神經網路學習小記錄66——Vision Transformer（VIT）模型的復現詳解
- 學習前言
- 什么是Vision Transformer（VIT）
- 代碼下載
- Vision Transforme的實作思路
- 一、整體結構決議
- 二、網路結構決議
- 1、特征提取部分介紹
- a、Patch+Position Embedding
- b、Transformer Encoder
- I、Self-attention結構決議
- II、Self-attention的矩陣運算
- III、MultiHead多頭注意力機制
- IV、TransformerBlock的構建，
- c、整個VIT模型的構建
- 2、分類部分
- Vision Transforme的構建代碼
學習前言
視覺Transformer最近非常的火熱，從VIT開始，我先學學看，
什么是Vision Transformer（VIT）
Vision Transformer是Transformer的視覺版本，Transformer基本上已經成為了自然語言處理的標配，但是在視覺中的運用還受到限制，
Vision Transformer打破了這種NLP與CV的隔離，將Transformer應用于影像圖塊（patch）序列上，進一步完成影像分類任務，簡單來理解，Vision Transformer就是將輸入進來的圖片，每隔一定的區域大小劃分圖片塊，然后將劃分后的圖片塊組合成序列，將組合后的結果傳入Transformer特有的Multi-head Self-attention進行特征提取，最后利用Cls Token進行分類，
代碼下載
Github原始碼下載地址為：
https://github.com/bubbliiiing/classification-keras
復制該路徑到地址欄跳轉，
Vision Transforme的實作思路
一、整體結構決議
與尋常的分類網路類似，整個Vision Transformer可以氛圍兩部分，一部分是特征提取部分，另一部分是分類部分，
在特征提取部分，VIT所做的作業是特征提取，特征提取部分在圖片中的對應區域是Patch+Position Embedding和Transformer Encoder，Patch+Position Embedding的作用主要是對輸入進來的圖片進行分塊處理，每隔一定的區域大小劃分圖片塊，然后將劃分后的圖片塊組合成序列，在獲得序列資訊后，傳入Transformer Encoder進行特征提取，這是Transformer特有的Multi-head Self-attention結構，通過自注意力機制，關注每個圖片塊的重要程度，
在分類部分，VIT所做的作業是利用提取到的特征進行分類，在進行特征提取的時候，我們會在圖片序列中添加上Cls Token，該Token會作為一個單位的序列資訊一起進行特征提取，提取的程序中，該Cls Token會與其它的特征進行特征互動，融合其它圖片序列的特征，最終，我們利用Multi-head Self-attention結構提取特征后的Cls Token進行全連接分類，
二、網路結構決議
1、特征提取部分介紹
a、Patch+Position Embedding
Patch+Position Embedding的作用主要是對輸入進來的圖片進行分塊處理，每隔一定的區域大小劃分圖片塊，然后將劃分后的圖片塊組合成序列，
該部分首先對輸入進來的圖片進行分塊處理，處理方式其實很簡單，使用的是現成的卷積，由于卷積使用的是滑動視窗的思想，我們只需要設定特定的步長，就可以輸入進來的圖片進行分塊處理了，
在VIT中，我們常設定這個卷積的卷積核大小為16x16，步長也為16x16，此時卷積就會每隔16個像素點進行一次特征提取，由于卷積核大小為16x16，兩個圖片區域的特征提取程序就不會有重疊，當我們輸入的圖片是224, 224, 3的時候，我們可以獲得一個14, 14, 768的特征層，
下一步就是將這個特征層組合成序列，組合的方式非常簡單，就是將高寬維度進行平鋪，14, 14, 768在高寬維度平鋪后，獲得一個196, 768的特征層，平鋪完成后，我們會在圖片序列中添加上Cls Token，該Token會作為一個單位的序列資訊一起進行特征提取，圖中的這個0*就是Cls Token，我們此時獲得一個197, 768的特征層，
添加完成Cls Token后，再為所有特征添加上位置資訊，這樣網路才有區分不同區域的能力，添加方式其實也非常簡單，我們生成一個197, 768的引數矩陣，這個引數矩陣是可訓練的，把這個矩陣加上197, 768的特征層即可，
到這里，Patch+Position Embedding就構建完成了，構建代碼如下：
#--------------------------------------------------------------------------------------------------------------------#
# classtoken部分是transformer的分類特征，用于堆疊到序列化后的圖片特征中，作為一個單位的序列特征進行特征提取，
#
# 在利用步長為16x16的卷積將輸入圖片劃分成14x14的部分后，將14x14部分的特征平鋪，一幅圖片會存在序列長度為196的特征，
# 此時生成一個classtoken，將classtoken堆疊到序列長度為196的特征上，獲得一個序列長度為197的特征，
# 在特征提取的程序中，classtoken會與圖片特征進行特征的互動，最終分類時，我們取出classtoken的特征，利用全連接分類，
#--------------------------------------------------------------------------------------------------------------------#
class ClassToken(Layer):
def __init__(self, cls_initializer='zeros', cls_regularizer=None, cls_constraint=None, **kwargs):
super(ClassToken, self).__init__(**kwargs)
self.cls_initializer = keras.initializers.get(cls_initializer)
self.cls_regularizer = keras.regularizers.get(cls_regularizer)
self.cls_constraint = keras.constraints.get(cls_constraint)
def get_config(self):
config = {
'cls_initializer': keras.initializers.serialize(self.cls_initializer),
'cls_regularizer': keras.regularizers.serialize(self.cls_regularizer),
'cls_constraint': keras.constraints.serialize(self.cls_constraint),
}
base_config = super(ClassToken, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def compute_output_shape(self, input_shape):
return (input_shape[0], input_shape[1] + 1, input_shape[2])
def build(self, input_shape):
self.num_features = input_shape[-1]
self.cls = self.add_weight(
shape = (1, 1, self.num_features),
initializer = self.cls_initializer,
