交
2846
        super(AverageLayer, self).__init__(
X
xuwei06 已提交
2847
            name, 'average', 0, inputs=inputs, **xargs)
Z
zhangjinchao01 已提交
2848
        self.config.average_strategy = average_strategy
2849 2850
        if trans_type == 'seq':
            config_assert(stride == -1, 'subseq does not support stride window')
Q
qijun 已提交
2851
        self.config.trans_type = trans_type
2852
        self.config.seq_pool_stride = stride
Z
zhangjinchao01 已提交
2853 2854 2855 2856 2857 2858
        config_assert(len(inputs) == 1, 'AverageLayer must have 1 input')
        for input_index in xrange(len(self.inputs)):
            input_layer = self.get_input_layer(input_index)
            self.set_layer_size(input_layer.size)
        self.create_bias_parameter(bias, self.config.size)

Q
qijun 已提交
2859

Z
zhangjinchao01 已提交
2860 2861
@config_layer('cos')
class CosSimLayer(LayerBase):
2862
    def __init__(self, name, inputs, cos_scale=1, device=None):
Z
zhangjinchao01 已提交
2863 2864 2865 2866 2867 2868
        super(CosSimLayer, self).__init__(
            name, 'cos', 1, inputs=inputs, device=device)
        config_assert(len(self.inputs) == 2, 'CosSimLayer must have 2 inputs')
        config_assert(
            self.get_input_layer(0).size == self.get_input_layer(1).size,
            'inputs of CosSimLayer must have same dim')
2869
        self.config.cos_scale = cos_scale
Z
zhangjinchao01 已提交
2870 2871 2872 2873


@config_layer('tensor')
class TensorLayer(LayerBase):
2874
    def __init__(self, name, size, inputs, bias=True, **xargs):
Q
qijun 已提交
2875
        super(TensorLayer, self).__init__(
2876
            name, 'tensor', size, inputs=inputs, **xargs)
Z
zhangjinchao01 已提交
2877 2878
        config_assert(len(self.inputs) == 2, 'TensorLayer must have 2 inputs')
        config_assert(size > 0, 'size must be positive')
Q
qijun 已提交
2879 2880
        config_assert(inputs[1].parameter_name == None,
                      'second parameter should be None.')
Z
zhangjinchao01 已提交
2881 2882 2883 2884 2885 2886 2887 2888 2889 2890
        input_layer0 = self.get_input_layer(0)
        input_layer1 = self.get_input_layer(1)
        psize = size * input_layer0.size * input_layer1.size
        dims = [input_layer0.size, input_layer1.size, size]
        self.create_input_parameter(0, psize, dims)
        self.create_bias_parameter(bias, size)


@config_layer('mixed')
class MixedLayer(LayerBase):
C
caoying03 已提交
2891
    def __init__(self, name, inputs, size=0, bias=True, **xargs):
Z
zhangjinchao01 已提交
2892 2893 2894 2895 2896 2897 2898 2899 2900 2901 2902 2903 2904 2905 2906 2907 2908
        config_assert(inputs, 'inputs cannot be empty')
        super(MixedLayer, self).__init__(
            name, 'mixed', size, inputs=inputs, **xargs)
        operator_input_index = []
        for operator in self.operators:
            operator_conf = operator.operator_conf
            for i in xrange(1, len(operator.input_layer_names)):
                input_index = len(self.config.inputs)
                operator_conf.input_indices.append(input_index)
                input_config = Input(operator.input_layer_names[i])
                self.inputs.append(input_config)
                layer_input = self.config.inputs.add()
                layer_input.input_layer_name = input_config.input_layer_name
            for input_index in operator_conf.input_indices:
                input_layer = self.get_input_layer(input_index)
                operator_conf.input_sizes.append(input_layer.size)
                operator_input_index.append(input_index)
2909
            if self.config.size == 0:
Z
zhangjinchao01 已提交
2910 2911 2912
                size = operator.calc_output_size(operator_conf.input_sizes)
                if size != 0:
                    self.set_layer_size(size)
2913
            else:
2914 2915
                sz = operator.calc_output_size(operator_conf.input_sizes)
                if sz != 0:
Q
qijun 已提交
2916 2917 2918 2919
                    config_assert(
                        sz == self.config.size,
                        "different inputs have different size: %s vs. %s" %
                        (sz, self.config.size))
Z
zhangjinchao01 已提交
2920 2921 2922 2923
        for input_index in xrange(len(self.inputs)):
            input_layer = self.get_input_layer(input_index)
            input = self.inputs[input_index]
            if input_index not in operator_input_index:
Q
qijun 已提交
2924 2925 2926
                config_assert(
                    isinstance(input, Projection),
                    "input should be projection or operation")
2927
            if self.config.size == 0 and isinstance(input, Projection):
Z
zhangjinchao01 已提交
2928 2929 2930
                size = input.calc_output_size(input_layer)
                if size != 0:
                    self.set_layer_size(size)
2931
            elif isinstance(input, Projection):
Q
qijun 已提交
2932 2933 2934 2935 2936 2937
                sz = input.calc_output_size(input_layer)
                if sz != 0:
                    config_assert(
                        sz == self.config.size,
                        "different inputs have different size: %s vs. %s" %
                        (sz, self.config.size))
Z
zhangjinchao01 已提交
2938 2939 2940 2941 2942 2943 2944 2945 2946 2947 2948
        config_assert(size != 0, "size is not set")

