feat: Add upscale model integration Add EIFE integration、 batch processing for video frames

- Integrated progress tracking with upscale model loading.
- Implemented conditional latent upscaling using the upscale model.
- Processed batch video frames using PyTorch and converted them to PIL images.
- load_torch_file for params_ema convert weights

inference/gradio_web_demo.py

@@ -1,4 +1,7 @@
+import gc
+import math
 import os
+import random
 import tempfile
 import threading
 import time
@@ -8,17 +11,33 @@ import numpy as np
 import torch
 from diffusers import CogVideoXPipeline
 from datetime import datetime, timedelta
+
+from diffusers.image_processor import VaeImageProcessor
 from openai import OpenAI
-import imageio
+import spaces
 import moviepy.editor as mp
 from typing import List, Union
 import PIL
+import utils
+from inference.rife_model import load_rife_model, rife_inference_with_latents
 
-dtype = torch.bfloat16
 device = "cuda" if torch.cuda.is_available() else "cpu"
-pipe = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-2b", torch_dtype=dtype)
+
+MODEL_PATH = os.environ.get('MODEL_PATH', "THUDM/CogVideoX-2b")
+UP_SCALE_MODEL_CKPT = os.environ.get('UP_SCALE_MODEL_CKPT', "")
+RIFE_MODEL_PATH = os.environ.get('RIFE_MODEL_PATH', "")
+pipe = CogVideoXPipeline.from_pretrained(MODEL_PATH, torch_dtype=torch.float16).to(
+    device)
 pipe.enable_model_cpu_offload()
 
+gc.collect()
+torch.cuda.empty_cache()
+torch.cuda.reset_accumulated_memory_stats()
+torch.cuda.reset_peak_memory_stats()
+
+# pipe.vae.enable_tiling()
+UP_SCALE_MODEL = None
+RIFE_MODEL = None
 sys_prompt = """You are part of a team of bots that creates videos. You work with an assistant bot that will draw anything you say in square brackets.
 
 For example , outputting " a beautiful morning in the woods with the sun peaking through the trees " will trigger your partner bot to output an video of a forest morning , as described. You will be prompted by people looking to create detailed , amazing videos. The way to accomplish this is to take their short prompts and make them extremely detailed and descriptive.
@@ -33,23 +52,64 @@ Video descriptions must have the same num of words as examples below. Extra word
 """
 
 
-def export_to_video_imageio(
+def load_sd_upscale(ckpt):
-    video_frames: Union[List[np.ndarray], List[PIL.Image.Image]], output_video_path: str = None, fps: int = 8
+    from spandrel import ModelLoader, ImageModelDescriptor   # Simulate a step in loading
-) -> str:
-    """
-    Export the video frames to a video file using imageio lib to Avoid "green screen" issue (for example CogVideoX)
-    """
-    if output_video_path is None:
-        output_video_path = tempfile.NamedTemporaryFile(suffix=".mp4").name
 
-    if isinstance(video_frames[0], PIL.Image.Image):
+    pbar = utils.ProgressBar(1, "Loading Upscale Model")
-        video_frames = [np.array(frame) for frame in video_frames]
+    sd = utils.load_torch_file(ckpt, device=device)
+    if "module.layers.0.residual_group.blocks.0.norm1.weight" in sd:
+        sd = utils.state_dict_prefix_replace(sd, {"module.": ""})
+    out = ModelLoader().load_from_state_dict(sd).half()
 
-    with imageio.get_writer(output_video_path, fps=fps) as writer:
+    pbar.update(1)  # Update progress by 1
-        for frame in video_frames:
+    return out
-            writer.append_data(frame)
+
+
+def upscale(upscale_model, tensor: torch.Tensor) -> torch.Tensor:
+    memory_required = utils.module_size(upscale_model.model)
+    memory_required += ((512 * 512 * 3) *
+                        tensor.element_size() *
+                        max(upscale_model.scale, 1.0) *
+                        384.0
+                        )  #The 384.0 is an estimate of how much some of these models take, TODO: make it more accurate
+    memory_required += tensor.nelement() * tensor.element_size()
+    print(f"Memory required: {memory_required / 1024 / 1024 / 1024} GB")
+
+    upscale_model.to(device)
+    # in_img = tensor.movedim(-1, -3).to(device)
+
+    tile = 512
+    overlap = 32
+
+    steps = tensor.shape[0] * utils.get_tiled_scale_steps(tensor.shape[3], tensor.shape[2], tile_x=tile,
+                                                          tile_y=tile, overlap=overlap)
+
+    pbar = utils.ProgressBar(steps, desc="Tiling and Upscaling")
+
+    s = utils.tiled_scale(tensor, lambda a: upscale_model(a), tile_x=tile, tile_y=tile, overlap=overlap,
+                          upscale_amount=upscale_model.scale, pbar=pbar)
+
+    upscale_model.to("cpu")
+    return s
+
+
+def upscale_batch_and_concatenate(upscale_model, latents):
+    # 初始化一个空列表来存储每个批次的放大结果
+    upscaled_latents = []
+
+    # 遍历第一个维度 (批次)
+    for i in range(latents.size(0)):
+        # 取出第 i 个批次数据 (形状为 [49, 3, 512, 480])
+        latent = latents[i]
+
+        # 调用放大模型对该批次数据进行放大
+        upscaled_latent = upscale(upscale_model, latent)
+
+        # 将放大的结果存储到列表中
+        upscaled_latents.append(upscaled_latent)
+
+    return torch.stack(upscaled_latents)
 
-    return output_video_path
 
 
 def convert_prompt(prompt: str, retry_times: int = 3) -> str:
@@ -62,82 +122,63 @@ def convert_prompt(prompt: str, retry_times: int = 3) -> str:
         response = client.chat.completions.create(
             messages=[
                 {"role": "system", "content": sys_prompt},
-                {
+                {"role": "user",
-                    "role": "user",
+                 "content": 'Create an imaginative video descriptive caption or modify an earlier caption for the user input : "a girl is on the beach"'},
-                    "content": 'Create an imaginative video descriptive caption or modify an earlier caption for the user input : "a girl is on the beach"',
+                {"role": "assistant",
-                },
+                 "content": "A radiant woman stands on a deserted beach, arms outstretched, wearing a beige trench coat, white blouse, light blue jeans, and chic boots, against a backdrop of soft sky and sea. Moments later, she is seen mid-twirl, arms exuberant, with the lighting suggesting dawn or dusk. Then, she runs along the beach, her attire complemented by an off-white scarf and black ankle boots, the tranquil sea behind her. Finally, she holds a paper airplane, her pose reflecting joy and freedom, with the ocean's gentle waves and the sky's soft pastel hues enhancing the serene ambiance."},
-                {
+                {"role": "user",
-                    "role": "assistant",
+                 "content": 'Create an imaginative video descriptive caption or modify an earlier caption for the user input : "A man jogging on a football field"'},
-                    "content": "A radiant woman stands on a deserted beach, arms outstretched, wearing a beige trench coat, white blouse, light blue jeans, and chic boots, against a backdrop of soft sky and sea. Moments later, she is seen mid-twirl, arms exuberant, with the lighting suggesting dawn or dusk. Then, she runs along the beach, her attire complemented by an off-white scarf and black ankle boots, the tranquil sea behind her. Finally, she holds a paper airplane, her pose reflecting joy and freedom, with the ocean's gentle waves and the sky's soft pastel hues enhancing the serene ambiance.",
+                {"role": "assistant",
-                },
+                 "content": "A determined man in athletic attire, including a blue long-sleeve shirt, black shorts, and blue socks, jogs around a snow-covered soccer field, showcasing his solitary exercise in a quiet, overcast setting. His long dreadlocks, focused expression, and the serene winter backdrop highlight his dedication to fitness. As he moves, his attire, consisting of a blue sports sweatshirt, black athletic pants, gloves, and sneakers, grips the snowy ground. He is seen running past a chain-link fence enclosing the playground area, with a basketball hoop and children's slide, suggesting a moment of solitary exercise amidst the empty field."},
-                {
+                {"role": "user",
-                    "role": "user",
+                 "content": 'Create an imaginative video descriptive caption or modify an earlier caption for the user input : " A woman is dancing, HD footage, close-up"'},
-                    "content": 'Create an imaginative video descriptive caption or modify an earlier caption for the user input : "A man jogging on a football field"',
+                {"role": "assistant",
-                },
+                 "content": "A young woman with her hair in an updo and wearing a teal hoodie stands against a light backdrop, initially looking over her shoulder with a contemplative expression. She then confidently makes a subtle dance move, suggesting rhythm and movement. Next, she appears poised and focused, looking directly at the camera. Her expression shifts to one of introspection as she gazes downward slightly. Finally, she dances with confidence, her left hand over her heart, symbolizing a poignant moment, all while dressed in the same teal hoodie against a plain, light-colored background."},
-                {
+                {"role": "user",
-                    "role": "assistant",
+                 "content": f'Create an imaginative video descriptive caption or modify an earlier caption in ENGLISH for the user input: "{text}"'},
-                    "content": "A determined man in athletic attire, including a blue long-sleeve shirt, black shorts, and blue socks, jogs around a snow-covered soccer field, showcasing his solitary exercise in a quiet, overcast setting. His long dreadlocks, focused expression, and the serene winter backdrop highlight his dedication to fitness. As he moves, his attire, consisting of a blue sports sweatshirt, black athletic pants, gloves, and sneakers, grips the snowy ground. He is seen running past a chain-link fence enclosing the playground area, with a basketball hoop and children's slide, suggesting a moment of solitary exercise amidst the empty field.",
-                },
-                {
-                    "role": "user",
-                    "content": 'Create an imaginative video descriptive caption or modify an earlier caption for the user input : " A woman is dancing, HD footage, close-up"',
-                },
-                {
-                    "role": "assistant",
-                    "content": "A young woman with her hair in an updo and wearing a teal hoodie stands against a light backdrop, initially looking over her shoulder with a contemplative expression. She then confidently makes a subtle dance move, suggesting rhythm and movement. Next, she appears poised and focused, looking directly at the camera. Her expression shifts to one of introspection as she gazes downward slightly. Finally, she dances with confidence, her left hand over her heart, symbolizing a poignant moment, all while dressed in the same teal hoodie against a plain, light-colored background.",
-                },
-                {
-                    "role": "user",
-                    "content": f'Create an imaginative video descriptive caption or modify an earlier caption in ENGLISH for the user input: "{text}"',
-                },
             ],
             model="glm-4-0520",
             temperature=0.01,
             top_p=0.7,
             stream=False,
-            max_tokens=250,
+            max_tokens=200,
         )
         if response.choices:
             return response.choices[0].message.content
     return prompt
 