regularizer = self.cls_regularizer,
constraint = self.cls_constraint,
name = 'cls',
)
super(ClassToken, self).build(input_shape)
def call(self, inputs):
batch_size = tf.shape(inputs)[0]
cls_broadcasted = tf.cast(tf.broadcast_to(self.cls, [batch_size, 1, self.num_features]), dtype = inputs.dtype)
return tf.concat([cls_broadcasted, inputs], 1)
#--------------------------------------------------------------------------------------------------------------------#
# 為網路提取到的特征添加上位置資訊，
# 以輸入圖片為224, 224, 3為例，我們獲得的序列化后的圖片特征為196, 768，加上classtoken后就是197, 768
# 此時生成的pos_Embedding的shape也為197, 768，代表每一個特征的位置資訊，
#--------------------------------------------------------------------------------------------------------------------#
class AddPositionEmbs(Layer):
def __init__(self, image_shape, patch_size, pe_initializer='zeros', pe_regularizer=None, pe_constraint=None, **kwargs):
super(AddPositionEmbs, self).__init__(**kwargs)
self.image_shape = image_shape
self.patch_size = patch_size
self.pe_initializer = keras.initializers.get(pe_initializer)
self.pe_regularizer = keras.regularizers.get(pe_regularizer)
self.pe_constraint = keras.constraints.get(pe_constraint)
def get_config(self):
config = {
'pe_initializer': keras.initializers.serialize(self.pe_initializer),
'pe_regularizer': keras.regularizers.serialize(self.pe_regularizer),
'pe_constraint': keras.constraints.serialize(self.pe_constraint),
}
base_config = super(AddPositionEmbs, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def compute_output_shape(self, input_shape):
return input_shape
def build(self, input_shape):
assert (len(input_shape) == 3), f"Number of dimensions should be 3, got {len(input_shape)}"
length = (224 // self.patch_size) * (224 // self.patch_size) + 1
self.pe = self.add_weight(
# shape = [1, input_shape[1], input_shape[2]],
shape = [1, length, input_shape[2]],
initializer = self.pe_initializer,
regularizer = self.pe_regularizer,
constraint = self.pe_constraint,
name = 'pos_embedding',
)
super(AddPositionEmbs, self).build(input_shape)
def call(self, inputs):
num_features = tf.shape(inputs)[2]
cls_token_pe = self.pe[:, 0:1, :]
img_token_pe = self.pe[:, 1: , :]
img_token_pe = tf.reshape(img_token_pe, [1, (224 // self.patch_size), (224 // self.patch_size), num_features])
img_token_pe = tf.image.resize_bicubic(img_token_pe, (self.image_shape[0] // self.patch_size, self.image_shape[1] // self.patch_size), align_corners=False)
img_token_pe = tf.reshape(img_token_pe, [1, -1, num_features])
pe = tf.concat([cls_token_pe, img_token_pe], axis = 1)
return inputs + tf.cast(pe, dtype=inputs.dtype)
def VisionTransformer(input_shape = [224, 224], patch_size = 16, num_layers = 12, num_features = 768, num_heads = 12, mlp_dim = 3072,
classes = 1000, dropout = 0.1):
#-----------------------------------------------#
# 224, 224, 3
#-----------------------------------------------#
inputs = Input(shape = (input_shape[0], input_shape[1], 3))
#-----------------------------------------------#
# 224, 224, 3 -> 14, 14, 768
#-----------------------------------------------#
x = Conv2D(num_features, patch_size, strides = patch_size, padding = "valid", name = "patch_embed.proj")(inputs)
#-----------------------------------------------#
# 14, 14, 768 -> 196, 768
#-----------------------------------------------#
x = Reshape(((input_shape[0] // patch_size) * (input_shape[1] // patch_size), num_features))(x)
#-----------------------------------------------#
# 196, 768 -> 197, 768
#-----------------------------------------------#
