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taylor_softmax.py
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taylor_softmax.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.cuda.amp as amp
'''
proposed in this paper: [Exploring Alternatives to Softmax Function](https://arxiv.org/pdf/2011.11538.pdf)
'''
##
# functions
import taylor_softmax_cpp
class TaylorSoftmaxFunc(torch.autograd.Function):
'''
use cpp/cuda to accelerate and shrink memory usage
'''
@staticmethod
@amp.custom_fwd
def forward(ctx, feat, dim=1, n=2, use_log=False):
ctx.vars = feat, dim, n, use_log
return taylor_softmax_cpp.taylor_softmax_forward(feat, dim, n, use_log)
@staticmethod
@amp.custom_bwd
def backward(ctx, grad_output):
'''
compute gradient of focal loss
'''
feat, dim, n, use_log = ctx.vars
return taylor_softmax_cpp.taylor_softmax_backward(grad_output, feat, dim, n, use_log), None, None, None
def taylor_softmax_v1(x, dim=1, n=4, use_log=False):
assert n % 2 == 0 and n > 0
fn = torch.ones_like(x)
denor = 1.
for i in range(1, n + 1):
denor *= i
fn = fn + x.pow(i) / denor
out = fn / fn.sum(dim=dim, keepdims=True)
if use_log: out = out.log()
return out
def taylor_softmax_v3(inten, dim=1, n=4, use_log=False):
assert n % 2 == 0 and n > 0
return TaylorSoftmaxFunc.apply(inten, dim, n, use_log)
### TaylorSoftmax
##
# version 1: use torch.autograd
class TaylorSoftmaxV1(nn.Module):
'''
This is the autograd version
'''
def __init__(self, dim=1, n=2):
super(TaylorSoftmaxV1, self).__init__()
self.dim = dim
self.n = n
def forward(self, x):
'''
usage similar to nn.Softmax:
>>> mod = TaylorSoftmaxV1(dim=1, n=4)
>>> inten = torch.randn(1, 32, 64, 64)
>>> out = mod(inten)
'''
return taylor_softmax_v1(x, self.dim, self.n, use_log=False)
##
# version 3: use cuda
class TaylorSoftmaxV3(nn.Module):
'''
This is the autograd version
'''
def __init__(self, dim=1, n=2):
super(TaylorSoftmaxV3, self).__init__()
assert n % 2 == 0 and n > 0
self.dim = dim
self.n = n
def forward(self, x):
'''
usage similar to nn.Softmax:
>>> mod = TaylorSoftmaxV3(dim=1, n=4)
>>> inten = torch.randn(1, 32, 64, 64)
>>> out = mod(inten)
'''
return taylor_softmax_v3(x, self.dim, self.n, use_log=False)
### LogSoftmax
##
# version 1: use torch.autograd
class LogTaylorSoftmaxV1(nn.Module):
'''
This is the autograd version
'''
def __init__(self, dim=1, n=2):
super(LogTaylorSoftmaxV1, self).__init__()
assert n % 2 == 0
self.dim = dim
self.n = n
def forward(self, x):
'''
usage similar to nn.Softmax:
>>> mod = LogTaylorSoftmaxV1(dim=1, n=4)
>>> inten = torch.randn(1, 32, 64, 64)
>>> out = mod(inten)
'''
return taylor_softmax_v1(x, self.dim, self.n, use_log=True)
##
# version 3: use cuda
class LogTaylorSoftmaxV3(nn.Module):
'''
This is the autograd version
'''
def __init__(self, dim=1, n=2):
super(LogTaylorSoftmaxV3, self).__init__()
assert n % 2 == 0
self.dim = dim
self.n = n
def forward(self, x):
'''
usage similar to nn.Softmax:
>>> mod = LogTaylorSoftmaxV3(dim=1, n=4)
>>> inten = torch.randn(1, 32, 64, 64)
>>> out = mod(inten)
'''
return taylor_softmax_v3(x, self.dim, self.n, use_log=True)
### SoftmaxCrossEntropy
##
# version 1: use torch.autograd
class TaylorCrossEntropyLoss(nn.Module):
'''
This is the autograd version
'''
def __init__(self, n=2, ignore_index=-1, reduction='mean'):
super(TaylorCrossEntropyLoss, self).__init__()
assert n % 2 == 0
self.taylor_softmax = LogTaylorSoftmaxV1(dim=1, n=n)
self.reduction = reduction
self.ignore_index = ignore_index
def forward(self, logits, labels):
