def __init__(self, input_vocab_size, opt_vocab_size, d_model, nhead,
num_encoder_layers, dim_feedforward, position_embed_size=300,
utter_n_layer=2, dropout=0.3, sos=0, pad=0, teach_force=1):
super(Transformer, self).__init__()
self.d_model = d_model
self.hidden_size = d_model
self.embed_src = nn.Embedding(input_vocab_size, d_model)
# position maxlen is 5000
self.pos_enc = PositionEmbedding(d_model, dropout=dropout,
max_len=position_embed_size)
self.input_vocab_size = input_vocab_size
self.utter_n_layer = utter_n_layer
self.opt_vocab_size = opt_vocab_size
self.pad, self.sos = pad, sos
self.teach_force = teach_force
encoder_layer = nn.TransformerEncoderLayer(d_model=d_model, nhead=nhead,
dim_feedforward=dim_feedforward,
dropout=dropout, activation='gelu')
self.encoder = nn.TransformerEncoder(encoder_layer,
num_layers=num_encoder_layers)
self.decoder = Decoder(d_model, d_model, opt_vocab_size,
n_layers=utter_n_layer, dropout=dropout, nhead=nhead)
def __init__(self, device):
super().__init__()
self.bert_tokenizer = BertTokenizer.from_pretrained(
"bert-base-uncased", do_lower_case=True)
self.bert_model = BertModel.from_pretrained("bert-base-uncased")
self.bert_feature_dim = self.bert_model.pooler.dense.in_features
self.indicator_vector = torch.nn.parameter.Parameter(
torch.rand(self.bert_feature_dim,
requires_grad=True,
dtype=torch.double),
requires_grad=True).to(device)
self.encoder_layer = nn.TransformerEncoderLayer(
d_model=self.bert_feature_dim, nhead=8)
self.transformer_encoder = nn.TransformerEncoder(self.encoder_layer,
num_layers=6)
self.norm_layer = nn.LayerNorm(self.bert_feature_dim)
self.projection_layer = nn.Linear(self.bert_feature_dim, 2)
self.device = device
#self.loss = nn.NLLLoss(weight=torch.tensor([0.17105,1]),reduction='mean', ignore_index=-1)
self.loss = nn.NLLLoss(weight=torch.tensor([1.0, 1.0]),
reduction='mean',
ignore_index=-1)
self.lsm = nn.LogSoftmax(dim=2)
def __init__(self, conf):
super().__init__()
self.save_hyperparameters(conf)
self.ke = nn.Embedding(4, 2, max_norm=1.)
self.conv1 = ConvBlock(3, 256, 13, stride=3, padding=6)
self.conv2 = ConvBlock(256, 256, 7, stride=1, padding=3)
self.conv3 = ConvBlock(256, 256, 3, stride=2, padding=1)
self.pos_encoder = PositionalEncoding(256, self.hparams.dropout)
encoder_layer = nn.TransformerEncoderLayer(256,
self.hparams.nhead,
self.hparams.dim_ff,
self.hparams.dropout,
activation='gelu')
self.encoder = nn.TransformerEncoder(encoder_layer,
self.hparams.nlayers)
self.fc1 = nn.Linear(256, 1)
self.train_acc = Accuracy()
self.val_acc = Accuracy(compute_on_step=False)
def __init__(self, id2char, model_para):
super(Transformer, self).__init__()
self.idim = model_para['encoder']['idim']
#FIXME: need to remove these hardcoded thing later
self.odim = len(id2char) + 2
self.sos_id = len(id2char) + 1
self.eos_id = len(id2char) + 1
self.blank_id = 0
self.space_id = -1 #FIXME: what is this
self.vgg_ch_dim = 128
self.feat_extractor = nn.Sequential(
nn.Conv2d(1, 64, 3, stride=1, padding=1),
nn.ReLU(),
nn.Conv2d(64, 64, 3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2d(2, stride=2),
nn.Conv2d(64, 128, 3, stride=1, padding=1),
nn.ReLU(),
nn.Conv2d(self.vgg_ch_dim,self.vgg_ch_dim, 3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2d(2, stride=2),
)
self.vgg_o_dim = self.vgg_ch_dim * floor(self.idim/4)
# self.vgg2enc = nn.Linear(self.vgg_o_dim, model_para['encoder']['d_model'])
self.vgg2enc = nn.Linear(self.vgg_o_dim, model_para['encoder']['d_model'])
self.pos_encoder = PositionalEncoding(model_para['encoder']['d_model'], model_para['encoder']['dropout'])
encoder_layer = nn.TransformerEncoderLayer(model_para['encoder']['d_model'], #512
model_para['encoder']['nhead'], # 2
model_para['encoder']['dim_inner'], # 2048
model_para['encoder']['dropout'] # 0.1
)
self.encoder = nn.TransformerEncoder(encoder_layer, model_para['encoder']['nlayers'])
def __init__(self, config: BertConfig):
super(Bert, self).__init__()
self.token_embeddings = nn.Embedding(config.vocab_size,
config.hidden_size)
self.token_type_embeddings = nn.Embedding(config.type_vocab_size,
config.hidden_size)
self.position_embeddings = nn.Embedding(config.max_position_embeddings,
config.hidden_size)
self.embedding_layer_norm = nn.LayerNorm(config.hidden_size)
self.embedding_dropout = nn.Dropout(p=config.hidden_dropout_prob)
self.encoders = nn.TransformerEncoder(
encoder_layer=nn.TransformerEncoderLayer(
d_model=config.hidden_size,
nhead=config.num_attention_heads,
dim_feedforward=config.intermediate_size,
dropout=config.attention_probs_dropout_prob,
activation=config.hidden_act,
),
num_layers=config.num_hidden_layers,
)
self.pooler_layer = nn.Linear(config.hidden_size, config.hidden_size)
self.pooled_output_activate = nn.Tanh()
def __init__(self,
d_model=512,
nhead=8,
num_encoder_layers=6,
num_decoder_layers=6,
dim_feedforward=2048,
dropout=0.1,
activation="relu",
custom_encoder=None,
custom_decoder=None):
super().__init__()
if custom_encoder is not None:
self.encoder = custom_encoder
