TorchFusion 是一个深度学习框架，主要用于 AI 系统加速研究和开发。
TorchFusion 基于 PyTorch 并且完全兼容纯 PyTorch 和其他 PyTorch 软件包，它供了一个全面的可扩展训练框架，可以轻松用开发者的 PyTorch 模型进行训练，评估和运行推理。
该框架具有高度可扩展性，所以开发者可以根据自己特定的目的来进行训练。
Github 链接：
https://github.com/johnolafenwa/TorchFusion
独特功能
高度可扩展
高度详细的汇总功能，不仅为您提供了有关参数，层数，输入和输出大小的详细信息，还为网络中的每个线性和卷积层提供了Flops（Multiply-Adds）的数量。 现在，只需一个功能就可以知道任何CNN架构的确切计算成本！
实时指标和损失可视化，并可选择永久保存它们
支持永久保存日志
易于使用的回调
注意：这只是 TorchFusion 的预发布版本，未来的 TorchFusion 将会跨越更多的深度学习领域。
安装
安装 TorchFusion
pip3 install https://github.com/johnolafenwa/TorchFusion/releases/download/0.1.1/torchfusion-0.1.1-py3-none-any.whl
在 Windows 上安装 PyTorch：
https://pytorch.org/
CPU Only
With Python 3.6
pip3installhttp://download.pytorch.org/whl/cpu/torch-0.4.0-cp36-cp36m-win_amd64.whl torchvision
With Python 3.5
pip3installhttp://download.pytorch.org/whl/cpu/torch-0.4.0-cp35-cp35m-win_amd64.whl torchvision
CUDA 支持
With Python 3.6
pip3installhttp://download.pytorch.org/whl/cu80/torch-0.4.0-cp36-cp36m-win_amd64.whl torchvision
With Python 3.5
pip3installhttp://download.pytorch.org/whl/cu80/torch-0.4.0-cp35-cp35m-win_amd64.whl
在 Linux 上安装 PyTorch：
https://pytorch.org/
CPU Only
With Python 3.6
pip3installhttp://download.pytorch.org/whl/cpu/torch-0.4.0-cp36-cp36m-linux_x86_64.whl torchvision
With Python 3.5
pip3installhttp://download.pytorch.org/whl/cpu/torch-0.4.0-cp35-cp35m-linux_x86_64.whl torchvision
CUDA 支持
pip3installtorch torchvision
在 OSX 上安装 PyTorch：
https://pytorch.org/
CPU Only
pip3installtorch torchvision
MNIST in Five Minutes
importtorchfusionastf
fromtorchvision.datasets.mnistimportMNIST
fromtorch.utils.dataimportDataLoader
fromtorchvision.transformsimporttransforms
importtorch.nnasnn
fromtorch.optimimportAdam
importtorch.cudaascuda
#Define a the classifier network
net = nn.Sequential(
tf.Flatten(),
nn.Linear(784,100),
nn.ReLU(),
nn.Linear(100,100),
nn.ReLU(),
nn.Linear(100,100),
nn.ReLU(),
nn.Linear(100,10)
batch_size =64
#Transformations and data augmentation
transformations = transforms.Compose([
transforms.Resize(28),
transforms.ToTensor(),
transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))
])
#Load the training and test sets
train_set = MNIST(root="./data",transform=transformations,download=True)
test_set = MNIST(root="./data",train=False,transform=transformations,download=True)
train_loader = DataLoader(train_set,shuffle=True,batch_size=batch_size,num_workers=4)
test_loader = DataLoader(test_set,shuffle=False,batch_size=batch_size,num_workers=4)
#Move to GPU if available
ifcuda.is_available():
net.cuda()
#Setup the optimize and a loss function
optimizer = Adam(net.parameters(),lr=0.001)
loss_fn = nn.CrossEntropyLoss()
#Top 1 Train accuracy
train_metrics = tf.Accuracy(topK=1)
#Top 1 and Top 2 test accuracy
test_metrics_top1 = tf.Accuracy(name="Top 1 Acc ",topK=1)
test_metrics_top2 = tf.Accuracy(name="Top 2 Acc ",topK=2)
#Create an instance of the StandardModel
model = tf.StandardModel(net)
deftrain():
#print a summary of the network
print(model.summary((1,28,28)))
model.train(train_loader, loss_fn, optimizer, [train_metrics], test_loader,
[test_metrics_top1, test_metrics_top2], num_epochs=20,
model_dir="mnist_mlp_saved_models",save_logs="logs.txt")
if__name__ =="__main__":
train()
GAN in Five Minutes
importtorchfusion.ganastfgan
fromtorchvision.datasetsimportMNIST
fromtorchvision.transformsimporttransforms
fromtorch.optimimportAdam
fromtorch.utils.dataimportDataLoader
importtorch.nnasnn
importtorch.cudaascuda
#Transformations and data augmentation
train_transformations = transforms.Compose([
transforms.Resize(28),
transforms.ToTensor(),
transforms.Normalize((0.5,0.5,0.5), (0.5,0.5,0.5))
])
batch_size =64
# Load the training set
train_set = MNIST(root="./data", train=True, transform=train_transformations, download=True)
train_data = DataLoader(train_set,batch_size=batch_size,shuffle=True,num_workers=4)
#Create an instance of the NormalDistribution
source = tfgan.NormalDistribution(length=len(train_set),size=(100))
source_data = DataLoader(source,batch_size=batch_size,shuffle=True,num_workers=4)
#Create an instance of the Generator and Discriminator
G = tfgan.MLPGenerator(latent_size=100,output_size=(1,28,28))
D = tfgan.MLPDiscriminator(input_size=(1,28,28))
#Move the networks to GPU if available
ifcuda.is_available():
G.cuda()
D.cuda()
#Setup the optimizers
g_optim = Adam(G.parameters(),lr=0.0002,betas=(0.5,0.999))
d_optim = Adam(D.parameters(),lr=0.0002,betas=(0.5,0.999))
#Define the loss function
loss_fn = nn.BCELoss()
if__name__ =="__main__":
#Create an instance of the StandardGANModel
trainer = tfgan.StandardGANModel(G,D,gen_loss_fn=loss_fn,disc_loss_fn=loss_fn)
#Train the two models
trainer.train(train_data,source_data,g_optim,d_optim,num_epochs=200,disc_steps=1,sa