【人工智能笔记】第四十一节:TF2实现VITGAN对抗生成网络,Generator生成器 实现

本文详细介绍VITGAN中Generator生成器的代码实现过程,包括Transformer编码器等关键组件,并探讨了不同位置编码方式对生成效果的影响。

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网络结构图
该章节介绍VITGAN对抗生成网络中,Generator生成器 部分的代码实现。

目录(文章发布后会补上链接):

  1. 网络结构简介
  2. Mapping NetWork 实现
  3. PositionalEmbedding 实现
  4. MLP 实现
  5. MSA多头注意力 实现
  6. SLN自调制 实现
  7. CoordinatesPositionalEmbedding 实现
  8. ModulatedLinear 实现
  9. Siren 实现
  10. Generator生成器 实现
  11. PatchEmbedding 实现
  12. ISN 实现
  13. Discriminator鉴别器 实现
  14. VITGAN 实现

Generator生成器 简介

Generator生成器
论文原文
上图是整个完整的 Generator生成器结构,由前面几章的模块组合而成。

代码实现

GeneratorEncoder代码实现

import tensorflow as tf

import sys

sys.path.append('')

from models.msa import MSA
from models.mlp import MLP
from models.sln import SLN


class GeneratorEncoderLayer(tf.keras.layers.Layer):
    def __init__(self, d_model, num_heads, dropout=0.0):
        super().__init__()
        self.d_model = d_model
        self.num_heads = num_heads
        self.sln1 = SLN(d_model)
        self.msa1 = MSA(d_model, num_heads, discriminator=False)
        self.sln2 = SLN(d_model)
        self.mlp1 = MLP(d_model, discriminator=False, dropout=dropout)

    def call(self, x, w, training):
        h = x
        x = self.sln1(h=x, w=w, training=training)
        x = self.msa1(v=x, k=x, q=x, mask=None)
        x = x + h
        h = x
        x = self.sln2(h=x, w=w, training=training)
        x = self.mlp1(x)
        x = x + h
        return x

class GeneratorEncoder(tf.keras.layers.Layer):
    def __init__(self, d_model, num_heads, num_layers, dropout=0.0):
        super().__init__()
        self.d_model = d_model
        self.num_heads = num_heads
        self.num_layers = num_layers
        self.encoder_layers = [GeneratorEncoderLayer(d_model, num_heads, dropout=dropout) for i in range(num_layers)]

    def call(self, x, w, training):
        for encoder_layer in self.encoder_layers:
            x = encoder_layer(x=x, w=w, training=training)
        return x

if __name__ == "__main__":
    # layer = EncoderLayer(256, 8)
    layer = GeneratorEncoder(256, 8, 4)
    x = tf.random.uniform([2,5,256], dtype=tf.float32)
    w = tf.random.uniform([2,5,256], dtype=tf.float32)
    o1 = layer(x, w, training=True)
    tf.print('o1:', tf.shape(o1))

Generator代码实现,不含博里叶位置编码

import tensorflow as tf
import sys

sys.path.append('')

from models.mapping_network import MappingNetwork
from models.generator_transformer_encoder import GeneratorEncoder
from models.coordinates_positional_embedding import CoordinatesPositionalEmbedding
from models.siren_test import Siren
from models.sln import SLN
from models.positional_embedding import PositionalEmbedding
from models.modulated_linear import ModulatedLinear
from models.mlp import MLP


class Generator(tf.keras.layers.Layer):
    """
    生成器
    """

    def __init__(
        self,
        image_size=224,
        patch_size=16,
        num_channels=3,
        d_model=768,
        dropout=0.0,
    ):
        super().__init__()
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.d_model = d_model
        self.dropout = dropout
        self.grid_size = image_size // patch_size
        self.num_patches = self.grid_size ** 2

        self.mapping_network = MappingNetwork(
            self.d_model,
            num_layers=8
        )

