config_parser.py 125.2 KB
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# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
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#
# 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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            pad=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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            make_layer_name_in_submodel=True, ):
        """
        @param make_layer_name_in_submodel True by defalut, you might need to
        set it carefully when adding Input in config_parser.py.
        """
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        self.add_keys(locals())
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        self.input_layer_name = MakeLayerNameInSubmodel(
            input_layer_name
        ) if make_layer_name_in_submodel else 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,
703
                   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
707 708 709 710 711 712 713 714 715

    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'
748 749 750

    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'
769 770 771 772 773 774 775

    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]),
781
                   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,
            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 Pad(Cfg):
    def __init__(self, channels, pad_c, pad_h, pad_w):
        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 create_data_config_proto(async_load_data=False,
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                             constant_slots=None,
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                             data_ratio=1,
                             is_main_data=True,
                             usage_ratio=None):
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    # 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 = create_data_config_proto(**xargs)
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    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 = create_data_config_proto(**xargs)
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    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):
999
    data_config = create_data_config_proto(**xargs)
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    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
1022
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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1029
@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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1038
    data_config = create_data_config_proto(**xargs)
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    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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1073 1074
#caffe_mode: compute the output size using floor instead of ceil,
#            which is consistent of caffe and CuDNN's convention.
1075 1076 1077 1078 1079 1080 1081
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))

1082

1083
#calcualte image_size based on output_size for de-convolution (ConvTransLayer).
1084
#It is the reverse function of cnn_output_size
1085
def cnn_image_size(output_size, filter_size, padding, stride, caffe_mode):
1086 1087 1088
    img_size = (output_size - 1) * stride + filter_size - 2 * padding
    if not caffe_mode:
        img_size = img_size + 1
1089 1090
    return img_size

1091

1092
def get_img_size(input_layer_name, channels):
1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110
    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


1111
def parse_pool(pool, input_layer_name, pool_conf, ceil_mode):
1112
    pool_conf.pool_type = pool.pool_type
1113 1114 1115
    config_assert(pool.pool_type in [
        'max-projection', 'avg-projection', 'cudnn-max-pool', 'cudnn-avg-pool'
    ], "pool-type %s is not in "
1116
                  "['max-projection', 'avg-projection', "
1117
                  "'cudnn-max-pool', 'cudnn-avg-pool']" % pool.pool_type)
1118 1119 1120 1121 1122 1123

    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)
1124
    pool_conf.stride_y = default(pool.stride_y, pool_conf.stride)
1125

1126
    pool_conf.img_size, pool_conf.img_size_y = \
1127
        get_img_size(input_layer_name, pool.channels)
1128

1129
    config_assert(not pool.start, "start is deprecated in pooling.")
1130

1131
    if pool.padding is not None:
1132
        pool_conf.padding = pool.padding
1133
    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,
1136
                                         not ceil_mode)
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    pool_conf.output_y = cnn_output_size(pool_conf.img_size_y, pool_conf.size_y,
                                         pool_conf.padding_y,
1139
                                         pool_conf.stride_y, not ceil_mode)
1140

1141

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def parse_spp(spp, input_layer_name, spp_conf):
1143
    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'],
1146 1147
                  "pool-type %s is not in "
                  "['max-projection', 'avg-projection']" % spp.pool_type)
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    spp_conf.pyramid_height = spp.pyramid_height
1149

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1151 1152
def parse_image(image, input_layer_name, image_conf):
    image_conf.channels = image.channels
1153
    image_conf.img_size, image_conf.img_size_y = \
1154
        get_img_size(input_layer_name, image_conf.channels)
1155

1156 1157 1158 1159

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'],
1160 1161
                  "norm-type %s is not in [rnorm, 'cmrnorm-projection']" %
                  norm.norm_type)
1162 1163 1164 1165 1166 1167
    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

1168
    norm_conf.img_size, norm_conf.img_size_y = \
1169
        get_img_size(input_layer_name, norm.channels)
1170
    norm_conf.output_x = norm_conf.img_size
1171
    norm_conf.output_y = norm_conf.img_size_y
1172 1173 1174
    if norm.norm_type in ['cmrnorm-projection']:
        norm_conf.scale /= norm.size
    else:
1175 1176
        norm_conf.scale /= norm.size**2

1177

1178 1179
#caffe_mode: compute the output size using floor instead of ceil,
#            which is consistent of caffe and CuDNN's convention.
1180
def parse_conv(conv, input_layer_name, conv_conf, num_filters, trans=False):
1181 1182 1183 1184 1185 1186 1187 1188 1189
    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
1190

1191
    if not trans:
1192
        conv_conf.filter_channels = conv.channels / conv.groups
1193
        conv_conf.img_size, conv_conf.img_size_y = \
1194
            get_img_size(input_layer_name, conv.channels)
1195
        conv_conf.output_x = cnn_output_size(
1196 1197
            conv_conf.img_size, conv_conf.filter_size, conv_conf.padding,
            conv_conf.stride, conv_conf.caffe_mode)
1198 1199 1200
        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)
1201
    else:
1202
        conv_conf.filter_channels = num_filters / conv.groups
1203
        conv_conf.output_x, conv_conf.output_y = \
1204
            get_img_size(input_layer_name, conv.channels)
1205
        conv_conf.img_size = cnn_image_size(
1206 1207
            conv_conf.output_x, conv_conf.filter_size, conv_conf.padding,
            conv_conf.stride, conv_conf.caffe_mode)
1208
        conv_conf.img_size_y = cnn_image_size(
1209 1210
            conv_conf.output_y, conv_conf.filter_size_y, conv_conf.padding_y,
            conv_conf.stride_y, conv_conf.caffe_mode)
1211

