170 lines
5.4 KiB
Python
170 lines
5.4 KiB
Python
import torch
|
|
import torch.nn as nn
|
|
|
|
from dassl.data import DataManager
|
|
from dassl.optim import build_optimizer, build_lr_scheduler
|
|
from dassl.utils import count_num_param
|
|
from dassl.engine import TRAINER_REGISTRY, TrainerX
|
|
from dassl.metrics import compute_accuracy
|
|
from dassl.engine.trainer import SimpleNet
|
|
from dassl.data.transforms import build_transform
|
|
from dassl.modeling.ops.utils import create_onehot
|
|
|
|
|
|
class Experts(nn.Module):
|
|
|
|
def __init__(self, n_source, fdim, num_classes):
|
|
super().__init__()
|
|
self.linears = nn.ModuleList(
|
|
[nn.Linear(fdim, num_classes) for _ in range(n_source)]
|
|
)
|
|
self.softmax = nn.Softmax(dim=1)
|
|
|
|
def forward(self, i, x):
|
|
x = self.linears[i](x)
|
|
x = self.softmax(x)
|
|
return x
|
|
|
|
|
|
@TRAINER_REGISTRY.register()
|
|
class DAELDG(TrainerX):
|
|
"""Domain Adaptive Ensemble Learning.
|
|
|
|
DG version: only use labeled source data.
|
|
|
|
https://arxiv.org/abs/2003.07325.
|
|
"""
|
|
|
|
def __init__(self, cfg):
|
|
super().__init__(cfg)
|
|
n_domain = cfg.DATALOADER.TRAIN_X.N_DOMAIN
|
|
batch_size = cfg.DATALOADER.TRAIN_X.BATCH_SIZE
|
|
if n_domain <= 0:
|
|
n_domain = self.num_source_domains
|
|
self.split_batch = batch_size // n_domain
|
|
self.n_domain = n_domain
|
|
|
|
self.conf_thre = cfg.TRAINER.DAEL.CONF_THRE
|
|
|
|
def check_cfg(self, cfg):
|
|
assert cfg.DATALOADER.TRAIN_X.SAMPLER == "RandomDomainSampler"
|
|
assert len(cfg.TRAINER.DAEL.STRONG_TRANSFORMS) > 0
|
|
|
|
def build_data_loader(self):
|
|
cfg = self.cfg
|
|
tfm_train = build_transform(cfg, is_train=True)
|
|
custom_tfm_train = [tfm_train]
|
|
choices = cfg.TRAINER.DAEL.STRONG_TRANSFORMS
|
|
tfm_train_strong = build_transform(cfg, is_train=True, choices=choices)
|
|
custom_tfm_train += [tfm_train_strong]
|
|
dm = DataManager(self.cfg, custom_tfm_train=custom_tfm_train)
|
|
self.train_loader_x = dm.train_loader_x
|
|
self.train_loader_u = dm.train_loader_u
|
|
self.val_loader = dm.val_loader
|
|
self.test_loader = dm.test_loader
|
|
self.num_classes = dm.num_classes
|
|
self.num_source_domains = dm.num_source_domains
|
|
self.lab2cname = dm.lab2cname
|
|
|
|
def build_model(self):
|
|
cfg = self.cfg
|
|
|
|
print("Building F")
|
|
self.F = SimpleNet(cfg, cfg.MODEL, 0)
|
|
self.F.to(self.device)
|
|
print("# params: {:,}".format(count_num_param(self.F)))
|
|
self.optim_F = build_optimizer(self.F, cfg.OPTIM)
|
|
self.sched_F = build_lr_scheduler(self.optim_F, cfg.OPTIM)
|
|
self.register_model("F", self.F, self.optim_F, self.sched_F)
|
|
fdim = self.F.fdim
|
|
|
|
print("Building E")
|
|
self.E = Experts(self.num_source_domains, fdim, self.num_classes)
|
|
self.E.to(self.device)
|
|
print("# params: {:,}".format(count_num_param(self.E)))
|
|
self.optim_E = build_optimizer(self.E, cfg.OPTIM)
|
|
self.sched_E = build_lr_scheduler(self.optim_E, cfg.OPTIM)
|
|
self.register_model("E", self.E, self.optim_E, self.sched_E)
|
|
|
|
def forward_backward(self, batch):
|
|
parsed_data = self.parse_batch_train(batch)
|
|
input, input2, label, domain = parsed_data
|
|
|
|
input = torch.split(input, self.split_batch, 0)
|
|
input2 = torch.split(input2, self.split_batch, 0)
|
|
label = torch.split(label, self.split_batch, 0)
|
|
domain = torch.split(domain, self.split_batch, 0)
|
|
domain = [d[0].item() for d in domain]
|
|
|
|
loss_x = 0
|
|
loss_cr = 0
|
|
acc = 0
|
|
|
|
feat = [self.F(x) for x in input]
|
|
feat2 = [self.F(x) for x in input2]
|
|
|
|
for feat_i, feat2_i, label_i, i in zip(feat, feat2, label, domain):
|
|
cr_s = [j for j in domain if j != i]
|
|
|
|
# Learning expert
|
|
pred_i = self.E(i, feat_i)
|
|
loss_x += (-label_i * torch.log(pred_i + 1e-5)).sum(1).mean()
|
|
expert_label_i = pred_i.detach()
|
|
acc += compute_accuracy(pred_i.detach(),
|
|
label_i.max(1)[1])[0].item()
|
|
|
|
# Consistency regularization
|
|
cr_pred = []
|
|
for j in cr_s:
|
|
pred_j = self.E(j, feat2_i)
|
|
pred_j = pred_j.unsqueeze(1)
|
|
cr_pred.append(pred_j)
|
|
cr_pred = torch.cat(cr_pred, 1)
|
|
cr_pred = cr_pred.mean(1)
|
|
loss_cr += ((cr_pred - expert_label_i)**2).sum(1).mean()
|
|
|
|
loss_x /= self.n_domain
|
|
loss_cr /= self.n_domain
|
|
acc /= self.n_domain
|
|
|
|
loss = 0
|
|
loss += loss_x
|
|
loss += loss_cr
|
|
self.model_backward_and_update(loss)
|
|
|
|
loss_summary = {
|
|
"loss_x": loss_x.item(),
|
|
"acc": acc,
|
|
"loss_cr": loss_cr.item()
|
|
}
|
|
|
|
if (self.batch_idx + 1) == self.num_batches:
|
|
self.update_lr()
|
|
|
|
return loss_summary
|
|
|
|
def parse_batch_train(self, batch):
|
|
input = batch["img"]
|
|
input2 = batch["img2"]
|
|
label = batch["label"]
|
|
domain = batch["domain"]
|
|
|
|
label = create_onehot(label, self.num_classes)
|
|
|
|
input = input.to(self.device)
|
|
input2 = input2.to(self.device)
|
|
label = label.to(self.device)
|
|
|
|
return input, input2, label, domain
|
|
|
|
def model_inference(self, input):
|
|
f = self.F(input)
|
|
p = []
|
|
for k in range(self.num_source_domains):
|
|
p_k = self.E(k, f)
|
|
p_k = p_k.unsqueeze(1)
|
|
p.append(p_k)
|
|
p = torch.cat(p, 1)
|
|
p = p.mean(1)
|
|
return p
|