else:
encoder_layer = nn.TransformerEncoderLayer(d_model, nhead,
dim_feedforward,
dropout, activation)
encoder_norm = nn.LayerNorm(d_model)
self.encoder = nn.TransformerEncoder(encoder_layer,
num_encoder_layers,
encoder_norm)
if custom_decoder is not None:
self.decoder = custom_decoder
else:
decoder_layer = TransformerDecoderLayerWithFastDecode(
d_model, nhead, dim_feedforward, dropout, activation)
decoder_norm = nn.LayerNorm(d_model)
self.decoder = TransformerDecoderWithFastDecode(
decoder_layer, num_decoder_layers, decoder_norm)
self._reset_parameters()
self.d_model = d_model
self.nhead = nhead
def __init__(self, config):
super(TransformerLM, self).__init__()
vocabSize = config.data.vocabSize
self.nemd = config.model.transformer.nemd
emd_drop_ratio = config.model.transformer.emd_drop_ratio
hidden_drop_ratio = config.model.transformer.hidden_drop_ratio
nhead = config.model.transformer.nhead
nhid = config.model.transformer.nhid
nlayer = config.model.transformer.nlayer
self.src_mask=None
tie_weight = config.model.transformer.tie_weight
self.embedding = nn.Embedding(vocabSize, self.nemd)
self.pos_encoder = PositionalEncoding(config)
self.dropout = nn.Dropout(emd_drop_ratio)
encoder_layers = nn.TransformerEncoderLayer(self.nemd, nhead, nhid, hidden_drop_ratio)
self.transformer_encoder = nn.TransformerEncoder(encoder_layers, nlayer)
self.out = nn.Linear(self.nemd, vocabSize)
# if not pretrained embedding
self.init_weights()
if tie_weight:
self.out.weight = self.embedding.weight
def __init__(self,
input_dim,
hidden_dim,
num_layers=1,
dropout=0,
use_categories=False):
super().__init__()
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.num_layers = num_layers
self.dropout = dropout
self.use_categories = use_categories
enc_layer = nn.TransformerEncoderLayer(hidden_dim,
2,
dim_feedforward=hidden_dim * 4,
dropout=dropout,
activation='gelu')
self.enc = nn.TransformerEncoder(enc_layer, num_layers)
self.input_fc = nn.Linear(input_dim, hidden_dim)
if use_categories:
self.dropout_mod = nn.Dropout(dropout)
self.stroke_cat_fc = nn.Linear(input_dim + hidden_dim, hidden_dim)
def __init__(self, args):
'''
transformer encoder for language, frames and action inputs
'''
super(EncoderVL, self).__init__()
# transofmer layers
encoder_layer = nn.TransformerEncoderLayer(
args.demb, args.encoder_heads, args.demb,
args.dropout['transformer']['encoder'])
self.enc_transformer = nn.TransformerEncoder(encoder_layer,
args.encoder_layers)
# how many last actions to attend to
self.num_input_actions = args.num_input_actions
# encodings
self.enc_pos = PosEncoding(args.demb) if args.enc['pos'] else None
self.enc_pos_learn = PosLearnedEncoding(
args.demb) if args.enc['pos_learn'] else None
self.enc_token = TokenLearnedEncoding(
args.demb) if args.enc['token'] else None
self.enc_layernorm = nn.LayerNorm(args.demb)
self.enc_dropout = nn.Dropout(args.dropout['emb'], inplace=True)
def __init__(self,
input_size,
emb_size,
hidden_size,
num_layer,
max_len=64):
super().__init__()
self.emb_size = emb_size
self.hidden_size = hidden_size
self.num_layer = num_layer
self.scale = math.sqrt(emb_size)
self.embedding = nn.Embedding(input_size, emb_size)
# additional length for sos and eos
self.pos_encoder = PositionEncoder(max_len + 10, emb_size)
encoder_layer = nn.TransformerEncoderLayer(d_model=emb_size,
nhead=8,
dim_feedforward=hidden_size,
dropout=0.1,
activation='gelu')
encoder_norm = nn.LayerNorm(emb_size)
self.encoder = nn.TransformerEncoder(encoder_layer,
num_layers=num_layer,
norm=encoder_norm)
def __init__(
self,
d_model: int,
nhead: int,
vocab_size: int,
max_len: int,
num_encoder_layers: int = 6,
num_decoder_layers: int = 6,
) -> None:
super(Transformer2, self).__init__()
encoder_layer = nn.TransformerEncoderLayer(d_model=d_model,
nhead=nhead)
decoder_layer = nn.TransformerDecoderLayer(d_model=d_model,
nhead=nhead)
self.encoder = nn.TransformerEncoder(encoder_layer, num_encoder_layers)
self.decoder = nn.TransformerDecoder(decoder_layer, num_decoder_layers)
self.embedding = nn.Embedding(vocab_size, d_model)
self.positional_encoding = PositionalEncoding(d_model, max_len)
self.output_bias = Parameter(torch.Tensor(vocab_size))
self._init_bias()
def __init__(self, num_slots, slot_dim, hidden_dim, num_heads, num_layers,
**kwargs):
super().__init__()
self.num_slots = num_slots
self.slot_dim = slot_dim
self.hidden_dim = hidden_dim
self.num_heads = num_heads
# TODO: for now we simply embed the slots to higher dim, create
# TODO: custom transformer layer
# TODO: non-relational Transformer is not directly comparable
# TODO: to non-relational GNN
self.slot_encode = nn.Linear(slot_dim, hidden_dim)
self.slot_decode = nn.Linear(hidden_dim, slot_dim)
transformer_layer = nn.TransformerEncoderLayer(d_model=hidden_dim,
nhead=num_heads)
self.transformer = nn.TransformerEncoder(
encoder_layer=transformer_layer, num_layers=num_layers)
def __init__(self,
input_window,
output_window,
num_layers=1,
dropout=0.1,
longueur_serie=23):
"""
Init.