1212

1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225
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:
1226
        block_expand_conf.output_x = cnn_output_size(
1227
            block_expand.img_size_x, block_expand.block_x,
1228
            block_expand.padding_x, block_expand.stride_x, False)
1229 1230

    if block_expand_conf.img_size_y == 0:
1231
        block_expand_conf.output_y = 0
1232
    else:
1233
        block_expand_conf.output_y = cnn_output_size(
1234
            block_expand.img_size_y, block_expand.block_y,
1235
            block_expand.padding_y, block_expand.stride_y, False)
1236

1237

1238
def parse_maxout(maxout, input_layer_name, maxout_conf):
1239
    parse_image(maxout, input_layer_name, maxout_conf.image_conf)
1240
    maxout_conf.groups = maxout.groups
1241

1242

1243 1244 1245 1246 1247 1248
# 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,
1256
        top_k=None,
1257 1258
        delimited=None,
        excluded_chunk_types=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
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    if top_k is not None:
        evaluator.top_k = top_k
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    if delimited is not None:
        evaluator.delimited = delimited
1288

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    if excluded_chunk_types:
        evaluator.excluded_chunk_types.extend(excluded_chunk_types)

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1293 1294 1295 1296 1297
class LayerBase(object):
    def __init__(
            self,
            name,
            type,
1298
            size,  # size can be 0. In this case, subclass should set it.
1299 1300 1301 1302
            inputs,
            device=None,
            active_type="",
            drop_rate=0.,
1303
            coeff=None):
1304
        config_assert('@' not in name,
1305
                      "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()
1321
        assert isinstance(self.config, LayerConfig)
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        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
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        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(
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                    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:
1359
                raise ValueError('Wrong type for inputs: %s' % type_of(input))
1360
            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
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            size,
1388 1389 1390
            dims=None,
            for_self=True,  # whether create bias for layer self
    ):
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        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,
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                    is_static=bias.is_static,
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                    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')
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                          % (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')
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                          % (input_config.parameter_name, para.dims, dims))
            return

        Parameter(
            input_config.parameter_name,
            size,
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            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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@config_layer('fc')
class FCLayer(LayerBase):
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    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
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            else:
                sparse = None
1538

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

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@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,
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                          ("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)
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        self.create_bias_parameter(bias, self.config.size)

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@config_layer('print')
class PrintLayer(LayerBase):
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    def __init__(self, name, inputs):
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        super(PrintLayer, self).__init__(name, 'print', 0, inputs)

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@config_layer('priorbox')
class PriorBoxLayer(LayerBase):
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    def __init__(self, name, inputs, size, min_size, max_size, aspect_ratio,
                 variance):
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        super(PriorBoxLayer, self).__init__(name, 'priorbox', 0, inputs)
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        config_assert(len(inputs) == 2, 'PriorBoxLayer must have 2 inputs')
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        input_layer = self.get_input_layer(1)
        config_assert(
            input_layer.type == 'data',
            'Expecting the second input layer of an priorbox layer to be '
            'a data layer')
        config_assert(input_layer.width > 0, 'The data layer must set width')
        config_assert(input_layer.height > 0, 'The data layer must set height')
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        config_assert(len(variance) == 4, 'The variance must have 4 inputs')
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        self.config.inputs[0].priorbox_conf.min_size.extend(min_size)
        self.config.inputs[0].priorbox_conf.max_size.extend(max_size)
        self.config.inputs[0].priorbox_conf.aspect_ratio.extend(aspect_ratio)
        self.config.inputs[0].priorbox_conf.variance.extend(variance)
        self.config.size = size

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1622 1623
@config_layer('data')
class DataLayer(LayerBase):
1624
    def __init__(self, name, size, height=None, width=None, device=None):
1625 1626
        super(DataLayer, self).__init__(
            name, 'data', size, inputs=[], device=device)
1627 1628
        if height and width:
            self.set_layer_height_width(height, width)
1629

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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
'''
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1659 1660
@config_layer('data_norm')
class DataNormLayer(LayerBase):
1661
    def __init__(self, name, inputs, data_norm_strategy="z-score", device=None):
1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672
        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)

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@config_layer('prelu')
class ParameterReluLayer(LayerBase):
    layer_type = 'prelu'
1677 1678

    def __init__(self, name, inputs, partial_sum=1, **args):
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        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)

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@config_layer('conv')
class ConvLayerBase(LayerBase):
    layer_type = 'conv'
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    def __init__(self,
                 name,
                 inputs=[],
                 bias=True,
                 num_filters=None,
                 shared_biases=False,
                 **xargs):
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        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
1715
            (parallel_nn == 0 or self.config.device > -1)):
1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727
            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
1728 1729
            parse_conv(self.inputs[input_index].conv, input_layer.name,
                       conv_conf, num_filters)
1730 1731
            psize = self.calc_parameter_size(conv_conf)
            self.create_input_parameter(input_index, psize)
1732 1733
            self.set_cnn_layer(name, conv_conf.output_y, conv_conf.output_x,
                               self.config.num_filters)
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        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)

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1745 1746 1747 1748
@config_layer('exconv')
class ConvLayer(ConvLayerBase):
    layer_type = 'exconv'

1749

1750 1751 1752 1753
@config_layer('cudnn_conv')
class ConvLayer(ConvLayerBase):
    layer_type = 'cudnn_conv'

1754 1755 1756 1757

@config_layer('convt')
class ConvTransLayerBase(LayerBase):
    layer_type = 'convt'
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    def __init__(self,
                 name,
                 inputs=[],
                 bias=True,
                 num_filters=None,
                 shared_biases=False,
                 **xargs):
1766
        super(ConvTransLayerBase, self).__init__(
1767 1768 1769 1770 1771 1772 1773 1774
            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))

