config_parser.py 121.9 KB
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# Copyright (c) 2016 Baidu, Inc. All Rights Reserved
#
# 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(
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    format='[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s', )
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logger = logging.getLogger('paddle')
logger.setLevel(logging.INFO)
__real_print__ = print
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print = logger.info
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# from layer type name to layer class
g_layer_type_map = {}

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# Initialize global variables. We use this function so that we can
# call parse_config() multiple times
def init_config_environment(
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        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={},
        g_extended_config_funcs={},
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        # store command args of paddle_trainer
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        g_command_config_args={},
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        # Used for PyDataProvider to avoid duplicate module name
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        g_py_module_name_list=[],
        g_current_submodel=None,
        g_root_submodel=None,
        g_submodel_map={},
        g_submodel_stack=[],
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        g_add_submodel_suffix=False,

        # Whether current layer needs to pass the image height and width.
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        # Default value is true, but if it encounters recurrent_layer_group,
        # it will be false. The reason is that image is converted to be sequence,
        # image height will be sequence length, and image width will be feature
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        # length of each timestep.
        g_pass_height_width=True, ):
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    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)

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g_config_funcs = {}

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# 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

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# decorator for indicating a class which can be used in config file
def config_class(cls):
    g_config_funcs[cls.__name__] = cls
    return cls

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# 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
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    return wrap

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def gen_parameter_name(layer_name, input_index):
    return '_%s.w%d' % (layer_name, input_index)

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def gen_bias_parameter_name(layer_name):
    return '_%s.wbias' % layer_name

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def default(x, default_value):
    return default_value if x is None else x

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class Cfg(object):
    def add_keys(self, locals):
        for k, v in locals.iteritems():
            if not k.startswith('_'):
                self.__setattr__(k, v)

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# functions available in config file

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# 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)

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@config_func
def HasInputsSet():
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    return len(g_current_submodel.input_layer_names) != 0
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# 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)

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    name = MakeLayerNameInParentSubmodel(name)  #rename in nested submodel
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    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

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@config_func
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def SubModelEnd(name=None):
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    global g_current_submodel, g_root_submodel, g_submodel_stack
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    config_assert(g_current_submodel is not g_root_submodel,
                  "submodel not begin")
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    if name is not None:
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        config_assert(
            g_current_submodel.name == MakeLayerNameInParentSubmodel(name),
            "submodel name error")
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    g_current_submodel = g_submodel_stack.pop()

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def MakeLayerNameInParentSubmodel(name):
    suffix = ""
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    if len(g_submodel_stack) > 1:
        suffix = "@" + g_submodel_stack[-1].name
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    return name + suffix

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def GetLayerBaseName(name):
    return name.split('@')[0]

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def MakeLayerNameInSubmodel(name, submodel_name=None):
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    global g_current_submodel
    global g_add_submodel_suffix
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    if (submodel_name is None and not g_add_submodel_suffix and
            not g_current_submodel.is_recurrent_layer_group):
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        return name
    if submodel_name is None:
        submodel_name = g_current_submodel.name
    return name + "@" + submodel_name

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# 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,
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                                            seq_reversed=False,
                                            target_inlinkname=""):
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    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
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    g_current_submodel.target_inlinkid = -1
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    in_links_count = 0
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    for linkid, link in enumerate(in_links):
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        if isinstance(link, basestring):
            name = link
            has_subseq = False
        else:
            name = link.link_name
            has_subseq = link.has_subseq
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        # assign target_inlinkid according to target_inlinkname
        if target_inlinkname == name:
            g_current_submodel.target_inlinkid = linkid

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        if in_links_count == 0:
            in_links_has_subseq = has_subseq
        else:
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            config_assert(
                in_links_has_subseq == has_subseq,
                "The sequence type of in_links should be the same in RecurrentLayerGroup"
            )
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        in_links_count += 1
        layer_name = MakeLayerNameInParentSubmodel(name)
        layer = g_layer_map[layer_name]
        if has_subseq:
            SequenceScatterAgentLayer(name=name, size=layer.size)
        else:
            ScatterAgentLayer(name=name, size=layer.size)
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        pair = g_current_submodel.in_links.add()
        pair.layer_name = layer_name
        pair.link_name = MakeLayerNameInSubmodel(name)
        pair.has_subseq = has_subseq

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@config_func
def RecurrentLayerGroupSetOutLink(link):
    if isinstance(link, basestring):
        name = link
        has_subseq = False
    else:
        name = link.link_name
        has_subseq = link.has_subseq
    layer_name = MakeLayerNameInParentSubmodel(name)
    pair = g_current_submodel.out_links.add()
    pair.layer_name = MakeLayerNameInSubmodel(name)
    pair.link_name = layer_name
    pair.has_subseq = has_subseq


def RecurrentLayerGroupSetGenerator(generator=None):
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    generator.eos_layer_name = MakeLayerNameInSubmodel(generator.eos_layer_name)
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    g_current_submodel.generator.CopyFrom(generator)


@config_func
def RecurrentLayerGroupBegin(name,
                             in_links,
                             out_links,
                             generator=None,
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                             target_inlinkname="",
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                             seq_reversed=False):
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    RecurrentLayerGroupWithoutOutLinksBegin(name, in_links, seq_reversed,
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                                            target_inlinkname)
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    for link in out_links:
        RecurrentLayerGroupSetOutLink(link)

    if generator is not None:
        RecurrentLayerGroupSetGenerator(generator)
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        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")
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@config_func
def RecurrentLayerGroupEnd(name):
    global g_current_submodel
    config_assert(g_current_submodel.is_recurrent_layer_group,
                  "RecurrentLayerGroup not begin")
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    for pair in g_current_submodel.memories:  #check exist
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        layer = g_layer_map[pair.layer_name]
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        config_assert(layer is not None,
                      "memory declare wrong name:%s" % pair.layer_name)
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        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)
        elif pair.has_subseq:
            SequenceGatherAgentLayer(name=agent_name, size=layer.size)
        else:
            GatherAgentLayer(name=agent_name, size=layer.size)

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# 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

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@config_class
class Bias(Cfg):
    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,
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            is_shared=None, ):
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        self.add_keys(locals())

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# Define one input for a layer
@config_class
class Input(Cfg):
    def __init__(
            self,
            input_layer_name,
            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,
            sparse_update=None,
            gradient_clipping_threshold=None,
            conv=None,
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            bilinear_interp=None,
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            norm=None,
            pool=None,
            image=None,
            block_expand=None,
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            maxout=None,
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            spp=None,
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            format=None,
            nnz=None,
            is_static=None,
            is_shared=None,
            update_hooks=None,
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            input_layer_argument=None, ):
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        self.add_keys(locals())
        self.input_layer_name = MakeLayerNameInSubmodel(input_layer_name)

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# Define a projection for iexed layer
@config_class
class Projection(Input):
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    type = None  # subclass should set it correctly

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    def __init__(
            self,
            input_layer_name,
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            size=0,  # projection output size
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            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,
            sparse_update=None,
            gradient_clipping_threshold=None,
            ptype=None,
            format=None,
            nnz=None,
            is_static=None,
            is_shared=None,
            update_hooks=None,
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            input_layer_argument=None, ):
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        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
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    def calc_parameter_size(self, input_size, output_size):
        raise NotimplementedError
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    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
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    def calc_parameter_size(self, input_size, output_size):
        return 0
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    def calc_parameter_dims(self, input_size, output_size):
        return []

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# 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'

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    def __init__(self, input_layer_name, offset, **xargs):
        super(IdentityOffsetProjection, self).__init__(input_layer_name,
                                                       **xargs)
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        self.proj_conf.offset = offset

    def calc_parameter_size(self, input_size, output_size):
        return 0
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    def calc_parameter_dims(self, input_size, output_size):
        return []

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

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# 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]

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@config_class
class TableProjection(Projection):
    type = 'table'

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

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@config_class
class FullMatrixProjection(Projection):
    type = 'fc'

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

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@config_class
class TransposedFullMatrixProjection(Projection):
    type = 'trans_fc'

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

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@config_class
class ContextProjection(Projection):
    type = 'context'

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    def __init__(self, input_layer_name, context_start, context_length,
                 trainable_padding, **xargs):
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        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


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@config_class
class ConvProjection(Projection):
    type = 'conv'

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

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

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        parse_conv(conv_conf, input_layer_name, self.proj_conf.conv_conf,
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                   num_filters)
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        self.proj_conf.output_size = self.proj_conf.conv_conf.output_x * \
                                     self.proj_conf.conv_conf.output_y * \
                                     num_filters
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    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
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        gr = self.proj_conf.conv_conf.groups
        return co * ci * fh * fw / gr
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    def calc_bias_size(self):
        return self.proj_conf.num_filters

