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models.py
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models.py
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import torch
from torch import nn
import torchvision
from collections import OrderedDict
import torch.nn.functional as F
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class CNN_Encoder(nn.Module):
"""
CNN_Encoder.
Resnet50.
"""
def __init__(self, encoded_image_size=14):
super(CNN_Encoder, self).__init__()
self.enc_image_size = encoded_image_size
resnet = torchvision.models.resnet50(pretrained=True)
# Remove last linear layer and pooling layers
modules = list(resnet.children())[:-2]
self.resnet = nn.Sequential(*modules)
# Resize image to fixed size to allow input images of variable size
self.adaptive_pool = nn.AdaptiveAvgPool2d((encoded_image_size, encoded_image_size))
self.fine_tune()
def forward(self, images):
"""
Forward propagation.
:param images: images, a tensor of dimensions (batch_size, 3, image_size, image_size)
:return: encoded images [batch_size, encoded_image_size=14, encoded_image_size=14, 2048]
"""
out = self.resnet(images) # (batch_size, 2048, image_size/32, image_size/32)
out = self.adaptive_pool(out) # [batch_size, 2048/512, 8, 8] -> [batch_size, 2048/512, 14, 14]
out = out.permute(0, 2, 3, 1)
return out
def fine_tune(self, fine_tune=True):
"""
Allow or prevent the computation of gradients for backbone CNN.
:param fine_tune: Allow?
"""
for p in self.resnet.parameters():
p.requires_grad = False
for c in list(self.resnet.children()):
for p in c.parameters():
p.requires_grad = fine_tune
class LF(nn.Module):
def __init__(self, encoded_image_size=14):
super(LF, self).__init__()
self.enc_image_size = encoded_image_size
resnet = torchvision.models.resnet50(pretrained = True)
modules = list(resnet.children())[:-2]
self.resnet = nn.Sequential(*modules)
self.adaptive_pool = nn.AdaptiveAvgPool2d((encoded_image_size, encoded_image_size))
self.layers = [
('input_image', lambda x:x),
('conv1', lambda x: self.resnet[0](x)),
('conv5', lambda x: self.resnet[4](self.resnet[3](
self.resnet[2](self.resnet[1](x))))),
('conv9', lambda x: self.resnet[5](x)),
('conv13', lambda x: self.resnet[6](x)),
('conv17', lambda x: self.resnet[7](x)),
('adp_pool', lambda x: self.adaptive_pool(x)),
]
def forward(self, x):
for name, operator in self.layers:
x = operator(x)
setattr(self, name, x)
# Take the max for each prediction map.
return x.permute(0, 2, 3, 1)
def partial_forward(self, start):
skip = True
for name, operator in self.layers:
if name == start:
x = getattr(self, name)
skip = False
elif skip:
continue
else:
x = operator(x)
setattr(self, name, x)
return x.permute(0, 2, 3, 1)
class Attention(nn.Module):
"""
Attention Network.
"""
def __init__(self, encoder_dim, decoder_dim, attention_dim):
"""
:param encoder_dim: feature size of encoded images
:param decoder_dim: size of decoder's RNN
:param attention_dim: size of the attention network
"""
super(Attention, self).__init__()
self.encoder_att = nn.Linear(encoder_dim, attention_dim) # linear layer to transform encoded image
self.decoder_att = nn.Linear(decoder_dim, attention_dim) # linear layer to transform decoder's output
self.full_att = nn.Linear(attention_dim, 1) # linear layer to calculate values to be softmax-ed
self.relu = nn.ReLU()
self.softmax = nn.Softmax(dim=1) # softmax layer to calculate weights
def forward(self, encoder_out, decoder_hidden):
"""
Forward propagation.
:param encoder_out: encoded images, a tensor of dimension (batch_size, num_pixels, encoder_dim)
:param decoder_hidden: previous decoder output, a tensor of dimension (batch_size, decoder_dim)
:return: attention weighted encoding, weights
"""
att1 = self.encoder_att(encoder_out) # [batch_size_t, num_pixels=196, 2048] -> [batch_size_t, num_pixels, attention_dim]
att2 = self.decoder_att(decoder_hidden) # [batch_size_t, decoder_dim=512] -> [batch_size_t, attention_dim]
att = self.full_att(self.relu(att1 + att2.unsqueeze(1))).squeeze(2) # [batch_size_t, num_pixels=196, attention_dim] -> [batch_size_t, num_pixels]
alpha = self.softmax(att) # [batch_size_t, num_pixels=196]
attention_weighted_encoding = (encoder_out * alpha.unsqueeze(2)).sum(dim=1) # [batch_size_t, encoder_dim=2048]
return attention_weighted_encoding, alpha
class DecoderWithAttention(nn.Module):
"""
Double-Decoder.
