<gh_stars>10-100
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#


from config.collections import AttrDict

# --------------------------------------------------------------------------- #
# optimizer options:
# --------------------------------------------------------------------------- #

Adamax_par = AttrDict()
Adamax_par.lr = 0.01
Adamax_par.weight_decay = 0
Adamax_par.eps = 0.00000001

OPTIMIZER = {"Adamax": Adamax_par}


# --------------------------------------------------------------------------- #
# loss options:
# --------------------------------------------------------------------------- #

LOSS = {"logit": AttrDict(), "softmax": AttrDict()}


# --------------------------------------------------------------------------- #
# image feature encoding options:
# --------------------------------------------------------------------------- #
finetune_faster_rcnn_fpn_fc7 = AttrDict()
finetune_faster_rcnn_fpn_fc7.weights_file = ""
finetune_faster_rcnn_fpn_fc7.bias_file = ""

default_feature = AttrDict()

IMAGE_FEATURE_MODEL = {
    "finetune_faster_rcnn_fpn_fc7": finetune_faster_rcnn_fpn_fc7,
    "default": default_feature,
}


# --------------------------------------------------------------------------- #
# question embedding model options:
# --------------------------------------------------------------------------- #

# Attention question embedding model
att_que_embed = AttrDict()
att_que_embed.embedding_dim = 300
att_que_embed.LSTM_hidden_size = 1024
att_que_embed.LSTM_layer = 1
att_que_embed.dropout = 0
att_que_embed.conv1_out = 512
att_que_embed.conv2_out = 2
att_que_embed.kernel_size = 1
att_que_embed.padding = 0
att_que_embed.embedding_init_file = "vqa2.0_glove.6B.300d.txt.npy"

QUESTION_MODEL = {"att_que_embed": att_que_embed}


# --------------------------------------------------------------------------- #
# modal combine options:
# --------------------------------------------------------------------------- #

non_linear_elmt_multiply = AttrDict()
non_linear_elmt_multiply.hidden_size = 5000
non_linear_elmt_multiply.dropout = 0

MFH = AttrDict()

MFH.order = 1
MFH.hidden_sizes = [5000]
MFH.dropout = 0.1
MFH.pool_size = 5

MODAL_COMBINE = {"non_linear_elmt_multiply": non_linear_elmt_multiply, "MFH": MFH}


# --------------------------------------------------------------------------- #
# linear_transform options
# --------------------------------------------------------------------------- #
linear_transform = AttrDict()
linear_transform.out_dim = 1

conv_transform = AttrDict()
conv_transform.out_dim = 2
conv_transform.hidden_dim = 512


# --------------------------------------------------------------------------- #
# logit classifier
# --------------------------------------------------------------------------- #

logit_classifier = AttrDict()
logit_classifier.txt_hidden_dim = 300
logit_classifier.img_hidden_dim = 5000


# --------------------------------------------------------------------------- #
# weigt_norm_classifier
# --------------------------------------------------------------------------- #
weight_norm_classifier = AttrDict()
weight_norm_classifier.hidden_dim = 5000
weight_norm_classifier.dropout = 0.1


# --------------------------------------------------------------------------- #
# linear_classifier
# --------------------------------------------------------------------------- #
linear_classifier = AttrDict()


# --------------------------------------------------------------------------- #
# Adamax optimizer
# --------------------------------------------------------------------------- #
adamax_opt = AttrDict()
adamax_opt.lr = 0.01
adamax_opt.weight_decay = 0
adamax_opt.eps = 0.00000001

# SUMMARY of all model parameters
MODEL_TYPE_PAR_DICT = {
    "linear_transform": linear_transform,
    "conv_transform": conv_transform,
    "non_linear_elmt_multiply": MODAL_COMBINE["non_linear_elmt_multiply"],
    "MFH": MODAL_COMBINE["MFH"],
    "att_que_embed": QUESTION_MODEL["att_que_embed"],
    "logit_classifier": logit_classifier,
    "Adamax": adamax_opt,
    "default_image": IMAGE_FEATURE_MODEL["default"],
    "finetune_faster_rcnn_fpn_fc7": IMAGE_FEATURE_MODEL["finetune_faster_rcnn_fpn_fc7"],
    "weight_norm_classifier": weight_norm_classifier,
    "linear_classifier": linear_classifier,
}


class ModelParPair(AttrDict):

    IMMUTABLE = "__immutable__"

    def __init__(self, model_type):
        super(ModelParPair, self).__init__()

        self.method = model_type
        if self.method not in MODEL_TYPE_PAR_DICT:
            exit(
                "unkown model type %s, please check \
                 config/function_config_lib.py for allowed options"
            )
        self.par = MODEL_TYPE_PAR_DICT[self.method]

    def update_type(self, updated_pair_type):
        if updated_pair_type != self.method:
            self.method = updated_pair_type
            self.par = MODEL_TYPE_PAR_DICT[self.method]

    def is_immutable(self):
        return self.__dict__[ModelParPair.IMMUTABLE]

# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#


from config.collections import AttrDict

# --------------------------------------------------------------------------- #
# optimizer options:
# --------------------------------------------------------------------------- #

Adamax_par = AttrDict()
Adamax_par.lr = 0.01
Adamax_par.weight_decay = 0
Adamax_par.eps = 0.00000001

OPTIMIZER = {"Adamax": Adamax_par}


# --------------------------------------------------------------------------- #
# loss options:
# --------------------------------------------------------------------------- #