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

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# Define a operator for mixed layer
@config_class
class Operator(Cfg):
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    type = None  # subclass should set it correctly

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    def __init__(
            self,
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            input_layer_names, ):
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        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

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@config_class
class DotMulOperator(Operator):
    type = 'dot_mul'
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    def __init__(self, input_layer_names, scale=None, **xargs):
        super(DotMulOperator, self).__init__(input_layer_names, **xargs)
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        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'
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    def __init__(self,
                 input_layer_names,
                 num_filters=None,
                 conv_conf=None,
                 **xargs):
        super(ConvOperator, self).__init__(input_layer_names, **xargs)
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        if num_filters is not None:
            self.operator_conf.num_filters = num_filters

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        parse_conv(conv_conf,
                   MakeLayerNameInSubmodel(input_layer_names[0]),
773
                   self.operator_conf.conv_conf, num_filters)
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        self.operator_conf.output_size = self.operator_conf.conv_conf.output_x * \
                                         self.operator_conf.conv_conf.output_y * \
                                         num_filters
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        config_assert(len(input_layer_names) == 2, "Conv is binary operator")

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    def calc_output_size(self, input_sizes):
        return self.operator_conf.output_size
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# please refer to the comments in proto/ModelConfig.proto
@config_class
class Conv(Cfg):
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    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):
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        self.add_keys(locals())
        if filter_size_y is None:
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            self.filter_size_y = filter_size
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        if padding_y is None:
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            self.padding_y = padding
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        if stride_y is None:
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            self.stride_y = stride
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        if output_x is not None:
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            config_assert(output_x <= 0)

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@config_class
class BilinearInterp(Cfg):
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    def __init__(self, out_size_x=None, out_size_y=None, channels=None):
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        self.add_keys(locals())

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@config_class
class Pool(Cfg):
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    def __init__(
            self,
            pool_type,
            channels,
            size_x,
            size_y=None,
            img_width=None,
            start=None,
            stride=None,  # 1 by defalut in protobuf
            stride_y=None,
            padding=None,  # 0 by defalut in protobuf
            padding_y=None):
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        self.add_keys(locals())
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@config_class
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class SpatialPyramidPool(Cfg):
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    def __init__(self, pool_type, pyramid_height, channels):
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        self.add_keys(locals())
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@config_class
class Norm(Cfg):
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    def __init__(self,
                 norm_type,
                 channels,
                 size,
                 scale,
                 pow,
                 output_x=None,
                 img_size=None,
                 blocked=None):
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        self.add_keys(locals())

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@config_class
class Image(Cfg):
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    def __init__(self, channels, img_size=None):
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        self.add_keys(locals())

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@config_class
class BlockExpand(Cfg):
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    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):
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        self.add_keys(locals())

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@config_class
class MaxOut(Cfg):
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    def __init__(self, channels, groups, img_size_x=0, img_size_y=0):
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        self.add_keys(locals())

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def DataBase(async_load_data=False,
             constant_slots=None,
             data_ratio=1,
             is_main_data=True,
             usage_ratio=None):
    # 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)
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    data_config.data_ratio = data_ratio
    data_config.is_main_data = is_main_data
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    usage_ratio = default(usage_ratio, settings_deprecated["usage_ratio"])
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    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

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@config_func
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def SimpleData(files=None,
               feat_dim=None,
               context_len=None,
               buffer_capacity=None,
               **xargs):
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    data_config = DataBase(**xargs)
    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

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@config_func
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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):
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    data_config = DataBase(**xargs)
    data_config.type = 'py'
    if load_data_module in g_py_module_name_list:
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        def get_path(module):
            m = __import__(load_data_module)
            return os.path.split(os.path.realpath(m.__file__))[0]
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        # 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.
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        module_new_name = "%s_copy_%d" % (load_data_module,
                                          len(g_py_module_name_list))
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        g_py_module_name_list.append(module_new_name)
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        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)
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        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

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@config_func
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def ProtoData(files=None,
              type=None,
              file_group_queue_capacity=None,
              load_file_count=None,
              constant_slots=None,
              load_thread_num=None,
              **xargs):
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    data_config = DataBase(**xargs)
    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

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#real data for training is actually provided by "sub_data" data providers.
@config_func
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def MultiData(sub_data=[]):
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    data_config = DataConfig()
    data_config.type = 'multi'
    data_config.sub_data_configs.extend(sub_data)
    return data_config

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1016
@config_func
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def Data(type,
         files=None,
         feat_dim=None,
         slot_dims=None,
         context_len=None,
         buffer_capacity=None,
         **xargs):
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    data_config = DataBase(**xargs)
    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

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1060 1061
#caffe_mode: compute the output size using floor instead of ceil,
#            which is consistent of caffe and CuDNN's convention.
1062 1063 1064 1065 1066 1067 1068
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))

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1070
#calcualte image_size based on output_size for de-convolution (ConvTransLayer).
1071
#It is the reverse function of cnn_output_size
1072
def cnn_image_size(output_size, filter_size, padding, stride, caffe_mode):
1073 1074 1075
    img_size = (output_size - 1) * stride + filter_size - 2 * padding
    if not caffe_mode:
        img_size = img_size + 1
1076 1077
    return img_size

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1079
def get_img_size(input_layer_name, channels):
1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097
    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


1098 1099
def parse_pool(pool, input_layer_name, pool_conf):
    pool_conf.pool_type = pool.pool_type
1100 1101 1102
    config_assert(pool.pool_type in [
        'max-projection', 'avg-projection', 'cudnn-max-pool', 'cudnn-avg-pool'
    ], "pool-type %s is not in "
1103
                  "['max-projection', 'avg-projection', "
1104
                  "'cudnn-max-pool', 'cudnn-avg-pool']" % pool.pool_type)
1105 1106 1107 1108 1109 1110

    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)
1111
    pool_conf.stride_y = default(pool.stride_y, pool_conf.stride)
1112

1113
    pool_conf.img_size, pool_conf.img_size_y = \
1114
        get_img_size(input_layer_name, pool.channels)
1115

1116
    config_assert(not pool.start, "start is deprecated in pooling.")
1117

1118
    if pool.padding is not None:
1119
        pool_conf.padding = pool.padding
1120
    pool_conf.padding_y = default(pool.padding_y, pool_conf.padding)
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    pool_conf.output_x = cnn_output_size(pool_conf.img_size, pool_conf.size_x,
                                         pool_conf.padding, pool_conf.stride,
                                         False)
    pool_conf.output_y = cnn_output_size(pool_conf.img_size_y, pool_conf.size_y,
                                         pool_conf.padding_y,
                                         pool_conf.stride_y, False)
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def parse_spp(spp, input_layer_name, spp_conf):
1130
    parse_image(spp, input_layer_name, spp_conf.image_conf)
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    spp_conf.pool_type = spp.pool_type
    config_assert(spp.pool_type in ['max-projection', 'avg-projection'],
1133 1134
                  "pool-type %s is not in "
                  "['max-projection', 'avg-projection']" % spp.pool_type)
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    spp_conf.pyramid_height = spp.pyramid_height
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def parse_image(image, input_layer_name, image_conf):
    image_conf.channels = image.channels
1140
    image_conf.img_size, image_conf.img_size_y = \
1141
        get_img_size(input_layer_name, image_conf.channels)
1142

1143 1144 1145 1146

def parse_norm(norm, input_layer_name, norm_conf):
    norm_conf.norm_type = norm.norm_type
    config_assert(norm.norm_type in ['rnorm', 'cmrnorm-projection'],
1147 1148
                  "norm-type %s is not in [rnorm, 'cmrnorm-projection']" %
                  norm.norm_type)
1149 1150 1151 1152 1153 1154
    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

1155
    norm_conf.img_size, norm_conf.img_size_y = \
1156
        get_img_size(input_layer_name, norm.channels)
1157
    norm_conf.output_x = norm_conf.img_size
1158
    norm_conf.output_y = norm_conf.img_size_y
1159 1160 1161
    if norm.norm_type in ['cmrnorm-projection']:
        norm_conf.scale /= norm.size
    else:
1162 1163
        norm_conf.scale /= norm.size**2

