demo.py

import gc
import os
import torch
from extern.depthcrafter.infer import DepthCrafterDemo
# from extern.video_depth_anything.vdademo import VDADemo
import numpy as np
import torch
from transformers import T5EncoderModel
from omegaconf import OmegaConf
from PIL import Image
from models.crosstransformer3d import CrossTransformer3DModel
from models.autoencoder_magvit import AutoencoderKLCogVideoX
from models.pipeline_trajectorycrafter import TrajCrafter_Pipeline
from models.utils import *
from diffusers import (AutoencoderKL, CogVideoXDDIMScheduler, DDIMScheduler,
                       DPMSolverMultistepScheduler,
                       EulerAncestralDiscreteScheduler, EulerDiscreteScheduler,
                       PNDMScheduler)
from transformers import AutoProcessor, Blip2ForConditionalGeneration

class TrajCrafter:
    def __init__(self, opts, gradio=False):
        self.funwarp = Warper(device=opts.device)
        # self.depth_estimater = VDADemo(pre_train_path=opts.pre_train_path_vda,device=opts.device)
        self.depth_estimater = DepthCrafterDemo(unet_path=opts.unet_path,pre_train_path=opts.pre_train_path,cpu_offload=opts.cpu_offload,device=opts.device)
        self.caption_processor = AutoProcessor.from_pretrained(opts.blip_path)
        self.captioner = Blip2ForConditionalGeneration.from_pretrained(opts.blip_path, torch_dtype=torch.float16).to(opts.device)
        self.setup_diffusion(opts)
        if gradio:
            self.opts=opts

    def infer_gradual(self,opts):
        frames = read_video_frames(opts.video_path,opts.video_length,opts.stride,opts.max_res)
        prompt = self.get_caption(opts,frames[opts.video_length//2])
        # depths= self.depth_estimater.infer(frames, opts.near, opts.far).to(opts.device)
        depths= self.depth_estimater.infer(frames, opts.near, opts.far, opts.depth_inference_steps, opts.depth_guidance_scale, window_size=opts.window_size, overlap=opts.overlap).to(opts.device)
        frames = torch.from_numpy(frames).permute(0,3,1,2).to(opts.device)*2.-1. # 49 576 1024 3 -> 49 3 576 1024, [-1,1]
        assert frames.shape[0] == opts.video_length
        pose_s, pose_t, K = self.get_poses(opts,depths,num_frames = opts.video_length)
        warped_images = []
        masks = []        
        for i in tqdm(range(opts.video_length)):
            warped_frame2, mask2, warped_depth2, flow12 = self.funwarp.forward_warp(frames[i:i+1], None, depths[i:i+1], pose_s[i:i+1], pose_t[i:i+1], K[i:i+1], None, opts.mask,twice=False)
            warped_images.append(warped_frame2)
            masks.append(mask2)
        cond_video = (torch.cat(warped_images)+1.)/2.
        cond_masks = torch.cat(masks)

        frames = F.interpolate(frames, size=opts.sample_size, mode='bilinear', align_corners=False)
        cond_video = F.interpolate(cond_video, size=opts.sample_size, mode='bilinear', align_corners=False)
        cond_masks = F.interpolate(cond_masks, size=opts.sample_size, mode='nearest')
        save_video((frames.permute(0,2,3,1)+1.)/2., os.path.join(opts.save_dir,'input.mp4'),fps=opts.fps)
        save_video(cond_video.permute(0,2,3,1), os.path.join(opts.save_dir,'render.mp4'),fps=opts.fps)
        save_video(cond_masks.repeat(1,3,1,1).permute(0,2,3,1), os.path.join(opts.save_dir,'mask.mp4'),fps=opts.fps)
        
        frames = (frames.permute(1,0,2,3).unsqueeze(0)+1.)/2.
        frames_ref = frames[:,:,:10,:,:]
        cond_video = cond_video.permute(1,0,2,3).unsqueeze(0)
        cond_masks = (1.-cond_masks.permute(1,0,2,3).unsqueeze(0))*255.
        generator = torch.Generator(device=opts.device).manual_seed(opts.seed)

