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attention.py
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attention.py
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import torch
import torch.nn as nn
from torch.nn.utils.weight_norm import weight_norm
from fc import FCNet, GTH, get_norm
# Default concat, 1 layer, output layer
class Att_0(nn.Module):
def __init__(self, v_dim, q_dim, num_hid, norm, act, dropout=0.0):
super(Att_0, self).__init__()
norm_layer = get_norm(norm)
self.nonlinear = FCNet([v_dim + q_dim, num_hid], dropout= dropout, norm= norm, act= act)
self.linear = norm_layer(nn.Linear(num_hid, 1), dim=None)
def forward(self, v, q):
"""
v: [batch, k, vdim]
q: [batch, qdim]
"""
logits = self.logits(v, q)
w = nn.functional.softmax(logits, 1)
return w
def logits(self, v, q):
num_objs = v.size(1)
q = q.unsqueeze(1).repeat(1, num_objs, 1)
vq = torch.cat((v, q), 2)
joint_repr = self.nonlinear(vq)
logits = self.linear(joint_repr)
return logits
# concat, 2 layer, output layer
class Att_1(nn.Module):
def __init__(self, v_dim, q_dim, num_hid, norm, act, dropout=0.0):
super(Att_1, self).__init__()
norm_layer = get_norm(norm)
self.nonlinear = FCNet([v_dim + q_dim, num_hid, num_hid], dropout= dropout, norm= norm, act= act)
self.linear = norm_layer(nn.Linear(num_hid, 1), dim=None)
def forward(self, v, q):
"""
v: [batch, k, vdim]
q: [batch, qdim]
"""
logits = self.logits(v, q)
w = nn.functional.softmax(logits, 1)
return w
def logits(self, v, q):
num_objs = v.size(1)
q = q.unsqueeze(1).repeat(1, num_objs, 1)
vq = torch.cat((v, q), 2)
joint_repr = self.nonlinear(vq)
logits = self.linear(joint_repr)
return logits
# 1 layer seperate, element-wise *, output layer
class Att_2(nn.Module):
def __init__(self, v_dim, q_dim, num_hid, norm, act, dropout=0.0):
super(Att_2, self).__init__()
norm_layer = get_norm(norm)
self.v_proj = FCNet([v_dim, num_hid], dropout= dropout, norm= norm, act= act)
self.q_proj = FCNet([q_dim, num_hid], dropout= dropout, norm= norm, act= act)
self.linear = norm_layer(nn.Linear(q_dim, 1), dim=None)
def forward(self, v, q):
"""
v: [batch, k, vdim]
q: [batch, qdim]
"""
logits = self.logits(v, q)
w = nn.functional.softmax(logits, 1)
return w
def logits(self, v, q):
batch, k, _ = v.size()
v_proj = self.v_proj(v) # [batch, k, num_hid]
q_proj = self.q_proj(q).unsqueeze(1).repeat(1, k, 1) # [batch, k, num_hid]
joint_repr = v_proj * q_proj
logits = self.linear(joint_repr)
return logits
# 1 layer seperate, element-wise *, 1 layer seperate, output layer
class Att_3(nn.Module):
def __init__(self, v_dim, q_dim, num_hid, norm, act, dropout=0.0):
super(Att_3, self).__init__()
norm_layer = get_norm(norm)
self.v_proj = FCNet([v_dim, num_hid], dropout= dropout, norm= norm, act= act)
self.q_proj = FCNet([q_dim, num_hid], dropout= dropout, norm= norm, act= act)
self.nonlinear = FCNet([num_hid, num_hid], dropout= dropout, norm= norm, act= act)
self.linear = norm_layer(nn.Linear(num_hid, 1), dim=None)
def forward(self, v, q):
"""
v: [batch, k, vdim]
q: [batch, qdim]
"""
logits = self.logits(v, q)
w = nn.functional.softmax(logits, 1)
return w
def logits(self, v, q):
batch, k, _ = v.size()
v_proj = self.v_proj(v) # [batch, k, num_hid]
q_proj = self.q_proj(q).unsqueeze(1).repeat(1, k, 1) # [batch, k, num_hid]
joint_repr = v_proj * q_proj
joint_repr = self.nonlinear(joint_repr)
logits = self.linear(joint_repr)
return logits
# 1 layer seperate, element-wise *, 1 layer seperate, output layer
class Att_3S(nn.Module):
def __init__(self, v_dim, q_dim, num_hid, norm, act, dropout=0.0):
super(Att_3S, self).__init__()
norm_layer = get_norm(norm)
self.v_proj = FCNet([v_dim, num_hid], dropout=dropout, norm=norm, act=act)
self.q_proj = FCNet([q_dim, num_hid], dropout=dropout, norm=norm, act=act)
self.nonlinear = FCNet([num_hid, num_hid], dropout=dropout, norm=norm, act=act)
self.linear = norm_layer(nn.Linear(num_hid, 1), dim=None)
def forward(self, v, q):
"""
v: [batch, k, vdim]
q: [batch, qdim]
"""
logits = self.logits(v, q)
w = nn.functional.sigmoid(logits)
#w = nn.functional.leaky_relu(logits)
return w
def logits(self, v, q):
batch, k, _ = v.size()
v_proj = self.v_proj(v) # [batch, k, num_hid]
q_proj = self.q_proj(q).unsqueeze(1).repeat(1, k, 1) # [batch, k, num_hid]
joint_repr = v_proj * q_proj
joint_repr = self.nonlinear(joint_repr)
logits = self.linear(joint_repr)
return logits
# concat w/ 2 layer seperate, element-wise *, output layer
class Att_PD(nn.Module):
def __init__(self, v_dim, q_dim, num_hid, norm, act, dropout=0.0):
super(Att_PD, self).__init__()
norm_layer = get_norm(norm)
self.nonlinear = FCNet([v_dim + q_dim, num_hid, num_hid], dropout= dropout, norm= norm, act= act)
self.nonlinear_gate = FCNet([v_dim + q_dim, num_hid, num_hid], dropout= dropout, norm= norm, act= 'Sigmoid')
self.linear = norm_layer(nn.Linear(num_hid, 1), dim=None)
def forward(self, v, q):
"""
v: [batch, k, vdim]
q: [batch, qdim]
"""
logits = self.logits(v, q)
w = nn.functional.softmax(logits, 1)
return w
def logits(self, v, q):
num_objs = v.size(1)
q = q.unsqueeze(1).repeat(1, num_objs, 1)
vq = torch.cat((v, q), 2)
joint_repr = self.nonlinear(vq)
gate = self.nonlinear_gate(vq)
logits = joint_repr*gate
logits = self.linear(logits)
return logits
# concat w/ 1 layer seperate, element-wise *, output layer
class Att_P(nn.Module):
def __init__(self, v_dim, q_dim, num_hid, norm, act, dropout=0.0):
super(Att_P, self).__init__()
norm_layer = get_norm(norm)
self.gated_tanh = GTH( in_dim= v_dim + q_dim, out_dim= num_hid, dropout= dropout, norm= norm, act= act)
self.linear = norm_layer(nn.Linear(num_hid, 1), dim=None)
def forward(self, v, q):
"""
v: [batch, k, vdim]
q: [batch, qdim]
"""
logits = self.logits(v, q)
w = nn.functional.softmax(logits, 1)
return w
def logits(self, v, q):
num_objs = v.size(1)
q = q.unsqueeze(1).repeat(1, num_objs, 1)
vq = torch.cat((v, q), 2)
joint_repr = self.gated_tanh(vq)
logits = self.linear(joint_repr)
return logits