 
-def infer(prompt: str, num_inference_steps: int, guidance_scale: float, progress=gr.Progress(track_tqdm=True)):
+@spaces.GPU(duration=300)
+def infer(
+        prompt: str,
+        num_inference_steps: int,
+        guidance_scale: float,
+        seed: int = -1,
+        progress=gr.Progress(track_tqdm=True),
+):
     torch.cuda.empty_cache()
 
-    prompt_embeds, _ = pipe.encode_prompt(
+    if seed == -1:
-        prompt=prompt,
+        seed = random.randint(0, 2**32 - 1)
-        negative_prompt=None,
-        do_classifier_free_guidance=True,
-        num_videos_per_prompt=1,
-        max_sequence_length=226,
-        device=device,
-        dtype=dtype,
-    )
 
-    video = pipe(
+    video_pt = pipe(
+        prompt=prompt,
         num_inference_steps=num_inference_steps,
         guidance_scale=guidance_scale,
-        prompt_embeds=prompt_embeds,
+        output_type="pt",
-        negative_prompt_embeds=torch.zeros_like(prompt_embeds),
+        generator=torch.Generator().manual_seed(seed),  # Set the seed for reproducibility
-    ).frames[0]
+    ).frames
 
-    return video
+    return (video_pt, seed)
 
 
-def save_video(tensor):
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    video_path = f"./output/{timestamp}.mp4"
-    os.makedirs(os.path.dirname(video_path), exist_ok=True)
-    export_to_video_imageio(tensor[1:], video_path)
-    return video_path
 
 
 def convert_to_gif(video_path):
     clip = mp.VideoFileClip(video_path)
     clip = clip.set_fps(8)
     clip = clip.resize(height=240)
-    gif_path = video_path.replace(".mp4", ".gif")
+    gif_path = video_path.replace('.mp4', '.gif')
     clip.write_gif(gif_path, fps=8)
     return gif_path
 
@@ -146,7 +187,8 @@ def delete_old_files():
     while True:
         now = datetime.now()
         cutoff = now - timedelta(minutes=10)
-        output_dir = "./output"
+        output_dir = './output'
+        os.makedirs(output_dir, exist_ok=True)
         for filename in os.listdir(output_dir):
             file_path = os.path.join(output_dir, filename)
             if os.path.isfile(file_path):
@@ -161,11 +203,13 @@ threading.Thread(target=delete_old_files, daemon=True).start()
 with gr.Blocks() as demo:
     gr.Markdown("""
            <div style="text-align: center; font-size: 32px; font-weight: bold; margin-bottom: 20px;">
-               CogVideoX-2B Huggingface Space🤗
+               CogVideoX-5B Huggingface Space🤗
            </div>
            <div style="text-align: center;">
-               <a href="https://huggingface.co/THUDM/CogVideoX-2b">🤗 Model Hub</a> |
+               <a href="https://huggingface.co/THUDM/CogVideoX-2B">🤗 2B Model Hub</a> |
-               <a href="https://github.com/THUDM/CogVideo">🌐 Github</a> 
+               <a href="https://huggingface.co/THUDM/CogVideoX-5B">🤗 5B Model Hub</a> |
+               <a href="https://github.com/THUDM/CogVideo">🌐 Github</a> |
+               <a href="https://arxiv.org/pdf/2408.06072">📜 arxiv </a>
            </div>
 
            <div style="text-align: center; font-size: 15px; font-weight: bold; color: red; margin-bottom: 20px;">
@@ -176,21 +220,25 @@ with gr.Blocks() as demo:
     with gr.Row():
         with gr.Column():
             prompt = gr.Textbox(label="Prompt (Less than 200 Words)", placeholder="Enter your prompt here", lines=5)
+
             with gr.Row():
                 gr.Markdown(
-                    "✨Upon pressing the enhanced prompt button, we will use [GLM-4 Model](https://github.com/THUDM/GLM-4) to polish the prompt and overwrite the original one."
+                    "✨Upon pressing the enhanced prompt button, we will use [GLM-4 Model](https://github.com/THUDM/GLM-4) to polish the prompt and overwrite the original one.")
-                )
                 enhance_button = gr.Button("✨ Enhance Prompt(Optional)")
 
             with gr.Column():
-                gr.Markdown(
+                gr.Markdown("**Optional Parameters** (default values are recommended)<br>"
-                    "**Optional Parameters** (default values are recommended)<br>"
+                            "Increasing the number of inference steps will produce more detailed videos, but it will slow down the process.<br>"
-                    "Turn Inference Steps larger if you want more detailed video, but it will be slower.<br>"
+                            "50 steps are recommended for most cases.<br>"
-                    "50 steps are recommended for most cases. will cause 120 seconds for inference.<br>"
+                            "For the 5B model, 50 steps will take approximately 350 seconds.")
-                )
                 with gr.Row():
                     num_inference_steps = gr.Number(label="Inference Steps", value=50)
                     guidance_scale = gr.Number(label="Guidance Scale", value=6.0)
+                with gr.Row():
+                    seed_param = gr.Number(label="Inference Seed", value=-1)
+                with gr.Row():
+                    enable_scale = gr.Checkbox(label="Enable Upscale", value=False)
+                    enable_rife = gr.Checkbox(label="Enable RIFE", value=False)
                 generate_button = gr.Button("🎬 Generate Video")
 
         with gr.Column():
@@ -198,61 +246,100 @@ with gr.Blocks() as demo:
             with gr.Row():
                 download_video_button = gr.File(label="📥 Download Video", visible=False)
                 download_gif_button = gr.File(label="📥 Download GIF", visible=False)
+                seed_text = gr.Number(label="seed", value=-1)
 