        del self.depth_estimater
        del self.caption_processor
        del self.captioner
        gc.collect()
        torch.cuda.empty_cache()
        with torch.no_grad():
            sample = self.pipeline(
                prompt, 
                num_frames = opts.video_length,
                negative_prompt = opts.negative_prompt,
                height      = opts.sample_size[0],
                width       = opts.sample_size[1],
                generator   = generator,
                guidance_scale = opts.diffusion_guidance_scale,
                num_inference_steps = opts.diffusion_inference_steps,
                video        = cond_video,
                mask_video   = cond_masks,
                reference    = frames_ref,
            ).videos
        save_video(sample[0].permute(1,2,3,0), os.path.join(opts.save_dir,'gen.mp4'), fps=opts.fps)

        viz = True
        if viz:
            tensor_left = frames[0].to(opts.device)
            tensor_right = sample[0].to(opts.device)
            interval = torch.ones(3, 49, 384, 30).to(opts.device)
            result = torch.cat((tensor_left, interval, tensor_right), dim=3)
            result_reverse = torch.flip(result, dims=[1])
            final_result = torch.cat((result, result_reverse[:,1:,:,:]), dim=1)
            save_video(final_result.permute(1,2,3,0), os.path.join(opts.save_dir,'viz.mp4'), fps=opts.fps*2)

    def infer_direct(self,opts):
        opts.cut = 20
        frames = read_video_frames(opts.video_path,opts.video_length,opts.stride,opts.max_res)
        prompt = self.get_caption(opts,frames[opts.video_length//2])
        # depths= self.depth_estimater.infer(frames, opts.near, opts.far).to(opts.device)
        depths= self.depth_estimater.infer(frames, opts.near, opts.far, opts.depth_inference_steps, opts.depth_guidance_scale, window_size=opts.window_size, overlap=opts.overlap).to(opts.device)
        frames = torch.from_numpy(frames).permute(0,3,1,2).to(opts.device)*2.-1. # 49 576 1024 3 -> 49 3 576 1024, [-1,1]
        assert frames.shape[0] == opts.video_length
        pose_s, pose_t, K = self.get_poses(opts,depths,num_frames = opts.cut)

        warped_images = []
        masks = []        
        for i in tqdm(range(opts.video_length)):
            if i < opts.cut:
                warped_frame2, mask2, warped_depth2, flow12 = self.funwarp.forward_warp(frames[0:1], None, depths[0:1], pose_s[0:1], pose_t[i:i+1], K[0:1], None, opts.mask,twice=False)
                warped_images.append(warped_frame2)
                masks.append(mask2)
            else:
                warped_frame2, mask2, warped_depth2, flow12 = self.funwarp.forward_warp(frames[i-opts.cut:i-opts.cut+1], None, depths[i-opts.cut:i-opts.cut+1], pose_s[0:1], pose_t[-1:], K[0:1], None, opts.mask,twice=False)
                warped_images.append(warped_frame2)
                masks.append(mask2)    
        cond_video = (torch.cat(warped_images)+1.)/2.
        cond_masks = torch.cat(masks)
        frames = F.interpolate(frames, size=opts.sample_size, mode='bilinear', align_corners=False)
        cond_video = F.interpolate(cond_video, size=opts.sample_size, mode='bilinear', align_corners=False)
        cond_masks = F.interpolate(cond_masks, size=opts.sample_size, mode='nearest')
        save_video((frames[:opts.video_length-opts.cut].permute(0,2,3,1)+1.)/2., os.path.join(opts.save_dir,'input.mp4'),fps=opts.fps)
        save_video(cond_video[opts.cut:].permute(0,2,3,1), os.path.join(opts.save_dir,'render.mp4'),fps=opts.fps)
        save_video(cond_masks[opts.cut:].repeat(1,3,1,1).permute(0,2,3,1), os.path.join(opts.save_dir,'mask.mp4'),fps=opts.fps)
        frames = (frames.permute(1,0,2,3).unsqueeze(0)+1.)/2.
        frames_ref = frames[:,:,:10,:,:]
        cond_video = cond_video.permute(1,0,2,3).unsqueeze(0)
        cond_masks = (1.-cond_masks.permute(1,0,2,3).unsqueeze(0))*255.
        generator = torch.Generator(device=opts.device).manual_seed(opts.seed)

