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.gitignore

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+*.ipynb
+*.pkl
+*.pth
+*.pt
+*/*.ipynb
+*/*.pkl
+*/*.pth
+*/*.pt
+*/*/*.ipynb
+*/*/*.pkl
+*/*/*.pth
+*/*/*.pt

README.md

@@ -1,12 +1,11 @@
 ---
 title: VfiTest
-emoji: 🏢
-colorFrom: blue
-colorTo: purple
+emoji: 👁
+colorFrom: yellow
+colorTo: blue
 sdk: gradio
 sdk_version: 4.19.2
 app_file: app.py
 pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+license: unknown
+---

START_COMMAND.txt

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+CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node 4 main.py --cfg cfgs/upr_freq_unimatch_exp001.yaml
+CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node 4 main.py --cfg cfgs/upr_freq_unimatch_exp002.yaml
+CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node 4 main.py --cfg cfgs/upr_freq003.yaml
+CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node 4 main.py --cfg cfgs/upr_freq004.yaml
+CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node 4 main.py --cfg cfgs/upr_freq005.yaml
+CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node 4 main.py --cfg cfgs/upr_freq006.yaml
+CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node 4 main.py --cfg cfgs/upr_freq007.yaml
+CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node 4 main.py --cfg cfgs/upr_freq008.yaml
+
+CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node 4 main.py --cfg cfgs/upr_freq_unimatch_exp002_stopmotion.yaml
+
+**inference 고려사항**
+class Model에서 패딩 위치: normalization -> padding
+utils.padder.py: constant padding mode
+pyr_level (Vimeo: 3, UCF: 3, SNU-FILM: 5)
+trainer.py: train function에서 epoch 들어가기 전에 return
+yaml: [results.txt, 이미지들 저장하고 싶으면][~~~every 전부 1로 설정] /// [그렇지 않으면][~~~every 전부 마지막 에폭 안 나눠떨어지는 수로 설정]

UPR_BASIC_INFERENCE_SPEED.txt

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+Test:  [  0/237]  eta: 0:30:27  psnr: 25.80  ssim: 0.79  time: 7.7092  data: 0.3797  max mem: 12902
+Test:  [ 10/237]  eta: 0:04:25  psnr: 25.25  ssim: 0.73  time: 1.1688  data: 0.0348  max mem: 12902
+Test:  [ 20/237]  eta: 0:02:57  psnr: 24.94  ssim: 0.71  time: 0.4738  data: 0.0002  max mem: 12902
+Test:  [ 30/237]  eta: 0:02:23  psnr: 25.29  ssim: 0.72  time: 0.4329  data: 0.0002  max mem: 12902
+Test:  [ 40/237]  eta: 0:02:03  psnr: 25.64  ssim: 0.77  time: 0.4309  data: 0.0002  max mem: 12902
+Test:  [ 50/237]  eta: 0:01:50  psnr: 27.04  ssim: 0.80  time: 0.4291  data: 0.0002  max mem: 12902
+Test:  [ 60/237]  eta: 0:01:39  psnr: 24.79  ssim: 0.76  time: 0.4310  data: 0.0002  max mem: 12902
+Test:  [ 70/237]  eta: 0:01:31  psnr: 25.93  ssim: 0.78  time: 0.4323  data: 0.0002  max mem: 12902
+Test:  [ 80/237]  eta: 0:01:23  psnr: 25.98  ssim: 0.76  time: 0.4320  data: 0.0002  max mem: 12902
+Test:  [ 90/237]  eta: 0:01:17  psnr: 24.59  ssim: 0.77  time: 0.4666  data: 0.0002  max mem: 12902
+Test:  [100/237]  eta: 0:01:11  psnr: 24.06  ssim: 0.72  time: 0.4651  data: 0.0002  max mem: 12902
+Test:  [110/237]  eta: 0:01:04  psnr: 24.65  ssim: 0.67  time: 0.4289  data: 0.0002  max mem: 12902
+Test:  [120/237]  eta: 0:00:58  psnr: 23.71  ssim: 0.73  time: 0.4287  data: 0.0002  max mem: 12902
+Test:  [130/237]  eta: 0:00:53  psnr: 28.94  ssim: 0.87  time: 0.4288  data: 0.0002  max mem: 12902
+Test:  [140/237]  eta: 0:00:47  psnr: 26.36  ssim: 0.73  time: 0.4301  data: 0.0002  max mem: 12902
+Test:  [150/237]  eta: 0:00:42  psnr: 29.65  ssim: 0.85  time: 0.4299  data: 0.0002  max mem: 12902
+Test:  [160/237]  eta: 0:00:37  psnr: 25.18  ssim: 0.74  time: 0.4287  data: 0.0002  max mem: 12902
+Test:  [170/237]  eta: 0:00:32  psnr: 25.57  ssim: 0.77  time: 0.4618  data: 0.0002  max mem: 12902
+Test:  [180/237]  eta: 0:00:27  psnr: 27.12  ssim: 0.80  time: 0.4618  data: 0.0002  max mem: 12902
+Test:  [190/237]  eta: 0:00:22  psnr: 23.36  ssim: 0.59  time: 0.4290  data: 0.0002  max mem: 12902
+Test:  [200/237]  eta: 0:00:17  psnr: 25.49  ssim: 0.78  time: 0.4296  data: 0.0002  max mem: 12902
+Test:  [210/237]  eta: 0:00:12  psnr: 27.89  ssim: 0.82  time: 0.4322  data: 0.0002  max mem: 12902
+Test:  [220/237]  eta: 0:00:08  psnr: 28.20  ssim: 0.81  time: 0.4336  data: 0.0002  max mem: 12902
+Test:  [230/237]  eta: 0:00:03  psnr: 26.18  ssim: 0.78  time: 0.4322  data: 0.0002  max mem: 12902
+Test:  [236/237]  eta: 0:00:00  psnr: 25.18  ssim: 0.77  time: 0.5410  data: 0.0002  max mem: 12902
+Test: Total time: 0:01:53 (0.4800 s / it)
+[12-01 16:11:10] Averaged validate stats:psnr: 25.48485653640427  ssim: 0.7470527462248057
+[12-01 16:11:10] best performance achieved at epoch -1 with performance of 25.48485653640427
+
+MEMORY: 6818MB