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        # cudnn_convt has not been implemented so use exconvt only
        self.layer_type = "exconvt"
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        # 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)
1785
            parse_conv(
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                self.inputs[input_index].conv,
                input_layer.name,
1788
                self.config.inputs[input_index].conv_conf,
1789
                num_filters,
1790
                trans=True)
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            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(
1796
                (conv_conf.img_size**2) * self.config.num_filters)
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        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):
1804
        return conv_conf.channels * conv_conf.filter_channels \
1805 1806
                    * (conv_conf.filter_size * conv_conf.filter_size_y)

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1808 1809 1810 1811
@config_layer('exconvt')
class ConvTransLayer(ConvTransLayerBase):
    layer_type = 'exconvt'

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1813 1814
@config_layer('norm')
class NormLayer(LayerBase):
1815 1816
    def __init__(self, name, inputs, **xargs):
        super(NormLayer, self).__init__(name, 'norm', 0, inputs=inputs, **xargs)
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        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
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            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)
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1825 1826 1827

@config_layer('pool')
class PoolLayer(LayerBase):
1828 1829
    def __init__(self, name, inputs, ceil_mode=True, **xargs):
        super(PoolLayer, self).__init__(name, 'pool', 0, inputs=inputs, **xargs)
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        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
1833
            parse_pool(self.inputs[input_index].pool, input_layer.name,
1834
                       pool_conf, ceil_mode)
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            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):
1841
    def __init__(self, name, inputs, **xargs):
1842
        super(SpatialPyramidPoolLayer, self).__init__(
1843
            name, 'spp', 0, inputs=inputs, **xargs)
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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
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            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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@config_layer('pad')
class PadLayer(LayerBase):
    def __init__(self, name, inputs, **xargs):
        super(PadLayer, self).__init__(name, 'pad', 0, inputs=inputs, **xargs)
        pad = self.inputs[0].pad
        self.config.inputs[0].pad_conf.pad_c.extend(pad.pad_c)
        self.config.inputs[0].pad_conf.pad_h.extend(pad.pad_h)
        self.config.inputs[0].pad_conf.pad_w.extend(pad.pad_w)

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


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@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,
                 use_global_stats=True,
                 moving_average_fraction=0.9,
                 batch_norm_type=None,
                 **xargs):
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        if inputs is None:
            inputs = []
        elif not isinstance(inputs, list):
            inputs = [inputs]
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        config_assert(
            len(inputs) == 1, "BatchNormLayer must have one and only one input")
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        # 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):
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            inputs.append(
                Input(
                    inputs[0].input_layer_name,
                    initial_std=0.0,
                    initial_mean=0.0,
                    is_static=True,
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                    is_shared=is_shared,
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                    make_layer_name_in_submodel=False, ))
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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 \
1913
            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,
            **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

1928
        input_layer = self.get_input_layer(0)
1929
        image_conf = self.config.inputs[0].image_conf
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        parse_image(self.inputs[0].image, input_layer.name, image_conf)
1931

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        # Only pass the width and height of input to batch_norm layer
        # when either of it is non-zero.
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        if input_layer.width != 0 or input_layer.height != 0:
            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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        else:
            self.set_layer_size(input_layer.size)
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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):
1954
    def __init__(self, name, inputs, **xargs):
1955
        super(TransLayer, self).__init__(
1956
            name, 'trans', 0, inputs=inputs, **xargs)
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        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):
1965
    def __init__(self, name, size, inputs, **xargs):
1966
        super(ResizeLayer, self).__init__(
1967
            name, 'resize', size=size, inputs=inputs, **xargs)
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        config_assert(
            len(self.inputs) == 1,
            'ResizeLayer must have one and only one input')

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1973 1974
@config_layer('rotate')
class RotateLayer(LayerBase):
1975
    def __init__(self, name, inputs, height, width, device=None):
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        super(RotateLayer, self).__init__(
            name, 'rotate', 0, inputs=inputs, device=device)
        config_assert(
            len(self.inputs) == 1,
            'RotateLayer must have one and only one input')
1981
        self.set_layer_height_width(height, width)
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        self.set_layer_size(self.get_input_layer(0).size)


1985 1986
@config_layer('blockexpand')
class BlockExpandLayer(LayerBase):
1987
    def __init__(self, name, inputs, **xargs):
1988
        super(BlockExpandLayer, self).__init__(
1989
            name, 'blockexpand', 0, inputs=inputs, **xargs)
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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,
1994
                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):
2004 2005 2006
    def __init__(self, name, inputs, **xargs):
        super(MaxOutLayer, self).__init__(
            name, 'maxout', 0, inputs=inputs, **xargs)
2007 2008
        input_layer = self.get_input_layer(0)
        maxout_conf = self.config.inputs[0].maxout_conf
2009
        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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2015 2016 2017 2018
# key: cost type
# value: cost class
g_cost_map = {}

2019

2020 2021 2022
# define a cost layer without any parameters
def define_cost(class_name, cost_type):
    def init(cls, name, inputs, device=None, coeff=1.):
2023 2024
        super(type(cls), cls).__init__(
            name, cost_type, 1, inputs, device=device, coeff=coeff)
2025

2026
    cls = type(class_name, (LayerBase, ), dict(__init__=init))
2027 2028 2029
    global g_cost_map
    g_cost_map[cost_type] = cls

