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(09-1講)マルチモード学習

1. Overview of multi-modal learning

マルチモード学習は、異なるタイプのデータを学習に一緒に適用する方法である.
質問する
1.データの表示方法はそれぞれ異なる(一つの画像:3 d、一つのテキスト:wordの埋め込みベクトルは一つのシーケンスに対応する)
2.異性特徴空間間のアンバランス
アボカド形の椅子(テキスト)-画像(画像)=1:Nマッチング
3.多様な形式を用いる場合の学習の限界
あまりにも多くの情報を受け取っているので、かえって勉強ができません(一つの形式だけを勉強するなどの問題)
これらの問題にもかかわらず、マルチモード学習は重要であり、そのタイプは以下の通りである.

2. Multi-modal tasks (1) - Visual data & Text

2.1 test embedding

word level text埋め込みベクトルを作成した後、各ベクトルを低次元投影することで各ベクトルの距離を測定する->他の単語関係で一般化可能(man~won:king~queen)

word2vec : Skip-gram model
学習中心語と周辺語の関係

2.2 Joint embedding

Image tagging

イメージ->タグ/タグ->イメージの作成

予雨の単一ピークモデルと組み合わせて実現
目測空間におけるMetric Learning in visual-semanticspace:2つのベクトルを同じ埋め込み空間にマッピングして距離を減らす方向に学習する(一致しないデータについては距離を大きくして学習する)
によって作成された連合埋め込み空間によるマルチモード解析
適用
[Image & food recipr retrival]([Marin et al., TPAMI 2019])
Cross modal translation
1. Image captioning : image to sentance - CNN 4 image + RNN 4 sentence

show and tell

cnnとrnnを統合する方法,Encoder:CNNモデル事前トレーニングImageNet/Decorder:LSTMモジュール

show, attend, and tell - Attention

ビデオ全体でtagを生成するために集中するローカル部分も見る方法.空間情報を保持するfixedimensinal vectorのfeature mapを作成しRNNに挿入

Soft Attention embedding

人間が視覚的に物体を判断する際に集中する特徴があるように,モデルも特徴と微調整重みにzを加えることでzを作成する.

sis isi:画像のどの部分が集中的に見る必要があるかを示すspatial soft attention map.
ziz izi:内部から特徴図とsoft attention mapの注意重みを取得する
yiy iii:以前に出力された単語
hih ihi:デコーダの非表示状態

Text-to-image by Generative model
テキスト-画像のように、1:nはモデルを生成する必要があります.

Generator Network
テキスト全体を固定次元ベクトルに変更
ガウス分布を加えて、出力を常に異なります
Codintial Generatorによるイメージ作成

Discriminator Network
生成された画像を入力として受信し、低次元フィーチャーマッピングを抽出
Gで使用する文の条件を組み合わせて、その条件で生成された画像をtrueまたはfalseと判定する
Crodd model reasoning (Referencing)
1. Visual question answering

multi streams

joint embedding

end-to-end training

3. Multi-modal tasks (2) - Visual data & Audio

3.1 Sound representation

時間軸のwave形式の1 d信号は,モデル学習時にパワースペクトルまたはスペクトルのみを必要とする.

Fourier transform : Short-time Fourier transform (STFT)
Fourier変換は短いウィンドウ範囲でのみ適用され、spectrogramでのみ描画/Hammming windowでは重み付け

が適用されます.
Further Question
(1)マルチモード学習において,feature間の意味を保つためにどのような学習方法を用いたか.
(2)選択タスクを完了する際,注意力はどのように用いられるか.
(3)Sound Sourceローカライズタスクを完了した場合,オーディオ情報はどのように利用されるのか.

（09－2）画像説明

タスク練習/論文:Show, Attend, and Tell

author's original implementation : https://github.com/kelvinxu/arctic-captions Show, Attend and Tell Concepts

Image captioning

Encoder-Decoder architecture

Attention

Transfer Learning

Beam Search

environment : Pytorch 0.4 Python 3.6

Encoder

class Encoder(nn.Module):
    """
    Encoder.
    """

    def __init__(self, encoded_image_size=14):
        super(Encoder, self).__init__()
        self.enc_image_size = encoded_image_size

        resnet = torchvision.models.resnet101(pretrained=True)  # pretrained ImageNet ResNet-101

        # Remove linear and pool layers (since we're not doing classification)
        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
        """
        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: Allow?
        """
        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

Decoder(with attention)

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, 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)
        attention_weighted_encoding = (encoder_out * alpha.unsqueeze(2)).sum(dim=1)  # (batch_size, encoder_dim)

        return attention_weighted_encoding, alpha


class DecoderWithAttention(nn.Module):
    """
    Decoder.
    """

    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__()

        self.encoder_dim = encoder_dim
        self.attention_dim = attention_dim
        self.embed_dim = embed_dim
        self.decoder_dim = decoder_dim
        self.vocab_size = vocab_size
        self.dropout = dropout

        self.attention = Attention(encoder_dim, decoder_dim, attention_dim)  # attention network

        self.embedding = nn.Embedding(vocab_size, embed_dim)  # embedding layer
        self.dropout = nn.Dropout(p=self.dropout)
        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.sigmoid = nn.Sigmoid()
        self.fc = nn.Linear(decoder_dim, vocab_size)  # linear layer to find scores over vocabulary
        self.init_weights()  # initialize some layers with the uniform distribution

    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 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_hidden_state(self, encoder_out):
        """
        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)
        h = self.init_h(mean_encoder_out)  # (batch_size, decoder_dim)
        c = self.init_c(mean_encoder_out)
        return h, c

    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; why? apparent below
        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)

        # Initialize LSTM state
        h, c = self.init_hidden_state(encoder_out)  # (batch_size, decoder_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(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

        return predictions, encoded_captions, decode_lengths, alphas, sort_ind

Further Reading
a-PyTorch-Tutorial-to-Image-Captioning

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