        for input_index in xrange(len(self.inputs)):
            input = self.inputs[input_index]
            if isinstance(input, Projection):
                input_layer = self.get_input_layer(input_index)
                input.proj_conf.input_size = input_layer.size
                input.proj_conf.output_size = size

                input_config = self.config.inputs[input_index]
                input_config.proj_conf.CopyFrom(input.proj_conf)
Q
qijun 已提交
2949 2950
                input_config.proj_conf.name = gen_parameter_name(name,
                                                                 input_index)
Z
zhangjinchao01 已提交
2951 2952 2953 2954 2955 2956 2957 2958 2959 2960 2961
                psize = input.calc_parameter_size(input_layer.size, size)
                dims = input.calc_parameter_dims(input_layer.size, size)
                self.create_input_parameter(input_index, psize, dims)

        for operator in self.operators:
            operator_conf = operator.operator_conf
            operator_conf.output_size = self.config.size
            operator.check_dims()
            record_operator_conf = self.config.operator_confs.add()
            record_operator_conf.CopyFrom(operator_conf)

2962 2963 2964 2965 2966 2967
        psize = self.config.size
        if isinstance(self.inputs[0], ConvProjection):
            self.config.shared_biases = True
            psize = 0
            for input in self.inputs:
                psize += input.calc_bias_size()
Z
zhangjinchao01 已提交
2968

2969 2970 2971
        if bias:
            self.config.bias_size = psize
            self.create_bias_parameter(bias, psize)
Z
zhangjinchao01 已提交
2972

Q
qijun 已提交
2973

Z
zhangjinchao01 已提交
2974 2975
# like MixedLayer, but no bias parameter
@config_func
Q
qijun 已提交
2976
def ExpressionLayer(name, inputs, **xargs):
Z
zhangjinchao01 已提交
2977 2978
    MixedLayer(name, inputs, bias=False, **xargs)

Q
qijun 已提交
2979

Z
zhangjinchao01 已提交
2980 2981
@config_layer('concat')
class ConcatenateLayer(LayerBase):
Q
qijun 已提交
2982
    def __init__(self, name, inputs, bias=False, **xargs):
Z
zhangjinchao01 已提交
2983
        config_assert(inputs, 'inputs cannot be empty')
2984
        config_assert(not bias, 'ConcatenateLayer cannot support bias.')
Z
zhangjinchao01 已提交
2985 2986 2987 2988 2989 2990
        super(ConcatenateLayer, self).__init__(
            name, 'concat', 0, inputs=inputs, **xargs)
        size = 0
        for input_index in xrange(len(self.inputs)):
            input_layer = self.get_input_layer(input_index)
            input = self.inputs[input_index]
Q
qijun 已提交
2991
            if self.config.size == 0:
Z
zhangjinchao01 已提交
2992 2993 2994 2995
                size += input_layer.size

        self.set_layer_size(size)

Q
qijun 已提交
2996

Z
zhangjinchao01 已提交
2997 2998 2999
# like concat layer, but each input layer was processed by a Projection.
@config_layer('concat2')
class ConcatenateLayer2(LayerBase):
Q
qijun 已提交
3000
    def __init__(self, name, inputs, bias=False, **xargs):
Z
zhangjinchao01 已提交
3001 3002 3003
        config_assert(inputs, 'inputs cannot be empty')
        super(ConcatenateLayer2, self).__init__(
            name, 'concat2', 0, inputs=inputs, **xargs)
3004 3005

        if isinstance(self.inputs[0], ConvProjection):
Q
qijun 已提交
3006 3007 3008 3009 3010 3011
            for input_index in xrange(len(self.inputs) - 1):
                input = self.inputs[input_index + 1]
                config_assert(
                    isinstance(input, ConvProjection),
                    "The first input of ConcatenateLayer2 is ConvProjection, "
                    "the other inputs should also be ConvProjection.")
3012

Z
zhangjinchao01 已提交
3013 3014 3015 3016 3017 3018 3019 3020 3021 3022 3023 3024 3025 3026 3027 3028 3029 3030 3031 3032
        size = 0
        for input_index in xrange(len(self.inputs)):
            input_layer = self.get_input_layer(input_index)
            input = self.inputs[input_index]
            output_size = input.calc_output_size(input_layer)
            config_assert(output_size != 0, "proj output size is not set")
            size += output_size

        self.set_layer_size(size)

        for input_index in xrange(len(self.inputs)):
            input_layer = self.get_input_layer(input_index)
            input = self.inputs[input_index]
            input.proj_conf.input_size = input_layer.size
            input.proj_conf.output_size = input.calc_output_size(input_layer)

            input_config = self.config.inputs[input_index]
            input_config.proj_conf.CopyFrom(input.proj_conf)
            input_config.proj_conf.name = gen_parameter_name(name, input_index)
            psize = input.calc_parameter_size(input.proj_conf.input_size,
Q
qijun 已提交
3033
                                              input.proj_conf.output_size)
Z
zhangjinchao01 已提交
3034
            dims = input.calc_parameter_dims(input.proj_conf.input_size,
Q
qijun 已提交
3035
                                             input.proj_conf.output_size)
Z
zhangjinchao01 已提交
3036 3037
            self.create_input_parameter(input_index, psize, dims)

3038 3039 3040 3041 3042 3043 3044
        psize = self.config.size
        if isinstance(self.inputs[0], ConvProjection):
            self.config.shared_biases = True
            psize = 0
            for input in self.inputs:
                psize += input.calc_bias_size()

3045 3046 3047
        if bias:
            self.config.bias_size = psize
            self.create_bias_parameter(bias, psize)
3048

