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import os
from typing import Union, Optional, Tuple, List, Dict, Set
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torch.utils.data.dataloader import default_collate
from torch.utils.tensorboard import SummaryWriter
from models.rgb_part_net import RGBPartNet
from utils.configuration import DataloaderConfiguration, \
HyperparameterConfiguration, DatasetConfiguration, ModelConfiguration, \
SystemConfiguration
from utils.dataset import CASIAB
from utils.sampler import TripletSampler
class Model:
def __init__(
self,
system_config: SystemConfiguration,
model_config: ModelConfiguration,
hyperparameter_config: HyperparameterConfiguration
):
self.disable_acc = system_config['disable_acc']
if self.disable_acc:
self.device = torch.device('cpu')
else: # Enable accelerator
if torch.cuda.is_available():
self.device = torch.device('cuda')
else:
print('No accelerator available, fallback to CPU.')
self.device = torch.device('cpu')
self.save_dir = system_config['save_dir']
self.checkpoint_dir = os.path.join(self.save_dir, 'checkpoint')
self.log_dir = os.path.join(self.save_dir, 'logs')
for dir_ in (self.save_dir, self.log_dir, self.checkpoint_dir):
if not os.path.exists(dir_):
os.mkdir(dir_)
self.meta = model_config
self.hp = hyperparameter_config
self.curr_iter = self.meta['restore_iter']
self.total_iter = self.meta['total_iter']
self.is_train: bool = True
self.train_size: int = 74
self.in_channels: int = 3
self.pr: Optional[int] = None
self.k: Optional[int] = None
self._model_sig: str = self._make_signature(self.meta, ['restore_iter'])
self._hp_sig: str = self._make_signature(self.hp)
self._dataset_sig: str = 'undefined'
self._log_sig: str = '_'.join((self._model_sig, self._hp_sig))
self.log_name: str = os.path.join(self.log_dir, self._log_sig)
self.rgb_pn: Optional[RGBPartNet] = None
self.optimizer: Optional[optim.Adam] = None
self.scheduler: Optional[optim.lr_scheduler.StepLR] = None
self.writer: Optional[SummaryWriter] = None
@property
def signature(self) -> str:
return '_'.join((self._model_sig, str(self.curr_iter), self._hp_sig,
self._dataset_sig, str(self.pr), str(self.k)))
@property
def checkpoint_name(self) -> str:
return os.path.join(self.checkpoint_dir, self.signature)
def fit(
self,
dataset_config: DatasetConfiguration,
dataloader_config: DataloaderConfiguration,
):
self.is_train = True
dataset = self._parse_dataset_config(dataset_config)
dataloader = self._parse_dataloader_config(dataset, dataloader_config)
# Prepare for model, optimizer and scheduler
hp = self.hp.copy()
lr, betas = hp.pop('lr', 1e-4), hp.pop('betas', (0.9, 0.999))
self.rgb_pn = RGBPartNet(self.train_size, self.in_channels, **hp)
self.optimizer = optim.Adam(self.rgb_pn.parameters(), lr, betas)
self.scheduler = optim.lr_scheduler.StepLR(self.optimizer, 500, 0.9)
self.writer = SummaryWriter(self.log_name)
if not self.disable_acc:
if torch.cuda.device_count() > 1:
self.rgb_pn = nn.DataParallel(self.rgb_pn)
self.rgb_pn = self.rgb_pn.to(self.device)
self.rgb_pn.train()
# Init weights at first iter
if self.curr_iter == 0:
self.rgb_pn.apply(self.init_weights)
else: # Load saved state dicts
checkpoint = torch.load(self.checkpoint_name)
iter_, loss = checkpoint['iter'], checkpoint['loss']
print('{0:5d} loss: {1:.3f}'.format(iter_, loss))
self.rgb_pn.load_state_dict(checkpoint['model_state_dict'])
self.optimizer.load_state_dict(checkpoint['optim_state_dict'])
for (batch_c1, batch_c2) in dataloader:
self.curr_iter += 1
# Zero the parameter gradients
self.optimizer.zero_grad()
# forward + backward + optimize
x_c1 = batch_c1['clip'].to(self.device)