        # 输入位置编码
        self.patch_positional_embedding = PositionalEmbedding(
            sequence_length=self.num_patches,
            emb_dim=self.d_model,
        )

        self.generator_transformer_encoder = GeneratorEncoder(
            d_model,
            num_heads=8,
            num_layers=4,
            dropout=dropout,
        )
        self.sln1 = SLN(d_model)

        self.siren = Siren(
            hidden_dim=d_model,
            hidden_layers=2,
            out_dim=d_model,
            first_omega_0=30,
            hidden_omega_0=30,
            demodulation=True,
            outermost_linear=False
        )

    def call(self, x, training):
        batch_size = tf.shape(x)[0]
        w = self.mapping_network(x, training=training)
        # 输入位置编码
        x_pos = self.patch_positional_embedding()
        x = self.generator_transformer_encoder(x=x_pos, w=w, training=training)
        x = self.sln1(x, w, training=training)
        x = self.siren(x) # (B, L, E=P*P*C)
        x = tf.reshape(x, [batch_size, self.grid_size, self.grid_size, self.patch_size, self.patch_size, self.num_channels])
        x = tf.transpose(x, perm=[0,1,3,2,4,5])
        x = tf.reshape(x, [batch_size, self.image_size, self.image_size, self.num_channels])
        return x


if __name__ == "__main__":
    layer = Generator(
        image_size=224,
        patch_size=16,
        num_channels=3,
        d_model=768
    )
    x = tf.random.uniform([2,1,768], dtype=tf.float32)
    o1 = layer(x, training=True)
    tf.print('o1:', tf.shape(o1))
    o1 = layer(x, training=False)
    tf.print('o1:', tf.shape(o1))

Generator代码实现,包含博里叶位置编码。论文没描述该结构细节,这是一种猜测,试过无效,可能有误。

import tensorflow as tf
import sys

sys.path.append('')

from models.mapping_network import MappingNetwork
from models.generator_transformer_encoder import GeneratorEncoder
from models.coordinates_positional_embedding import CoordinatesPositionalEmbedding
from models.siren import Siren
from models.sln import SLN
from models.positional_embedding import PositionalEmbedding
from models.modulated_linear import ModulatedLinear
from models.mlp import MLP


class Generator(tf.keras.layers.Layer):
    """
    生成器
    """

    def __init__(
        self,
        image_size=224,
        patch_size=16,
        num_channels=3,
        d_model=768,
        dropout=0.0,
    ):
        super().__init__()
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.d_model = d_model
        self.dropout = dropout
        self.grid_size = image_size // patch_size
        self.num_patches = self.grid_size ** 2

        self.mapping_network = MappingNetwork(
            self.d_model,
            num_layers=8
        )

        # 输入位置编码
        self.patch_positional_embedding = PositionalEmbedding(
            sequence_length=self.num_patches,
            emb_dim=self.d_model,
        )

        self.generator_transformer_encoder = GeneratorEncoder(
            d_model,
            num_heads=8,
            num_layers=4,
            dropout=dropout,
        )
        self.sln1 = SLN(d_model)
        # 博里叶位置编码
        self.coordinates_positional_embedding = CoordinatesPositionalEmbedding(
            patch_size=patch_size,
            emb_dim=d_model,
            )

        self.siren = Siren(
            hidden_dim=d_model,
            hidden_layers=2,
            out_dim=num_channels,
            first_omega_0=30,
            hidden_omega_0=30,
            demodulation=True,
            outermost_linear=False
        )

        self.modulated_linear = ModulatedLinear(
            hidden_dim=d_model,
            output_dim=d_model,
            demodulation=True,
            use_bias=False,
            kernel_initializer=tf.initializers.GlorotNormal(),
        )

    def call(self, x, training):
        batch_size = tf.shape(x)[0]
        w = self.mapping_network(x, training=training)
        # 输入位置编码
        x_pos = self.patch_positional_embedding()
        x = self.generator_transformer_encoder(x=x_pos, w=w, training=training)
        x = self.sln1(x, w, training=training)
        # 博里叶位置编码
        e_fou = self.coordinates_positional_embedding(x)
        x = self.siren((e_fou, x)) # (B*L, P*P, E)
        x = tf.reshape(x, [batch_size, self.grid_size, self.grid_size, self.patch_size, self.patch_size, self.num_channels])
        x = tf.transpose(x, perm=[0,1,3,2,4,5])
        x = tf.reshape(x, [batch_size, self.image_size, self.image_size, self.num_channels])
        return x


if __name__ == "__main__":
    layer = Generator(
        image_size=224,
        patch_size=16,
        num_channels=3,
        d_model=768
    )
    x = tf.random.uniform([2,1,768], dtype=tf.float32)
    o1 = layer(x, training=True)
    tf.print('o1:', tf.shape(o1))
    o1 = layer(x, training=False)
    tf.print('o1:', tf.shape(o1))

