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sleep.py
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sleep.py
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import argparse
import time
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import models
import torch.utils.data as data
parser = argparse.ArgumentParser(description='PyTorch Example')
parser.add_argument('--batch-size', '-b', type=int, default=64, metavar='N',
help='input batch size for training (default: 64)')
class PseudoDataset(data.Dataset):
def __init__(self):
pass
def __getitem__(self, item):
return torch.rand(3, 224, 224), np.random.randint(1000)
def __len__(self):
return 50000
def train(train_loader, model, criterion, optimizer):
model.train()
for i, (images, target) in enumerate(train_loader):
images, target = images.cuda(), target.cuda()
output = model(images)
loss = criterion(output, target)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# time.sleep(0.1)
def main():
args = parser.parse_args()
model = models.__dict__['resnet50']().cuda()
model = nn.DataParallel(model)
optimizer = optim.SGD(model.parameters(), lr=0.01)
criterion = nn.CrossEntropyLoss().cuda()
dataset = PseudoDataset()
train_loader = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size)
while True:
train(train_loader, model, criterion, optimizer)
if __name__ == '__main__':
main()