def init_encoder(self):
dummy_batch = torch.zeros((2, self.hparams.image_channels, self.hparams.image_height,
self.hparams.image_height))
encoder_name = self.hparams.encoder
if encoder_name == 'amdim_encoder':
encoder = AMDIMEncoder(
dummy_batch,
num_channels=self.hparams.image_channels,
encoder_feature_dim=self.hparams.encoder_feature_dim,
embedding_fx_dim=self.hparams.embedding_fx_dim,
conv_block_depth=self.hparams.conv_block_depth,
encoder_size=self.hparams.image_height,
use_bn=self.hparams.use_bn
)
encoder.init_weights()
return encoder
else:
return torchvision_ssl_encoder(encoder_name, return_all_feature_maps=True)
def __init__(self, hparams):
super().__init__()
self.hparams = hparams
dummy_batch = torch.zeros(
(2, 3, hparams.image_height, hparams.image_height))
self.encoder = AMDIMEncoder(dummy_batch,
num_channels=3,
ndf=hparams.ndf,
n_rkhs=hparams.n_rkhs,
n_depth=hparams.n_depth,
encoder_size=hparams.image_height,
use_bn=hparams.use_bn)
self.encoder.init_weights()
# the loss has learnable parameters
self.nce_loss = AMDIMLossNCE(tclip=self.hparams.tclip)
self.tng_split = None
self.val_split = None
class AMDIM(pl.LightningModule):
def __init__(self, hparams):
super().__init__()
self.hparams = hparams
dummy_batch = torch.zeros(
(2, 3, hparams.image_height, hparams.image_height))
self.encoder = AMDIMEncoder(dummy_batch,
num_channels=3,
ndf=hparams.ndf,
n_rkhs=hparams.n_rkhs,
n_depth=hparams.n_depth,
encoder_size=hparams.image_height,
use_bn=hparams.use_bn)
self.encoder.init_weights()
# the loss has learnable parameters
self.nce_loss = AMDIMLossNCE(tclip=self.hparams.tclip)
self.tng_split = None
self.val_split = None
def forward(self, img_1, img_2):
# feats for img 1
# r1 = last layer out
# r5 = last layer with (b, c, 5, 5) size
# r7 = last layer with (b, c, 7, 7) size
r1_x1, r5_x1, r7_x1 = self.encoder(img_1)
# feats for img 2
r1_x2, r5_x2, r7_x2 = self.encoder(img_2)
# first number = resnet block. second = image 1 or 2
return r1_x1, r5_x1, r7_x1, r1_x2, r5_x2, r7_x2
def training_step(self, batch, batch_nb):
[img_1, img_2], _ = batch
# ------------------
# FEATURE EXTRACTION
# extract features from various blocks for each image
# _x1 are from image 1
# _x2 from image 2
r1_x1, r5_x1, r7_x1, r1_x2, r5_x2, r7_x2 = self.forward(img_1, img_2)
result = {
'r1_x1': r1_x1,
'r5_x1': r5_x1,
'r7_x1': r7_x1,
'r1_x2': r1_x2,
'r5_x2': r5_x2,
'r7_x2': r7_x2,
}
return result
def training_end(self, outputs):
r1_x1 = outputs['r1_x1']
r5_x1 = outputs['r5_x1']
r7_x1 = outputs['r7_x1']
r1_x2 = outputs['r1_x2']
r5_x2 = outputs['r5_x2']
r7_x2 = outputs['r7_x2']
# ------------------
# NCE LOSS
loss_1t5, loss_1t7, loss_5t5, lgt_reg = self.nce_loss(
r1_x1, r5_x1, r7_x1, r1_x2, r5_x2, r7_x2)
unsupervised_loss = loss_1t5 + loss_1t7 + loss_5t5 + lgt_reg
# ------------------
# FULL LOSS
total_loss = unsupervised_loss
tensorboard_logs = {'train_nce_loss': total_loss}
result = {'loss': total_loss, 'log': tensorboard_logs}
return result
def validation_step(self, batch, batch_nb):
[img_1, img_2], labels = batch
# generate features
r1_x1, r5_x1, r7_x1, r1_x2, r5_x2, r7_x2 = self.forward(img_1, img_2)
# NCE LOSS
loss_1t5, loss_1t7, loss_5t5, lgt_reg = self.nce_loss(
r1_x1, r5_x1, r7_x1, r1_x2, r5_x2, r7_x2)
unsupervised_loss = loss_1t5 + loss_1t7 + loss_5t5 + lgt_reg
result = {'val_nce': unsupervised_loss}
return result
def validation_epoch_end(self, outputs):
val_nce = 0
for output in outputs:
val_nce += output['val_nce']
val_nce = val_nce / len(outputs)
tensorboard_logs = {'val_nce': val_nce}
return {'val_loss': val_nce, 'log': tensorboard_logs}
def configure_optimizers(self):
opt = optim.Adam(params=self.parameters(),
lr=self.hparams.learning_rate,
betas=(0.8, 0.999),
weight_decay=1e-5,
eps=1e-7)
if self.hparams.dataset_name in ['CIFAR10', 'stl_10', 'CIFAR100']:
lr_scheduler = MultiStepLR(opt, milestones=[250, 280], gamma=0.2)
else:
lr_scheduler = MultiStepLR(opt, milestones=[30, 45], gamma=0.2)
return opt # [opt], [lr_scheduler]
def train_dataloader(self):