Generator代码实现,包含博里叶位置编码。论文没描述该结构细节,这是另一种猜测,试过无效,可能有误。

import tensorflow as tf
import sys

sys.path.append('')

from models.mapping_network import MappingNetwork
from models.generator_transformer_encoder import GeneratorEncoder
from models.coordinates_positional_embedding import CoordinatesPositionalEmbedding
from models.siren import Siren
from models.sln import SLN
from models.positional_embedding import PositionalEmbedding
from models.modulated_linear import ModulatedLinear
from models.mlp import MLP


class Generator(tf.keras.layers.Layer):
    """
    生成器
    """

    def __init__(
        self,
        image_size=224,
        patch_size=16,
        num_channels=3,
        d_model=768,
        dropout=0.0,
    ):
        super().__init__()
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.d_model = d_model
        self.dropout = dropout
        self.grid_size = image_size // patch_size
        self.num_patches = self.grid_size ** 2

        self.mapping_network = MappingNetwork(
            self.d_model,
            num_layers=8
        )

        # 输入位置编码
        self.patch_positional_embedding = PositionalEmbedding(
            sequence_length=self.num_patches,
            emb_dim=self.d_model,
        )

        self.generator_transformer_encoder = GeneratorEncoder(
            d_model,
            num_heads=8,
            num_layers=4,
            dropout=dropout,
        )
        self.sln1 = SLN(d_model)
        # 博里叶位置编码
        self.coordinates_positional_embedding = CoordinatesPositionalEmbedding(
            patch_size=patch_size,
            emb_dim=d_model,
            )

        self.siren = Siren(
            hidden_dim=d_model,
            hidden_layers=2,
            out_dim=d_model,
            first_omega_0=30,
            hidden_omega_0=30,
            demodulation=True,
            outermost_linear=False
        )

        self.modulated_linear = ModulatedLinear(
            hidden_dim=d_model,
            output_dim=d_model,
            demodulation=True,
            use_bias=False,
            kernel_initializer=tf.initializers.GlorotNormal(),
        )
        self.mlp1 = MLP(d_model, discriminator=False, dropout=dropout)
        self.mlp2 = MLP(num_channels, discriminator=False, dropout=dropout)

    def call(self, x, training):
        batch_size = tf.shape(x)[0]
        w = self.mapping_network(x, training=training)
        # 输入位置编码
        x_pos = self.patch_positional_embedding()
        x = self.generator_transformer_encoder(x=x_pos, w=w, training=training)
        x = self.sln1(x, w, training=training)
        # 博里叶位置编码
        e_fou = self.coordinates_positional_embedding(x) # (B*L, P*P, E)
        e_fou = self.siren(e_fou) # (B*L, P*P, E)
        x = self.modulated_linear((e_fou, x)) # (B*L, P*P, E)
        x = self.mlp1(x, training=training)
        x = self.mlp2(x, training=training)
        x = tf.math.sin(x)
        x = tf.reshape(x, [batch_size, self.grid_size, self.grid_size, self.patch_size, self.patch_size, self.num_channels])
        x = tf.transpose(x, perm=[0,1,3,2,4,5])
        x = tf.reshape(x, [batch_size, self.image_size, self.image_size, self.num_channels])
        return x


if __name__ == "__main__":
    layer = Generator(
        image_size=224,
        patch_size=16,
        num_channels=3,
        d_model=768
    )
    x = tf.random.uniform([2,1,768], dtype=tf.float32)
    o1 = layer(x, training=True)
    tf.print('o1:', tf.shape(o1))
    o1 = layer(x, training=False)
    tf.print('o1:', tf.shape(o1))

参考资料:

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