add qwen2.5vl model

src/models/qwen2_5vl/config.rs

@@ -0,0 +1,97 @@
+use candle_nn::Activation;
+
+#[derive(Debug, Clone, PartialEq, serde::Deserialize)]
+pub struct VisionConfig {
+    pub depth: usize,
+    pub hidden_act: Activation,
+    pub hidden_size: usize,
+    pub intermediate_size: usize,
+    pub num_heads: usize,
+    pub in_chans: usize,
+    pub out_hidden_size: usize,
+    pub patch_size: usize,
+    pub spatial_merge_size: usize,
+    pub spatial_patch_size: usize,
+    pub window_size: usize,
+    pub fullatt_block_indexes: Vec<usize>,
+    pub tokens_per_second: usize,
+    pub temporal_patch_size: usize,
+}
+#[derive(Debug, Clone, PartialEq, serde::Deserialize)]
+pub struct RopeScaling {
+    pub r#type: String,
+    pub mrope_section: Vec<usize>,
+}
+
+#[derive(Debug, Clone, PartialEq, serde::Deserialize)]
+pub struct Config {
+    pub attention_dropout: f32,
+    pub bos_token_id: usize,
+    pub eos_token_id: usize,
+    pub vision_start_token_id: usize,
+    pub vision_end_token_id: usize,
+    pub vision_token_id: usize,
+    pub image_token_id: usize,
+    pub video_token_id: usize,
+    pub hidden_act: Activation,
+    pub hidden_size: usize,
+    pub initializer_range: f32,
+    pub intermediate_size: usize,
+    pub max_position_embeddings: usize,
+    pub max_window_layers: usize,
+    pub num_attention_heads: usize,
+    pub num_hidden_layers: usize,
+    pub num_key_value_heads: usize,
+    pub rms_norm_eps: f64,
+    pub rope_theta: f32,
+    pub sliding_window: usize,
+    pub tie_word_embeddings: bool,
+    pub torch_dtype: String,
+    pub use_sliding_window: bool,
+    pub vision_config: VisionConfig,
+    pub rope_scaling: RopeScaling,
+    pub vocab_size: usize,
+}
+
+pub struct VisionSetting {
+    pub image_factor: u32,
+    pub min_pixels: u32,
+    pub max_pixels: u32,
+    pub max_ratio: u32,
+    pub temporal_patch_size: usize,
+    pub patch_size: usize,
+    pub merge_size: usize,
+    pub video_min_pixels: u32,
+    pub video_max_pixels: u32,
+    pub video_total_pixels: u32,
+    pub frame_factor: u32,
+    pub fps: f32,
+    pub fps_min_frames: u32,
+    pub fps_max_frames: u32,
+    pub image_mean: Vec<f32>,
+    pub image_std: Vec<f32>,
+}
+
+impl VisionSetting {
+    pub fn default() -> Self {
+        Self {
+            image_factor: 28,
+            min_pixels: 4 * 28 * 28,
+            max_pixels: 16384 * 28 * 28,
+            // max_pixels: 1000 * 28 * 28,
+            max_ratio: 200,
+            temporal_patch_size: 2,
+            patch_size: 14,
+            merge_size: 2,
+            video_min_pixels: 128 * 28 * 28,
+            video_max_pixels: 768 * 28 * 28,
+            video_total_pixels: 24576 * 28 * 28,
+            frame_factor: 2,
+            fps: 2.0,
+            fps_min_frames: 4,
+            fps_max_frames: 768,
+            image_mean: vec![0.48145466_f32, 0.4578275, 0.40821073],
+            image_std: vec![0.26862954, 0.26130258, 0.27577711],
+        }
+    }
+}

src/models/qwen2_5vl/generate.rs

@@ -0,0 +1,137 @@
+use crate::models::qwen2_5vl::config::Config;
+use crate::utils::utils::{find_safetensors_files, get_device, get_dtype};
+use crate::{
+    chat_template::chat_template::ChatTemplate,
+    models::{
+        GenerateModel,
+        qwen2_5vl::{model::Qwen2_5VLModel, processor::Qwen2_5VLProcessor},
+    },
+    tokenizer::tokenizer::TokenizerModel,
+};
+use anyhow::{Result, anyhow};
+use candle_core::{D, DType, Device, IndexOp, Tensor};
