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