-
Notifications
You must be signed in to change notification settings - Fork 0
/
VGGNet.py
195 lines (168 loc) · 8.74 KB
/
VGGNet.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
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
import os
import sys
sys.path.append('/mnt/hdd1/wearly/kaggle/shopee/pytorch-image-models')
import timm
import torch
import torch.nn as nn
from config import CFG
import math
# VGGNet11 Custom
# ====================================================
# class VGG_11(nn.Module): # Paper model A
# def __init__(self,
# num_classes=CFG.n_classes,
# init_weights=True):
# super(VGG_11, self).__init__()
# self.convnet = nn.Sequential(
# # Input Channel (RGB: 3)
# nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, padding=1, stride=1),
# nn.ReLU(inplace=True),
# nn.MaxPool2d(kernel_size=2, stride=2),
# nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1, stride=1),
# nn.ReLU(inplace=True),
# nn.MaxPool2d(kernel_size=2, stride=2),
# nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=1, stride=1),
# nn.ReLU(inplace=True),
# nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, padding=1, stride=1),
# nn.ReLU(inplace=True),
# nn.MaxPool2d(kernel_size=2, stride=2),
# nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, padding=1, stride=1),
# nn.ReLU(inplace=True),
# nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1, stride=1),
# nn.ReLU(inplace=True),
# nn.MaxPool2d(kernel_size=2, stride=2),
# nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1, stride=1),
# nn.ReLU(inplace=True),
# nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1, stride=1),
# nn.ReLU(inplace=True),
# nn.MaxPool2d(kernel_size=2, stride=2),
# )
# self.fclayer = nn.Sequential(
# nn.Linear(512 * 7 * 7, 4096),
# nn.ReLU(inplace=True),
# nn.Dropout(p=0.5),
# nn.Linear(4096, 4096),
# nn.ReLU(inplace=True),
# nn.Dropout(p=0.5),
# nn.Linear(4096, num_classes),
# )
# if init_weights:
# self._initialize_weights()
# # https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html
# # filter의 값(가중치값)은 초기에 랜덤난수로서 뿌려짐 - 그렇기 때문에 torch.seed 를 고정해주는 것임.
# # 필터를 정의할 때 초기 가중치값은 위 doc에서 설명함
# def _initialize_weights(self):
# for m in self.modules(): # 모델 클래스에서 정의된 layer들을 iterable(ex : list, dict ...)한 객체로 반환
# if isinstance(m, nn.Conv2d): # 위에서 정의된 m번째 layer가 nn.Conv2d함수에서 비롯된 인스턴스인가? (True or False)
# n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels # 해당 filer의 모든 뉴런 개수
# # https://reniew.github.io/13/
# m.weight.data.normal_(0, math.sqrt(2. / n)) # He Initialization (relu에 적합한 편이라고 함)
# if m.bias is not None:
# m.bias.data.zero_()
# elif isinstance(m, nn.BatchNorm2d): # batchNorm layer의 경우
# m.weight.data.fill_(1) # weight값들은 전부 1로 초기화
# m.bias.data.zero_() # bias는 0으로 초기화
# elif isinstance(m, nn.Linear):
# m.weight.data.normal_(0, 0.01) # 논문에 명시 (we sampled the weights from a normal distribution with the zero mean and 10−2 variance)
# m.bias.data.zero_()
# def forward(self, x):
# # batch_size = x.shape[0]
# x = self.convnet(x)
# x = torch.flatten(x, 1) # x = x.view(-1, .view(batch_size, -1))
# x = self.fclayer(x)
# return x
# # VGGNet19 Custom
# # ====================================================
# class VGG_19(nn.Module): # Paper model E
# def __init__(self,
# num_classes=CFG.n_classes,
# init_weights=True):
# super(VGG_19, self).__init__()
# self.convnet = nn.Sequential(
# # Input Channel (RGB: 3)
# nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, padding=1, stride=1),
# nn.ReLU(inplace=True),
# nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1, stride=1),
# nn.ReLU(inplace=True),
# nn.MaxPool2d(kernel_size=2, stride=2),
# nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1, stride=1),
# nn.ReLU(inplace=True),
# nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, padding=1, stride=1),
# nn.ReLU(inplace=True),
# nn.MaxPool2d(kernel_size=2, stride=2),
# nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=1, stride=1),
# nn.ReLU(inplace=True),
# nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, padding=1, stride=1),
# nn.ReLU(inplace=True),
# nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, padding=1, stride=1),
# nn.ReLU(inplace=True),
# nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, padding=1, stride=1),
# nn.ReLU(inplace=True),
# nn.MaxPool2d(kernel_size=2, stride=2),
# nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, padding=1, stride=1),
# nn.ReLU(inplace=True),
# nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1, stride=1),
# nn.ReLU(inplace=True),
# nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1, stride=1),
# nn.ReLU(inplace=True),
# nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1, stride=1),
# nn.ReLU(inplace=True),
# nn.MaxPool2d(kernel_size=2, stride=2),
# nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1, stride=1),
# nn.ReLU(inplace=True),
# nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1, stride=1),
# nn.ReLU(inplace=True),
# nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1, stride=1),
# nn.ReLU(inplace=True),
# nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1, stride=1),
# nn.ReLU(inplace=True),
# nn.MaxPool2d(kernel_size=2, stride=2),
# )
# self.fclayer = nn.Sequential(
# nn.Linear(512 * 7 * 7, 4096),
# nn.ReLU(inplace=True),
# nn.Dropout(p=0.5),
# nn.Linear(4096, 4096),
# nn.ReLU(inplace=True),
# nn.Dropout(p=0.5),
# nn.Linear(4096, num_classes),
# )
# if init_weights:
# self._initialize_weights()
# def _initialize_weights(self):
# for m in self.modules():
# if isinstance(m, nn.Conv2d):
# n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
# m.weight.data.normal_(0, math.sqrt(2. / n)) # He Initialization
# if m.bias is not None:
# m.bias.data.zero_()
# elif isinstance(m, nn.BatchNorm2d):
# m.weight.data.fill_(1)
# m.bias.data.zero_()
# elif isinstance(m, nn.Linear):
# m.weight.data.normal_(0, 0.01) # 논문에 명시 (we sampled the weights from a normal distribution with the zero mean and 10−2 variance)
# m.bias.data.zero_()
# def forward(self, x):
# x = self.convnet(x)
# x = torch.flatten(x, 1)
# x = self.fclayer(x)
# return x
# Pretrained VGGNet19 (Timm)
# ====================================================
class Pretrained_VGG_19(nn.Module): # Paper model E
def __init__(self,
pretrained,
n_classes=CFG.n_classes,
model_name=CFG.model_name):
super(Pretrained_VGG_19, self).__init__()
if 'vgg' in model_name:
self.model = timm.create_model(model_name, pretrained=pretrained)
self.n_features = self.model.head.fc.in_features
self.model.head.fc = nn.Identity() # backbone 모델의 classifier 초기화
self.model.head.fc = nn.Linear(self.n_features, n_classes)
def forward(self, image):
img_embedding = self.feature_extractor(image)
return img_embedding # shape(batch_size, class개수)
def feature_extractor(self, x):
x = self.model(x)
return x