-
Notifications
You must be signed in to change notification settings - Fork 0
/
ResNet18.py
146 lines (128 loc) · 4.92 KB
/
ResNet18.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
145
146
# -*- coding: utf-8 -*-
# @File : ResNet18.py
# @Author : Hua Guo
# @Time : 2019/12/25 下午9:56
# @Disc :
import tensorflow as tf
from tensorflow.keras import (
layers,
Model
)
import matplotlib.pyplot as plt
from src.BaseClass.DLModel import DLModel
class ResidualBlock(Model):
def __init__(self, filter_num, conv_bypass=False, strides=1):
super(ResidualBlock, self).__init__()
self.conv1 = layers.Conv2D(filters=filter_num, kernel_size=3, padding='same', strides=strides)
self.bn1 = layers.BatchNormalization()
self.activation1 = layers.Activation('relu')
self.conv2 = layers.Conv2D(
filters=filter_num,
kernel_size=(3, 3),
strides=strides,
padding="same"
)
self.bn2 = layers.BatchNormalization()
self.activation2 = layers.Activation("relu")
if conv_bypass:
self.conv3 = layers.Conv2D(filters=filter_num, kernel_size=1, padding='valid', strides=strides)
else:
self.conv3 = None
def call(self, inputs, training=None, mask=None):
print(f'Residual block')
print('='*20)
x = self.activation1(self.bn1(self.conv1(inputs)))
for layer in [
self.conv2
, self.bn2
]:
print(x.shape)
x = layer(x)
if self.conv3:
inputs = self.conv3(inputs)
print(x.shape)
print(inputs.shape)
return self.activation2(tf.add(inputs, x))
class ResidualCompoent(DLModel):
"""
seveal residual block in one residualcomponent
"""
def __init__(self, filter_num, residul_block_num, first_component=False):
super(ResidualCompoent, self).__init__()
for idx in range(residul_block_num):
if idx == 0 and first_component:
self.layers_lst.append(ResidualBlock(filter_num=filter_num, strides=2, conv_bypass=True))
else:
self.layers_lst.append(ResidualBlock(filter_num=filter_num, strides=1))
class ResNet18(DLModel):
def __init__(self, label_num):
super(ResNet18, self).__init__()
# first component
self.layers_lst.append(layers.Conv2D(filters=7, kernel_size=3, padding='same', strides=2))
self.layers_lst.append(layers.BatchNormalization())
self.layers_lst.append(layers.Activation('relu'))
# Second - fifth component
self.layers_lst.append(ResidualCompoent(filter_num=64, residul_block_num=2, first_component=True))
for filter_num in [128, 256, 512]:
self.layers_lst.append(ResidualCompoent(filter_num=filter_num, residul_block_num=2))
# Sixth component
self.layers_lst.append(layers.GlobalAvgPool2D())
self.layers_lst.append(layers.Dense(units=label_num))
if __name__ == "__main__":
# config
debug = True
img_dimension = 28
target_dimension = 224
batch_size = 8
epochs = 10
label_num = 10
if debug:
train_num = 60
test_num = 10
else:
train_num = -1
test_num = -1
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()
x_train = x_train[:train_num, :, :]
y_train = y_train[:train_num, ]
x_test = x_test[:test_num, :, :]
y_test = y_test[:test_num, ]
x_train = (x_train.astype('float32')/225).reshape(x_train.shape[0], img_dimension, img_dimension, 1).astype('float32')
x_test = (x_test.astype('float32')/225).reshape(x_test.shape[0], img_dimension, img_dimension, 1).astype('float32')
x_train = tf.image.resize(x_train, [target_dimension, target_dimension])
x_test = tf.image.resize(x_test, [target_dimension, target_dimension])
model = ResNet18(label_num=10)
model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])
sample_input = tf.ones(shape=[1, target_dimension, target_dimension, 1])
print(sample_input.shape)
# for layer in model.layers:
# print(layer)
# sample_input = layer(sample_input)
# print(sample_input.shape)
# model(sample_input)
# print(model.summary())
history = model.fit(
x=x_train
, y=y_train
, validation_data=(x_test, y_test)
, epochs=epochs
, batch_size=batch_size
)
print(model.summary())
acc = history.history['accuracy']
val_ac = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs_range = range(epochs)
plt.figure(figsize=(10, 10))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, acc, label='Train acc')
plt.plot(epochs_range, val_ac, label='Val acc')
plt.legend(loc='lower right')
plt.title("Train & val accuracy")
plt.subplot(1, 2, 2)
plt.plot(epochs_range, loss, label='Train loss')
plt.plot(epochs_range, val_loss, label='Val loss')
plt.legend(loc='upper right')
plt.title("Train & val loss")
plt.show()