app.py

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+import gradio as gr
+import numpy as np
+import cv2
+import os
+import glob
+import torch
+import shutil
+from PIL import Image
+from tqdm import tqdm
+from torch.nn import functional as F
+from torchvision.transforms import functional as TF
+from matplotlib import pyplot as plt
+from modules.components.upr_net_freq import upr_freq as upr_freq002
+from modules.components.upr_basic import upr as upr_basic
+import datetime
+import zipfile
+
+os.system('python -m pip install --upgrade pip')
+
+#from scipy.interpolate import make_interp_spline
+
+# python3 -m vfi_inference_triplet --cuda_index 0 \
+# --root ../VFI_Inference/thistriplet_notarget --pretrain_path ./pretrained/upr_freq002.pth \
+# --pyr_level 7 --nr_lvl_skipped 0 --splat_mode average --down_scale 1
+
+# 아이디, 비밀번호 튜플, 리스트에 추가하면 여러 사용자가 사용 가능합니다.
+# 다른 파일로 만들어 사용하기를 권장합니다.
+KEY = [("test", "test"),
+       ]
+
+# 로그인 시 호출되는 함수입니다.
+#혹시 로그인에 대한 정보나, 다ip 등을 얻고 싶으면 이 부분 수정바랍니다.
+def check_valid_login(user_name, password):
+    #client_ip = request.client.host
+    #print(client_ip)
+    flag = (user_name, password) in KEY
+    return flag
+
+# 비디오에서 처음 몇 프레임을 자를지 변수입니다.
+MAX_FRAME = 24
+
+#VFI inference 코드를 그대로 가져왔습니다.
+DEVICE = 0#"cuda"
+torch.cuda.set_device(DEVICE)
+#ROOT = args.root
+#SAVE_ROOT = f'output'
+SCALE = 1
+pyr_level = 7
+nr_lvl_skipped = 0
+splat_mode = "average"
+pretrain_path = "./pretrained/upr_freq002.pth"
+
+model = upr_freq002.Model(pyr_level=pyr_level,
+                              nr_lvl_skipped=nr_lvl_skipped,
+                              splat_mode=splat_mode)
+sd = torch.load(pretrain_path, map_location='cpu')
+sd = sd['model'] if 'model' in sd.keys() else sd
+print(model.load_state_dict(sd))
+model = model.to(DEVICE)
+
+def get_sorted_img(file_path):
+    return sorted(glob.glob(os.path.join(file_path, f"*.png")), key = lambda x : float(x.split("_")[-1][:-4]))
+
+def multiple_pad(image, multiple):
+    _,_,H,W = image.size()
+    pad1 = multiple-(H%multiple) if H%multiple!=0 else 0
+    pad2 = multiple-(W%multiple) if W%multiple!=0 else 0
+    return TF.pad(image, (0,0,pad2,pad1))
+
+#이미지 1(path1), 2를 VFI하여 가운데 이미지를 생성하는 함수입니다.
+def multiple_VFIx2(path1, path2, output_name):
+    file_list = [path1, path2]
+    img_list = [(torch.from_numpy(cv2.imread(file)[:,:,[2,1,0]])/255).permute(2,0,1).unsqueeze(0).to(DEVICE) for file in file_list]
+    img_list = [multiple_pad(img, SCALE) for k, img in enumerate(img_list)]
+    img_list = [F.interpolate(img, scale_factor=1/SCALE, mode='bicubic') for k, img in enumerate(img_list)]
+    img0,img1 = img_list
+    _,_,Hori,Wori = img0.size()
+
+    with torch.no_grad():
+        result_dict, extra_dict = model(img0, img1, pyr_level=pyr_level, nr_lvl_skipped=nr_lvl_skipped, time_step=0.5)
+    out = F.interpolate(result_dict['imgt_pred'], scale_factor=SCALE, mode='bicubic')[:,:,:Hori,:Wori].clamp(0,1)
+    cv2.imwrite(output_name, (out[0].cpu().permute(1,2,0)*255).numpy().astype(np.uint8)[:,:,[2,1,0]])
+    torch.cuda.empty_cache()
+
+#1, 2를 3번 VFI하여 3장을 만드는 함수입니다.
+"""
+def multiple_VFIx4(path1, path2, name1, name2, name3):
+    multiple_VFIx2(path1, path2, name2)
+    multiple_VFIx2(path1, name2, name1)
+    multiple_VFIx2(name2, path2, name3)
+"""
+def multiple_VFIx4(path1, path2):
+    frac = [".25", ".5", ".75"]
+    name1 , name2, name3 = [f"{path1[:-4]}{f}.png" for f in frac]
+    multiple_VFIx2(path1, path2, name2)
+    multiple_VFIx2(path1, name2, name1)
+    multiple_VFIx2(name2, path2, name3)
+
+#0, 0.125 , 0.25, 0.5, 0.75, 0.875, 1로 5장 생성
+def multiple_VFIx6(path1, path2):
+    frac = [".125", ".25", ".75", ".875"]
+    name_inf1 , name1, name2, name_inf2 = [f"{path1[:-4]}{f}.png" for f in frac]
+    multiple_VFIx4(path1, path2)
+    multiple_VFIx2(path1, name1, name_inf1)
+    multiple_VFIx2(name2, path2, name_inf2)
+
+#비디오에서 fix를 하여 이미지를 대체하여 출력하는 함수입니다.
+def fix_img(idx, fixed_list, input_dir = "input", output_dir = "output"):
+    idx = int(idx)
+    #올바르지 않거나, 이미 fix 했다면 변화 x
+    if idx < 1 or idx > MAX_FRAME - 2 or fixed_list[idx] == 1:
+        return {
+            fix_result_gallery : gr.Gallery(),
+            fix_result_group : gr.Group(),
+            fixed_frame : gr.Text()
+            }
+    now_time = os.path.basename(input_dir)
+    output_dir = os.path.join(output_dir, f"fix_{now_time}")
+    os.makedirs(output_dir, exist_ok = True)
+    output_name = os.path.join(output_dir, f"img_{idx:03d}.png")
+
+    multiple_VFIx2(os.path.join(input_dir, f"img_{idx - 1:03d}.png"),
+                    os.path.join(input_dir, f"img_{idx + 1:03d}.png"),
+                    output_name)
+    fixed_list[idx] = 1
+
+    fixed_frame_string = ""
+    result_list = []
+    name_list = []
+    #순차적으로 결과 갤러리 갱신
+    for i in range(MAX_FRAME):
+        if fixed_list[i] == 1:
+            name_list.append(f"(fixed) frame {i}")
+            result_list.append(os.path.join(output_dir, f"img_{i:03d}.png"))
+            fixed_frame_string += f"{i}, "
+        else:
+            name_list.append(f"frame {i}")
+            result_list.append(os.path.join(input_dir, f"img_{i:03d}.png"))
+    return {
+        fix_result_gallery : gr.Gallery(value = [(img, name) for img, name in zip(result_list, name_list)], selected_index = idx),
+        fix_result_group : gr.Group(visible=True),
+        fixed_frame : gr.Text(visible=True, value = fixed_frame_string[:-2]),
+    }
+
+#주어진 ease_val 리스트의 값 바탕으로 ease를 실행시키는 함수입니다.
+def ease_frames(ease_val, input_dir = "input", output_dir = "output", progress=gr.Progress(track_tqdm=False)):
+    #now = os.path.basename(input_dir)
+    now = datetime.datetime.now().strftime('%Y%m%d_%H%M%S')
+    output_dir = os.path.join(output_dir, f"ease_{now}")
+    os.makedirs(output_dir, exist_ok = True)
+    out_frame_list = [os.path.join(output_dir, f"img_{i:03d}.png") for i in range(MAX_FRAME)]
+    for i, f in enumerate([os.path.join(input_dir, f"img_{i:03d}.png") for i in range(MAX_FRAME)]):
+        shutil.copyfile(f, out_frame_list[i])
+    img_name = []
+    for i in progress.tqdm(range(MAX_FRAME - 1), desc = "VFI frames..."):
+        img_name.append(f"frame {i}")
+        if ease_val[i] == 1: pass
+        #x1는 아무것도, x2는 한 장, x4는 3장
+        # 아래 글자 추가 부분은 새로운 이미지의 제목 바꾸는 부분입니다.
+        elif ease_val[i] == 2:
+            multiple_VFIx2(out_frame_list[i], out_frame_list[i + 1]
+                           , os.path.join(output_dir, f"img_{i:03d}.5.png"))
+            img_name.append(f"(new) frame {i + 0.5}")
+        elif ease_val[i] == 3:
+            multiple_VFIx4(out_frame_list[i], out_frame_list[i + 1])
+            img_name.append(f"(new) frame {i + 0.25}")
+            img_name.append(f"(new) frame {i + 0.5}")
+            img_name.append(f"(new) frame {i + 0.75}")
+    img_name.append(f"frame {MAX_FRAME - 1}")
+    files = get_sorted_img(output_dir)
+
+    #다운로드용 zip 파일
+    zip_name = os.path.join(output_dir,"frame_list.zip")
+    with zipfile.ZipFile(zip_name, 'w', compression=zipfile.ZIP_DEFLATED) as new_zip:
+        for x in progress.tqdm(files, desc ="compress file..."):
+            new_zip.write(x, os.path.basename(x))
+    
+    return {
+        ease_result_gallery : [(file, name) for file, name in zip(files, img_name)],
+        ease_make_video : gr.Accordion(visible = True),
+        last_ease_dir : output_dir,
+        ease_zip : gr.File(value = zip_name)
+    }
+# 이미지 두 장을 받아 VFI를 수행하는 함수입니다.
+def VFI_two(l, r, flag ,output_dir = "output"):
+    now = datetime.datetime.now().strftime('%Y%m%d_%H%M%S')
+    output_dir = os.path.join(output_dir, f"fix_img_{now}")
+    os.makedirs(output_dir, exist_ok = True)
+    l = Image.fromarray(l)
+    r = Image.fromarray(r)
+    #메모리 초과를 막기 위해 적당한 크기 픽셀 이하가 되도록 관리
+    W, H = l.size
+    #1920 * 1080 * 1.2 * 1.2 가 대충 3e6라 그걸 기준으로 잡았습니다.
+    mul = ((3e6) / (W * H)) ** (1/2)
+    H, W = int(H * mul), int(W * mul)
+    #이미지가 커서 줄여야 한다면 감소, 아님 그냥 입력
+    if mul < 1:
+        l = l.resize((W, H))
+        r = r.resize((W, H))
+    l_name, r_name = f"{output_dir}/img_000.png", f"{output_dir}/img_001.png"
+    l.save(l_name)
+    r.save(r_name)
+
+    if flag == "x4":
+        multiple_VFIx4(l_name, r_name)
+    elif flag == "x2":
+        output_name = f"{output_dir}/img_000.5.png"
+        multiple_VFIx2(l_name, r_name, output_name)
+    else:
+        multiple_VFIx6(l_name, r_name)
+
+    return {
+        frame_gen_result_gallery :  gr.Gallery(visible=True, value=get_sorted_img(output_dir))
+    }
+#다른 이미지 입력을 위해 입력된 이미지를 날리는 합수입니다.
+def clear_fix():
+    return{
+        img_0 : gr.Image(label="start image", sources =["upload"], value = None),
+        img_1 : gr.Image(label="end image", sources =["upload"], value = None),
+        frame_gen_result_gallery :  gr.Gallery(visible=True, value=None)
+    }
+
+with gr.Blocks(theme=gr.themes.Default(), title = "Inshorts Animator V. 0.5") as demo:
+    def info(request: gr.Request):
+        #ip를 얻는 부분입니다.
+        #추후 특정 ip 허용, 차단 등이 필요하면 이쪽 참고해 주세요
+        headers = request.headers
+        print(headers["x-forwarded-for"].split(","))
+    demo.load(info, None)
+    gr.Markdown(f"""# Inshorts Animator V. 0.5 WebUI (Permitted User Only)""")
+    with gr.Tab("Mid Frame Generator"):
+        with gr.Column():
+            
+            with gr.Row():
+                img_0 = gr.Image(label="start image", sources =["upload"])
+                img_1 = gr.Image(label="end image", sources =["upload"])
+            with gr.Row():
+                VFI_flag = gr.Radio(["x2", "x4", "x6(side ease)"], label="VFI ratio", value = "x2", interactive = True)
+                image_button = gr.Button("Run model")
+            frame_gen_result_gallery = gr.Gallery(visible=True,
+                                             label="result", columns=[5], rows=[1], object_fit="contain", height="auto", preview = True,
+                                             interactive = False)
+            image_button.click(VFI_two, inputs=[img_0, img_1, VFI_flag],
+                            outputs=[frame_gen_result_gallery])
+            clear_button = gr.Button("Clear images")
+            clear_button.click(clear_fix, inputs=[],
+                            outputs=[img_0, img_1, frame_gen_result_gallery])
+    with gr.Tab("Video"):
+        with gr.Group(visible=True) as video_input_group:
+            gr.Markdown(f"""#### only can handle {MAX_FRAME} frames""")
+            with gr.Column():
+                input_dir = gr.State("")
+                fps = gr.Number(visible=False)
+                video_input =  gr.Video(label="Input Video", interactive=True, sources=['upload'])
+                gr.Markdown(f"""If video frame size is big, it will be resized""")
+                upload_button = gr.Button("upload video")
+        with gr.Group(visible=False) as image_edit_group:
+            with gr.Row():
+                with gr.Column():
+                    with gr.Tab("Original Frame (for Monitoring)"):
+                        fixed_list = gr.State([0] * (MAX_FRAME))
+                        selected = gr.Number(visible=False, label = "selected frame", interactive = False)
+                        image_gallery = gr.Gallery(
+                            label="inputs", columns=[MAX_FRAME], rows=[1], object_fit="contain", height="auto", preview = True,
+                            show_download_button=False)
+                        clear_video = gr.Button("clear video")
+                with gr.Tab("Frame Fixer"):
+                    with gr.Column():
+                        #with gr.Row():
+                        #with gr.Row():
+                            with gr.Group(visible=True) as fix_result_group:
+                                fix_result_gallery = gr.Gallery(
+                                             label="result", columns=[MAX_FRAME], rows=[1], object_fit="contain", height="auto", preview = True,
+                                             interactive = False)
+                            fix_button = gr.Button(visible = True)
+                            with gr.Row():
+                                fixed_frame = gr.Text(visible=False, label = "fixed frame", interactive = False)
+                    fix_button.click(fix_img, inputs=[selected, fixed_list, input_dir],
+                                outputs=[fix_result_gallery, fix_result_group, fixed_frame])
+                    def update_fix_button_visible(evt: gr.SelectData):
+                        flag = 0 < evt.index < MAX_FRAME - 1
+                        msg = f"fix frame {evt.index}" if flag else f"can only fix 1 ~ {MAX_FRAME - 2}"
+                        return {
+                            fix_button:gr.Button(msg, visible=True),
+                            selected : evt.index,
+                            fix_result_gallery : gr.Gallery(selected_index = evt.index)
+                        }
+                    image_gallery.select(update_fix_button_visible, None, [fix_button, selected, fix_result_gallery])
+                with gr.Tab("Motion easer"):
+                    with gr.Column():
+                        with gr.Column():
+                        #with gr.Row():
+                            with gr.Group(visible=True) as ease_result_group:
+                                last_ease_dir = gr.State("")
+                                ease_result_gallery = gr.Gallery(
+                                             label="result", columns=[MAX_FRAME], rows=[4], object_fit="contain", height="auto", preview = True,
+                                             interactive = False)
+                            ease_button = gr.Button("ease", visible = True)
+                        plt_data = gr.State([1] * (MAX_FRAME - 1))
+                        VFI_x = gr.Radio([("x1", 1), ("x2", 2), ("x4", 3)], value = 1, label="Slow ratio", info="adjust Slow ratio", interactive = True)
+                        with gr.Row():
+                            edit_one_button = gr.Button("edit one scale", visible = True)
+                            edit_all_button = gr.Button("edit all scale", visible = True)
+                        now_frame =  gr.Slider(0, MAX_FRAME - 1 - 1, step=1, label="Start frame", info="Choose Start frame to make slow. Interpolation will apply to (frame ~ frame + 1)")
+
+                        def plt_edit(data):
+                            fig = plt.figure()
+                            x = np.arange(0, MAX_FRAME - 1) + 0.5
+                            y = np.array(data)
+                            plt.plot(x , y, color = 'black', marker = "o", linewidth = "2.5")
+                            plt.xticks(np.arange(0, MAX_FRAME))
+                            plt.yticks([1, 2, 3], ["x1", "x2\nslow", "x4\nslow"])
+                            plt.gca().invert_yaxis()
+                            plt.grid(True)
+                            plt.rcParams['xtick.bottom'] = plt.rcParams['xtick.labelbottom'] = False
+                            plt.rcParams['xtick.top'] = plt.rcParams['xtick.labeltop'] = True
+                            return fig
+                        ease_plot = gr.Plot(value = plt_edit(plt_data.value), show_label=False)
+                        with gr.Accordion("get result", visible = False) as ease_make_video:
+                            ease_zip = gr.File(label = "Download all image frames in Zip", interactive = False)
+                            make_video_button = gr.Button("make video")
+                            result_video = gr.Video(interactive = False)
+                            def make_video(frame_dir, fps):
+                                t = os.path.basename(frame_dir)
+                                output_name = f"{frame_dir}/{t}.mp4"
+                                if os.path.exists(output_name):
+                                    os.remove(output_name)
+                                frame_list = get_sorted_img(frame_dir)
+                                with open(f"{frame_dir}/input.txt", "w") as f:
+                                    for line in frame_list:
+                                        f.write(f"file '{os.path.basename(line)}'\n")
+                                cmd = f'ffmpeg -r {fps} -f concat -safe 0 -i {frame_dir}/input.txt -c:v libx264 -preset veryslow -crf 10 {output_name}'
+                                os.system(cmd)
+                                return output_name
+                            make_video_button.click(make_video, inputs = [last_ease_dir, fps], outputs = [result_video])
+                    ease_button.click(ease_frames, inputs=[plt_data, input_dir], outputs=[ease_result_gallery, ease_make_video, last_ease_dir, ease_zip])
+
+                    def edit_one_scale(data, idx, x):
+                        if idx < MAX_FRAME - 1:
+                            data[idx] = x if x else 1
+                        return plt_edit(data)
+                    edit_one_button.click(edit_one_scale, inputs=[plt_data, now_frame, VFI_x] , outputs=[ease_plot])
+                    def edit_all_scale(data, x):
+                        for i in range(len(data)): data[i] = x if x else 1
+                        return plt_edit(data)
+                    edit_all_button.click(edit_all_scale, inputs=[plt_data, VFI_x], outputs=[ease_plot])
+        def clear_vd(plt_data, fixed_list):
+            for i in range(len(plt_data)): plt_data[i] = 1
+            for i in range(len(fixed_list)): fixed_list[i] = 0
+            return {video_input:gr.Video(label="Input Video", interactive=True, sources=['upload'], value = None),
+                    ease_result_gallery : gr.Gallery(
+                                             label="result", columns=[MAX_FRAME], rows=[4], object_fit="contain", height="auto", preview = True,
+                                             interactive = False, value = None),
+                    fix_result_gallery : gr.Gallery(
+                                             label="result", columns=[MAX_FRAME], rows=[1], object_fit="contain", height="auto", preview = True,
+                                             interactive = False, value = None),
+                    fixed_frame : gr.Text(visible=False, label = "fixed frame", interactive = False, value = None),
+                    ease_make_video : gr.Accordion(visible = True),
+                    video_input_group:gr.Group(visible=True),
+                    image_edit_group:gr.Group(visible=False),
+                    ease_plot : gr.Plot(value = plt_edit(plt_data))}
+        clear_video.click(clear_vd, inputs=[plt_data, fixed_list],outputs=[video_input, ease_result_gallery, fix_result_gallery, fixed_frame, ease_make_video, video_input_group, image_edit_group, ease_plot])
+        def update_video_visible(video):
+            if not video:
+                 return {video_input_group:gr.Group(visible=True),
+                    image_edit_group:gr.Group(visible=False),
+                    image_gallery:[],
+                    input_dir : "",
+                    fps : 0
+                    }
+            now = datetime.datetime.now().strftime('%Y%m%d_%H%M%S')
+            input_now = os.path.join("input", now)
+            os.makedirs(input_now, exist_ok = True)
+            cap = cv2.VideoCapture(video)
+            frame_count = 0
+            video_fps = cap.get(cv2.CAP_PROP_FPS)
+            #print('video fps:', video_fps)
+
+            H = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
+            W = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
+            mul = ((3e6) / (W * H)) ** (1/2)
+            H, W = int(H * mul), int(W * mul)
+
+            frame_name_list = []
+            while cap.isOpened():
+                ret, frame = cap.read()
+                if not ret:
+                    break
+                img_name = os.path.join(input_now, f"img_{frame_count:03d}.png")
+                if mul < 1:
+                    frame = cv2.resize(frame, (W, H), interpolation=cv2.INTER_CUBIC)
+                cv2.imwrite(img_name, frame)
+                frame_name_list.append((img_name, f"frame {frame_count}"))
+                frame_count += 1
+                if frame_count >= MAX_FRAME: break
+            cap.release()
+            return {video_input_group:gr.Group(visible=False),
+                    image_edit_group:gr.Group(visible=True),
+                    image_gallery:frame_name_list,
+                    input_dir : input_now,
+                    fps : video_fps
+                    }
+        upload_button.click(update_video_visible,
+                                                 [video_input],
+                                                 [video_input_group, image_edit_group, image_gallery, input_dir, fps])
+
+if __name__ == '__main__':
+    demo.launch(allowed_paths=["./input", "./output"], auth = check_valid_login, auth_message = "Inshorts Animator V. 0.5 WebUI (Permitted User Only)", share = True)