Q
qijun 已提交
3049

Z
zhangjinchao01 已提交
3050 3051
@config_layer('recurrent')
class RecurrentLayer(LayerBase):
Q
qijun 已提交
3052
    def __init__(self, name, inputs, reversed=False, bias=True, **xargs):
Y
Yu Yang 已提交
3053 3054
        super(RecurrentLayer, self).__init__(name, 'recurrent', 0, inputs,
                                             **xargs)
Z
zhangjinchao01 已提交
3055 3056 3057 3058 3059 3060 3061 3062 3063
        config_assert(len(self.inputs) == 1, 'RecurrentLayer must have 1 input')
        input_layer = self.get_input_layer(0)
        size = input_layer.size
        self.set_layer_size(size)
        self.config.reversed = reversed
        dims = [size, size]
        self.create_input_parameter(0, size * size, dims)
        self.create_bias_parameter(bias, self.config.size)

Q
qijun 已提交
3064

Z
zhangjinchao01 已提交
3065 3066
@config_layer('lstmemory')
class LstmLayer(LayerBase):
Q
qijun 已提交
3067 3068 3069 3070 3071 3072 3073 3074
    def __init__(self,
                 name,
                 inputs,
                 reversed=False,
                 active_gate_type="sigmoid",
                 active_state_type="sigmoid",
                 bias=True,
                 **xargs):
Z
zhangjinchao01 已提交
3075 3076 3077 3078 3079 3080 3081 3082
        super(LstmLayer, self).__init__(name, 'lstmemory', 0, inputs, **xargs)
        config_assert(len(self.inputs) == 1, 'LstmLayer must have 1 input')
        input_layer = self.get_input_layer(0)
        #check input_layer.size is divided by 4
        config_assert(input_layer.size % 4 == 0, "size % 4 should be 0!")
        size = input_layer.size / 4
        self.set_layer_size(size)
        self.config.reversed = reversed
Q
qijun 已提交
3083
        self.config.active_gate_type = active_gate_type
Z
zhangjinchao01 已提交
3084 3085 3086 3087 3088
        self.config.active_state_type = active_state_type
        self.create_input_parameter(0, size * size * 4, [size, size, 4])
        #bias includes 3 kinds of peephole, 4 + 3 = 7
        self.create_bias_parameter(bias, size * 7)

Q
qijun 已提交
3089

Z
zhangjinchao01 已提交
3090 3091
@config_layer('lstm_step')
class LstmStepLayer(LayerBase):
Q
qijun 已提交
3092 3093 3094 3095 3096 3097 3098 3099 3100 3101
    def __init__(self,
                 name,
                 size,
                 inputs,
                 active_gate_type="sigmoid",
                 active_state_type="sigmoid",
                 bias=True,
                 **xargs):
        super(LstmStepLayer, self).__init__(name, 'lstm_step', size, inputs,
                                            **xargs)
Z
zhangjinchao01 已提交
3102 3103 3104
        config_assert(len(inputs) == 2, 'LstmStepLayer must have 2 inputs')
        input_layer0 = self.get_input_layer(0)
        input_layer1 = self.get_input_layer(1)
Q
qijun 已提交
3105 3106 3107 3108 3109
        config_assert(input_layer0.size == 4 * size,
                      'input_layer0.size != 4 * layer.size')
        config_assert(input_layer1.size == size,
                      'input_layer1.size != layer.size')
        self.config.active_gate_type = active_gate_type
Z
zhangjinchao01 已提交
3110 3111 3112
        self.config.active_state_type = active_state_type
        self.create_bias_parameter(bias, size * 3)

Q
qijun 已提交
3113

Z
zhangjinchao01 已提交
3114 3115 3116
# get the specific output from the input layer.
@config_layer('get_output')
class GetOutputLayer(LayerBase):
Q
qijun 已提交
3117 3118 3119 3120
    def __init__(self, name, size, inputs):
        super(GetOutputLayer, self).__init__(name, 'get_output', size, inputs)
        config_assert(
            len(self.inputs) == 1, 'GetOutputLayer must have 1 inputs')
Z
zhangjinchao01 已提交
3121 3122 3123 3124
        inputs = self.inputs[0]
        config_assert(inputs.input_layer_argument,
                      'input_layer_argument cannot be empty')

Q
qijun 已提交
3125

Z
zhangjinchao01 已提交
3126 3127
@config_layer('mdlstmemory')
class MDLstmLayer(LayerBase):
Q
qijun 已提交
3128 3129 3130 3131 3132 3133 3134 3135
    def __init__(self,
                 name,
                 inputs,
                 directions=True,
                 active_gate_type="sigmoid",
                 active_state_type="sigmoid",
                 bias=True,
                 **xargs):
Y
Yu Yang 已提交
3136 3137
        super(MDLstmLayer, self).__init__(name, 'mdlstmemory', 0, inputs,
                                          **xargs)
Z
zhangjinchao01 已提交
3138 3139 3140 3141
        config_assert(len(self.inputs) == 1, 'MDLstmLayer must have 1 input')
        input_layer = self.get_input_layer(0)
        dim_num = len(directions)
        #check input_layer.size is divided by (3+dim_num)
Y
Yu Yang 已提交
3142 3143
        config_assert(input_layer.size % (3 + dim_num) == 0,
                      "size % (dim_num) should be 0!")
Q
qijun 已提交
3144
        size = input_layer.size / (3 + dim_num)
Z
zhangjinchao01 已提交
3145
        self.set_layer_size(size)
Q
qijun 已提交
3146
        self.config.active_gate_type = active_gate_type
Z
zhangjinchao01 已提交
3147 3148 3149
        self.config.active_state_type = active_state_type
        for i in xrange(len(directions)):
            self.config.directions.append(int(directions[i]))
Y
Yu Yang 已提交
3150 3151
        self.create_input_parameter(0, size * size * (3 + dim_num),
                                    [size, size, 3 + dim_num])
Z
zhangjinchao01 已提交
3152
        #bias includes 3 kinds of peephole, 3+dim_num+2+dim_num
Q
qijun 已提交
3153 3154
        self.create_bias_parameter(bias, size * (5 + 2 * dim_num))

