ViT模型学习

Vision Transformer（ViT）简介
ViT则是自然语言处理和计算机视觉两个领域的融合结晶。在不依赖卷积操作的情况下，依然可以在图像分类任务上达到很好的效果。

模型特点
ViT模型主要应用于图像分类领域。因此，其模型结构相较于传统的Transformer有以下几个特点：

• 数据集的原图像被划分为多个patch（图像块）后，将二维patch（不考虑channel）转换为一维向量，再加上类别向量与位置向量作为模型输入。
• 模型主体的Block结构是基于Transformer的Encoder结构，但是调整了Normalization的位置，其中，最主要的结构依然是Multi-head Attention结构。
• 模型在Blocks堆叠后接全连接层，接受类别向量的输出作为输入并用于分类。通常情况下，我们将最后的全连接层称为Head，Transformer Encoder部分为backbone。

构建模型

Multi-Head Attention

• 多头注意力可以实现并行加速，且可以保持参数量

from mindspore import nn, ops


class Attention(nn.Cell):
    def __init__(self,
                 dim: int,
                 num_heads: int = 8,
                 keep_prob: float = 1.0,
                 attention_keep_prob: float = 1.0):
        super(Attention, self).__init__()

        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = ms.Tensor(head_dim ** -0.5)

        self.qkv = nn.Dense(dim, dim * 3)
        self.attn_drop = nn.Dropout(p=1.0-attention_keep_prob)
        self.out = nn.Dense(dim, dim)
        self.out_drop = nn.Dropout(p=1.0-keep_prob)
        self.attn_matmul_v = ops.BatchMatMul()
        self.q_matmul_k = ops.BatchMatMul(transpose_b=True)
        self.softmax = nn.Softmax(axis=-1)

    def construct(self, x):
        """Attention construct."""
        b, n, c = x.shape
        qkv = self.qkv(x)
        qkv = ops.reshape(qkv, (b, n, 3, self.num_heads, c // self.num_heads))
        qkv = ops.transpose(qkv, (2, 0, 3, 1, 4))
        q, k, v = ops.unstack(qkv, axis=0)
        attn = self.q_matmul_k(q, k)
        attn = ops.mul(attn, self.scale)
        attn = self.softmax(attn)
        attn = self.attn_drop(attn)
        out = self.attn_matmul_v(attn, v)
        out = ops.transpose(out, (0, 2, 1, 3))
        out = ops.reshape(out, (b, n, c))
        out = self.out(out)
        out = self.out_drop(out)

        return out

TransformerEncoder

class TransformerEncoder(nn.Cell):
    def __init__(self,
                 dim: int,
                 num_layers: int,
                 num_heads: int,
                 mlp_dim: int,
                 keep_prob: float = 1.,
                 attention_keep_prob: float = 1.0,
                 drop_path_keep_prob: float = 1.0,
                 activation: nn.Cell = nn.GELU,
                 norm: nn.Cell = nn.LayerNorm):
        super(TransformerEncoder, self).__init__()
        layers = []

        for _ in range(num_layers):
            normalization1 = norm((dim,))
            normalization2 = norm((dim,))
            attention = Attention(dim=dim,
                                  num_heads=num_heads,
                                  keep_prob=keep_prob,
                                  attention_keep_prob=attention_keep_prob)

            feedforward = FeedForward(in_features=dim,
                                      hidden_features=mlp_dim,
                                      activation=activation,
                                      keep_prob=keep_prob)

            layers.append(
                nn.SequentialCell([
                    ResidualCell(nn.SequentialCell([normalization1, attention])),
                    ResidualCell(nn.SequentialCell([normalization2, feedforward]))
                ])
            )
        self.layers = nn.SequentialCell(layers)

    def construct(self, x):
        """Transformer construct."""
        return self.layers(x)

pos_embedding

class PatchEmbedding(nn.Cell):
    MIN_NUM_PATCHES = 4

    def __init__(self,
                 image_size: int = 224,
                 patch_size: int = 16,
                 embed_dim: int = 768,
                 input_channels: int = 3):
        super(PatchEmbedding, self).__init__()

        self.image_size = image_size
        self.patch_size = patch_size
        self.num_patches = (image_size // patch_size) ** 2
        self.conv = nn.Conv2d(input_channels, embed_dim, kernel_size=patch_size, stride=patch_size, has_bias=True)

    def construct(self, x):
        """Path Embedding construct."""
        x = self.conv(x)
        b, c, h, w = x.shape
        x = ops.reshape(x, (b, c, h * w))
        x = ops.transpose(x, (0, 2, 1))

        return x

Vit部分实现

from mindspore.common.initializer import Normal
from mindspore.common.initializer import initializer
from mindspore import Parameter


def init(init_type, shape, dtype, name, requires_grad):
    """Init."""
    initial = initializer(init_type, shape, dtype).init_data()
    return Parameter(initial, name=name, requires_grad=requires_grad)


class ViT(nn.Cell):
    def __init__(self,
                 image_size: int = 224,
                 input_channels: int = 3,
                 patch_size: int = 16,
                 embed_dim: int = 768,
                 num_layers: int = 12,
                 num_heads: int = 12,
                 mlp_dim: int = 3072,
                 keep_prob: float = 1.0,
                 attention_keep_prob: float = 1.0,
                 drop_path_keep_prob: float = 1.0,
                 activation: nn.Cell = nn.GELU,
                 norm: Optional[nn.Cell] = nn.LayerNorm,
                 pool: str = 'cls') -> None:
        super(ViT, self).__init__()

        self.patch_embedding = PatchEmbedding(image_size=image_size,
                                              patch_size=patch_size,
                                              embed_dim=embed_dim,
                                              input_channels=input_channels)
        num_patches = self.patch_embedding.num_patches

        self.cls_token = init(init_type=Normal(sigma=1.0),
                              shape=(1, 1, embed_dim),
                              dtype=ms.float32,
                              name='cls',
                              requires_grad=True)

        self.pos_embedding = init(init_type=Normal(sigma=1.0),
                                  shape=(1, num_patches + 1, embed_dim),
                                  dtype=ms.float32,
                                  name='pos_embedding',
                                  requires_grad=True)

        self.pool = pool
        self.pos_dropout = nn.Dropout(p=1.0-keep_prob)
        self.norm = norm((embed_dim,))
        self.transformer = TransformerEncoder(dim=embed_dim,
                                              num_layers=num_layers,
                                              num_heads=num_heads,
                                              mlp_dim=mlp_dim,
                                              keep_prob=keep_prob,
                                              attention_keep_prob=attention_keep_prob,
                                              drop_path_keep_prob=drop_path_keep_prob,
                                              activation=activation,
                                              norm=norm)
        self.dropout = nn.Dropout(p=1.0-keep_prob)
        self.dense = nn.Dense(embed_dim, num_classes)

    def construct(self, x):
        """ViT construct."""
        x = self.patch_embedding(x)
        cls_tokens = ops.tile(self.cls_token.astype(x.dtype), (x.shape[0], 1, 1))
        x = ops.concat((cls_tokens, x), axis=1)
        x += self.pos_embedding

        x = self.pos_dropout(x)
        x = self.transformer(x)
        x = self.norm(x)
        x = x[:, 0]
        if self.training:
            x = self.dropout(x)
        x = self.dense(x)

        return x

推理结果

此章节学习到此结束，感谢昇思平台。