     gr.Markdown("""
-        <table border="1" style="width: 100%; text-align: left; margin-top: 20px;">
+    <table border="0" style="width: 100%; text-align: left; margin-top: 20px;">
-            <tr>
+         <div style="text-align: center; font-size: 24px; font-weight: bold; margin-bottom: 20px;">
-                <th>Prompt</th>
+               Demo Videos with 50 Inference Steps and 6.0 Guidance Scale.
-                <th>Video URL</th>
+         </div>
-                <th>Inference Steps</th>
+        <tr>
-                <th>Guidance Scale</th>
+            <td style="width: 25%; vertical-align: top; font-size: 1.2em;">
-            </tr>
+                <p>A detailed wooden toy ship with intricately carved masts and sails is seen gliding smoothly over a plush, blue carpet that mimics the waves of the sea. The ship's hull is painted a rich brown, with tiny windows. The carpet, soft and textured, provides a perfect backdrop, resembling an oceanic expanse. Surrounding the ship are various other toys and children's items, hinting at a playful environment. The scene captures the innocence and imagination of childhood, with the toy ship's journey symbolizing endless adventures in a whimsical, indoor setting.</p>
-            <tr>
+            </td>
-                <td>A detailed wooden toy ship with intricately carved masts and sails is seen gliding smoothly over a plush, blue carpet that mimics the waves of the sea. The ship's hull is painted a rich brown, with tiny windows. The carpet, soft and textured, provides a perfect backdrop, resembling an oceanic expanse. Surrounding the ship are various other toys and children's items, hinting at a playful environment. The scene captures the innocence and imagination of childhood, with the toy ship's journey symbolizing endless adventures in a whimsical, indoor setting.</td>
+            <td style="width: 25%; vertical-align: top;">
-                <td><a href="https://github.com/THUDM/CogVideo/raw/main/resources/videos/1.mp4">Video 1</a></td>
+                <video src="https://github.com/user-attachments/assets/ea3af39a-3160-4999-90ec-2f7863c5b0e9" width="100%" controls autoplay></video>
-                <td>50</td>
+            </td>
-                <td>6</td>
+            <td style="width: 25%; vertical-align: top; font-size: 1.2em;">
-            </tr>
+                <p>The camera follows behind a white vintage SUV with a black roof rack as it speeds up a steep dirt road surrounded by pine trees on a steep mountain slope, dust kicks up from its tires, the sunlight shines on the SUV as it speeds along the dirt road, casting a warm glow over the scene. The dirt road curves gently into the distance, with no other cars or vehicles in sight. The trees on either side of the road are redwoods, with patches of greenery scattered throughout. The car is seen from the rear following the curve with ease, making it seem as if it is on a rugged drive through the rugged terrain. The dirt road itself is surrounded by steep hills and mountains, with a clear blue sky above with wispy clouds.</p>
-            <tr>
+            </td>
-                <td>The camera follows behind a white vintage SUV with a black roof rack as it speeds up a steep dirt road surrounded by pine trees on a steep mountain slope, dust kicks up from it’s tires, the sunlight shines on the SUV as it speeds along the dirt road, casting a warm glow over the scene. The dirt road curves gently into the distance, with no other cars or vehicles in sight. The trees on either side of the road are redwoods, with patches of greenery scattered throughout. The car is seen from the rear following the curve with ease, making it seem as if it is on a rugged drive through the rugged terrain. The dirt road itself is surrounded by steep hills and mountains, with a clear blue sky above with wispy clouds.</td>
+            <td style="width: 25%; vertical-align: top;">
-                <td><a href="https://github.com/THUDM/CogVideo/raw/main/resources/videos/2.mp4">Video 2</a></td>
+                <video src="https://github.com/user-attachments/assets/9de41efd-d4d1-4095-aeda-246dd834e91d" width="100%" controls autoplay></video>
-                <td>50</td>
+            </td>
-                <td>6</td>
+        </tr>
-            </tr>
+        <tr>
-            <tr>
+            <td style="width: 25%; vertical-align: top; font-size: 1.2em;">
-                <td>A street artist, clad in a worn-out denim jacket and a colorful bandana, stands before a vast concrete wall in the heart, holding a can of spray paint, spray-painting a colorful bird on a mottled wall.</td>
+                <p>A street artist, clad in a worn-out denim jacket and a colorful bandana, stands before a vast concrete wall in the heart, holding a can of spray paint, spray-painting a colorful bird on a mottled wall.</p>
-                <td><a href="https://github.com/THUDM/CogVideo/raw/main/resources/videos/3.mp4">Video 3</a></td>
+            </td>
-                <td>50</td>
+            <td style="width: 25%; vertical-align: top;">
-                <td>6</td>
+                <video src="https://github.com/user-attachments/assets/941d6661-6a8d-4a1b-b912-59606f0b2841" width="100%" controls autoplay></video>
-            </tr>
+            </td>
-            <tr>
+            <td style="width: 25%; vertical-align: top; font-size: 1.2em;">
-                <td>In the haunting backdrop of a war-torn city, where ruins and crumbled walls tell a story of devastation, a poignant close-up frames a young girl. Her face is smudged with ash, a silent testament to the chaos around her. Her eyes glistening with a mix of sorrow and resilience, capturing the raw emotion of a world that has lost its innocence to the ravages of conflict.</td>
+                <p>In the haunting backdrop of a war-torn city, where ruins and crumbled walls tell a story of devastation, a poignant close-up frames a young girl. Her face is smudged with ash, a silent testament to the chaos around her. Her eyes glistening with a mix of sorrow and resilience, capturing the raw emotion of a world that has lost its innocence to the ravages of conflict.</p>
-                <td><a href="https://github.com/THUDM/CogVideo/raw/main/resources/videos/4.mp4">Video 4</a></td>
+            </td>
-                <td>50</td>
+            <td style="width: 25%; vertical-align: top;">
-                <td>6</td>
+                <video src="https://github.com/user-attachments/assets/938529c4-91ae-4f60-b96b-3c3947fa63cb" width="100%" controls autoplay></video>
-            </tr>
+            </td>
-        </table>
+        </tr>
+    </table>
     """)
 
-    def generate(prompt, num_inference_steps, guidance_scale, progress=gr.Progress(track_tqdm=True)):
+
-        tensor = infer(prompt, num_inference_steps, guidance_scale, progress=progress)
+    def generate(prompt, num_inference_steps, guidance_scale,seed_value, scale_status, rife_status, progress=gr.Progress(track_tqdm=True)):
-        video_path = save_video(tensor)
+        global UP_SCALE_MODEL, RIFE_MODEL
+        if not UP_SCALE_MODEL and len(str(UP_SCALE_MODEL_CKPT).strip()) > 0:
+            # Load the upscale model with progress tracking
+            UP_SCALE_MODEL = load_sd_upscale(UP_SCALE_MODEL_CKPT)
+        if not RIFE_MODEL and len(str(RIFE_MODEL_PATH).strip()) > 0:
+            # Load the RIFE model with progress tracking
+            RIFE_MODEL = load_rife_model(RIFE_MODEL_PATH)
+
+        latents, seed = infer(prompt, num_inference_steps, guidance_scale, seed=seed_value, progress=progress)
+        if UP_SCALE_MODEL and scale_status:
+            latents = upscale_batch_and_concatenate(UP_SCALE_MODEL, latents)
+
+        if RIFE_MODEL and rife_status:
+            latents = rife_inference_with_latents(RIFE_MODEL, latents)
+
+        batch_size = latents.shape[0]
+        batch_video_frames = []
+        for batch_idx in range(batch_size):
+            pt_image = latents[batch_idx]
+            pt_image = torch.stack(
+                [pt_image[i] for i in range(pt_image.shape[0])]
+            )
+
+            image_np = VaeImageProcessor.pt_to_numpy(pt_image)  # (to [49, 512, 480, 3])
+
+            image_pil = VaeImageProcessor.numpy_to_pil(image_np)
+
+            batch_video_frames.append(image_pil)
+
+        # fps (len(batch_video_frames[0])-1) /6
+        video_path = utils.save_video(batch_video_frames[0], fps=math.ceil((len(batch_video_frames[0])-1) / 6))
         video_update = gr.update(visible=True, value=video_path)
         gif_path = convert_to_gif(video_path)
         gif_update = gr.update(visible=True, value=gif_path)
 
-        return video_path, video_update, gif_update
+        seed_update = gr.update(value=seed)
+        return video_path, video_update, gif_update, seed_update
+
 
     def enhance_prompt_func(prompt):
         return convert_prompt(prompt, retry_times=1)
 
+
     generate_button.click(
         generate,
-        inputs=[prompt, num_inference_steps, guidance_scale],
+        inputs=[prompt, num_inference_steps, guidance_scale, seed_param, enable_scale, enable_rife],
-        outputs=[video_output, download_video_button, download_gif_button],
+        outputs=[video_output, download_video_button, download_gif_button, seed_text]
     )
 
