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from typing import Any, Dict, List
from schema import Schema
from data import Scenario, MergedDataset
from methods.base.alg import BaseAlg
from methods.base.model import BaseModel
from data import build_dataloader
import torch.optim
import tqdm
import os
import time
from abc import abstractmethod
import matplotlib.pyplot as plt
from copy import deepcopy
from torch import nn
import torch.optim
def tent_as_detector(online_model, x, num_iters=1, lr=1e-4, l1_wd=0., strategy='ours'):
model = online_model.models_dict['main']
before_model = deepcopy(model)
# from methods.tent import tent
optimizer = torch.optim.SGD(
model.parameters(), lr=lr, weight_decay=l1_wd)
# from .tent import configure_model, forward_and_adapt
# configure_model(model)
output = online_model.infer(x)
entropy = online_model.get_output_entropy(output).mean()
entropy.backward()
# for _ in range(num_iters):
# forward_and_adapt(x, model, optimizer)
# entropy_loss = model.
filters_sen_info = {}
last_conv_name = None
for (name, m1), m2 in zip(model.named_modules(), before_model.modules()):
if isinstance(m1, nn.Linear):
last_conv_name = name
if not isinstance(m1, nn.LayerNorm):
continue
with torch.no_grad():
features_weight_diff = ((m1.weight.data - m2.weight.data).abs())
features_bias_diff = ((m1.bias.data - m2.bias.data).abs())
features_diff = features_weight_diff + features_bias_diff
features_diff_order = features_diff.argsort(descending=False)
if strategy == 'ours':
untrained_filters_index = features_diff_order[: int(len(features_diff) * 0.8)]
elif strategy == 'random':
untrained_filters_index = torch.randperm(len(features_diff))[: int(len(features_diff) * 0.8)]
elif strategy == 'inversed_ours':
untrained_filters_index = features_diff_order.flip(0)[: int(len(features_diff) * 0.8)]
elif strategy == 'none':
untrained_filters_index = None
filters_sen_info[name] = dict(untrained_filters_index=untrained_filters_index, conv_name=last_conv_name)
return filters_sen_info
class SGDF(torch.optim.SGD):
@torch.no_grad()
def step(self, p_names, conv_filters_sen_info, filters_sen_info, closure=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
weight_decay = group['weight_decay']
momentum = group['momentum']
dampening = group['dampening']
nesterov = group['nesterov']
# assert len([i for i in model.named_parameters()]) == len([j for j in group['params']])
for name, p in zip(p_names, group['params']):
if p.grad is None:
continue
layer_name = '.'.join(name.split('.')[0:-1])
if layer_name in filters_sen_info.keys():
untrained_filters_index = filters_sen_info[layer_name]['untrained_filters_index']
elif layer_name in conv_filters_sen_info.keys():
untrained_filters_index = conv_filters_sen_info[layer_name]['untrained_filters_index']
else:
untrained_filters_index = []
d_p = p.grad
if weight_decay != 0:
d_p = d_p.add(p, alpha=weight_decay)
if momentum != 0:
param_state = self.state[p]
if 'momentum_buffer' not in param_state:
buf = param_state['momentum_buffer'] = torch.clone(d_p).detach()
else:
buf = param_state['momentum_buffer']
buf.mul_(momentum).add_(d_p, alpha=1 - dampening)
if nesterov:
d_p = d_p.add(buf, alpha=momentum)
else:
d_p = buf
try:
d_p[untrained_filters_index] = 0.
p.add_(d_p, alpha=-group['lr'])
except Exception as e:
print('SGDF error', name)
return loss
class OnlineFeatAlignModel(BaseModel):
def get_required_model_components(self) -> List[str]:
return ['main']
@abstractmethod
def get_feature_hook(self):
pass
@abstractmethod
def forward_to_get_task_loss(self, x, y):
pass
@abstractmethod
def get_trained_params(self):
pass
@abstractmethod
def get_mmd_loss(self, f1, f2):
pass
@abstractmethod
def get_output_entropy(self, output):
pass
class FeatAlignAlg(BaseAlg):
def get_required_models_schema(self) -> Schema:
return Schema({
'main': OnlineFeatAlignModel
})
def get_required_hyp_schema(self) -> Schema:
return Schema({
'train_batch_size': int,
'val_batch_size': int,
'num_workers': int,
'optimizer': str,
'optimizer_args': dict,
'scheduler': str,
'scheduler_args': dict,
'num_iters': int,
'val_freq': int,
'feat_align_loss_weight': float,
'trained_neuron_selection_strategy': str
})
def run(self, scenario: Scenario, hyps: Dict) -> Dict[str, Any]:
super().run(scenario, hyps)
assert isinstance(self.models['main'], OnlineFeatAlignModel) # for auto completion
cur_domain_name = scenario.target_domains_order[scenario.cur_domain_index]
datasets_for_training = scenario.get_online_cur_domain_datasets_for_training()
train_dataset = datasets_for_training[cur_domain_name]['train']