        del self.depth_estimater
        del self.caption_processor
        del self.captioner
        gc.collect()
        torch.cuda.empty_cache()
        with torch.no_grad():        
            sample = self.pipeline(
                prompt, 
                num_frames = opts.video_length,
                negative_prompt = opts.negative_prompt,
                height      = opts.sample_size[0],
                width       = opts.sample_size[1],
                generator   = generator,
                guidance_scale = opts.diffusion_guidance_scale,
                num_inference_steps = opts.diffusion_inference_steps,
                video        = cond_video,
                mask_video   = cond_masks,
                reference    = frames_ref,
            ).videos
        save_video(sample[0].permute(1,2,3,0)[opts.cut:], os.path.join(opts.save_dir,'gen.mp4'), fps=opts.fps)
        
        viz = True
        if viz:
            tensor_left = frames[0][:,:opts.video_length-opts.cut,...].to(opts.device)
            tensor_right = sample[0][:,opts.cut:,...].to(opts.device)
            interval = torch.ones(3, opts.video_length-opts.cut, 384, 30).to(opts.device)
            result = torch.cat((tensor_left, interval, tensor_right), dim=3)
            result_reverse = torch.flip(result, dims=[1])
            final_result = torch.cat((result, result_reverse[:,1:,:,:]), dim=1)
            save_video(final_result.permute(1,2,3,0), os.path.join(opts.save_dir,'viz.mp4'), fps=opts.fps*2)
    
    def infer_bullet(self,opts):
        frames = read_video_frames(opts.video_path,opts.video_length,opts.stride,opts.max_res)
        prompt = self.get_caption(opts,frames[opts.video_length//2])        
        # depths= self.depth_estimater.infer(frames, opts.near, opts.far).to(opts.device)
        depths= self.depth_estimater.infer(frames, opts.near, opts.far, opts.depth_inference_steps, opts.depth_guidance_scale, window_size=opts.window_size, overlap=opts.overlap).to(opts.device)

        frames = torch.from_numpy(frames).permute(0,3,1,2).to(opts.device)*2.-1. # 49 576 1024 3 -> 49 3 576 1024, [-1,1]
        assert frames.shape[0] == opts.video_length
        pose_s, pose_t, K = self.get_poses(opts,depths, num_frames = opts.video_length)

        warped_images = []
        masks = []        
        for i in tqdm(range(opts.video_length)):
            warped_frame2, mask2, warped_depth2, flow12 = self.funwarp.forward_warp(frames[-1:], None, depths[-1:], pose_s[0:1], pose_t[i:i+1], K[0:1], None, opts.mask,twice=False)
            warped_images.append(warped_frame2)
            masks.append(mask2)   
        cond_video = (torch.cat(warped_images)+1.)/2.
        cond_masks = torch.cat(masks)
        frames = F.interpolate(frames, size=opts.sample_size, mode='bilinear', align_corners=False)
        cond_video = F.interpolate(cond_video, size=opts.sample_size, mode='bilinear', align_corners=False)
        cond_masks = F.interpolate(cond_masks, size=opts.sample_size, mode='nearest')
        save_video((frames.permute(0,2,3,1)+1.)/2., os.path.join(opts.save_dir,'input.mp4'),fps=opts.fps)
        save_video(cond_video.permute(0,2,3,1), os.path.join(opts.save_dir,'render.mp4'),fps=opts.fps)
        save_video(cond_masks.repeat(1,3,1,1).permute(0,2,3,1), os.path.join(opts.save_dir,'mask.mp4'),fps=opts.fps)
        frames = (frames.permute(1,0,2,3).unsqueeze(0)+1.)/2.
        frames_ref = frames[:,:,-10:,:,:]
        cond_video = cond_video.permute(1,0,2,3).unsqueeze(0)
        cond_masks = (1.-cond_masks.permute(1,0,2,3).unsqueeze(0))*255.
        generator = torch.Generator(device=opts.device).manual_seed(opts.seed)

        del self.depth_estimater
        del self.caption_processor
        del self.captioner
        gc.collect()
        torch.cuda.empty_cache()
        with torch.no_grad():
            sample = self.pipeline(
                prompt, 
                num_frames = opts.video_length,
                negative_prompt = opts.negative_prompt,
                height      = opts.sample_size[0],
                width       = opts.sample_size[1],
                generator   = generator,
                guidance_scale = opts.diffusion_guidance_scale,
                num_inference_steps = opts.diffusion_inference_steps,
                video        = cond_video,
                mask_video   = cond_masks,
                reference    = frames_ref,
            ).videos
        save_video(sample[0].permute(1,2,3,0), os.path.join(opts.save_dir,'gen.mp4'), fps=opts.fps)
        
