สร้าง app.py สำหรับ G01_Computer_Vision_Joy-caption-pre-alpha

app.py

@@ -0,0 +1,129 @@
+import spaces
+import gradio as gr
+from huggingface_hub import InferenceClient
+from torch import nn
+from transformers import AutoModel, AutoProcessor, AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast, AutoModelForCausalLM
+from pathlib import Path
+import torch
+import torch.amp.autocast_mode
+from PIL import Image
+import os
+
+
+CLIP_PATH = "google/siglip-so400m-patch14-384"
+VLM_PROMPT = "A descriptive caption for this image:\n"
+MODEL_PATH = "meta-llama/Meta-Llama-3.1-8B"
+CHECKPOINT_PATH = Path("wpkklhc6")
+TITLE = "<h1><center>JoyCaption Pre-Alpha (2024-07-30a)</center></h1>"
+
+HF_TOKEN = os.environ.get("HF_TOKEN", None)
+
+
+class ImageAdapter(nn.Module):
+	def __init__(self, input_features: int, output_features: int):
+		super().__init__()
+		self.linear1 = nn.Linear(input_features, output_features)
+		self.activation = nn.GELU()
+		self.linear2 = nn.Linear(output_features, output_features)
+	
+	def forward(self, vision_outputs: torch.Tensor):
+		x = self.linear1(vision_outputs)
+		x = self.activation(x)
+		x = self.linear2(x)
+		return x
+
+
+# Load CLIP
+print("Loading CLIP")
+clip_processor = AutoProcessor.from_pretrained(CLIP_PATH)
+clip_model = AutoModel.from_pretrained(CLIP_PATH)
+clip_model = clip_model.vision_model
+clip_model.eval()
+clip_model.requires_grad_(False)
+clip_model.to("cuda")
+
+
+# Tokenizer
+print("Loading tokenizer")
+tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, use_fast=False)
+assert isinstance(tokenizer, PreTrainedTokenizer) or isinstance(tokenizer, PreTrainedTokenizerFast), f"Tokenizer is of type {type(tokenizer)}"
+
+# LLM
+print("Loading LLM")
+text_model = AutoModelForCausalLM.from_pretrained(MODEL_PATH, device_map="auto", torch_dtype=torch.bfloat16)
+text_model.eval()
+
+# Image Adapter
+print("Loading image adapter")
+image_adapter = ImageAdapter(clip_model.config.hidden_size, text_model.config.hidden_size)
+image_adapter.load_state_dict(torch.load(CHECKPOINT_PATH / "image_adapter.pt", map_location="cpu"))
+image_adapter.eval()
+image_adapter.to("cuda")
+
+
+@spaces.GPU()
+@torch.no_grad()
+def stream_chat(input_image: Image.Image):
+	torch.cuda.empty_cache()
+
+	# Preprocess image
+	image = clip_processor(images=input_image, return_tensors='pt').pixel_values
+	image = image.to('cuda')
+
+	# Tokenize the prompt
+	prompt = tokenizer.encode(VLM_PROMPT, return_tensors='pt', padding=False, truncation=False, add_special_tokens=False)
+
+	# Embed image
+	with torch.amp.autocast_mode.autocast('cuda', enabled=True):
+		vision_outputs = clip_model(pixel_values=image, output_hidden_states=True)
+		image_features = vision_outputs.hidden_states[-2]
+		embedded_images = image_adapter(image_features)
+		embedded_images = embedded_images.to('cuda')
+	
+	# Embed prompt
+	prompt_embeds = text_model.model.embed_tokens(prompt.to('cuda'))
+	assert prompt_embeds.shape == (1, prompt.shape[1], text_model.config.hidden_size), f"Prompt shape is {prompt_embeds.shape}, expected {(1, prompt.shape[1], text_model.config.hidden_size)}"
+	embedded_bos = text_model.model.embed_tokens(torch.tensor([[tokenizer.bos_token_id]], device=text_model.device, dtype=torch.int64))
+
+	# Construct prompts
+	inputs_embeds = torch.cat([
+		embedded_bos.expand(embedded_images.shape[0], -1, -1),
+		embedded_images.to(dtype=embedded_bos.dtype),
+		prompt_embeds.expand(embedded_images.shape[0], -1, -1),
+	], dim=1)
+
+	input_ids = torch.cat([
+		torch.tensor([[tokenizer.bos_token_id]], dtype=torch.long),
+		torch.zeros((1, embedded_images.shape[1]), dtype=torch.long),
+		prompt,
+	], dim=1).to('cuda')
+	attention_mask = torch.ones_like(input_ids)
+
+	#generate_ids = text_model.generate(input_ids, inputs_embeds=inputs_embeds, attention_mask=attention_mask, max_new_tokens=300, do_sample=False, suppress_tokens=None)
+	generate_ids = text_model.generate(input_ids, inputs_embeds=inputs_embeds, attention_mask=attention_mask, max_new_tokens=300, do_sample=True, top_k=10, temperature=0.5, suppress_tokens=None)
+
+	# Trim off the prompt
+	generate_ids = generate_ids[:, input_ids.shape[1]:]
+	if generate_ids[0][-1] == tokenizer.eos_token_id:
+		generate_ids = generate_ids[:, :-1]
+
+	caption = tokenizer.batch_decode(generate_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False)[0]
+
+	return caption.strip()
+
+
+with gr.Blocks() as demo:
+	gr.HTML(TITLE)
+	with gr.Row():
+		with gr.Column():
+			input_image = gr.Image(type="pil", label="Input Image")
+			run_button = gr.Button("Caption")
+		
+		with gr.Column():
+			output_caption = gr.Textbox(label="Caption")
+	
+	run_button.click(fn=stream_chat, inputs=[input_image], outputs=[output_caption])
+
+
+if __name__ == "__main__":
+    demo.launch()