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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 torchvision.transforms import Compose
import torch.nn.functional as F
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
class FeatAlignAlg(BaseAlg):
def get_required_models_schema(self) -> Schema:
return Schema({
'main': OnlineFeatAlignModel
})
def get_required_hyp_schema(self) -> Schema:
from schema import Optional
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,
Optional('transform'): Compose,
})
def run(self, scenario: Scenario, hyps: Dict, collate_fn=None) -> 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]
if 'transform' in hyps.keys():
datasets_for_training = scenario.get_online_cur_domain_datasets_for_training(transform=hyps['transform'])
else:
datasets_for_training = scenario.get_online_cur_domain_datasets_for_training()
train_dataset = datasets_for_training[cur_domain_name]['train']
if 'transform' in hyps.keys():
datasets_for_inference = scenario.get_online_cur_domain_datasets_for_inference(transform=hyps['transform'])
else:
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, collate_fn=collate_fn))
# test_loader = build_dataloader(test_dataset, hyps['val_batch_size'], hyps['num_workers'],
# False, False, collate_fn=collate_fn)
source_datasets = [d['train'] for n, d in datasets_for_training.items() if n != cur_domain_name]
source_dataset = MergedDataset(source_datasets)
# source_dataset_val = [d['val'] for n, d in datasets_for_training.items() if n != cur_domain_name][0]
source_train_loader = iter(build_dataloader(source_dataset, hyps['train_batch_size'], hyps['num_workers'],
True, None, collate_fn=collate_fn))
# source_val_loader = build_dataloader(source_dataset_val, hyps['val_batch_size'], hyps['num_workers'],
# False, False, collate_fn=collate_fn)
# 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 = self.models['main'].get_trained_params()
optimizer = torch.optim.__dict__[hyps['optimizer']](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 = []
total_train_time = 0.
feature_hook = self.models['main'].get_feature_hook()
num_test = 256
for iter_index in pbar:
if iter_index % hyps['val_freq'] == 0:
from data import split_dataset
if 'transform' in hyps.keys():
cur_test_batch_dataset = split_dataset(test_dataset, num_test, iter_index, transform=hyps['transform'])[0]
else:
cur_test_batch_dataset = split_dataset(test_dataset, num_test, iter_index)[0]
cur_test_batch_dataloader = build_dataloader(cur_test_batch_dataset, hyps['val_batch_size'], hyps['num_workers'], False, False, collate_fn=collate_fn)
cur_acc = self.models['main'].get_accuracy(cur_test_batch_dataloader)
# cur_source_acc = self.models['main'].get_accuracy(source_val_loader)
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
source_vis_features = source_features[1]
tlist = []
for vis in source_vis_features:
tmp = F.max_pool2d(input=vis, kernel_size=2, stride=2, return_indices=False)
tmp = tmp.view(vis.shape[0], -1)
tlist.append(tmp)
source_vis_features = torch.cat(tlist, dim=1)
source_lang_features = source_features[3]
source_lang_aggregate_feature = source_lang_features['aggregate']
source_lang_embedded_feature = source_lang_features['embedded'].flatten(1)
source_lang_hidden_feature = source_lang_features['hidden'].flatten(1)
source_features = torch.cat([source_vis_features, source_lang_aggregate_feature, source_lang_embedded_feature, source_lang_hidden_feature], dim=1)
self.models['main'].infer(x)
target_features = feature_hook.input
target_vis_features = target_features[1]
tlist = []
for vis in target_vis_features:
tmp = F.max_pool2d(input=vis, kernel_size=2, stride=2, return_indices=False)
tmp = tmp.view(vis.shape[0], -1)
tlist.append(tmp)
target_vis_features = torch.cat(tlist, dim=1)
target_lang_features = target_features[3]
target_lang_aggregate_feature = target_lang_features['aggregate']
target_lang_embedded_feature = target_lang_features['embedded'].flatten(1)
target_lang_hidden_feature = target_lang_features['hidden'].flatten(1)
target_features = torch.cat([target_vis_features, target_lang_aggregate_feature, target_lang_hidden_feature, target_lang_hidden_feature], dim=1)
mmd_loss = self.models['main'].get_mmd_loss(source_features, target_features)
loss = task_loss + hyps['feat_align_loss_weight'] * mmd_loss
optimizer.zero_grad()
loss.backward()
optimizer.step()
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()
if 'transform' in hyps.keys():
cur_test_batch_dataset = split_dataset(test_dataset, num_test, iter_index + 1, transform=hyps['transform'])[0]
else:
cur_test_batch_dataset = split_dataset(test_dataset, num_test, iter_index + 1)[0]
cur_test_batch_dataloader = build_dataloader(cur_test_batch_dataset, hyps['val_batch_size'], hyps['num_workers'], False, False, collate_fn=collate_fn)
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 |