-
Notifications
You must be signed in to change notification settings - Fork 0
/
models.py
144 lines (115 loc) · 5.26 KB
/
models.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
import tensorflow as tf
from keras import layers
from keras.layers import Dense, Flatten, Average
from tensorflow import keras
import numpy as np
from utils import Parameters
class Patches(layers.Layer):
def __init__(self, patch_size):
super(Patches, self).__init__()
self.patch_size = patch_size
def call(self, images):
batch_size = tf.shape(images)[0]
patches = tf.image.extract_patches(
images=images,
sizes=[1, self.patch_size, self.patch_size, 1],
strides=[1, self.patch_size, self.patch_size, 1],
rates=[1, 1, 1, 1],
padding="VALID",
)
patch_dims = patches.shape[-1]
patches = tf.reshape(patches, [batch_size, -1, patch_dims])
return patches
class PatchEncoder(layers.Layer):
def __init__(self, num_patches, projection_dim):
super(PatchEncoder, self).__init__()
self.num_patches = num_patches
self.projection = layers.Dense(units=projection_dim)
self.position_embedding = layers.Embedding(
input_dim=num_patches, output_dim=projection_dim
)
def call(self, patch):
positions = tf.range(start=0, limit=self.num_patches, delta=1)
return self.projection(patch) + self.position_embedding(positions)
def mlp(x, hidden_units, dropout_rate):
for units in hidden_units:
x = layers.Dense(units, activation=tf.nn.gelu)(x)
x = layers.Dropout(dropout_rate)(x)
return x
def ViT(input_shape: tuple,
x_train: np.array):
data_augmentation = keras.Sequential(
[
layers.Normalization(),
layers.Resizing(Parameters.image_size, Parameters.image_size),
layers.RandomFlip("horizontal"),
layers.RandomRotation(factor=0.02),
layers.RandomZoom(
height_factor=0.2, width_factor=0.2
),
],
name="data_augmentation",
)
data_augmentation.layers[0].adapt(x_train)
inputs = layers.Input(shape=input_shape)
augmented = data_augmentation(inputs)
patches = Patches(Parameters.patch_size)(augmented)
encoded_patches = PatchEncoder(Parameters.num_patches, Parameters.projection_dim)(patches)
for _ in range(Parameters.transformer_layers):
x1 = layers.LayerNormalization(epsilon=1e-6)(encoded_patches)
attention_output = layers.MultiHeadAttention(
num_heads=Parameters.num_heads, key_dim=Parameters.projection_dim, dropout=0.1
)(x1, x1)
x2 = layers.Add()([attention_output, encoded_patches])
x3 = layers.LayerNormalization(epsilon=1e-6)(x2)
x3 = mlp(x3, hidden_units=Parameters.transformer_units, dropout_rate=0.1)
encoded_patches = layers.Add()([x3, x2])
representation = layers.LayerNormalization(epsilon=1e-6)(encoded_patches)
representation = layers.Flatten()(representation)
representation = layers.Dropout(0.5)(representation)
features = mlp(representation, hidden_units=Parameters.mlp_head_units, dropout_rate=0.5)
logits = layers.Dense(Parameters.num_classes)(features)
return keras.Model(inputs=inputs, outputs=logits)
def RESNET50(input_shape: tuple):
model = keras.Sequential()
resnet50_model = keras.applications.ResNet50(include_top=False,
input_shape=input_shape,
pooling='avg', classes=Parameters.num_classes)
model.add(resnet50_model)
model.add(Flatten())
model.add(Dense(Parameters.num_classes, activation='softmax'))
model.summary()
return model
def RESNET101(input_shape: tuple):
model = keras.Sequential()
resnet101_model = keras.applications.ResNet101(include_top=False, input_shape=input_shape,
pooling='avg', classes=Parameters.num_classes)
model.add(resnet101_model)
model.add(Flatten())
model.add(Dense(Parameters.num_classes, activation='softmax'))
model.summary()
return model
def ensemble_model(create_model,
input_shape: tuple,
weight_paths: dict):
def_models = {heading: create_model(input_shape) for heading in [0, 90, 180, 270]}
for heading in def_models:
def_models[heading].load_weights(weight_paths[heading])
models = list(def_models.values())
model_input = keras.Input(shape=input_shape)
model_outputs = [model(model_input) for model in models]
ensemble_output = Average()(model_outputs)
return keras.Model(inputs=model_input, outputs=ensemble_output, name='ensemble')
def boosted_ensemble_model(create_model,
input_shape: tuple,
weight_paths: dict):
def_models = {heading: create_model(input_shape) for heading in [0, 90, 180, 270]}
for heading in def_models:
def_models[heading].load_weights(weight_paths[heading])
models = list(def_models.values())
model_input = keras.Input(shape=input_shape)
model_outputs = [model(model_input) for model in models]
merged = keras.layers.Concatenate(axis=1)(model_outputs)
perceptron = Dense(1000, activation='relu', input_dim=4)(merged)
output = Dense(50, activation='softmax')(perceptron)
return keras.Model(inputs=model_input, outputs=output, name='ensemble')