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models.py
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import torch
import torch.nn as nn
import numpy as np
from torchvision import models
import torch.nn.functional as F
class JointEmbedModel(nn.Module):
def __init__(self, config, vlen, embeds=None):
super(JointEmbedModel, self).__init__()
self.vlen = vlen
self.embedding_dim = config['embedding_dim']
self.embed = nn.Embedding(self.vlen + 1, self.embedding_dim, padding_idx=self.vlen)
if embeds is not None:
self.embed.weight.data.copy_(torch.from_numpy(embeds))
self.embed.eval()
for param in self.embed.parameters():
param.requires_grad = False
self.lstm_hidden_size = 512
self.lstm_num_layers = 2
self.lstmq = nn.LSTM(input_size=self.embedding_dim, hidden_size=self.lstm_hidden_size, num_layers=self.lstm_num_layers, batch_first=True)
self.lstm_transform = nn.Sequential(
nn.Linear(2*self.lstm_num_layers*self.lstm_hidden_size, 1024),
nn.BatchNorm1d(1024),
nn.Tanh())
# self.lstm_transform.apply(init_weights)
self.vggnet = models.vgg16(pretrained=True)
self.vggnet.classifier = self.vggnet.classifier[:-1]
self.vggnet.eval()
for param in self.vggnet.parameters():
param.requires_grad = False
self.vggnet_transform = nn.Sequential(
nn.Linear(4096, 1024),
nn.BatchNorm1d(1024),
nn.Tanh())
# self.vggnet_transform.apply(init_weights)
self.mlp = nn.Sequential(
nn.Linear(1024, 1000),
nn.BatchNorm1d(1000),
nn.Tanh(),
nn.Dropout(p=0.5),
nn.Linear(1000, 1000),
nn.BatchNorm1d(1000),
nn.Tanh(),
nn.Dropout(p=0.5),
nn.Linear(1000, config['n_classes']))
# self.mlp.apply(init_weights)
def forward(self, images, q_idxs):
# self.lstmq.flatten_parameters()
ques_embedded = self.embed(q_idxs)
lstm_out, (hn, cn) = self.lstmq(ques_embedded)
hn = hn.permute(1, 0, 2)
cn = cn.permute(1, 0, 2)
hn = hn.contiguous().view(images.shape[0], self.lstm_hidden_size*self.lstm_num_layers)
cn = cn.contiguous().view(images.shape[0], self.lstm_hidden_size*self.lstm_num_layers)
ques_embedding = torch.cat((hn,cn), 1)
ques_embedding_transformed = self.lstm_transform(ques_embedding)
imgs_embedding = self.vggnet(images)
imgs_embedding = nn.functional.normalize(imgs_embedding)
imgs_embedding_transformed = self.vggnet_transform(imgs_embedding)
joint_embedding = ques_embedding_transformed*imgs_embedding_transformed
output = self.mlp(joint_embedding)
return output
class JointEmbedResNet(nn.Module):
def __init__(self, config, vlen, embeds=None):
super(JointEmbedResNet, self).__init__()
self.vlen = vlen
self.embedding_dim = config['embedding_dim']
self.embed = nn.Embedding(self.vlen + 1, self.embedding_dim, padding_idx=self.vlen)
if embeds is not None:
self.embed.weight.data.copy_(torch.from_numpy(embeds))
self.lstm_hidden_size = 512
self.lstm_num_layers = 2
self.lstmq = nn.LSTM(input_size=self.embedding_dim, hidden_size=self.lstm_hidden_size, num_layers=self.lstm_num_layers, batch_first=True)
self.lstm_transform = nn.Sequential(
nn.Linear(2*self.lstm_num_layers*self.lstm_hidden_size, 1024),
nn.BatchNorm1d(1024),
nn.Tanh())
# self.lstm_transform.apply(init_weights)
self.resnet = models.resnet152(pretrained=True)
self.resnet.fc = nn.Sequential()
self.resnet.eval()
for param in self.resnet.parameters():
param.requires_grad = False
self.resnet_transform = nn.Sequential(
nn.Linear(2048, 1024),
nn.BatchNorm1d(1024),
nn.Tanh())
self.mlp = nn.Sequential(
nn.Linear(1024, 1000),
nn.BatchNorm1d(1000),
nn.Tanh(),
nn.Dropout(p=0.5),
