def test_from_pretrained(tmpdir):
vae = VAE(input_height=32)
ae = AE(input_height=32)
assert len(VAE.pretrained_weights_available()) > 0
assert len(AE.pretrained_weights_available()) > 0
exception_raised = False
try:
vae = vae.from_pretrained('cifar10-resnet18')
vae = vae.from_pretrained(
'stl10-resnet18'
) # try loading weights not compatible with exact architecture
ae = ae.from_pretrained('cifar10-resnet18')
except Exception as e:
exception_raised = True
assert exception_raised is False, "error in loading weights"
keyerror = False
try:
vae = vae.from_pretrained('abc')
ae = ae.from_pretrained('xyz')
except KeyError:
keyerror = True
assert keyerror is True, "KeyError not raised when provided with illegal checkpoint name"
def test_from_pretrained(datadir):
dm = CIFAR10DataModule(data_dir=datadir, batch_size=4)
dm.setup()
dm.prepare_data()
data_loader = dm.val_dataloader()
vae = VAE(input_height=32)
ae = AE(input_height=32)
assert len(VAE.pretrained_weights_available()) > 0
assert len(AE.pretrained_weights_available()) > 0
exception_raised = False
try:
vae = vae.from_pretrained('cifar10-resnet18')
# test forward method on pre-trained weights
for x, y in data_loader:
x_hat = vae(x)
break
vae = vae.from_pretrained(
'stl10-resnet18'
) # try loading weights not compatible with exact architecture
ae = ae.from_pretrained('cifar10-resnet18')
# test forward method on pre-trained weights
for x, y in data_loader:
x_hat = ae(x)
break
except Exception:
exception_raised = True
assert exception_raised is False, "error in loading weights"
keyerror = False
try:
vae = vae.from_pretrained('abc')
ae = ae.from_pretrained('xyz')
except KeyError:
keyerror = True
assert keyerror is True, "KeyError not raised when provided with illegal checkpoint name"
def test_ae(tmpdir):
reset_seed()
model = AE(data_dir=tmpdir, batch_size=2)
trainer = pl.Trainer(fast_dev_run=True, default_root_dir=tmpdir)
trainer.fit(model)
trainer.test(model)
def __init__(self, pretrained_path):
super().__init__()
self.pretrained_path = pretrained_path
self.output_dim = 800 * 800
# ------------------
# PRE-TRAINED MODEL
# ------------------
ae = AE.load_from_checkpoint(pretrained_path)
ae.freeze()
self.backbone = ae.encoder
self.backbone.c3_only = True
self.backbone.out_channels = 32
# ------------------
# FAST RCNN
# ------------------
anchor_generator = AnchorGenerator(sizes=((32, 64, 128, 256, 512), ),
aspect_ratios=((0.5, 1.0, 2.0), ))
roi_pooler = torchvision.ops.MultiScaleRoIAlign(featmap_names=['0'],
output_size=7,
sampling_ratio=2)
self.fast_rcnn = FasterRCNN(self.backbone,
num_classes=9,
rpn_anchor_generator=anchor_generator,
box_roi_pool=roi_pooler)
# for unfreezing encoder later
self.frozen = True
def test_ae(tmpdir, datadir, dm_cls):
seed_everything()
dm = dm_cls(data_dir=datadir, batch_size=4)
model = AE(input_height=dm.size()[-1])
trainer = Trainer(
fast_dev_run=True,
default_root_dir=tmpdir,
gpus=None,
)
trainer.fit(model, datamodule=dm)
def test_ae(tmpdir, datadir, dm_cls):
seed_everything()
dm = dm_cls(data_dir=datadir, batch_size=4)
model = AE(input_height=dm.size()[-1])
trainer = pl.Trainer(fast_dev_run=True,
default_root_dir=tmpdir,
max_epochs=1,
gpus=None)
result = trainer.fit(model, dm)
assert result == 1
def basic_ae_example():
from pl_bolts.models.autoencoders import AE
from pytorch_lightning.plugins import DDPPlugin
# Data.
train_dataset = torchvision.datasets.CIFAR10(
"",
train=True,
download=True,
transform=torchvision.transforms.ToTensor())
val_dataset = torchvision.datasets.CIFAR10(
"",
train=False,
download=True,
transform=torchvision.transforms.ToTensor())
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=32,
num_workers=12)
#train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=32, num_workers=12, persistent_workers=True)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=32,
num_workers=12)
#val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=32, num_workers=12, persistent_workers=True)
# Model.
model = AE(input_height=32)
"""
# Override any part of this AE to build your own variation.
class MyAEFlavor(AE):
def init_encoder(self, hidden_dim, latent_dim, input_width, input_height):
encoder = YourSuperFancyEncoder(...)
return encoder
model = MyAEFlavor(...)
"""
# Fit.
trainer = pl.Trainer(gpus=2,
accelerator="ddp",
plugins=DDPPlugin(find_unused_parameters=False))
trainer.fit(model, train_loader, val_loader)
#--------------------
# CIFAR-10 pretrained model:
ae = AE(input_height=32)
print(AE.pretrained_weights_available())
ae = ae.from_pretrained("cifar10-resnet18")
ae.freeze()
required=True,
help='root directory that contains captions')
parser.add_argument('--fasttext_model',
type=str,
required=True,
help='pretrained fastText model (binary file)')
parser.add_argument('--max_nwords',
type=int,
default=50,
help='maximum number of words (default: 50)')
parser.add_argument('--img_model',
type=str,
required=True,
help='pretrained autoencoder model')
args = parser.parse_args()
if __name__ == '__main__':
caption_root = args.caption_root.split('/')[-1]
if (caption_root + '_vec') not in os.listdir(
args.caption_root.replace(caption_root, '')):
os.makedirs(args.caption_root + '_vec')
print('Loading a pretrained image model...')
img_model = AE.load_from_checkpoint(checkpoint_path=args.img_model)
model = nn.Sequential(img_model.encoder, img_model.fc)
model = model.eval()
print('Loading a pretrained fastText model...')
word_embedding = fastText.load_model(args.fasttext_model)
print('Making vectorized caption data files...')
ConvertCapVec().convert_and_save3(args.caption_root, word_embedding,
args.max_nwords, model)
import argparse
import pytorch_lightning as pl
from pl_bolts.models.autoencoders import AE
from ae_datasets import iCLEVRDataModule
from pytorch_lightning.callbacks import ModelCheckpoint
parser = argparse.ArgumentParser()
parser = AE.add_model_specific_args(parser)
parser = iCLEVRDataModule.add_dataset_specific_args(parser)
parser = pl.Trainer.add_argparse_args(parser)
opt = parser.parse_args()
model = AE(**vars(opt))
data = iCLEVRDataModule(**vars(opt))
trainer = pl.Trainer.from_argparse_args(
opt,
log_gpu_memory='all',
checkpoint_callback=ModelCheckpoint(filepath="{epoch:02d}.ckpt"))
trainer.fit(model, data)