Z
zhangjinchao01 已提交
3155 3156 3157

@config_layer('gated_recurrent')
class GatedRecurrentLayer(LayerBase):
Q
qijun 已提交
3158 3159 3160 3161 3162 3163 3164 3165 3166 3167 3168
    def __init__(self,
                 name,
                 inputs,
                 reversed=False,
                 active_gate_type="sigmoid",
                 bias=True,
                 **xargs):
        super(GatedRecurrentLayer, self).__init__(name, 'gated_recurrent', 0,
                                                  inputs, **xargs)
        config_assert(
            len(self.inputs) == 1, 'GatedRecurrentLayer must have 1 input')
Z
zhangjinchao01 已提交
3169 3170 3171 3172 3173 3174
        input_layer = self.get_input_layer(0)
        #check input_layer.size is divided by 3
        config_assert(input_layer.size % 3 == 0, "size % 3 should be 0!")
        size = input_layer.size / 3
        self.set_layer_size(size)
        self.config.reversed = reversed
Q
qijun 已提交
3175
        self.config.active_gate_type = active_gate_type
Z
zhangjinchao01 已提交
3176 3177 3178
        self.create_input_parameter(0, size * size * 3, [size, size * 3])
        self.create_bias_parameter(bias, size * 3)

Q
qijun 已提交
3179

Z
zhangjinchao01 已提交
3180 3181
@config_layer('gru_step')
class GruStepLayer(LayerBase):
Q
qijun 已提交
3182 3183 3184 3185 3186 3187 3188
    def __init__(self,
                 name,
                 size,
                 inputs,
                 active_gate_type="sigmoid",
                 bias=True,
                 **xargs):
Y
Yu Yang 已提交
3189 3190
        super(GruStepLayer, self).__init__(name, 'gru_step', size, inputs,
                                           **xargs)
Z
zhangjinchao01 已提交
3191 3192 3193
        config_assert(len(self.inputs) == 2, 'GruStepLayer must have 2 input')
        input_layer0 = self.get_input_layer(0)
        input_layer1 = self.get_input_layer(1)
Q
qijun 已提交
3194 3195 3196 3197 3198
        config_assert(input_layer0.size == 3 * size,
                      'input_layer0.size != 3 * layer.size')
        config_assert(input_layer1.size == size,
                      'input_layer1.size != layer.size')
        self.config.active_gate_type = active_gate_type
H
Haonan 已提交
3199
        self.create_input_parameter(0, size * size * 3, [size, size * 3])
Z
zhangjinchao01 已提交
3200 3201
        self.create_bias_parameter(bias, size * 3)

Q
qijun 已提交
3202

Z
zhangjinchao01 已提交
3203 3204 3205 3206 3207 3208 3209
'''
 A layer for calculating the cost of sequential conditional random field model.
 Example: CRFLayer(name="crf_cost", size=label_num,
                   inputs=["output", "label", "weight"])
          where "weight" is optional, one weight for each sequence
 @param coeff: weight of the layer
'''
Q
qijun 已提交
3210 3211


Z
zhangjinchao01 已提交
3212 3213
@config_layer('crf')
class CRFLayer(LayerBase):
Q
qijun 已提交
3214
    def __init__(self, name, size, inputs, coeff=1.0, device=None):
Z
zhangjinchao01 已提交
3215
        super(CRFLayer, self).__init__(name, 'crf', size, inputs, device=device)
Q
qijun 已提交
3216 3217
        config_assert(2 <= len(self.inputs) <= 3,
                      'CRFLayer must have 2 or 3 inputs')
3218
        self.create_input_parameter(0, size * (size + 2), [size + 2, size])
Z
zhangjinchao01 已提交
3219 3220
        self.config.coeff = coeff

Q
qijun 已提交
3221

Z
zhangjinchao01 已提交
3222 3223 3224 3225 3226 3227 3228 3229
'''
 A layer for calculating the decoding sequence of sequential conditional
 random field model.
 The decoding sequence is stored in output_.ids
 If a second input is provided, it is treated as the ground-truth label, and
 this layer will also calculate error, output_.value[i] is 1 for incorrect
 decoding or 0 for correct decoding
'''
Q
qijun 已提交
3230 3231


Z
zhangjinchao01 已提交
3232 3233
@config_layer('crf_decoding')
class CRFDecodingLayer(LayerBase):
Q
qijun 已提交
3234
    def __init__(self, name, size, inputs, device=None):
Z
zhangjinchao01 已提交
3235 3236 3237 3238 3239
        super(CRFDecodingLayer, self).__init__(
            name, 'crf_decoding', size, inputs, device=device)
        config_assert(
            len(self.inputs) <= 2,
            'CRFDecodingLayer cannot have more than 2 inputs')
3240
        self.create_input_parameter(0, size * (size + 2), [size + 2, size])
Z
zhangjinchao01 已提交
3241

