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kd_da_grl_alt_multi_target_cst_fac.py
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kd_da_grl_alt_multi_target_cst_fac.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from KD.base_kd import hinton_distillation, hinton_distillation_sw, hinton_distillation_wo_ce
import os
import DA.DA_datasets as DA_datasets
import cmodels.ResNet as ResNet
import cmodels.DAN_model as DAN_model
from utils import eval, LoggerForSacred, adjust_learning_rate, get_config_var
save_dir = get_config_var()["SAVE_DIR"]
def grl_multi_target_hinton_train_alt(current_ep, epochs, teacher_models, student_model, optimizer_das, optimizer_kd, device,
source_dataloader, targets_dataloader, T, alpha, beta, gamma, batch_norm, is_cst, is_debug=False, **kwargs):
logger = kwargs["logger"]
if "logger_id" not in kwargs:
logger_id = ""
else:
logger_id = kwargs["logger_id"]
if batch_norm:
for teacher_model in teacher_models:
teacher_model.train()
student_model.train()
total_losses = torch.zeros(len(teacher_models))
teacher_da_temp_losses = torch.zeros(len(teacher_models))
kd_temp_losses = torch.zeros(len(teacher_models))
kd_target_loss = 0.
kd_source_loss = 0.
iter_targets = [0] * len(targets_dataloader)
for i, d in enumerate(targets_dataloader):
iter_targets[i] = iter(d)
iter_source = iter(source_dataloader)
for i in range(1, len(source_dataloader) + 1):
data_source, label_source = iter_source.next()
data_source = data_source.to(device)
label_source = label_source.to(device)
for ix, it in enumerate(iter_targets):
try:
data_target, _ = it.next()
except StopIteration:
it = iter(targets_dataloader[ix])
data_target, _ = it.next()
if data_target.shape[0] != data_source.shape[0]:
data_target = data_target[: data_source.shape[0]]
data_target = data_target.to(device)
optimizer_das[ix].zero_grad()
p = float(i + (current_ep -1) * len(source_dataloader)) / epochs / len(source_dataloader)
delta = 2. / (1. + np.exp(-10 * p)) - 1
teacher_label_source_pred, teacher_source_loss_adv = teacher_models[ix](data_source, delta)
teacher_source_loss_cls = F.cross_entropy(F.log_softmax(teacher_label_source_pred, dim=1), label_source)
_, teacher_target_loss_adv = teacher_models[ix](data_target, delta, source=False)
teacher_loss_adv = teacher_source_loss_adv + teacher_target_loss_adv
teacher_da_grl_loss = (1 - beta) * (teacher_source_loss_cls + gamma * teacher_loss_adv)
teacher_da_temp_losses[ix] += teacher_da_grl_loss.mean().item()
teacher_da_grl_loss.mean().backward()
optimizer_das[ix].step() # May need to have 2 optimizers
optimizer_das[ix].zero_grad()
optimizer_kd.zero_grad()
teacher_source_logits, _ = teacher_models[ix](data_source, delta, source=True)
teacher_target_logits, _ = teacher_models[ix](data_target, delta, source=True)
student_source_logits, _ = student_model(data_source, delta, source=True)
student_target_logits, student_target_loss_adv = student_model(data_target, delta, source=False)
source_kd_loss = hinton_distillation_sw(teacher_source_logits, student_source_logits, label_source, T, alpha).abs()
if is_cst:
target_kd_loss = hinton_distillation_wo_ce(teacher_target_logits, student_target_logits, T).abs() + alpha * student_target_loss_adv
else:
target_kd_loss = hinton_distillation_wo_ce(teacher_target_logits, student_target_logits, T).abs()
kd_source_loss += source_kd_loss.mean().item()
kd_target_loss += target_kd_loss.mean().item()
kd_loss = beta * (target_kd_loss + source_kd_loss)
kd_temp_losses[ix] += kd_loss.mean().item()
total_losses[ix] += teacher_da_grl_loss.mean().item() + kd_loss.mean().item()
kd_loss.mean().backward()
optimizer_kd.step()
optimizer_kd.zero_grad()
if is_debug:
break
del kd_loss
del teacher_da_grl_loss
# torch.cuda.empty_cache()
return total_losses / len(source_dataloader), teacher_da_temp_losses / len(source_dataloader), \
kd_temp_losses/ len(source_dataloader)
def grl_multi_target_hinton_alt(init_lr_da, init_lr_kd, device, epochs, T, alpha, gamma, growth_rate, init_beta,
source_dloader, targets_dloader, targets_testloader, optimizer_das, optimizer_kd, teacher_models, student_model,
is_scheduler_da=True, is_scheduler_kd=False, scheduler_da=None, scheduler_kd=None, is_debug=False, save_name="", batch_norm=False, is_cst=True, **kwargs):
logger = kwargs["logger"]
if "logger_id" not in kwargs:
logger_id = ""
else:
logger_id = kwargs["logger_id"]
best_student_acc = 0.
best_teacher_acc = 0.