x = ClassToken(name="cls_token")(x)
#-----------------------------------------------#
# 197, 768 -> 197, 768
#-----------------------------------------------#
x = AddPositionEmbs(input_shape, patch_size, name="pos_embed")(x)
b、Transformer Encoder
在上一步獲得shape為197, 768的序列資訊后，將序列資訊傳入Transformer Encoder進行特征提取，這是Transformer特有的Multi-head Self-attention結構，通過自注意力機制，關注每個圖片塊的重要程度，
I、Self-attention結構決議
看懂Self-attention結構，其實看懂下面這個動圖就可以了，動圖中存在一個序列的三個單位輸入，每一個序列單位的輸入都可以通過三個處理（比如全連接）獲得Query、Key、Value，Query是查詢向量、Key是鍵向量、Value值向量，
如果我們想要獲得input-1的輸出，那么我們進行如下幾步：
1、利用input-1的查詢向量，分別乘上input-1、input-2、input-3的鍵向量，此時我們獲得了三個score，
2、然后對這三個score取softmax，獲得了input-1、input-2、input-3各自的重要程度，
3、然后將這個重要程度乘上input-1、input-2、input-3的值向量，求和，
4、此時我們獲得了input-1的輸出，
如圖所示，我們進行如下幾步：
1、input-1的查詢向量為[1, 0, 2]，分別乘上input-1、input-2、input-3的鍵向量，獲得三個score為2，4，4，
2、然后對這三個score取softmax，獲得了input-1、input-2、input-3各自的重要程度，獲得三個重要程度為0.0，0.5，0.5，
3、然后將這個重要程度乘上input-1、input-2、input-3的值向量，求和，即
0.0
?
[
1
,
2
,
3
]
+
0.5
?
[
2
,
8
,
0
]
+
0.5
?
[
2
,
6
,
3
]
=
[
1.0
,
3.0
,
1.5
]
0.0 * [1, 2, 3] + 0.5 * [2, 8, 0] + 0.5 * [2, 6, 3] = [1.0, 3.0, 1.5]
0.0?[1,2,3]+0.5?[2,8,0]+0.5?[2,6,3]=[1.0,3.0,1.5]，
4、此時我們獲得了input-1的輸出 [1.0, 3.0, 1.5]，
上述的例子中，序列長度僅為3，每個單位序列的特征長度僅為3，在VIT的Transformer Encoder中，序列長度為197，每個單位序列的特征長度為768 // num_heads，但計算程序是一樣的，在實際運算時，我們采用矩陣進行運算，
II、Self-attention的矩陣運算
實際的矩陣運算程序如下圖所示，我以實際矩陣為例子給大家決議：
輸入的Query、Key、Value如下圖所示：
首先利用 查詢向量query 點乘 轉置后的鍵向量key，這一步可以通俗的理解為，利用查詢向量去查詢序列的特征，獲得序列每個部分的重要程度score，
輸出的每一行，都代表input-1、input-2、input-3，對當前input的貢獻，我們對這個貢獻值取一個softmax，
然后利用 score 點乘 value，這一步可以通俗的理解為，將序列每個部分的重要程度重新施加到序列的值上去，
這個矩陣運算的代碼如下所示，各位同學可以自己試試，
import numpy as np
def soft_max(z):
t = np.exp(z)
a = np.exp(z) / np.expand_dims(np.sum(t, axis=1), 1)
return a
Query = np.array([
[1,0,2],
[2,2,2],
[2,1,3]
])
Key = np.array([
[0,1,1],
[4,4,0],
[2,3,1]
])
Value = np.array([
[1,2,3],
[2,8,0],
[2,6,3]
])
scores = Query @ Key.T
print(scores)
scores = soft_max(scores)
print(scores)
out = scores @ Value
print(out)
III、MultiHead多頭注意力機制
多頭注意力機制的示意圖如圖所示：
這幅圖給人的感覺略顯迷茫，我們跳脫出這個圖，直接從矩陣的shape入手會清晰很多，
在第一步進行影像的分割后，我們獲得的特征層為197, 768，
在施加多頭的時候，我們直接對196, 768的最后一維度進行分割，比如我們想分割成12個頭，那么矩陣的shepe就變成了196, 12, 64，
然后我們將196, 12, 64進行轉置，將12放到前面去，獲得的特征層為12, 196, 64，之后我們忽略這個12，把它和batch維度同等對待，只對196, 64進行處理，其實也就是上面的注意力機制的程序了，
#--------------------------------------------------------------------------------------------------------------------#
# Attention機制
# 將輸入的特征qkv特征進行劃分，首先生成query, key, value，query是查詢向量、key是鍵向量、v是值向量，
# 然后利用 查詢向量query 點乘 轉置后的鍵向量key，這一步可以通俗的理解為，利用查詢向量去查詢序列的特征，獲得序列每個部分的重要程度score，
# 然后利用 score 點乘 value，這一步可以通俗的理解為，將序列每個部分的重要程度重新施加到序列的值上去，
#--------------------------------------------------------------------------------------------------------------------#
class Attention(Layer):
def __init__(self, num_features, num_heads, **kwargs):
super(Attention, self).__init__(**kwargs)
self.num_features = num_features
self.num_heads = num_heads
self.projection_dim = num_features // num_heads
def compute_output_shape(self, input_shape):
return (input_shape[0], input_shape[1], input_shape[2] // 3)
def call(self, inputs):
#-----------------------------------------------#
# 獲得batch_size
#-----------------------------------------------#
bs = tf.shape(inputs)[0]
#-----------------------------------------------#
# b, 197, 3 * 768 -> b, 197, 3, 12, 64
#-----------------------------------------------#
inputs = tf.reshape(inputs, [bs, -1, 3, self.num_heads, self.projection_dim])
