训练大规模SNN
本教程作者： fangwei123456
使用 spikingjelly.clock_driven.model
在 spikingjelly.clock_driven.model 中定义了一些常用的网络，下面以 spikingjelly.clock_driven.model.spiking_resnet
为例展示如何使用这些网络。
大多数 spikingjelly.clock_driven.model 中的网络，都提供单步和多步两种网络。例如，我们创建一个逐步传播的Spiking ResNet-18 1 ：
import torch
import torch.nn.functional as F
from spikingjelly.clock_driven import neuron, surrogate, functional
from spikingjelly.clock_driven.model import spiking_resnet
net = spiking_resnet.spiking_resnet18(pretrained=False, progress=True, single_step_neuron=neuron.IFNode, v_threshold=1., surrogate_function=surrogate.ATan())
print(net)
函数 spiking_resnet18(pretrained=False, progress=True, single_step_neuron: callable=None, **kwargs)
的运行参数中，single_step_neuron 是单步神经元，而 **kwargs 是神经元的参数。如果设置 pretrained=True 则可以加载ANN，即
ResNet-18的预训练模型参数。运行结果为：
SpikingResNet(
(conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(sn1): IFNode(
v_threshold=1.0, v_reset=0.0, detach_reset=False
(surrogate_function): ATan(alpha=2.0, spiking=True)
)
(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(layer1): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(sn1): IFNode(
v_threshold=1.0, v_reset=0.0, detach_reset=False
(surrogate_function): ATan(alpha=2.0, spiking=True)
)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(sn2): IFNode(
v_threshold=1.0, v_reset=0.0, detach_reset=False
(surrogate_function): ATan(alpha=2.0, spiking=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(sn1): IFNode(
v_threshold=1.0, v_reset=0.0, detach_reset=False
(surrogate_function): ATan(alpha=2.0, spiking=True)
)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(sn2): IFNode(
v_threshold=1.0, v_reset=0.0, detach_reset=False
(surrogate_function): ATan(alpha=2.0, spiking=True)
)
)
)
(layer2): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(sn1): IFNode(
v_threshold=1.0, v_reset=0.0, detach_reset=False
(surrogate_function): ATan(alpha=2.0, spiking=True)
)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(sn2): IFNode(
v_threshold=1.0, v_reset=0.0, detach_reset=False
(surrogate_function): ATan(alpha=2.0, spiking=True)
)
(downsample): Sequential(
(0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(sn1): IFNode(
v_threshold=1.0, v_reset=0.0, detach_reset=False
(surrogate_function): ATan(alpha=2.0, spiking=True)
)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(sn2): IFNode(
v_threshold=1.0, v_reset=0.0, detach_reset=False
(surrogate_function): ATan(alpha=2.0, spiking=True)
)
)
)
(layer3): Sequential(
(0): BasicBlock(
(conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(sn1): IFNode(
v_threshold=1.0, v_reset=0.0, detach_reset=False
(surrogate_function): ATan(alpha=2.0, spiking=True)
)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(sn2): IFNode(
v_threshold=1.0, v_reset=0.0, detach_reset=False
(surrogate_function): ATan(alpha=2.0, spiking=True)
)
(downsample): Sequential(
(0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(sn1): IFNode(
v_threshold=1.0, v_reset=0.0, detach_reset=False
(surrogate_function): ATan(alpha=2.0, spiking=True)
)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(sn2): IFNode(
v_threshold=1.0, v_reset=0.0, detach_reset=False
(surrogate_function): ATan(alpha=2.0, spiking=True)
)
)
)
(layer4): Sequential(
(0): BasicBlock(
(conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(sn1): IFNode(
v_threshold=1.0, v_reset=0.0, detach_reset=False
(surrogate_function): ATan(alpha=2.0, spiking=True)
)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(sn2): IFNode(
v_threshold=1.0, v_reset=0.0, detach_reset=False
(surrogate_function): ATan(alpha=2.0, spiking=True)
)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(sn1): IFNode(
v_threshold=1.0, v_reset=0.0, detach_reset=False
(surrogate_function): ATan(alpha=2.0, spiking=True)
)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(sn2): IFNode(
v_threshold=1.0, v_reset=0.0, detach_reset=False
(surrogate_function): ATan(alpha=2.0, spiking=True)
)
)
)
(avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
(fc): Linear(in_features=512, out_features=1000, bias=True)
)
单步网络的输入不包含时间维度，每次需要给网络单个时间步的输入：
net = spiking_resnet.spiking_resnet18(pretrained=False, progress=True, single_step_neuron=neuron.IFNode, v_threshold=1., surrogate_function=surrogate.ATan())
T = 4
N = 2
x = torch.rand([T, N, 3, 224, 224])
fr = 0.
