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项目:CV和NLP结合的Attention视频字幕生成算法实现

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课程:学堂在线的清华训练营《驭风计划:培养人工智能青年人才》(满分作业)

 

代码: sgrvinod/a-PyTorch-Tutorial-to-Image-Captioning: Show, Attend, and Tell | a PyTorch Tutorial to Image Captioning (github.com)

 

paper:《Show, Attend and Tell Neural Image Caption Generation with Visual Attention》

 

需要的理论知识:LSTM BLEU Resnet-101 COCO数据集 Attention beam算法

 

理论知识也可以参考博客:

Monte Carlo

详解BLEU的原理和计算

 

《Show, Attend and Tell: Neural Image Caption Generation with Visual Attention》论文阅读(详细

 

《Show and Tell: A Neural Image Caption Generator》论文解读

 

项目配置、训练过程(环境:百度飞浆平台 V100 cuda9.2):

 

conda create --prefix=/home/aistudio/external-libraries/caption python=3.6 -y 
conda init bash
conda activate /home/aistudio/external-libraries/caption
conda install pytorch=0.4.1 cuda92 -c https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/linux-64/ -y
pip install torchvision==0.2.2 numpy nltk tqdm h5py pillow matplotlib scikit-image scipy==1.1.0 -i https://pypi.tuna.tsinghua.edu.cn/simple
python train.py > train_att.log
python caption.py --img '/home/aistudio/data/data124850/test2014/COCO_test2014_000000090228.jpg' --model '../code/BEST_checkpoint_coco_5_cap_per_img_5_min_word_freq.pth.tar' --word_map '/home/aistudio/data/data124851/WORDMAP_coco_5_cap_per_img_5_min_word_freq.json'

 

实验结果示例:

 

图中的白色亮光是指注意力,很明显,可以看到对于清晰和明显的图片这个模型还是有较好的效果,注意力也能关注到图片中的重要部分,而且注意力是随着词的变化而改变位置的,例如第2张图,当词是a laptop的时候,注意力正好关注电脑;当词是wooden desk的时候,注意力正好关注着桌子。

 

 

核心代码:

 

import torch
from torch import nn
import torchvision
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class Encoder(nn.Module):
    """
    Encoder
    """
    def __init__(self, encoded_image_size=14):
        super(Encoder, self).__init__()
        self.enc_image_size = encoded_image_size
        # Pretrained ImageNet ResNet-101
        # Remove linear and pool layers
        resnet = torchvision.models.resnet101(pretrained=True)
        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(fine_tune=True)
    def forward(self, images):
        """
        Forward propagation.
        :param images: images, a tensor of dimensions (batch_size, 3, image_size, image_size)
        :return: encoded images
        """
        out = self.resnet(images)  # (batch_size, 2048, image_size/32, image_size/32)
        out = self.adaptive_pool(out)  # (batch_size, 2048, encoded_image_size, encoded_image_size)
        out = out.permute(0, 2, 3, 1)  # (batch_size, encoded_image_size, encoded_image_size, 2048)
        return out
    def fine_tune(self, fine_tune=True):
        """
        Allow or prevent the computation of gradients for convolutional blocks 2 through 4 of the encoder.
        :param fine_tune: boolean
        """
        for p in self.resnet.parameters():
            p.requires_grad = False
        # If fine-tuning, only fine-tune convolutional blocks 2 through 4
        for c in list(self.resnet.children())[5:]:
            for p in c.parameters():
                p.requires_grad = fine_tune
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 pass.
        :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, num_pixels, attention_dim)
        att2 = self.decoder_att(decoder_hidden)  # (batch_size, attention_dim)
        att = self.full_att(self.relu(att1 + att2.unsqueeze(1))).squeeze(2)  # (batch_size, num_pixels)
        alpha = self.softmax(att)  # (batch_size, num_pixels)
        z = (encoder_out * alpha.unsqueeze(2)).sum(dim=1)  # (batch_size, encoder_dim)
        return z, alpha
class DecoderWithAttention(nn.Module):
    """
    Decoder.
    """
    def __init__(self, cfg, encoder_dim=2048):
        """
        :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__()
        self.encoder_dim = encoder_dim
        self.decoder_dim = cfg['decoder_dim']
        self.attention_dim = cfg['attention_dim']
        self.embed_dim = cfg['embed_dim']
        self.vocab_size = cfg['vocab_size']
        self.dropout = cfg['dropout']
        self.device = cfg['device']
        self.attention = Attention(encoder_dim, self.decoder_dim, self.attention_dim)  # attention network
        self.embedding = nn.Embedding(self.vocab_size, self.embed_dim)  # embedding layer
        self.dropout = nn.Dropout(0.1)
        self.decode_step = nn.LSTMCell(self.embed_dim + encoder_dim, self.decoder_dim, bias=True)  # decoding LSTMCell
        self.init_h = nn.Linear(encoder_dim, self.decoder_dim)  # linear layer to find initial hidden state of LSTMCell
        self.init_c = nn.Linear(encoder_dim, self.decoder_dim)  # linear layer to find initial cell state of LSTMCell
        self.f_beta = nn.Linear(self.decoder_dim, encoder_dim)  # linear layer to create a sigmoid-activated gate
        self.sigmoid = nn.Sigmoid()
        self.fc = nn.Linear(self.decoder_dim, self.vocab_size)  # linear layer to find scores over vocabulary
        self.init_weights()  # initialize some layers with the uniform distribution
        # initialize some layers with the uniform distribution
        self.embedding.weight.data.uniform_(-0.1, 0.1)
        self.fc.bias.data.fill_(0)
        self.fc.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)
    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
    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)
    def forward(self, encoder_out, encoded_captions, caption_lengths):
        """
        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_size = self.vocab_size
        # Flatten image
        encoder_out = encoder_out.view(batch_size, -1, encoder_dim)  # (batch_size, num_pixels, encoder_dim)
        num_pixels = encoder_out.size(1)
        # Sort input data by decreasing lengths;
        caption_lengths, sort_ind = caption_lengths.squeeze(1).sort(dim=0, descending=True)
        encoder_out = encoder_out[sort_ind]
        encoded_captions = encoded_captions[sort_ind]
        # Embedding
        embeddings = self.embedding(encoded_captions)  # (batch_size, max_caption_length, embed_dim)
        # 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()
        # Create tensors to hold word predicion scores and alphas
        predictions = torch.zeros(batch_size, max(decode_lengths), vocab_size).to(self.device)
        alphas = torch.zeros(batch_size, max(decode_lengths), num_pixels).to(self.device)
        # Initialize LSTM state
        mean_encoder_out = encoder_out.mean(dim=1)
        h = self.init_h(mean_encoder_out)  # (batch_size, decoder_dim)
        c = self.init_c(mean_encoder_out)
        for t in range(max(decode_lengths)):
            batch_size_t = sum([l > t for l in decode_lengths])
            preds, alpha, h, c = self.one_step(t, batch_size_t, embeddings, encoder_out, h, c)
            predictions[:batch_size_t, t, :] = preds
            alphas[:batch_size_t, t, :] = alpha
       
        return predictions, encoded_captions, decode_lengths, alphas, sort_ind
    def one_step(self, t, batch_size_t, embeddings, encoder_out, h, c):
        """
        :param t:
        :param batch_size_t:
        :param embeddings:
        :param encoder_out:
        :param h:
        :param c:
        :return:
        """
        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)     
        return preds, alpha, h, c

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