-    enhance_button.click(enhance_prompt_func, inputs=[prompt], outputs=[prompt])
+    enhance_button.click(
+        enhance_prompt_func,
+        inputs=[prompt],
+        outputs=[prompt]
+    )
 
 if __name__ == "__main__":
-    demo.launch(server_name="127.0.0.1", server_port=7870, share=True)
+    demo.launch(server_port=7870)

inference/rife_model.py

@@ -0,0 +1,177 @@
+import importlib
+
+import torch
+from diffusers.image_processor import VaeImageProcessor
+from torch.nn import functional as F
+import cv2
+from inference import utils
+from rife.pytorch_msssim import ssim_matlab
+import numpy as np
+import logging
+import os
+import skvideo.io
+
+logger = logging.getLogger(__name__)
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+
+def pad_image(img, scale):
+    _, _, h, w = img.shape
+    tmp = max(32, int(32 / scale))
+    ph = ((h - 1) // tmp + 1) * tmp
+    pw = ((w - 1) // tmp + 1) * tmp
+    padding = (0, 0, pw - w, ph - h)
+    return F.pad(img, padding)
+
+
+def make_inference(model, I0, I1, upscale_amount, n):
+    middle = model.inference(I0, I1, upscale_amount)
+    if n == 1:
+        return [middle]
+    first_half = make_inference(model, I0, middle, upscale_amount, n=n // 2)
+    second_half = make_inference(model, middle, I1, upscale_amount, n=n // 2)
+    if n % 2:
+        return [*first_half, middle, *second_half]
+    else:
+        return [*first_half, *second_half]
+
+
+@torch.inference_mode()
+def ssim_interpolation_rife(model, samples, exp=1, upscale_amount=1, output_device="cpu", pbar=None):
+    print(f"samples dtype:{samples.dtype}")
+    print(f"samples shape:{samples.shape}")
+    output = []
+    # [f, c, h, w]
+    for b in range(samples.shape[0]):
+        frame = samples[b:b + 1]
+        _, _, h, w = frame.shape
+        I0 = samples[b:b + 1]
+        I1 = samples[b + 1: b + 2] if b + 2 < samples.shape[0] else samples[-1:]
+        I1 = pad_image(I1, upscale_amount)
+        # [c, h, w]
+        I0_small = F.interpolate(I0, (32, 32), mode='bilinear', align_corners=False)
+        I1_small = F.interpolate(I1, (32, 32), mode='bilinear', align_corners=False)
+
+        ssim = ssim_matlab(I0_small[:, :3], I1_small[:, :3])
+
+        if ssim > 0.996:
+            I1 = I0
+            I1 = pad_image(I1, upscale_amount)
+            I1 = make_inference(model, I0, I1, upscale_amount, 1)
+
+            I1_small = F.interpolate(I1[0], (32, 32), mode='bilinear', align_corners=False)
+            ssim = ssim_matlab(I0_small[:, :3], I1_small[:, :3])
+            frame = I1[0]
+            I1 = I1[0]
+
+        tmp_output = []
+        if ssim < 0.2:
+            for i in range((2 ** exp) - 1):
+                tmp_output.append(I0)
+
+        else:
+            tmp_output = make_inference(model, I0, I1, upscale_amount, 2 ** exp - 1) if exp else []
+
+        # frame to tensor output
+        frame = pad_image(frame, upscale_amount)
+        tmp_output = [frame] + tmp_output
+        # output拼接 tmp_output
+        for i, frame in enumerate(tmp_output):
+            output.append(frame.to(output_device))
+
+        if pbar:
+            pbar.update(1)
+    return output
+
+
+def load_rife_model(model_path):
+    pbar = utils.ProgressBar(1, desc="Loading RIFE model")
+    torch.set_grad_enabled(False)
+    if torch.cuda.is_available():
+        torch.backends.cudnn.enabled = True
+        torch.backends.cudnn.benchmark = True
+        torch.set_default_tensor_type(torch.cuda.FloatTensor)
+
+    from rife.RIFE_HDv3 import Model
+    model = Model()
+    model.load_model(model_path, -1)
+    print("Loaded v3.x HD model.")
+    model.eval()
+    model.device()
+
+    pbar.update(1)  # Update progress by 1
+    return model
+
+
+# Create a generator that yields each frame, similar to cv2.VideoCapture
+def frame_generator(video_capture):
+    while True:
+        ret, frame = video_capture.read()
+        if not ret:
+            break
+        yield frame
+    video_capture.release()
+
+
+def rife_inference_with_path(model, video_path):
+    video_capture = cv2.VideoCapture(video_path)
+    tot_frame = video_capture.get(cv2.CAP_PROP_FRAME_COUNT)
+    pt_frame_data = []
+    pt_frame = skvideo.io.vreader(video_path)
+    for frame in pt_frame:
+        # Process each frame
+        pt_frame_data.append(
+            torch.from_numpy(
+                np.transpose(frame, (2, 0, 1))
+            )
+            .to("cpu", non_blocking=True).float() / 255.
+        )
+
+    pt_frame = torch.from_numpy(np.stack(pt_frame_data))
+    pt_frame = pt_frame.to(device)
+    pbar = utils.ProgressBar(tot_frame, desc="RIFE inference")
+    frames = ssim_interpolation_rife(model, pt_frame, pbar=pbar)
+    pt_image = torch.stack(
+        [frames[i].squeeze(0) for i in range(len(frames))]
+    )
+
+    image_np = VaeImageProcessor.pt_to_numpy(pt_image)  # (to [49, 512, 480, 3])
+
+    image_pil = VaeImageProcessor.numpy_to_pil(image_np)
+    video_path = utils.save_video(image_pil, fps=16)
+    print(f"Video saved to {video_path}")
+    # frame = next(pt_frame)
+    # I1 = torch.from_numpy(np.transpose(frame, (2,0,1))).to("cpu", non_blocking=True).unsqueeze(0).float() / 255.
+    # I1 = pad_image(I1, 1)
+    # I0 = I1
+    # I0_small = F.interpolate(I0, (32, 32), mode='bilinear', align_corners=False)
+    # I1_small = F.interpolate(I1, (32, 32), mode='bilinear', align_corners=False)
+    # ssim = ssim_matlab(I0_small[:, :3], I1_small[:, :3])
+    # print(I1.shape)
+
+    return video_path
+
+
+def rife_inference_with_latents(model, latents):
+    pbar = utils.ProgressBar(latents.shape[0], desc="RIFE inference")
+    rife_results = []
+    latents = latents.to(device)
+    for i in range(latents.size(0)):
+        #  [f, c, w, h]
+        latent = latents[i]
+
+        frames = ssim_interpolation_rife(model, latent, pbar=pbar)
+        pt_image = torch.stack(
+            [frames[i].squeeze(0) for i in range(len(frames))]
+        )  # (to [f, c, w, h])
+        rife_results.append(pt_image)
+
+    return torch.stack(rife_results)
+
+
+# if __name__ == '__main__':
+#     model_path = "/media/gpt4-pdf-chatbot-langchain/ECCV2022-RIFE/train_log"
+#     video_path = "/media/gpt4-pdf-chatbot-langchain/CogVideo/inference/output/20240823_110325.mp4"
+#     model = load_rife_model(model_path)
+#     rife_inference_with_path(model, video_path)

inference/test_upscale.py

@@ -0,0 +1,28 @@
+import utils
+
+import torch
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+
+def load_sd_upscale(ckpt):
+    from spandrel import ModelLoader, ImageModelDescriptor  # Simulate a step in loading
+
+    pbar = utils.ProgressBar(1, desc="Loading upscale model")
+    sd = utils.load_torch_file(ckpt, device=device)
+    if "module.layers.0.residual_group.blocks.0.norm1.weight" in sd:
+        sd = utils.state_dict_prefix_replace(sd, {"module.": ""})
+    out = ModelLoader().load_from_state_dict(sd).half()
+
+    pbar.update(1)  # Update progress by 1
+    return out
+
+
+def test_load_sd_upscale():
+    model = load_sd_upscale("/media/gpt4-pdf-chatbot-langchain/ComfyUI/models/upscale_models/RealESRNet_x4plus.pth")
+
+    print(model.dtype)
+
+
+if __name__ == "__main__":
+    test_load_sd_upscale()

