-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathmain.py
206 lines (182 loc) · 10.2 KB
/
main.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
196
197
198
199
200
201
202
203
204
205
206
import argparse
import os
import pandas as pd
import torch
import torch.optim as optim
from torch.utils.data import DataLoader
from tqdm import tqdm
from data.HSI_new import HSI
from EADNet import EADNet
from model import Model
from torchvision import transforms
# train for one epoch to learn unique features
def train(net, data_loader, train_optimizer):
net.train()
total_loss, total_num, train_bar = 0.0, 0, tqdm(data_loader)
# print(train_bar)
for pos_1, pos_2, target in train_bar:
pos0_0, pos0_1, pos0_2, pos0_3 = pos_1[0].cuda(non_blocking=True), pos_1[1].cuda(non_blocking=True), \
pos_1[2].cuda(non_blocking=True), pos_1[3].cuda(non_blocking=True)
pos1_0, pos1_1, pos1_2, pos1_3 = pos_2[0].cuda(non_blocking=True), pos_2[1].cuda(non_blocking=True), \
pos_2[2].cuda(non_blocking=True), pos_2[3].cuda(non_blocking=True)
# print(pos0_0.shape)
feature_1, out_1 = net(pos0_0, pos0_1, pos0_2, pos0_3)
feature_2, out_2 = net(pos1_0, pos1_1, pos1_2, pos1_3)
# [2*B, D]
out = torch.cat([out_1, out_2], dim=0)
# [2*B, 2*B]
sim_matrix = torch.exp(torch.mm(out, out.t().contiguous()) / temperature)
mask = (torch.ones_like(sim_matrix) - torch.eye(2 * batch_size, device=sim_matrix.device)).bool()
# [2*B, 2*B-1]
sim_matrix = sim_matrix.masked_select(mask).view(2 * batch_size, -1)
# compute loss [B]
pos_sim = torch.exp(torch.sum(out_1 * out_2, dim=-1) / temperature)
# [2*B]
pos_sim = torch.cat([pos_sim, pos_sim], dim=0)
loss = (- torch.log(pos_sim / sim_matrix.sum(dim=-1))).mean()
train_optimizer.zero_grad()
loss.backward()
train_optimizer.step()
total_num += batch_size
total_loss += loss.item() * batch_size
train_bar.set_description('Train Epoch: [{}/{}] Loss: {:.4f}'.format(epoch, epochs, total_loss / total_num))
return total_loss / total_num
# test for one epoch, use weighted knn to find the most similar images' label to assign the test image
def test(net, memory_data_loader, test_data_loader):
net.eval()
total_top1, total_top5, total_num, feature_bank = 0.0, 0.0, 0, []
with torch.no_grad():
# generate feature bank
for data, _, target in tqdm(memory_data_loader, desc='Feature extracting'):
data_0, data_1, data_2, data_3 = data[0].cuda(non_blocking=True), data[1].cuda(non_blocking=True), \
data[2].cuda(non_blocking=True), data[3].cuda(non_blocking=True)
feature, out = net(data_0, data_1, data_2, data_3)
feature_bank.append(feature)
# [D, N]
feature_bank = torch.cat(feature_bank, dim=0).t().contiguous()
# [N]
feature_labels = torch.tensor(memory_data_loader.dataset.targets, device=feature_bank.device)
# loop test data to predict the label by weighted knn search
test_bar = tqdm(test_data_loader)
for data, _, target in test_bar:
data_0, data_1, data_2, data_3 = data[0].cuda(non_blocking=True), data[1].cuda(non_blocking=True), \
data[2].cuda(non_blocking=True), data[3].cuda(non_blocking=True)
target = target.cuda(non_blocking=True)
feature, out = net(data_0, data_1, data_2, data_3)
total_num += data[0].size(0)
# compute cos similarity between each feature vector and feature bank ---> [B, N]
sim_matrix = torch.mm(feature, feature_bank)
# [B, K]
sim_weight, sim_indices = sim_matrix.topk(k=k, dim=-1)
# [B, K]
sim_labels = torch.gather(feature_labels.expand(data[0].size(0), -1), dim=-1, index=sim_indices)
sim_weight = (sim_weight / temperature).exp()
# counts for each class
one_hot_label = torch.zeros(data[0].size(0) * k, c, device=sim_labels.device)
# [B*K, C]
one_hot_label = one_hot_label.scatter(dim=-1, index=sim_labels.view(-1, 1), value=1.0)
one_hot_label[:, 0] = 0
# weighted score ---> [B, C]
pred_scores = torch.sum(one_hot_label.view(data[0].size(0), -1, c) * sim_weight.unsqueeze(dim=-1), dim=1)
pred_labels = pred_scores.argsort(dim=-1, descending=True)
total_top1 += torch.sum((pred_labels[:, :1] == target.unsqueeze(dim=-1)).any(dim=-1).float()).item()
total_top5 += torch.sum((pred_labels[:, :5] == target.unsqueeze(dim=-1)).any(dim=-1).float()).item()
test_bar.set_description('Test Epoch: [{}/{}] Acc@1:{:.2f}% Acc@5:{:.2f}%'
.format(epoch, epochs, total_top1 / total_num * 100, total_top5 / total_num * 100))
return total_top1 / total_num * 100, total_top5 / total_num * 100
class MyCoTransform(object):