if self.hparams.dataset_name == 'CIFAR10':
dataset = AMDIMPretraining.cifar10_train(self.hparams.data_dir)
if self.hparams.dataset_name == 'stl_10':
self.tng_split, self.val_split = AMDIMPretraining.stl_train(
self.hparams.data_dir)
dataset = self.tng_split
if self.hparams.dataset_name == 'imagenet_128':
dataset = AMDIMPretraining.imagenet_train(self.hparams.data_dir,
self.hparams.nb_classes)
# LOADER
loader = DataLoader(
dataset=dataset,
batch_size=self.hparams.batch_size,
pin_memory=True,
drop_last=True,
num_workers=16,
)
return loader
def val_dataloader(self):
if self.hparams.dataset_name == 'CIFAR10':
dataset = AMDIMPretraining.cifar10_val(self.hparams.data_dir)
if self.hparams.dataset_name == 'stl_10':
dataset = self.val_split
if self.hparams.dataset_name == 'imagenet_128':
dataset = AMDIMPretraining.imagenet_val(self.hparams.data_dir,
self.hparams.nb_classes)
# LOADER
loader = DataLoader(
dataset=dataset,
batch_size=self.hparams.batch_size,
pin_memory=True,
drop_last=True,
num_workers=16,
)
return loader
@staticmethod
def add_model_specific_args(parent_parser):
parser = ArgumentParser(parents=[parent_parser], add_help=False)
# CIFAR 10
cf_root_lr = 2e-4
cifar_10 = {
'dataset_name':
'CIFAR10',
'ndf':
320,
'n_rkhs':
1280,
'depth':
10,
'image_height':
32,
'batch_size':
200,
'nb_classes':
10,
'lr_options': [
cf_root_lr * 32,
cf_root_lr * 16,
cf_root_lr * 8,
cf_root_lr * 4,
cf_root_lr * 2,
cf_root_lr,
cf_root_lr * 1 / 2,
cf_root_lr * 1 / 4,
cf_root_lr * 1 / 8,
cf_root_lr * 1 / 16,
cf_root_lr * 1 / 32,
]
}
# stl-10
stl_root_lr = 2e-4
stl_10 = {
'dataset_name':
'stl_10',
'ndf':
192,
'n_rkhs':
1536,
'depth':
8,
'image_height':
64,
'batch_size':
200,
'nb_classes':
10,
'lr_options': [
stl_root_lr * 32,
stl_root_lr * 16,
stl_root_lr * 8,
stl_root_lr * 4,
stl_root_lr * 2,
stl_root_lr,
stl_root_lr * 1 / 2,
stl_root_lr * 1 / 4,
stl_root_lr * 1 / 8,
stl_root_lr * 1 / 16,
stl_root_lr * 1 / 32,
]
}
imagenet_root_lr = 2e-4
imagenet_128 = {
'dataset_name':
'imagenet_128',
'ndf':
320,
'n_rkhs':
2560,
'depth':
10,
'image_height':
128,
'batch_size':
200,
'nb_classes':
1000,
'lr_options': [
imagenet_root_lr * 32,
imagenet_root_lr * 16,
imagenet_root_lr * 8,
imagenet_root_lr * 4,
imagenet_root_lr * 2,
imagenet_root_lr,
imagenet_root_lr * 1 / 2,
imagenet_root_lr * 1 / 4,
imagenet_root_lr * 1 / 8,
imagenet_root_lr * 1 / 16,
imagenet_root_lr * 1 / 32,
]
}
imagenet_128_large = {
'dataset_name':
'imagenet_128',
'ndf':
320,
'n_rkhs':
2560,
'depth':
10,
'image_height':
128,
'batch_size':
200,
'nb_classes':
1000,
'lr_options': [
imagenet_root_lr * 32,
imagenet_root_lr * 16,
imagenet_root_lr * 8,
imagenet_root_lr * 4,
imagenet_root_lr * 2,
imagenet_root_lr,
imagenet_root_lr * 1 / 2,
imagenet_root_lr * 1 / 4,
imagenet_root_lr * 1 / 8,
imagenet_root_lr * 1 / 16,
imagenet_root_lr * 1 / 32,
]
}
# dataset = cifar_10
# dataset = stl_10
dataset = cifar_10
# dataset options
parser.add_argument('--nb_classes',
default=dataset['nb_classes'],
type=int)
# network params
parser.add_argument('--tclip',
type=float,
default=20.0,
help='soft clipping range for NCE scores')
parser.add_argument('--use_bn', type=int, default=0)
parser.add_argument('--ndf',
type=int,
default=dataset['ndf'],
help='feature width for encoder')
parser.add_argument(
'--n_rkhs',
type=int,
default=dataset['n_rkhs'],
help='number of dimensions in fake RKHS embeddings')
parser.add_argument('--n_depth', type=int, default=dataset['depth'])
parser.add_argument('--image_height',
type=int,
default=dataset['image_height'])
# trainin params
resnets = [
'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152',
'resnext50_32x4d', 'resnext101_32x8d', 'wide_resnet50_2',
'wide_resnet101_2'
]
parser.add_argument('--dataset_name',
type=str,
default=dataset['dataset_name'])
parser.add_argument('--batch_size',
type=int,
default=dataset['batch_size'],
help='input batch size (default: 200)')
parser.add_argument('--learning_rate', type=float, default=0.0002)
# data
parser.add_argument('--data_dir', default=os.getcwd(), type=str)
return parser
