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import argparse
import os.path
from dataclasses import dataclass
from pathlib import Path
from typing import Iterable, Callable
import sys
import torch
import yaml
from torch.utils.data import Dataset
from torchvision.datasets import CIFAR10, CIFAR100, ImageNet
from torchvision.transforms import transforms
path = str(Path(Path(__file__).parent.absolute()).parent.absolute())
sys.path.insert(0, path)
from libs.optimizers import LARS
from libs.logging import Loggers, BaseBatchLogRecord, BaseEpochLogRecord
from libs.utils import BaseConfig
from simclr.main import SimCLRTrainer, SimCLRConfig
from simclr.models import CIFARSimCLRResNet50, ImageNetSimCLRResNet50, CIFARSimCLRViTTiny
def parse_args_and_config():
parser = argparse.ArgumentParser(
description='Contrastive baseline SimCLR (evaluation)',
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument('--codename', default='cifar10-simclr-linear',
type=str, help="Model descriptor")
parser.add_argument('--log-dir', default='logs', type=str,
help="Path to log directory")
parser.add_argument('--checkpoint-dir', default='checkpoints', type=str,
help="Path to checkpoints directory")
parser.add_argument('--seed', default=None, type=int,
help='Random seed for reproducibility')
parser.add_argument('--num-iters', default=50, type=int,
help='Number of iters')
parser.add_argument('--config', type=argparse.FileType(mode='r'),
help='Path to config file (optional)')
# TODO: Add model hyperparams dataclass
parser.add_argument('--encoder', default='resnet', type=str,
choices=('resnet', 'vit'),
help='Backbone of encoder')
parser.add_argument('--label-smooth', default=0., type=float,
help='Label smoothing in cross entropy')
parser.add_argument('--hid-dim', default=2048, type=int,
help='Number of dimension of embedding')
parser.add_argument('--out-dim', default=128, type=int,
help='Number of dimension after projection')
parser.add_argument('--pretrained-checkpoint', type=str, required=True,
help='Pretrained checkpoint location')
parser.add_argument('--finetune', default=False,
action=argparse.BooleanOptionalAction,
help='Finetune backbone or linear head only')
dataset_group = parser.add_argument_group('Dataset parameters')
dataset_group.add_argument('--dataset-dir', default='dataset', type=str,
help="Path to dataset directory")
dataset_group.add_argument('--dataset', default='cifar10', type=str,
choices=('cifar10, cifar100', 'imagenet'),
help="Name of dataset")
dataset_group.add_argument('--train-size', default=32, type=int,
help='Resize during training')
dataset_group.add_argument('--test-size', default=32, type=int,
help='Resize during testing')
dataset_group.add_argument('--test-crop-size', default=32, type=int,
help='Center crop size during testing')
dataset_group.add_argument('--hflip-prob', default=0.5, type=float,
help='Random horizontal flip probability')
dataloader_group = parser.add_argument_group('Dataloader parameters')
dataloader_group.add_argument('--batch-size', default=256, type=int,
help='Batch size')
dataloader_group.add_argument('--num-workers', default=2, type=int,
help='Number of dataloader processes')
optim_group = parser.add_argument_group('Optimizer parameters')
optim_group.add_argument('--optim', default='sgd', type=str,
choices=('adam', 'sgd', 'lars'),
help="Name of optimizer")
optim_group.add_argument('--lr', default=1e-3, type=float,
help='Learning rate')
optim_group.add_argument('--betas', nargs=2, default=(0.9, 0.999), type=float,
help='Adam betas', metavar=('beta1', 'beta2'))
optim_group.add_argument('--momentum', default=0.9, type=float,
help='SDG momentum')
optim_group.add_argument('--weight-decay', default=0., type=float,
help='Weight decay (l2 regularization)')
sched_group = parser.add_argument_group('Scheduler parameters')
sched_group.add_argument('--sched', default=None, type=str,
choices=('const', None, 'linear', 'warmup-anneal'),
help="Name of scheduler")
sched_group.add_argument('--warmup-iters', default=5, type=int,
help='Epochs for warmup (`warmup-anneal` scheduler only)')
args = parser.parse_args()
if args.config:
config = yaml.safe_load(args.config)
args.__dict__ |= {
k: tuple(v) if isinstance(v, list) else v
for k, v in config.items()
}
args.checkpoint_dir = os.path.join(args.checkpoint_dir, args.codename)
args.log_dir = os.path.join(args.log_dir, args.codename)
return args
@dataclass
class SimCLREvalConfig(SimCLRConfig):
@dataclass
class DatasetConfig(BaseConfig.DatasetConfig):
dataset_dir: str
train_size: int
test_size: int
test_crop_size: int
hflip_prob: float
class SimCLREvalTrainer(SimCLRTrainer):
def __init__(self, pretrained_checkpoint, finetune, **kwargs):
self.pretrained_checkpoint = pretrained_checkpoint
self.finetune = finetune
super(SimCLREvalTrainer, self).__init__(**kwargs)
@dataclass
class BatchLogRecord(BaseBatchLogRecord):
lr: float
train_loss: float
@dataclass
class EpochLogRecord(BaseEpochLogRecord):
eval_loss: float
eval_accuracy: float
@staticmethod
def _prepare_dataset(dataset_config: SimCLREvalConfig.DatasetConfig) -> tuple[Dataset, Dataset]:
