[英]Pytorch lightning print accuracy and loss at the end of each epoch
在 tensorflow keras 中,当我训练模型时,它会在每个时期打印准确度和损失,我想使用 pythorch 闪电做同样的事情。
我已经创建了我的模块,但我不知道该怎么做。
import torch
import torch.nn as nn
from residual_block import ResidualBlock
import pytorch_lightning as pl
from torchmetrics import Accuracy
class ResNet(pl.LightningModule):
def __init__(self, block, layers, image_channels, num_classes, learning_rate):
super(ResNet, self).__init__()
self.in_channels = 64
self.conv1 = nn.Conv2d(
image_channels, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU()
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(
block, layers[0], intermediate_channels=64, stride=1)
self.layer2 = self._make_layer(
block, layers[1], intermediate_channels=128, stride=2)
self.layer3 = self._make_layer(
block, layers[2], intermediate_channels=256, stride=2)
self.layer4 = self._make_layer(
block, layers[3], intermediate_channels=512, stride=2)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512 * 4, num_classes)
self.learning_rate = learning_rate
self.train_accuracy = Accuracy()
self.val_accuracy = Accuracy()
self.test_accuracy = Accuracy()
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.reshape(x.shape[0], -1)
x = self.fc(x)
return x
def configure_optimizers(self):
optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)
return optimizer
def training_step(self, train_batch, batch_idx):
images, labels = train_batch
outputs = self(images)
criterion = nn.CrossEntropyLoss()
loss = criterion(outputs, labels)
self.train_accuracy(outputs, labels)
self.log('train_loss', loss)
self.log('train_accuracy', self.train_accuracy)
return loss
def validation_step(self, val_batch, batch_idx):
images, labels = val_batch
outputs = self(images)
criterion = nn.CrossEntropyLoss()
loss = criterion(outputs, labels)
self.val_accuracy(outputs, labels)
self.log('val_loss', loss)
self.log('val_accuracy', self.val_accuracy)
def test_step(self, test_batch, batch_idx):
images, labels = test_batch
outputs = self(images)
criterion = nn.CrossEntropyLoss()
loss = criterion(outputs, labels)
self.test_accuracy(outputs, labels)
self.log('test_loss', loss)
self.log('test_accuracy', self.test_accuracy)
def _make_layer(self, block, num_residual_blocks, intermediate_channels, stride):
identity_downsample = None
layers = []
if stride != 1 or self.in_channels != intermediate_channels * 4:
identity_downsample = nn.Sequential(nn.Conv2d(self.in_channels, intermediate_channels * 4,
kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(intermediate_channels * 4),)
layers.append(
block(self.in_channels, intermediate_channels, identity_downsample, stride))
self.in_channels = intermediate_channels * 4
for i in range(num_residual_blocks - 1):
layers.append(block(self.in_channels, intermediate_channels))
return nn.Sequential(*layers)
@classmethod
def ResNet50(cls, img_channels, num_classes, learning_rate):
return ResNet(ResidualBlock, [3, 4, 6, 3], img_channels, num_classes, learning_rate)
@classmethod
def ResNet101(cls, img_channels, num_classes, learning_rate):
return ResNet(ResidualBlock, [3, 4, 23, 3], img_channels, num_classes, learning_rate)
@classmethod
def ResNet152(cls, img_channels, num_classes, learning_rate):
return ResNet(ResidualBlock, [3, 8, 36, 3], img_channels, num_classes, learning_rate)
我只想在每个时期结束时打印训练和验证的准确性和损失。
self.log("train_loss", loss, prog_bar=True, on_step=False, on_epoch=True)
上面的代码将train_loss
记录到进度条。
https://pytorch-lightning.readthedocs.io/en/stable/extensions/logging.html#automatic-logging
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.