+use candle_nn::VarBuilder;
+use candle_transformers::generation::LogitsProcessor;
+use openai_dive::v1::resources::chat::ChatCompletionParameters;
+
+pub struct Qwen2_5VLGenerateModel<'a> {
+    chat_template: ChatTemplate<'a>,
+    tokenizer: TokenizerModel,
+    pre_processor: Qwen2_5VLProcessor,
+    qwen2_5_vl: Qwen2_5VLModel,
+    device: Device,
+    dtype: DType,
+}
+
+impl<'a> GenerateModel for Qwen2_5VLGenerateModel<'a> {
+    fn init(path: &str, device: Option<&Device>, dtype: Option<DType>) -> Result<Self> {
+        let chat_template = ChatTemplate::init(path)?;
+        let tokenizer = TokenizerModel::init(path)?;
+        let config_path = path.to_string() + "/config.json";
+        let cfg: Config = serde_json::from_slice(&std::fs::read(config_path)?)?;
+        let device = &get_device(device);
+        let cfg_dtype = cfg.torch_dtype.as_str();
+        let dtype = get_dtype(dtype, cfg_dtype);
+        let pre_processor = Qwen2_5VLProcessor::new(device, dtype)?;       
+        
+        let model_list = find_safetensors_files(&path)?;
+        let vb = unsafe { VarBuilder::from_mmaped_safetensors(&model_list, dtype, device)? };
+        let qwen2_5_vl = Qwen2_5VLModel::new(cfg, vb)?;
+        Ok(Qwen2_5VLGenerateModel {
+            chat_template,
+            tokenizer,
+            pre_processor,
+            qwen2_5_vl,
+            device: device.clone(),
+            dtype: dtype,
+        })
+    }
+    fn generate(&mut self, mes: ChatCompletionParameters) -> Result<String> {
+        let temperature = match mes.temperature {
+            Some(temp) => Some(temp as f64),
+            None => None,
+        };
+        let top_p = match mes.top_p {
+            Some(tp) => Some(tp as f64),
+            None => None,
+        };
+        let mut logit_processor = LogitsProcessor::new(34562, temperature, top_p);
+        let mes_render = self.chat_template.apply_chat_template(&mes)?;
+        let input = self.pre_processor.process_info(&mes, &mes_render)?;
+        let mut input_ids = self
+            .tokenizer
+            .text_encode(input.replace_text.clone(), &self.device)?;
+        let mut seq_len = input_ids.dim(1)?;
+        let mut seqlen_offset = 0;
+        let end_of_text_id = self.qwen2_5_vl.cfg.bos_token_id as u32;
+        let im_end_id = self.qwen2_5_vl.cfg.eos_token_id as u32;
+        let mut pixel_values = if input.pixel_values.is_some() {
+            Some(&input.pixel_values.unwrap().clone())
+        } else {
+            None
+        };
+        let image_grid_thw = if input.image_grid_thw.is_some() {
+            Some(&input.image_grid_thw.unwrap().clone())
+        } else {
+            None
+        };
+        let mut pixel_values_video = if input.pixel_values_video.is_some() {
+            Some(&input.pixel_values_video.unwrap().clone())
+        } else {
+            None
+        };
+        let video_grid_thw = if input.video_grid_thw.is_some() {
+            Some(&input.video_grid_thw.unwrap().clone())
+        } else {
+            None
+        };
+        let second_per_grid_ts = if input.second_per_grid_ts.is_some() {
+            Some(input.second_per_grid_ts.unwrap().clone())
+        } else {
+            None
+        };
+
+        let mut mask = Tensor::ones_like(&input_ids)?;
+        let mut cache_position = Tensor::ones_like(&input_ids.i(0)?)?
+            .to_dtype(candle_core::DType::F64)?
+            .cumsum(D::Minus1)?
+            .to_dtype(candle_core::DType::U32)?
