
# instantiate clip transform
clip_transform = CLIPTransform()

# pass the transform to your dataset. Here we use coco captions
dataset = CocoCaptions(root= ..., annFile=..., transforms=clip_transform)
dataloader = DataLoader(dataset, batch_size=16)

# instantiate model. Here we use clip with vit-L as the image encoder
model= clip_vit_l14()

# define loss and other things needed for training
clip_loss = ContrastiveLossWithTemperature()
optim = torch.optim.AdamW(model.parameters(), lr = 1e-5)
epochs = 1

# write your train loop
for _ in range(epochs):
    for batch_idx, batch in enumerate(dataloader):
        image, text = batch
        image_embeddings, text_embeddings = model(image, text)
        loss = contrastive_loss_with_temperature(image_embeddings, text_embeddings)
        loss.backward()
        optimizer.step()