inference/utils.py

@@ -0,0 +1,178 @@
+import math
+from typing import Union, List
+
+import torch
+import os
+from datetime import datetime, timedelta
+import imageio
+import numpy as np
+import itertools
+
+import tempfile
+import PIL
+import safetensors.torch
+import tqdm
+import logging
+
+logger = logging.getLogger(__file__)
+
+
+def load_torch_file(ckpt, device=None, dtype=torch.float16):
+    if device is None:
+        device = torch.device("cpu")
+    if ckpt.lower().endswith(".safetensors") or ckpt.lower().endswith(".sft"):
+        sd = safetensors.torch.load_file(ckpt, device=device.type)
+    else:
+        if not 'weights_only' in torch.load.__code__.co_varnames:
+            logger.warning(
+                "Warning torch.load doesn't support weights_only on this pytorch version, loading unsafely.")
+
+        pl_sd = torch.load(ckpt, map_location=device, weights_only=True)
+        if "global_step" in pl_sd:
+            logger.debug(f"Global Step: {pl_sd['global_step']}")
+        if "state_dict" in pl_sd:
+            sd = pl_sd["state_dict"]
+        elif 'params_ema' in pl_sd:
+            sd = pl_sd['params_ema']
+        else:
+            sd = pl_sd
+
+    # Convert all tensors in the state_dict to the specified dtype
+    sd = {k: v.to(dtype) for k, v in sd.items()}
+    return sd
+
+
+def state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=False):
+    if filter_keys:
+        out = {}
+    else:
+        out = state_dict
+    for rp in replace_prefix:
+        replace = list(map(lambda a: (a, "{}{}".format(replace_prefix[rp], a[len(rp):])),
+                           filter(lambda a: a.startswith(rp), state_dict.keys())))
+        for x in replace:
+            w = state_dict.pop(x[0])
+            out[x[1]] = w
+    return out
+
+
+def module_size(module):
+    module_mem = 0
+    sd = module.state_dict()
+    for k in sd:
+        t = sd[k]
+        module_mem += t.nelement() * t.element_size()
+    return module_mem
+
+
+def get_tiled_scale_steps(width, height, tile_x, tile_y, overlap):
+    return math.ceil((height / (tile_y - overlap))) * math.ceil((width / (tile_x - overlap)))
+
+
+@torch.inference_mode()
+def tiled_scale_multidim(samples, function, tile=(64, 64), overlap=8, upscale_amount=4, out_channels=3,
+                         output_device="cpu", pbar=None):
+    dims = len(tile)
+    print(f"samples dtype:{samples.dtype}")
+    output = torch.empty(
+        [samples.shape[0], out_channels] + list(map(lambda a: round(a * upscale_amount), samples.shape[2:])),
+        device=output_device)
+
+    for b in range(samples.shape[0]):
+        s = samples[b:b + 1]
+        out = torch.zeros([s.shape[0], out_channels] + list(map(lambda a: round(a * upscale_amount), s.shape[2:])),
+                          device=output_device)
+        out_div = torch.zeros([s.shape[0], out_channels] + list(map(lambda a: round(a * upscale_amount), s.shape[2:])),
+                              device=output_device)
+
+        for it in itertools.product(*map(lambda a: range(0, a[0], a[1] - overlap), zip(s.shape[2:], tile))):
+            s_in = s
+            upscaled = []
+
+            for d in range(dims):
+                pos = max(0, min(s.shape[d + 2] - overlap, it[d]))
+                l = min(tile[d], s.shape[d + 2] - pos)
+                s_in = s_in.narrow(d + 2, pos, l)
+                upscaled.append(round(pos * upscale_amount))
+
+            ps = function(s_in).to(output_device)
+            mask = torch.ones_like(ps)
+            feather = round(overlap * upscale_amount)
+            for t in range(feather):
+                for d in range(2, dims + 2):
+                    m = mask.narrow(d, t, 1)
+                    m *= ((1.0 / feather) * (t + 1))
+                    m = mask.narrow(d, mask.shape[d] - 1 - t, 1)
+                    m *= ((1.0 / feather) * (t + 1))
+
+            o = out
+            o_d = out_div
+            for d in range(dims):
+                o = o.narrow(d + 2, upscaled[d], mask.shape[d + 2])
+                o_d = o_d.narrow(d + 2, upscaled[d], mask.shape[d + 2])
+
+            o += ps * mask
+            o_d += mask
+
+            if pbar is not None:
+                pbar.update(1)
+
+        output[b:b + 1] = out / out_div
+    return output
+
+
+def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap=8, upscale_amount=4, out_channels=3,
+                output_device="cpu", pbar=None):
+    return tiled_scale_multidim(samples, function, (tile_y, tile_x), overlap, upscale_amount, out_channels,
+                                output_device, pbar)
+
+
+def export_to_video_imageio(
+        video_frames: Union[List[np.ndarray], List[PIL.Image.Image]], output_video_path: str = None, fps: int = 8
+) -> str:
+    """
+    Export the video frames to a video file using imageio lib to Avoid "green screen" issue (for example CogVideoX)
+    """
+    if output_video_path is None:
+        output_video_path = tempfile.NamedTemporaryFile(suffix=".mp4").name
+
+    if isinstance(video_frames[0], PIL.Image.Image):
+        video_frames = [np.array(frame) for frame in video_frames]
+
+    with imageio.get_writer(output_video_path, fps=fps) as writer:
+        for frame in video_frames:
+            writer.append_data(frame)
+
+    return output_video_path
+
+
+def save_video(tensor: Union[List[np.ndarray], List[PIL.Image.Image]], fps: int = 8):
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    video_path = f"./output/{timestamp}.mp4"
+    os.makedirs(os.path.dirname(video_path), exist_ok=True)
+    export_to_video_imageio(tensor, video_path, fps=fps)
+    return video_path
+
+
+class ProgressBar:
+    def __init__(self, total, desc=None):
+
+        self.total = total
+        self.current = 0
+        self.b_unit = tqdm.tqdm(
+            total=total, desc="ProgressBar context index: 0" if desc is None else desc
+        )
+
+    def update(self, value):
+
+        if value > self.total:
+            value = self.total
+        self.current = value
+        if self.b_unit is not None:
+            self.b_unit.set_description(
+                "ProgressBar context index: {}".format(self.current)
+            )
+            self.b_unit.refresh()
+
+            # 更新进度
+            self.b_unit.update(self.current)

requirements.txt

@@ -11,4 +11,10 @@ streamlit==1.37.0 # For streamlit web demo
 imageio==2.34.2 # For diffusers inference export video
 imageio-ffmpeg==0.5.1 # For diffusers inference export video
 openai==1.40.6 # For prompt refiner
-moviepy==1.0.3 # For export video
+moviepy==1.0.3 # For export video
+
+#
+safetensors>=0.4.2
+spandrel
+spaces
+tqdm

