pytorch空間變換網絡

本文將學習如何使用稱為空間變換器網絡的視覺注意機制來擴充網絡。可以在DeepMind paper 有關空間變換器網絡的內容。
空間變換器網絡是對任何空間變換的差異化關注的概括。空間變換器網絡（簡稱STN）允許神經網絡學習如何在輸入圖像上執行空間變換， 以增強模型的幾何不變性。例如，它可以裁剪感興趣的區域，縮放并校正圖像的方向。而這可能是一種有用的機制，因為CNN對于旋轉和 縮放以及更一般的仿射變換并不是不變的。
STN的最棒的事情之一，能夠簡單地將其插入任何現有的CNN，而且只需很少的修改。
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.加載數據
嘗試了經典的 MNIST 數據集。使用標準卷積網絡增強空間變換器網絡。
device = torch.device(“cuda” if torch.cuda.is_available() else “cpu”)

訓練數據集

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)

測試數據集

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)
? 輸出結果
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!
2.什么是空間變換網絡？
空間變換器網絡歸結為三個主要組成部分：
? 本地網絡（Localisation Network）是常規CNN，其對變換參數進行回歸。不會從該數據集中明確地學習轉換，而是網絡自動學習增強全局準確性的空間變換。
? 網格生成器( Grid Genator)在輸入圖像中生成與輸出圖像中的每個像素相對應的坐標網格。
? 采樣器（Sampler）使用變換的參數并將其應用于輸入圖像。

注意：使用最新版本的Pytorch，它應該包含affine_grid和grid_sample模塊。
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)

    # 空間變換器定位 - 網絡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矩陣的回歸量self.fc_loc = nn.Sequential(nn.Linear(10 * 3 * 3, 32),nn.ReLU(True),nn.Linear(32, 3 * 2))# 使用身份轉換初始化權重/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))# 空間變換器網絡轉發功能
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 xdef forward(self, x):# transform the inputx = self.stn(x)# 執行一般的前進傳遞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.訓練模型
訓練模型
使用 SGD（隨機梯度下降）算法來訓練模型。網絡正在以有監督的方式學習分類任務。同時，該模型以端到端的方式自動學習STN。
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()))

一種簡單的測試程序，用于測量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)

        # 累加批量損失test_loss += F.nll_loss(output, target, size_average=False).item()# 獲取最大對數概率的索引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.可視化 STN 結果
檢查學習的視覺注意機制的結果。
定義了一個小輔助函數，以便在訓練時可視化變換。
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

我們想要在訓練之后可視化空間變換器層的輸出

我們使用STN可視化一批輸入圖像和相應的變換批次。

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-sidef, 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()

在某些輸入批處理上可視化STN轉換

visualize_stn()

plt.ioff()
plt.show()
? 輸出結果

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%)
腳本的總運行時間：1分48.736秒

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