语义分割可以用什么代码

语义分割可以使用以下代码：

1. PyTorch:

import torch
import torchvision
import torchvision.transforms as transforms

# Load the dataset
transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5,), (0.5,))])

trainset = torchvision.datasets.MNIST(root='./data', train=True,
                                        download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
                                          shuffle=True, num_workers=0)

# Define the model
import torch.nn as nn
import torch.nn.functional as F

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 4 * 4, 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 = x.view(-1, 16 * 4 * 4)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

net = Net()

# Define the loss function and optimizer
import torch.optim as optim

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

# Train the model
for epoch in range(2):  # loop over the dataset multiple times

    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        # get the inputs; data is a list of [inputs, labels]
        inputs, labels = data

        # zero the parameter gradients
        optimizer.zero_grad()

        # forward + backward + optimize
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # print statistics
        running_loss += loss.item()
        if i % 2000 == 1999:    # print every 2000 mini-batches
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

print('Finished Training')

2. TensorFlow:

import tensorflow as tf

# Load the dataset
mnist = tf.keras.datasets.mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

# Define the model
model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10)
])

# Define the loss function and optimizer
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
optimizer = tf.keras.optimizers.Adam()

# Train the model
model.compile(optimizer=optimizer,
              loss=loss_fn,
              metrics=['accuracy'])

model.fit(x_train, y_train, epochs=5)

model.evaluate(x_test,  y_test, verbose=2)

3. Caffe:

# Load the dataset
import caffe
import numpy as np

caffe.set_device(0)
caffe.set_mode_gpu()

solver = caffe.SGDSolver('/path/to/solver.prototxt')

# Define the model
net = caffe.Net('/path/to/deploy.prototxt', caffe.TEST)

# Define the loss function and optimizer
solver.net.blobs['data'].reshape(4, 1, 28, 28)
solver.net.blobs['label'].reshape(4,)

# Train the model
for i in range(10000):
    solver.step(1)
    if i % 1000 == 0:
        print('iteration', i)

# Evaluate the model
test_net = caffe.Net('/path/to/deploy.prototxt', caffe.TEST)
test_net.copy_from('/path/to/weights.caffemodel')

test_net.blobs['data'].reshape(1, 1, 28, 28)
test_net.blobs['data'].data[...] = x_test[0:1, :, :, :]
out = test_net.forward()
print('predicted class:', out['prob'].argmax())