#-----------------------------------------------#
# b, 197, 3, 12, 64 -> 3, b, 12, 197, 64
#-----------------------------------------------#
inputs = tf.transpose(inputs, [2, 0, 3, 1, 4])
#-----------------------------------------------#
# 將query, key, value劃分開
# query b, 12, 197, 64
# key b, 12, 197, 64
# value b, 12, 197, 64
#-----------------------------------------------#
query, key, value = inputs[0], inputs[1], inputs[2]
#-----------------------------------------------#
# b, 12, 197, 64 @ b, 12, 197, 64 = b, 12, 197, 197
#-----------------------------------------------#
score = tf.matmul(query, key, transpose_b=True)
#-----------------------------------------------#
# 進行數量級的縮放
#-----------------------------------------------#
scaled_score = score / tf.math.sqrt(tf.cast(self.projection_dim, score.dtype))
#-----------------------------------------------#
# b, 12, 197, 197 -> b, 12, 197, 197
#-----------------------------------------------#
weights = tf.nn.softmax(scaled_score, axis=-1)
#-----------------------------------------------#
# b, 12, 197, 197 @ b, 12, 197, 64 = b, 12, 197, 64
#-----------------------------------------------#
value = tf.matmul(weights, value)
#-----------------------------------------------#
# b, 12, 197, 64 -> b, 197, 12, 64
#-----------------------------------------------#
value = tf.transpose(value, perm=[0, 2, 1, 3])
#-----------------------------------------------#
# b, 197, 12, 64 -> b, 197, 768
#-----------------------------------------------#
output = tf.reshape(value, (tf.shape(value)[0], tf.shape(value)[1], -1))
return output
def MultiHeadSelfAttention(inputs, num_features, num_heads, dropout, name):
#-----------------------------------------------#
# qkv b, 197, 768 -> b, 197, 3 * 768
#-----------------------------------------------#
qkv = Dense(int(num_features * 3), name = name + "qkv")(inputs)
#-----------------------------------------------#
# b, 197, 3 * 768 -> b, 197, 768
#-----------------------------------------------#
x = Attention(num_features, num_heads)(qkv)
#-----------------------------------------------#
# 197, 768 -> 197, 768
#-----------------------------------------------#
x = Dense(num_features, name = name + "proj")(x)
x = Dropout(dropout)(x)
return x
IV、TransformerBlock的構建，
在完成MultiHeadSelfAttention的構建后，我們需要在其后加上兩個全連接，就構建了整個TransformerBlock，
def MLP(y, num_features, mlp_dim, dropout, name):
y = Dense(mlp_dim, name = name + "fc1")(y)
y = Gelu()(y)
y = Dropout(dropout)(y)
y = Dense(num_features, name = name + "fc2")(y)
return y
def TransformerBlock(inputs, num_features, num_heads, mlp_dim, dropout, name):
#-----------------------------------------------#
# 施加層標準化
#-----------------------------------------------#
x = LayerNormalization(epsilon=1e-6, name = name + "norm1")(inputs)
#-----------------------------------------------#
# 施加多頭注意力機制
#-----------------------------------------------#
x = MultiHeadSelfAttention(x, num_features, num_heads, dropout, name = name + "attn.")
x = Dropout(dropout)(x)
#-----------------------------------------------#
# 施加殘差結構
#-----------------------------------------------#
x = Add()([x, inputs])
#-----------------------------------------------#
# 施加層標準化
#-----------------------------------------------#
y = LayerNormalization(epsilon=1e-6, name = name + "norm2")(x)
#-----------------------------------------------#
# 施加兩次全連接
#-----------------------------------------------#
y = MLP(y, num_features, mlp_dim, dropout, name = name + "mlp.")
