def __init__(self, v_dim, q_dim, num_hid, norm='weight', act='LeakyReLU', dropout=0.3):
super(Att_PD_layer2, self).__init__()
self.nonlinear_1 = FCNet([v_dim + q_dim, num_hid], output_dropout=0.3)
self.nonlinear_2 = FCNet([num_hid, num_hid], output_dropout=0.3)
self.nonlinear_gate_1 = FCNet_sigmoid([v_dim + q_dim, num_hid])
self.nonlinear_gate_2 = FCNet_sigmoid([num_hid, num_hid])
self.linear = nn.Linear(num_hid, 1)
def __init__(self, v_dim, q_dim, num_hid, norm='weight', act='LeakyReLU', dropout=0.3):
super(Att_3S_layer2, self).__init__()
# norm_layer = get_norm(norm)
self.v_proj = FCNet([v_dim, num_hid], output_dropout=0.3)
self.q_proj = FCNet([q_dim, num_hid], output_dropout=0.3)
self.nonlinear = FCNet([num_hid, num_hid], output_dropout=0.3)
self.linear = nn.Linear(num_hid, 1)
def __init__(self, v_dim, q_dim, num_hid, norm='weight', act='LeakyReLU', dropout=0.3):
super(Att_4_layer2_keycat_textual_visual, self).__init__()
# norm_layer = get_norm(norm)
self.v_proj = FCNet([v_dim, num_hid], output_dropout=0.3)
self.q_proj = FCNet([q_dim, num_hid], output_dropout=0.3)
# self.v_linear = nn.Linear(num_hid, num_hid)
# self.q_linear = nn.Linear(num_hid, num_hid)
# self.v_proj = torch.nn.Sequential(
# nn.Linear(v_dim, num_hid),
# torch.nn.Dropout(0.3, inplace=False)
# )
# self.q_proj = torch.nn.Sequential(
# nn.Linear(q_dim, num_hid),
# torch.nn.Dropout(0.3, inplace=False)
# )
self.num_hid = num_hid
def __init__(self, v_dim, q_dim, num_hid):
super(Att_0_layer2, self).__init__()
self.nonlinear = FCNet([v_dim + q_dim, num_hid], output_dropout=0.3)
self.linear = nn.Linear(num_hid, 1)
def __init__(self, v_dim, q_dim, num_hid, norm='weight', act='LeakyReLU', dropout=0.3):
super(Att_4_layer2_huge_negative_mask, self).__init__()
# norm_layer = get_norm(norm)
self.v_proj = FCNet([v_dim, num_hid], output_dropout=0.3)
self.q_proj = FCNet([q_dim, num_hid], output_dropout=0.3)
def __init__(self, v_dim, q_dim, num_hid):
super(Att_2_layer2, self).__init__()
self.v_proj = FCNet([v_dim, num_hid], output_dropout=0.3)
self.q_proj = FCNet([q_dim, num_hid], output_dropout=0.3)
self.linear = nn.Linear(num_hid, 1)
def __init__(self, v_dim, q_dim, num_hid, norm='weight', act='LeakyReLU', dropout=0.3):
super(Att_1_layer2_keycat_textual_visual, self).__init__()
self.nonlinear_1 = FCNet([v_dim + q_dim, num_hid], output_dropout=0.3)
self.nonlinear_2 = FCNet([num_hid, num_hid], output_dropout=0.3)
self.linear = nn.Linear(num_hid, 1)
def __init__(self, query_size, key_size, hidden_size):
super().__init__()
self.query_proj = FCNet([query_size, hidden_size], output_dropout=0.3)
self.key_proj = FCNet([key_size, hidden_size], output_dropout=0.3)