added cnn with tests and tensoboard

This commit is contained in:
wangobango 2021-12-19 22:37:44 +01:00
parent addd9af657
commit 768526a595
3 changed files with 630 additions and 1 deletions

5
.gitignore vendored
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@ -63,4 +63,7 @@ Thumbs.db.meta
*.vscode
data/*
venv/*
venv/*
new_data/*
cnn/runs/*
cnn/new_data/*

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cnn/main.py Normal file
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import os
import cv2
from torch.utils.data import DataLoader, Dataset
from torch.utils.data import random_split
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
class TreesDataset(Dataset):
def __init__(self, data_links) -> None:
self.X, self.Y = readData(data_links)
def __len__(self):
return len(self.X)
def __getitem__(self, index):
return (self.X[index], self.Y[index])
class Net(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(3264, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = torch.flatten(x, 1)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
def create_datalinks(root_dir):
data_links = os.listdir(root_dir)
data_links = [root_dir + "/" + x for x in data_links]
return data_links
def preprocess(img):
scale_percent = 10
width = int(img.shape[1] * scale_percent / 100)
height = int(img.shape[0] * scale_percent / 100)
dim = (width, height)
resized = cv2.resize(img, dim, interpolation = cv2.INTER_AREA)
resized = torchvision.transforms.functional.to_tensor(resized)
return resized
def readData(data_links):
x, y = [], []
for link in data_links:
img = cv2.imread(link, cv2.IMREAD_COLOR)
img = preprocess(img)
if("ground" in link):
label = 1
elif("AS12" in link):
label = 0
x.append(img)
y.append(label)
return x, y
links_3_plus_ground = create_datalinks("new_data/AS12_3") + create_datalinks("new_data/ground")
dataset = TreesDataset(links_3_plus_ground)
train_set, test_set = random_split(dataset, [300, 50], generator=torch.Generator().manual_seed(42))
trainloader = DataLoader(train_set, batch_size=10, shuffle=True, num_workers=2)
testloader = DataLoader(test_set, batch_size=10, shuffle=True, num_workers=2)
classes = ('tree', 'ground')
epochs_num = 15
net = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
for epoch in range(epochs_num):
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
inputs, labels = data
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 10 == 0:
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 10))
running_loss = 0.0
print('Finished Training')
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
images, labels = data
outputs = net(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy : %d %%' % (100 * correct / total))

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