val_dataset = datasets_for_training[cur_domain_name]['val']
datasets_for_inference = scenario.get_online_cur_domain_datasets_for_inference()
test_dataset = datasets_for_inference
train_loader = iter(build_dataloader(train_dataset, hyps['train_batch_size'], hyps['num_workers'],
True, None))
test_loader = build_dataloader(test_dataset, hyps['val_batch_size'], hyps['num_workers'],
False, False)
source_datasets = [d['train'] for n, d in datasets_for_training.items() if n != cur_domain_name]
source_dataset = MergedDataset(source_datasets)
source_train_loader = iter(build_dataloader(source_dataset, hyps['train_batch_size'], hyps['num_workers'],
True, None))
# 1. generate surrogate DNN
# for n, m in self.models['main'].models_dict['md'].named_modules():
# if isinstance(m, nn.Linear):
# m.reset_parameters()
# from utils.dl.common.model import set_module
# for n, m in self.models['main'].models_dict['md'].named_modules():
# if m.__class__.__name__ == 'KTakesAll':
# set_module(self.models['main'].models_dict['md'], n, KTakesAll(0.5))
# self.models['main'].set_sd_sparsity(hyps['sd_sparsity'])
device = self.models['main'].device
# surrogate_dnn = self.models['main'].generate_sd_by_target_samples(next(train_loader)[0].to(device))
# self.models['sd'] = surrogate_dnn
# 2. train surrogate DNN
# TODO: train only a part of filters
trained_params, p_name = self.models['main'].get_trained_params()
# optimizer = torch.optim.__dict__[hyps['optimizer']](trained_params, **hyps['optimizer_args'])
optimizer = SGDF(trained_params, **hyps['optimizer_args'])
if hyps['scheduler'] != '':
scheduler = torch.optim.lr_scheduler.__dict__[hyps['scheduler']](optimizer, **hyps['scheduler_args'])
else:
scheduler = None
pbar = tqdm.tqdm(range(hyps['num_iters']), dynamic_ncols=True, desc='da...')
task_losses, mmd_losses = [], []
accs = []
x, _ = next(train_loader)
if isinstance(x, dict):
for k, v in x.items():
if isinstance(v, torch.Tensor):
x[k] = v.to(device)
else:
x = x.to(device)
filters_sen_info = tent_as_detector(self.models['main'], x, strategy=hyps['trained_neuron_selection_strategy'])
conv_filters_sen_info = {v['conv_name']: v for _, v in filters_sen_info.items()}
total_train_time = 0.
feature_hook = self.models['main'].get_feature_hook()
for iter_index in pbar:
if iter_index % hyps['val_freq'] == 0:
from data import split_dataset
cur_test_batch_dataset = split_dataset(test_dataset, hyps['val_batch_size'], iter_index)[0]
cur_test_batch_dataloader = build_dataloader(cur_test_batch_dataset, hyps['train_batch_size'], hyps['num_workers'], False, False)
cur_acc = self.models['main'].get_accuracy(cur_test_batch_dataloader)
accs += [{
'iter': iter_index,
'acc': cur_acc
}]
cur_start_time = time.time()
self.models['main'].to_train_mode()
x, _ = next(train_loader)
if isinstance(x, dict):
for k, v in x.items():
if isinstance(v, torch.Tensor):
x[k] = v.to(device)
else:
x = x.to(device)
source_x, source_y = next(source_train_loader)
if isinstance(source_x, dict):
for k, v in source_x.items():
if isinstance(v, torch.Tensor):
source_x[k] = v.to(device)
source_y = source_y.to(device)
else:
source_x, source_y = source_x.to(device), source_y.to(device)
task_loss = self.models['main'].forward_to_get_task_loss(source_x, source_y)
source_features = feature_hook.input
self.models['main'].infer(x)
target_features = feature_hook.input
mmd_loss = hyps['feat_align_loss_weight'] * self.models['main'].get_mmd_loss(source_features, target_features)
loss = task_loss + mmd_loss
optimizer.zero_grad()
loss.backward()
# optimizer.step()
optimizer.step(p_name, conv_filters_sen_info, filters_sen_info)
if scheduler is not None:
scheduler.step()
pbar.set_description(f'da... | cur_acc: {cur_acc:.4f}, task_loss: {task_loss:.6f}, mmd_loss: {mmd_loss:.6f}')
task_losses += [float(task_loss.cpu().item())]
mmd_losses += [float(mmd_loss.cpu().item())]
total_train_time += time.time() - cur_start_time
feature_hook.remove()
time_usage = total_train_time
plt.plot(task_losses, label='task')
plt.plot(mmd_losses, label='mmd')
plt.xlabel('iteration')
plt.ylabel('loss')
plt.savefig(os.path.join(self.res_save_dir, 'loss.png'))
plt.clf()
cur_test_batch_dataset = split_dataset(test_dataset, hyps['train_batch_size'], iter_index + 1)[0]
cur_test_batch_dataloader = build_dataloader(cur_test_batch_dataset, len(cur_test_batch_dataset), hyps['num_workers'], False, False)
cur_acc = self.models['main'].get_accuracy(cur_test_batch_dataloader)
accs += [{
'iter': iter_index + 1,
'acc': cur_acc
}]
return {
'accs': accs,
'time': time_usage
}, self.models |