Q
qijun 已提交
3242

Z
zhangjinchao01 已提交
3243 3244
@config_layer('ctc')
class CTCLayer(LayerBase):
Q
qijun 已提交
3245
    def __init__(self, name, size, inputs, norm_by_times=False, device=None):
Z
zhangjinchao01 已提交
3246 3247 3248 3249
        super(CTCLayer, self).__init__(name, 'ctc', size, inputs, device=device)
        self.config.norm_by_times = norm_by_times
        config_assert(len(self.inputs) == 2, 'CTCLayer must have 2 inputs')

Q
qijun 已提交
3250

3251 3252 3253 3254 3255 3256 3257 3258 3259 3260
@config_layer('kmax_seq_score')
class KmaxSeqScoreLayer(LayerBase):
    def __init__(self, name, inputs, beam_size, **xargs):
        super(KmaxSeqScoreLayer, self).__init__(
            name, 'kmax_seq_score', 0, inputs=inputs, **xargs)
        config_assert(
            len(self.inputs) == 1, 'KmaxSeqScoreLayer has only one input.')
        self.config.beam_size = beam_size


3261 3262 3263 3264 3265 3266 3267 3268 3269 3270 3271 3272 3273 3274 3275 3276 3277 3278 3279 3280 3281
@config_layer('warp_ctc')
class WarpCTCLayer(LayerBase):
    def __init__(self,
                 name,
                 size,
                 inputs,
                 blank=0,
                 norm_by_times=False,
                 device=None):
        super(WarpCTCLayer, self).__init__(
            name, 'warp_ctc', size=size, inputs=inputs, device=device)
        self.config.blank = blank
        self.config.norm_by_times = norm_by_times
        config_assert(len(self.inputs) == 2, 'WarpCTCLayer must have 2 inputs')
        input_layer = self.get_input_layer(0)
        config_assert(
            (input_layer.active_type == '' or
             input_layer.active_type == 'linear'),
            "Expecting the active_type of input layer to be linear or null")


Z
zhangjinchao01 已提交
3282 3283
@config_layer('recurrent_layer_group')
class RecurrentLayerGroup(LayerBase):
Q
qijun 已提交
3284
    def __init__(self, name, device=None):
Z
zhangjinchao01 已提交
3285 3286 3287 3288 3289 3290
        super(RecurrentLayerGroup, self).__init__(
            name, 'recurrent_layer_group', 0, inputs=[], device=device)


# Deprecated, use a new layer specific class instead
@config_func
Q
qijun 已提交
3291
def Layer(name, type, **xargs):
Z
zhangjinchao01 已提交
3292 3293 3294 3295
    layers = {}
    layers.update(g_cost_map)
    layers.update(g_layer_type_map)
    layer_func = layers.get(type)
Q
qijun 已提交
3296
    config_assert(layer_func, "layer type '%s' not supported." % type)
X
xuwei06 已提交
3297
    return layer_func(name, **xargs)
Z
zhangjinchao01 已提交
3298

Q
qijun 已提交
3299

Z
zhangjinchao01 已提交
3300
@config_func
Q
qijun 已提交
3301
def ParameterHook(type, **kwargs):
3302
    if type == 'pruning':
Z
zhangjinchao01 已提交
3303 3304
        hook = ParameterUpdaterHookConfig()
        hook.type = type
X
xzl 已提交
3305
        sparsity_ratio = kwargs.get('sparsity_ratio', None)
X
xzl 已提交
3306 3307
        if sparsity_ratio is not None:
            hook.sparsity_ratio = sparsity_ratio
Z
zhangjinchao01 已提交
3308
        return hook
3309 3310 3311 3312
    elif type == 'dpruning':
        hook = ParameterUpdaterHookConfig()
        hook.type = type
        return hook
Z
zhangjinchao01 已提交
3313 3314 3315 3316 3317
    else:
        return None


@config_func
Q
qijun 已提交
3318 3319 3320 3321 3322 3323 3324 3325 3326 3327 3328 3329 3330 3331 3332 3333 3334 3335 3336 3337 3338
def Parameter(name,
              size,
              device,
              dims,
              learning_rate=None,
              momentum=None,
              decay_rate=None,
              decay_rate_l1=None,
              initial_mean=None,
              initial_std=None,
              initial_strategy=None,
              initial_smart=None,
              num_batches_regularization=None,
              sparse_remote_update=None,
              sparse_update=None,
              gradient_clipping_threshold=None,
              sparse=None,
              format=None,
              need_compact=None,
              is_static=None,
              is_shared=None,
X
xuwei06 已提交
3339 3340
              update_hooks=None,
              initializer=None):
Z
zhangjinchao01 已提交
3341 3342 3343 3344 3345 3346 3347

    config_assert(name not in g_parameter_map,
                  'Duplicated parameter name: ' + name)

    para = g_config.model_config.parameters.add()
    para.name = name
    para.size = size
3348 3349 3350 3351 3352 3353 3354 3355 3356 3357 3358
    if device is not None:
        para.device = int(device)
    para.dims.extend(dims)

    if learning_rate is not None:
        para.learning_rate = float(learning_rate)

    momentum = default(momentum, g_default_momentum)
    if momentum is not None:
        para.momentum = float(momentum)

