model.py

import torch
import torch.nn as nn
import torchvision.models as models
from torch.nn.utils.rnn import pack_padded_sequence
from torch.autograd import Variable

#pretrained resent152 model encoder
class EncoderCNN(nn.Module):
    def __init__(self, embed_size):
        """Load the pretrained ResNet-152 and replace top fc layer."""
        super(EncoderCNN, self).__init__()
        resnet = models.resnet152(pretrained=True)
        modules = list(resnet.children())[:-1]      # delete the last fc layer.
        self.resnet = nn.Sequential(*modules)
        self.linear = nn.Linear(resnet.fc.in_features, embed_size)
        self.bn = nn.BatchNorm1d(embed_size, momentum=0.01)
        self.init_weights()
        
    def init_weights(self):
        """Initialize the weights."""
        self.linear.weight.data.normal_(0.0, 0.02)
        self.linear.bias.data.fill_(0)
        
    def forward(self, images):
        """Extract the image feature vectors."""
        features = self.resnet(images)
        features = Variable(features.data)
        features = features.view(features.size(0), -1)
        features = self.bn(self.linear(features))
        return features

# 3x3 Convolution
def conv3x3(in_channels, out_channels, stride=1):
    return nn.Conv2d(in_channels, out_channels, kernel_size=3,
                     stride=stride, padding=1, bias=False)

# Residual Block
class ResidualBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1, downsample=None):
        super(ResidualBlock, self).__init__()
        self.conv1 = conv3x3(in_channels, out_channels, stride)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(out_channels, out_channels)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.downsample = downsample

    def forward(self, x):
        residual = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        if self.downsample:
            residual = self.downsample(x)
        out += residual
        out = self.relu(out)
        return out

# ResNet Module
class ResNet(nn.Module):
    def __init__(self, block, layers, embed_size):
        super(ResNet, self).__init__()
        self.in_channels = 32
        self.conv = conv3x3(3, 32)
        self.bn = nn.BatchNorm2d(32)
        self.relu = nn.ReLU(inplace=True)
        self.layer1 = self.make_layer(block, 32, layers[0])
        self.layer2 = self.make_layer(block, 64, layers[0],2)
        self.layer3 = self.make_layer(block, 128, layers[1],2)
        self.avg_pool = nn.AvgPool2d(8)
        self.fc = nn.Linear(32768, embed_size)
        #self.fc2 = nn.Linear(2048, num_classes)
        #self.init_weights()

    def init_weights(self):
        """Initialize the weights."""
        self.fc.weight.data.normal_(0.0, 0.02)
        self.fc.bias.data.fill_(0)
        self.fc2.weight.data.normal_(0.0, 0.02)
        self.fc2.bias.data.fill_(0)

    def make_layer(self, block, out_channels, blocks, stride=1):
        downsample = None
        if (stride != 1) or (self.in_channels != out_channels):
            downsample = nn.Sequential(
                conv3x3(self.in_channels, out_channels, stride=stride),
                nn.BatchNorm2d(out_channels))
        layers = []
        layers.append(block(self.in_channels, out_channels, stride, downsample))
        self.in_channels = out_channels
        for i in range(1, blocks):
            layers.append(block(out_channels, out_channels))
        return nn.Sequential(*layers)

    def forward(self, x):
        out = self.conv(x)
        out = self.bn(out)
        out = self.relu(out)
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        #out = self.avg_pool(out)
        out = out.view(out.size(0), -1)
        out = self.fc(out)
        #out = self.fc2(out)
        return out

#Basic decoder RNN
class DecoderRNN(nn.Module):
    def __init__(self, embed_size, hidden_size, vocab_size, num_layers):
        """Set the hyper-parameters and build the layers."""
        super(DecoderRNN, self).__init__()
        self.embed = nn.Embedding(vocab_size, embed_size)
        self.embed_size = embed_size
        self.lstm = nn.LSTM(embed_size, hidden_size, num_layers, batch_first=True)
        self.linear = nn.Linear(hidden_size, vocab_size)
        self.init_weights()
    
    def init_weights(self):
        """Initialize weights."""
        self.embed.weight.data.uniform_(-0.1, 0.1)
        self.linear.weight.data.uniform_(-0.1, 0.1)
        self.linear.bias.data.fill_(0)
        
    def forward(self, features, captions, lengths):
        """Decode image feature vectors and generates captions."""
        captions = self.embed(captions)
        embeddings = torch.cat((features.unsqueeze(1), captions), 1)
        packed = pack_padded_sequence(embeddings, lengths, batch_first=True) 
        #packed = torch.cat((features.unsqueeze(1), captions),1)
        hiddens, _ = self.lstm(packed)
        outputs = self.linear(hiddens[0])
        return outputs
    
    def sample(self, features, states=None):
        """Samples captions for given image features (Greedy search)."""
        sampled_ids = []
        inputs = features.unsqueeze(1)
        for i in range(100):                                      # maximum sampling length
            hiddens, states = self.lstm(inputs, states)          # (batch_size, 1, hidden_size), 
            outputs = self.linear(hiddens.squeeze(1))            # (batch_size, vocab_size)
            predicted = outputs.max(1)[1]
            sampled_ids.append(predicted)
            inputs = self.embed(predicted)
            inputs = inputs.unsqueeze(1)                   # (batch_size, 1, embed_size)
        #print(sampled_ids)
        #sampled_ids = torch.cat(sampled_ids, 1)                  # (batch_size, 20)
        return sampled_ids