'''
usage similar to nn.CrossEntropyLoss:
>>> crit = TaylorCrossEntropyLoss(n=4)
>>> inten = torch.randn(1, 10, 64, 64)
>>> label = torch.randint(0, 10, (1, 64, 64))
>>> out = crit(inten, label)
'''
log_probs = self.taylor_softmax(logits)
loss = F.nll_loss(log_probs, labels, reduction=self.reduction,
ignore_index=self.ignore_index)
return loss
if __name__ == '__main__':
import numpy as np
import torchvision
torch.backends.cudnn.deterministic = True
# tsoftmax = TaylorSoftmaxV3(dim=0, n=4)
# inten = torch.randn(3, 4, 5, 6).cuda()
# out = tsoftmax(inten)
# print(out.size())
# print(out)
# print(out[:, 0, 0, :4])
# print(out[:, 0, 0, :4].sum(dim=0))
class Model(nn.Module):
def __init__(self, softmax='v1'):
super(Model, self).__init__()
net = torchvision.models.resnet18(pretrained=False)
self.conv1 = net.conv1
self.bn1 = net.bn1
self.maxpool = net.maxpool
self.relu = net.relu
self.layer1 = net.layer1
self.layer2 = net.layer2
self.layer3 = net.layer3
self.layer4 = net.layer4
self.fc = nn.Conv2d(512, 19 * 32 * 32, 3, 1, 1)
self.upsample = nn.PixelShuffle(32)
self.softmax = softmax
if softmax == 'v1':
obj = LogTaylorSoftmaxV1
self.softmax1 = obj(dim=0, n=2)
self.softmax2 = obj(dim=1, n=4)
self.softmax3 = obj(dim=2, n=6)
self.softmax4 = obj(dim=3, n=8)
else:
obj = LogTaylorSoftmaxV3
self.softmax1 = obj(dim=0, n=2)
self.softmax2 = obj(dim=1, n=4)
self.softmax3 = obj(dim=2, n=6)
self.softmax4 = obj(dim=3, n=8)
def forward(self, x):
feat = self.conv1(x)
feat = self.bn1(feat)
feat = self.relu(feat)
feat = self.maxpool(feat)
feat = self.layer1(feat)
feat = self.softmax1(feat)
feat = self.layer2(feat)
# arr = feat.cpu().detach().numpy().tofile('tmp.npy')
# size = feat.size()
# arr = np.fromfile('tmp.npy', dtype=np.float32)
# feat = torch.from_numpy(arr).cuda().view(size)
feat = self.softmax2(feat)
feat = self.layer3(feat)
feat = self.softmax3(feat)
feat = self.layer4(feat)
feat = self.softmax4(feat)
feat = self.fc(feat)
out = self.upsample(feat)
# out = F.interpolate(feat, x.size()[2:], mode='bilinear', align_corners=True)
return out
red = 'mean'
bs = 64
net1 = Model(softmax='v1')
net2 = Model(softmax='v3')
net2.load_state_dict(net1.state_dict())
net1.cuda()
net2.cuda()
net1.train()
net2.train()
criteria1 = TaylorCrossEntropyLoss(n=4, ignore_index=255, reduction=red)
criteria2 = TaylorCrossEntropyLoss(n=4, ignore_index=255, reduction=red)
# criteria1 = nn.CrossEntropyLoss(ignore_index=255)
# criteria2 = nn.CrossEntropyLoss(ignore_index=255)
optim1 = torch.optim.SGD(net1.parameters(), lr=1e-2)
optim2 = torch.optim.SGD(net2.parameters(), lr=1e-2)
for it in range(5000):
inten = torch.randn(bs, 3, 224, 224).cuda()
lbs = torch.randint(0, 19, (bs, 224, 224)).cuda()
lbs[1, 1, 1] = 255
lbs[30, 3, 2:200] = 255
lbs[18, 4:7, 8:200] = 255
# net2.load_state_dict(net1.state_dict())
logits1 = net1(inten)
logits2 = net2(inten)
# print('logits2.size(): ', logits2.size())
# print('torch.isnan(logits2.sum()): ', torch.isnan(logits2).sum())
loss1 = criteria1(logits1, lbs)
loss2 = criteria2(logits2, lbs)
optim1.zero_grad()
optim2.zero_grad()
loss1.backward()
loss2.backward()
optim1.step()
optim2.step()
# _ = input()
with torch.no_grad():
if (it+1) % 50 == 0:
# if True:
print('iter: {}, ================='.format(it+1))
print('out.weight: ', torch.max(torch.abs(net1.fc.weight - net2.fc.weight)).item())
print('conv1.weight: ', torch.max(torch.abs(net1.conv1.weight - net2.conv1.weight)).item())
print('\nloss: ', loss1.item() - loss2.item())