+            .broadcast_sub(&Tensor::new(vec![1_u32], input_ids.device())?)?;
+
+        let mut generate = Vec::new();
+        let sample_len = match mes.max_tokens {
+            Some(max) => max,
+            None => 512,
+        };
+        for _ in 0..sample_len {
+            let logits = self.qwen2_5_vl.forward(
+                &input_ids,
+                pixel_values,
+                image_grid_thw,
+                pixel_values_video,
+                video_grid_thw,
+                &mask,
+                Some(&cache_position),
+                seqlen_offset,
+                second_per_grid_ts.clone(),
+            )?;
+            let logits = logits.squeeze(0)?.squeeze(0)?.to_dtype(DType::F32)?;
+            let next_token = logit_processor.sample(&logits)?;
+            generate.push(next_token);
+            if next_token == end_of_text_id || next_token == im_end_id {
+                break;
+            }
+            seqlen_offset += seq_len;
+            seq_len = 1;
+            input_ids = Tensor::from_vec(vec![next_token], (1, 1), &self.device)?;
+            let appendd_mask = Tensor::ones((1, 1), mask.dtype(), &self.device)?;
+            mask = Tensor::cat(&[mask, appendd_mask], 1)?;
+            cache_position = Tensor::from_vec(vec![seqlen_offset as u32], 1, &self.device)?;
+            pixel_values = None;
+            pixel_values_video = None;
+        }
+        let res = self.tokenizer.token_decode(generate)?;
+        self.qwen2_5_vl.clear_kv_cache();
+        Ok(res)
+    }
+}

src/models/qwen2_5vl/mod.rs

@@ -0,0 +1,4 @@
+pub mod config;
+pub mod model;
+pub mod processor;
+pub mod generate;

src/models/qwen2_5vl/processor.rs

@@ -0,0 +1,462 @@
+use std::collections::HashMap;
+
+use anyhow::{Result, anyhow};
+use candle_core::{DType, Device, IndexOp, Shape, Tensor};
+use ffmpeg_next as ffmpeg;
+use image::DynamicImage;
+use num::integer::lcm;
+use openai_dive::v1::resources::chat::{
+    ChatCompletionParameters, ChatMessage, ChatMessageContent, ChatMessageContentPart,
+};
+
+use crate::{
+    models::qwen2_5vl::config::VisionSetting,
+    utils::{
+        img_utils::get_image,
+        utils::{ceil_by_factor, floor_by_factor, round_by_factor},
+    },
+};
+
+#[derive(Clone)]
+pub struct VisionInput {
+    pub data: Tensor,
+    pub grid_thw: Tensor,
+}
+
+#[derive(Clone)]
+pub struct GeneralInput {
+    pub replace_text: String,
+    pub pixel_values: Option<Tensor>,
+    pub image_grid_thw: Option<Tensor>,
+    pub pixel_values_video: Option<Tensor>,
+    pub video_grid_thw: Option<Tensor>,
+    pub second_per_grid_ts: Option<Vec<f32>>,
+}
+
+pub struct Qwen2_5VLProcessor {
+    vision_setting: VisionSetting,
+    device: Device,
+    dtype: DType,
+    image_token: String,
+    video_token: String,
+}
+
+impl Qwen2_5VLProcessor {
+    pub fn new(device: &Device, dtype: DType) -> Result<Self> {
+        let vision_setting = VisionSetting::default();
+        let image_token = "<|image_pad|>".to_string();
+        let video_token = "<|video_pad|>".to_string();
+        Ok(Self {
+            vision_setting,
+            device: device.clone(),
+            dtype,
+            image_token,
+            video_token,
+        })
+    }
+
+    pub fn extract_vision_info(
+        &self,
+        mes: &ChatCompletionParameters,
+    ) -> Result<HashMap<String, Vec<String>>> {
+        let mut vision_map = HashMap::new();
+        vision_map.insert("image".to_string(), Vec::new());
+        vision_map.insert("video".to_string(), Vec::new());
+        for chat_mes in mes.messages.clone() {
+            match chat_mes {
+                ChatMessage::User { content, name } => match content {
+                    ChatMessageContent::ContentPart(part_vec) => {
+                        for part in part_vec {
+                            match part {
+                                ChatMessageContentPart::Image(img_part) => {
+                                    let img_url = img_part.image_url;
+                                    vision_map.get_mut("image").unwrap().push(img_url.url);
+                                }
+                                _ => {}
+                            }
+                        }
+                    }
+                    _ => {}
+                },
+                _ => {}
+            }
+        }
+        Ok(vision_map)
+    }
+
+    pub fn process_img(
+        &self,
+        img: &DynamicImage,
+        img_mean: &Tensor,
+        img_std: &Tensor,
+    ) -> Result<Tensor> {
+        let img_h = img.height();
+        let img_w = img.width();
+        //  h,w resize成 28的倍数
+        let (resize_h, resize_w) = smart_resize(img_h, img_w, &self.vision_setting, true, None)?;
+        let img = img.resize_exact(resize_w, resize_h, image::imageops::FilterType::CatmullRom);
+        let img_vec = img.to_rgb8().into_raw();
+        // (h, w, c) => (c, h, w)
+        let img_tensor = Tensor::from_slice(
+            &img_vec,
+            (resize_h as usize, resize_w as usize, 3),
+            &self.device,
+        )?
