-
Notifications
You must be signed in to change notification settings - Fork 690
/
SpatialTranNet.md
335 lines (251 loc) · 11.2 KB
/
SpatialTranNet.md
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
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
# 空间变换器网络
在本教程中，您将学习如何使用称为空间变换器网络的视觉注意机制来扩充您的网络。你可以在[DeepMind paper](https://arxiv.org/abs/1506.02025)
阅读更多有关空间变换器网络的内容。
空间变换器网络是对任何空间变换的差异化关注的概括。空间变换器网络（简称STN）允许神经网络学习如何在输入图像上执行空间变换，
以增强模型的几何不变性。例如，它可以裁剪感兴趣的区域，缩放并校正图像的方向。而这可能是一种有用的机制，因为CNN对于旋转和
缩放以及更一般的仿射变换并不是不变的。
关于STN的最棒的事情之一是能够简单地将其插入任何现有的CNN，而且只需很少的修改。
```buildoutcfg
from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import numpy as np
plt.ion() # 交互模式
```
### 1.加载数据
在这篇文章中，我们尝试了经典的 MNIST 数据集。使用标准卷积网络增强空间变换器网络。
```buildoutcfg
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# 训练数据集
train_loader = torch.utils.data.DataLoader(
datasets.MNIST(root='.', train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])), batch_size=64, shuffle=True, num_workers=4)
# 测试数据集
test_loader = torch.utils.data.DataLoader(
datasets.MNIST(root='.', train=False, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])), batch_size=64, shuffle=True, num_workers=4)
```
* 输出结果
```buildoutcfg
Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./MNIST/raw/train-images-idx3-ubyte.gz
Extracting ./MNIST/raw/train-images-idx3-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./MNIST/raw/train-labels-idx1-ubyte.gz
Extracting ./MNIST/raw/train-labels-idx1-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./MNIST/raw/t10k-images-idx3-ubyte.gz
Extracting ./MNIST/raw/t10k-images-idx3-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./MNIST/raw/t10k-labels-idx1-ubyte.gz
Extracting ./MNIST/raw/t10k-labels-idx1-ubyte.gz
Processing...
Done!
```
### 2.什么是空间变换器网络？
空间变换器网络归结为三个主要组成部分：
* 本地网络（Localisation Network）是常规CNN，其对变换参数进行回归。不会从该数据集中明确地学习转换，而是网络自动学习增强
全局准确性的空间变换。
* 网格生成器( Grid Genator)在输入图像中生成与输出图像中的每个像素相对应的坐标网格。
* 采样器（Sampler）使用变换的参数并将其应用于输入图像。
> *注意：*<br/>
我们使用最新版本的Pytorch，它应该包含affine_grid和grid_sample模块。
```buildoutcfg
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linear(320, 50)
self.fc2 = nn.Linear(50, 10)
# 空间变换器定位 - 网络
self.localization = nn.Sequential(
nn.Conv2d(1, 8, kernel_size=7),
nn.MaxPool2d(2, stride=2),
nn.ReLU(True),
nn.Conv2d(8, 10, kernel_size=5),
nn.MaxPool2d(2, stride=2),
nn.ReLU(True)
)
# 3 * 2 affine矩阵的回归量
self.fc_loc = nn.Sequential(
nn.Linear(10 * 3 * 3, 32),
nn.ReLU(True),
nn.Linear(32, 3 * 2)
)
# 使用身份转换初始化权重/偏差
self.fc_loc[2].weight.data.zero_()
self.fc_loc[2].bias.data.copy_(torch.tensor([1, 0, 0, 0, 1, 0], dtype=torch.float))
# 空间变换器网络转发功能
def stn(self, x):
xs = self.localization(x)
xs = xs.view(-1, 10 * 3 * 3)
theta = self.fc_loc(xs)
theta = theta.view(-1, 2, 3)
grid = F.affine_grid(theta, x.size())
x = F.grid_sample(x, grid)
return x
def forward(self, x):
# transform the input
x = self.stn(x)
# 执行一般的前进传递
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
x = x.view(-1, 320)
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
x = self.fc2(x)
return F.log_softmax(x, dim=1)
model = Net().to(device)
```
### 3.训练模型
训练模型
现在我们使用 SGD（随机梯度下降）算法来训练模型。网络正在以有监督的方式学习分类任务。同时，该模型以端到端的方式自动学习STN。
```buildoutcfg
optimizer = optim.SGD(model.parameters(), lr=0.01)
def train(epoch):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.step()
if batch_idx % 500 == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))
#
# 一种简单的测试程序，用于测量STN在MNIST上的性能。.
#
def test():
with torch.no_grad():
model.eval()
test_loss = 0
correct = 0
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)
# 累加批量损失
test_loss += F.nll_loss(output, target, size_average=False).item()
# 获取最大对数概率的索引
pred = output.max(1, keepdim=True)[1]
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'
.format(test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
```
### 4.可视化 STN 结果
现在，我们将检查我们学习的视觉注意机制的结果。
我们定义了一个小辅助函数，以便在训练时可视化变换。
```buildoutcfg
def convert_image_np(inp):
"""Convert a Tensor to numpy image."""