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2031 2032 2033 2034 2035 2036 2037 2038
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')
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@config_layer('hsigmoid')
class HierarchicalSigmoidLayer(LayerBase):
2044
    def __init__(self, name, num_classes, inputs, device=None, bias=True):
2045 2046
        super(HierarchicalSigmoidLayer, self).__init__(
            name, 'hsigmoid', 1, inputs=inputs, device=device)
2047 2048 2049
        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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2085 2086
@config_layer('lambda_cost')
class LambdaCost(LayerBase):
2087
    def __init__(self, name, inputs, NDCG_num=5, max_sort_size=-1, device=None):
2088 2089
        super(LambdaCost, self).__init__(
            name, 'lambda_cost', 1, inputs=inputs, device=device)
2090
        config_assert(len(self.inputs) == 2, 'lambdaCost must have 2 inputs')
2091 2092
        self.config.NDCG_num = NDCG_num
        if max_sort_size != -1:
2093 2094 2095
            config_assert(
                NDCG_num <= max_sort_size,
                'NDCG_num must be less than or equal to max_sort_size')
2096 2097
        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):
2109
        super(NCELayer, self).__init__(name, 'nce', 1, inputs=inputs, **xargs)
2110 2111
        config_assert(
            len(self.inputs) >= 2, 'NCELayer must have at least 2 inputs')
2112 2113
        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))
2118
            s = sum(neg_sampling_dist)
2119 2120 2121
            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
2127
        input_layer = self.get_input_layer(num_real_inputs)
2128 2129 2130 2131
        config_assert(input_layer.type == 'data',
                      'Expecting the last input layer of an nce layer to be '
                      'a data layer')

2132 2133
        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):
2147
    def __init__(self, name, inputs, bias=True, **xargs):
2148 2149
        super(AddToLayer, self).__init__(
            name, 'addto', 0, inputs=inputs, **xargs)
2150
        config_assert(len(inputs) > 0, 'inputs cannot be empty for AddToLayer')
2151 2152 2153 2154 2155
        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)

2163 2164 2165

@config_layer('sequence_agent')
class SequenceAgentLayer(LayerBase):
2166
    def __init__(self, name, size, device=None):
2167 2168 2169
        super(SequenceAgentLayer, self).__init__(
            name, 'sequence_agent', size, inputs=[], device=device)

2170

2171 2172
@config_layer('gather_agent')
class GatherAgentLayer(LayerBase):
2173
    def __init__(self, name, size, device=None):
2174 2175 2176
        super(GatherAgentLayer, self).__init__(
            name, 'gather_agent', size, inputs=[], device=device)

2177

2178 2179
@config_layer('scatter_agent')
class ScatterAgentLayer(LayerBase):
2180
    def __init__(self, name, size, device=None):
2181 2182 2183
        super(ScatterAgentLayer, self).__init__(
            name, 'scatter_agent', size, inputs=[], device=device)

2184

2185 2186
@config_layer('sequence_gather_agent')
class SequenceGatherAgentLayer(LayerBase):
2187
    def __init__(self, name, size, device=None):
2188
        super(SequenceGatherAgentLayer, self).__init__(
2189 2190
            name, 'sequence_gather_agent', size, inputs=[], device=device)

2191 2192 2193

@config_layer('sequence_scatter_agent')
class SequenceScatterAgentLayer(LayerBase):
2194
    def __init__(self, name, size, device=None):
2195
        super(SequenceScatterAgentLayer, self).__init__(
2196 2197
            name, 'sequence_scatter_agent', size, inputs=[], device=device)

2198 2199 2200

@config_layer('multiplex')
class MultiplexLayer(LayerBase):
2201 2202 2203 2204 2205
    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.')
2206
        for i in range(1, len(inputs)):
2207 2208 2209 2210 2211
            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.")

2212 2213

@config_func
2214 2215 2216
def Link(
        name,
        has_subseq=False, ):
2217 2218 2219 2220 2221
    link_config = LinkConfig()
    link_config.link_name = name
    link_config.has_subseq = has_subseq
    return link_config

2222

2223 2224 2225 2226 2227 2228 2229 2230 2231 2232 2233 2234 2235 2236
# 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
2237 2238 2239 2240 2241 2242 2243 2244
def Memory(
        name,
        size,
        is_sequence=False,
        boot_layer=None,
        boot_bias=False,
        boot_bias_active_type="",
        boot_with_const_id=None, ):
2245 2246 2247 2248 2249 2250
    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,
2251
                  'Memory should be used in recurrent layer group only')
2252 2253 2254 2255
    memory = g_current_submodel.memories.add()
    memory.layer_name = MakeLayerNameInSubmodel(name)
    memory.link_name = MakeLayerNameInSubmodel(agent_name)
    memory.is_sequence = is_sequence
2256
    options = sum((boot_layer is not None, bool(boot_bias),
2257
                   boot_with_const_id is not None))
2258 2259 2260 2261
    config_assert(
        options <= 1,
        'take one option at most from boot_layer, boot_bias, or boot_with_const_id'
    )
2262 2263 2264
    if boot_layer is not None:
        boot_layer = MakeLayerNameInParentSubmodel(boot_layer)
        config_assert(boot_layer in g_layer_map,
2265 2266
                      'boot_layer "%s" does not correspond to a layer name' %
                      boot_layer)
2267 2268 2269
        memory.boot_layer_name = boot_layer
    elif boot_bias:
        memory.boot_bias_parameter_name = agent_layer.create_bias_parameter(
2270
            boot_bias, size, for_self=False)
2271 2272 2273 2274 2275
        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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# 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,
2288 2289 2290 2291
        eos_layer_name="eos_check",
        num_results_per_sample=1,
        beam_size=1,
        log_prob=None, ):
2292 2293 2294 2295 2296 2297 2298 2299 2300
    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

2301

2302 2303
@config_layer('expand')
class ExpandLayer(LayerBase):
2304 2305 2306 2307 2308 2309 2310 2311 2312 2313 2314 2315 2316 2317 2318 2319
    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)