Two seperate decoders share the same attention layer.
"""
def __init__(self, attention_dim, embed_dim, decoder_dim, vocab_size, encoder_dim=2048, dropout=0.5):
"""
:param attention_dim: size of attention network
:param embed_dim: embedding size
:param decoder_dim: size of decoder's RNN
:param vocab_size: size of vocabulary
:param encoder_dim: feature size of encoded images
:param dropout: dropout
"""
super(DecoderWithAttention, self).__init__()
assert(len(vocab_size)==2)
self.encoder_dim = encoder_dim
self.attention_dim = attention_dim
self.embed_dim = embed_dim
self.decoder_dim = decoder_dim
self.vocab_size1 = vocab_size[0]
self.vocab_size2 = vocab_size[1]
self.dropout = dropout
self.attention = Attention(encoder_dim, decoder_dim, attention_dim) # attention network
self.embedding = nn.Embedding(self.vocab_size1, embed_dim, padding_idx=0) # embedding layer
self.decode_step = nn.LSTMCell(embed_dim + encoder_dim, decoder_dim, bias=True) # decoding LSTMCell
self.init_h = nn.Linear(encoder_dim, decoder_dim) # linear layer to find initial hidden state of LSTMCell
self.init_c = nn.Linear(encoder_dim, decoder_dim) # linear layer to find initial cell state of LSTMCell
self.f_beta = nn.Linear(decoder_dim, encoder_dim) # linear layer to create a sigmoid-activated gate
self.fc = nn.Linear(decoder_dim, self.vocab_size1) # linear layer to find scores over vocabulary
self.dropout = nn.Dropout(p=self.dropout)
self.sigmoid = nn.Sigmoid()
self.embedding_sec = nn.Embedding(self.vocab_size2, embed_dim, padding_idx=0) # embedding layer
self.decode_step_sec = nn.LSTMCell(embed_dim + encoder_dim, decoder_dim, bias=True) # decoding LSTMCell
self.init_h_sec = nn.Linear(encoder_dim, decoder_dim) # linear layer to find initial hidden state of LSTMCell
self.init_c_sec = nn.Linear(encoder_dim, decoder_dim) # linear layer to find initial cell state of LSTMCell
self.f_beta_sec = nn.Linear(decoder_dim, encoder_dim) # linear layer to create a sigmoid-activated gate
self.fc_sec = nn.Linear(decoder_dim, self.vocab_size2) # linear layer to find scores over vocabulary
self.init_weights() # initialize some layers with the uniform distribution
self.fine_tune_embeddings()
def init_weights(self):
"""
Initializes some parameters with values from the uniform distribution, for easier convergence.
"""
self.embedding.weight.data.uniform_(-0.1, 0.1)
self.fc.bias.data.fill_(0)
self.fc.weight.data.uniform_(-0.1, 0.1)
self.embedding_sec.weight.data.uniform_(-0.1, 0.1)
self.fc_sec.bias.data.fill_(0)
self.fc_sec.weight.data.uniform_(-0.1, 0.1)
def load_pretrained_embeddings(self, embeddings):
"""
Loads embedding layer with pre-trained embeddings.
:param embeddings: pre-trained embeddings
"""
self.embedding.weight = nn.Parameter(embeddings)
self.embedding_sec.weight = nn.Parameter(embeddings_sec)
def fine_tune_embeddings(self, fine_tune=True):
"""
Allow fine-tuning of embedding layer? (Only makes sense to not-allow if using pre-trained embeddings).
:param fine_tune: Allow?
"""
for p in self.embedding.parameters():
p.requires_grad = fine_tune
for p in self.embedding_sec.parameters():
p.requires_grad = fine_tune
def init_hidden_state(self, encoder_out, language):
"""
Creates the initial hidden and cell states for the decoder's LSTM based on the encoded images.
:param encoder_out: encoded images, a tensor of dimension (batch_size, num_pixels, encoder_dim)
:return: hidden state, cell state
"""
mean_encoder_out = encoder_out.mean(dim=1)
if language == 'EN':
h = self.init_h(mean_encoder_out)
c = self.init_c(mean_encoder_out)
elif language == 'DE':
h = self.init_h_sec(mean_encoder_out)
c = self.init_c_sec(mean_encoder_out)
else:
print('Input language is not defined.')
assert(0)
return h, c
def forward(self, encoder_out, encoded_captions, caption_lengths, language):
"""
Forward propagation.