        viz = True
        if viz:
            tensor_left = frames[0].to(opts.device)
            tensor_left_full = torch.cat([tensor_left,tensor_left[:,-1:,:,:].repeat(1,48,1,1)],dim=1)
            tensor_right = sample[0].to(opts.device)
            tensor_right_full = torch.cat([tensor_left,tensor_right[:,1:,:,:]],dim=1)
            interval = torch.ones(3, 49*2-1, 384, 30).to(opts.device)
            result = torch.cat((tensor_left_full, interval, tensor_right_full), dim=3)
            result_reverse = torch.flip(result, dims=[1])
            final_result = torch.cat((result, result_reverse[:,1:,:,:]), dim=1)
            save_video(final_result.permute(1,2,3,0), os.path.join(opts.save_dir,'viz.mp4'), fps=opts.fps*4)

    def get_caption(self,opts,image):
        image_array = (image * 255).astype(np.uint8)
        pil_image = Image.fromarray(image_array)
        inputs = self.caption_processor(images=pil_image, return_tensors="pt").to(opts.device, torch.float16)
        generated_ids = self.captioner.generate(**inputs)
        generated_text = self.caption_processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip() 
        return generated_text + opts.refine_prompt      

    def get_poses(self,opts,depths,num_frames):
        radius = depths[0,0,depths.shape[-2]//2,depths.shape[-1]//2].cpu()*opts.radius_scale     
        radius = min(radius, 5)  
        cx = 512. #depths.shape[-1]//2 
        cy = 288. #depths.shape[-2]//2 
        f = 500 #500.
        K = torch.tensor([[f,   0., cx],[  0., f, cy],[  0.,   0.,   1.]]).repeat(num_frames,1,1).to(opts.device)
        c2w_init = torch.tensor([[-1.,  0.,  0.,  0.],
                                [ 0.,  1.,  0.,  0.],
                                [ 0.,  0., -1., 0.],
                                [ 0.,  0.,  0.,  1.]]).to(opts.device).unsqueeze(0)
        if opts.camera == 'target':
            dtheta, dphi, dr, dx, dy = opts.target_pose
            poses = generate_traj_specified(c2w_init, dtheta, dphi, dr*radius, dx, dy, num_frames, opts.device)
        elif opts.camera =='traj':
            with open(opts.traj_txt, 'r') as file:
                lines = file.readlines()
                theta = [float(i) for i in lines[0].split()]
                phi = [float(i) for i in lines[1].split()]
                r = [float(i)*radius for i in lines[2].split()]
            poses = generate_traj_txt(c2w_init, phi, theta, r, num_frames, opts.device)
        poses[:,2, 3] = poses[:,2, 3] + radius
        pose_s = poses[opts.anchor_idx:opts.anchor_idx+1].repeat(num_frames,1,1)
        pose_t = poses
        return pose_s, pose_t, K

    # def setup_diffusion(self,opts):
    #     # transformer = CrossTransformer3DModel.from_pretrained_cus(opts.transformer_path).to(opts.weight_dtype)
    #     transformer = CrossTransformer3DModel.from_pretrained(opts.transformer_path).to(opts.weight_dtype)
    #     # transformer = transformer.to(opts.weight_dtype)
    #     vae = AutoencoderKLCogVideoX.from_pretrained(
    #         opts.model_name, 
    #         subfolder="vae"
    #     ).to(opts.weight_dtype)
    #     text_encoder = T5EncoderModel.from_pretrained(
    #         opts.model_name, subfolder="text_encoder", torch_dtype=opts.weight_dtype
    #     )
    #     # Get Scheduler
    #     Choosen_Scheduler  = {
    #         "Euler": EulerDiscreteScheduler,
    #         "Euler A": EulerAncestralDiscreteScheduler,
    #         "DPM++": DPMSolverMultistepScheduler, 
    #         "PNDM": PNDMScheduler,
    #         "DDIM_Cog": CogVideoXDDIMScheduler,
    #         "DDIM_Origin": DDIMScheduler,
    #     }[opts.sampler_name]
    #     scheduler = Choosen_Scheduler.from_pretrained(
    #         opts.model_name, 
    #         subfolder="scheduler"
    #     )