Z
zhangjinchao01 已提交
3359 3360
    config_assert(not momentum or not decay_rate_l1,
                  "momentum and decay_rate_l1 cannot both be non-zero")
3361 3362 3363 3364 3365

    decay_rate = default(decay_rate, g_default_decay_rate)
    if decay_rate is not None:
        para.decay_rate = decay_rate

Z
zhangjinchao01 已提交
3366 3367 3368 3369
    if decay_rate_l1 is not None:
        para.decay_rate_l1 = decay_rate_l1
    para.initial_std = default(initial_std, g_default_initial_std)
    para.initial_mean = default(initial_mean, g_default_initial_mean)
3370

Q
qijun 已提交
3371 3372
    num_batches_regularization = default(num_batches_regularization,
                                         g_default_num_batches_regularization)
3373 3374 3375
    if num_batches_regularization is not None:
        para.num_batches_regularization = int(num_batches_regularization)

Z
zhangjinchao01 已提交
3376 3377 3378 3379 3380 3381
    if sparse_remote_update is not None:
        para.sparse_remote_update = sparse_remote_update
        if sparse_remote_update:
            g_config.opt_config.use_sparse_remote_updater = True
    if sparse_update is not None:
        para.sparse_update = sparse_update
Q
qijun 已提交
3382 3383
    gradient_clipping_threshold = default(gradient_clipping_threshold,
                                          g_default_gradient_clipping_threshold)
3384 3385
    if gradient_clipping_threshold is not None:
        para.gradient_clipping_threshold = gradient_clipping_threshold
Q
qijun 已提交
3386 3387
    para.initial_strategy = default(initial_strategy,
                                    g_default_initial_strategy)
Z
zhangjinchao01 已提交
3388 3389 3390 3391 3392 3393
    para.initial_smart = default(initial_smart, g_default_initial_smart)
    if para.initial_smart:
        para.initial_mean = 0.
        if len(para.dims) != 0:
            para.initial_std = 1. / math.sqrt(para.dims[0])
        else:
Q
qijun 已提交
3394 3395 3396
            print(
                "Use initial_smart, but dims not set. Initial_smart may not be used in this layer"
            )
Z
zhangjinchao01 已提交
3397 3398 3399 3400
            traceback.print_exc()
            para.initial_std = 1. / math.sqrt(para.size)
    if g_default_compact_func is not None:
        sparse, format, need_compact = g_default_compact_func(para.name)
3401 3402 3403 3404 3405 3406 3407

    if sparse is not None:
        para.is_sparse = sparse
    if format is not None:
        para.format = format
    if need_compact is not None:
        para.need_compact = need_compact
Z
zhangjinchao01 已提交
3408 3409 3410 3411
    if is_static is not None:
        para.is_static = is_static
    config_assert(not para.sparse_remote_update or not para.is_static,
                  "sparse_remote_update and is_static cannot both be true")
3412 3413
    if is_shared is not None:
        para.is_shared = is_shared
Z
zhangjinchao01 已提交
3414 3415 3416 3417 3418

    update_hooks = default(update_hooks, g_default_update_hooks)

    if update_hooks is not None:
        if hasattr(update_hooks, '__call__'):
X
xzl 已提交
3419
            update_hooks = update_hooks()
Z
zhangjinchao01 已提交
3420 3421 3422 3423 3424

        if isinstance(update_hooks, list):
            for hook in update_hooks:
                para.update_hooks.extend([hook])
        else:
X
xzl 已提交
3425
            para.update_hooks.extend([update_hooks])
Z
zhangjinchao01 已提交
3426 3427

    g_parameter_map[name] = para
X
xuwei06 已提交
3428 3429 3430 3431 3432
    if initializer is not None:
        config_assert(
            callable(initializer),
            "parameter initializer should be a callable object")
        g_parameter_initializer_map[name] = initializer
Z
zhangjinchao01 已提交
3433 3434 3435 3436 3437 3438 3439


@config_func
def default_initial_std(val):
    global g_default_initial_std
    g_default_initial_std = val

Q
qijun 已提交
3440

Z
zhangjinchao01 已提交
3441 3442 3443 3444 3445
@config_func
def default_initial_mean(val):
    global g_default_initial_mean
    g_default_initial_mean = val

Q
qijun 已提交
3446

Z
zhangjinchao01 已提交
3447 3448 3449 3450 3451
@config_func
def default_initial_strategy(val):
    global g_default_initial_strategy
    g_default_initial_strategy = val

Q
qijun 已提交
3452

Z
zhangjinchao01 已提交
3453 3454 3455 3456 3457
@config_func
def default_initial_smart(val):
    global g_default_initial_smart
    g_default_initial_smart = val

Q
qijun 已提交
3458

Z
zhangjinchao01 已提交
3459 3460 3461 3462 3463
@config_func
def default_momentum(val):
    global g_default_momentum
    g_default_momentum = val

Q
qijun 已提交
3464

Z
zhangjinchao01 已提交
3465 3466 3467 3468 3469
@config_func
def default_decay_rate(val):
    global g_default_decay_rate
    g_default_decay_rate = val

Q
qijun 已提交
3470

Z
zhangjinchao01 已提交
3471 3472 3473 3474 3475
@config_func
def default_num_batches_regularization(val):
    global g_default_num_batches_regularization
    g_default_num_batches_regularization = val

Q
qijun 已提交
3476

Z
zhangjinchao01 已提交
3477 3478 3479 3480 3481
@config_func
def default_gradient_clipping_threshold(val):
    global g_default_gradient_clipping_threshold
    g_default_gradient_clipping_threshold = val