+        .permute((2, 0, 1))?
+        .to_dtype(self.dtype)?;
+        // 0-255 rescale to 0-1
+        let img_tensor = img_tensor.affine(1.0 / 255.0, 0.)?;
+        // normalize
+        let img_tensor = img_tensor
+            .broadcast_sub(&img_mean)?
+            .broadcast_div(&img_std)?;
+        // (c, h, w) => (1, c, h, w)
+        let img_tensor = img_tensor.unsqueeze(0)?;
+        Ok(img_tensor)
+    }
+
+    pub fn process_vision_tensor(&self, img_tensor: &Tensor) -> Result<(Tensor, Tensor)> {
+        let channel = img_tensor.dim(1)?;
+        let grid_t = img_tensor.dim(0)? / self.vision_setting.temporal_patch_size;
+        let grid_h = img_tensor.dim(2)? / self.vision_setting.patch_size;
+        let grid_w = img_tensor.dim(3)? / self.vision_setting.patch_size;
+        let shape = Shape::from(vec![
+            grid_t,
+            self.vision_setting.temporal_patch_size,
+            channel,
+            grid_h / self.vision_setting.merge_size,
+            self.vision_setting.merge_size,
+            self.vision_setting.patch_size,
+            grid_w / self.vision_setting.merge_size,
+            self.vision_setting.merge_size,
+            self.vision_setting.patch_size,
+        ]);
+        let img_tensor = img_tensor.reshape(shape)?;
+        // shape to // grid_t,
+        // grid_h / merge_size,
+        // grid_w / merge_size,
+        // merge_size,
+        // merge_size,
+        // channel,
+        // temporal_patch_size,
+        // patch_size,
+        // patch_size,
+        let img_tensor = img_tensor.permute(vec![0, 3, 6, 4, 7, 2, 1, 5, 8])?;
+        let img_tensor = img_tensor
+            .reshape((
+                grid_t * grid_h * grid_w,
+                channel
+                    * self.vision_setting.temporal_patch_size
+                    * self.vision_setting.patch_size
+                    * self.vision_setting.patch_size,
+            ))?