x_c2 = batch_c2['clip'].to(self.device)
y = batch_c1['label'].to(self.device)
loss, metrics = self.rgb_pn(x_c1, x_c2, y)
loss.backward()
self.optimizer.step()
# Step scheduler
self.scheduler.step()
# Write losses to TensorBoard
self.writer.add_scalar('Loss/all', loss.item(), self.curr_iter)
self.writer.add_scalars('Loss/details', dict(zip([
'Cross reconstruction loss', 'Pose similarity loss',
'Canonical consistency loss', 'Batch All triplet loss'
], metrics)), self.curr_iter)
if self.curr_iter % 100 == 0:
print('{0:5d} loss: {1:.3f}'.format(self.curr_iter, loss),
'(xrecon = {:f}, pose_sim = {:f},'
' cano_cons = {:f}, ba_trip = {:f})'.format(*metrics),
'lr:', self.scheduler.get_last_lr()[0])
if self.curr_iter % 1000 == 0:
torch.save({
'iter': self.curr_iter,
'model_state_dict': self.rgb_pn.state_dict(),
'optim_state_dict': self.optimizer.state_dict(),
'loss': loss,
}, self.checkpoint_name)
if self.curr_iter == self.total_iter:
self.writer.close()
break
@staticmethod
def init_weights(m):
if isinstance(m, nn.modules.conv._ConvNd):
nn.init.normal_(m.weight, 0.0, 0.01)
elif isinstance(m, nn.modules.batchnorm._NormBase):
nn.init.normal_(m.weight, 1.0, 0.01)
nn.init.zeros_(m.bias)
elif isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight)
elif isinstance(m, RGBPartNet):
nn.init.xavier_uniform_(m.fc_mat)
def _parse_dataset_config(
self,
dataset_config: DatasetConfiguration
) -> Union[CASIAB]:
self.train_size = dataset_config.get('train_size', 74)
self.in_channels = dataset_config.get('num_input_channels', 3)
self._dataset_sig = self._make_signature(
dataset_config,
popped_keys=['root_dir', 'cache_on']
)
self.log_name = '_'.join((self.log_name, self._dataset_sig))
config: Dict = dataset_config.copy()
name = config.pop('name')
if name == 'CASIA-B':
return CASIAB(**config, is_train=self.is_train)
elif name == 'FVG':
# TODO
pass
raise ValueError('Invalid dataset: {0}'.format(name))
def _parse_dataloader_config(
self,
dataset: Union[CASIAB],
dataloader_config: DataloaderConfiguration
) -> DataLoader:
config: Dict = dataloader_config.copy()
if self.is_train:
(self.pr, self.k) = config.pop('batch_size')
self.log_name = '_'.join((self.log_name, str(self.pr), str(self.k)))
triplet_sampler = TripletSampler(dataset, (self.pr, self.k))
return DataLoader(dataset,
batch_sampler=triplet_sampler,
collate_fn=self._batch_splitter,
**config)
else: # is_test
config.pop('batch_size')
return DataLoader(dataset, **config)
def _batch_splitter(
self,
batch: List[Dict[str, Union[np.int64, str, torch.Tensor]]]
) -> Tuple[Dict[str, Union[List[str], torch.Tensor]],
Dict[str, Union[List[str], torch.Tensor]]]:
"""
Disentanglement need two random conditions, this function will
split pr * k * 2 samples to 2 dicts each containing pr * k
samples. labels and clip data are tensor, and others are list.
"""
_batch = [[], []]
for i in range(0, self.pr * self.k * 2, self.k * 2):
_batch[0] += batch[i:i + self.k]
_batch[1] += batch[i + self.k:i + self.k * 2]
return default_collate(_batch[0]), default_collate(_batch[1])
def _make_signature(self,
config: Dict,
popped_keys: Optional[List] = None) -> str:
_config = config.copy()
if popped_keys:
for key in popped_keys:
_config.pop(key)
return self._gen_sig(list(_config.values()))
def _gen_sig(self, values: Union[Tuple, List, Set, str, int, float]) -> str:
strings = []
for v in values:
if isinstance(v, str):
strings.append(v)
elif isinstance(v, (Tuple, List, Set)):
strings.append(self._gen_sig(v))
elif isinstance(v, Dict):
strings.append(self._gen_sig(list(v.values())))
else:
strings.append(str(v))
return '_'.join(strings)
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