cfgs/upr_backward_unimatch_exp1.yaml

@@ -0,0 +1,75 @@
+exp_name: backwarp_resize
+
+mode: train
+
+train_dataset:
+  name: vimeo
+  args:
+    root_path: ../datasets/upr_backward
+    split: train
+    patch_size: 256
+    flow: none
+  loader:
+    batch_size: 32
+    num_workers: 8
+
+test_dataset:
+  name: vimeo
+  args:
+    root_path: ../datasets/Vimeo90K
+    split: val
+  loader:
+    batch_size: 32
+    num_workers: 8
+
+demo_dataset:
+  name: demo
+  args:
+    root_path: ../data/animation
+    split: animation
+
+model:
+  name: upr_net_mod
+  args:
+    pyr_level: 3
+    nr_lvl_skipped: 0
+    splat_mode: average
+
+
+optimizer:
+  name: adamW
+  args: {lr: 0.00015, weight_decay: 1.e-4}
+
+lr_scheduler:
+  name: one_cycle_lr
+  args:
+    max_lr: 0.00015
+    pct_start: 0.01
+    cycle_momentum: False
+    anneal_strategy: cos
+
+loss:
+  - {
+    name: multiple_flow,
+    args: { weight: 0.005 }
+  }
+  - {
+    name: charbonnier,
+    args: { weight: 1 }
+  }
+  - {
+    name: ternary,
+    args: { weight: 1 }
+  }
+
+max_epoch: 838
+
+validate_every: 5
+save_every: 10
+vis_every: 40
+
+seed: 1234
+
+dist_url: env://
+resume: ./save/upr_backward_unimatch_exp1_backwarp_resize/checkpoints/model_200.pth
+# pretrained: ./pretrained/upr_backwarp_corr_resize_model.pth