    #     self.pipeline = TrajCrafter_Pipeline.from_pretrained(
    #         opts.model_name,
    #         vae=vae,
    #         text_encoder=text_encoder,
    #         transformer=transformer,
    #         scheduler=scheduler,
    #         torch_dtype=opts.weight_dtype
    #     )

    #     if opts.low_gpu_memory_mode:
    #         self.pipeline.enable_sequential_cpu_offload()
    #     else:
    #         self.pipeline.enable_model_cpu_offload()

    def setup_diffusion(self, opts):
        import torch
    
        # 1) 选择设备
        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    
        # （可选）在 CPU 跑时避免 efficient attention 报错；在 CUDA 上也无害
        try:
            torch.backends.cuda.enable_flash_sdp(False)
            torch.backends.cuda.enable_mem_efficient_sdp(True)
            torch.backends.cuda.enable_math_sdp(True)
        except Exception:
            pass
    
        # 2) 加载/放置子模块到 device + dtype
        # 注意：原代码只 .to(dtype)，未指定 device；这里补齐
        transformer = CrossTransformer3DModel.from_pretrained(opts.transformer_path)
        transformer = transformer.to(device=device, dtype=opts.weight_dtype)
    
        vae = AutoencoderKLCogVideoX.from_pretrained(
            opts.model_name,
            subfolder="vae",
            # 仅指定 dtype；后面统一 .to(device)
            # 某些 from_pretrained 不支持 device 形参
        ).to(dtype=opts.weight_dtype).to(device)
    
        text_encoder = T5EncoderModel.from_pretrained(
            opts.model_name,
            subfolder="text_encoder",
            torch_dtype=opts.weight_dtype,
        ).to(device)
    
        # 3) 调度器照旧
        Choosen_Scheduler  = {
            "Euler": EulerDiscreteScheduler,
            "Euler A": EulerAncestralDiscreteScheduler,
            "DPM++": DPMSolverMultistepScheduler,
            "PNDM": PNDMScheduler,
            "DDIM_Cog": CogVideoXDDIMScheduler,
            "DDIM_Origin": DDIMScheduler,
        }[opts.sampler_name]
        scheduler = Choosen_Scheduler.from_pretrained(
            opts.model_name,
            subfolder="scheduler"
        )
    
        # 4) 组装 pipeline，并确保在正确 device/dtype
        self.pipeline = TrajCrafter_Pipeline.from_pretrained(
            opts.model_name,
            vae=vae,
            text_encoder=text_encoder,
            transformer=transformer,
            scheduler=scheduler,
            torch_dtype=opts.weight_dtype,
        )
    
        # Offload 策略：
        # - 如果你机器有足够显存，建议直接 to(device) 获得最稳的行为
        # - 如果显存紧张，再启用 offload（需要 accelerate 支持）
        if opts.low_gpu_memory_mode:
            # 这两种 offload 会在计算时把块迁移到 GPU，空闲时回收；加速略低但更省显存
            # 二选一：根据你之前的使用习惯保留其一
            # self.pipeline.enable_sequential_cpu_offload()
            self.pipeline.enable_model_cpu_offload()
        else:
            self.pipeline.to(device)

    def run_gradio(self,input_video, stride, radius_scale, pose, steps, seed):
        frames = read_video_frames(input_video, self.opts.video_length, stride,self.opts.max_res)
        prompt = self.get_caption(self.opts,frames[self.opts.video_length//2])
        # depths= self.depth_estimater.infer(frames, opts.near, opts.far).to(opts.device)
        depths= self.depth_estimater.infer(frames, self.opts.near, self.opts.far, self.opts.depth_inference_steps, self.opts.depth_guidance_scale, window_size=self.opts.window_size, overlap=self.opts.overlap).to(self.opts.device)
        frames = torch.from_numpy(frames).permute(0,3,1,2).to(self.opts.device)*2.-1. # 49 576 1024 3 -> 49 3 576 1024, [-1,1]
        num_frames = frames.shape[0]
        assert num_frames == self.opts.video_length
        radius_scale = float(radius_scale)
        radius = depths[0,0,depths.shape[-2]//2,depths.shape[-1]//2].cpu()*radius_scale     
        radius = min(radius, 5)  
        cx = 512. #depths.shape[-1]//2 
        cy = 288. #depths.shape[-2]//2 
        f = 500 #500.
        K = torch.tensor([[f,   0., cx],[  0., f, cy],[  0.,   0.,   1.]]).repeat(num_frames,1,1).to(self.opts.device)
        c2w_init = torch.tensor([[-1.,  0.,  0.,  0.],
                                [ 0.,  1.,  0.,  0.],
                                [ 0.,  0., -1., 0.],
                                [ 0.,  0.,  0.,  1.]]).to(self.opts.device).unsqueeze(0)