Q
qijun 已提交
3482

Z
zhangjinchao01 已提交
3483 3484 3485 3486 3487
@config_func
def default_device(val):
    global g_default_device
    g_default_device = val

Q
qijun 已提交
3488

Z
zhangjinchao01 已提交
3489 3490 3491 3492 3493
@config_func
def default_update_hooks(val):
    global g_default_update_hooks
    g_default_update_hooks = val

Q
qijun 已提交
3494

Z
zhangjinchao01 已提交
3495 3496 3497 3498 3499
@config_func
def default_compact_func(val):
    global g_default_compact_func
    g_default_compact_func = val

Q
qijun 已提交
3500

Z
zhangjinchao01 已提交
3501 3502 3503 3504 3505
def make_importer(config_dir, config_args):
    def Import(config_file, local_args={}):
        if not config_file.startswith('/'):
            config_file = config_dir + '/' + config_file
            g_config.config_files.append(config_file)
Q
qijun 已提交
3506 3507 3508
        execfile(config_file,
                 make_config_environment(config_file, config_args), local_args)

Z
zhangjinchao01 已提交
3509 3510
    return Import

Q
qijun 已提交
3511

X
xuwei06 已提交
3512
DEFAULT_SETTING = dict(
Z
zhangjinchao01 已提交
3513 3514 3515 3516 3517
    batch_size=None,
    mini_batch_size=None,
    algorithm='async_sgd',
    async_lagged_grad_discard_ratio=1.5,
    learning_method='momentum',
3518
    gradient_clipping_threshold=None,
Z
zhangjinchao01 已提交
3519 3520 3521 3522 3523 3524 3525 3526 3527 3528 3529 3530 3531 3532 3533 3534 3535 3536 3537 3538 3539 3540
    num_batches_per_send_parameter=None,
    num_batches_per_get_parameter=None,
    center_parameter_update_method=None,
    learning_rate=1.,
    learning_rate_decay_a=0.,
    learning_rate_decay_b=0.,
    learning_rate_schedule='poly',
    learning_rate_args='',
    l1weight=0.1,
    l2weight=0.,
    l2weight_zero_iter=0,
    c1=0.0001,
    backoff=0.5,
    owlqn_steps=10,
    max_backoff=5,
    average_window=0,
    do_average_in_cpu=False,
    max_average_window=None,
    ada_epsilon=1e-6,
    ada_rou=0.95,
    delta_add_rate=1.0,
    shrink_parameter_value=0,
Q
qijun 已提交
3541 3542 3543
    adam_beta1=0.9,
    adam_beta2=0.999,
    adam_epsilon=1e-8, )
Z
zhangjinchao01 已提交
3544

X
xuwei06 已提交
3545
settings = copy.deepcopy(DEFAULT_SETTING)
X
xuwei06 已提交
3546

Q
qijun 已提交
3547
settings_deprecated = dict(usage_ratio=1., )
Z
zhangjinchao01 已提交
3548 3549 3550 3551

trainer_settings = dict(
    save_dir="./output/model",
    init_model_path=None,
Q
qijun 已提交
3552 3553
    start_pass=0, )

Z
zhangjinchao01 已提交
3554 3555 3556 3557 3558

@config_func
def Settings(**args):
    for k, v in args.iteritems():
        if k == "usage_ratio":
Q
qijun 已提交
3559 3560
            logger.warning(
                "Deprecated: define usage_ratio in DataConfig instead")
Z
zhangjinchao01 已提交
3561 3562 3563 3564 3565 3566 3567 3568 3569 3570 3571
            if g_config.HasField("data_config"):
                g_config.data_config.__setattr__(k, v)
            settings_deprecated[k] = v
            continue
        elif k in settings:
            settings[k] = v
        elif k in trainer_settings:
            trainer_settings[k] = v
        else:
            logger.fatal('Unkown setting: %s' % k)

Q
qijun 已提交
3572

Z
zhangjinchao01 已提交
3573 3574 3575 3576
@config_func
def cluster_config(**args):
    pass

Q
qijun 已提交
3577

Z
zhangjinchao01 已提交
3578 3579 3580 3581 3582 3583 3584 3585 3586
@config_func
def EnableSubmodelSuffix(flag=True):
    """
    If enabled, the layer and evaluator names in submodel will be automatically
    appended with @submodel_name
    """
    global g_add_submodel_suffix
    g_add_submodel_suffix = flag

Q
qijun 已提交
3587

Z
zhangjinchao01 已提交
3588 3589 3590 3591
def make_config_environment(config_file, config_args):
    def make_setter(k):
        def setter(v):
            logger.fatal("Obsolete: use Settings(%s=%s, ...) instead" % (k, v))
Q
qijun 已提交
3592

Z
zhangjinchao01 已提交
3593 3594 3595 3596 3597 3598 3599 3600 3601 3602 3603 3604 3605 3606 3607
        return setter

    funcs = {}
    funcs.update(g_config_funcs)

    for k in settings.iterkeys():
        funcs[k] = make_setter(k)
    for k in settings_deprecated.iterkeys():
        funcs[k] = make_setter(k)
    config_dir = os.path.dirname(config_file)
    if not config_dir:
        config_dir = '.'