:param encoder_out: encoded images, a tensor of dimension (batch_size, enc_image_size, enc_image_size, encoder_dim)
:param encoded_captions: encoded captions, a tensor of dimension (batch_size, max_caption_length)
:param caption_lengths: caption lengths, a tensor of dimension (batch_size, 1)
:return: scores for vocabulary, sorted encoded captions, decode lengths, weights, sort indices
"""
batch_size = encoder_out.size(0)
encoder_dim = encoder_out.size(-1)
vocab_size1 = self.vocab_size1
vocab_size2 = self.vocab_size2
# Flatten image -> [batch_size, num_pixels=196, encoder_dim=2048]
encoder_out = encoder_out.view(batch_size, -1, encoder_dim)
num_pixels = encoder_out.size(1)
# Sort input data by decreasing lengths
if len(caption_lengths.size()) == 1:
caption_lengths, sort_ind = caption_lengths.sort(dim=0, descending=True)
else:
caption_lengths, sort_ind = caption_lengths.squeeze(1).sort(dim=0, descending=True)
encoder_out = encoder_out[sort_ind] # sort encoded image based on length of caption 1 languegs
encoded_captions = encoded_captions[sort_ind]
# We won't decode at the <end> position, since we've finished generating as soon as we generate <end>
# So, decoding lengths are actual lengths - 1
decode_lengths = (caption_lengths - 1).tolist()
if language == 'EN':
# Embedding
embeddings = self.embedding(encoded_captions) # (batch_size, max_caption_length, embed_dim)
# Initialize LSTM state
h, c = self.init_hidden_state(encoder_out, language) # [batch_size, decoder_dim]
# Create tensors to hold word predicion scores and alphas
predictions = torch.zeros(batch_size, max(decode_lengths), vocab_size1).to(device)
alphas = torch.zeros(batch_size, max(decode_lengths), num_pixels).to(device)
# At each time-step, decode by
# attention-weighing the encoder's output based on the decoder's previous hidden state output
# then generate a new word in the decoder with the previous word and the attention weighted encoding
for t in range(max(decode_lengths)):
batch_size_t = sum([l > t for l in decode_lengths])
attention_weighted_encoding, alpha = self.attention(encoder_out[:batch_size_t],
h[:batch_size_t])
gate = self.sigmoid(self.f_beta(h[:batch_size_t])) # gating scalar, (batch_size_t, encoder_dim)
attention_weighted_encoding = gate * attention_weighted_encoding
h, c = self.decode_step(
torch.cat([embeddings[:batch_size_t, t, :], attention_weighted_encoding], dim=1),
(h[:batch_size_t], c[:batch_size_t])) # (batch_size_t, decoder_dim)
preds = self.fc(self.dropout(h)) # (batch_size_t, vocab_size)
predictions[:batch_size_t, t, :] = preds
alphas[:batch_size_t, t, :] = alpha
elif language == 'DE':
embeddings = self.embedding_sec(encoded_captions)
# Initialize LSTM state
h, c = self.init_hidden_state(encoder_out, language) # [batch_size, decoder_dim]
# Create tensors to hold word predicion scores and alphas
predictions = torch.zeros(batch_size, max(decode_lengths), vocab_size2).to(device)
alphas = torch.zeros(batch_size, max(decode_lengths), num_pixels).to(device)
for t in range(max(decode_lengths)):
batch_size_t = sum([l > t for l in decode_lengths])
attention_weighted_encoding, alpha = self.attention(encoder_out[:batch_size_t],
h[:batch_size_t])
gate = self.sigmoid(self.f_beta_sec(h[:batch_size_t])) # gating scalar, (batch_size_t, encoder_dim)
attention_weighted_encoding = gate * attention_weighted_encoding
h, c = self.decode_step_sec(
torch.cat([embeddings[:batch_size_t, t, :], attention_weighted_encoding], dim=1),
(h[:batch_size_t], c[:batch_size_t])) # (batch_size_t, decoder_dim)
preds = self.fc_sec(self.dropout(h)) # (batch_size_t, vocab_size)
predictions[:batch_size_t, t, :] = preds
alphas[:batch_size_t, t, :] = alpha
else:
assert(0)
return predictions, encoded_captions, decode_lengths, alphas, sort_ind