y = Dropout(dropout)(y)
#-----------------------------------------------#
# 施加殘差結構
#-----------------------------------------------#
y = Add()([x, y])
return y
c、整個VIT模型的構建
整個VIT模型由一個Patch+Position Embedding加上多個TransformerBlock組成，典型的TransforerBlock的數量為12個，
def VisionTransformer(input_shape = [224, 224], patch_size = 16, num_layers = 12, num_features = 768, num_heads = 12, mlp_dim = 3072,
classes = 1000, dropout = 0.1):
#-----------------------------------------------#
# 224, 224, 3
#-----------------------------------------------#
inputs = Input(shape = (input_shape[0], input_shape[1], 3))
#-----------------------------------------------#
# 224, 224, 3 -> 14, 14, 768
#-----------------------------------------------#
x = Conv2D(num_features, patch_size, strides = patch_size, padding = "valid", name = "patch_embed.proj")(inputs)
#-----------------------------------------------#
# 14, 14, 768 -> 196, 768
#-----------------------------------------------#
x = Reshape(((input_shape[0] // patch_size) * (input_shape[1] // patch_size), num_features))(x)
#-----------------------------------------------#
# 196, 768 -> 197, 768
#-----------------------------------------------#
x = ClassToken(name="cls_token")(x)
#-----------------------------------------------#
# 197, 768 -> 197, 768
#-----------------------------------------------#
x = AddPositionEmbs(input_shape, patch_size, name="pos_embed")(x)
#-----------------------------------------------#
# 197, 768 -> 197, 768 12次
#-----------------------------------------------#
for n in range(num_layers):
x = TransformerBlock(
x,
num_features= num_features,
num_heads = num_heads,
mlp_dim = mlp_dim,
dropout = dropout,
name = "blocks." + str(n) + ".",
)
x = LayerNormalization(
epsilon=1e-6, name="norm"
)(x)
2、分類部分
在分類部分，VIT所做的作業是利用提取到的特征進行分類，
在進行特征提取的時候，我們會在圖片序列中添加上Cls Token，該Token會作為一個單位的序列資訊一起進行特征提取，提取的程序中，該Cls Token會與其它的特征進行特征互動，融合其它圖片序列的特征，
最終，我們利用Multi-head Self-attention結構提取特征后的Cls Token進行全連接分類，
def VisionTransformer(input_shape = [224, 224], patch_size = 16, num_layers = 12, num_features = 768, num_heads = 12, mlp_dim = 3072,
classes = 1000, dropout = 0.1):
#-----------------------------------------------#
# 224, 224, 3
#-----------------------------------------------#
inputs = Input(shape = (input_shape[0], input_shape[1], 3))
#-----------------------------------------------#
# 224, 224, 3 -> 14, 14, 768
#-----------------------------------------------#
x = Conv2D(num_features, patch_size, strides = patch_size, padding = "valid", name = "patch_embed.proj")(inputs)
#-----------------------------------------------#
# 14, 14, 768 -> 196, 768
#-----------------------------------------------#
x = Reshape(((input_shape[0] // patch_size) * (input_shape[1] // patch_size), num_features))(x)
#-----------------------------------------------#
# 196, 768 -> 197, 768
#-----------------------------------------------#
x = ClassToken(name="cls_token")(x)
#-----------------------------------------------#
# 197, 768 -> 197, 768
#-----------------------------------------------#
x = AddPositionEmbs(input_shape, patch_size, name="pos_embed")(x)
#-----------------------------------------------#
# 197, 768 -> 197, 768 12次
#-----------------------------------------------#
for n in range(num_layers):
x = TransformerBlock(
x,
num_features= num_features,
num_heads = num_heads,
mlp_dim = mlp_dim,
dropout = dropout,
name = "blocks." + str(n) + ".",
)
x = LayerNormalization(
epsilon=1e-6, name="norm"
)(x)
x = Lambda(lambda v: v[:, 0], name="ExtractToken")(x)
x = Dense(classes, name="head")(x)
x = Softmax()(x)
return keras.models.Model(inputs, x)
Vision Transforme的構建代碼
import math
import keras
import tensorflow as tf
from keras import backend as K
from keras.layers import (Add, Conv2D, Dense, Dropout, Input, Lambda, Layer,
Reshape, Softmax)
#--------------------------------------#
# LayerNormalization
# 層標準化的實作
#--------------------------------------#
class LayerNormalization(keras.layers.Layer):
def __init__(self,
center=True,
scale=True,
epsilon=None,
gamma_initializer='ones',
beta_initializer='zeros',
gamma_regularizer=None,
beta_regularizer=None,
gamma_constraint=None,
beta_constraint=None,
**kwargs):
"""Layer normalization layer
See: [Layer Normalization](https://arxiv.org/pdf/1607.06450.pdf)
:param center: Add an offset parameter if it is True.
:param scale: Add a scale parameter if it is True.
:param epsilon: Epsilon for calculating variance.
:param gamma_initializer: Initializer for the gamma weight.
:param beta_initializer: Initializer for the beta weight.
