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import torch
class SMOLLm_VISION_ImageCaptioning(torch.nn.Module):
def __init__(self, llm_model, hidden_dim):
super(ImageCaptioningModel, self).__init__()
self.llm_model = llm_model
self.fc = torch.nn.Linear(768, 960)
self.relu=torch.nn.GELU()
def forward(self, images, input_ids,att):
# Encode images
image_features = self.relu(self.fc(images))
#image_att=torch.zeros([images.shape[0],]).view(-1,1).to('cuda:0')
# Prepare text inputs for LLaMA2
llama_inputs = self.llm_model.prepare_inputs_for_generation(input_ids)
with torch.no_grad():
llama_embeds=self.llm_model.get_input_embeddings()(llama_inputs['input_ids'])
# Concatenate image features with LLaMA2 text inputs
combined_inputs = torch.cat([image_features.unsqueeze(1).float(),llama_embeds], dim=1)
#attention_mask=torch.cat((image_att,att),dim=-1)
outputs = self.llm_model(inputs_embeds=combined_inputs,attention_mask=att)
return outputs.logits[:,1:,:],combined_inputs
#return
class SmoLLM_processor():
def __init__(self,image_model,image_processor):
self.image_model=image_model
self.image_processor=image_processor
def get_features(self,image):
inputs = clip_processor(images=image, return_tensors="pt")
with torch.no_grad():
image_features = clip_model.get_image_features(**inputs.to('cuda:0')).squeeze()
#tokenized=tokenizer(prompt,padding=True, return_tensors='pt', max_length=50)
#input_ids=tokenized['input_ids'].squeeze() #image=tfms(image/255.)
#attention_mask=tokenized['attention_mask'].squeeze()
#x=input_ids[:-1]
#y=input_ids[1:]
return image_features |