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from .clip import clip
from PIL import Image
import torch.nn as nn
CHANNELS = {
"RN50" : 1024,
"ViT-L/14" : 768
}
class CLIPModel(nn.Module):
def __init__(self, name, num_classes=1):
super(CLIPModel, self).__init__()
self.model, self.preprocess = clip.load(name, device="cpu") # self.preprocess will not be used during training, which is handled in Dataset class
self.fc = nn.Linear( CHANNELS[name], num_classes )
def forward(self, x, return_feature=False):
features = self.model.encode_image(x)
if return_feature:
return features
return self.fc(features)
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