with torch.no_grad():
for t in range(T):
fr += net(x[t])
fr /= T
print('firing rate =', fr)
搭建多步网络的方式类似，只需要把 spikingjelly.clock_driven.model.spiking_resnet.spiking_resnet18 换成 spikingjelly.clock_driven.model.spiking_resnet.multi_step_spiking_resnet18 并将单步神经元换成多步神经元：
net_ms = spiking_resnet.multi_step_spiking_resnet18(pretrained=False, progress=True, multi_step_neuron=neuron.MultiStepIFNode, v_threshold=1., surrogate_function=surrogate.ATan(), backend='torch')
print(net_ms)
运行结果为：
MultiStepSpikingResNet(
(conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(sn1): MultiStepIFNode(
v_threshold=1.0, v_reset=0.0, detach_reset=False, backend=cupy
(surrogate_function): ATan(alpha=2.0, spiking=True)
)
(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(layer1): Sequential(
(0): MultiStepBasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(sn1): MultiStepIFNode(
v_threshold=1.0, v_reset=0.0, detach_reset=False, backend=cupy
(surrogate_function): ATan(alpha=2.0, spiking=True)
)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(sn2): MultiStepIFNode(
v_threshold=1.0, v_reset=0.0, detach_reset=False, backend=cupy
(surrogate_function): ATan(alpha=2.0, spiking=True)
)
)
(1): MultiStepBasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(sn1): MultiStepIFNode(
v_threshold=1.0, v_reset=0.0, detach_reset=False, backend=cupy
(surrogate_function): ATan(alpha=2.0, spiking=True)
)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(sn2): MultiStepIFNode(
v_threshold=1.0, v_reset=0.0, detach_reset=False, backend=cupy
(surrogate_function): ATan(alpha=2.0, spiking=True)
)
)
)
(layer2): Sequential(
(0): MultiStepBasicBlock(
(conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(sn1): MultiStepIFNode(
v_threshold=1.0, v_reset=0.0, detach_reset=False, backend=cupy
(surrogate_function): ATan(alpha=2.0, spiking=True)
)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(sn2): MultiStepIFNode(
v_threshold=1.0, v_reset=0.0, detach_reset=False, backend=cupy
(surrogate_function): ATan(alpha=2.0, spiking=True)
)
(downsample): Sequential(
(0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): MultiStepBasicBlock(
(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(sn1): MultiStepIFNode(
v_threshold=1.0, v_reset=0.0, detach_reset=False, backend=cupy
(surrogate_function): ATan(alpha=2.0, spiking=True)
)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(sn2): MultiStepIFNode(
v_threshold=1.0, v_reset=0.0, detach_reset=False, backend=cupy
(surrogate_function): ATan(alpha=2.0, spiking=True)
)
)
)
(layer3): Sequential(
(0): MultiStepBasicBlock(
(conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(sn1): MultiStepIFNode(
v_threshold=1.0, v_reset=0.0, detach_reset=False, backend=cupy
(surrogate_function): ATan(alpha=2.0, spiking=True)
)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(sn2): MultiStepIFNode(
v_threshold=1.0, v_reset=0.0, detach_reset=False, backend=cupy
(surrogate_function): ATan(alpha=2.0, spiking=True)
)
(downsample): Sequential(