:param gamma_regularizer: Optional regularizer for the gamma weight.
:param beta_regularizer: Optional regularizer for the beta weight.
:param gamma_constraint: Optional constraint for the gamma weight.
:param beta_constraint: Optional constraint for the beta weight.
:param kwargs:
"""
super(LayerNormalization, self).__init__(**kwargs)
self.supports_masking = True
self.center = center
self.scale = scale
if epsilon is None:
epsilon = K.epsilon() * K.epsilon()
self.epsilon = epsilon
self.gamma_initializer = keras.initializers.get(gamma_initializer)
self.beta_initializer = keras.initializers.get(beta_initializer)
self.gamma_regularizer = keras.regularizers.get(gamma_regularizer)
self.beta_regularizer = keras.regularizers.get(beta_regularizer)
self.gamma_constraint = keras.constraints.get(gamma_constraint)
self.beta_constraint = keras.constraints.get(beta_constraint)
self.gamma, self.beta = None, None
def get_config(self):
config = {
'center': self.center,
'scale': self.scale,
'epsilon': self.epsilon,
'gamma_initializer': keras.initializers.serialize(self.gamma_initializer),
'beta_initializer': keras.initializers.serialize(self.beta_initializer),
'gamma_regularizer': keras.regularizers.serialize(self.gamma_regularizer),
'beta_regularizer': keras.regularizers.serialize(self.beta_regularizer),
'gamma_constraint': keras.constraints.serialize(self.gamma_constraint),
'beta_constraint': keras.constraints.serialize(self.beta_constraint),
}
base_config = super(LayerNormalization, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def compute_output_shape(self, input_shape):
return input_shape
def compute_mask(self, inputs, input_mask=None):
return input_mask
def build(self, input_shape):
shape = input_shape[-1:]
if self.scale:
self.gamma = self.add_weight(
shape=shape,
initializer=self.gamma_initializer,
regularizer=self.gamma_regularizer,
constraint=self.gamma_constraint,
name='gamma',
)
if self.center:
self.beta = self.add_weight(
shape=shape,
initializer=self.beta_initializer,
regularizer=self.beta_regularizer,
constraint=self.beta_constraint,
name='beta',
)
super(LayerNormalization, self).build(input_shape)
def call(self, inputs, training=None):
mean = K.mean(inputs, axis=-1, keepdims=True)
variance = K.mean(K.square(inputs - mean), axis=-1, keepdims=True)
std = K.sqrt(variance + self.epsilon)
outputs = (inputs - mean) / std
if self.scale:
outputs *= self.gamma
if self.center:
outputs += self.beta
return outputs
#--------------------------------------#
# Gelu激活函式的實作
# 利用近似的數學公式
#--------------------------------------#
class Gelu(Layer):
def __init__(self, **kwargs):
super(Gelu, self).__init__(**kwargs)
self.supports_masking = True
def call(self, inputs):
return 0.5 * inputs * (1 + tf.tanh(tf.sqrt(2 / math.pi) * (inputs + 0.044715 * tf.pow(inputs, 3))))
def get_config(self):
config = super(Gelu, self).get_config()
return config
def compute_output_shape(self, input_shape):
return input_shape
#--------------------------------------------------------------------------------------------------------------------#
# classtoken部分是transformer的分類特征，用于堆疊到序列化后的圖片特征中，作為一個單位的序列特征進行特征提取，
#
# 在利用步長為16x16的卷積將輸入圖片劃分成14x14的部分后，將14x14部分的特征平鋪，一幅圖片會存在序列長度為196的特征，
# 此時生成一個classtoken，將classtoken堆疊到序列長度為196的特征上，獲得一個序列長度為197的特征，
# 在特征提取的程序中，classtoken會與圖片特征進行特征的互動，最終分類時，我們取出classtoken的特征，利用全連接分類，
#--------------------------------------------------------------------------------------------------------------------#
class ClassToken(Layer):
def __init__(self, cls_initializer='zeros', cls_regularizer=None, cls_constraint=None, **kwargs):
super(ClassToken, self).__init__(**kwargs)
self.cls_initializer = keras.initializers.get(cls_initializer)
self.cls_regularizer = keras.regularizers.get(cls_regularizer)
self.cls_constraint = keras.constraints.get(cls_constraint)
def get_config(self):
config = {
'cls_initializer': keras.initializers.serialize(self.cls_initializer),
'cls_regularizer': keras.regularizers.serialize(self.cls_regularizer),
'cls_constraint': keras.constraints.serialize(self.cls_constraint),
}
base_config = super(ClassToken, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def compute_output_shape(self, input_shape):
return (input_shape[0], input_shape[1] + 1, input_shape[2])
def build(self, input_shape):
self.num_features = input_shape[-1]
self.cls = self.add_weight(
shape = (1, 1, self.num_features),
initializer = self.cls_initializer,
regularizer = self.cls_regularizer,
constraint = self.cls_constraint,
name = 'cls',