2320 2321 2322

@config_layer('featmap_expand')
class FeatMapExpandLayer(LayerBase):
2323 2324 2325 2326 2327 2328
    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:
2329
            self.config.num_filters = num_filters
2330
        else:
2331
            logger.fatal("FeatMapExpandLayer must specify num_filters.")
2332
        self.set_layer_size(self.get_input_layer(0).size * num_filters)
2333 2334 2335 2336


@config_layer('max')
class MaxLayer(LayerBase):
2337 2338 2339 2340 2341 2342 2343 2344 2345 2346
    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)
2347
        config_assert(len(self.inputs) == 1, 'MaxLayer must have 1 input')
2348 2349
        self.config.trans_type = trans_type
        self.config.active_type = active_type
2350 2351 2352 2353
        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)
2354 2355
        if output_max_index is not None:
            self.config.output_max_index = output_max_index
2356 2357 2358 2359


@config_layer('maxid')
class MaxIdLayer(LayerBase):
2360
    def __init__(self, name, inputs, beam_size=None, device=None):
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        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):
2378
    def __init__(self, name, inputs, eos_id, device=None):
2379 2380 2381
        super(EosIdLayer, self).__init__(
            name, 'eos_id', 0, inputs=inputs, device=device)
        config_assert(len(self.inputs) == 1, 'EosIdLayer must have 1 input')
2382
        self.set_layer_size(2)  # boolean output
2383 2384
        self.config.eos_id = eos_id

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2386 2387
@config_layer('seqlastins')
class SequenceLastInstanceLayer(LayerBase):
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    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
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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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2411 2412 2413 2414 2415 2416 2417 2418 2419
@config_layer('seqfirstins')
class SequenceFirstInstanceLayer(SequenceLastInstanceLayer):
    def __init__(
            self,
            name,
            inputs,
            active_type='linear',
            trans_type='non-seq',
            device=None,
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            bias=False, ):
        super(SequenceFirstInstanceLayer, self).__init__(
            name,
            inputs=inputs,
            active_type=active_type,
            device=device,
            bias=bias)
        self.config.trans_type = trans_type
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        self.config.select_first = True

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2431 2432
@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')
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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('seqreshape')
class SequenceReshapeLayer(LayerBase):
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    def __init__(self,
                 name,
                 size,
                 inputs,
                 active_type='linear',
                 device=None,
                 bias=False):
        super(SequenceReshapeLayer, self).__init__(
            name,
            'seqreshape',
2466
            size,
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            inputs=inputs,
            device=device,
            active_type=active_type)
        config_assert(
            len(inputs) == 1, 'SequenceReshapeLayer must have 1 inputs')
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        self.set_layer_size(size)
        self.create_bias_parameter(bias, size)

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@config_layer('subseq')
class SubSequenceLayer(LayerBase):
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    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)
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        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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@config_layer('out_prod')
class OuterProdLayer(LayerBase):
2500 2501 2502
    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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2509 2510
@config_layer('power')
class PowerLayer(LayerBase):
2511 2512 2513
    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)
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        config_assert(1 == input_layer0.size,
                      'The left input is the exponent and should be of size 1')

2521 2522 2523

@config_layer('slope_intercept')
class SlopeInterceptLayer(LayerBase):
2524 2525 2526
    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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2534 2535
@config_layer('scaling')
class ScalingLayer(LayerBase):
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    def __init__(self, name, inputs, device=None):
        super(ScalingLayer, self).__init__(
            name, 'scaling', 0, inputs=inputs, device=device)
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        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')

2546 2547 2548

@config_layer('conv_shift')
class ConvShiftLayer(LayerBase):
2549 2550 2551
    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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2557 2558
@config_layer('convex_comb')
class ConvexCombinationLayer(LayerBase):
2559
    def __init__(self, name, size, inputs, device=None):
2560
        super(ConvexCombinationLayer, self).__init__(
2561 2562 2563
            name, 'convex_comb', size, inputs=inputs, device=device)
        config_assert(
            len(self.inputs) == 2, 'ConvexCombinationLayer 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 ConvexCombinationLayer')
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        self.set_layer_size(size)

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@config_layer('interpolation')
class InterpolationLayer(LayerBase):
2572
    def __init__(self, name, inputs, device=None):
2573 2574
        super(InterpolationLayer, self).__init__(
            name, 'interpolation', 0, inputs=inputs, device=device)
2575 2576
        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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2586 2587
@config_layer('bilinear_interp')
class BilinearInterpLayer(LayerBase):
2588
    def __init__(self, name, inputs, **xargs):
2589
        super(BilinearInterpLayer, self).__init__(
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            name, 'bilinear_interp', 0, inputs=inputs, **xargs)
2591
        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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@config_layer('sum_to_one_norm')
class SumToOneNormLayer(LayerBase):
2600
    def __init__(self, name, inputs, device=None):
2601
        super(SumToOneNormLayer, self).__init__(
2602 2603 2604
            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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2609 2610
@config_layer('cos_vm')
class CosSimVecMatLayer(LayerBase):
2611
    def __init__(self, name, size, inputs, cos_scale=1.0, device=None):
2612
        super(CosSimVecMatLayer, self).__init__(
2613
            name, 'cos_vm', size, inputs=inputs, device=device)
2614
        self.config.cos_scale = cos_scale
2615 2616
        config_assert(
            len(self.inputs) == 2, 'CosSimVecMatLayer must have 2 inputs')
2617 2618 2619
        config_assert(
            size * self.get_input_layer(0).size == self.get_input_layer(1).size,
            'Wrong input size for CosSimVecMatLayer')
2620

