def __init__(self, opt):
super(QMultiTask, self).__init__()
self.device = opt.device
self.input_dims = opt.input_dims
self.total_input_dim = sum(self.input_dims)
self.embed_dim = opt.embed_dim
self.speaker_num = opt.speaker_num
self.dataset_name = opt.dataset_name
self.features = opt.features
# MELD data
# The one-hot vectors are not the global user ID
if self.dataset_name.lower() == 'meld':
self.speaker_num = 1
self.n_classes_emo = opt.output_dim_emo
self.n_classes_act = opt.output_dim_act
self.projections = nn.ModuleList(
[nn.Linear(dim, self.embed_dim) for dim in self.input_dims])
self.multiply = ComplexMultiply()
self.outer = QOuter()
self.norm = L2Norm(dim=-1)
self.mixture = QMixture(device=self.device)
self.output_cell_dim = opt.output_cell_dim
self.phase_embeddings = nn.ModuleList([
PositionEmbedding(
self.embed_dim, input_dim=self.speaker_num, device=self.device)
] * len(self.input_dims))
self.out_dropout_rate = opt.out_dropout_rate
self.measurement_emotion = QMeasurement(self.embed_dim)
self.measurement_act = QMeasurement(self.embed_dim)
self.fc_out_emo = SimpleNet(self.embed_dim,
self.output_cell_dim,
self.out_dropout_rate,
self.n_classes_emo,
output_activation=nn.Tanh())
self.fc_out_act = SimpleNet(self.embed_dim,
self.output_cell_dim,
self.out_dropout_rate,
self.n_classes_act,
output_activation=nn.Tanh())
self.num_layers = opt.num_layers
#self.rnn=nn.ModuleList([QRNNCell(self.embed_dim, device = self.device)]*self.num_layers)
self.RNNs = nn.ModuleList([
QRNN(self.embed_dim, self.device, self.num_layers)
for i in range(len(opt.features))
])
self.rnn_outer = QOuter()
self.action_qrnn = QRNN(self.embed_dim, self.device, self.num_layers)
def __init__(self, opt):
super(QMPN, self).__init__()
self.device = opt.device
self.input_dims = opt.input_dims
self.total_input_dim = sum(self.input_dims)
if type(opt.embed_dims) == int:
self.embed_dims = [opt.embed_dims]
self.embed_dims = [int(s) for s in opt.embed_dims.split(',')]
self.speaker_num = opt.speaker_num
self.dataset_name = opt.dataset_name
# MELD data
# The one-hot vectors are not the global user ID
if self.dataset_name.lower() == 'meld':
self.speaker_num = 1
self.n_classes = opt.output_dim
self.projections = nn.ModuleList([
nn.Linear(dim, embed_dim)
for dim, embed_dim in zip(self.input_dims, self.embed_dims)
])
self.multiply = ComplexMultiply()
self.outer = QOuter()
self.norm = L2Norm(dim=-1)
self.mixture = QMixture(device=self.device)
self.output_cell_dim = opt.output_cell_dim
self.phase_embeddings = nn.ModuleList([
PositionEmbedding(embed_dim,
input_dim=self.speaker_num,
device=self.device)
for embed_dim in self.embed_dims
])
self.out_dropout_rate = opt.out_dropout_rate
self.num_layers = opt.num_layers
self.state_product = QProduct()
self.recurrent_dim = 1
for embed_dim in self.embed_dims:
self.recurrent_dim = self.recurrent_dim * embed_dim
self.recurrent_cells = nn.ModuleList(
[QRNNCell(self.recurrent_dim, device=self.device)] *
self.num_layers)
self.out_dropout = QDropout(p=self.out_dropout_rate)
self.activation = QActivation(scale_factor=1, beta=0.8)
self.measurement = QMeasurement(self.recurrent_dim)
self.fc_out = SimpleNet(self.recurrent_dim,
self.output_cell_dim,
self.out_dropout_rate,
self.n_classes,
output_activation=nn.Tanh())
def __init__(self, opt):
super(QAttN, self).__init__()
self.device = opt.device
self.input_dims = opt.input_dims
self.total_input_dim = sum(self.input_dims)
self.embed_dim = opt.embed_dim
self.speaker_num = opt.speaker_num