+            .contiguous()?;
+        let grid_thw = Tensor::from_vec(
+            vec![grid_t as u32, grid_h as u32, grid_w as u32],
+            (1, 3),
+            &self.device,
+        )?;
+        Ok((img_tensor, grid_thw))
+    }
+
+    pub fn process_images(
+        &self,
+        imgs: Vec<DynamicImage>,
+        img_mean: &Tensor,
+        img_std: &Tensor,
+    ) -> Result<VisionInput> {
+        let mut pixel_values_vec = Vec::new();
+        let mut vision_grid_thws_vec = Vec::new();
+
+        for img in imgs {
+            let img_tensor = self.process_img(&img, &img_mean, &img_std)?;
+            let img_tensor = Tensor::cat(&[&img_tensor, &img_tensor], 0)?.contiguous()?;
+            let (img_tensor, grid_thw) = self.process_vision_tensor(&img_tensor)?;
+            pixel_values_vec.push(img_tensor);
+            vision_grid_thws_vec.push(grid_thw);
+        }
+        let pixel_values = Tensor::cat(&pixel_values_vec, 0)?;
+        let vision_grid_thws = Tensor::cat(&vision_grid_thws_vec, 0)?;
+        Ok(VisionInput {
+            data: pixel_values,
+            grid_thw: vision_grid_thws,
+        })
+    }
+
+    pub fn process_videos(
+        &self,
+        data: Vec<Tensor>,
+        img_mean: &Tensor,
+        img_std: &Tensor,
+    ) -> Result<VisionInput> {
+        let mut pixel_values_vec = Vec::new();
+        let mut vision_grid_thws_vec = Vec::new();
+        for single_video in data {
+            // 0-255 rescale to 0-1
+            let video_tensor = single_video.to_dtype(self.dtype)?.affine(1.0 / 255.0, 0.)?;
+            // normalize
+            let video_tensor = video_tensor
+                .broadcast_sub(&img_mean)?
+                .broadcast_div(&img_std)?
+                .contiguous()?;
+            let (video_tensor, video_grid_thw) = self.process_vision_tensor(&video_tensor)?;
+            pixel_values_vec.push(video_tensor);
+            vision_grid_thws_vec.push(video_grid_thw);
+        }
+        let pixel_values = Tensor::cat(&pixel_values_vec, 0)?.contiguous()?;
+        let vision_grid_thws = Tensor::cat(&vision_grid_thws_vec, 0)?.contiguous()?;
+        Ok(VisionInput {
+            data: pixel_values,
+            grid_thw: vision_grid_thws,
+        })
+    }
+
+    pub fn process_info(
+        &self,
+        messages: &ChatCompletionParameters,
+        text: &str,
+    ) -> Result<GeneralInput> {
+        let mut pixel_values = None;
+        let mut image_grid_thw = None;
+        let mut pixel_values_video = None;
+        let mut video_grid_thw = None;
+        let mut second_per_grid_ts = None;
+        let vision_map = self.extract_vision_info(messages)?;
+        let img_mean =
+            Tensor::from_slice(&self.vision_setting.image_mean, (3, 1, 1), &self.device)?
+                .to_dtype(self.dtype)?;
+        let img_std = Tensor::from_slice(&self.vision_setting.image_std, (3, 1, 1), &self.device)?
+            .to_dtype(self.dtype)?;
+        for (key, vec) in vision_map {
+            // println!("key: {}, \nvalue: {:?}", key, vec);
+            if key.eq("image") {
+                let mut file_vec = Vec::new();
+                for file in &vec {
+                    let image = get_image(file);
+                    match image {
+                        Ok(img) => file_vec.push(img),
+                        Err(e) => println!("get_image err: {:?}", e),
+                    };
+                }
+                if file_vec.len() > 0 {
+                    let vision_input = self.process_images(file_vec, &img_mean, &img_std);
+                    match vision_input {
+                        Ok(img_input) => {
+                            pixel_values = Some(img_input.data);
+                            image_grid_thw = Some(img_input.grid_thw);
+                        }
+                        Err(e) => println!("img process_images err: {:?}", e),
+                    };
+                }
+            }
+            if key.eq("video") {
+                let mut file_vec = Vec::new();
+                for file in &vec {
+                    let video_data = get_video_data(file, &self.vision_setting, &self.device);
+                    match video_data {
+                        Ok(tensor) => file_vec.push(tensor),