        # import pdb
        # pdb.set_trace()
        theta,phi,r,x,y = [float(i) for i in pose.split(';')]
        # theta,phi,r,x,y = [float(i) for i in theta.split()],[float(i) for i in phi.split()],[float(i) for i in r.split()],[float(i) for i in x.split()],[float(i) for i in y.split()]
        # target mode
        poses = generate_traj_specified(c2w_init, theta, phi, r*radius, x, y, num_frames, self.opts.device)
        poses[:,2, 3] = poses[:,2, 3] + radius
        pose_s = poses[self.opts.anchor_idx:self.opts.anchor_idx+1].repeat(num_frames,1,1)
        pose_t = poses
        
        warped_images = []
        masks = []        
        for i in tqdm(range(self.opts.video_length)):
            warped_frame2, mask2, warped_depth2, flow12 = self.funwarp.forward_warp(frames[i:i+1], None, depths[i:i+1], pose_s[i:i+1], pose_t[i:i+1], K[i:i+1], None, self.opts.mask,twice=False)
            warped_images.append(warped_frame2)
            masks.append(mask2)
        cond_video = (torch.cat(warped_images)+1.)/2.
        cond_masks = torch.cat(masks)

        frames = F.interpolate(frames, size=self.opts.sample_size, mode='bilinear', align_corners=False)
        cond_video = F.interpolate(cond_video, size=self.opts.sample_size, mode='bilinear', align_corners=False)
        cond_masks = F.interpolate(cond_masks, size=self.opts.sample_size, mode='nearest')
        save_video((frames.permute(0,2,3,1)+1.)/2., os.path.join(self.opts.save_dir,'input.mp4'),fps=self.opts.fps)
        save_video(cond_video.permute(0,2,3,1), os.path.join(self.opts.save_dir,'render.mp4'),fps=self.opts.fps)
        save_video(cond_masks.repeat(1,3,1,1).permute(0,2,3,1), os.path.join(self.opts.save_dir,'mask.mp4'),fps=self.opts.fps)
        
        frames = (frames.permute(1,0,2,3).unsqueeze(0)+1.)/2.
        frames_ref = frames[:,:,:10,:,:]
        cond_video = cond_video.permute(1,0,2,3).unsqueeze(0)
        cond_masks = (1.-cond_masks.permute(1,0,2,3).unsqueeze(0))*255.
        generator = torch.Generator(device=self.opts.device).manual_seed(seed)

        # del self.depth_estimater
        # del self.caption_processor
        # del self.captioner
        # gc.collect()
        torch.cuda.empty_cache()
        with torch.no_grad():
            sample = self.pipeline(
                prompt, 
                num_frames = self.opts.video_length,
                negative_prompt = self.opts.negative_prompt,
                height      = self.opts.sample_size[0],
                width       = self.opts.sample_size[1],
                generator   = generator,
                guidance_scale = self.opts.diffusion_guidance_scale,
                num_inference_steps = steps,
                video        = cond_video,
                mask_video   = cond_masks,
                reference    = frames_ref,
            ).videos
        save_video(sample[0].permute(1,2,3,0), os.path.join(self.opts.save_dir,'gen.mp4'), fps=self.opts.fps)

        viz = True
        if viz:
            tensor_left = frames[0].to(self.opts.device)
            tensor_right = sample[0].to(self.opts.device)
            interval = torch.ones(3, 49, 384, 30).to(self.opts.device)
            result = torch.cat((tensor_left, interval, tensor_right), dim=3)
            result_reverse = torch.flip(result, dims=[1])
            final_result = torch.cat((result, result_reverse[:,1:,:,:]), dim=1)
            save_video(final_result.permute(1,2,3,0), os.path.join(self.opts.save_dir,'viz.mp4'), fps=self.opts.fps*2)
        return os.path.join(self.opts.save_dir,'viz.mp4')