self.n_classes = opt.output_dim
self.output_cell_dim = opt.output_cell_dim
self.dataset_name = opt.dataset_name
self.projections = nn.ModuleList(
[nn.Linear(dim, self.embed_dim) for dim in self.input_dims])
if self.dataset_name.lower() == 'meld':
self.speaker_num = 1
self.multiply = ComplexMultiply()
self.outer = QOuter()
self.norm = L2Norm(dim=-1)
self.mixture = QMixture(device=self.device)
self.phase_embeddings = nn.ModuleList([
PositionEmbedding(self.embed_dim, input_dim=1, device=self.device)
] * len(self.input_dims))
self.out_dropout_rate = opt.out_dropout_rate
self.attention = QAttention()
self.num_modalities = len(self.input_dims)
#self.out_dropout = QDropout(p=self.out_dropout_rate)
#self.dense = QDense(self.embed_dim, self.n_classes)
self.measurement_type = opt.measurement_type
if opt.measurement_type == 'quantum':
self.measurement = QMeasurement(self.embed_dim)
self.fc_out = SimpleNet(self.embed_dim * self.num_modalities,
self.output_cell_dim,
self.out_dropout_rate,
self.n_classes,
output_activation=nn.Tanh())
else:
self.measurement = ComplexMeasurement(self.embed_dim *
self.num_modalities,
units=20)
self.fc_out = SimpleNet(20,
self.output_cell_dim,
self.out_dropout_rate,
self.n_classes,
output_activation=nn.Tanh())
def __init__(self, opt):
super(QMNAblation, self).__init__()
self.device = opt.device
self.input_dims = opt.input_dims
self.total_input_dim = sum(self.input_dims)
self.embed_dim = opt.embed_dim
self.speaker_num = opt.speaker_num
self.dataset_name = opt.dataset_name
# MELD data
if self.dataset_name.lower() == 'meld':
self.speaker_num = 1
self.n_classes = opt.output_dim
self.input_concat = opt.input_concat
self.zero_phase = opt.zero_phase
self.measurement_type = opt.measurement_type
self.classical_recurrent = opt.classical_recurrent
self.quantum_recurrent = opt.quantum_recurrent
if self.input_concat:
self.projections = nn.ModuleList(
[nn.Linear(self.total_input_dim, self.embed_dim)])
elif self.classical_recurrent:
self.projections = nn.ModuleList(
[nn.GRU(dim, self.embed_dim, 1) for dim in self.input_dims])
else:
self.projections = nn.ModuleList(
[nn.Linear(dim, self.embed_dim) for dim in self.input_dims])
self.multiply = ComplexMultiply()
self.outer = QOuter()
self.norm = L2Norm(dim=-1)
self.mixture = QMixture(device=self.device)
self.output_cell_dim = opt.output_cell_dim
self.phase_embeddings = nn.ModuleList([PositionEmbedding(self.embed_dim, input_dim = self.speaker_num,\
zero_phase = self.zero_phase, device = self.device)]*len(self.input_dims))
self.out_dropout_rate = opt.out_dropout_rate
self.num_layers = opt.num_layers
self.recurrent_cells = nn.ModuleList(
[QRNNCell(self.embed_dim, device=self.device)] * self.num_layers)
self.out_dropout = QDropout(p=self.out_dropout_rate)
self.activation = QActivation(scale_factor=1, beta=0.8)
self.measurement_type = opt.measurement_type
if self.measurement_type == 'quantum':
self.measurement = QMeasurement(self.embed_dim)
self.fc_out = SimpleNet(self.embed_dim,
self.output_cell_dim,
self.out_dropout_rate,
self.n_classes,
output_activation=nn.Tanh())
elif self.measurement_type == 'flatten':
self.fc_out = SimpleNet(self.embed_dim,
self.output_cell_dim,
self.out_dropout_rate,
self.n_classes,
output_activation=nn.Tanh())
else:
self.measurement = ComplexMeasurement(self.embed_dim,
units=self.embed_dim)
self.fc_out = SimpleNet(self.embed_dim,
self.output_cell_dim,
self.out_dropout_rate,
self.n_classes,
output_activation=nn.Tanh())