+                        Err(e) => println!("get_video_data err: {:?}", e),
+                    };
+                }
+                if file_vec.len() > 0 {
+                    let vision_input = self.process_videos(file_vec, &img_mean, &img_std);
+                    match vision_input {
+                        Ok(video_input) => {
+                            let video_num = video_input.grid_thw.dim(0)?;
+                            pixel_values_video = Some(video_input.data);
+                            video_grid_thw = Some(video_input.grid_thw);
+                            let second_per_grid = vec![
+                                self.vision_setting.temporal_patch_size
+                                    as f32
+                                    / self.vision_setting.fps;
+                                video_num
+                            ];
+                            second_per_grid_ts = Some(second_per_grid);
+                        }
+                        Err(e) => println!("video process_videos err: {:?}", e),
+                    };
+                }
+            }
+        }
+        let merge_length = self.vision_setting.merge_size.pow(2);
+        let mut text = text.to_string();
+        if image_grid_thw.is_some() {
+            let mut index = 0;
+            while text.contains(&self.image_token) {
+                let grid_i = image_grid_thw.as_ref().unwrap().i(index)?;
+                let repeat_num =
+                    grid_i.to_vec1::<u32>()?.iter().product::<u32>() as usize / merge_length;
+                let replace = "<|placeholder|>".repeat(repeat_num);
+                text = text.replacen(&self.image_token, &replace, 1);
+                index += 1;
+            }
+            text = text.replace("<|placeholder|>", &self.image_token);
+        }
+        if video_grid_thw.is_some() {
+            let mut index = 0;
+            while text.contains(&self.video_token) {
+                let grid_i = video_grid_thw.as_ref().unwrap().i(index)?;
+                let repeat_num =
+                    grid_i.to_vec1::<u32>()?.iter().product::<u32>() as usize / merge_length;
+                let replace = "<|placeholder|>".repeat(repeat_num);
+                text = text.replacen(&self.video_token, &replace, 1);
+                index += 1;
+            }
+            text = text.replace("<|placeholder|>", &self.video_token);
+        }
+        let input = GeneralInput {
+            replace_text: text,
+            pixel_values,
+            image_grid_thw,
+            pixel_values_video,
+            video_grid_thw,
+            second_per_grid_ts,
+        };
+        Ok(input)
+    }
+}
+
+pub fn smart_resize(
+    img_h: u32,
+    img_w: u32,
+    vision_setting: &VisionSetting,
+    is_img: bool,
+    video_ratio: Option<u32>,
+) -> Result<(u32, u32)> {
+    if std::cmp::max(img_h, img_w) / std::cmp::min(img_h, img_w) > vision_setting.max_ratio {
+        return Err(anyhow!(format!(
+            "absolute aspect ratio mush be smaller than {}, got {}",
+            vision_setting.max_ratio,
+            std::cmp::max(img_h, img_w) / std::cmp::min(img_h, img_w)
+        )));
+    }
+    let mut image_factor = vision_setting.image_factor;
+    if let Some(ratio) = video_ratio {
+        image_factor = lcm(image_factor, ratio);
+    }
+    let mut h_bar = std::cmp::max(image_factor, round_by_factor(img_h, image_factor));
+    let mut w_bar = std::cmp::max(image_factor, round_by_factor(img_w, image_factor));
+    let mut max_pixels = 0u32;
+    let mut min_pixels = 0u32;
+    if is_img {
+        min_pixels = vision_setting.min_pixels;
+        max_pixels = vision_setting.max_pixels;
+    } else {
+        min_pixels = vision_setting.video_min_pixels;
+        max_pixels = vision_setting.video_max_pixels;
+    }
+    if h_bar * w_bar > max_pixels {
+        let beta = ((img_h * img_w) as f32 / max_pixels as f32).sqrt();
+        h_bar = floor_by_factor(img_h as f32 / beta, image_factor);
+        w_bar = floor_by_factor(img_w as f32 / beta, image_factor);
+    } else if h_bar * w_bar < min_pixels {
+        let beta = (min_pixels as f32 / (img_h * img_w) as f32).sqrt();
+        h_bar = ceil_by_factor(img_h as f32 * beta, image_factor);