train_transform = transforms.Compose([
transforms.RandomHorizontalFlip(dataset_config.hflip_prob),
transforms.Resize(dataset_config.train_size),
transforms.ToTensor(),
])
test_transform = transforms.Compose([
transforms.Resize(dataset_config.test_size),
transforms.CenterCrop(dataset_config.test_crop_size),
transforms.ToTensor(),
])
if dataset_config.dataset in {'cifar10', 'cifar100', 'cifar'}:
if dataset_config.dataset in {'cifar10', 'cifar'}:
train_set = CIFAR10(dataset_config.dataset_dir, train=True,
transform=train_transform, download=True)
test_set = CIFAR10(dataset_config.dataset_dir, train=False,
transform=test_transform)
else: # CIFAR-100
train_set = CIFAR100(dataset_config.dataset_dir, train=True,
transform=train_transform, download=True)
test_set = CIFAR100(dataset_config.dataset_dir, train=False,
transform=test_transform)
elif dataset_config.dataset in {'imagenet1k', 'imagenet'}:
train_set = ImageNet(dataset_config.dataset_dir, 'train',
transform=train_transform)
test_set = ImageNet(dataset_config.dataset_dir, 'val',
transform=test_transform)
else:
raise NotImplementedError(f"Unimplemented dataset: '{dataset_config.dataset}")
return train_set, test_set
def _init_models(self, dataset: str) -> Iterable[tuple[str, torch.nn.Module]]:
if dataset in {'cifar10', 'cifar100', 'cifar'}:
if self.encoder == 'resnet':
backbone = CIFARSimCLRResNet50(self.hid_dim, pretrain=False)
elif self.encoder == 'vit':
backbone = CIFARSimCLRViTTiny(self.hid_dim, pretrain=False)
else:
raise NotImplementedError(f"Unimplemented encoder: '{self.encoder}")
if dataset in {'cifar10', 'cifar'}:
classifier = torch.nn.Linear(self.hid_dim, 10)
else:
classifier = torch.nn.Linear(self.hid_dim, 100)
elif dataset in {'imagenet1k', 'imagenet'}:
if self.encoder == 'resnet':
backbone = ImageNetSimCLRResNet50(self.hid_dim, pretrain=False)
else:
raise NotImplementedError(f"Unimplemented encoder: '{self.encoder}")
classifier = torch.nn.Linear(self.hid_dim, 1000)
else:
raise NotImplementedError(f"Unimplemented dataset: '{dataset}")
yield 'backbone', backbone
yield 'classifier', classifier
def _custom_init_fn(self, config: SimCLREvalConfig):
self.optims = {n: LARS(o) if config.optim_config.optim == 'lars' else o
for n, o in self.optims.items()}
if self.restore_iter == 0:
pretrained_checkpoint = torch.load(self.pretrained_checkpoint)
backbone_checkpoint = pretrained_checkpoint['model_state_dict']
backbone_state_dict = {k: v for k, v in backbone_checkpoint.items()
if k in self.models['backbone'].state_dict()}
self.models['backbone'].load_state_dict(backbone_state_dict)
def train(self, num_iters: int, loss_fn: Callable, logger: Loggers, device: torch.device):
backbone, classifier = self.models.values()
optim_b, optim_c = self.optims.values()
sched_b, sched_c = self.scheds.values()
loader_size = len(self.train_loader)
num_batches = num_iters * loader_size
for iter_ in range(self.restore_iter, num_iters):
if self.finetune:
backbone.train()
else:
backbone.eval()
classifier.train()
for batch, (images, targets) in enumerate(self.train_loader):
global_batch = iter_ * loader_size + batch
images, targets = images.to(device), targets.to(device)
classifier.zero_grad()
if self.finetune:
backbone.zero_grad()
embedding = backbone(images)
else:
with torch.no_grad():
embedding = backbone(images)
logits = classifier(embedding)
train_loss = loss_fn(logits, targets)
train_loss.backward()
if self.finetune:
optim_b.step()
optim_c.step()
self.log(logger, self.BatchLogRecord(
batch, num_batches, global_batch, iter_, num_iters,
optim_c.param_groups[0]['lr'], train_loss.item()
))
if (iter_ + 1) % (num_iters // 10) == 0:
metrics = torch.Tensor(list(self.eval(loss_fn, device))).mean(0)
eval_loss = metrics[0].item()
eval_accuracy = metrics[1].item()
epoch_log = self.EpochLogRecord(iter_, num_iters,
eval_loss, eval_accuracy)
self.log(logger, epoch_log)
self.save_checkpoint(epoch_log)
if sched_b is not None and self.finetune:
sched_b.step()
if sched_c is not None:
sched_c.step()
def eval(self, loss_fn: Callable, device: torch.device):
backbone, classifier = self.models.values()
backbone.eval()
classifier.eval()
with torch.no_grad():
for images, targets in self.test_loader:
images, targets = images.to(device), targets.to(device)
embedding = backbone(images)
logits = classifier(embedding)
loss = loss_fn(logits, targets)
prediction = logits.argmax(1)
accuracy = (prediction == targets).float().mean()
yield loss.item(), accuracy.item()
if __name__ == '__main__':
args = parse_args_and_config()
config = SimCLREvalConfig.from_args(args)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
trainer = SimCLREvalTrainer(
seed=args.seed,
checkpoint_dir=args.checkpoint_dir,
device=device,
inf_mode=False,
num_iters=args.num_iters,
config=config,
encoder=args.encoder,
hid_dim=args.hid_dim,
out_dim=args.out_dim,
pretrained_checkpoint=args.pretrained_checkpoint,
finetune=args.finetune,
)
loggers = trainer.init_logger(args.log_dir)
loss_fn = torch.nn.CrossEntropyLoss(label_smoothing=args.label_smooth)
trainer.train(args.num_iters, loss_fn, loggers, device)
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