inp = inp.numpy().transpose((1, 2, 0))
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
inp = std * inp + mean
inp = np.clip(inp, 0, 1)
return inp
# 我们想要在训练之后可视化空间变换器层的输出
# 我们使用STN可视化一批输入图像和相应的变换批次。
def visualize_stn():
with torch.no_grad():
# Get a batch of training data
data = next(iter(test_loader))[0].to(device)
input_tensor = data.cpu()
transformed_input_tensor = model.stn(data).cpu()
in_grid = convert_image_np(
torchvision.utils.make_grid(input_tensor))
out_grid = convert_image_np(
torchvision.utils.make_grid(transformed_input_tensor))
# Plot the results side-by-side
f, axarr = plt.subplots(1, 2)
axarr[0].imshow(in_grid)
axarr[0].set_title('Dataset Images')
axarr[1].imshow(out_grid)
axarr[1].set_title('Transformed Images')
for epoch in range(1, 20 + 1):
train(epoch)
test()
# 在某些输入批处理上可视化STN转换
visualize_stn()
plt.ioff()
plt.show()
```
* 输出结果
```buildoutcfg
Train Epoch: 1 [0/60000 (0%)] Loss: 2.336866
Train Epoch: 1 [32000/60000 (53%)] Loss: 0.841600
Test set: Average loss: 0.2624, Accuracy: 9212/10000 (92%)
Train Epoch: 2 [0/60000 (0%)] Loss: 0.527656
Train Epoch: 2 [32000/60000 (53%)] Loss: 0.428908
Test set: Average loss: 0.1176, Accuracy: 9632/10000 (96%)
Train Epoch: 3 [0/60000 (0%)] Loss: 0.305364
Train Epoch: 3 [32000/60000 (53%)] Loss: 0.263615
Test set: Average loss: 0.1099, Accuracy: 9677/10000 (97%)
Train Epoch: 4 [0/60000 (0%)] Loss: 0.169776
Train Epoch: 4 [32000/60000 (53%)] Loss: 0.408683
Test set: Average loss: 0.0861, Accuracy: 9734/10000 (97%)
Train Epoch: 5 [0/60000 (0%)] Loss: 0.286635
Train Epoch: 5 [32000/60000 (53%)] Loss: 0.122162
Test set: Average loss: 0.0817, Accuracy: 9743/10000 (97%)
Train Epoch: 6 [0/60000 (0%)] Loss: 0.331074
Train Epoch: 6 [32000/60000 (53%)] Loss: 0.126413
Test set: Average loss: 0.0589, Accuracy: 9822/10000 (98%)
Train Epoch: 7 [0/60000 (0%)] Loss: 0.109780
Train Epoch: 7 [32000/60000 (53%)] Loss: 0.172199
Test set: Average loss: 0.0629, Accuracy: 9814/10000 (98%)
Train Epoch: 8 [0/60000 (0%)] Loss: 0.078934
Train Epoch: 8 [32000/60000 (53%)] Loss: 0.156452
Test set: Average loss: 0.0563, Accuracy: 9839/10000 (98%)
Train Epoch: 9 [0/60000 (0%)] Loss: 0.063500
Train Epoch: 9 [32000/60000 (53%)] Loss: 0.186023
Test set: Average loss: 0.0713, Accuracy: 9799/10000 (98%)
Train Epoch: 10 [0/60000 (0%)] Loss: 0.199808
Train Epoch: 10 [32000/60000 (53%)] Loss: 0.083502
Test set: Average loss: 0.0528, Accuracy: 9850/10000 (98%)
Train Epoch: 11 [0/60000 (0%)] Loss: 0.092909
Train Epoch: 11 [32000/60000 (53%)] Loss: 0.204410
Test set: Average loss: 0.0471, Accuracy: 9857/10000 (99%)
Train Epoch: 12 [0/60000 (0%)] Loss: 0.078322
Train Epoch: 12 [32000/60000 (53%)] Loss: 0.041391
Test set: Average loss: 0.0634, Accuracy: 9796/10000 (98%)
Train Epoch: 13 [0/60000 (0%)] Loss: 0.061228
Train Epoch: 13 [32000/60000 (53%)] Loss: 0.137952
Test set: Average loss: 0.0654, Accuracy: 9802/10000 (98%)
Train Epoch: 14 [0/60000 (0%)] Loss: 0.068635
Train Epoch: 14 [32000/60000 (53%)] Loss: 0.084583
Test set: Average loss: 0.0515, Accuracy: 9853/10000 (99%)
Train Epoch: 15 [0/60000 (0%)] Loss: 0.263158
Train Epoch: 15 [32000/60000 (53%)] Loss: 0.127036
Test set: Average loss: 0.0493, Accuracy: 9851/10000 (99%)
Train Epoch: 16 [0/60000 (0%)] Loss: 0.083642
Train Epoch: 16 [32000/60000 (53%)] Loss: 0.028274
Test set: Average loss: 0.0461, Accuracy: 9867/10000 (99%)
Train Epoch: 17 [0/60000 (0%)] Loss: 0.076734
Train Epoch: 17 [32000/60000 (53%)] Loss: 0.034796
Test set: Average loss: 0.0409, Accuracy: 9864/10000 (99%)
Train Epoch: 18 [0/60000 (0%)] Loss: 0.122501
Train Epoch: 18 [32000/60000 (53%)] Loss: 0.152187
Test set: Average loss: 0.0474, Accuracy: 9860/10000 (99%)
Train Epoch: 19 [0/60000 (0%)] Loss: 0.050512
Train Epoch: 19 [32000/60000 (53%)] Loss: 0.270055
Test set: Average loss: 0.0416, Accuracy: 9878/10000 (99%)
Train Epoch: 20 [0/60000 (0%)] Loss: 0.073357
Train Epoch: 20 [32000/60000 (53%)] Loss: 0.017542
Test set: Average loss: 0.0713, Accuracy: 9816/10000 (98%)
```
脚本的总运行时间：1分48.736秒