rife/IFNet.py

@@ -0,0 +1,104 @@
+from rife.refine import *
+
+def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1):
+    return nn.Sequential(
+        torch.nn.ConvTranspose2d(in_channels=in_planes, out_channels=out_planes, kernel_size=4, stride=2, padding=1),
+        nn.PReLU(out_planes)
+    )
+
+def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
+    return nn.Sequential(
+        nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
+                  padding=padding, dilation=dilation, bias=True),
+        nn.PReLU(out_planes)
+    )
+
+class IFBlock(nn.Module):
+    def __init__(self, in_planes, c=64):
+        super(IFBlock, self).__init__()
+        self.conv0 = nn.Sequential(
+            conv(in_planes, c//2, 3, 2, 1),
+            conv(c//2, c, 3, 2, 1),
+            )
+        self.convblock = nn.Sequential(
+            conv(c, c),
+            conv(c, c),
+            conv(c, c),
+            conv(c, c),
+            conv(c, c),
+            conv(c, c),
+            conv(c, c),
+            conv(c, c),
+        )
+        self.lastconv = nn.ConvTranspose2d(c, 5, 4, 2, 1)
+
+    def forward(self, x, flow, scale):
+        if scale != 1:
+            x = F.interpolate(x, scale_factor = 1. / scale, mode="bilinear", align_corners=False)
+        if flow != None:
+            flow = F.interpolate(flow, scale_factor = 1. / scale, mode="bilinear", align_corners=False) * 1. / scale
+            x = torch.cat((x, flow), 1)
+        x = self.conv0(x)
+        x = self.convblock(x) + x
+        tmp = self.lastconv(x)
+        tmp = F.interpolate(tmp, scale_factor = scale * 2, mode="bilinear", align_corners=False)
+        flow = tmp[:, :4] * scale * 2
+        mask = tmp[:, 4:5]
+        return flow, mask
+    
+class IFNet(nn.Module):
+    def __init__(self):
+        super(IFNet, self).__init__()
+        self.block0 = IFBlock(6, c=240)
+        self.block1 = IFBlock(13+4, c=150)
+        self.block2 = IFBlock(13+4, c=90)
+        self.block_tea = IFBlock(16+4, c=90)
+        self.contextnet = Contextnet()
+        self.unet = Unet()
+
+    def forward(self, x, scale=[4,2,1], timestep=0.5):
+        img0 = x[:, :3]
+        img1 = x[:, 3:6]
+        gt = x[:, 6:] # In inference time, gt is None
+        flow_list = []
+        merged = []
+        mask_list = []
+        warped_img0 = img0
+        warped_img1 = img1
+        flow = None 
+        loss_distill = 0
+        stu = [self.block0, self.block1, self.block2]
+        for i in range(3):
+            if flow != None:
+                flow_d, mask_d = stu[i](torch.cat((img0, img1, warped_img0, warped_img1, mask), 1), flow, scale=scale[i])
+                flow = flow + flow_d
+                mask = mask + mask_d
+            else:
+                flow, mask = stu[i](torch.cat((img0, img1), 1), None, scale=scale[i])
+            mask_list.append(torch.sigmoid(mask))
+            flow_list.append(flow)
+            warped_img0 = warp(img0, flow[:, :2])
+            warped_img1 = warp(img1, flow[:, 2:4])
+            merged_student = (warped_img0, warped_img1)
+            merged.append(merged_student)
+        if gt.shape[1] == 3:
+            flow_d, mask_d = self.block_tea(torch.cat((img0, img1, warped_img0, warped_img1, mask, gt), 1), flow, scale=1)
+            flow_teacher = flow + flow_d
+            warped_img0_teacher = warp(img0, flow_teacher[:, :2])
+            warped_img1_teacher = warp(img1, flow_teacher[:, 2:4])
+            mask_teacher = torch.sigmoid(mask + mask_d)
+            merged_teacher = warped_img0_teacher * mask_teacher + warped_img1_teacher * (1 - mask_teacher)
+        else:
+            flow_teacher = None
+            merged_teacher = None
+        for i in range(3):
+            merged[i] = merged[i][0] * mask_list[i] + merged[i][1] * (1 - mask_list[i])
+            if gt.shape[1] == 3:
+                loss_mask = ((merged[i] - gt).abs().mean(1, True) > (merged_teacher - gt).abs().mean(1, True) + 0.01).float().detach()
+                loss_distill += (((flow_teacher.detach() - flow_list[i]) ** 2).mean(1, True) ** 0.5 * loss_mask).mean()
+        c0 = self.contextnet(img0, flow[:, :2])
+        c1 = self.contextnet(img1, flow[:, 2:4])
+        tmp = self.unet(img0, img1, warped_img0, warped_img1, mask, flow, c0, c1)
+        res = tmp[:, :3] * 2 - 1
+        merged[2] = torch.clamp(merged[2] + res, 0, 1)
+        return flow_list, mask_list[2], merged, flow_teacher, merged_teacher, loss_distill

rife/IFNet_2R.py

@@ -0,0 +1,104 @@
+from rife.refine_2R import *
+
+def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1):
+    return nn.Sequential(
+        torch.nn.ConvTranspose2d(in_channels=in_planes, out_channels=out_planes, kernel_size=4, stride=2, padding=1),
+        nn.PReLU(out_planes)
+    )
+
+def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
+    return nn.Sequential(
+        nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
+                  padding=padding, dilation=dilation, bias=True),
+        nn.PReLU(out_planes)
+    )
+
+class IFBlock(nn.Module):
+    def __init__(self, in_planes, c=64):
+        super(IFBlock, self).__init__()
+        self.conv0 = nn.Sequential(
+            conv(in_planes, c//2, 3, 1, 1),
+            conv(c//2, c, 3, 2, 1),
+            )
+        self.convblock = nn.Sequential(
+            conv(c, c),
+            conv(c, c),
+            conv(c, c),
+            conv(c, c),
+            conv(c, c),
+            conv(c, c),
+            conv(c, c),
+            conv(c, c),
+        )
+        self.lastconv = nn.ConvTranspose2d(c, 5, 4, 2, 1)
+
+    def forward(self, x, flow, scale):
+        if scale != 1:
+            x = F.interpolate(x, scale_factor = 1. / scale, mode="bilinear", align_corners=False)
+        if flow != None:
+            flow = F.interpolate(flow, scale_factor = 1. / scale, mode="bilinear", align_corners=False) * 1. / scale
+            x = torch.cat((x, flow), 1)
+        x = self.conv0(x)
+        x = self.convblock(x) + x
+        tmp = self.lastconv(x)
+        tmp = F.interpolate(tmp, scale_factor = scale, mode="bilinear", align_corners=False)
+        flow = tmp[:, :4] * scale
+        mask = tmp[:, 4:5]
+        return flow, mask
+    
+class IFNet(nn.Module):
+    def __init__(self):
+        super(IFNet, self).__init__()
+        self.block0 = IFBlock(6, c=240)
+        self.block1 = IFBlock(13+4, c=150)
+        self.block2 = IFBlock(13+4, c=90)
+        self.block_tea = IFBlock(16+4, c=90)
+        self.contextnet = Contextnet()
+        self.unet = Unet()
+
+    def forward(self, x, scale=[4,2,1], timestep=0.5):
+        img0 = x[:, :3]
+        img1 = x[:, 3:6]
+        gt = x[:, 6:] # In inference time, gt is None
+        flow_list = []
+        merged = []
+        mask_list = []
+        warped_img0 = img0
+        warped_img1 = img1
+        flow = None 
+        loss_distill = 0
+        stu = [self.block0, self.block1, self.block2]
+        for i in range(3):
+            if flow != None:
+                flow_d, mask_d = stu[i](torch.cat((img0, img1, warped_img0, warped_img1, mask), 1), flow, scale=scale[i])
+                flow = flow + flow_d
+                mask = mask + mask_d
+            else:
+                flow, mask = stu[i](torch.cat((img0, img1), 1), None, scale=scale[i])
+            mask_list.append(torch.sigmoid(mask))
+            flow_list.append(flow)
+            warped_img0 = warp(img0, flow[:, :2])
+            warped_img1 = warp(img1, flow[:, 2:4])
+            merged_student = (warped_img0, warped_img1)
+            merged.append(merged_student)
+        if gt.shape[1] == 3:
+            flow_d, mask_d = self.block_tea(torch.cat((img0, img1, warped_img0, warped_img1, mask, gt), 1), flow, scale=1)
+            flow_teacher = flow + flow_d
+            warped_img0_teacher = warp(img0, flow_teacher[:, :2])
+            warped_img1_teacher = warp(img1, flow_teacher[:, 2:4])
+            mask_teacher = torch.sigmoid(mask + mask_d)
+            merged_teacher = warped_img0_teacher * mask_teacher + warped_img1_teacher * (1 - mask_teacher)
+        else:
+            flow_teacher = None
+            merged_teacher = None
+        for i in range(3):
+            merged[i] = merged[i][0] * mask_list[i] + merged[i][1] * (1 - mask_list[i])
+            if gt.shape[1] == 3:
+                loss_mask = ((merged[i] - gt).abs().mean(1, True) > (merged_teacher - gt).abs().mean(1, True) + 0.01).float().detach()
+                loss_distill += (((flow_teacher.detach() - flow_list[i]) ** 2).mean(1, True) ** 0.5 * loss_mask).mean()
+        c0 = self.contextnet(img0, flow[:, :2])
+        c1 = self.contextnet(img1, flow[:, 2:4])
+        tmp = self.unet(img0, img1, warped_img0, warped_img1, mask, flow, c0, c1)
+        res = tmp[:, :3] * 2 - 1
+        merged[2] = torch.clamp(merged[2] + res, 0, 1)
+        return flow_list, mask_list[2], merged, flow_teacher, merged_teacher, loss_distill