)
super(ClassToken, self).build(input_shape)
def call(self, inputs):
batch_size = tf.shape(inputs)[0]
cls_broadcasted = tf.cast(tf.broadcast_to(self.cls, [batch_size, 1, self.num_features]), dtype = inputs.dtype)
return tf.concat([cls_broadcasted, inputs], 1)
#--------------------------------------------------------------------------------------------------------------------#
# 為網路提取到的特征添加上位置資訊，
# 以輸入圖片為224, 224, 3為例，我們獲得的序列化后的圖片特征為196, 768，加上classtoken后就是197, 768
# 此時生成的pos_Embedding的shape也為197, 768，代表每一個特征的位置資訊，
#--------------------------------------------------------------------------------------------------------------------#
class AddPositionEmbs(Layer):
def __init__(self, image_shape, patch_size, pe_initializer='zeros', pe_regularizer=None, pe_constraint=None, **kwargs):
super(AddPositionEmbs, self).__init__(**kwargs)
self.image_shape = image_shape
self.patch_size = patch_size
self.pe_initializer = keras.initializers.get(pe_initializer)
self.pe_regularizer = keras.regularizers.get(pe_regularizer)
self.pe_constraint = keras.constraints.get(pe_constraint)
def get_config(self):
config = {
'pe_initializer': keras.initializers.serialize(self.pe_initializer),
'pe_regularizer': keras.regularizers.serialize(self.pe_regularizer),
'pe_constraint': keras.constraints.serialize(self.pe_constraint),
}
base_config = super(AddPositionEmbs, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def compute_output_shape(self, input_shape):
return input_shape
def build(self, input_shape):
assert (len(input_shape) == 3), f"Number of dimensions should be 3, got {len(input_shape)}"
length = (224 // self.patch_size) * (224 // self.patch_size) + 1
self.pe = self.add_weight(
# shape = [1, input_shape[1], input_shape[2]],
shape = [1, length, input_shape[2]],
initializer = self.pe_initializer,
regularizer = self.pe_regularizer,
constraint = self.pe_constraint,
name = 'pos_embedding',
)
super(AddPositionEmbs, self).build(input_shape)
def call(self, inputs):
num_features = tf.shape(inputs)[2]
cls_token_pe = self.pe[:, 0:1, :]
img_token_pe = self.pe[:, 1: , :]
img_token_pe = tf.reshape(img_token_pe, [1, (224 // self.patch_size), (224 // self.patch_size), num_features])
img_token_pe = tf.image.resize_bicubic(img_token_pe, (self.image_shape[0] // self.patch_size, self.image_shape[1] // self.patch_size), align_corners=False)
img_token_pe = tf.reshape(img_token_pe, [1, -1, num_features])
pe = tf.concat([cls_token_pe, img_token_pe], axis = 1)
return inputs + tf.cast(pe, dtype=inputs.dtype)
#--------------------------------------------------------------------------------------------------------------------#
# Attention機制
# 將輸入的特征qkv特征進行劃分，首先生成query, key, value，query是查詢向量、key是鍵向量、v是值向量，
# 然后利用 查詢向量query 點乘 轉置后的鍵向量key，這一步可以通俗的理解為，利用查詢向量去查詢序列的特征，獲得序列每個部分的重要程度score，
# 然后利用 score 點乘 value，這一步可以通俗的理解為，將序列每個部分的重要程度重新施加到序列的值上去，
#--------------------------------------------------------------------------------------------------------------------#
class Attention(Layer):
def __init__(self, num_features, num_heads, **kwargs):
super(Attention, self).__init__(**kwargs)
self.num_features = num_features
self.num_heads = num_heads
self.projection_dim = num_features // num_heads
def compute_output_shape(self, input_shape):
return (input_shape[0], input_shape[1], input_shape[2] // 3)
def call(self, inputs):
#-----------------------------------------------#
# 獲得batch_size
#-----------------------------------------------#
bs = tf.shape(inputs)[0]
#-----------------------------------------------#
# b, 197, 3 * 768 -> b, 197, 3, 12, 64
#-----------------------------------------------#
inputs = tf.reshape(inputs, [bs, -1, 3, self.num_heads, self.projection_dim])
#-----------------------------------------------#
# b, 197, 3, 12, 64 -> 3, b, 12, 197, 64
#-----------------------------------------------#
inputs = tf.transpose(inputs, [2, 0, 3, 1, 4])
#-----------------------------------------------#
# 將query, key, value劃分開
# query b, 12, 197, 64
# key b, 12, 197, 64
# value b, 12, 197, 64
#-----------------------------------------------#
query, key, value = inputs[0], inputs[1], inputs[2]
#-----------------------------------------------#
# b, 12, 197, 64 @ b, 12, 197, 64 = b, 12, 197, 197
#-----------------------------------------------#
score = tf.matmul(query, key, transpose_b=True)
#-----------------------------------------------#
# 進行數量級的縮放
#-----------------------------------------------#
scaled_score = score / tf.math.sqrt(tf.cast(self.projection_dim, score.dtype))