2621

2622 2623
@config_layer('sampling_id')
class SamplingIdLayer(LayerBase):
2624
    def __init__(self, name, inputs, device=None):
2625 2626
        super(SamplingIdLayer, self).__init__(
            name, 'sampling_id', 0, inputs=inputs, device=device)
2627 2628
        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):
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    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)
2656
        self.config.average_strategy = average_strategy
2657
        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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2665 2666
@config_layer('cos')
class CosSimLayer(LayerBase):
2667
    def __init__(self, name, inputs, cos_scale=1, device=None):
2668 2669 2670 2671 2672 2673
        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')
2674
        self.config.cos_scale = cos_scale
2675 2676 2677 2678


@config_layer('tensor')
class TensorLayer(LayerBase):
2679 2680 2681
    def __init__(self, name, size, inputs, device=None, bias=True, **xargs):
        super(TensorLayer, self).__init__(
            name, 'tensor', size, inputs=inputs, device=device, **xargs)
2682 2683
        config_assert(len(self.inputs) == 2, 'TensorLayer must have 2 inputs')
        config_assert(size > 0, 'size must be positive')
2684 2685
        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):
2703 2704 2705 2706 2707 2708 2709 2710 2711 2712 2713 2714 2715 2716 2717 2718 2719
        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)
2720
            if self.config.size == 0:
2721 2722 2723
                size = operator.calc_output_size(operator_conf.input_sizes)
                if size != 0:
                    self.set_layer_size(size)
2724
            else:
2725 2726
                sz = operator.calc_output_size(operator_conf.input_sizes)
                if sz != 0:
2727 2728 2729 2730
                    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:
2735 2736 2737
                config_assert(
                    isinstance(input, Projection),
                    "input should be projection or operation")
2738
            if self.config.size == 0 and isinstance(input, Projection):
2739 2740 2741
                size = input.calc_output_size(input_layer)
                if size != 0:
                    self.set_layer_size(size)
2742
            elif isinstance(input, Projection):
2743 2744 2745 2746 2747 2748
                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))
2749 2750 2751 2752 2753 2754 2755 2756 2757 2758 2759
        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)
2760 2761
                input_config.proj_conf.name = gen_parameter_name(name,
                                                                 input_index)
2762 2763 2764 2765 2766 2767 2768 2769 2770 2771 2772
                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)

2773 2774 2775 2776 2777 2778
        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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2780 2781 2782
        if bias:
            self.config.bias_size = psize
            self.create_bias_parameter(bias, psize)
2783

2784 2785
        if error_clipping_threshold is not None:
            self.config.error_clipping_threshold = error_clipping_threshold
2786

2787

2788 2789
# like MixedLayer, but no bias parameter
@config_func
2790
def ExpressionLayer(name, inputs, **xargs):
2791 2792
    MixedLayer(name, inputs, bias=False, **xargs)

2793

2794 2795
@config_layer('concat')
class ConcatenateLayer(LayerBase):
2796
    def __init__(self, name, inputs, bias=False, **xargs):
2797
        config_assert(inputs, 'inputs cannot be empty')
2798
        config_assert(not bias, 'ConcatenateLayer cannot support bias.')
2799 2800 2801 2802 2803 2804
        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]
2805
            if self.config.size == 0:
2806 2807 2808 2809
                size += input_layer.size

        self.set_layer_size(size)

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2811 2812 2813
# like concat layer, but each input layer was processed by a Projection.
@config_layer('concat2')
class ConcatenateLayer2(LayerBase):
2814
    def __init__(self, name, inputs, bias=False, **xargs):
2815 2816 2817
        config_assert(inputs, 'inputs cannot be empty')
        super(ConcatenateLayer2, self).__init__(
            name, 'concat2', 0, inputs=inputs, **xargs)
2818 2819

        if isinstance(self.inputs[0], ConvProjection):
2820 2821 2822 2823 2824 2825
            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.")
2826

2827 2828 2829 2830 2831 2832 2833 2834 2835 2836 2837 2838 2839 2840 2841 2842 2843 2844 2845 2846
        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,
2847
                                              input.proj_conf.output_size)
2848
            dims = input.calc_parameter_dims(input.proj_conf.input_size,
2849
                                             input.proj_conf.output_size)
2850 2851
            self.create_input_parameter(input_index, psize, dims)

2852 2853 2854 2855 2856 2857 2858
        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()

2859 2860 2861
        if bias:
            self.config.bias_size = psize
            self.create_bias_parameter(bias, psize)
2862

2863

2864 2865
@config_layer('recurrent')
class RecurrentLayer(LayerBase):
2866
    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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2879 2880
@config_layer('lstmemory')
class LstmLayer(LayerBase):
2881 2882 2883 2884 2885 2886 2887 2888
    def __init__(self,
                 name,
                 inputs,
                 reversed=False,
                 active_gate_type="sigmoid",
                 active_state_type="sigmoid",
                 bias=True,
                 **xargs):
2889 2890 2891 2892 2893 2894 2895 2896
        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
2897
        self.config.active_gate_type = active_gate_type
2898 2899 2900 2901 2902
        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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2904 2905
@config_layer('lstm_step')
class LstmStepLayer(LayerBase):
2906 2907 2908 2909 2910 2911 2912 2913 2914 2915
    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)
2916 2917 2918
        config_assert(len(inputs) == 2, 'LstmStepLayer must have 2 inputs')
        input_layer0 = self.get_input_layer(0)
        input_layer1 = self.get_input_layer(1)
2919 2920 2921 2922 2923
        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
2924 2925 2926
        self.config.active_state_type = active_state_type
        self.create_bias_parameter(bias, size * 3)

2927

2928 2929 2930
# get the specific output from the input layer.
@config_layer('get_output')
class GetOutputLayer(LayerBase):
2931 2932 2933 2934
    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')
2935 2936 2937 2938
        inputs = self.inputs[0]
        config_assert(inputs.input_layer_argument,
                      'input_layer_argument cannot be empty')