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1165 1166
#caffe_mode: compute the output size using floor instead of ceil,
#            which is consistent of caffe and CuDNN's convention.
1167
def parse_conv(conv, input_layer_name, conv_conf, num_filters, trans=False):
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    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
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1178
    if not trans:
1179
        conv_conf.filter_channels = conv.channels / conv.groups
1180
        conv_conf.img_size, conv_conf.img_size_y = \
1181
            get_img_size(input_layer_name, conv.channels)
1182
        conv_conf.output_x = cnn_output_size(
1183 1184
            conv_conf.img_size, conv_conf.filter_size, conv_conf.padding,
            conv_conf.stride, conv_conf.caffe_mode)
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        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)
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    else:
1189
        conv_conf.filter_channels = num_filters / conv.groups
1190
        conv_conf.output_x, conv_conf.output_y = \
1191
            get_img_size(input_layer_name, conv.channels)
1192
        conv_conf.img_size = cnn_image_size(
1193 1194
            conv_conf.output_x, conv_conf.filter_size, conv_conf.padding,
            conv_conf.stride, conv_conf.caffe_mode)
1195
        conv_conf.img_size_y = cnn_image_size(
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            conv_conf.output_y, conv_conf.filter_size_y, conv_conf.padding_y,
            conv_conf.stride_y, conv_conf.caffe_mode)
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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:
1213
        block_expand_conf.output_x = cnn_output_size(
1214
            block_expand.img_size_x, block_expand.block_x,
1215
            block_expand.padding_x, block_expand.stride_x, False)
1216 1217

    if block_expand_conf.img_size_y == 0:
1218
        block_expand_conf.output_y = 0
1219
    else:
1220
        block_expand_conf.output_y = cnn_output_size(
1221
            block_expand.img_size_y, block_expand.block_y,
1222
            block_expand.padding_y, block_expand.stride_y, False)
1223

1224

1225
def parse_maxout(maxout, input_layer_name, maxout_conf):
1226
    parse_image(maxout, input_layer_name, maxout_conf.image_conf)
1227
    maxout_conf.groups = maxout.groups
1228

1229

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# Define an evaluator
@config_func
def Evaluator(
        name,
        type,
        inputs,
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        chunk_scheme=None,
        num_chunk_types=None,
        classification_threshold=None,
        positive_label=None,
        dict_file=None,
        result_file=None,
        num_results=None,
        delimited=None, ):
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    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)

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    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
    if delimited is not None:
        evaluator.delimited = delimited
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class LayerBase(object):
    def __init__(
            self,
            name,
            type,
1278
            size,  # size can be 0. In this case, subclass should set it.
1279 1280 1281 1282
            inputs,
            device=None,
            active_type="",
            drop_rate=0.,
1283
            coeff=None):
1284
        config_assert('@' not in name,
1285
                      "layer name: %s contain special character @" % name)
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        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()
1301
        assert isinstance(self.config, LayerConfig)
1302 1303 1304
        self.config.name = name
        self.config.type = type
        self.config.active_type = active_type
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        if coeff is not None:
            self.config.coeff = float(coeff)
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        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
1314
        elif g_default_device is not None:
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            self.config.device = g_default_device

        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(
1324 1325
                    input_layer_name=input,
                    parameter_name=gen_parameter_name(name, input_index))
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                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):
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                self.operators.append(input)
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                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:
1339
                raise ValueError('Wrong type for inputs: %s' % type_of(input))
1340
            config_assert(input_layer_name in g_layer_map,
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                          "Unknown input layer '%s' for layer %s" %
                          (input_layer_name, name))
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            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)

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        if self.config.type != 'data' and g_pass_height_width:
            height = self.get_input_layer(0).height
            width = self.get_input_layer(0).width
            if height and width:
                self.set_layer_height_width(height, width)

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    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,
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            bias,  # True/False or BiasCfg
1367
            size,
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            dims=None,
            for_self=True,  # whether create bias for layer self
    ):
1371 1372 1373 1374 1375 1376

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

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        config_assert(
            type_of(bias) == bool or type_of(bias) == Bias,
            'Incorrect type for bias: %s' % type_of(bias))
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        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:
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                assert isinstance(self.config, LayerConfig)

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                Parameter(
                    bias.parameter_name,
                    size,
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                    self.config.device
                    if self.config.HasField('device') else None,
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                    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,
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                    gradient_clipping_threshold=bias.
                    gradient_clipping_threshold,
1409
                    is_static=bias.is_static,
1410
                    is_shared=bias.is_shared, )
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            if for_self:
                self.config.bias_parameter_name = bias.parameter_name
            else:
                return bias.parameter_name

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    def create_input_parameter(self,
                               input_index,
                               size,
                               dims=None,
                               sparse=None,
                               format=None):
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        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]
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            config_assert(size == para.size, (
                'Shared parameter "%s" does not ' + 'have same size: %s vs. %s')
1438 1439
                          % (input_config.parameter_name, para.size, size))

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            config_assert(dims == para.dims, (
                'Shared parameter "%s" does not ' + 'have same dims: %s vs. %s')
1442 1443 1444 1445 1446 1447
                          % (input_config.parameter_name, para.dims, dims))
            return

        Parameter(
            input_config.parameter_name,
            size,
1448
            self.config.device if self.config.HasField("device") else None,
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            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,
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            gradient_clipping_threshold=input_config.
            gradient_clipping_threshold,
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            sparse=sparse,
            format=format,
            is_static=input_config.is_static,
            is_shared=input_config.is_shared,
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            update_hooks=input_config.update_hooks)
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    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)

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    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))

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@config_layer('multi_class_cross_entropy_with_selfnorm')
class MultiClassCrossEntropySelfNormCostLayer(LayerBase):
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    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)
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        self.config.softmax_selfnorm_alpha = softmax_selfnorm_alpha

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1503 1504
@config_layer('fc')
class FCLayer(LayerBase):
1505
    def __init__(self, name, size, inputs, bias=True, **xargs):
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        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
1516 1517
            else:
                sparse = None
1518

1519 1520
            self.create_input_parameter(input_index, psize, dims, sparse,
                                        format)
1521 1522
        self.create_bias_parameter(bias, self.config.size)

1523

1524 1525
@config_layer('selective_fc')
class SelectiveFCLayer(LayerBase):
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    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):
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        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,
1556 1557
                          ("if indices of selected columns are not specified, "
                           "selective_fc Layer has at least two inputs"))
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            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

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            self.create_input_parameter(input_index, psize, dims, sparse,
                                        format)
1572 1573
        self.create_bias_parameter(bias, self.config.size)

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1575 1576
@config_layer('print')
class PrintLayer(LayerBase):
1577
    def __init__(self, name, inputs):
1578 1579
        super(PrintLayer, self).__init__(name, 'print', 0, inputs)

1580

1581 1582
@config_layer('data')
class DataLayer(LayerBase):
1583
    def __init__(self, name, size, height=None, width=None, device=None):
1584 1585
        super(DataLayer, self).__init__(
            name, 'data', size, inputs=[], device=device)
1586 1587
        if height and width:
            self.set_layer_height_width(height, width)
1588

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'''
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
'''
1616 1617


1618 1619
@config_layer('data_norm')
class DataNormLayer(LayerBase):
1620
    def __init__(self, name, inputs, data_norm_strategy="z-score", device=None):
1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631
        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)

1632

1633 1634 1635
@config_layer('prelu')
class ParameterReluLayer(LayerBase):
    layer_type = 'prelu'
1636 1637

    def __init__(self, name, inputs, partial_sum=1, **args):
1638 1639 1640 1641 1642 1643 1644 1645
        super(ParameterReluLayer, self).__init__(
            name, self.layer_type, 0, inputs=inputs, **args)
        config_assert(len(self.inputs) == 1)
        config_assert(self.input_layer.size % partial_sum == 0)
        input_layer = self.get_input_layer(0)
        self.set_layer_size(input_layer.size)
        self.create_input_parameter(0, input_layer.size / partial_sum)

1646

1647 1648 1649
@config_layer('conv')
class ConvLayerBase(LayerBase):
    layer_type = 'conv'
1650 1651 1652 1653 1654 1655 1656 1657

    def __init__(self,
                 name,
                 inputs=[],
                 bias=True,
                 num_filters=None,
                 shared_biases=False,
                 **xargs):
1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673
        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
1674
            (parallel_nn == 0 or self.config.device > -1)):
1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686
            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
1687 1688
            parse_conv(self.inputs[input_index].conv, input_layer.name,
                       conv_conf, num_filters)
1689 1690
            psize = self.calc_parameter_size(conv_conf)
            self.create_input_parameter(input_index, psize)
1691 1692
            self.set_cnn_layer(name, conv_conf.output_y, conv_conf.output_x,
                               self.config.num_filters)
1693 1694 1695 1696 1697 1698 1699 1700 1701 1702