cfgs/upr_backward_unimatch_exp2.yaml

@@ -0,0 +1,76 @@
+exp_name: backwarp_resize
+
+mode: train
+
+train_dataset:
+  name: vimeo
+  args:
+    root_path: ../datasets/Vimeo90Kx2
+    flow_root: ../datasets/unimatch_flow
+    split: train
+    patch_size: 512
+    flow: t0
+  loader:
+    batch_size: 16
+    num_workers: 16
+
+test_dataset:
+  name: vimeo
+  args:
+    root_path: ../datasets/Vimeo90K
+    split: val
+  loader:
+    batch_size: 32
+    num_workers: 8
+
+demo_dataset:
+  name: demo
+  args:
+    root_path: ../data/animation
+    split: animation
+
+model:
+  name: upr_net_mod
+  args:
+    pyr_level: 3
+    nr_lvl_skipped: 0
+    splat_mode: average
+
+
+optimizer:
+  name: adamW
+  args: {lr: 0.00015, weight_decay: 1.e-4}
+
+lr_scheduler:
+  name: one_cycle_lr
+  args:
+    max_lr: 0.00015
+    pct_start: 0.01
+    cycle_momentum: False
+    anneal_strategy: cos
+
+loss:
+  - {
+    name: multiple_flow,
+    args: { weight: 0.005 }
+  }
+  - {
+    name: charbonnier,
+    args: { weight: 1 }
+  }
+  - {
+    name: ternary,
+    args: { weight: 1 }
+  }
+
+max_epoch: 250
+
+validate_every: 5
+save_every: 10
+vis_every: 40
+
+seed: 1234
+
+dist_url: env://
+resume: ./save/upr_backward_unimatch_exp2_backwarp_resize/checkpoints/model_30.pth
+# pretrained: ./pretrained/upr_backwarp_corr_resize_model.pth

cfgs/upr_basic.yaml

@@ -0,0 +1,72 @@
+exp_name: UPR_basic
+
+mode: train
+
+train_dataset:
+  name: vimeo
+  args:
+    root_path: ../datasets/Vimeo90K
+    flow_root: ../datasets/unimatch_flow
+    split: train
+    patch_size: 256
+    flow: none
+    use_distance: False
+  loader:
+    batch_size: 32
+    num_workers: 8
+
+test_dataset:
+  name: vimeo
+  args:
+    root_path: ../datasets/Vimeo90K
+    split: val
+    use_distance: False
+  loader:
+    batch_size: 16
+    num_workers: 4
+  save_imgs: False
+
+demo_dataset:
+  name: demo
+  args:
+    root_path: ../data/animation
+    split: animation
+
+model:
+  name: upr_basic
+  args:
+    pyr_level: 3
+    nr_lvl_skipped: 0
+
+optimizer:
+  name: adamW
+  args: {lr: 1.5e-4, weight_decay: 1.e-4}
+
+lr_scheduler:
+  name: one_cycle_lr
+  args:
+    max_lr: 1.5e-4
+    pct_start: 0.01
+    cycle_momentum: False
+    anneal_strategy: cos
+
+loss:
+  - {
+    name: charbonnier,
+    args: { weight: 1 }
+  }
+  - {
+    name: ternary,
+    args: { weight: 1 }
+  }
+
+
+max_epoch: 540
+
+validate_every: 10
+save_every: 10
+vis_every: 20
+
+seed: 1234
+
+dist_url: env://

cfgs/upr_custom_4090_exp1.yaml

@@ -0,0 +1,70 @@
+exp_name: backwarp_resize
+
+mode: train
+
+train_dataset:
+  name: vimeo
+  args:
+    root_path: /mnt/nas/512_new
+    split: train
+    patch_size: 512
+    flow: none
+  loader:
+    batch_size: 16
+    num_workers: 8
+
+test_dataset:
+  name: vimeo
+  args:
+    root_path: /mnt/nas/vimeo_triplet
+    split: val
+  loader:
+    batch_size: 16
+    num_workers: 8
+
+demo_dataset:
+  name: demo
+  args:
+    root_path: ../data/animation
+    split: animation
+
+model:
+  name: upr_net_mod
+  args:
+    pyr_level: 5
+    nr_lvl_skipped: 0
+    splat_mode: average
+
+
+optimizer:
+  name: adamW
+  args: {lr: 0.000075, weight_decay: 1.e-4}
+
+lr_scheduler:
+  name: one_cycle_lr
+  args:
+    max_lr: 0.000075
+    pct_start: 0.01
+    cycle_momentum: False
+    anneal_strategy: cos
+
+loss:
+  - {
+    name: charbonnier,
+    args: { weight: 1 }
+  }
+  - {
+    name: ternary,
+    args: { weight: 1 }
+  }
+
+max_epoch: 533
+
+validate_every: 1
+save_every: 10
+vis_every: 20
+
+seed: 1234
+
+dist_url: env://
+pretrained: ./pretrained/upr_backwarp_corr_resize_model.pth

cfgs/upr_custom_4090_exp1_10epoch_test.yaml

@@ -0,0 +1,70 @@
+exp_name: backwarp_resize
+
+mode: train
+
+train_dataset:
+  name: vimeo
+  args:
+    root_path: /mnt/nas/512_new
+    split: train
+    patch_size: 512
+    flow: none
+  loader:
+    batch_size: 16
+    num_workers: 8
+
+test_dataset:
+  name: vimeo
+  args:
+    root_path: /mnt/nas/vimeo_triplet
+    split: val
+  loader:
+    batch_size: 16
+    num_workers: 8
+
+demo_dataset:
+  name: demo
+  args:
+    root_path: ../data/animation
+    split: animation
+
+model:
+  name: upr_net_mod
+  args:
+    pyr_level: 5
+    nr_lvl_skipped: 0
+    splat_mode: average
+
+
+optimizer:
+  name: adamW
+  args: {lr: 0.000075, weight_decay: 1.e-4}
+
+lr_scheduler:
+  name: one_cycle_lr
+  args:
+    max_lr: 0.000075
+    pct_start: 0.01
+    cycle_momentum: False
+    anneal_strategy: cos
+
+loss:
+  - {
+    name: charbonnier,
+    args: { weight: 1 }
+  }
+  - {
+    name: ternary,
+    args: { weight: 1 }
+  }
+
+max_epoch: 533
+
+validate_every: 1
+save_every: 10
+vis_every: 20
+
+seed: 1234
+
+dist_url: env://
+pretrained: ./pretrained/upr_backwarp_corr_resize_model.pth

cfgs/upr_custom_4090_exp1_extract.yaml

@@ -0,0 +1,70 @@
+exp_name: backwarp_resize
+
+mode: train
+
+train_dataset:
+  name: vimeo
+  args:
+    root_path: /mnt/nas/512_new
+    split: train
+    patch_size: 512
+    flow: none
+  loader:
+    batch_size: 16
+    num_workers: 8
+
+test_dataset:
+  name: vimeo
+  args:
+    root_path: ../datasets/Vimeo90K
+    split: val
+  loader:
+    batch_size: 4
+    num_workers: 8
+
+demo_dataset:
+  name: demo
+  args:
+    root_path: ../data/animation
+    split: animation
+
+model:
+  name: upr_net_mod
+  args:
+    pyr_level: 5
+    nr_lvl_skipped: 0
+    splat_mode: average
+
+
+optimizer:
+  name: adamW
+  args: {lr: 0.000075, weight_decay: 1.e-4}
+
+lr_scheduler:
+  name: one_cycle_lr
+  args:
+    max_lr: 0.000075
+    pct_start: 0.01
+    cycle_momentum: False
+    anneal_strategy: cos
+
+loss:
+  - {
+    name: charbonnier,
+    args: { weight: 1 }
+  }
+  - {
+    name: ternary,
+    args: { weight: 1 }
+  }
+
+max_epoch: 533
+
+validate_every: 1
+save_every: 10
+vis_every: 20
+
+seed: 1234
+
+dist_url: env://
+pretrained: ./pretrained/upr_backwarp_corr_resize_model.pth

cfgs/upr_custom_4090_exp2.yaml

@@ -0,0 +1,70 @@
+exp_name: backwarp_resize
+
+mode: train
+
+train_dataset:
+  name: vimeo
+  args:
+    root_path: ../datasets/upr_backward
+    split: train
+    patch_size: 256
+    flow: none
+  loader:
+    batch_size: 32
+    num_workers: 8
+
+test_dataset:
+  name: vimeo
+  args:
+    root_path: ../datasets/Vimeo90K
+    split: val
+  loader:
+    batch_size: 32
+    num_workers: 8
+
+demo_dataset:
+  name: demo
+  args:
+    root_path: ../data/animation
+    split: animation
+
+model:
+  name: upr_net_mod
+  args:
+    pyr_level: 5
+    nr_lvl_skipped: 0
+    splat_mode: average
+
+
+optimizer:
+  name: adamW
+  args: {lr: 0.00015, weight_decay: 1.e-4}
+
+lr_scheduler:
+  name: one_cycle_lr
+  args:
+    max_lr: 0.00015
+    pct_start: 0.01
+    cycle_momentum: False
+    anneal_strategy: cos
+
+loss:
+  - {
+    name: charbonnier,
+    args: { weight: 1 }
+  }
+  - {
+    name: ternary,
+    args: { weight: 1 }
+  }
+
+max_epoch: 533
+
+validate_every: 1
+save_every: 10
+vis_every: 20
+
+seed: 1234
+
+dist_url: env://
+pretrained: ./pretrained/upr_backwarp_corr_resize_model.pth