Parameters
----------
input_window: int
Représente le nombre de jour de la séquence d'entrée
Longueur de la séquence d'entrée: 24 * input_window
output_window: int
Représente le nombre d'heure de la séquence de sortie
Longueur de la séquence de sortie: output_window
"""
super(Transformer, self).__init__()
self.name_model = 'Transformer'
self.input_window = input_window
self.output_window = output_window
self.feature_size = self.output_window * 4
self.src_mask = None
self.pos_encoder = PositionalEncoding(self.feature_size)
self.encoder_layer = nn.TransformerEncoderLayer(
d_model=self.feature_size,
dim_feedforward=self.feature_size * 4,
nhead=self.output_window,
dropout=dropout)
self.transformer_encoder = nn.TransformerEncoder(
self.encoder_layer, num_layers=num_layers).float()
self.decoder = nn.Linear(self.feature_size, 1)
self.init_weights()
def __init__(self, numberTokens, embeddingSize, maxLength, numberEncoderLayers, numberDecoderLayers, attentionHeadCount, transformerHiddenDenseSize, batch_size=32):
# Based on https://pytorch.org/tutorials/beginner/transformer_tutorial.html
super(Transformer, self).__init__()
self.batch_size=batch_size
self.model_type = 'Transformer'
self.embeddingSize = embeddingSize
self.numberTokens = numberTokens
self.encoderEmbedding = nn.Embedding(numberTokens, embeddingSize)
self.maxLength = maxLength
encoderLayer = nn.TransformerEncoderLayer(embeddingSize, attentionHeadCount, transformerHiddenDenseSize)
self.encoder = nn.TransformerEncoder(encoderLayer, numberEncoderLayers)
self.decoderEmbedding = nn.Embedding(numberTokens, embeddingSize)
decoderLayer = nn.TransformerDecoderLayer(embeddingSize, attentionHeadCount, transformerHiddenDenseSize)
self.decoder = nn.TransformerDecoder(decoderLayer, numberDecoderLayers)
self.decoderLinear = nn.Linear(embeddingSize, numberTokens)
self.decoderSoftmax = nn.Softmax(dim=2)
def __init__(self, d_model, seq_len, nhead, dim_feedforward, dropout,
num_layers):
super(Encoder, self).__init__()
# 時系列データの入力処理
self.embedding_layer = nn.Linear(51, d_model)
# Positional Encodeingの処理
self.positionalencoding_layer = PositionalEncoding(d_model, seq_len)
# Dropoutの処理
self.dropout_layer = nn.Dropout(p=dropout)
# Transformer decoder側の処理 default dim_feedforward =2048
self.encoder_layer = nn.TransformerEncoderLayer(
d_model=d_model,
nhead=nhead,
dim_feedforward=dim_feedforward,
dropout=dropout)
self.transformer_encoder = nn.TransformerEncoder(
self.encoder_layer, num_layers)
# Discriminatorへの入力用
self.linear_layer_dis = nn.Linear(seq_len * d_model, d_model)
def __init__(self,
input_dim,
rnn_hidden_dims,
max_ponder=3,
epsilon=0.05,
last_relu=True,
act_steps=3,
act_fixed=False):
super(ATC_TFencoder, self).__init__()
self.rnn_hidden_dim = rnn_hidden_dims[-1]
self.epsilon = epsilon
# self.rnn_cell = GRUEXND(input_dim, rnn_hidden_dims, last_relu)
self.transformer_encoder_layer = nn.TransformerEncoderLayer(
d_model=50, nhead=2, dim_feedforward=150)
self.transformer_encoder = nn.TransformerEncoder(
self.transformer_encoder_layer, num_layers=1)
#TransformerEncoder(source_dims=13, k_dims=16, v_dims=16, n_heads=3, layer_cnt=1)
self.transition_layer = nn.Linear(13, rnn_hidden_dims[-1])
self.max_ponder = max_ponder
self.ponder_linear = nn.Linear(rnn_hidden_dims[-1], 1)
self.act_fixed = act_fixed
self.act_steps = act_steps
def __init__(self,
n_skill,
max_seq=100,
embed_dim=128,
num_heads=8,
dropout=0.2):
super(SAKTModel, self).__init__()
self.n_skill = n_skill
self.embed_dim = embed_dim
embed_dim = 32 * 6 + 256
self.embedding = nn.Embedding(4, 32)
self.user_answer_embedding = nn.Embedding(6, 32)
self.prior_question_had_explanation_embedding = nn.Embedding(4, 32)
self.e_embedding = nn.Embedding(n_skill + 1, 256)
self.part_embedding = nn.Embedding(8, 32)
self.elapsed_time_embedding = nn.Embedding(302, 32)
self.duration_previous_content_embedding = nn.Embedding(302, 32)
encoder_layer = nn.TransformerEncoderLayer(d_model=embed_dim,
nhead=num_heads,
dropout=dropout)
self.transformer_enc = nn.TransformerEncoder(
encoder_layer=encoder_layer, num_layers=4)
self.gru = nn.GRU(input_size=embed_dim, hidden_size=embed_dim)
self.continuous_embedding = nn.Sequential(nn.BatchNorm1d(99),
nn.Linear(1, embed_dim // 2),