        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)

1703

1704 1705 1706 1707
@config_layer('exconv')
class ConvLayer(ConvLayerBase):
    layer_type = 'exconv'

1708

1709 1710 1711 1712
@config_layer('cudnn_conv')
class ConvLayer(ConvLayerBase):
    layer_type = 'cudnn_conv'

1713 1714 1715 1716

@config_layer('convt')
class ConvTransLayerBase(LayerBase):
    layer_type = 'convt'
1717 1718 1719 1720 1721 1722 1723 1724

    def __init__(self,
                 name,
                 inputs=[],
                 bias=True,
                 num_filters=None,
                 shared_biases=False,
                 **xargs):
1725
        super(ConvTransLayerBase, self).__init__(
1726 1727 1728 1729 1730 1731 1732 1733
            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))

1734 1735
        # cudnn_convt has not been implemented so use exconvt only
        self.layer_type = "exconvt"
1736 1737 1738 1739 1740 1741 1742 1743
        # 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)
1744
            parse_conv(
1745 1746
                self.inputs[input_index].conv,
                input_layer.name,
1747
                self.config.inputs[input_index].conv_conf,
1748
                num_filters,
1749
                trans=True)
1750 1751 1752 1753 1754
            conv_conf = self.config.inputs[input_index].conv_conf
            psize = self.calc_parameter_size(conv_conf)
            print("output size for %s is %d " % (name, conv_conf.output_x))
            self.create_input_parameter(input_index, psize)
            self.set_layer_size(
1755
                (conv_conf.img_size**2) * self.config.num_filters)
1756 1757 1758 1759 1760 1761 1762

        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):
1763
        return conv_conf.channels * conv_conf.filter_channels \
1764 1765
                    * (conv_conf.filter_size * conv_conf.filter_size_y)

1766

1767 1768 1769 1770
@config_layer('exconvt')
class ConvTransLayer(ConvTransLayerBase):
    layer_type = 'exconvt'

1771

1772 1773
@config_layer('norm')
class NormLayer(LayerBase):
1774 1775 1776
    def __init__(self, name, inputs, device=None):
        super(NormLayer, self).__init__(
            name, 'norm', 0, inputs=inputs, device=device)
1777 1778 1779
        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
1780 1781 1782 1783
            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)
1784

1785 1786 1787

@config_layer('pool')
class PoolLayer(LayerBase):
1788 1789 1790
    def __init__(self, name, inputs, device=None):
        super(PoolLayer, self).__init__(
            name, 'pool', 0, inputs=inputs, device=device)
1791 1792 1793
        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
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            parse_pool(self.inputs[input_index].pool, input_layer.name,
                       pool_conf)
            self.set_cnn_layer(name, pool_conf.output_y, pool_conf.output_x,
                               pool_conf.channels)
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@config_layer('spp')
class SpatialPyramidPoolLayer(LayerBase):
1802 1803 1804
    def __init__(self, name, inputs, device=None):
        super(SpatialPyramidPoolLayer, self).__init__(
            name, 'spp', 0, inputs=inputs, device=device)
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        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
1808 1809 1810
            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)
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1812

1813 1814 1815
@config_layer('batch_norm')
class BatchNormLayer(LayerBase):
    layer_type = 'batch_norm'
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    def __init__(self,
                 name,
                 inputs,
                 active_type="linear",
                 bias=True,
                 device=None,
                 use_global_stats=True,
                 moving_average_fraction=0.9,
                 batch_norm_type=None,
                 **xargs):
1827 1828 1829 1830
        if inputs is None:
            inputs = []
        elif not isinstance(inputs, list):
            inputs = [inputs]
1831 1832
        config_assert(
            len(inputs) == 1, "BatchNormLayer must have one and only one input")
1833 1834 1835 1836 1837 1838 1839 1840
        # 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):
1841 1842 1843 1844 1845 1846 1847
            inputs.append(
                Input(
                    inputs[0].input_layer_name,
                    initial_std=0.0,
                    initial_mean=0.0,
                    is_static=True,
                    is_shared=is_shared, ))
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        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 \
            ((not parallel_nn) or self.config.device > -1) and \
1855
            cudnn_version >= 4007
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        self.layer_type = "cudnn_batch_norm" if use_cudnn else "batch_norm"
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        super(BatchNormLayer, self).__init__(
            name,
            self.layer_type,
            0,
            active_type=active_type,
            inputs=inputs,
            device=device,
            **xargs)
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        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

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        input_layer = self.get_input_layer(0)
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        image_conf = self.config.inputs[0].image_conf
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        parse_image(self.inputs[0].image, input_layer.name, image_conf)
        self.set_cnn_layer(name, image_conf.img_size_y, image_conf.img_size,
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                           image_conf.channels, False)
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        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

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@config_layer('trans')
class TransLayer(LayerBase):
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    def __init__(self, name, inputs, device=None):
        super(TransLayer, self).__init__(
            name, 'trans', 0, inputs=inputs, device=device)
        config_assert(
            len(self.inputs) == 1,
            'TransLayer must have one and only one input')
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        self.set_layer_size(self.get_input_layer(0).size)

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@config_layer('resize')
class ResizeLayer(LayerBase):
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    def __init__(self, name, size, inputs, device=None):
        super(ResizeLayer, self).__init__(
            name, 'resize', size=size, inputs=inputs, device=device)
        config_assert(
            len(self.inputs) == 1,
            'ResizeLayer must have one and only one input')

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@config_layer('blockexpand')
class BlockExpandLayer(LayerBase):
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    def __init__(self, name, inputs, device=None):
        super(BlockExpandLayer, self).__init__(
            name, 'blockexpand', 0, inputs=inputs, device=device)
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        for input_index in xrange(len(self.inputs)):
            input_layer = self.get_input_layer(input_index)
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            parse_block_expand(
                self.inputs[input_index].block_expand, input_layer.name,
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                self.config.inputs[input_index].block_expand_conf)
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            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)

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@config_layer('maxout')
class MaxOutLayer(LayerBase):
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    def __init__(self, name, inputs, **xargs):
        super(MaxOutLayer, self).__init__(
            name, 'maxout', 0, inputs=inputs, **xargs)
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        input_layer = self.get_input_layer(0)
        maxout_conf = self.config.inputs[0].maxout_conf
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        parse_maxout(self.inputs[0].maxout, input_layer.name, maxout_conf)
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        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)
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# key: cost type
# value: cost class
g_cost_map = {}

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# define a cost layer without any parameters
def define_cost(class_name, cost_type):
    def init(cls, name, inputs, device=None, coeff=1.):
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        super(type(cls), cls).__init__(
            name, cost_type, 1, inputs, device=device, coeff=coeff)
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    cls = type(class_name, (LayerBase, ), dict(__init__=init))
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    global g_cost_map
    g_cost_map[cost_type] = cls

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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')
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define_cost('SumCost', 'sum_cost')
1965

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@config_layer('hsigmoid')
class HierarchicalSigmoidLayer(LayerBase):
1969
    def __init__(self, name, num_classes, inputs, device=None, bias=True):
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        super(HierarchicalSigmoidLayer, self).__init__(
            name, 'hsigmoid', 1, inputs=inputs, device=device)
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        config_assert(
            len(self.inputs) >= 2,
            'HierarchicalSigmoidLayer must have at least 2 inputs')
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        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)

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'''
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.
'''
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2010 2011
@config_layer('lambda_cost')
class LambdaCost(LayerBase):
2012
    def __init__(self, name, inputs, NDCG_num=5, max_sort_size=-1, device=None):
2013 2014
        super(LambdaCost, self).__init__(
            name, 'lambda_cost', 1, inputs=inputs, device=device)
2015
        config_assert(len(self.inputs) == 2, 'lambdaCost must have 2 inputs')
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        self.config.NDCG_num = NDCG_num
        if max_sort_size != -1:
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            config_assert(
                NDCG_num <= max_sort_size,
                'NDCG_num must be less than or equal to max_sort_size')
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        self.config.max_sort_size = max_sort_size