LOSS = {"logit": AttrDict(), "softmax": AttrDict()}


# --------------------------------------------------------------------------- #
# image feature encoding options:
# --------------------------------------------------------------------------- #
finetune_faster_rcnn_fpn_fc7 = AttrDict()
finetune_faster_rcnn_fpn_fc7.weights_file = ""
finetune_faster_rcnn_fpn_fc7.bias_file = ""

default_feature = AttrDict()

IMAGE_FEATURE_MODEL = {
    "finetune_faster_rcnn_fpn_fc7": finetune_faster_rcnn_fpn_fc7,
    "default": default_feature,
}


# --------------------------------------------------------------------------- #
# question embedding model options:
# --------------------------------------------------------------------------- #

# Attention question embedding model
att_que_embed = AttrDict()
att_que_embed.embedding_dim = 300
att_que_embed.LSTM_hidden_size = 1024
att_que_embed.LSTM_layer = 1
att_que_embed.dropout = 0
att_que_embed.conv1_out = 512
att_que_embed.conv2_out = 2
att_que_embed.kernel_size = 1
att_que_embed.padding = 0
att_que_embed.embedding_init_file = "vqa2.0_glove.6B.300d.txt.npy"

QUESTION_MODEL = {"att_que_embed": att_que_embed}


# --------------------------------------------------------------------------- #
# modal combine options:
# --------------------------------------------------------------------------- #

non_linear_elmt_multiply = AttrDict()
non_linear_elmt_multiply.hidden_size = 5000
non_linear_elmt_multiply.dropout = 0

MFH = AttrDict()

MFH.order = 1
MFH.hidden_sizes = [5000]
MFH.dropout = 0.1
MFH.pool_size = 5

MODAL_COMBINE = {"non_linear_elmt_multiply": non_linear_elmt_multiply, "MFH": MFH}


# --------------------------------------------------------------------------- #
# linear_transform options
# --------------------------------------------------------------------------- #
linear_transform = AttrDict()
linear_transform.out_dim = 1

conv_transform = AttrDict()
conv_transform.out_dim = 2
conv_transform.hidden_dim = 512


# --------------------------------------------------------------------------- #
# logit classifier
# --------------------------------------------------------------------------- #

logit_classifier = AttrDict()
logit_classifier.txt_hidden_dim = 300
logit_classifier.img_hidden_dim = 5000


# --------------------------------------------------------------------------- #
# weigt_norm_classifier
# --------------------------------------------------------------------------- #
weight_norm_classifier = AttrDict()
weight_norm_classifier.hidden_dim = 5000
weight_norm_classifier.dropout = 0.1


# --------------------------------------------------------------------------- #
# linear_classifier
# --------------------------------------------------------------------------- #
linear_classifier = AttrDict()


# --------------------------------------------------------------------------- #
# Adamax optimizer
# --------------------------------------------------------------------------- #
adamax_opt = AttrDict()
adamax_opt.lr = 0.01
adamax_opt.weight_decay = 0
adamax_opt.eps = 0.00000001

# SUMMARY of all model parameters
MODEL_TYPE_PAR_DICT = {
    "linear_transform": linear_transform,
    "conv_transform": conv_transform,
    "non_linear_elmt_multiply": MODAL_COMBINE["non_linear_elmt_multiply"],
    "MFH": MODAL_COMBINE["MFH"],
    "att_que_embed": QUESTION_MODEL["att_que_embed"],
    "logit_classifier": logit_classifier,
    "Adamax": adamax_opt,
    "default_image": IMAGE_FEATURE_MODEL["default"],
    "finetune_faster_rcnn_fpn_fc7": IMAGE_FEATURE_MODEL["finetune_faster_rcnn_fpn_fc7"],
    "weight_norm_classifier": weight_norm_classifier,
    "linear_classifier": linear_classifier,
}


class ModelParPair(AttrDict):

    IMMUTABLE = "__immutable__"

    def __init__(self, model_type):
        super(ModelParPair, self).__init__()

        self.method = model_type
        if self.method not in MODEL_TYPE_PAR_DICT:
            exit(
                "unkown model type %s, please check \
                 config/function_config_lib.py for allowed options"
            )
        self.par = MODEL_TYPE_PAR_DICT[self.method]

    def update_type(self, updated_pair_type):
        if updated_pair_type != self.method:
            self.method = updated_pair_type
            self.par = MODEL_TYPE_PAR_DICT[self.method]

    def is_immutable(self):
        return self.__dict__[ModelParPair.IMMUTABLE]
# Copyright (c) Facebook, Inc. and its affiliates. # All rights reserved. # # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. # # --------------------------------------------------------------------------- # # optimizer options: # --------------------------------------------------------------------------- # # --------------------------------------------------------------------------- # # loss options: # --------------------------------------------------------------------------- # # --------------------------------------------------------------------------- # # image feature encoding options: # --------------------------------------------------------------------------- # # --------------------------------------------------------------------------- # # question embedding model options: # --------------------------------------------------------------------------- # # Attention question embedding model # --------------------------------------------------------------------------- # # modal combine options: # --------------------------------------------------------------------------- # # --------------------------------------------------------------------------- # # linear_transform options # --------------------------------------------------------------------------- # # --------------------------------------------------------------------------- # # logit classifier # --------------------------------------------------------------------------- # # --------------------------------------------------------------------------- # # weigt_norm_classifier # --------------------------------------------------------------------------- # # --------------------------------------------------------------------------- # # linear_classifier # --------------------------------------------------------------------------- # # --------------------------------------------------------------------------- # # Adamax optimizer # --------------------------------------------------------------------------- # # SUMMARY of all model parameters