    funcs.update(
        Import=make_importer(config_dir, config_args),
Q
qijun 已提交
3608
        get_config_arg=make_get_config_arg(config_args), )
Z
zhangjinchao01 已提交
3609 3610 3611 3612 3613

    funcs.update(g_extended_config_funcs)

    return funcs

Q
qijun 已提交
3614

Z
zhangjinchao01 已提交
3615 3616 3617 3618 3619 3620 3621 3622 3623 3624 3625 3626 3627 3628 3629 3630
def make_get_config_arg(config_args):
    def get_config_arg(name, type, default=None):
        if type == bool:
            s = config_args.get(name)
            if not s:
                return default
            if s == 'True' or s == '1' or s == 'true':
                return True
            if s == 'False' or s == '0' or s == 'false':
                return False
            raise ValueError('Value of config_arg %s is not boolean' % name)
        else:
            return type(config_args.get(name, default))

    return get_config_arg

Q
qijun 已提交
3631

Z
zhangjinchao01 已提交
3632 3633 3634 3635 3636 3637 3638 3639 3640 3641 3642 3643
def importlib(name):
    __import__(name)
    return sys.modules[name]


def find_caller():
    stack = traceback.extract_stack()
    for s in stack[-4::-1]:
        if not s[0].endswith('config_parser.py'):
            return s[0], s[1], s[2]
    return "(unknown file)", 0, "(unknown function)"

Q
qijun 已提交
3644

Z
zhangjinchao01 已提交
3645 3646 3647 3648
def my_fatal(s):
    logger.critical(s)
    raise Exception()

Y
Yu Yang 已提交
3649

3650
_parse_config_hooks = set()
Y
Yu Yang 已提交
3651 3652


3653 3654 3655 3656 3657 3658 3659
def register_parse_config_hook(f):
    """
    Register a hook function for parse_config. parse_config will invoke the hook
    at the beginning of parse. This make it possible to reset global state for
    for constructing the model.
    """
    _parse_config_hooks.add(f)
Q
qijun 已提交
3660

Y
Yu Yang 已提交
3661

3662
def update_g_config():
Z
zhangjinchao01 已提交
3663
    '''
3664 3665 3666 3667 3668 3669 3670 3671 3672 3673 3674 3675 3676 3677 3678 3679 3680 3681 3682 3683 3684 3685 3686
    Update g_config after execute config_file or config_functions.
    '''
    for k, v in settings.iteritems():
        if v is None:
            continue
        g_config.opt_config.__setattr__(k, v)

    for k, v in trainer_settings.iteritems():
        if v is None:
            continue
        g_config.__setattr__(k, v)

    for name in g_config.model_config.input_layer_names:
        assert name in g_layer_map, \
            'input name "%s" does not correspond to a layer name' % name
        assert (g_layer_map[name].type == "data" or g_layer_map[name].type == "data_trim"), \
            'The type of input layer "%s" is not "data"' % name
    for name in g_config.model_config.output_layer_names:
        assert name in g_layer_map, \
            'input name "%s" does not correspond to a layer name' % name
    return g_config


3687
def begin_parse():
Z
zhangjinchao01 已提交
3688
    init_config_environment()
3689 3690
    for hook in _parse_config_hooks:
        hook()
Z
zhangjinchao01 已提交
3691 3692 3693 3694 3695

    logger.findCaller = find_caller
    logger.fatal = my_fatal

    g_config.model_config.type = "nn"
X
xuwei06 已提交
3696 3697 3698 3699 3700 3701 3702 3703 3704

    global g_current_submodel, g_root_submodel
    g_root_submodel = g_config.model_config.sub_models.add()
    g_root_submodel.name = 'root'
    g_root_submodel.is_recurrent_layer_group = False
    g_current_submodel = g_root_submodel


def parse_config(trainer_config, config_arg_str):
3705 3706 3707 3708
    '''
    @param config_arg_str: a string of the form var1=val1,var2=val2. It will be
    passed to config script as a dictionary CONFIG_ARGS
    '''
X
xuwei06 已提交
3709

3710
    begin_parse()
X
xuwei06 已提交
3711 3712
    config_args = {}

Z
zhangjinchao01 已提交
3713 3714 3715 3716 3717 3718 3719 3720 3721 3722 3723 3724
    if config_arg_str:
        config_args = dict([f.split('=') for f in config_arg_str.split(',')])

    global g_command_config_args
    g_command_config_args.update(config_args)

    extension_module_name = config_args.get('extension_module_name')
    if extension_module_name:
        global g_extended_config_funcs
        extension_module = importlib(extension_module_name)
        g_extended_config_funcs = extension_module.get_config_funcs(g_config)

3725 3726
    if hasattr(trainer_config, '__call__'):
        trainer_config.func_globals.update(
L
Luo Tao 已提交
3727
            make_config_environment("", config_args))
3728
        trainer_config()
H
hanchao 已提交
3729
    else:
3730 3731
        execfile(trainer_config,
                 make_config_environment(trainer_config, config_args))
Z
zhangjinchao01 已提交
3732

3733
    return update_g_config()
Z
zhangjinchao01 已提交
3734 3735


3736
def parse_config_and_serialize(trainer_config, config_arg_str):
Z
zhangjinchao01 已提交
3737
    try:
3738
        config = parse_config(trainer_config, config_arg_str)
Z
zhangjinchao01 已提交
3739 3740 3741 3742 3743 3744
        #logger.info(config)
        return config.SerializeToString()
    except:
        traceback.print_exc()
        raise

Q
qijun 已提交
3745

Z
zhangjinchao01 已提交
3746 3747 3748 3749 3750 3751 3752 3753
if __name__ == '__main__':
    try:
        config = parse_config(sys.argv[1], '')
        config.SerializeToString()
        __real_print__(str(config))
    except:
        traceback.print_exc()
        raise