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@config_layer('nce')
class NCELayer(LayerBase):
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    def __init__(self,
                 name,
                 num_classes,
                 inputs,
                 num_neg_samples=10,
                 neg_sampling_dist=None,
                 bias=True,
                 **xargs):
2034
        super(NCELayer, self).__init__(name, 'nce', 1, inputs=inputs, **xargs)
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        config_assert(
            len(self.inputs) >= 2, 'NCELayer must have at least 2 inputs')
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        self.config.num_classes = num_classes
        if neg_sampling_dist is not None:
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            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))
2043
            s = sum(neg_sampling_dist)
2044 2045 2046
            config_assert(
                abs(s - 1) < 1e-5,
                'The sum of neg_sampling_dist (%s) is not 1' % s)
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            self.config.neg_sampling_dist.extend(neg_sampling_dist)

        self.config.num_neg_samples = num_neg_samples
        num_real_inputs = len(self.inputs) - 1
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        input_layer = self.get_input_layer(num_real_inputs)
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        config_assert(input_layer.type == 'data',
                      'Expecting the last input layer of an nce layer to be '
                      'a data layer')

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        if (num_real_inputs > 1 and input_layer.size == 1 and
                self.get_input_layer(num_real_inputs - 1).type == 'data'):
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            # 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):
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    def __init__(self, name, inputs, bias=True, **xargs):
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        super(AddToLayer, self).__init__(
            name, 'addto', 0, inputs=inputs, **xargs)
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        config_assert(len(inputs) > 0, 'inputs cannot be empty for AddToLayer')
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        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)

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@config_layer('agent')
class AgentLayer(LayerBase):
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    def __init__(self, name, size, device=None):
        super(AgentLayer, self).__init__(
            name, 'agent', size, inputs=[], device=device)

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@config_layer('sequence_agent')
class SequenceAgentLayer(LayerBase):
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    def __init__(self, name, size, device=None):
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        super(SequenceAgentLayer, self).__init__(
            name, 'sequence_agent', size, inputs=[], device=device)

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@config_layer('gather_agent')
class GatherAgentLayer(LayerBase):
2098
    def __init__(self, name, size, device=None):
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        super(GatherAgentLayer, self).__init__(
            name, 'gather_agent', size, inputs=[], device=device)

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@config_layer('scatter_agent')
class ScatterAgentLayer(LayerBase):
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    def __init__(self, name, size, device=None):
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        super(ScatterAgentLayer, self).__init__(
            name, 'scatter_agent', size, inputs=[], device=device)

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@config_layer('sequence_gather_agent')
class SequenceGatherAgentLayer(LayerBase):
2112
    def __init__(self, name, size, device=None):
2113
        super(SequenceGatherAgentLayer, self).__init__(
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            name, 'sequence_gather_agent', size, inputs=[], device=device)

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@config_layer('sequence_scatter_agent')
class SequenceScatterAgentLayer(LayerBase):
2119
    def __init__(self, name, size, device=None):
2120
        super(SequenceScatterAgentLayer, self).__init__(
2121 2122
            name, 'sequence_scatter_agent', size, inputs=[], device=device)

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@config_layer('multiplex')
class MultiplexLayer(LayerBase):
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    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.')
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        for i in range(1, len(inputs)):
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            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.")

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@config_func
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def Link(
        name,
        has_subseq=False, ):
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    link_config = LinkConfig()
    link_config.link_name = name
    link_config.has_subseq = has_subseq
    return link_config

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# memory for recurrent layer group.
# *name* and *size* are actual layer's name and size.
# will return name of the memory,
# 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
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def Memory(
        name,
        size,
        is_sequence=False,
        boot_layer=None,
        boot_bias=False,
        boot_bias_active_type="",
        boot_with_const_id=None, ):
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    agent_name = name + "+delay1"
    if is_sequence:
        agent_layer = SequenceAgentLayer(agent_name, size)
    else:
        agent_layer = AgentLayer(agent_name, size)
    config_assert(g_current_submodel.is_recurrent_layer_group,
2176
                  'Memory should be used in recurrent layer group only')
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    memory = g_current_submodel.memories.add()
    memory.layer_name = MakeLayerNameInSubmodel(name)
    memory.link_name = MakeLayerNameInSubmodel(agent_name)
    memory.is_sequence = is_sequence
2181
    options = sum((boot_layer is not None, bool(boot_bias),
2182
                   boot_with_const_id is not None))
2183 2184 2185 2186
    config_assert(
        options <= 1,
        'take one option at most from boot_layer, boot_bias, or boot_with_const_id'
    )
2187 2188 2189
    if boot_layer is not None:
        boot_layer = MakeLayerNameInParentSubmodel(boot_layer)
        config_assert(boot_layer in g_layer_map,
2190 2191
                      'boot_layer "%s" does not correspond to a layer name' %
                      boot_layer)
2192 2193 2194
        memory.boot_layer_name = boot_layer
    elif boot_bias:
        memory.boot_bias_parameter_name = agent_layer.create_bias_parameter(
2195
            boot_bias, size, for_self=False)
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        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

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2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 2212
# 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,
2213 2214 2215 2216
        eos_layer_name="eos_check",
        num_results_per_sample=1,
        beam_size=1,
        log_prob=None, ):
2217 2218 2219 2220 2221 2222 2223 2224 2225
    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

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@config_layer('expand')
class ExpandLayer(LayerBase):
2229 2230 2231 2232 2233 2234 2235 2236 2237 2238 2239 2240 2241 2242 2243 2244
    def __init__(self,
                 name,
                 inputs,
                 trans_type='non-seq',
                 device=None,
                 bias=False):
        super(ExpandLayer, self).__init__(
            name, 'expand', 0, inputs=inputs, device=device)
        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)

2245 2246 2247

@config_layer('featmap_expand')
class FeatMapExpandLayer(LayerBase):
2248 2249 2250 2251 2252 2253
    def __init__(self, name, inputs, device=None, num_filters=None, bias=False):
        super(FeatMapExpandLayer, self).__init__(
            name, 'featmap_expand', 0, inputs=inputs, device=device)
        config_assert(
            len(self.inputs) == 1, 'ExpandLayer takes 1 and only 1 inputs')
        if num_filters is not None:
2254
            self.config.num_filters = num_filters
2255
        else:
2256
            logger.fatal("FeatMapExpandLayer must specify num_filters.")
2257
        self.set_layer_size(self.get_input_layer(0).size * num_filters)
2258 2259 2260 2261


@config_layer('max')
class MaxLayer(LayerBase):
2262 2263 2264 2265 2266 2267 2268 2269 2270 2271
    def __init__(self,
                 name,
                 inputs,
                 trans_type='non-seq',
                 active_type='linear',
                 device=None,
                 bias=False,
                 output_max_index=None):
        super(MaxLayer, self).__init__(
            name, 'max', 0, inputs=inputs, device=device)
2272
        config_assert(len(self.inputs) == 1, 'MaxLayer must have 1 input')
2273 2274
        self.config.trans_type = trans_type
        self.config.active_type = active_type
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        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)
2279 2280
        if output_max_index is not None:
            self.config.output_max_index = output_max_index
2281 2282 2283 2284


@config_layer('maxid')
class MaxIdLayer(LayerBase):
2285
    def __init__(self, name, inputs, beam_size=None, device=None):
2286 2287 2288 2289 2290 2291 2292 2293 2294 2295 2296 2297 2298 2299 2300 2301 2302
        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):
2303
    def __init__(self, name, inputs, eos_id, device=None):
2304 2305 2306
        super(EosIdLayer, self).__init__(
            name, 'eos_id', 0, inputs=inputs, device=device)
        config_assert(len(self.inputs) == 1, 'EosIdLayer must have 1 input')
2307
        self.set_layer_size(2)  # boolean output
2308 2309
        self.config.eos_id = eos_id

2310

2311 2312
@config_layer('seqlastins')
class SequenceLastInstanceLayer(LayerBase):
2313 2314 2315 2316 2317 2318 2319 2320 2321 2322 2323 2324 2325 2326 2327 2328 2329
    def __init__(self,
                 name,
                 inputs,
                 active_type='linear',
                 trans_type='non-seq',
                 device=None,
                 bias=False):
        super(SequenceLastInstanceLayer, self).__init__(
            name,
            'seqlastins',
            0,
            inputs=inputs,
            device=device,
            active_type=active_type)
        config_assert(
            len(inputs) == 1, 'SequenceLastInstanceLayer must have 1 input')
        self.config.trans_type = trans_type
2330 2331 2332 2333 2334
        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)

2335

2336 2337 2338 2339 2340 2341 2342 2343 2344
@config_layer('seqfirstins')
class SequenceFirstInstanceLayer(SequenceLastInstanceLayer):
    def __init__(
            self,
            name,
            inputs,
            active_type='linear',
            trans_type='non-seq',
            device=None,
2345 2346 2347 2348 2349 2350 2351 2352
            bias=False, ):
        super(SequenceFirstInstanceLayer, self).__init__(
            name,
            inputs=inputs,
            active_type=active_type,
            device=device,
            bias=bias)
        self.config.trans_type = trans_type
2353 2354
        self.config.select_first = True