rife/IFNet_HDv3.py

@@ -0,0 +1,115 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from rife.warplayer import warp
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
+    return nn.Sequential(
+        nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
+                  padding=padding, dilation=dilation, bias=True),        
+        nn.PReLU(out_planes)
+    )
+
+def conv_bn(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
+    return nn.Sequential(
+        nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
+                  padding=padding, dilation=dilation, bias=False),
+        nn.BatchNorm2d(out_planes),
+        nn.PReLU(out_planes)
+    )
+
+class IFBlock(nn.Module):
+    def __init__(self, in_planes, c=64):
+        super(IFBlock, self).__init__()
+        self.conv0 = nn.Sequential(
+            conv(in_planes, c//2, 3, 2, 1),
+            conv(c//2, c, 3, 2, 1),
+            )
+        self.convblock0 = nn.Sequential(
+            conv(c, c),
+            conv(c, c)
+        )
+        self.convblock1 = nn.Sequential(
+            conv(c, c),
+            conv(c, c)
+        )
+        self.convblock2 = nn.Sequential(
+            conv(c, c),
+            conv(c, c)
+        )
+        self.convblock3 = nn.Sequential(
+            conv(c, c),
+            conv(c, c)
+        )
+        self.conv1 = nn.Sequential(
+            nn.ConvTranspose2d(c, c//2, 4, 2, 1),
+            nn.PReLU(c//2),
+            nn.ConvTranspose2d(c//2, 4, 4, 2, 1),
+        )
+        self.conv2 = nn.Sequential(
+            nn.ConvTranspose2d(c, c//2, 4, 2, 1),
+            nn.PReLU(c//2),
+            nn.ConvTranspose2d(c//2, 1, 4, 2, 1),
+        )
+
+    def forward(self, x, flow, scale=1):
+        x = F.interpolate(x, scale_factor= 1. / scale, mode="bilinear", align_corners=False, recompute_scale_factor=False)
+        flow = F.interpolate(flow, scale_factor= 1. / scale, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 1. / scale
+        feat = self.conv0(torch.cat((x, flow), 1))
+        feat = self.convblock0(feat) + feat
+        feat = self.convblock1(feat) + feat
+        feat = self.convblock2(feat) + feat
+        feat = self.convblock3(feat) + feat        
+        flow = self.conv1(feat)
+        mask = self.conv2(feat)
+        flow = F.interpolate(flow, scale_factor=scale, mode="bilinear", align_corners=False, recompute_scale_factor=False) * scale
+        mask = F.interpolate(mask, scale_factor=scale, mode="bilinear", align_corners=False, recompute_scale_factor=False)
+        return flow, mask
+        
+class IFNet(nn.Module):
+    def __init__(self):
+        super(IFNet, self).__init__()
+        self.block0 = IFBlock(7+4, c=90)
+        self.block1 = IFBlock(7+4, c=90)
+        self.block2 = IFBlock(7+4, c=90)
+        self.block_tea = IFBlock(10+4, c=90)
+        # self.contextnet = Contextnet()
+        # self.unet = Unet()
+
+    def forward(self, x, scale_list=[4, 2, 1], training=False):
+        if training == False:
+            channel = x.shape[1] // 2
+            img0 = x[:, :channel]
+            img1 = x[:, channel:]
+        flow_list = []
+        merged = []
+        mask_list = []
+        warped_img0 = img0
+        warped_img1 = img1
+        flow = (x[:, :4]).detach() * 0
+        mask = (x[:, :1]).detach() * 0
+        loss_cons = 0
+        block = [self.block0, self.block1, self.block2]
+        for i in range(3):
+            f0, m0 = block[i](torch.cat((warped_img0[:, :3], warped_img1[:, :3], mask), 1), flow, scale=scale_list[i])
+            f1, m1 = block[i](torch.cat((warped_img1[:, :3], warped_img0[:, :3], -mask), 1), torch.cat((flow[:, 2:4], flow[:, :2]), 1), scale=scale_list[i])
+            flow = flow + (f0 + torch.cat((f1[:, 2:4], f1[:, :2]), 1)) / 2
+            mask = mask + (m0 + (-m1)) / 2
+            mask_list.append(mask)
+            flow_list.append(flow)
+            warped_img0 = warp(img0, flow[:, :2])
+            warped_img1 = warp(img1, flow[:, 2:4])
+            merged.append((warped_img0, warped_img1))
+        '''
+        c0 = self.contextnet(img0, flow[:, :2])
+        c1 = self.contextnet(img1, flow[:, 2:4])
+        tmp = self.unet(img0, img1, warped_img0, warped_img1, mask, flow, c0, c1)
+        res = tmp[:, 1:4] * 2 - 1
+        '''
+        for i in range(3):
+            mask_list[i] = torch.sigmoid(mask_list[i])
+            merged[i] = merged[i][0] * mask_list[i] + merged[i][1] * (1 - mask_list[i])
+            # merged[i] = torch.clamp(merged[i] + res, 0, 1)        
+        return flow_list, mask_list[2], merged

rife/IFNet_m.py

@@ -0,0 +1,108 @@
+from rife.refine import *
+
+def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1):
+    return nn.Sequential(
+        torch.nn.ConvTranspose2d(in_channels=in_planes, out_channels=out_planes, kernel_size=4, stride=2, padding=1),
+        nn.PReLU(out_planes)
+    )
+
+def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
+    return nn.Sequential(
+        nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
+                  padding=padding, dilation=dilation, bias=True),
+        nn.PReLU(out_planes)
+    )
+
+class IFBlock(nn.Module):
+    def __init__(self, in_planes, c=64):
+        super(IFBlock, self).__init__()
+        self.conv0 = nn.Sequential(
+            conv(in_planes, c//2, 3, 2, 1),
+            conv(c//2, c, 3, 2, 1),
+            )
+        self.convblock = nn.Sequential(
+            conv(c, c),
+            conv(c, c),
+            conv(c, c),
+            conv(c, c),
+            conv(c, c),
+            conv(c, c),
+            conv(c, c),
+            conv(c, c),
+        )
+        self.lastconv = nn.ConvTranspose2d(c, 5, 4, 2, 1)
+
+    def forward(self, x, flow, scale):
+        if scale != 1:
+            x = F.interpolate(x, scale_factor = 1. / scale, mode="bilinear", align_corners=False)
+        if flow != None:
+            flow = F.interpolate(flow, scale_factor = 1. / scale, mode="bilinear", align_corners=False) * 1. / scale
+            x = torch.cat((x, flow), 1)
+        x = self.conv0(x)
+        x = self.convblock(x) + x
+        tmp = self.lastconv(x)
+        tmp = F.interpolate(tmp, scale_factor = scale * 2, mode="bilinear", align_corners=False)
+        flow = tmp[:, :4] * scale * 2
+        mask = tmp[:, 4:5]
+        return flow, mask
+    
+class IFNet_m(nn.Module):
+    def __init__(self):
+        super(IFNet_m, self).__init__()
+        self.block0 = IFBlock(6+1, c=240)
+        self.block1 = IFBlock(13+4+1, c=150)
+        self.block2 = IFBlock(13+4+1, c=90)
+        self.block_tea = IFBlock(16+4+1, c=90)
+        self.contextnet = Contextnet()
+        self.unet = Unet()
+
+    def forward(self, x, scale=[4,2,1], timestep=0.5, returnflow=False):
+        timestep = (x[:, :1].clone() * 0 + 1) * timestep
+        img0 = x[:, :3]
+        img1 = x[:, 3:6]
+        gt = x[:, 6:] # In inference time, gt is None
+        flow_list = []
+        merged = []
+        mask_list = []
+        warped_img0 = img0
+        warped_img1 = img1
+        flow = None 
+        loss_distill = 0
+        stu = [self.block0, self.block1, self.block2]
+        for i in range(3):
+            if flow != None:
+                flow_d, mask_d = stu[i](torch.cat((img0, img1, timestep, warped_img0, warped_img1, mask), 1), flow, scale=scale[i])
+                flow = flow + flow_d
+                mask = mask + mask_d
+            else:
+                flow, mask = stu[i](torch.cat((img0, img1, timestep), 1), None, scale=scale[i])
+            mask_list.append(torch.sigmoid(mask))
+            flow_list.append(flow)
+            warped_img0 = warp(img0, flow[:, :2])
+            warped_img1 = warp(img1, flow[:, 2:4])
+            merged_student = (warped_img0, warped_img1)
+            merged.append(merged_student)
+        if gt.shape[1] == 3:
+            flow_d, mask_d = self.block_tea(torch.cat((img0, img1, timestep, warped_img0, warped_img1, mask, gt), 1), flow, scale=1)
+            flow_teacher = flow + flow_d
+            warped_img0_teacher = warp(img0, flow_teacher[:, :2])
+            warped_img1_teacher = warp(img1, flow_teacher[:, 2:4])
+            mask_teacher = torch.sigmoid(mask + mask_d)
+            merged_teacher = warped_img0_teacher * mask_teacher + warped_img1_teacher * (1 - mask_teacher)
+        else:
+            flow_teacher = None
+            merged_teacher = None
+        for i in range(3):
+            merged[i] = merged[i][0] * mask_list[i] + merged[i][1] * (1 - mask_list[i])
+            if gt.shape[1] == 3:
+                loss_mask = ((merged[i] - gt).abs().mean(1, True) > (merged_teacher - gt).abs().mean(1, True) + 0.01).float().detach()
+                loss_distill += (((flow_teacher.detach() - flow_list[i]) ** 2).mean(1, True) ** 0.5 * loss_mask).mean()
+        if returnflow:
+            return flow
+        else:
+            c0 = self.contextnet(img0, flow[:, :2])
+            c1 = self.contextnet(img1, flow[:, 2:4])
+            tmp = self.unet(img0, img1, warped_img0, warped_img1, mask, flow, c0, c1)
+            res = tmp[:, :3] * 2 - 1
+            merged[2] = torch.clamp(merged[2] + res, 0, 1)
+        return flow_list, mask_list[2], merged, flow_teacher, merged_teacher, loss_distill