nn.LayerNorm(embed_dim // 2))
self.cat_embedding = nn.Sequential(
nn.Linear(embed_dim, embed_dim // 2), nn.LayerNorm(embed_dim // 2))
self.layer_normal = nn.LayerNorm(embed_dim)
self.ffn = FFN(embed_dim)
self.dropout = nn.Dropout(dropout / 2)
self.pred = nn.Linear(embed_dim, 1)
def __init__(self,
d_model: int = 300,
nhead: int = 6,
num_encoder_layers: int = 4,
num_decoder_layers: int = 4,
dim_feedforward: int = 1024,
dropout: float = 0.1,
activation: str = "relu",
words_num: int = 0) -> None:
super(Transformer, self).__init__()
self.source_embedding = nn.Embedding(words_num, 300)
self.pos_encoder = PositionalEncoding(d_model=d_model,
dropout=dropout,
max_len=75)
self.pos_decoder = PositionalEncoding(d_model=d_model,
dropout=dropout,
max_len=74)
encoder_layer = nn.TransformerEncoderLayer(d_model, nhead,
dim_feedforward, dropout,
activation)
encoder_norm = nn.LayerNorm(d_model)
self.encoder = nn.TransformerEncoder(encoder_layer, num_encoder_layers,
encoder_norm)
self.target_embedding = nn.Embedding(words_num, 300)
decoder_layer = nn.TransformerDecoderLayer(d_model, nhead,
dim_feedforward, dropout,
activation)
decoder_norm = nn.LayerNorm(d_model)
self.decoder = nn.TransformerDecoder(decoder_layer, num_decoder_layers,
decoder_norm)
self.out = nn.Linear(d_model, words_num)
self._reset_parameters()
self.d_model = d_model
self.nhead = nhead
def __init__(self,
input_size,
encoder_size,
n_head,
feedforward_size,
n_layers,
dropout=0):
'''
input_size:
'''
super(EncoderTransformer, self).__init__()
# self.model_type = 'Transformer'
# self.src_mask = None
# self.pos_encoder = PositionalEncoding(ninp, dropout)
encoder_layers = nn.TransformerEncoderLayer(encoder_size,
n_head,
feedforward_size,
dropout=dropout)
self.transformer_encoder = nn.TransformerEncoder(
encoder_layers, n_layers)
def __init__(self):
super(sequence_encoder, self).__init__()
self.embedding_layer = nn.Embedding(5, 512)
self.encoder_layer = nn.TransformerEncoderLayer(512, 8, 1024)
self.encoder = nn.TransformerEncoder(self.encoder_layer, 5)
def __init__(self, config: Config):
super().__init__(config)
conv_H = config.experiment.cmax_h
conv_W = config.experiment.cmax_w
conv_layers = []
in_channels = 1
for index, filters in enumerate(config.experiment.cnn_filters):
out_channels = filters
conv_layers.extend([
nn.Conv2d(in_channels=in_channels,
out_channels=out_channels,
kernel_size=(3, 3),
stride=(2, 2),
padding=1),
nn.ReLU(),
nn.BatchNorm2d(num_features=out_channels),
])
if index != len(config.experiment.cnn_filters) - 1:
conv_layers.append(nn.Dropout(config.experiment.dropout))
conv_W = math.ceil(conv_W / 2)
conv_H = math.ceil(conv_H / 2)
in_channels = out_channels
self.conv = nn.Sequential(
*conv_layers, nn.Flatten(),
nn.Linear(in_features=conv_W * conv_H * out_channels,
out_features=conv_W * conv_H * out_channels))
self.conv_time_distributed = TimeDistributed(self.conv)
self.embed_dim = self.features_len * (
config.experiment.time2vec_embedding_size +
1) + conv_W * conv_H * out_channels
if config.experiment.use_all_gfs_as_input:
self.time_2_vec_time_distributed = TimeDistributed(
Time2Vec(
self.features_len + len(
process_config(
config.experiment.train_parameters_config_file)),
config.experiment.time2vec_embedding_size),
batch_first=True)
self.embed_dim += len(
process_config(
config.experiment.train_parameters_config_file)) * (
config.experiment.time2vec_embedding_size + 1)
self.pos_encoder = PositionalEncoding(self.embed_dim, self.dropout,
self.sequence_length)
encoder_layer = nn.TransformerEncoderLayer(
d_model=self.embed_dim,
nhead=config.experiment.transformer_attention_heads,
dim_feedforward=config.experiment.transformer_ff_dim,
dropout=config.experiment.dropout,
batch_first=True)
encoder_norm = nn.LayerNorm(self.embed_dim)
self.encoder = nn.TransformerEncoder(
encoder_layer, config.experiment.transformer_attention_layers,
encoder_norm)
dense_layers = []
features = self.embed_dim + 1
for neurons in config.experiment.transformer_head_dims:
dense_layers.append(
nn.Linear(in_features=features, out_features=neurons))
features = neurons
dense_layers.append(nn.Linear(in_features=features, out_features=1))
self.classification_head = nn.Sequential(*dense_layers)
self.classification_head_time_distributed = TimeDistributed(