(0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): MultiStepBasicBlock(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(sn1): MultiStepIFNode(
v_threshold=1.0, v_reset=0.0, detach_reset=False, backend=cupy
(surrogate_function): ATan(alpha=2.0, spiking=True)
)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(sn2): MultiStepIFNode(
v_threshold=1.0, v_reset=0.0, detach_reset=False, backend=cupy
(surrogate_function): ATan(alpha=2.0, spiking=True)
)
)
)
(layer4): Sequential(
(0): MultiStepBasicBlock(
(conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(sn1): MultiStepIFNode(
v_threshold=1.0, v_reset=0.0, detach_reset=False, backend=cupy
(surrogate_function): ATan(alpha=2.0, spiking=True)
)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(sn2): MultiStepIFNode(
v_threshold=1.0, v_reset=0.0, detach_reset=False, backend=cupy
(surrogate_function): ATan(alpha=2.0, spiking=True)
)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): MultiStepBasicBlock(
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(sn1): MultiStepIFNode(
v_threshold=1.0, v_reset=0.0, detach_reset=False, backend=cupy
(surrogate_function): ATan(alpha=2.0, spiking=True)
)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(sn2): MultiStepIFNode(
v_threshold=1.0, v_reset=0.0, detach_reset=False, backend=cupy
(surrogate_function): ATan(alpha=2.0, spiking=True)
)
)
)
(avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
(fc): Linear(in_features=512, out_features=1000, bias=True)
)
对于多步网络，输入应该是带有时间维度的：
net = spiking_resnet.spiking_resnet18(pretrained=False, progress=True, single_step_neuron=neuron.IFNode, v_threshold=1.,
surrogate_function=surrogate.ATan())
T = 4
N = 2
x = torch.rand([T, N, 3, 224, 224])
fr = 0.
with torch.no_grad():
for t in range(T):
fr += net(x[t])
fr /= T
net_ms = spiking_resnet.multi_step_spiking_resnet18(pretrained=False, progress=True, multi_step_neuron=neuron.MultiStepIFNode, v_threshold=1., surrogate_function=surrogate.ATan(), backend='torch')
net_ms.load_state_dict(net.state_dict())
with torch.no_grad():
print('mse of single/multi step network outputs', F.mse_loss(net_ms(x).mean(0), fr))
但是多步网络也允许输入不带时间维度的数据，在这种情况下，必须在构造网络时或构造后指定 T。
在构造时指定 T：
net_ms = spiking_resnet.multi_step_spiking_resnet18(pretrained=False, progress=True, T=4, multi_step_neuron=neuron.MultiStepIFNode, v_threshold=1., surrogate_function=surrogate.ATan(), backend='torch')
或者在构造后指定 T：
net_ms = spiking_resnet.multi_step_spiking_resnet18(pretrained=False, progress=True, multi_step_neuron=neuron.MultiStepIFNode, v_threshold=1., surrogate_function=surrogate.ATan(), backend='torch')
net_ms.T = 4
网络在 forward 时会将输入自动复制 T 次，和我们把输入复制是一样的：
net_ms = spiking_resnet.multi_step_spiking_resnet18(pretrained=False, progress=True, multi_step_neuron=neuron.MultiStepIFNode, v_threshold=1., surrogate_function=surrogate.ATan(), backend='torch')
T = 4
N = 2
with torch.no_grad():
x = torch.rand([N, 3, 224, 224])
y1 = net_ms(x.unsqueeze(0).repeat(T, 1, 1, 1, 1))
functional.reset_net(net_ms)
net_ms.T = T
y2 = net_ms(x)
print(F.mse_loss(y1, y2))
输出是：
tensor(0.)