+        w_bar = ceil_by_factor(img_w as f32 * beta, image_factor);
+    }
+    Ok((h_bar, w_bar))
+}
+
+pub fn get_video_data(
+    file: &String,
+    vision_setting: &VisionSetting,
+    device: &Device,
+) -> Result<Tensor> {
+    ffmpeg::init().map_err(|e| anyhow!(format!("Failed to initialize ffmpeg: {}", e)))?;
+
+    let mut ictx = ffmpeg::format::input(&file)
+        .map_err(|e| anyhow!(format!("Failed to open video file: {}", e)))?;
+    let input = ictx
+        .streams()
+        .best(ffmpeg::media::Type::Video)
+        .ok_or_else(|| anyhow!(format!("No video stream found")))?;
+    let video_stream_index = input.index();
+    let context_decoder = ffmpeg::codec::context::Context::from_parameters(input.parameters())
+        .map_err(|e| anyhow!(format!("Failed to crate decoder context: {}", e)))?;
+    let mut decoder = context_decoder
+        .decoder()
+        .video()
+        .map_err(|e| anyhow!(format!("Failed to decoder video: {}", e)))?;
+
+    let video_h = decoder.height();
+    let video_w = decoder.width();
+    let format = decoder.format();
+
+    let frames = input.frames();
+    let rate = (input.rate().0 as f32 / input.rate().1 as f32).round() as u32;
+    // 1s取两帧
+    let min_frames = ceil_by_factor(
+        vision_setting.fps_min_frames as f32,
+        vision_setting.frame_factor,
+    );
+    let max_frames = floor_by_factor(
+        vision_setting.fps_max_frames as f32,
+        vision_setting.frame_factor,
+    );
+    let nframes = (frames as f32 / rate as f32 * vision_setting.fps) as u32;
+    let nframes = std::cmp::min(std::cmp::max(nframes, min_frames), max_frames);
+    let nframes = round_by_factor(nframes, vision_setting.frame_factor);
+    let sample_interval = (frames as f32 / nframes as f32).round() as u32;
+    let mut frame_id = 0_u32;
+
+    // 图片帧使用scaler reshape的时候需要保证宽高是16的倍数,不然reshape出来的是损坏的图片
+    // 所以计算resize的目标宽高时,需要用16和image_factor的最小公倍数
+    let (resize_h, resize_w) = smart_resize(video_h, video_w, vision_setting, false, Some(16))?;
+    let mut scaler = ffmpeg::software::scaling::context::Context::get(
+        format,
+        video_w,
+        video_h,
+        ffmpeg::format::Pixel::RGB24,
+        resize_w,
+        resize_h,
+        ffmpeg::software::scaling::flag::Flags::BILINEAR
+            | ffmpeg::software::scaling::flag::Flags::ACCURATE_RND,
+    )
+    .map_err(|e| anyhow!(format!("Failed to crate scaler: {}", e)))?;
+
+    let mut frames_vec = Vec::new();
+    let mut receive_and_process_decoded_frames =
+        |decoder: &mut ffmpeg::decoder::Video| -> Result<()> {
+            let mut decoded = ffmpeg::frame::Video::empty();
+            while decoder.receive_frame(&mut decoded).is_ok() {
+                if frame_id % sample_interval == 0 {
+                    let mut rgb_frame = ffmpeg::frame::Video::empty();
+                    scaler
+                        .run(&decoded, &mut rgb_frame)
+                        .map_err(|e| anyhow!(format!("Failed to scaler run decoded: {}", e)))?;
+
+                    // save_file(&rgb_frame, frame_id as usize);
+                    let frame_data = rgb_frame.data(0);
+                    let frame_tensor = Tensor::from_slice(
+                        frame_data,
+                        (resize_h as usize, resize_w as usize, 3),
+                        device,
+                    )?
+                    .permute((2, 0, 1))?;
+                    frames_vec.push(frame_tensor);
+                }
+                frame_id += 1;
+            }
+            Ok(())
+        };
+
+    for (stream, packet) in ictx.packets() {
+        if stream.index() == video_stream_index {
+            decoder
+                .send_packet(&packet)
+                .map_err(|e| anyhow!(format!("Failed to send packet: {}", e)))?;
+            receive_and_process_decoded_frames(&mut decoder)?;
+        }
+    }
+    decoder
+        .send_eof()
+        .map_err(|e| anyhow!(format!("Failed to decoder.send_eof(): {}", e)))?;
+    receive_and_process_decoded_frames(&mut decoder)?;
+
+    if frames_vec.is_empty() {
+        return Err(anyhow!("No frames extracted from video".to_string()));
+    }
+    // (t, c, h, w)
+    let frames_tensor = Tensor::stack(&frames_vec, 0)?.contiguous()?;
+    Ok(frames_tensor)
+}