2939

2940 2941
@config_layer('mdlstmemory')
class MDLstmLayer(LayerBase):
2942 2943 2944 2945 2946 2947 2948 2949
    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)
2952 2953 2954 2955
        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!")
2958
        size = input_layer.size / (3 + dim_num)
2959
        self.set_layer_size(size)
2960
        self.config.active_gate_type = active_gate_type
2961 2962 2963
        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])
2966
        #bias includes 3 kinds of peephole, 3+dim_num+2+dim_num
2967 2968
        self.create_bias_parameter(bias, size * (5 + 2 * dim_num))

2969 2970 2971

@config_layer('gated_recurrent')
class GatedRecurrentLayer(LayerBase):
2972 2973 2974 2975 2976 2977 2978 2979 2980 2981 2982
    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')
2983 2984 2985 2986 2987 2988
        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
2989
        self.config.active_gate_type = active_gate_type
2990 2991 2992
        self.create_input_parameter(0, size * size * 3, [size, size * 3])
        self.create_bias_parameter(bias, size * 3)

2993

2994 2995
@config_layer('gru_step')
class GruStepLayer(LayerBase):
2996 2997 2998 2999 3000 3001 3002
    def __init__(self,
                 name,
                 size,
                 inputs,
                 active_gate_type="sigmoid",
                 bias=True,
                 **xargs):
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3003 3004
        super(GruStepLayer, self).__init__(name, 'gru_step', size, inputs,
                                           **xargs)
3005 3006 3007
        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)
3008 3009 3010 3011 3012
        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
3013
        self.create_input_parameter(0, size * size * 3, [size, size * 3])
3014 3015
        self.create_bias_parameter(bias, size * 3)

3016

3017 3018 3019 3020 3021 3022 3023
'''
 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
'''
3024 3025


3026 3027
@config_layer('crf')
class CRFLayer(LayerBase):
3028
    def __init__(self, name, size, inputs, coeff=1.0, device=None):
3029
        super(CRFLayer, self).__init__(name, 'crf', size, inputs, device=device)
3030 3031
        config_assert(2 <= len(self.inputs) <= 3,
                      'CRFLayer must have 2 or 3 inputs')
3032 3033 3034
        self.create_input_parameter(0, size * (size + 2), [size, size + 2])
        self.config.coeff = coeff

3035

3036 3037 3038 3039 3040 3041 3042 3043
'''
 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
'''
3044 3045


3046 3047
@config_layer('crf_decoding')
class CRFDecodingLayer(LayerBase):
3048
    def __init__(self, name, size, inputs, device=None):
3049 3050 3051 3052 3053 3054 3055
        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])

3056

3057 3058
@config_layer('ctc')
class CTCLayer(LayerBase):
3059
    def __init__(self, name, size, inputs, norm_by_times=False, device=None):
3060 3061 3062 3063
        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')

3064

3065 3066 3067 3068 3069 3070 3071 3072 3073 3074 3075 3076 3077 3078 3079 3080 3081 3082 3083 3084 3085
@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")


3086 3087
@config_layer('recurrent_layer_group')
class RecurrentLayerGroup(LayerBase):
3088
    def __init__(self, name, device=None):
3089 3090
        global g_pass_height_width
        g_pass_height_width = False
3091 3092 3093 3094 3095 3096
        super(RecurrentLayerGroup, self).__init__(
            name, 'recurrent_layer_group', 0, inputs=[], device=device)


# Deprecated, use a new layer specific class instead
@config_func
3097
def Layer(name, type, **xargs):
3098 3099 3100 3101
    layers = {}
    layers.update(g_cost_map)
    layers.update(g_layer_type_map)
    layer_func = layers.get(type)
3102
    config_assert(layer_func, "layer type '%s' not supported." % type)
3103
    return layer_func(name, **xargs)
3104

3105

3106
@config_func
3107
def ParameterHook(type, **kwargs):
3108 3109 3110 3111 3112 3113 3114 3115 3116 3117 3118 3119
    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
3120 3121 3122 3123 3124 3125 3126 3127 3128 3129 3130 3131 3132 3133 3134 3135 3136 3137 3138 3139 3140 3141
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):
3142 3143 3144 3145 3146 3147 3148

    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
3149 3150 3151 3152 3153 3154 3155 3156 3157 3158 3159
    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)

3160 3161
    config_assert(not momentum or not decay_rate_l1,
                  "momentum and decay_rate_l1 cannot both be non-zero")
3162 3163 3164 3165 3166

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

3167 3168 3169 3170
    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)
3171

3172 3173
    num_batches_regularization = default(num_batches_regularization,
                                         g_default_num_batches_regularization)
3174 3175 3176
    if num_batches_regularization is not None:
        para.num_batches_regularization = int(num_batches_regularization)

3177 3178 3179 3180 3181 3182
    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
3183 3184
    gradient_clipping_threshold = default(gradient_clipping_threshold,
                                          g_default_gradient_clipping_threshold)
3185 3186
    if gradient_clipping_threshold is not None:
        para.gradient_clipping_threshold = gradient_clipping_threshold
3187 3188
    para.initial_strategy = default(initial_strategy,
                                    g_default_initial_strategy)
3189 3190 3191 3192 3193 3194
    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:
3195 3196 3197
            print(
                "Use initial_smart, but dims not set. Initial_smart may not be used in this layer"
            )
3198 3199 3200 3201
            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)
3202 3203 3204 3205 3206 3207 3208

    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
3209 3210 3211 3212
    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")
3213 3214
    if is_shared is not None:
        para.is_shared = is_shared
3215 3216 3217 3218 3219 3220 3221 3222 3223 3224 3225 3226 3227 3228 3229 3230 3231 3232 3233 3234 3235