2355

2356 2357
@config_layer('seqconcat')
class SequenceConcatLayer(LayerBase):
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    def __init__(self,
                 name,
                 inputs,
                 active_type='linear',
                 device=None,
                 bias=False):
        super(SequenceConcatLayer, self).__init__(
            name,
            'seqconcat',
            0,
            inputs=inputs,
            device=device,
            active_type=active_type)
        config_assert(
            len(inputs) == 2, 'SequenceConcatLayer must have 2 inputs')
2373 2374 2375 2376 2377
        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)

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2379 2380
@config_layer('seqreshape')
class SequenceReshapeLayer(LayerBase):
2381 2382 2383 2384 2385 2386 2387 2388 2389 2390
    def __init__(self,
                 name,
                 size,
                 inputs,
                 active_type='linear',
                 device=None,
                 bias=False):
        super(SequenceReshapeLayer, self).__init__(
            name,
            'seqreshape',
2391
            size,
2392 2393 2394 2395 2396
            inputs=inputs,
            device=device,
            active_type=active_type)
        config_assert(
            len(inputs) == 1, 'SequenceReshapeLayer must have 1 inputs')
2397 2398 2399
        self.set_layer_size(size)
        self.create_bias_parameter(bias, size)

2400

2401 2402
@config_layer('subseq')
class SubSequenceLayer(LayerBase):
2403 2404 2405 2406 2407 2408 2409 2410 2411 2412 2413 2414 2415
    def __init__(self,
                 name,
                 inputs,
                 active_type='linear',
                 device=None,
                 bias=False):
        super(SubSequenceLayer, self).__init__(
            name,
            'subseq',
            0,
            inputs=inputs,
            device=device,
            active_type=active_type)
2416 2417 2418 2419 2420 2421
        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)

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2423 2424
@config_layer('out_prod')
class OuterProdLayer(LayerBase):
2425 2426 2427
    def __init__(self, name, inputs, device=None):
        super(OuterProdLayer, self).__init__(
            name, 'out_prod', 0, inputs=inputs, device=device)
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        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)

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2434 2435
@config_layer('power')
class PowerLayer(LayerBase):
2436 2437 2438
    def __init__(self, name, inputs, device=None):
        super(PowerLayer, self).__init__(
            name, 'power', 0, inputs=inputs, device=device)
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        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)
2443 2444 2445
        config_assert(1 == input_layer0.size,
                      'The left input is the exponent and should be of size 1')

2446 2447 2448

@config_layer('slope_intercept')
class SlopeInterceptLayer(LayerBase):
2449 2450 2451
    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)
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        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)

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@config_layer('scaling')
class ScalingLayer(LayerBase):
2461 2462 2463
    def __init__(self, name, inputs, device=None):
        super(ScalingLayer, self).__init__(
            name, 'scaling', 0, inputs=inputs, device=device)
2464 2465 2466 2467
        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)
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        config_assert(1 == input_layer0.size,
                      'The left input should be of size 1')

2471 2472 2473

@config_layer('conv_shift')
class ConvShiftLayer(LayerBase):
2474 2475 2476
    def __init__(self, name, inputs, device=None):
        super(ConvShiftLayer, self).__init__(
            name, 'conv_shift', 0, inputs=inputs, device=device)
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        config_assert(len(inputs) == 2, 'ConvShiftLayer must have 2 inputs')
        input_layer0 = self.get_input_layer(0)
        self.set_layer_size(input_layer0.size)

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2482 2483
@config_layer('convex_comb')
class ConvexCombinationLayer(LayerBase):
2484
    def __init__(self, name, size, inputs, device=None):
2485
        super(ConvexCombinationLayer, self).__init__(
2486 2487 2488
            name, 'convex_comb', size, inputs=inputs, device=device)
        config_assert(
            len(self.inputs) == 2, 'ConvexCombinationLayer must have 2 inputs')
2489 2490 2491
        config_assert(
            size * self.get_input_layer(0).size == self.get_input_layer(1).size,
            'Wrong input size for ConvexCombinationLayer')
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        self.set_layer_size(size)

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@config_layer('interpolation')
class InterpolationLayer(LayerBase):
2497
    def __init__(self, name, inputs, device=None):
2498 2499
        super(InterpolationLayer, self).__init__(
            name, 'interpolation', 0, inputs=inputs, device=device)
2500 2501
        config_assert(
            len(self.inputs) == 3, 'InterpolationLayer must have 3 inputs')
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        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')

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@config_layer('bilinear_interp')
class BilinearInterpLayer(LayerBase):
2513
    def __init__(self, name, inputs, **xargs):
2514
        super(BilinearInterpLayer, self).__init__(
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            name, 'bilinear_interp', 0, inputs=inputs, **xargs)
2516
        input_layer = self.get_input_layer(0)
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        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)
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2523 2524
@config_layer('sum_to_one_norm')
class SumToOneNormLayer(LayerBase):
2525
    def __init__(self, name, inputs, device=None):
2526
        super(SumToOneNormLayer, self).__init__(
2527 2528 2529
            name, 'sum_to_one_norm', 0, inputs=inputs, device=device)
        config_assert(
            len(self.inputs) == 1, 'SumToOneNormLayer must have 1 input')
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        input_layer0 = self.get_input_layer(0)
        self.set_layer_size(input_layer0.size)

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2534 2535
@config_layer('cos_vm')
class CosSimVecMatLayer(LayerBase):
2536
    def __init__(self, name, size, inputs, cos_scale=1.0, device=None):
2537
        super(CosSimVecMatLayer, self).__init__(
2538
            name, 'cos_vm', size, inputs=inputs, device=device)
2539
        self.config.cos_scale = cos_scale
2540 2541
        config_assert(
            len(self.inputs) == 2, 'CosSimVecMatLayer must have 2 inputs')
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        config_assert(
            size * self.get_input_layer(0).size == self.get_input_layer(1).size,
            'Wrong input size for CosSimVecMatLayer')
2545

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2547 2548
@config_layer('sampling_id')
class SamplingIdLayer(LayerBase):
2549
    def __init__(self, name, inputs, device=None):
2550 2551
        super(SamplingIdLayer, self).__init__(
            name, 'sampling_id', 0, inputs=inputs, device=device)
2552 2553
        config_assert(
            len(self.inputs) == 1, 'SamplingIdLayer must have 1 input')
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        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):
2566 2567 2568 2569 2570 2571 2572 2573 2574 2575 2576 2577 2578 2579 2580
    def __init__(self,
                 name,
                 inputs,
                 average_strategy='average',
                 trans_type='non-seq',
                 active_type='linear',
                 device=None,
                 bias=False):
        super(AverageLayer, self).__init__(
            name,
            'average',
            0,
            inputs=inputs,
            device=device,
            active_type=active_type)
2581
        self.config.average_strategy = average_strategy
2582
        self.config.trans_type = trans_type
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        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)

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2590 2591
@config_layer('cos')
class CosSimLayer(LayerBase):
2592
    def __init__(self, name, inputs, cos_scale=5, device=None):
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        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')
2599
        self.config.cos_scale = cos_scale
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@config_layer('tensor')
class TensorLayer(LayerBase):
2604 2605 2606
    def __init__(self, name, size, inputs, device=None, bias=True, **xargs):
        super(TensorLayer, self).__init__(
            name, 'tensor', size, inputs=inputs, device=device, **xargs)
2607 2608
        config_assert(len(self.inputs) == 2, 'TensorLayer must have 2 inputs')
        config_assert(size > 0, 'size must be positive')
2609 2610
        config_assert(inputs[1].parameter_name == None,
                      'second parameter should be None.')
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        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):
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    def __init__(self,
                 name,
                 inputs,
                 size=0,
                 bias=True,
                 error_clipping_threshold=None,
                 **xargs):
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        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)
2645
            if self.config.size == 0:
2646 2647 2648
                size = operator.calc_output_size(operator_conf.input_sizes)
                if size != 0:
                    self.set_layer_size(size)
2649
            else:
2650 2651
                sz = operator.calc_output_size(operator_conf.input_sizes)
                if sz != 0:
2652 2653 2654 2655
                    config_assert(
                        sz == self.config.size,
                        "different inputs have different size: %s vs. %s" %
                        (sz, self.config.size))
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        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:
2660 2661 2662
                config_assert(
                    isinstance(input, Projection),
                    "input should be projection or operation")
2663
            if self.config.size == 0 and isinstance(input, Projection):
2664 2665 2666
                size = input.calc_output_size(input_layer)
                if size != 0:
                    self.set_layer_size(size)
2667
            elif isinstance(input, Projection):
2668 2669 2670 2671 2672 2673
                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))
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        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)
2685 2686
                input_config.proj_conf.name = gen_parameter_name(name,
                                                                 input_index)
2687 2688 2689 2690 2691 2692 2693 2694 2695 2696 2697
                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)

