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import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class Encoder(nn.Module):
def __init__(self):
super(Encoder, self).__init__()
resnet = models.resnet50(pretrained=True)
for param in resnet.parameters():
param.requires_grad_(False)
modules = list(resnet.children())[:-2] # extracting the last conv layer from the model
self.resnet = nn.Sequential(*modules)
def forward(self, imgs):
features = self.resnet(imgs)
features = features.permute(0, 2, 3, 1) # batch x 7 x 7 x 2048
features = features.view(features.size(0), -1, features.size(-1)) # batch x 49 x 2048
return features
class Attention(nn.Module):
def __init__(self, encoder_dims, decoder_dims, attention_dims):
super(Attention, self).__init__()
self.attention_dims = attention_dims # size of attention network
self.U = nn.Linear(encoder_dims, attention_dims) # a^(t)
self.W = nn.Linear(decoder_dims, attention_dims) # s^(t` - 1)
self.A = nn.Linear(attention_dims, 1) # cvt the attention dims back to 1
def forward(self, features, hidden):
u_as = self.U(features)
w_as = self.W(hidden)
combined_state = torch.tanh(u_as + w_as.unsqueeze(1))
attention_score = self.A(combined_state)
attention_score = attention_score.squeeze(2)
alpha = F.softmax(attention_score, dim=1)
attention_weights = features * alpha.unsqueeze(2) # batch x num_timesteps (49) x features
attention_weights = attention_weights.sum(dim=1)
return alpha, attention_weights
class Decoder(nn.Module):
def __init__(self, embed_size, vocab_size, attention_dim, encoder_dim, decoder_dim, fc_dims, p=0.3,
embeddings=None):
super().__init__()
self.vocab_size = vocab_size
self.attention_dim = attention_dim
self.decoder_dim = decoder_dim
self.embedding = nn.Embedding(vocab_size, embedding_dim=embed_size)
self.attention = Attention(encoder_dim, decoder_dim, attention_dim)
self.init_h = nn.Linear(encoder_dim, decoder_dim)
self.init_c = nn.Linear(encoder_dim, decoder_dim)
self.lstm = nn.LSTMCell(encoder_dim + embed_size, decoder_dim, bias=True)
self.fcn1 = nn.Linear(decoder_dim, vocab_size)
self.fcn2 = nn.Linear(fc_dims, vocab_size)
self.drop = nn.Dropout(p)
if embeddings is not None:
self.load_pretrained_embed(embeddings)
def forward(self, features, captions):
seq_length = len(captions[0]) - 1 # Exclude the last one
batch_size = captions.size(0)
num_timesteps = features.size(1)
embed = self.embedding(captions)
h, c = self.init_hidden_state(features) # initialize h and c for LSTM
preds = torch.zeros(batch_size, seq_length, self.vocab_size).to(device)
alphas = torch.zeros(batch_size, seq_length, num_timesteps).to(device)
for s in range(seq_length):
alpha, context = self.attention(features, h)
lstm_inp = torch.cat((embed[:, s], context), dim=1)
h, c = self.lstm(lstm_inp, (h, c))
out = self.drop(self.fcn1(h))
preds[:, s] = out
alphas[:, s] = alpha
return preds, alphas
def gen_captions(self, features, max_len=20, vocab=None):
h, c = self.init_hidden_state(features)
alphas = []
captions = []
word = torch.tensor(vocab.stoi["<SOS>"]).view(1, -1).to(device)
embed = self.embedding(word)
for i in range(max_len):
alpha, context = self.attention(features, h)
alphas.append(alpha.cpu().detach().numpy())
lstm_inp = torch.cat((embed[:, 0], context), dim=1)
h, c = self.lstm(lstm_inp, (h, c))
out = self.drop(self.fcn1(h))
word_out_idx = torch.argmax(out, dim=1)
captions.append(word_out_idx.item())
if vocab.itos[word_out_idx.item()] == "<EOS>":
break
embed = self.embedding(word_out_idx.unsqueeze(0))
return [vocab.itos[word] for word in captions], alphas
def load_pretrained_embed(self, embeddings):
self.embedding.weight = nn.Parameter(embeddings)
for p in self.embedding.parameters():
p.requires_grad = True
def init_hidden_state(self, encoder_output):
mean_encoder_out = encoder_output.mean(dim=1)
h = self.init_h(mean_encoder_out)
c = self.init_c(mean_encoder_out)
return h, c
class EncoderDecoder(nn.Module):
def __init__(self, embed_size, vocab_size, attention_dim, encoder_dim, decoder_dim, fc_dims, p=0.3,
embeddings=None):
super().__init__()
self.EncoderCNN = Encoder()
self.DecoderLSTM = Decoder(embed_size, vocab_size, attention_dim, encoder_dim, decoder_dim, fc_dims, p,
embeddings)
def forward(self, imgs, caps):
features = self.EncoderCNN(imgs)
out = self.DecoderLSTM(features, caps)
return out
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