self.classification_head, batch_first=True)
def __init__(self, ):
super(TextTansformer, self).__init__()
self.encoder = nn.TransformerEncoder()
self.decoder = nn.TransformerDecoder()
pass
def __init__(self,
vocab,
feature_dim=(1024, 14, 14),
stem_num_layers=2,
stem_batchnorm=False,
module_dim=128,
text_dim=1,
module_residual=True,
module_batchnorm=False,
classifier_proj_dim=512,
classifier_downsample='maxpool2',
classifier_fc_layers=(1024, ),
classifier_batchnorm=False,
classifier_dropout=0,
verbose=True):
super(ModuleNet, self).__init__()
self.stem = build_stem(feature_dim[0],
module_dim,
num_layers=stem_num_layers,
with_batchnorm=stem_batchnorm)
if verbose:
print('Here is my stem:')
print(self.stem)
self.char_lstm = nn.LSTM(input_size=28,
hidden_size=98,
bidirectional=True,
batch_first=True)
encoder_layer = nn.TransformerEncoderLayer(d_model=28, nhead=7)
self.char_transformer = nn.TransformerEncoder(
encoder_layer=encoder_layer, num_layers=3)
self.char_linear = nn.Linear(28, 196)
num_answers = len(vocab['answer_idx_to_token'])
module_H, module_W = feature_dim[1], feature_dim[2]
self.classifier = build_classifier(module_dim + text_dim,
module_H,
module_W,
num_answers,
classifier_fc_layers,
classifier_proj_dim,
classifier_downsample,
with_batchnorm=classifier_batchnorm,
dropout=classifier_dropout)
if verbose:
print('Here is my classifier:')
print(self.classifier)
self.stem_times = []
self.module_times = []
self.classifier_times = []
self.timing = False
self.function_modules = {}
self.function_modules_num_inputs = {}
self.vocab = vocab
self.module_list = []
for idx, fn_str in enumerate(vocab['program_token_to_idx']):
num_inputs = iep.programs.get_num_inputs(fn_str)
self.function_modules_num_inputs[fn_str] = num_inputs
if fn_str == 'scene' or num_inputs == 1:
mod = ResidualBlock(module_dim + text_dim,
with_residual=module_residual,
with_batchnorm=module_batchnorm)
elif num_inputs == 2:
mod = ConcatBlock(module_dim + text_dim,
with_residual=module_residual,
with_batchnorm=module_batchnorm)
self.add_module(fn_str, mod)
self.module_list.append(mod)
self.function_modules[fn_str] = idx
self.module_list = nn.ModuleList(self.module_list)
self.save_module_outputs = False
def __init__(self):
layer = nn.TransformerEncoderLayer(128, 8)
self.encoder = nn.TransformerEncoder(layer, 8)
self.linear = nn.Linear(128, 2048)
def __init__(self,
char_embedding_dim: int,
out_dim: int,
image_feature_dim: int = 512,
nheaders: int = 8,
nlayers: int = 6,
feedforward_dim: int = 2048,
dropout: float = 0.1,
max_len: int = 100,
image_encoder: str = 'resnet50',
roi_pooling_mode: str = 'roi_align',
roi_pooling_size: Tuple[int, int] = (7, 7)):
'''
convert image segments and text segments to node embedding.
:param char_embedding_dim:
:param out_dim:
:param image_feature_dim:
:param nheaders:
:param nlayers:
:param feedforward_dim:
:param dropout:
:param max_len:
:param image_encoder:
:param roi_pooling_mode:
:param roi_pooling_size:
'''
super().__init__()
self.dropout = dropout
assert roi_pooling_mode in [
'roi_align', 'roi_pool'
], 'roi pooling model: {} not support.'.format(roi_pooling_mode)
self.roi_pooling_mode = roi_pooling_mode
assert roi_pooling_size and len(
roi_pooling_size) == 2, 'roi_pooling_size not be set properly.'
self.roi_pooling_size = tuple(roi_pooling_size) # (h, w)
transformer_encoder_layer = nn.TransformerEncoderLayer(
d_model=char_embedding_dim,
nhead=nheaders,
dim_feedforward=feedforward_dim,
dropout=dropout)
self.transformer_encoder = nn.TransformerEncoder(
transformer_encoder_layer, num_layers=nlayers)
if image_encoder == 'resnet18':
self.cnn = resnet.resnet18(output_channels=out_dim)
elif image_encoder == 'resnet34':
self.cnn = resnet.resnet34(output_channels=out_dim)
elif image_encoder == 'resnet50':
self.cnn = resnet.resnet50(output_channels=out_dim)
elif image_encoder == 'resnet101':
self.cnn = resnet.resnet101(output_channels=out_dim)
elif image_encoder == 'resnet152':
self.cnn = resnet.resnet152(output_channels=out_dim)
else:
raise NotImplementedError()
self.conv = nn.Conv2d(image_feature_dim, out_dim,
self.roi_pooling_size)
self.bn = nn.BatchNorm2d(out_dim)
self.projection = nn.Linear(2 * out_dim, out_dim)
self.norm = nn.LayerNorm(out_dim)
