SMOLL_image_captioner / SMOLLM_VisionModel.py

Create SMOLLM_VisionModel.py

a1fa153 verified over 1 year ago

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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

	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