cfgs/upr_freq003.yaml

@@ -0,0 +1,88 @@
+exp_name: FET-VFI_BiFreqLoss_Distance
+
+mode: train
+
+train_dataset:
+  name: vimeo
+  args:
+    root_path: ../datasets/Vimeo90K
+    flow_root: ../datasets/unimatch_flow
+    split: train
+    patch_size: 256
+    flow: 't0'
+    use_distance: True
+    distance_root: ../datasets/distance_map
+  loader:
+    batch_size: 32
+    num_workers: 8
+
+test_dataset:
+  name: vimeo
+  args:
+    root_path: ../datasets/Vimeo90K
+    split: val
+    use_distance: True
+  loader:
+    batch_size: 16
+    num_workers: 4
+  save_imgs: False
+
+demo_dataset:
+  name: demo
+  args:
+    root_path: ../data/animation
+    split: animation
+
+model:
+  name: upr_net_freq
+  args:
+    pyr_level: 3
+    nr_lvl_skipped: 0
+    splat_mode: average
+    fftshift: False
+
+
+optimizer:
+  name: adamW
+  args: {lr: 1.5e-4, weight_decay: 1.e-4}
+
+lr_scheduler:
+  name: one_cycle_lr
+  args:
+    max_lr: 1.5e-4
+    pct_start: 0.01
+    cycle_momentum: False
+    anneal_strategy: cos
+
+loss:
+  - {
+    name: frequency,
+    args: { weight: 0.01 }
+  }
+  - {
+    name: bi_frequency,
+    args: { weight: 0.01 }
+  }
+  - {
+    name: multiple_flow,
+    args: { weight: 0.005 }
+  }
+  - {
+    name: charbonnier,
+    args: { weight: 1 }
+  }
+  - {
+    name: ternary,
+    args: { weight: 1 }
+  }
+
+
+max_epoch: 540
+
+validate_every: 10
+save_every: 10
+vis_every: 20
+
+seed: 1234
+
+dist_url: env://

cfgs/upr_freq004.yaml

@@ -0,0 +1,88 @@
+exp_name: FET-VFI_BiFreqLoss_Distance
+
+mode: train
+
+train_dataset:
+  name: vimeo
+  args:
+    root_path: ../datasets/Vimeo90K
+    flow_root: ../datasets/unimatch_flow
+    split: train
+    patch_size: 256
+    flow: 't0'
+    use_distance: True
+    distance_root: ../datasets/distance_map
+  loader:
+    batch_size: 32
+    num_workers: 8
+
+test_dataset:
+  name: vimeo
+  args:
+    root_path: ../datasets/Vimeo90K
+    split: val
+    use_distance: True
+  loader:
+    batch_size: 16
+    num_workers: 4
+  save_imgs: False
+
+demo_dataset:
+  name: demo
+  args:
+    root_path: ../data/animation
+    split: animation
+
+model:
+  name: upr_net_freq
+  args:
+    pyr_level: 3
+    nr_lvl_skipped: 0
+    splat_mode: average
+    fftshift: False
+
+
+optimizer:
+  name: adamW
+  args: {lr: 1.5e-4, weight_decay: 1.e-4}
+
+lr_scheduler:
+  name: one_cycle_lr
+  args:
+    max_lr: 1.5e-4
+    pct_start: 0.01
+    cycle_momentum: False
+    anneal_strategy: cos
+
+loss:
+  - {
+    name: frequency,
+    args: { weight: 0.01 }
+  }
+  - {
+    name: bi_frequency,
+    args: { weight: 0.01 }
+  }
+  - {
+    name: multiple_flow,
+    args: { weight: 0.005 }
+  }
+  - {
+    name: charbonnier,
+    args: { weight: 1 }
+  }
+  - {
+    name: ternary,
+    args: { weight: 1 }
+  }
+
+
+max_epoch: 540
+
+validate_every: 10
+save_every: 10
+vis_every: 20
+
+seed: 1234
+
+dist_url: env://

cfgs/upr_freq005.yaml

@@ -0,0 +1,87 @@
+exp_name: FET-VFI_EncFreqs_AsymFreqDec
+
+mode: train
+
+train_dataset:
+  name: vimeo
+  args:
+    root_path: ../datasets/Vimeo90K
+    flow_root: ../datasets/unimatch_flow
+    split: train
+    patch_size: 256
+    flow: 't0'
+    use_distance: False
+  loader:
+    batch_size: 32
+    num_workers: 8
+
+test_dataset:
+  name: vimeo
+  args:
+    root_path: ../datasets/Vimeo90K
+    split: val
+    use_distance: False
+  loader:
+    batch_size: 16
+    num_workers: 4
+  save_imgs: False
+
+demo_dataset:
+  name: demo
+  args:
+    root_path: ../data/animation
+    split: animation
+
+model:
+  name: upr_net_freq
+  args:
+    pyr_level: 3
+    nr_lvl_skipped: 0
+    splat_mode: average
+    fftshift: False
+
+
+optimizer:
+  name: adamW
+  args: {lr: 1.5e-4, weight_decay: 1.e-4}
+
+lr_scheduler:
+  name: one_cycle_lr
+  args:
+    max_lr: 1.5e-4
+    pct_start: 0.01
+    cycle_momentum: False
+    anneal_strategy: cos
+
+loss:
+  - {
+    name: frequency,
+    args: { weight: 0.01 }
+  }
+  - {
+    name: bi_frequency,
+    args: { weight: 0.01 }
+  }
+  - {
+    name: multiple_flow,
+    args: { weight: 0.005 }
+  }
+  - {
+    name: charbonnier,
+    args: { weight: 1 }
+  }
+  - {
+    name: ternary,
+    args: { weight: 1 }
+  }
+
+
+max_epoch: 540
+
+validate_every: 10
+save_every: 10
+vis_every: 20
+
+seed: 1234
+
+dist_url: env://

cfgs/upr_freq006.yaml

@@ -0,0 +1,83 @@
+exp_name: FET-VFI_Basic_ChangeFreqCNN
+
+mode: train
+
+train_dataset:
+  name: vimeo
+  args:
+    root_path: ../datasets/Vimeo90K
+    flow_root: ../datasets/unimatch_flow
+    split: train
+    patch_size: 256
+    flow: none
+    use_distance: False
+  loader:
+    batch_size: 32
+    num_workers: 8
+
+test_dataset:
+  name: vimeo
+  args:
+    root_path: ../datasets/Vimeo90K
+    split: val
+    use_distance: False
+  loader:
+    batch_size: 16
+    num_workers: 4
+  save_imgs: False
+
+demo_dataset:
+  name: demo
+  args:
+    root_path: ../data/animation
+    split: animation
+
+model:
+  name: upr_net_freq2
+  args:
+    pyr_level: 3
+    nr_lvl_skipped: 0
+    splat_mode: average
+    fftshift: True
+
+
+optimizer:
+  name: adamW
+  args: {lr: 1.5e-4, weight_decay: 1.e-4}
+
+lr_scheduler:
+  name: one_cycle_lr
+  args:
+    max_lr: 1.5e-4
+    pct_start: 0.01
+    cycle_momentum: False
+    anneal_strategy: cos
+
+loss:
+  - {
+    name: frequency,
+    args: { weight: 0.01 }
+  }
+  - {
+    name: bi_frequency,
+    args: { weight: 0.01 }
+  }
+  - {
+    name: charbonnier,
+    args: { weight: 1 }
+  }
+  - {
+    name: ternary,
+    args: { weight: 1 }
+  }
+
+
+max_epoch: 540
+
+validate_every: 10
+save_every: 10
+vis_every: 20
+
+seed: 1234
+
+dist_url: env://

cfgs/upr_freq007.yaml

@@ -0,0 +1,83 @@
+exp_name: FET-VFI_FETAmpPhaResid
+
+mode: train
+
+train_dataset:
+  name: vimeo
+  args:
+    root_path: ../datasets/Vimeo90K
+    flow_root: ../datasets/unimatch_flow
+    split: train
+    patch_size: 256
+    flow: none
+    use_distance: False
+  loader:
+    batch_size: 32
+    num_workers: 8
+
+test_dataset:
+  name: vimeo
+  args:
+    root_path: ../datasets/Vimeo90K
+    split: val
+    use_distance: False
+  loader:
+    batch_size: 16
+    num_workers: 4
+  save_imgs: False
+
+demo_dataset:
+  name: demo
+  args:
+    root_path: ../data/animation
+    split: animation
+
+model:
+  name: upr_net_freq2
+  args:
+    pyr_level: 3
+    nr_lvl_skipped: 0
+    splat_mode: average
+    fftshift: True
+
+
+optimizer:
+  name: adamW
+  args: {lr: 1.5e-4, weight_decay: 1.e-4}
+
+lr_scheduler:
+  name: one_cycle_lr
+  args:
+    max_lr: 1.5e-4
+    pct_start: 0.01
+    cycle_momentum: False
+    anneal_strategy: cos
+
+loss:
+  - {
+    name: frequency,
+    args: { weight: 0.01 }
+  }
+  - {
+    name: bi_frequency,
+    args: { weight: 0.01 }
+  }
+  - {
+    name: charbonnier,
+    args: { weight: 1 }
+  }
+  - {
+    name: ternary,
+    args: { weight: 1 }
+  }
+
+
+max_epoch: 540
+
+validate_every: 10
+save_every: 10
+vis_every: 20
+
+seed: 1234
+
+dist_url: env://

cfgs/upr_freq008.yaml

@@ -0,0 +1,83 @@
+exp_name: FET-VFI_FETAmpPhaResid
+
+mode: train
+
+train_dataset:
+  name: vimeo
+  args:
+    root_path: ../datasets/Vimeo90K
+    flow_root: ../datasets/unimatch_flow
+    split: train
+    patch_size: 256
+    flow: none
+    use_distance: False
+  loader:
+    batch_size: 32
+    num_workers: 8
+
+test_dataset:
+  name: vimeo
+  args:
+    root_path: ../datasets/Vimeo90K
+    split: val
+    use_distance: False
+  loader:
+    batch_size: 16
+    num_workers: 4
+  save_imgs: False
+
+demo_dataset:
+  name: demo
+  args:
+    root_path: ../data/animation
+    split: animation
+
+model:
+  name: upr_net_freq2
+  args:
+    pyr_level: 3
+    nr_lvl_skipped: 0
+    splat_mode: average
+    fftshift: False
+
+
+optimizer:
+  name: adamW
+  args: {lr: 1.5e-4, weight_decay: 1.e-4}
+
+lr_scheduler:
+  name: one_cycle_lr
+  args:
+    max_lr: 1.5e-4
+    pct_start: 0.01
+    cycle_momentum: False
+    anneal_strategy: cos
+
+loss:
+  - {
+    name: frequency,
+    args: { weight: 0.01 }
+  }
+  - {
+    name: bi_frequency,
+    args: { weight: 0.01 }
+  }
+  - {
+    name: charbonnier,
+    args: { weight: 1 }
+  }
+  - {
+    name: ternary,
+    args: { weight: 1 }
+  }
+
+
+max_epoch: 540
+
+validate_every: 10
+save_every: 10
+vis_every: 20
+
+seed: 1234
+
+dist_url: env://