# Compute the positional encodings once in log space.
position_embedding = torch.zeros(max_len, char_embedding_dim)
position = torch.arange(0, max_len).unsqueeze(1).float()
div_term = torch.exp(
torch.arange(0, char_embedding_dim, 2).float() *
-(math.log(10000.0) / char_embedding_dim))
position_embedding[:, 0::2] = torch.sin(position * div_term)
position_embedding[:, 1::2] = torch.cos(position * div_term)
position_embedding = position_embedding.unsqueeze(0).unsqueeze(
0) # 1, 1, max_len, char_embedding_dim
self.register_buffer('position_embedding', position_embedding)
self.pe_droput = nn.Dropout(self.dropout)
def __init__(self, input_dim=40, hidden_dim=128, output_dim=2, num_layers=2):
super(LSTMTrans1_deep, self).__init__()
self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, dropout=0.2, batch_first=True)
encoder_layer = nn.TransformerEncoderLayer(d_model=hidden_dim, nhead=2)
self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=4)
self.hidden2out = nn.Linear(hidden_dim, output_dim)
def __init__(self, hidden_size=20, num_heads=16):
super(TransformerModel, self).__init__()
self.encoder_layer = nn.TransformerEncoderLayer(d_model=hidden_size,
nhead=num_heads)
self.transformer_encoder = nn.TransformerEncoder(self.encoder_layer,
num_layers=4)
def __init__(self, hps, obs_config):
super(TransformerPolicy7, self).__init__()
assert obs_config.drones > 0 or obs_config.minerals > 0,\
'Must have at least one mineral or drones observation'
assert obs_config.drones >= obs_config.allies
assert not hps.use_privileged or (
hps.nmineral > 0 and hps.nally > 0 and
(hps.nenemy > 0 or hps.ally_enemy_same))
assert hps.nally == obs_config.allies
assert hps.nenemy == obs_config.drones - obs_config.allies
assert hps.nmineral == obs_config.minerals
assert hps.ntile == obs_config.tiles
self.version = 'transformer_v7'
self.kwargs = dict(hps=hps, obs_config=obs_config)
self.hps = hps
self.obs_config = obs_config
self.agents = hps.agents
self.nally = hps.nally
self.nenemy = hps.nenemy
self.nmineral = hps.nmineral
self.nconstant = hps.nconstant
self.ntile = hps.ntile
self.nitem = hps.nally + hps.nenemy + hps.nmineral + hps.nconstant + hps.ntile
self.fp16 = hps.fp16
self.d_agent = hps.d_agent
self.d_item = hps.d_item
self.naction = hps.objective.naction() + obs_config.extra_actions()
if hasattr(obs_config, 'global_drones'):
self.global_drones = obs_config.global_drones
else:
self.global_drones = 0
if hps.norm == 'none':
norm_fn = lambda x: nn.Sequential()
elif hps.norm == 'batchnorm':
norm_fn = lambda n: nn.BatchNorm2d(n)
elif hps.norm == 'layernorm':
norm_fn = lambda n: nn.LayerNorm(n)
else:
raise Exception(f'Unexpected normalization layer {hps.norm}')
endglobals = self.obs_config.endglobals()
endallies = self.obs_config.endallies()
endenemies = self.obs_config.endenemies()
endmins = self.obs_config.endmins()
endtiles = self.obs_config.endtiles()
endallenemies = self.obs_config.endallenemies()
self.agent_embedding = ItemBlock(
obs_config.dstride() + obs_config.global_features(),
hps.d_agent,
hps.d_agent * hps.dff_ratio,
norm_fn,
True,
mask_feature=7, # Feature 7 is hitpoints
)
self.relpos_net = ItemBlock(3, hps.d_item // 2,
hps.d_item // 2 * hps.dff_ratio, norm_fn,
hps.item_ff)
self.item_nets = nn.ModuleList()
if hps.ally_enemy_same:
self.item_nets.append(
PosItemBlock(
obs_config.dstride(),
hps.d_item // 2,
hps.d_item // 2 * hps.dff_ratio,
norm_fn,
hps.item_ff,
mask_feature=7, # Feature 7 is hitpoints
count=obs_config.drones,
start=endglobals,
end=endenemies,
))
else:
if self.nally > 0:
self.item_nets.append(
PosItemBlock(
obs_config.dstride(),
hps.d_item // 2,
hps.d_item // 2 * hps.dff_ratio,
norm_fn,
hps.item_ff,
mask_feature=7, # Feature 7 is hitpoints
count=obs_config.allies,
start=endglobals,
end=endallies,
))
if self.nenemy > 0:
self.item_nets.append(
PosItemBlock(
obs_config.dstride(),
hps.d_item // 2,
hps.d_item // 2 * hps.dff_ratio,
norm_fn,
hps.item_ff,
mask_feature=7, # Feature 7 is hitpoints
count=obs_config.drones - self.obs_config.allies,
start=endallies,
end=endenemies,
start_privileged=endtiles
if hps.use_privileged else None,
end_privileged=endallenemies
if hps.use_privileged else None,
))
if hps.nmineral > 0:
self.item_nets.append(
PosItemBlock(
obs_config.mstride(),
hps.d_item // 2,
hps.d_item // 2 * hps.dff_ratio,
norm_fn,
hps.item_ff,
mask_feature=2, # Feature 2 is size
count=obs_config.minerals,
start=endenemies,
end=endmins,
))
if hps.ntile > 0:
self.item_nets.append(
PosItemBlock(
obs_config.tstride(),
hps.d_item // 2,
hps.d_item // 2 * hps.dff_ratio,
norm_fn,
hps.item_ff,
mask_feature=2, # Feature is elapsed since last visited time
count=obs_config.tiles,
start=endmins,
end=endtiles,
))
if hps.nconstant > 0:
self.constant_items = nn.Parameter(
torch.normal(0, 1, (hps.nconstant, hps.d_item)))
if hps.item_item_attn_layers > 0:
encoder_layer = nn.TransformerEncoderLayer(d_model=hps.d_item,
nhead=8)
self.item_item_attn = nn.TransformerEncoder(
encoder_layer, num_layers=hps.item_item_attn_layers)
else:
self.item_item_attn = None