2698 2699 2700 2701 2702 2703
        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()
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2705 2706 2707
        if bias:
            self.config.bias_size = psize
            self.create_bias_parameter(bias, psize)
2708

2709 2710
        if error_clipping_threshold is not None:
            self.config.error_clipping_threshold = error_clipping_threshold
2711

2712

2713 2714
# like MixedLayer, but no bias parameter
@config_func
2715
def ExpressionLayer(name, inputs, **xargs):
2716 2717
    MixedLayer(name, inputs, bias=False, **xargs)

2718

2719 2720
@config_layer('concat')
class ConcatenateLayer(LayerBase):
2721
    def __init__(self, name, inputs, bias=False, **xargs):
2722
        config_assert(inputs, 'inputs cannot be empty')
2723
        config_assert(not bias, 'ConcatenateLayer cannot support bias.')
2724 2725 2726 2727 2728 2729
        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]
2730
            if self.config.size == 0:
2731 2732 2733 2734
                size += input_layer.size

        self.set_layer_size(size)

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2736 2737 2738
# like concat layer, but each input layer was processed by a Projection.
@config_layer('concat2')
class ConcatenateLayer2(LayerBase):
2739
    def __init__(self, name, inputs, bias=False, **xargs):
2740 2741 2742
        config_assert(inputs, 'inputs cannot be empty')
        super(ConcatenateLayer2, self).__init__(
            name, 'concat2', 0, inputs=inputs, **xargs)
2743 2744

        if isinstance(self.inputs[0], ConvProjection):
2745 2746 2747 2748 2749 2750
            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.")
2751

2752 2753 2754 2755 2756 2757 2758 2759 2760 2761 2762 2763 2764 2765 2766 2767 2768 2769 2770 2771
        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,
2772
                                              input.proj_conf.output_size)
2773
            dims = input.calc_parameter_dims(input.proj_conf.input_size,
2774
                                             input.proj_conf.output_size)
2775 2776
            self.create_input_parameter(input_index, psize, dims)

2777 2778 2779 2780 2781 2782 2783
        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()

2784 2785 2786
        if bias:
            self.config.bias_size = psize
            self.create_bias_parameter(bias, psize)
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2789 2790
@config_layer('recurrent')
class RecurrentLayer(LayerBase):
2791
    def __init__(self, name, inputs, reversed=False, bias=True, **xargs):
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        super(RecurrentLayer, self).__init__(name, 'recurrent', 0, inputs,
                                             **xargs)
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        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)

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2804 2805
@config_layer('lstmemory')
class LstmLayer(LayerBase):
2806 2807 2808 2809 2810 2811 2812 2813
    def __init__(self,
                 name,
                 inputs,
                 reversed=False,
                 active_gate_type="sigmoid",
                 active_state_type="sigmoid",
                 bias=True,
                 **xargs):
2814 2815 2816 2817 2818 2819 2820 2821
        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
2822
        self.config.active_gate_type = active_gate_type
2823 2824 2825 2826 2827
        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)

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2829 2830
@config_layer('lstm_step')
class LstmStepLayer(LayerBase):
2831 2832 2833 2834 2835 2836 2837 2838 2839 2840
    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)
2841 2842 2843
        config_assert(len(inputs) == 2, 'LstmStepLayer must have 2 inputs')
        input_layer0 = self.get_input_layer(0)
        input_layer1 = self.get_input_layer(1)
2844 2845 2846 2847 2848
        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
2849 2850 2851
        self.config.active_state_type = active_state_type
        self.create_bias_parameter(bias, size * 3)

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2853 2854 2855
# get the specific output from the input layer.
@config_layer('get_output')
class GetOutputLayer(LayerBase):
2856 2857 2858 2859
    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')
2860 2861 2862 2863
        inputs = self.inputs[0]
        config_assert(inputs.input_layer_argument,
                      'input_layer_argument cannot be empty')

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@config_layer('mdlstmemory')
class MDLstmLayer(LayerBase):
2867 2868 2869 2870 2871 2872 2873 2874
    def __init__(self,
                 name,
                 inputs,
                 directions=True,
                 active_gate_type="sigmoid",
                 active_state_type="sigmoid",
                 bias=True,
                 **xargs):
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        super(MDLstmLayer, self).__init__(name, 'mdlstmemory', 0, inputs,
                                          **xargs)
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        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)
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        config_assert(input_layer.size % (3 + dim_num) == 0,
                      "size % (dim_num) should be 0!")
2883
        size = input_layer.size / (3 + dim_num)
2884
        self.set_layer_size(size)
2885
        self.config.active_gate_type = active_gate_type
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        self.config.active_state_type = active_state_type
        for i in xrange(len(directions)):
            self.config.directions.append(int(directions[i]))
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        self.create_input_parameter(0, size * size * (3 + dim_num),
                                    [size, size, 3 + dim_num])
2891
        #bias includes 3 kinds of peephole, 3+dim_num+2+dim_num
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        self.create_bias_parameter(bias, size * (5 + 2 * dim_num))

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@config_layer('gated_recurrent')
class GatedRecurrentLayer(LayerBase):
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    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')
2908 2909 2910 2911 2912 2913
        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
2914
        self.config.active_gate_type = active_gate_type
2915 2916 2917
        self.create_input_parameter(0, size * size * 3, [size, size * 3])
        self.create_bias_parameter(bias, size * 3)

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2919 2920
@config_layer('gru_step')
class GruStepLayer(LayerBase):
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    def __init__(self,
                 name,
                 size,
                 inputs,
                 active_gate_type="sigmoid",
                 bias=True,
                 **xargs):
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        super(GruStepLayer, self).__init__(name, 'gru_step', size, inputs,
                                           **xargs)
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        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)
2933 2934 2935 2936 2937
        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
2938 2939 2940
        self.create_input_parameter(0, size * size * 3, [size, size * 3])
        self.create_bias_parameter(bias, size * 3)

2941

2942 2943 2944 2945 2946 2947 2948
'''
 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
'''
2949 2950


2951 2952
@config_layer('crf')
class CRFLayer(LayerBase):
2953
    def __init__(self, name, size, inputs, coeff=1.0, device=None):
2954
        super(CRFLayer, self).__init__(name, 'crf', size, inputs, device=device)
2955 2956
        config_assert(2 <= len(self.inputs) <= 3,
                      'CRFLayer must have 2 or 3 inputs')
2957 2958 2959
        self.create_input_parameter(0, size * (size + 2), [size, size + 2])
        self.config.coeff = coeff

2960

2961 2962 2963 2964 2965 2966 2967 2968
'''
 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
'''
2969 2970


2971 2972
@config_layer('crf_decoding')
class CRFDecodingLayer(LayerBase):
2973
    def __init__(self, name, size, inputs, device=None):
2974 2975 2976 2977 2978 2979 2980
        super(CRFDecodingLayer, self).__init__(
            name, 'crf_decoding', size, inputs, device=device)
        config_assert(
            len(self.inputs) <= 2,
            'CRFDecodingLayer cannot have more than 2 inputs')
        self.create_input_parameter(0, size * (size + 2), [size, size + 2])

2981

2982 2983
@config_layer('ctc')
class CTCLayer(LayerBase):
2984
    def __init__(self, name, size, inputs, norm_by_times=False, device=None):
2985 2986 2987 2988
        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')

2989

2990 2991 2992 2993 2994 2995 2996 2997 2998 2999 3000 3001 3002 3003 3004 3005 3006 3007 3008 3009 3010
@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")


3011 3012
@config_layer('recurrent_layer_group')
class RecurrentLayerGroup(LayerBase):
3013
    def __init__(self, name, device=None):
3014 3015
        global g_pass_height_width
        g_pass_height_width = False
3016 3017 3018 3019 3020 3021
        super(RecurrentLayerGroup, self).__init__(
            name, 'recurrent_layer_group', 0, inputs=[], device=device)


# Deprecated, use a new layer specific class instead
@config_func
3022
def Layer(name, type, **xargs):
3023 3024 3025 3026
    layers = {}
    layers.update(g_cost_map)
    layers.update(g_layer_type_map)
    layer_func = layers.get(type)
3027
    config_assert(layer_func, "layer type '%s' not supported." % type)
3028
    return layer_func(name, **xargs)
3029