cfgs/upr_freq_unimatch_exp001.yaml

@@ -0,0 +1,83 @@
+exp_name: FreqeuncyEnhancementTransformer
+
+mode: train
+
+train_dataset:
+  name: vimeo
+  args:
+    root_path: ../datasets/Vimeo90K
+    flow_root: ../datasets/unimatch_flow
+    split: train
+    patch_size: 256
+    flow: 't0'
+  loader:
+    batch_size: 32
+    num_workers: 8
+
+test_dataset:
+  name: vimeo
+  args:
+    root_path: ../datasets/Vimeo90K
+    split: val
+  loader:
+    batch_size: 16
+    num_workers: 4
+  save_imgs: False
+
+demo_dataset:
+  name: demo
+  args:
+    root_path: ../data/animation
+    split: animation
+
+model:
+  name: upr_net_freq
+  args:
+    pyr_level: 3
+    nr_lvl_skipped: 0
+    splat_mode: average
+    fftshift: False
+
+
+optimizer:
+  name: adamW
+  args: {lr: 1.5e-4, weight_decay: 1.e-4}
+
+lr_scheduler:
+  name: one_cycle_lr
+  args:
+    max_lr: 1.5e-4
+    pct_start: 0.01
+    cycle_momentum: False
+    anneal_strategy: cos
+
+loss:
+  - {
+    name: frequency,
+    args: { weight: 0.01 }
+  }
+  - {
+    name: multiple_flow,
+    args: { weight: 0.005 }
+  }
+  - {
+    name: charbonnier,
+    args: { weight: 1 }
+  }
+  - {
+    name: ternary,
+    args: { weight: 1 }
+  }
+
+
+max_epoch: 540
+
+validate_every: 10
+save_every: 10
+vis_every: 20
+
+seed: 1234
+
+dist_url: env://
+
+resume: ./save/upr_freq_unimatch_exp001_FreqeuncyEnhancementTransformer/checkpoints/model_420.pth

cfgs/upr_freq_unimatch_exp002.yaml

@@ -0,0 +1,85 @@
+exp_name: FET-VFI_BiFrequencyLoss
+
+mode: train
+
+train_dataset:
+  name: vimeo
+  args:
+    root_path: ../datasets/Vimeo90K
+    flow_root: ../datasets/unimatch_flow
+    split: train
+    patch_size: 256
+    flow: 't0'
+  loader:
+    batch_size: 32
+    num_workers: 8
+
+test_dataset:
+  name: vimeo
+  args:
+    root_path: ../datasets/Vimeo90K
+    split: val
+  loader:
+    batch_size: 16
+    num_workers: 4
+  save_imgs: False
+
+demo_dataset:
+  name: demo
+  args:
+    root_path: ../data/animation
+    split: animation
+
+model:
+  name: upr_net_freq
+  args:
+    pyr_level: 3
+    nr_lvl_skipped: 0
+    splat_mode: average
+    fftshift: False
+
+
+optimizer:
+  name: adamW
+  args: {lr: 1.5e-4, weight_decay: 1.e-4}
+
+lr_scheduler:
+  name: one_cycle_lr
+  args:
+    max_lr: 1.5e-4
+    pct_start: 0.01
+    cycle_momentum: False
+    anneal_strategy: cos
+
+loss:
+  - {
+    name: frequency,
+    args: { weight: 0.01 }
+  }
+  - {
+    name: bi_frequency,
+    args: { weight: 0.01 }
+  }
+  - {
+    name: multiple_flow,
+    args: { weight: 0.005 }
+  }
+  - {
+    name: charbonnier,
+    args: { weight: 1 }
+  }
+  - {
+    name: ternary,
+    args: { weight: 1 }
+  }
+
+
+max_epoch: 540
+
+validate_every: 10
+save_every: 10
+vis_every: 20
+
+seed: 1234
+
+dist_url: env://

cfgs/upr_freq_unimatch_exp002_20240111_extratraining1.yaml

@@ -0,0 +1,86 @@
+exp_name: FET-VFI_BiFrequencyLoss
+
+mode: train
+
+train_dataset:
+  name: vimeo
+  args:
+    root_path: ../datasets/frames1_triplet
+    flow_root: ../datasets/frames1_unimatch_flow
+    tri_trainlist: tri_trainlist.txt
+    split: train
+    patch_size: 256
+    flow: 't0'
+  loader:
+    batch_size: 16
+    num_workers: 4
+
+# test_dataset:
+#   name: vimeo
+#   args:
+#     root_path: ../datasets/Vimeo90K
+#     split: val
+#   loader:
+#     batch_size: 16
+#     num_workers: 4
+#   save_imgs: False
+
+# demo_dataset:
+#   name: demo
+#   args:
+#     root_path: ../data/animation
+#     split: animation
+
+model:
+  name: upr_net_freq
+  args:
+    pyr_level: 3
+    nr_lvl_skipped: 0
+    splat_mode: average
+    fftshift: False
+
+
+optimizer:
+  name: adamW
+  args: {lr: 4.e-6, weight_decay: 1.e-4}
+
+lr_scheduler:
+  name: one_cycle_lr
+  args:
+    max_lr: 4.e-6
+    pct_start: 0.01
+    cycle_momentum: False
+    anneal_strategy: cos
+
+loss:
+  - {
+    name: frequency,
+    args: { weight: 0.01 }
+  }
+  - {
+    name: bi_frequency,
+    args: { weight: 0.01 }
+  }
+  - {
+    name: multiple_flow,
+    args: { weight: 0.005 }
+  }
+  - {
+    name: charbonnier,
+    args: { weight: 1 }
+  }
+  - {
+    name: ternary,
+    args: { weight: 1 }
+  }
+
+pretrained: ./pretrained/upr_freq002.pth
+max_epoch: 300
+
+validate_every: 10
+save_every: 10
+vis_every: 20
+
+seed: 1234
+
+dist_url: env://

cfgs/upr_freq_unimatch_exp002_20240111_extratraining1000_2_1.yaml

@@ -0,0 +1,86 @@
+exp_name: FET-VFI_BiFrequencyLoss
+
+mode: train
+
+train_dataset:
+  name: vimeo
+  args:
+    root_path: ../datasets/frames2_triplet
+    flow_root: ../datasets/frames2_unimatch_flow
+    tri_trainlist: tri_trainlist.txt
+    split: train
+    patch_size: 256
+    flow: 't0'
+  loader:
+    batch_size: 16
+    num_workers: 4
+
+# test_dataset:
+#   name: vimeo
+#   args:
+#     root_path: ../datasets/Vimeo90K
+#     split: val
+#   loader:
+#     batch_size: 16
+#     num_workers: 4
+#   save_imgs: False
+
+# demo_dataset:
+#   name: demo
+#   args:
+#     root_path: ../data/animation
+#     split: animation
+
+model:
+  name: upr_net_freq
+  args:
+    pyr_level: 3
+    nr_lvl_skipped: 0
+    splat_mode: average
+    fftshift: False
+
+
+optimizer:
+  name: adamW
+  args: {lr: 7.5e-6, weight_decay: 1.e-4}
+
+lr_scheduler:
+  name: one_cycle_lr
+  args:
+    max_lr: 7.5e-6
+    pct_start: 0.01
+    cycle_momentum: False
+    anneal_strategy: cos
+
+loss:
+  - {
+    name: frequency,
+    args: { weight: 0.01 }
+  }
+  - {
+    name: bi_frequency,
+    args: { weight: 0.01 }
+  }
+  - {
+    name: multiple_flow,
+    args: { weight: 0.005 }
+  }
+  - {
+    name: charbonnier,
+    args: { weight: 1 }
+  }
+  - {
+    name: ternary,
+    args: { weight: 1 }
+  }
+
+pretrained: ./pretrained/upr_freq002.pth
+max_epoch: 1000
+
+validate_every: 10
+save_every: 10
+vis_every: 20
+
+seed: 1234
+
+dist_url: env://

cfgs/upr_freq_unimatch_exp002_20240111_extratraining1000_2_2.yaml

@@ -0,0 +1,86 @@
+exp_name: FET-VFI_BiFrequencyLoss
+
+mode: train
+
+train_dataset:
+  name: vimeo
+  args:
+    root_path: ../datasets/frames2_triplet
+    flow_root: ../datasets/frames2_unimatch_flow
+    tri_trainlist: tri_trainlist.txt
+    split: train
+    patch_size: 256
+    flow: 't0'
+  loader:
+    batch_size: 16
+    num_workers: 4
+
+# test_dataset:
+#   name: vimeo
+#   args:
+#     root_path: ../datasets/Vimeo90K
+#     split: val
+#   loader:
+#     batch_size: 16
+#     num_workers: 4
+#   save_imgs: False
+
+# demo_dataset:
+#   name: demo
+#   args:
+#     root_path: ../data/animation
+#     split: animation
+
+model:
+  name: upr_net_freq
+  args:
+    pyr_level: 3
+    nr_lvl_skipped: 0
+    splat_mode: average
+    fftshift: False
+
+
+optimizer:
+  name: adamW
+  args: {lr: 4.e-6, weight_decay: 1.e-4}
+
+lr_scheduler:
+  name: one_cycle_lr
+  args:
+    max_lr: 4.e-6
+    pct_start: 0.01
+    cycle_momentum: False
+    anneal_strategy: cos
+
+loss:
+  - {
+    name: frequency,
+    args: { weight: 0.01 }
+  }
+  - {
+    name: bi_frequency,
+    args: { weight: 0.01 }
+  }
+  - {
+    name: multiple_flow,
+    args: { weight: 0.005 }
+  }
+  - {
+    name: charbonnier,
+    args: { weight: 1 }
+  }
+  - {
+    name: ternary,
+    args: { weight: 1 }
+  }
+
+pretrained: ./pretrained/upr_freq002.pth
+max_epoch: 1000
+
+validate_every: 10
+save_every: 10
+vis_every: 20
+
+seed: 1234
+
+dist_url: env://

