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Starting
on
L40S
import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
import torch.optim as optim | |
import torchvision.transforms as transforms | |
from torch.utils.data import DataLoader | |
from torchvision.datasets import CIFAR10 | |
from mmcv.parallel import MMDataParallel | |
from mmcv.runner import EpochBasedRunner | |
from mmcv.utils import get_logger | |
class Model(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(16 * 5 * 5, 120) | |
self.fc2 = nn.Linear(120, 84) | |
self.fc3 = nn.Linear(84, 10) | |
self.loss_fn = nn.CrossEntropyLoss() | |
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 * 5 * 5) | |
x = F.relu(self.fc1(x)) | |
x = F.relu(self.fc2(x)) | |
x = self.fc3(x) | |
return x | |
def train_step(self, data, optimizer): | |
images, labels = data | |
predicts = self(images) # -> self.__call__() -> self.forward() | |
loss = self.loss_fn(predicts, labels) | |
return {'loss': loss} | |
if __name__ == '__main__': | |
model = Model() | |
if torch.cuda.is_available(): | |
# only use gpu:0 to train | |
# Solved issue https://github.com/open-mmlab/mmcv/issues/1470 | |
model = MMDataParallel(model.cuda(), device_ids=[0]) | |
# dataset and dataloader | |
transform = transforms.Compose([ | |
transforms.ToTensor(), | |
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) | |
]) | |
trainset = CIFAR10( | |
root='data', train=True, download=True, transform=transform) | |
trainloader = DataLoader( | |
trainset, batch_size=128, shuffle=True, num_workers=2) | |
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9) | |
logger = get_logger('mmcv') | |
# runner is a scheduler to manage the training | |
runner = EpochBasedRunner( | |
model, | |
optimizer=optimizer, | |
work_dir='./work_dir', | |
logger=logger, | |
max_epochs=4) | |
# learning rate scheduler config | |
lr_config = dict(policy='step', step=[2, 3]) | |
# configuration of optimizer | |
optimizer_config = dict(grad_clip=None) | |
# configuration of saving checkpoints periodically | |
checkpoint_config = dict(interval=1) | |
# save log periodically and multiple hooks can be used simultaneously | |
log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) | |
# register hooks to runner and those hooks will be invoked automatically | |
runner.register_training_hooks( | |
lr_config=lr_config, | |
optimizer_config=optimizer_config, | |
checkpoint_config=checkpoint_config, | |
log_config=log_config) | |
runner.run([trainloader], [('train', 1)]) | |