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import torch
import torch.nn as nn
import torch.nn.functional as F
import yaml
import numpy as np
from clip.encoders.image_encoder import ImageEncoder
from clip.encoders.text_encoder import TextEncoder
from helper.tokenizer import Tokenizer
class CLIP(nn.Module):
def __init__(self, config_path):
super().__init__()
with open(config_path, "r") as file:
config = yaml.safe_load(file)
self.image_encoder = ImageEncoder(**config["image_encoder"])
self.text_encoder = TextEncoder(**config["text_encoder"])
self.tokenizer = Tokenizer()
self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
# initialize
for module in self.modules():
if isinstance(module, nn.Linear):
nn.init.xavier_normal_(module.weight)
if module.bias is not None:
nn.init.constant_(module.bias, 0)
elif isinstance(module, nn.Conv2d):
nn.init.kaiming_normal_(module.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(module, (nn.LayerNorm, nn.BatchNorm2d)):
nn.init.constant_(module.weight, 1)
nn.init.constant_(module.bias, 0)
def loss(self, image, text):
image_features, text_features = self(image, text, tokenize=False)
# Normalize features
image_features = F.normalize(image_features, dim=1)
text_features = F.normalize(text_features, dim=1)
# Cosine similarity as logits with learned temperature
logits = torch.matmul(image_features, text_features.t()) * self.logit_scale.exp()
labels = torch.arange(logits.shape[0], dtype=torch.long, device=logits.device)
# Cross-entropy loss
loss_i2t = F.cross_entropy(logits, labels)
loss_t2i = F.cross_entropy(logits.t(), labels)
return (loss_i2t + loss_t2i) / 2
def text_encode(self, text, tokenize=True):
if tokenize:
tokens = self.tokenizer.tokenize(text)
else:
tokens = text
text_features = self.text_encoder(tokens)
if text_features.dim() < 2:
text_features = text_features.unsqueeze(0)
return text_features
def forward(self, image, text, tokenize=True):
image_features = self.image_encoder(image)
text_features = self.text_encoder(text, tokenize)
if image_features.dim() < 2:
image_features = image_features.unsqueeze(0)
if text_features.dim() < 2:
text_features = text_features.unsqueeze(0)
return image_features, text_features |