#-----------------------------------------------#
# b, 12, 197, 197 -> b, 12, 197, 197
#-----------------------------------------------#
weights = tf.nn.softmax(scaled_score, axis=-1)
#-----------------------------------------------#
# b, 12, 197, 197 @ b, 12, 197, 64 = b, 12, 197, 64
#-----------------------------------------------#
value = tf.matmul(weights, value)
#-----------------------------------------------#
# b, 12, 197, 64 -> b, 197, 12, 64
#-----------------------------------------------#
value = tf.transpose(value, perm=[0, 2, 1, 3])
#-----------------------------------------------#
# b, 197, 12, 64 -> b, 197, 768
#-----------------------------------------------#
output = tf.reshape(value, (tf.shape(value)[0], tf.shape(value)[1], -1))
return output
def MultiHeadSelfAttention(inputs, num_features, num_heads, dropout, name):
#-----------------------------------------------#
# qkv b, 197, 768 -> b, 197, 3 * 768
#-----------------------------------------------#
qkv = Dense(int(num_features * 3), name = name + "qkv")(inputs)
#-----------------------------------------------#
# b, 197, 3 * 768 -> b, 197, 768
#-----------------------------------------------#
x = Attention(num_features, num_heads)(qkv)
#-----------------------------------------------#
# 197, 768 -> 197, 768
#-----------------------------------------------#
x = Dense(num_features, name = name + "proj")(x)
x = Dropout(dropout)(x)
return x
def MLP(y, num_features, mlp_dim, dropout, name):
y = Dense(mlp_dim, name = name + "fc1")(y)
y = Gelu()(y)
y = Dropout(dropout)(y)
y = Dense(num_features, name = name + "fc2")(y)
return y
def TransformerBlock(inputs, num_features, num_heads, mlp_dim, dropout, name):
#-----------------------------------------------#
# 施加層標準化
#-----------------------------------------------#
x = LayerNormalization(epsilon=1e-6, name = name + "norm1")(inputs)
#-----------------------------------------------#
# 施加多頭注意力機制
#-----------------------------------------------#
x = MultiHeadSelfAttention(x, num_features, num_heads, dropout, name = name + "attn.")
x = Dropout(dropout)(x)
#-----------------------------------------------#
# 施加殘差結構
#-----------------------------------------------#
x = Add()([x, inputs])
#-----------------------------------------------#
# 施加層標準化
#-----------------------------------------------#
y = LayerNormalization(epsilon=1e-6, name = name + "norm2")(x)
#-----------------------------------------------#
# 施加兩次全連接
#-----------------------------------------------#
y = MLP(y, num_features, mlp_dim, dropout, name = name + "mlp.")
y = Dropout(dropout)(y)
#-----------------------------------------------#
# 施加殘差結構
#-----------------------------------------------#
y = Add()([x, y])
return y
def VisionTransformer(input_shape = [224, 224], patch_size = 16, num_layers = 12, num_features = 768, num_heads = 12, mlp_dim = 3072,
classes = 1000, dropout = 0.1):
#-----------------------------------------------#
# 224, 224, 3
#-----------------------------------------------#
inputs = Input(shape = (input_shape[0], input_shape[1], 3))
#-----------------------------------------------#
# 224, 224, 3 -> 14, 14, 768
#-----------------------------------------------#
x = Conv2D(num_features, patch_size, strides = patch_size, padding = "valid", name = "patch_embed.proj")(inputs)
#-----------------------------------------------#
# 14, 14, 768 -> 196, 768
#-----------------------------------------------#
x = Reshape(((input_shape[0] // patch_size) * (input_shape[1] // patch_size), num_features))(x)
#-----------------------------------------------#
# 196, 768 -> 197, 768
#-----------------------------------------------#
x = ClassToken(name="cls_token")(x)
#-----------------------------------------------#
# 197, 768 -> 197, 768
#-----------------------------------------------#
x = AddPositionEmbs(input_shape, patch_size, name="pos_embed")(x)
#-----------------------------------------------#
# 197, 768 -> 197, 768 12次
#-----------------------------------------------#
for n in range(num_layers):
x = TransformerBlock(
x,
num_features= num_features,
num_heads = num_heads,
mlp_dim = mlp_dim,
dropout = dropout,
name = "blocks." + str(n) + ".",
)
x = LayerNormalization(
epsilon=1e-6, name="norm"
)(x)
x = Lambda(lambda v: v[:, 0], name="ExtractToken")(x)
x = Dense(classes, name="head")(x)
x = Softmax()(x)
return keras.models.Model(inputs, x)
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