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lovasz_softmax.py
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lovasz_softmax.py
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#!/usr/bin/python
# -*- encoding: utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.cuda.amp as amp
# grads = {}
##
# version 1: use torch.autograd
class LovaszSoftmaxV1(nn.Module):
'''
This is the autograd version, used in the multi-category classification case
'''
def __init__(self, reduction='mean', ignore_index=-100):
super(LovaszSoftmaxV1, self).__init__()
self.reduction = reduction
self.lb_ignore = ignore_index
def forward(self, logits, label):
'''
Same usage method as nn.CrossEntropyLoss:
>>> criteria = LovaszSoftmaxV1()
>>> logits = torch.randn(8, 19, 384, 384) # nchw, float/half
>>> lbs = torch.randint(0, 19, (8, 384, 384)) # nhw, int64_t
>>> loss = criteria(logits, lbs)
'''
# overcome ignored label
n, c, h, w = logits.size()
logits = logits.transpose(0, 1).reshape(c, -1).float() # use fp32 to avoid nan
label = label.view(-1)
idx = label.ne(self.lb_ignore).nonzero(as_tuple=False).squeeze()
probs = logits.softmax(dim=0)[:, idx]
label = label[idx]
lb_one_hot = torch.zeros_like(probs).scatter_(
0, label.unsqueeze(0), 1).detach()
errs = (lb_one_hot - probs).abs()
errs_sort, errs_order = torch.sort(errs, dim=1, descending=True)
n_samples = errs.size(1)
# lovasz extension grad
with torch.no_grad():
# lb_one_hot_sort = lb_one_hot[
# torch.arange(c).unsqueeze(1).repeat(1, n_samples), errs_order
# ].detach()
lb_one_hot_sort = torch.cat([
lb_one_hot[i, ord].unsqueeze(0)
for i, ord in enumerate(errs_order)], dim=0)
n_pos = lb_one_hot_sort.sum(dim=1, keepdim=True)
inter = n_pos - lb_one_hot_sort.cumsum(dim=1)
union = n_pos + (1. - lb_one_hot_sort).cumsum(dim=1)
jacc = 1. - inter / union
if n_samples > 1:
jacc[:, 1:] = jacc[:, 1:] - jacc[:, :-1]
losses = torch.einsum('ab,ab->a', errs_sort, jacc)
if self.reduction == 'sum':
losses = losses.sum()
elif self.reduction == 'mean':
losses = losses.mean()
return losses, errs
##
# version 3: use cuda
import lovasz_softmax_cpp
class LovaszSoftmaxFunctionV3(torch.autograd.Function):
@staticmethod
@amp.custom_fwd(cast_inputs=torch.float32)
def forward(ctx, logits, labels, ignore_index):
losses, jacc = lovasz_softmax_cpp.lovasz_softmax_forward(logits,
labels, ignore_index)
ctx.vars = logits, labels, jacc, ignore_index
# grads['one_hot'] = jacc
return losses
@staticmethod
@amp.custom_bwd
def backward(ctx, grad_output):
logits, labels, jacc, ignore_index = ctx.vars
grad = lovasz_softmax_cpp.lovasz_softmax_backward(grad_output, logits, labels, jacc, ignore_index)
return grad, None, None
class LovaszSoftmaxV3(nn.Module):
'''
'''
def __init__(self, reduction='mean', ignore_index=-100):
super(LovaszSoftmaxV3, self).__init__()
self.reduction = reduction
self.lb_ignore = ignore_index
def forward(self, logits, label):
'''
Same usage method as nn.CrossEntropyLoss:
>>> criteria = LovaszSoftmaxV3()
>>> logits = torch.randn(8, 19, 384, 384) # nchw, float/half
>>> lbs = torch.randint(0, 19, (8, 384, 384)) # nhw, int64_t
>>> loss = criteria(logits, lbs)
'''
# overcome ignored label
losses = LovaszSoftmaxFunctionV3.apply(logits, label, self.lb_ignore)
if self.reduction == 'sum':
losses = losses.sum()
elif self.reduction == 'mean':
losses = losses.mean()
return losses
if __name__ == '__main__':
torch.manual_seed(123)
torch.cuda.manual_seed(123)
# crit1 = LovaszSoftmaxV1(reduction='none', ignore_index=255)
# crit2 = lovasz_softmax_cpp.lovasz_softmax_forward
#
# bs, c, h, w = 2, 19, 1000, 1000
# # bs, c, h, w = 2, 18, 1240, 1240
# inten = torch.randn(bs, c, h, w).cuda()
# # inten2 = inten1.clone()
# label = torch.randint(0, c, (bs, h, w)).cuda()
# # label[0, :, :] = 255
# # label[1, 13:20, 6] = 255
#
# loss1, errs1, jacc1 = crit1(inten, label)
# loss2, jacc2 = crit2(inten, label, 255)
# print(loss1.size())
# print(loss2.size())
# print((loss1.view(-1) - loss2.view(-1)).abs().sum())
# print((jacc1.view(-1) - jacc2.view(-1)).abs().sum())
# print(loss1)
# print(loss2)
# print((jac1 - jac2).sum())
# print(jac1[1, :8])
# print(jac2[1, :8])
import torchvision
import torch
import numpy as np
import random
torch.manual_seed(15)
random.seed(15)
np.random.seed(15)
torch.backends.cudnn.deterministic = True
scaler = amp.GradScaler()
class Model(nn.Module):
def __init__(self, n_classes):
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, n_classes, 3, 1, 1)
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.layer2(feat)
feat = self.layer3(feat)
feat = self.layer4(feat)
feat = self.fc(feat)
out = F.interpolate(feat, x.size()[2:], mode='bilinear', align_corners=True)
return out
c = 227
net1 = Model(c)
net2 = Model(c)
net2.load_state_dict(net1.state_dict())
red = 'none'
criteria1 = LovaszSoftmaxV1(reduction='sum', ignore_index=255)
criteria2 = LovaszSoftmaxV3(reduction='sum', ignore_index=255)
net1.cuda()
net2.cuda()
net1.train()
net2.train()
# net1 = net1.half()
# net2 = net2.half()
criteria1.cuda()
criteria2.cuda()
optim1 = torch.optim.SGD(net1.parameters(), lr=1e-2)
optim2 = torch.optim.SGD(net2.parameters(), lr=1e-2)
weight = torch.randn(c).softmax(dim=0).cuda().detach()
bs, h, w = 2, 400, 400
use_fp16 = False
for it in range(1000):
inten = torch.randn(bs, 3, h, w).cuda()#.half()
lbs = torch.randint(0, c, (bs, h, w)).cuda()
# lbs2 = lbs.clone()
# lbs[1, 1, 1] = 255
# lbs[0, 3:100, 2:100] = 255
# lbs[1, 4:70, 28:200] = 255
optim1.zero_grad()
logits1 = net1(inten)
# logits1.retain_grad()
loss1, one_hot = criteria1(logits1, lbs)
loss1 = loss1.mul(weight).sum()
loss1.backward()
optim1.step()
optim2.zero_grad()
logits2 = net2(inten)
loss2 = criteria2(logits2, lbs).mul(weight).sum()
loss2.backward()
optim2.step()
# o1 = one_hot
# o2 = grads['one_hot']
# print((o1 - o2).abs().max())
# print(o1.size())
# print(o2.size())
with torch.no_grad():
if (it+1) % 50 == 0:
print('iter: {}, ================='.format(it+1))
# print(net1.fc.weight.numel())
print('fc 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('loss: ', loss1.item() - loss2.item())