Hello,
Im trying to load my model from a chekpoint, which I trained earlier with pytorch lightning.
I get the error:
TypeError: The classmethod SimCLR_Model.load_from_checkpoint cannot be called on an instance. Please call it on the class type and make sure the return value is used.
I double checked the docs and i cannot see where the error is. Its strange, because it worked before and I updated pytorch lightning and now Im seeing this error.
Here is the code:
emb_model_path = "models/version_876873/checkpoints/epoch=99-step=31600.ckpt"
e_model = SimCLR_Model(args, args.embedding_space).load_from_checkpoint(emb_model_path, args=args, emb_space=args.embedding_space)
e_model.freeze()
model = LinearModel(args, e_model)
print(model)
trainer = L.Trainer(max_epochs=args.epochs)
trainer.fit(model, train_loader, val_loader)
And the code from the SimCLR model:
import torch
import lightning as L
from torchvision.models import convnext_base, ConvNeXt_Base_Weights
from simclr_loss import SimCLR_Loss
class SimCLR_Model(L.LightningModule):
def __init__(self, args, emb_space):
super().__init__()
self.args = args
self.encoder = convnext_base(num_classes=emb_space)
self.loss = SimCLR_Loss(self.args.batch_size)
def forward(self, x):
return self.encoder(x)
def configure_optimizers(self):
optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
return optimizer
def training_step(self, batch, batch_idx):
x, y = batch
images = torch.cat([x, y], dim=0)
#put here contiguous(): images.contiguous()
embeddings = self.encoder(images)
loss = self.loss(embeddings)
self.log("train_loss", loss, on_epoch=True)
return loss
def validation_step(self, batch, batch_idx):
x, y = batch
images = torch.cat([x, y], dim=0)
embeddings = self.encoder(images)
loss = self.loss(embeddings)
self.log("val_loss", loss, on_epoch=True)
return loss