让网络自行复制，计算效率会稍微高一些，原因参见 时间驱动：使用卷积SNN识别Fashion-MNIST。
在ImageNet上训练
ImageNet 2 是计算机视觉常用的数据集，对于SNN而言颇具挑战性。惊蜇框架提供了一个训练ImageNet的代码样例，位于 spikingjelly.clock_driven.model.train_imagenet 。 该代码样例的实现参考了 torchvision 。使用 时只需要构建好网络、损失函数和正确率计算方式，就可以快速训练，下面是使用示例：
import torch
import torch.nn.functional as F
from spikingjelly.clock_driven.model import train_imagenet, spiking_resnet, train_classify
from spikingjelly.clock_driven import neuron, surrogate
def ce_loss(x_seq: torch.Tensor, label: torch.Tensor):
# x_seq.shape = [T, N, C]
return F.cross_entropy(input=x_seq.mean(0), target=label)
def cal_acc1_acc5(output, target):
return train_classify.default_cal_acc1_acc5(output.mean(0), target)
if __name__ == '__main__':
net = spiking_resnet.multi_step_spiking_resnet18(T=4, multi_step_neuron=neuron.MultiStepIFNode, surrogate_function=surrogate.ATan(), detach_reset=True, backend='cupy')
args = train_imagenet.parse_args()
train_imagenet.main(model=net, criterion=ce_loss, args=args, cal_acc1_acc5=cal_acc1_acc5)
我们把这段代码保存为 resnet18_imagenet.py。查看运行参数：
(pytorch-env) wfang@onebrain-dgx-a100-01:~/ssd/temp_dir$ python resnet18_imagenet.py -h
[--step-gamma STEP_GAMMA] [--cosa-tmax COSA_TMAX] [--momentum M] [--wd W] [--output-dir OUTPUT_DIR] [--resume RESUME] [--start-epoch N] [--cache-dataset]
[--sync-bn] [--amp] [--world-size WORLD_SIZE] [--dist-url DIST_URL] [--tb] [--T T] [--local_rank LOCAL_RANK]
PyTorch Classification Training
optional arguments:
-h, --help show this help message and exit
--data-path DATA_PATH
dataset
--device DEVICE device
-b BATCH_SIZE, --batch-size BATCH_SIZE
--epochs N number of total epochs to run
-j N, --workers N number of data loading workers (default: 16)
--lr LR initial learning rate
--opt OPT optimizer (sgd or adam)
--lrs LRS lr schedule (cosa(CosineAnnealingLR), step(StepLR)) or None
--step-size STEP_SIZE
step_size for StepLR
--step-gamma STEP_GAMMA
gamma for StepLR
--cosa-tmax COSA_TMAX
T_max for CosineAnnealingLR. If none, it will be set to epochs
--momentum M Momentum for SGD
--wd W, --weight-decay W
weight decay (default: 0)
--output-dir OUTPUT_DIR
path where to save
--resume RESUME resume from checkpoint
--start-epoch N start epoch
--cache-dataset Cache the datasets for quicker initialization. It also serializes the transforms
--sync-bn Use sync batch norm
--amp Use AMP training
--world-size WORLD_SIZE
number of distributed processes
--dist-url DIST_URL url used to set up distributed training
--tb Use TensorBoard to record logs
--T T simulation steps
--local_rank LOCAL_RANK
在单卡上训练：
python resnet18_imagenet.py --data-path /raid/wfang/datasets/ImageNet --lr 0.1 --opt sgd --lrs cosa --amp --tb --device cuda:7
在多卡上训练：
python -m torch.distributed.launch --nproc_per_node=8 resnet18_imagenet.py --data-path /raid/wfang/datasets/ImageNet --lr 0.1 --opt sgd --lrs cosa --amp --tb
- 1
He, Kaiming, et al. “Deep residual learning for image recognition.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
- 2
Deng, Jia, et al. “Imagenet: A large-scale hierarchical image database.” 2009 IEEE conference on computer vision and pattern recognition. IEEE, 2009.