nn.Linear(1000, 1000),
nn.BatchNorm1d(1000),
nn.Tanh(),
nn.Dropout(p=0.5),
nn.Linear(1000, config['n_classes']))
# self.mlp.apply(init_weights)
def forward(self, images, q_idxs):
# self.lstmq.flatten_parameters()
ques_embedded = self.embed(q_idxs)
lstm_out, (hn, cn) = self.lstmq(ques_embedded)
hn = hn.permute(1, 0, 2)
cn = cn.permute(1, 0, 2)
hn = hn.contiguous().view(images.shape[0], self.lstm_hidden_size*self.lstm_num_layers)
cn = cn.contiguous().view(images.shape[0], self.lstm_hidden_size*self.lstm_num_layers)
ques_embedding = torch.cat((hn,cn), 1)
ques_embedding_transformed = self.lstm_transform(ques_embedding)
imgs_embedding = self.resnet(images)
imgs_embedding = imgs_embedding.view(imgs_embedding.shape[0], -1)
imgs_embedding = nn.functional.normalize(imgs_embedding)
imgs_embedding_transformed = self.resnet_transform(imgs_embedding)
joint_embedding = ques_embedding_transformed*imgs_embedding_transformed
output = self.mlp(joint_embedding)
return output
class Attention(nn.Module):
def __init__(self, v_dim, q_dim, mid_dim, glimpses, drop=0.0):
super(Attention, self).__init__()
self.glimpses = glimpses
self.mid_dim = mid_dim
self.conv1 = nn.Conv2d(v_dim, mid_dim, 1, bias=False)
self.fc1 = nn.Linear(q_dim, mid_dim)
self.conv2 = nn.Conv2d(mid_dim, glimpses, 1)
self.drop = nn.Dropout(drop)
self.alpha = None
self.relu = nn.ReLU()
def forward(self, v, q):
n = v.shape[0]
c = v.shape[1]
x = self.conv1(self.drop(v))
y = self.fc1(self.drop(q))
y = y.view(n, self.mid_dim, 1, 1).expand_as(x)
x = self.relu(x + y)
a = self.conv2(self.drop(x))
v = v.view(n, c, -1)
a = a.view(n, self.glimpses, -1)
s = v.shape[2]
a = a.view(n * self.glimpses, -1)
a = F.softmax(a, dim=1)
self.alpha = a
v = v.unsqueeze(1).expand(n, self.glimpses, c, s)
a = a.view(n, self.glimpses, -1).unsqueeze(2).expand(n, self.glimpses, c, s)
x = v * a
out = x.sum(dim=3)
return out.view(n, -1)
class AttentionModel(nn.Module):
def __init__(self, config, vlen, embeds=None):
super(AttentionModel, self).__init__()
self.vlen = vlen
self.embedding_dim = config['embedding_dim']
self.drop = config['dropout']
self.glimpses = config['glimpses']
self.embed = nn.Embedding(self.vlen + 1, self.embedding_dim, padding_idx=self.vlen)
if embeds is not None:
self.embed.weight.data.copy_(torch.from_numpy(embeds))
self.lstm_dim = 1024
self.lstm_layers = 1
self.lstm = nn.LSTM(self.embedding_dim, self.lstm_dim, num_layers=self.lstm_layers)
self.v_dim = 2048
self.mid_dim = 512
self.drop_layer = nn.Dropout(self.drop)
self.tanh = nn.Tanh()
self.resnet152 = models.resnet152(pretrained=True)
mods = list(self.resnet152.children())[:-2]
self.resnet152 = nn.Sequential(*mods)
for param in self.resnet152.parameters():
param.requires_grad = False
self.attention = Attention(self.v_dim, self.lstm_dim, self.mid_dim, self.glimpses, self.drop)
self.classifier = nn.Sequential(
nn.Dropout(self.drop),
nn.Linear(self.glimpses * self.v_dim + self.lstm_dim, 1024),
nn.ReLU(),
nn.Dropout(self.drop),
nn.Linear(1024, config['n_classes']))
def forward(self, images, q_idxs):
ques = self.embed(q_idxs)
ques = self.tanh(self.drop_layer(ques))
_, (_, c) = self.lstm(ques.permute(1, 0, 2))
q = c.squeeze(0)
v = self.resnet152(images)
v = F.normalize(v, p=2, dim=1)
v = self.attention(v, q)
x = torch.cat((v, q), dim=1)
out = self.classifier(x)
return out
class SelfAttention(nn.Module):
def __init__(self, q_dim=1024, mid_dim=512, drop=0.0):
super(SelfAttention, self).__init__()