cfgs/upr_freq_unimatch_exp002_20240111_extratraining1000_2_3.yaml

@@ -0,0 +1,86 @@
+exp_name: FET-VFI_BiFrequencyLoss
+
+mode: train
+
+train_dataset:
+  name: vimeo
+  args:
+    root_path: ../datasets/frames2_triplet
+    flow_root: ../datasets/frames2_unimatch_flow
+    tri_trainlist: tri_trainlist.txt
+    split: train
+    patch_size: 256
+    flow: 't0'
+  loader:
+    batch_size: 16
+    num_workers: 4
+
+# test_dataset:
+#   name: vimeo
+#   args:
+#     root_path: ../datasets/Vimeo90K
+#     split: val
+#   loader:
+#     batch_size: 16
+#     num_workers: 4
+#   save_imgs: False
+
+# demo_dataset:
+#   name: demo
+#   args:
+#     root_path: ../data/animation
+#     split: animation
+
+model:
+  name: upr_net_freq
+  args:
+    pyr_level: 3
+    nr_lvl_skipped: 0
+    splat_mode: average
+    fftshift: False
+
+
+optimizer:
+  name: adamW
+  args: {lr: 1.e-6, weight_decay: 1.e-4}
+
+lr_scheduler:
+  name: one_cycle_lr
+  args:
+    max_lr: 1.e-6
+    pct_start: 0.01
+    cycle_momentum: False
+    anneal_strategy: cos
+
+loss:
+  - {
+    name: frequency,
+    args: { weight: 0.01 }
+  }
+  - {
+    name: bi_frequency,
+    args: { weight: 0.01 }
+  }
+  - {
+    name: multiple_flow,
+    args: { weight: 0.005 }
+  }
+  - {
+    name: charbonnier,
+    args: { weight: 1 }
+  }
+  - {
+    name: ternary,
+    args: { weight: 1 }
+  }
+
+pretrained: ./pretrained/upr_freq002.pth
+max_epoch: 10000
+
+validate_every: 1000
+save_every: 1000
+vis_every: 2000
+
+seed: 1234
+
+dist_url: env://

cfgs/upr_freq_unimatch_exp002_20240111_extratraining1000_2_4.yaml

@@ -0,0 +1,86 @@
+exp_name: FET-VFI_BiFrequencyLoss
+
+mode: train
+
+train_dataset:
+  name: vimeo
+  args:
+    root_path: ../datasets/frames2_triplet
+    flow_root: ../datasets/frames2_unimatch_flow
+    tri_trainlist: tri_trainlist.txt
+    split: train
+    patch_size: 256
+    flow: 't0'
+  loader:
+    batch_size: 16
+    num_workers: 4
+
+# test_dataset:
+#   name: vimeo
+#   args:
+#     root_path: ../datasets/Vimeo90K
+#     split: val
+#   loader:
+#     batch_size: 16
+#     num_workers: 4
+#   save_imgs: False
+
+# demo_dataset:
+#   name: demo
+#   args:
+#     root_path: ../data/animation
+#     split: animation
+
+model:
+  name: upr_net_freq
+  args:
+    pyr_level: 3
+    nr_lvl_skipped: 0
+    splat_mode: average
+    fftshift: False
+
+
+optimizer:
+  name: adamW
+  args: {lr: 4.e-6, weight_decay: 1.e-4}
+
+lr_scheduler:
+  name: one_cycle_lr
+  args:
+    max_lr: 4.e-6
+    pct_start: 0.01
+    cycle_momentum: False
+    anneal_strategy: cos
+
+loss:
+  - {
+    name: frequency,
+    args: { weight: 0.01 }
+  }
+  - {
+    name: bi_frequency,
+    args: { weight: 0.01 }
+  }
+  - {
+    name: multiple_flow,
+    args: { weight: 0.005 }
+  }
+  - {
+    name: charbonnier,
+    args: { weight: 1 }
+  }
+  - {
+    name: ternary,
+    args: { weight: 1 }
+  }
+
+pretrained: ./pretrained/upr_freq002.pth
+max_epoch: 10000
+
+validate_every: 1000
+save_every: 1000
+vis_every: 2000
+
+seed: 1234
+
+dist_url: env://

cfgs/upr_freq_unimatch_exp002_20240111_extratraining1000_3_1.yaml

@@ -0,0 +1,86 @@
+exp_name: FET-VFI_BiFrequencyLoss
+
+mode: train
+
+train_dataset:
+  name: vimeo
+  args:
+    root_path: ../datasets/frames3_triplet
+    flow_root: ../datasets/frames3_unimatch_flow
+    tri_trainlist: tri_trainlist.txt
+    split: train
+    patch_size: 256
+    flow: 't0'
+  loader:
+    batch_size: 16
+    num_workers: 4
+
+# test_dataset:
+#   name: vimeo
+#   args:
+#     root_path: ../datasets/Vimeo90K
+#     split: val
+#   loader:
+#     batch_size: 16
+#     num_workers: 4
+#   save_imgs: False
+
+# demo_dataset:
+#   name: demo
+#   args:
+#     root_path: ../data/animation
+#     split: animation
+
+model:
+  name: upr_net_freq
+  args:
+    pyr_level: 3
+    nr_lvl_skipped: 0
+    splat_mode: average
+    fftshift: False
+
+
+optimizer:
+  name: adamW
+  args: {lr: 7.5e-6, weight_decay: 1.e-4}
+
+lr_scheduler:
+  name: one_cycle_lr
+  args:
+    max_lr: 7.5e-6
+    pct_start: 0.01
+    cycle_momentum: False
+    anneal_strategy: cos
+
+loss:
+  - {
+    name: frequency,
+    args: { weight: 0.01 }
+  }
+  - {
+    name: bi_frequency,
+    args: { weight: 0.01 }
+  }
+  - {
+    name: multiple_flow,
+    args: { weight: 0.005 }
+  }
+  - {
+    name: charbonnier,
+    args: { weight: 1 }
+  }
+  - {
+    name: ternary,
+    args: { weight: 1 }
+  }
+
+pretrained: ./pretrained/upr_freq002.pth
+max_epoch: 1000
+
+validate_every: 10
+save_every: 10
+vis_every: 20
+
+seed: 1234
+
+dist_url: env://

cfgs/upr_freq_unimatch_exp002_20240111_extratraining1000_3_2.yaml

@@ -0,0 +1,86 @@
+exp_name: FET-VFI_BiFrequencyLoss
+
+mode: train
+
+train_dataset:
+  name: vimeo
+  args:
+    root_path: ../datasets/frames3_triplet
+    flow_root: ../datasets/frames3_unimatch_flow
+    tri_trainlist: tri_trainlist.txt
+    split: train
+    patch_size: 256
+    flow: 't0'
+  loader:
+    batch_size: 16
+    num_workers: 4
+
+# test_dataset:
+#   name: vimeo
+#   args:
+#     root_path: ../datasets/Vimeo90K
+#     split: val
+#   loader:
+#     batch_size: 16
+#     num_workers: 4
+#   save_imgs: False
+
+# demo_dataset:
+#   name: demo
+#   args:
+#     root_path: ../data/animation
+#     split: animation
+
+model:
+  name: upr_net_freq
+  args:
+    pyr_level: 3
+    nr_lvl_skipped: 0
+    splat_mode: average
+    fftshift: False
+
+
+optimizer:
+  name: adamW
+  args: {lr: 4.e-6, weight_decay: 1.e-4}
+
+lr_scheduler:
+  name: one_cycle_lr
+  args:
+    max_lr: 4.e-6
+    pct_start: 0.01
+    cycle_momentum: False
+    anneal_strategy: cos
+
+loss:
+  - {
+    name: frequency,
+    args: { weight: 0.01 }
+  }
+  - {
+    name: bi_frequency,
+    args: { weight: 0.01 }
+  }
+  - {
+    name: multiple_flow,
+    args: { weight: 0.005 }
+  }
+  - {
+    name: charbonnier,
+    args: { weight: 1 }
+  }
+  - {
+    name: ternary,
+    args: { weight: 1 }
+  }
+
+pretrained: ./pretrained/upr_freq002.pth
+max_epoch: 1000
+
+validate_every: 10
+save_every: 10
+vis_every: 20
+
+seed: 1234
+
+dist_url: env://

cfgs/upr_freq_unimatch_exp002_20240111_extratraining2.yaml

@@ -0,0 +1,86 @@
+exp_name: FET-VFI_BiFrequencyLoss
+
+mode: train
+
+train_dataset:
+  name: vimeo
+  args:
+    root_path: ../datasets/frames2_triplet
+    flow_root: ../datasets/frames2_unimatch_flow
+    tri_trainlist: tri_trainlist.txt
+    split: train
+    patch_size: 256
+    flow: 't0'
+  loader:
+    batch_size: 16
+    num_workers: 4
+
+# test_dataset:
+#   name: vimeo
+#   args:
+#     root_path: ../datasets/Vimeo90K
+#     split: val
+#   loader:
+#     batch_size: 16
+#     num_workers: 4
+#   save_imgs: False
+
+# demo_dataset:
+#   name: demo
+#   args:
+#     root_path: ../data/animation
+#     split: animation
+
+model:
+  name: upr_net_freq
+  args:
+    pyr_level: 3
+    nr_lvl_skipped: 0
+    splat_mode: average
+    fftshift: False
+
+
+optimizer:
+  name: adamW
+  args: {lr: 4.e-6, weight_decay: 1.e-4}
+
+lr_scheduler:
+  name: one_cycle_lr
+  args:
+    max_lr: 4.e-6
+    pct_start: 0.01
+    cycle_momentum: False
+    anneal_strategy: cos
+
+loss:
+  - {
+    name: frequency,
+    args: { weight: 0.01 }
+  }
+  - {
+    name: bi_frequency,
+    args: { weight: 0.01 }
+  }
+  - {
+    name: multiple_flow,
+    args: { weight: 0.005 }
+  }
+  - {
+    name: charbonnier,
+    args: { weight: 1 }
+  }
+  - {
+    name: ternary,
+    args: { weight: 1 }
+  }
+
+pretrained: ./pretrained/upr_freq002.pth
+max_epoch: 300
+
+validate_every: 10
+save_every: 10
+vis_every: 20
+
+seed: 1234
+
+dist_url: env://