epochs += 1
for epoch in range(1, epochs):
beta = init_beta * torch.exp(growth_rate * (epoch - 1))
beta = beta.to(device)
if is_scheduler_da:
new_lr_da = init_lr_da / np.power((1 + 10 * (epoch - 1) / epochs), 0.75) # 10*
for optimizer_da in optimizer_das:
adjust_learning_rate(optimizer_da, new_lr_da)
if is_scheduler_kd:
new_lr_kd = init_lr_kd / np.power((1 + 10 * (epoch - 1) / epochs), 0.75) # 10*
adjust_learning_rate(optimizer_kd, new_lr_kd)
total_losses, da_losses, kd_losses = grl_multi_target_hinton_train_alt(epoch, epochs, teacher_models, student_model, optimizer_das,
optimizer_kd, device, source_dloader, targets_dloader, T,
alpha, beta, gamma, batch_norm, is_cst, is_debug, logger=None)
teachers_targets_acc = np.zeros(len(teacher_models))
students_targets_acc = np.zeros(len(teacher_models))
for i, d in enumerate(targets_testloader):
teachers_targets_acc[i] = eval(teacher_models[i], device, d, is_debug)
students_targets_acc[i] = eval(student_model, device, d, is_debug)
total_student_target_acc = students_targets_acc.mean()
total_teacher_target_acc = teachers_targets_acc.mean()
if total_student_target_acc > best_student_acc:
best_student_acc = total_student_target_acc
torch.save({'student_model': student_model.state_dict(), 'acc': best_student_acc, 'epoch': epoch},
"{}/kd_da_alt_pth_student_best_model.pth".format(save_dir))
if save_name != "":
torch.save(student_model, save_name)
if logger is not None:
logger.log_scalar("beta_epoch".format(logger_id), beta.item(), epoch)
for i in range(len(teacher_models)):
logger.log_scalar("training_loss_t_{}".format(i), total_losses[i].item(), epoch)
for i in range(len(teacher_models)):
logger.log_scalar("da_loss_t_{}".format(i), da_losses[i].item(), epoch)
for i in range(len(teacher_models)):
logger.log_scalar("kd_loss_{}".format(i), kd_losses[i].item(), epoch)
logger.log_scalar("da_lr_epoch".format(logger_id), new_lr_da, epoch)
logger.log_scalar("kd_lr_epoch".format(logger_id), optimizer_kd.param_groups[0]['lr'], epoch)
for i in range(len(teacher_models)):
logger.log_scalar("teacher_{}_val_target_acc".format(i, logger_id), teachers_targets_acc[i], epoch)
logger.log_scalar("student_{}_val_target_1_acc".format(i, logger_id), students_targets_acc[i], epoch)
logger.log_scalar("student_val_target_total_acc".format(logger_id), total_student_target_acc, epoch)
logger.log_scalar("teacher_val_target_total_acc".format(logger_id), total_teacher_target_acc, epoch)
if scheduler_da is not None:
scheduler_da.step()
if scheduler_kd is not None:
scheduler_kd.step()
return best_student_acc
def main():
batch_size = 32
test_batch_size = 32
webcam = os.path.expanduser("~/datasets/webcam/images")
amazon = os.path.expanduser("~/datasets/amazon/images")
dslr = os.path.expanduser("~/datasets/dslr/images")
is_debug = False
epochs = 400
init_lr_da = 0.001
init_lr_kd = 0.001
momentum = 0.9
weight_decay = 5e-4
device = torch.device("cuda")
T = 20
alpha = 0.3
init_beta = 0.1
end_beta = 0.9
student_pretrained = True
if torch.cuda.device_count() > 1:
teacher_model = nn.DataParallel(DAN_model.DANNet_ResNet(ResNet.resnet50, True)).to(device)
student_model = nn.DataParallel(DAN_model.DANNet_ResNet(ResNet.resnet34, student_pretrained)).to(device)
else:
teacher_model = DAN_model.DANNet_ResNet(ResNet.resnet50, True).to(device)
student_model = DAN_model.DANNet_ResNet(ResNet.resnet34, student_pretrained).to(device)
growth_rate = torch.log(torch.FloatTensor([end_beta / init_beta])) / torch.FloatTensor([epochs])
optimizer_da = torch.optim.SGD(list(teacher_model.parameters()) + list(student_model.parameters()), init_lr_da,
momentum=momentum, weight_decay=weight_decay)
optimizer_kd = torch.optim.SGD(list(teacher_model.parameters()) + list(student_model.parameters()), init_lr_kd,
momentum=momentum, weight_decay=weight_decay)
source_dataloader = DA_datasets.office_loader(amazon, batch_size, 1)
target_dataloader = DA_datasets.office_loader(webcam, batch_size, 1)
target_testloader = DA_datasets.office_test_loader(webcam, test_batch_size, 1)
logger = LoggerForSacred(None,None, True)
grl_multi_target_hinton_alt(init_lr_da, device, epochs, T, alpha, growth_rate, init_beta, source_dataloader,
target_dataloader, target_testloader, optimizer_da, optimizer_kd, teacher_model, student_model, logger=logger, scheduler=None, is_debug=False)
if __name__ == "__main__":
main()