self.attention_network = nn.Sequential(
nn.Dropout(drop),
nn.Linear(2*q_dim,mid_dim),
nn.Tanh(),
nn.Dropout(drop),
nn.Linear(mid_dim,1))
self.softmax = nn.Softmax(dim=1)
self.alpha = None
def forward(self, memory, annotations):
batch_size, seq_len, hid_size = annotations.size()
expanded_memory = memory.unsqueeze(1).expand(batch_size,seq_len,hid_size)
concat = torch.cat((expanded_memory,annotations),2)
attention_output = self.attention_network(concat)
normalized_attention = self.softmax(attention_output) # batch_size x seq_len x 1
self.alpha = normalized_attention
context = torch.sum(normalized_attention*annotations, 1) # batch_size x hid_size
return context
class InteractiveAttention(nn.Module):
def __init__(self, v_dim=2048, q_dim=1024, mid_dim=512, drop=0.0):
super(InteractiveAttention, self).__init__()
self.glimpses = 1
self.mid_dim = mid_dim
self.conv1 = nn.Conv2d(v_dim, mid_dim, 1)
self.fc1 = nn.Linear(q_dim, mid_dim)
self.conv2 = nn.Conv2d(mid_dim, self.glimpses, 1)
self.drop = nn.Dropout(drop)
self.alpha = None
self.relu = nn.ReLU()
def forward(self, v, q):
n = v.shape[0]
c = v.shape[1]
x = self.conv1(self.drop(v))
y = self.fc1(self.drop(q))
y = y.view(n, self.mid_dim, 1, 1).expand_as(x)
x = self.relu(x + y)
a = self.conv2(self.drop(x))
v = v.view(n, c, -1)
a = a.view(n, self.glimpses, -1)
s = v.shape[2]
a = a.view(n * self.glimpses, -1)
a = F.softmax(a, dim=1)
self.alpha = a
v = v.unsqueeze(1).expand(n, self.glimpses, c, s)
a = a.view(n, self.glimpses, -1).unsqueeze(2).expand(n, self.glimpses, c, s)
x = v * a
out = x.sum(dim=3)
return out.view(n, -1)
class MultihopAttentionModel(nn.Module):
def __init__(self, config, vlen, embeds=None):
super(MultihopAttentionModel, self).__init__()
self.vlen = vlen
self.embedding_dim = config['embedding_dim']
self.drop = config['dropout']
self.hops = config['hops']
self.embed = nn.Embedding(self.vlen + 1, self.embedding_dim, padding_idx=self.vlen)
if embeds is not None:
self.embed.weight.data.copy_(torch.from_numpy(embeds))
self.v_dim = 2048
self.mid_dim = 512
self.drop_layer = nn.Dropout(self.drop)
self.lstm_dim = 1024
self.lstm_layers = 2
self.lstmq = nn.LSTM(input_size=self.embedding_dim, hidden_size=self.lstm_dim, num_layers=self.lstm_layers, batch_first=True, dropout=self.drop)
self.resnet = models.resnet152(pretrained=True)
mods = list(self.resnet.children())[:-2]
self.resnet = nn.Sequential(*mods)
self.resnet.eval()
for param in self.resnet.parameters():
param.requires_grad = False
self.self_attention = SelfAttention(self.lstm_dim, self.mid_dim, self.drop)
self.interactive_attention = InteractiveAttention(self.v_dim, self.lstm_dim, self.mid_dim, self.drop)
self.mlp = nn.Sequential(
nn.Linear(self.v_dim + self.lstm_dim, 1024),
nn.BatchNorm1d(1024),
nn.ReLU(),
nn.Dropout(self.drop),
nn.Linear(1024, config['n_classes']))
def forward(self, images, q_idxs):
ques = self.embed(q_idxs)
q_annot, (hn, cn) = self.lstmq(ques) # batch_size x seq_len x hid_size
v_feat = self.resnet(images)
v_feat = F.normalize(v_feat, p=2, dim=1)
q = q_annot.mean(dim=1)
q_memory = q
v = self.interactive_attention(v_feat, q)
v_memory = v
for hop_no in range(self.hops):
q = self.self_attention(q_memory, q_annot)
q_memory += q
v = self.interactive_attention(v_feat, q)
v_memory += v
x = torch.cat((v_memory, q_memory), dim=1)
out = self.mlp(x)
return out
def init_weights(net):
if type(net) == nn.Linear():
torch_init.xavier_normal_(net.weight)