-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathgenerate_coco_auroc.py
158 lines (115 loc) · 5.02 KB
/
generate_coco_auroc.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
import torch
from torch import manual_seed
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset
from torchmetrics import Accuracy,AUROC
import pickle
import time
import numpy as np
# import matplotlib.pyplot as plt
# import seaborn as sbs
DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
import sys
# DEVICE = torch.device("cuda",int(sys.argv[1]))
#DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class Net(nn.Module):
def __init__(self):
super().__init__()
self.conv1=nn.Conv2d(in_channels=3,out_channels=64,kernel_size=5, padding='same')
self.conv2=nn.Conv2d(in_channels=64,out_channels=128,kernel_size=3, padding='same')
self.conv3=nn.Conv2d(in_channels=128,out_channels=256,kernel_size=5, padding='same')
self.maxPooling2=nn.MaxPool2d(kernel_size=2)
self.maxPooling4_0=nn.MaxPool2d(kernel_size=4)
self.maxPooling4_1=nn.MaxPool2d(kernel_size=4)
# self.adPooling=nn.AdaptiveAvgPool1d(256)
self.fc1=nn.Linear(in_features=12544,out_features=128)
self.fc2=nn.Linear(in_features=128,out_features=64)
self.out=nn.Linear(in_features=64,out_features=2)
def forward(self,x):
x=self.conv1(x)
x=self.maxPooling4_0(x)
x=F.relu(x)
x=self.conv2(x)
x=self.maxPooling4_1(x)
x=F.relu(x)
x=self.conv3(x)
x=self.maxPooling2(x)
x=F.relu(x)
x=F.dropout(x)
x=x.view(1,x.size()[0],-1) #stretch to 1d data
#x=self.adPooling(x).squeeze()
x=self.fc1(x)
x=F.relu(x)
x=self.fc2(x)
x=F.relu(x)
x=self.out(x)
return x[0]
def load_trained(path):
model = Net()
model.load_state_dict(torch.load(path,map_location=DEVICE))
return model
def print_AUC(loader, model_ind=0):
cnn_conf=load_trained(f'./coco_conf_{model_ind}.pt').to(DEVICE)
cnn_sup=load_trained(f'./coco_sup_{model_ind}.pt').to(DEVICE)
cnn_no=load_trained(f'./coco_norm_{model_ind}.pt').to(DEVICE)
#models -> cnn_no; cnn_sup; cnn_conf
softmax = nn.Softmax(dim=1)
metric_conf = AUROC(num_classes=2, task='binary')
metric_sup = AUROC(num_classes=2, task='binary')
metric_no = AUROC(num_classes=2, task='binary')
for i_v, data_test in enumerate(loader):
inputs_test, labels_test = data_test
inputs_test = inputs_test.to(DEVICE,dtype=torch.float)
labels_test = labels_test.type(torch.LongTensor)
labels_test=labels_test.to(DEVICE)
outputs_conf = cnn_conf(inputs_test).squeeze()
outputs_sup = cnn_sup(inputs_test).squeeze()
outputs_no = cnn_no(inputs_test).squeeze()
out_pred_conf=softmax(outputs_conf)
out_pred_sup=softmax(outputs_sup)
out_pred_no=softmax(outputs_no)
metric_conf.update(torch.tensor(out_pred_conf[:,1].cpu().detach().numpy()),torch.tensor(labels_test.cpu().numpy()))
metric_sup.update(torch.tensor(out_pred_sup[:,1].cpu().detach().numpy()),torch.tensor(labels_test.cpu().numpy()))
metric_no.update(torch.tensor(out_pred_no[:,1].cpu().detach().numpy()),torch.tensor(labels_test.cpu().numpy()))
auroc_conf = metric_conf.compute()
auroc_sup = metric_sup.compute()
auroc_no = metric_no.compute()
print('model: confounder',auroc_conf)
print('model: suppressor',auroc_sup)
print('model: norm',auroc_no)
print()
return [auroc_conf, auroc_sup, auroc_no]
batch_size=32
with open(f'coco_data_norm.pkl', 'rb') as f:
[(_, _, _), (_, _, _), (x_test, y_test, _)] = pickle.load(f)
norm_loader = DataLoader(TensorDataset(torch.tensor(x_test.transpose(0,3,1,2)), torch.tensor(y_test)), batch_size=batch_size, shuffle=False, num_workers=4)
with open(f'coco_data_conf.pkl', 'rb') as f:
[(_, _, _), (_, _, _), (x_test, y_test, _)] = pickle.load(f)
conf_loader = DataLoader(TensorDataset(torch.tensor(x_test.transpose(0,3,1,2)), torch.tensor(y_test)), batch_size=batch_size, shuffle=False, num_workers=4)
with open(f'coco_data_sup.pkl', 'rb') as f:
[(_, _, _), (_, _, _), (x_test, y_test, _)] = pickle.load(f)
sup_loader = DataLoader(TensorDataset(torch.tensor(x_test.transpose(0,3,1,2)), torch.tensor(y_test)), batch_size=batch_size, shuffle=False, num_workers=4)
confounder_data_results = []
suppressor_data_results = []
no_mark_data_results = []
for i in range(1):
SEEDS = [12031212,1234,5845389,23423,343495,2024,3842834,23402304]
SEED=SEEDS[0]
np.random.seed(SEED)
torch.manual_seed(SEED)
import os
os.environ['PYTHONHASHSEED']=str(SEED)
import random
random.seed(SEED)
print(f'MODEL {i}')
print('confounder data:')
confounder_data_results.append(print_AUC(conf_loader, i))
print()
print('suppressor data:')
suppressor_data_results.append(print_AUC(sup_loader, i))
print()
print('norm data:')
no_mark_data_results.append(print_AUC(norm_loader, i))
print()