cfgs/upr_freq_unimatch_exp002_20240111_extratraining3.yaml

@@ -0,0 +1,86 @@
+exp_name: FET-VFI_BiFrequencyLoss
+
+mode: train
+
+train_dataset:
+  name: vimeo
+  args:
+    root_path: ../datasets/frames3_triplet
+    flow_root: ../datasets/frames3_unimatch_flow
+    tri_trainlist: tri_trainlist.txt
+    split: train
+    patch_size: 256
+    flow: 't0'
+  loader:
+    batch_size: 16
+    num_workers: 4
+
+# test_dataset:
+#   name: vimeo
+#   args:
+#     root_path: ../datasets/Vimeo90K
+#     split: val
+#   loader:
+#     batch_size: 16
+#     num_workers: 4
+#   save_imgs: False
+
+# demo_dataset:
+#   name: demo
+#   args:
+#     root_path: ../data/animation
+#     split: animation
+
+model:
+  name: upr_net_freq
+  args:
+    pyr_level: 3
+    nr_lvl_skipped: 0
+    splat_mode: average
+    fftshift: False
+
+
+optimizer:
+  name: adamW
+  args: {lr: 4.e-6, weight_decay: 1.e-4}
+
+lr_scheduler:
+  name: one_cycle_lr
+  args:
+    max_lr: 4.e-6
+    pct_start: 0.01
+    cycle_momentum: False
+    anneal_strategy: cos
+
+loss:
+  - {
+    name: frequency,
+    args: { weight: 0.01 }
+  }
+  - {
+    name: bi_frequency,
+    args: { weight: 0.01 }
+  }
+  - {
+    name: multiple_flow,
+    args: { weight: 0.005 }
+  }
+  - {
+    name: charbonnier,
+    args: { weight: 1 }
+  }
+  - {
+    name: ternary,
+    args: { weight: 1 }
+  }
+
+pretrained: ./pretrained/upr_freq002.pth
+max_epoch: 300
+
+validate_every: 10
+save_every: 10
+vis_every: 20
+
+seed: 1234
+
+dist_url: env://

cfgs/upr_freq_unimatch_exp002_20240111_extratraining4.yaml

@@ -0,0 +1,86 @@
+exp_name: FET-VFI_BiFrequencyLoss
+
+mode: train
+
+train_dataset:
+  name: vimeo
+  args:
+    root_path: ../datasets/frames4_triplet
+    flow_root: ../datasets/frames4_unimatch_flow
+    tri_trainlist: tri_trainlist.txt
+    split: train
+    patch_size: 256
+    flow: 't0'
+  loader:
+    batch_size: 4
+    num_workers: 4
+
+# test_dataset:
+#   name: vimeo
+#   args:
+#     root_path: ../datasets/Vimeo90K
+#     split: val
+#   loader:
+#     batch_size: 16
+#     num_workers: 4
+#   save_imgs: False
+
+# demo_dataset:
+#   name: demo
+#   args:
+#     root_path: ../data/animation
+#     split: animation
+
+model:
+  name: upr_net_freq
+  args:
+    pyr_level: 3
+    nr_lvl_skipped: 0
+    splat_mode: average
+    fftshift: False
+
+
+optimizer:
+  name: adamW
+  args: {lr: 1.e-6, weight_decay: 1.e-4}
+
+lr_scheduler:
+  name: one_cycle_lr
+  args:
+    max_lr: 1.e-6
+    pct_start: 0.01
+    cycle_momentum: False
+    anneal_strategy: cos
+
+loss:
+  - {
+    name: frequency,
+    args: { weight: 0.01 }
+  }
+  - {
+    name: bi_frequency,
+    args: { weight: 0.01 }
+  }
+  - {
+    name: multiple_flow,
+    args: { weight: 0.005 }
+  }
+  - {
+    name: charbonnier,
+    args: { weight: 1 }
+  }
+  - {
+    name: ternary,
+    args: { weight: 1 }
+  }
+
+pretrained: ./pretrained/upr_freq002.pth
+max_epoch: 300
+
+validate_every: 10
+save_every: 10
+vis_every: 20
+
+seed: 1234
+
+dist_url: env://

cfgs/upr_freq_unimatch_exp002_stopmotion.yaml

@@ -0,0 +1,86 @@
+exp_name: FET-VFI_BiFrequencyLoss
+
+mode: train
+
+train_dataset:
+  name: vimeo
+  args:
+    root_path: ../datasets/256_new
+    flow_root: ../datasets/256_new_unimatch_flow
+    tri_trainlist: tri_trainlist_011.txt
+    split: train
+    patch_size: 256
+    flow: 't0'
+  loader:
+    batch_size: 32
+    num_workers: 8
+
+test_dataset:
+  name: vimeo
+  args:
+    root_path: ../datasets/Vimeo90K
+    split: val
+  loader:
+    batch_size: 16
+    num_workers: 4
+  save_imgs: False
+
+demo_dataset:
+  name: demo
+  args:
+    root_path: ../data/animation
+    split: animation
+
+model:
+  name: upr_net_freq
+  args:
+    pyr_level: 3
+    nr_lvl_skipped: 0
+    splat_mode: average
+    fftshift: False
+
+
+optimizer:
+  name: adamW
+  args: {lr: 4.e-6, weight_decay: 1.e-4}
+
+lr_scheduler:
+  name: one_cycle_lr
+  args:
+    max_lr: 4.e-6
+    pct_start: 0.01
+    cycle_momentum: False
+    anneal_strategy: cos
+
+loss:
+  - {
+    name: frequency,
+    args: { weight: 0.01 }
+  }
+  - {
+    name: bi_frequency,
+    args: { weight: 0.01 }
+  }
+  - {
+    name: multiple_flow,
+    args: { weight: 0.005 }
+  }
+  - {
+    name: charbonnier,
+    args: { weight: 1 }
+  }
+  - {
+    name: ternary,
+    args: { weight: 1 }
+  }
+
+pretrained: ./pretrained/upr_freq002.pth
+max_epoch: 300
+
+validate_every: 10
+save_every: 10
+vis_every: 20
+
+seed: 1234
+
+dist_url: env://

cfgs/upr_vimeo_exp45.yaml

@@ -0,0 +1,76 @@
+exp_name: upsample_guided_filter_v2
+
+mode: train
+
+train_dataset:
+  name: vimeo
+  args:
+    root_path: ../data/vimeo_triplet
+    split: train
+    patch_size: 256
+    flow: 't0'
+    seg: False
+  loader:
+    batch_size: 32
+    num_workers: 8
+
+test_dataset:
+  name: vimeo
+  args:
+    root_path: ../data/vimeo_triplet
+    split: val
+  loader:
+    batch_size: 16
+    num_workers: 8
+  save_imgs: False
+
+demo_dataset:
+  name: demo
+  args:
+    root_path: ../data/animation
+    split: animation
+
+model:
+  name: upr_net_mod2
+  args:
+    pyr_level: 3
+    nr_lvl_skipped: 0
+    splat_mode: average
+
+
+optimizer:
+  name: adamW
+  args: {lr: 1.5e-4, weight_decay: 1.e-4}
+
+lr_scheduler:
+  name: one_cycle_lr
+  args:
+    max_lr: 1.5e-4
+    pct_start: 0.01
+    cycle_momentum: False
+    anneal_strategy: cos
+
+loss:
+  - {
+    name: multiple_flow,
+    args: { weight: 0.005 }
+  }
+  - {
+    name: charbonnier,
+    args: { weight: 1 }
+  }
+  - {
+    name: ternary,
+    args: { weight: 1 }
+  }
+
+
+max_epoch: 540
+
+validate_every: 10
+save_every: 10
+vis_every: 20
+
+seed: 1234
+
+dist_url: env://

check_images.py

@@ -0,0 +1,39 @@
+import os
+import cv2
+
+def process_directory(directory, output_file):
+    for root, dirs, files in os.walk(directory):
+        if not dirs:
+            # 더 이상 하위 디렉토리가 없는 경우
+            print("Directory:", root)
+    
+            # 이미지 파일 경로 검사
+            image_found = False
+            for filename in files:
+                if filename in ["im1.png","im1.jpg"]:
+                    image_found = True
+                    image_path = os.path.join(root, filename)
+                
+                    # 이미지 읽기
+                    image = cv2.imread(image_path)[:, :, ::-1]
+                    if image is not None:
+                        print("Image:", image_path)
+    
+                    # 이미지를 찾지 못한 경우 경로를 파일에 쓰기
+                    if not image_found:
+                        with open(output_file, "a") as f:
+                            f.write(root + "\n")
+    
+if __name__ == "__main__":
+    # 디렉토리 경로 지정
+    start_directory = "../../ins4/Triplet_250p_new/sequences"
+    
+    # 결과를 저장할 텍스트 파일 경로 지정
+    output_file = "missing_images.txt"
+    
+    # 텍스트 파일 초기화
+    open(output_file, "w").close()
+    
+    # 시작 디렉토리에서 순회 시작
+    process_directory(start_directory, output_file)
+

datasets/__init__.py

@@ -0,0 +1,5 @@
+from .datasets import *
+from .vimeo import *
+from .snu_film import *
+from .xiph import *
+from .ucf101 import *