3030

3031
@config_func
3032
def ParameterHook(type, **kwargs):
3033 3034 3035 3036 3037 3038 3039 3040 3041 3042 3043 3044
    if type == 'pruning':
        mask_filename = kwargs.get('mask_filename', None)
        assert mask_filename is not None
        hook = ParameterUpdaterHookConfig()
        hook.type = type
        hook.purning_mask_filename = mask_filename
        return hook
    else:
        return None


@config_func
3045 3046 3047 3048 3049 3050 3051 3052 3053 3054 3055 3056 3057 3058 3059 3060 3061 3062 3063 3064 3065 3066
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,
              update_hooks=None):
3067 3068 3069 3070 3071 3072 3073

    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
3074 3075 3076 3077 3078 3079 3080 3081 3082 3083 3084
    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)

3085 3086
    config_assert(not momentum or not decay_rate_l1,
                  "momentum and decay_rate_l1 cannot both be non-zero")
3087 3088 3089 3090 3091

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

3092 3093 3094 3095
    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)
3096

3097 3098
    num_batches_regularization = default(num_batches_regularization,
                                         g_default_num_batches_regularization)
3099 3100 3101
    if num_batches_regularization is not None:
        para.num_batches_regularization = int(num_batches_regularization)

3102 3103 3104 3105 3106 3107
    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
3108 3109
    gradient_clipping_threshold = default(gradient_clipping_threshold,
                                          g_default_gradient_clipping_threshold)
3110 3111
    if gradient_clipping_threshold is not None:
        para.gradient_clipping_threshold = gradient_clipping_threshold
3112 3113
    para.initial_strategy = default(initial_strategy,
                                    g_default_initial_strategy)
3114 3115 3116 3117 3118 3119
    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:
3120 3121 3122
            print(
                "Use initial_smart, but dims not set. Initial_smart may not be used in this layer"
            )
3123 3124 3125 3126
            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)
3127 3128 3129 3130 3131 3132 3133

    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
3134 3135 3136 3137
    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")
3138 3139
    if is_shared is not None:
        para.is_shared = is_shared
3140 3141 3142 3143 3144 3145 3146 3147 3148 3149 3150 3151 3152 3153 3154 3155 3156 3157 3158 3159 3160

    update_hooks = default(update_hooks, g_default_update_hooks)

    if update_hooks is not None:
        if hasattr(update_hooks, '__call__'):
            update_hooks = update_hooks(para.name)

        if isinstance(update_hooks, list):
            for hook in update_hooks:
                para.update_hooks.extend([hook])
        else:
            para.update_hooks.extend(update_hooks)

    g_parameter_map[name] = para


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

3161

3162 3163 3164 3165 3166
@config_func
def default_initial_mean(val):
    global g_default_initial_mean
    g_default_initial_mean = val

3167

3168 3169 3170 3171 3172
@config_func
def default_initial_strategy(val):
    global g_default_initial_strategy
    g_default_initial_strategy = val

3173

3174 3175 3176 3177 3178
@config_func
def default_initial_smart(val):
    global g_default_initial_smart
    g_default_initial_smart = val

3179

3180 3181 3182 3183 3184
@config_func
def default_momentum(val):
    global g_default_momentum
    g_default_momentum = val

3185

3186 3187 3188 3189 3190
@config_func
def default_decay_rate(val):
    global g_default_decay_rate
    g_default_decay_rate = val

3191

3192 3193 3194 3195 3196
@config_func
def default_num_batches_regularization(val):
    global g_default_num_batches_regularization
    g_default_num_batches_regularization = val

3197

3198 3199 3200 3201 3202
@config_func
def default_gradient_clipping_threshold(val):
    global g_default_gradient_clipping_threshold
    g_default_gradient_clipping_threshold = val

3203

3204 3205 3206 3207 3208
@config_func
def default_device(val):
    global g_default_device
    g_default_device = val

3209

3210 3211 3212 3213 3214
@config_func
def default_update_hooks(val):
    global g_default_update_hooks
    g_default_update_hooks = val

3215

3216 3217 3218 3219 3220
@config_func
def default_compact_func(val):
    global g_default_compact_func
    g_default_compact_func = val

3221

3222 3223 3224 3225 3226
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)
3227 3228 3229
        execfile(config_file,
                 make_config_environment(config_file, config_args), local_args)

3230 3231
    return Import

3232

3233 3234 3235 3236 3237 3238 3239 3240 3241 3242 3243 3244 3245 3246 3247 3248 3249 3250 3251 3252 3253 3254 3255 3256 3257 3258 3259 3260
settings = dict(
    batch_size=None,
    mini_batch_size=None,
    algorithm='async_sgd',
    async_lagged_grad_discard_ratio=1.5,
    learning_method='momentum',
    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,
3261 3262 3263
    adam_beta1=0.9,
    adam_beta2=0.999,
    adam_epsilon=1e-8, )
3264

3265
settings_deprecated = dict(usage_ratio=1., )
3266 3267 3268 3269

trainer_settings = dict(
    save_dir="./output/model",
    init_model_path=None,
3270 3271
    start_pass=0, )

3272 3273 3274 3275 3276

@config_func
def Settings(**args):
    for k, v in args.iteritems():
        if k == "usage_ratio":
3277 3278
            logger.warning(
                "Deprecated: define usage_ratio in DataConfig instead")
3279 3280 3281 3282 3283 3284 3285 3286 3287 3288 3289
            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)

3290

3291 3292 3293 3294
@config_func
def cluster_config(**args):
    pass

3295

3296 3297 3298 3299 3300 3301 3302 3303 3304
@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

3305

3306 3307 3308 3309
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))
3310

3311 3312 3313 3314 3315 3316 3317 3318 3319 3320 3321 3322 3323 3324 3325
        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),
3326
        get_config_arg=make_get_config_arg(config_args), )
3327 3328 3329 3330 3331

    funcs.update(g_extended_config_funcs)

    return funcs

3332

3333 3334 3335 3336 3337 3338 3339 3340 3341 3342 3343 3344 3345 3346 3347 3348
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

3349

3350 3351 3352 3353 3354 3355 3356 3357 3358 3359 3360 3361
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)"

3362

3363 3364 3365 3366
def my_fatal(s):
    logger.critical(s)
    raise Exception()

3367 3368 3369 3370 3371 3372 3373 3374
_parse_config_hooks = set()
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)
3375

3376 3377 3378 3379 3380 3381
def parse_config(config_file, config_arg_str):
    '''
    @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
    '''
    init_config_environment()
3382 3383
    for hook in _parse_config_hooks:
        hook()
3384 3385 3386 3387 3388 3389 3390 3391 3392 3393 3394 3395 3396 3397 3398 3399 3400 3401 3402 3403 3404 3405 3406 3407 3408 3409 3410

    config_args = {}

    logger.findCaller = find_caller
    logger.fatal = my_fatal

    g_config.model_config.type = "nn"
    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)

    g_config.model_config.type = 'nn'

    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

3411 3412 3413 3414 3415 3416 3417 3418 3419 3420
    # for paddle on spark, need support non-file config.
    # you can use parse_config like below:
    #
    # from paddle.trainer.config_parser import parse_config
    # def configs():
    #    #your paddle config code, which is same as config file.
    #
    # config = parse_config(configs, "is_predict=1")
    # # then you get config proto object.
    if hasattr(config_file, '__call__'):
L
Luo Tao 已提交
3421 3422 3423
        config_file.func_globals.update(
            make_config_environment("", config_args))
        config_file()
3424
    else:
L
Luo Tao 已提交
3425
        execfile(config_file, make_config_environment(config_file, config_args))
3426 3427 3428
    for k, v in settings.iteritems():
        if v is None:
            continue
3429
        g_config.opt_config.__setattr__(k, v)
3430 3431 3432 3433 3434 3435 3436 3437 3438 3439 3440 3441 3442 3443 3444 3445 3446 3447 3448 3449 3450 3451 3452 3453 3454 3455

    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


def parse_config_and_serialize(config_file, config_arg_str):
    try:
        config = parse_config(config_file, config_arg_str)
        #logger.info(config)
        return config.SerializeToString()
    except:
        traceback.print_exc()
        raise

3456

3457 3458 3459 3460 3461 3462 3463 3464
if __name__ == '__main__':
    try:
        config = parse_config(sys.argv[1], '')
        config.SerializeToString()
        __real_print__(str(config))
    except:
        traceback.print_exc()
        raise
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