def __init__(self, mode='train'):
self.mode = mode
pass
def __call__(self, input0, input1, input2, input3):
train_transform_0 = transforms.Compose([
transforms.RandomResizedCrop(31),
transforms.ToTensor(),
# ip
transforms.Normalize([0.23749262, 0.48883094, 0.4582705], [0.00555777, 0.00690145, 0.04093194])
# sv
# transforms.Normalize([0.34324876, 0.25490125, 0.40289667], [0.00757429, 0.0043157, 0.0362956])
# houston 2013
# transforms.Normalize([0.442528, 0.344754, 0.115389], [0.000489, 0.006495, 0.005109])
])
train_transform_1 = transforms.Compose([
transforms.RandomResizedCrop(31),
transforms.ToTensor(),
# ip
transforms.Normalize([0.23749262, 0.48883094, 0.4582705], [0.00555777, 0.00690145, 0.04093194])
# sv
# transforms.Normalize([0.34324876, 0.25490125, 0.40289667], [0.00757429, 0.0043157, 0.0362956])
# houston 2013
# transforms.Normalize([0.442528, 0.344754, 0.115389], [0.000489, 0.006495, 0.005109])
])
test_transform = transforms.Compose([
transforms.ToTensor(),
# indian pines
transforms.Normalize([0.23749262, 0.48883094, 0.4582705], [0.00555777, 0.00690145, 0.04093194])
# salinas
# transforms.Normalize([0.34324876, 0.25490125, 0.40289667], [0.00757429, 0.0043157, 0.0362956])
# houston 2013
# transforms.Normalize([0.442528, 0.344754, 0.115389], [0.000489, 0.006495, 0.005109])
])
if self.mode == 'train_0':
img0 = train_transform_0(input0)
img1 = train_transform_0(input1)
img2 = train_transform_0(input2)
img3 = train_transform_0(input3)
elif self.mode == 'train_1':
img0 = train_transform_1(input0)
img1 = train_transform_1(input1)
img2 = train_transform_1(input2)
img3 = train_transform_1(input3)
elif self.mode == 'test':
img0 = test_transform(input0)
img1 = test_transform(input1)
img2 = test_transform(input2)
img3 = test_transform(input3)
return [img0, img1, img2, img3]
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Train SimCLR')
parser.add_argument('--feature_dim', default=128, type=int, help='Feature dim for latent vector')
parser.add_argument('--temperature', default=0.5, type=float, help='Temperature used in softmax')
parser.add_argument('--k', default=200, type=int, help='Top k most similar images used to predict the label')
parser.add_argument('--batch_size', default=64, type=int, help='Number of images in each mini-batch')
parser.add_argument('--epochs', default=100, type=int, help='Number of sweeps over the dataset to train')
# args parse
args = parser.parse_args()
feature_dim, temperature, k = args.feature_dim, args.temperature, args.k
batch_size, epochs = args.batch_size, args.epochs
train_data = HSI(root_dir='./dataset/HSIdata/ip_50p_17c/', mode='train',
transform=[MyCoTransform(mode='train_0'), MyCoTransform(mode='train_1')])
train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True, num_workers=16, pin_memory=True,
drop_last=True)
memory_data = HSI(root_dir='./dataset/HSIdata/ip_50p_17c/', mode='train',
transform=[MyCoTransform(mode='test'), MyCoTransform(mode='test')])
memory_loader = DataLoader(memory_data, batch_size=batch_size, shuffle=False, num_workers=16, pin_memory=True)
test_data = HSI(root_dir='./dataset/HSIdata/ip_50p_17c/', mode='val',
transform=[MyCoTransform(mode='test'), MyCoTransform(mode='test')])
test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=False, num_workers=16, pin_memory=True)
# model setup and optimizer config
# model = Model(feature_dim=feature_dim, finetune=False).cuda()
model = EADNet(cl_dim=feature_dim, finetune=False).cuda()
optimizer = optim.Adam(model.parameters(), lr=1e-3, weight_decay=1e-6)
c = 17
# training loop
exp_id = 'sceadnet'
results = {'train_loss': [], 'test_acc@1': [], 'test_acc@5': []}
save_name_pre = '{}_{}_{}_{}_{}_{}'.format('ip', feature_dim, temperature, batch_size, epochs, exp_id)
if not os.path.exists('results'):
os.mkdir('results')
best_acc = 0.0
for epoch in range(1, epochs + 1):
train_loss = train(model, train_loader, optimizer)
results['train_loss'].append(train_loss)
test_acc_1, test_acc_5 = test(model, memory_loader, test_loader)
results['test_acc@1'].append(test_acc_1)
results['test_acc@5'].append(test_acc_5)
# save statistics
data_frame = pd.DataFrame(data=results, index=range(1, epoch + 1))
data_frame.to_csv('result/{}_statistics.csv'.format(save_name_pre), index_label='epoch')
if test_acc_1 > best_acc:
best_acc = test_acc_1
torch.save(model.state_dict(), 'result/{}_model.pth'.format(save_name_pre))