    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

3236

3237 3238 3239 3240 3241
@config_func
def default_initial_mean(val):
    global g_default_initial_mean
    g_default_initial_mean = val

3242

3243 3244 3245 3246 3247
@config_func
def default_initial_strategy(val):
    global g_default_initial_strategy
    g_default_initial_strategy = val

3248

3249 3250 3251 3252 3253
@config_func
def default_initial_smart(val):
    global g_default_initial_smart
    g_default_initial_smart = val

3254

3255 3256 3257 3258 3259
@config_func
def default_momentum(val):
    global g_default_momentum
    g_default_momentum = val

3260

3261 3262 3263 3264 3265
@config_func
def default_decay_rate(val):
    global g_default_decay_rate
    g_default_decay_rate = val

3266

3267 3268 3269 3270 3271
@config_func
def default_num_batches_regularization(val):
    global g_default_num_batches_regularization
    g_default_num_batches_regularization = val

3272

3273 3274 3275 3276 3277
@config_func
def default_gradient_clipping_threshold(val):
    global g_default_gradient_clipping_threshold
    g_default_gradient_clipping_threshold = val

3278

3279 3280 3281 3282 3283
@config_func
def default_device(val):
    global g_default_device
    g_default_device = val

3284

3285 3286 3287 3288 3289
@config_func
def default_update_hooks(val):
    global g_default_update_hooks
    g_default_update_hooks = val

3290

3291 3292 3293 3294 3295
@config_func
def default_compact_func(val):
    global g_default_compact_func
    g_default_compact_func = val

3296

3297 3298 3299 3300 3301
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)
3302 3303 3304
        execfile(config_file,
                 make_config_environment(config_file, config_args), local_args)

3305 3306
    return Import

3307

3308 3309 3310 3311 3312 3313 3314 3315 3316 3317 3318 3319 3320 3321 3322 3323 3324 3325 3326 3327 3328 3329 3330 3331 3332 3333 3334 3335
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,
3336 3337 3338
    adam_beta1=0.9,
    adam_beta2=0.999,
    adam_epsilon=1e-8, )
3339

3340
settings_deprecated = dict(usage_ratio=1., )
3341 3342 3343 3344

trainer_settings = dict(
    save_dir="./output/model",
    init_model_path=None,
3345 3346
    start_pass=0, )

3347 3348 3349 3350 3351

@config_func
def Settings(**args):
    for k, v in args.iteritems():
        if k == "usage_ratio":
3352 3353
            logger.warning(
                "Deprecated: define usage_ratio in DataConfig instead")
3354 3355 3356 3357 3358 3359 3360 3361 3362 3363 3364
            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)

3365

3366 3367 3368 3369
@config_func
def cluster_config(**args):
    pass

3370

3371 3372 3373 3374 3375 3376 3377 3378 3379
@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

3380

3381 3382 3383 3384
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))
3385

3386 3387 3388 3389 3390 3391 3392 3393 3394 3395 3396 3397 3398 3399 3400
        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),
3401
        get_config_arg=make_get_config_arg(config_args), )
3402 3403 3404 3405 3406

    funcs.update(g_extended_config_funcs)

    return funcs

3407

3408 3409 3410 3411 3412 3413 3414 3415 3416 3417 3418 3419 3420 3421 3422 3423
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

3424

3425 3426 3427 3428 3429 3430 3431 3432 3433 3434 3435 3436
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)"

3437

3438 3439 3440 3441
def my_fatal(s):
    logger.critical(s)
    raise Exception()

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3442

3443
_parse_config_hooks = set()
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Yu Yang 已提交
3444 3445


3446 3447 3448 3449 3450 3451 3452
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)
3453

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3454

3455
def update_g_config():
3456
    '''
3457 3458 3459 3460 3461 3462 3463 3464 3465 3466 3467 3468 3469 3470 3471 3472 3473 3474 3475 3476
    Update g_config after execute config_file or config_functions.
    '''
    for k, v in settings.iteritems():
        if v is None:
            continue
        g_config.opt_config.__setattr__(k, v)

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

    for name in g_config.model_config.input_layer_names:
        assert name in g_layer_map, \
            'input name "%s" does not correspond to a layer name' % name
        assert (g_layer_map[name].type == "data" or g_layer_map[name].type == "data_trim"), \
            'The type of input layer "%s" is not "data"' % name
    for name in g_config.model_config.output_layer_names:
        assert name in g_layer_map, \
            'input name "%s" does not correspond to a layer name' % name
3477 3478
    for hook in _parse_config_hooks:
        hook()
3479 3480 3481 3482 3483 3484 3485
    return g_config


def parse_config(trainer_config, config_arg_str):
    '''
    @param trainer_config: can be a string of config file name or a function name
    with config logic
3486 3487 3488 3489
    @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()
3490 3491
    # for hook in _parse_config_hooks:
    #     hook()
3492 3493 3494 3495 3496 3497 3498 3499 3500 3501 3502 3503 3504 3505 3506 3507 3508 3509 3510 3511 3512 3513 3514 3515 3516 3517 3518

    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

3519 3520
    if hasattr(trainer_config, '__call__'):
        trainer_config.func_globals.update(
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3521
            make_config_environment("", config_args))
3522
        trainer_config()
3523
    else:
3524 3525
        execfile(trainer_config,
                 make_config_environment(trainer_config, config_args))
3526

3527
    return update_g_config()
3528 3529


3530
def parse_config_and_serialize(trainer_config, config_arg_str):
3531
    try:
3532
        config = parse_config(trainer_config, config_arg_str)
3533 3534 3535 3536 3537 3538
        #logger.info(config)
        return config.SerializeToString()
    except:
        traceback.print_exc()
        raise

3539

3540 3541 3542 3543 3544 3545 3546 3547
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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