self.multihead_attention = MultiheadAttention(
embed_dim=hps.d_agent,
kdim=hps.d_item,
vdim=hps.d_item,
num_heads=hps.nhead,
dropout=hps.dropout,
)
self.linear1 = nn.Linear(hps.d_agent, hps.d_agent * hps.dff_ratio)
self.linear2 = nn.Linear(hps.d_agent * hps.dff_ratio, hps.d_agent)
self.norm1 = nn.LayerNorm(hps.d_agent)
self.norm2 = nn.LayerNorm(hps.d_agent)
self.map_channels = hps.d_agent // (hps.nm_nrings * hps.nm_nrays)
map_item_channels = self.map_channels - 2 if self.hps.map_embed_offset else self.map_channels
self.downscale = nn.Linear(hps.d_item, map_item_channels)
self.norm_map = norm_fn(map_item_channels)
self.conv1 = spatial.ZeroPaddedCylindricalConv2d(self.map_channels,
hps.dff_ratio *
self.map_channels,
kernel_size=3)
self.conv2 = spatial.ZeroPaddedCylindricalConv2d(hps.dff_ratio *
self.map_channels,
self.map_channels,
kernel_size=3)
self.norm_conv = norm_fn(self.map_channels)
final_width = hps.d_agent
if hps.nearby_map:
final_width += hps.d_agent
self.final_layer = nn.Sequential(
nn.Linear(final_width, hps.d_agent * hps.dff_ratio),
nn.ReLU(),
)
self.policy_head = nn.Linear(hps.d_agent * hps.dff_ratio, self.naction)
if hps.small_init_pi:
self.policy_head.weight.data *= 0.01
self.policy_head.bias.data.fill_(0.0)
if hps.use_privileged:
self.value_head = nn.Linear(
hps.d_agent * hps.dff_ratio + hps.d_item, 1)
else:
self.value_head = nn.Linear(hps.d_agent * hps.dff_ratio, 1)
if hps.zero_init_vf:
self.value_head.weight.data.fill_(0.0)
self.value_head.bias.data.fill_(0.0)
self.epsilon = 1e-4 if hps.fp16 else 1e-8
def __init__(self,
early_fusion,
d_model,
n_head,
dim_feedforward,
dropout,
num_layers,
layer_norm,
embed_dropout,
output_dim,
out_dropout,
multimodal_transformer=True):
super(Transformer, self).__init__()
self.multimodal_transformer = multimodal_transformer
if self.multimodal_transformer:
self.d_mult = d_model
self.mult = mult_model.MULTModel(
orig_d_l=300,
orig_d_a=74,
orig_d_v=35,
d_l=self.d_mult, # different from MulT
d_a=self.d_mult, # different from MulT
d_v=self.d_mult, # different from MulT
vonly=True,
aonly=True,
lonly=True,
num_heads=n_head,
layers=num_layers,
attn_dropout=0.1,
attn_dropout_a=0.0,
attn_dropout_v=0.0,
relu_dropout=0.1,
res_dropout=0.1,
out_dropout=out_dropout,
embed_dropout=embed_dropout,
attn_mask=True)
self.t_in_dim = self.v_in_dim = self.a_in_dim = self.d_mult * 2
combined_dim = 6 * d_model
else:
self.early_fusion = early_fusion
'Only late fusion implemented; early fusion will be implemented later'
# Late fusion
if not self.early_fusion:
self.orig_d_t = 300
self.orig_d_a = 74
self.orig_d_v = 35
self.d_t = self.d_a = self.d_v = d_model
# Temporal convolutional layers
self.proj_t = nn.Conv1d(self.orig_d_t,
self.d_t,
kernel_size=1,
padding=0,
bias=False)
self.proj_a = nn.Conv1d(self.orig_d_a,
self.d_a,
kernel_size=1,
padding=0,
bias=False)
self.proj_v = nn.Conv1d(self.orig_d_v,
self.d_v,
kernel_size=1,
padding=0,
bias=False)
# Transformer Layers
self.encoder_layer_t = nn.TransformerEncoderLayer(
d_model, nhead=n_head, dim_feedforward=dim_feedforward)
self.encoder_layer_a = nn.TransformerEncoderLayer(
d_model, nhead=n_head, dim_feedforward=dim_feedforward)
self.encoder_layer_v = nn.TransformerEncoderLayer(
d_model, nhead=n_head, dim_feedforward=dim_feedforward)
'Remember to implement layer norm option here'
if layer_norm:
print(
"layer norm not implemented yet for vanilla transformer"
)
assert False
else:
self.transformer_encoder_t = nn.TransformerEncoder(
self.encoder_layer_t, num_layers=num_layers)
self.transformer_encoder_a = nn.TransformerEncoder(
self.encoder_layer_a, num_layers=num_layers)
self.transformer_encoder_v = nn.TransformerEncoder(
self.encoder_layer_v, num_layers=num_layers)
self.embed_dropout = embed_dropout
'Change here for other dataset since number of modalities might be different'
combined_dim = 3 * d_model
self.proj1 = nn.Linear(combined_dim, combined_dim)
self.proj2 = nn.Linear(combined_dim, combined_dim)
self.out_layer = nn.Linear(combined_dim, output_dim)
self.out_dropout = out_dropout
def __init__(self, config):
super(MISA, self).__init__()
self.config = config
self.text_size = config.embedding_size
self.visual_size = config.visual_size
self.acoustic_size = config.acoustic_size
self.input_sizes = input_sizes = [
self.text_size, self.visual_size, self.acoustic_size
]
self.hidden_sizes = hidden_sizes = [
int(self.text_size),
int(self.visual_size),
int(self.acoustic_size)
]
self.output_size = output_size = config.num_classes
self.dropout_rate = dropout_rate = config.dropout
self.activation = self.config.activation()
self.tanh = nn.Tanh()
rnn = nn.LSTM if self.config.rnncell == "lstm" else nn.GRU
# defining modules - two layer bidirectional LSTM with layer norm in between
if self.config.use_bert:
# Initializing a BERT bert-base-uncased style configuration
bertconfig = BertConfig.from_pretrained('bert-base-uncased',
output_hidden_states=True)
self.bertmodel = BertModel.from_pretrained('bert-base-uncased',
config=bertconfig)
else:
self.embed = nn.Embedding(len(config.word2id), input_sizes[0])
self.trnn1 = rnn(input_sizes[0],
