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import os
from typing import Union, Optional
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 models 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.device = system_config['device']
self.save_dir = system_config['save_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.rgb_pn: Optional[RGBPartNet] = None
self.optimizer: Optional[optim.Adam] = None
self.scheduler: Optional[optim.lr_scheduler.StepLR] = None
@property
def signature(self) -> str:
return '_'.join((self._model_sig, str(self.curr_iter), self._hp_sig,
self._dataset_sig, str(self.batch_size)))
@property
def batch_size(self) -> int:
if self.is_train:
if self.pr and self.k:
return self.pr * self.k
raise AttributeError('No dataset loaded')
else:
return 1
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.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(os.path.join(self.save_dir, self.signature))
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 (x_c1, x_c2) in dataloader:
self.curr_iter += 1
# Zero the parameter gradients
self.optimizer.zero_grad()
# forward + backward + optimize
loss, metrics = self.rgb_pn(x_c1['clip'], x_c2['clip'],
x_c1['label'])
loss.backward()
self.optimizer.step()
# Step scheduler
self.scheduler.step(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())
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,
}, os.path.join(self.save_dir, self.signature))
if self.curr_iter == self.total_iter:
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['train_size']
self.in_channels = dataset_config['num_input_channels']
self._dataset_sig = self._make_signature(
dataset_config,
popped_keys=['root_dir', 'cache_on']
)
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')
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])
@staticmethod
def _make_signature(config: dict,
popped_keys: Optional[list] = None) -> str:
_config = config.copy()
for (key, value) in config.items():
if popped_keys and key in popped_keys:
_config.pop(key)
continue
if isinstance(value, str):
pass
elif isinstance(value, (tuple, list)):
_config[key] = '_'.join([str(v) for v in value])
else:
_config[key] = str(value)
return '_'.join(_config.values())
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