hidden_sizes[0],
bidirectional=True)
self.trnn2 = rnn(2 * hidden_sizes[0],
hidden_sizes[0],
bidirectional=True)
self.vrnn1 = rnn(input_sizes[1], hidden_sizes[1], bidirectional=True)
self.vrnn2 = rnn(2 * hidden_sizes[1],
hidden_sizes[1],
bidirectional=True)
self.arnn1 = rnn(input_sizes[2], hidden_sizes[2], bidirectional=True)
self.arnn2 = rnn(2 * hidden_sizes[2],
hidden_sizes[2],
bidirectional=True)
##########################################
# mapping modalities to same sized space
##########################################
if self.config.use_bert:
self.project_t = nn.Sequential()
self.project_t.add_module(
'project_t',
nn.Linear(in_features=768, out_features=config.hidden_size))
self.project_t.add_module('project_t_activation', self.activation)
self.project_t.add_module('project_t_layer_norm',
nn.LayerNorm(config.hidden_size))
else:
self.project_t = nn.Sequential()
self.project_t.add_module(
'project_t',
nn.Linear(in_features=hidden_sizes[0] * 4,
out_features=config.hidden_size))
self.project_t.add_module('project_t_activation', self.activation)
self.project_t.add_module('project_t_layer_norm',
nn.LayerNorm(config.hidden_size))
self.project_v = nn.Sequential()
self.project_v.add_module(
'project_v',
nn.Linear(in_features=hidden_sizes[1] * 4,
out_features=config.hidden_size))
self.project_v.add_module('project_v_activation', self.activation)
self.project_v.add_module('project_v_layer_norm',
nn.LayerNorm(config.hidden_size))
self.project_a = nn.Sequential()
self.project_a.add_module(
'project_a',
nn.Linear(in_features=hidden_sizes[2] * 4,
out_features=config.hidden_size))
self.project_a.add_module('project_a_activation', self.activation)
self.project_a.add_module('project_a_layer_norm',
nn.LayerNorm(config.hidden_size))
##########################################
# private encoders
##########################################
self.private_t = nn.Sequential()
self.private_t.add_module(
'private_t_1',
nn.Linear(in_features=config.hidden_size,
out_features=config.hidden_size))
self.private_t.add_module('private_t_activation_1', nn.Sigmoid())
self.private_v = nn.Sequential()
self.private_v.add_module(
'private_v_1',
nn.Linear(in_features=config.hidden_size,
out_features=config.hidden_size))
self.private_v.add_module('private_v_activation_1', nn.Sigmoid())
self.private_a = nn.Sequential()
self.private_a.add_module(
'private_a_3',
nn.Linear(in_features=config.hidden_size,
out_features=config.hidden_size))
self.private_a.add_module('private_a_activation_3', nn.Sigmoid())
##########################################
# shared encoder
##########################################
self.shared = nn.Sequential()
self.shared.add_module(
'shared_1',
nn.Linear(in_features=config.hidden_size,
out_features=config.hidden_size))
self.shared.add_module('shared_activation_1', nn.Sigmoid())
##########################################
# reconstruct
##########################################
self.recon_t = nn.Sequential()
self.recon_t.add_module(
'recon_t_1',
nn.Linear(in_features=config.hidden_size,
out_features=config.hidden_size))
self.recon_v = nn.Sequential()
self.recon_v.add_module(
'recon_v_1',
nn.Linear(in_features=config.hidden_size,
out_features=config.hidden_size))
self.recon_a = nn.Sequential()
self.recon_a.add_module(
'recon_a_1',
nn.Linear(in_features=config.hidden_size,
out_features=config.hidden_size))
##########################################
# shared space adversarial discriminator
##########################################
if not self.config.use_cmd_sim:
self.discriminator = nn.Sequential()
self.discriminator.add_module(
'discriminator_layer_1',
nn.Linear(in_features=config.hidden_size,
out_features=config.hidden_size))
self.discriminator.add_module('discriminator_layer_1_activation',
self.activation)
self.discriminator.add_module('discriminator_layer_1_dropout',
nn.Dropout(dropout_rate))
self.discriminator.add_module(
'discriminator_layer_2',
nn.Linear(in_features=config.hidden_size,
out_features=len(hidden_sizes)))
##########################################
# shared-private collaborative discriminator
##########################################
self.sp_discriminator = nn.Sequential()
self.sp_discriminator.add_module(
'sp_discriminator_layer_1',
nn.Linear(in_features=config.hidden_size, out_features=4))
self.fusion = nn.Sequential()
self.fusion.add_module(
'fusion_layer_1',
nn.Linear(in_features=self.config.hidden_size * 6,
out_features=self.config.hidden_size * 3))
self.fusion.add_module('fusion_layer_1_dropout',
nn.Dropout(dropout_rate))
self.fusion.add_module('fusion_layer_1_activation', self.activation)
self.fusion.add_module(
'fusion_layer_3',
nn.Linear(in_features=self.config.hidden_size * 3,
out_features=output_size))
self.tlayer_norm = nn.LayerNorm((hidden_sizes[0] * 2, ))
self.vlayer_norm = nn.LayerNorm((hidden_sizes[1] * 2, ))
self.alayer_norm = nn.LayerNorm((hidden_sizes[2] * 2, ))
encoder_layer = nn.TransformerEncoderLayer(
d_model=self.config.hidden_size, nhead=2)
self.transformer_encoder = nn.TransformerEncoder(encoder_layer,
num_layers=1)