rife/RIFE.py

@@ -0,0 +1,94 @@
+from torch.optim import AdamW
+from torch.nn.parallel import DistributedDataParallel as DDP
+from rife.IFNet import *
+from rife.IFNet_m import *
+from rife.loss import *
+from rife.laplacian import *
+from rife.refine import *
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+class Model:
+    def __init__(self, local_rank=-1, arbitrary=False):
+        if arbitrary == True:
+            self.flownet = IFNet_m()
+        else:
+            self.flownet = IFNet()
+        self.device()
+        self.optimG = AdamW(self.flownet.parameters(), lr=1e-6,
+                            weight_decay=1e-3)  # use large weight decay may avoid NaN loss
+        self.epe = EPE()
+        self.lap = LapLoss()
+        self.sobel = SOBEL()
+        if local_rank != -1:
+            self.flownet = DDP(self.flownet, device_ids=[local_rank], output_device=local_rank)
+
+    def train(self):
+        self.flownet.train()
+
+    def eval(self):
+        self.flownet.eval()
+
+    def device(self):
+        self.flownet.to(device)
+
+    def load_model(self, path, rank=0):
+        def convert(param):
+            return {
+                k.replace("module.", ""): v
+                for k, v in param.items()
+                if "module." in k
+            }
+
+        if rank <= 0:
+            self.flownet.load_state_dict(convert(torch.load('{}/flownet.pkl'.format(path))))
+
+    def save_model(self, path, rank=0):
+        if rank == 0:
+            torch.save(self.flownet.state_dict(), '{}/flownet.pkl'.format(path))
+
+    def inference(self, img0, img1, scale=1, scale_list=[4, 2, 1], TTA=False, timestep=0.5):
+        for i in range(3):
+            scale_list[i] = scale_list[i] * 1.0 / scale
+        imgs = torch.cat((img0, img1), 1)
+        flow, mask, merged, flow_teacher, merged_teacher, loss_distill = self.flownet(imgs, scale_list,
+                                                                                      timestep=timestep)
+        if TTA == False:
+            return merged[2]
+        else:
+            flow2, mask2, merged2, flow_teacher2, merged_teacher2, loss_distill2 = self.flownet(imgs.flip(2).flip(3),
+                                                                                                scale_list,
+                                                                                                timestep=timestep)
+            return (merged[2] + merged2[2].flip(2).flip(3)) / 2
+
+    def update(self, imgs, gt, learning_rate=0, mul=1, training=True, flow_gt=None):
+        for param_group in self.optimG.param_groups:
+            param_group['lr'] = learning_rate
+        img0 = imgs[:, :3]
+        img1 = imgs[:, 3:]
+        if training:
+            self.train()
+        else:
+            self.eval()
+        flow, mask, merged, flow_teacher, merged_teacher, loss_distill = self.flownet(torch.cat((imgs, gt), 1),
+                                                                                      scale=[4, 2, 1])
+        loss_l1 = (self.lap(merged[2], gt)).mean()
+        loss_tea = (self.lap(merged_teacher, gt)).mean()
+        if training:
+            self.optimG.zero_grad()
+            loss_G = loss_l1 + loss_tea + loss_distill * 0.01  # when training RIFEm, the weight of loss_distill should be 0.005 or 0.002
+            loss_G.backward()
+            self.optimG.step()
+        else:
+            flow_teacher = flow[2]
+        return merged[2], {
+            'merged_tea': merged_teacher,
+            'mask': mask,
+            'mask_tea': mask,
+            'flow': flow[2][:, :2],
+            'flow_tea': flow_teacher,
+            'loss_l1': loss_l1,
+            'loss_tea': loss_tea,
+            'loss_distill': loss_distill,
+        }

rife/RIFE_HDv3.py

@@ -0,0 +1,88 @@
+import torch
+import torch.nn as nn
+import numpy as np
+from torch.optim import AdamW
+import torch.optim as optim
+import itertools
+from rife.warplayer import warp
+from torch.nn.parallel import DistributedDataParallel as DDP
+from .IFNet_HDv3 import *
+import torch.nn.functional as F
+from rife.loss import *
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    
+class Model:
+    def __init__(self, local_rank=-1):
+        self.flownet = IFNet()
+        self.device()
+        self.optimG = AdamW(self.flownet.parameters(), lr=1e-6, weight_decay=1e-4)
+        self.epe = EPE()
+        # self.vgg = VGGPerceptualLoss().to(device)
+        self.sobel = SOBEL()
+        if local_rank != -1:
+            self.flownet = DDP(self.flownet, device_ids=[local_rank], output_device=local_rank)
+
+    def train(self):
+        self.flownet.train()
+
+    def eval(self):
+        self.flownet.eval()
+
+    def device(self):
+        self.flownet.to(device)
+
+    def load_model(self, path, rank=0):
+        def convert(param):
+            if rank == -1:
+                return {
+                    k.replace("module.", ""): v
+                    for k, v in param.items()
+                    if "module." in k
+                }
+            else:
+                return param
+        if rank <= 0:
+            if torch.cuda.is_available():
+                self.flownet.load_state_dict(convert(torch.load('{}/flownet.pkl'.format(path))))
+            else:
+                self.flownet.load_state_dict(convert(torch.load('{}/flownet.pkl'.format(path), map_location ='cpu')))
+        
+    def save_model(self, path, rank=0):
+        if rank == 0:
+            torch.save(self.flownet.state_dict(),'{}/flownet.pkl'.format(path))
+
+    def inference(self, img0, img1, scale=1.0):
+        imgs = torch.cat((img0, img1), 1)
+        scale_list = [4/scale, 2/scale, 1/scale]
+        flow, mask, merged = self.flownet(imgs, scale_list)
+        return merged[2]
+    
+    def update(self, imgs, gt, learning_rate=0, mul=1, training=True, flow_gt=None):
+        for param_group in self.optimG.param_groups:
+            param_group['lr'] = learning_rate
+        img0 = imgs[:, :3]
+        img1 = imgs[:, 3:]
+        if training:
+            self.train()
+        else:
+            self.eval()
+        scale = [4, 2, 1]
+        flow, mask, merged = self.flownet(torch.cat((imgs, gt), 1), scale=scale, training=training)
+        loss_l1 = (merged[2] - gt).abs().mean()
+        loss_smooth = self.sobel(flow[2], flow[2]*0).mean()
+        # loss_vgg = self.vgg(merged[2], gt)
+        if training:
+            self.optimG.zero_grad()
+            loss_G = loss_cons + loss_smooth * 0.1
+            loss_G.backward()
+            self.optimG.step()
+        else:
+            flow_teacher = flow[2]
+        return merged[2], {
+            'mask': mask,
+            'flow': flow[2][:, :2],
+            'loss_l1': loss_l1,
+            'loss_cons': loss_cons,
+            'loss_smooth': loss_smooth,
+            }

rife/laplacian.py

@@ -0,0 +1,59 @@
+import torch
+import numpy as np
+import torch.nn as nn
+import torch.nn.functional as F
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+import torch
+
+def gauss_kernel(size=5, channels=3):
+    kernel = torch.tensor([[1., 4., 6., 4., 1],
+                           [4., 16., 24., 16., 4.],
+                           [6., 24., 36., 24., 6.],
+                           [4., 16., 24., 16., 4.],
+                           [1., 4., 6., 4., 1.]])
+    kernel /= 256.
+    kernel = kernel.repeat(channels, 1, 1, 1)
+    kernel = kernel.to(device)
+    return kernel
+
+def downsample(x):
+    return x[:, :, ::2, ::2]
+
+def upsample(x):
+    cc = torch.cat([x, torch.zeros(x.shape[0], x.shape[1], x.shape[2], x.shape[3]).to(device)], dim=3)
+    cc = cc.view(x.shape[0], x.shape[1], x.shape[2]*2, x.shape[3])
+    cc = cc.permute(0,1,3,2)
+    cc = torch.cat([cc, torch.zeros(x.shape[0], x.shape[1], x.shape[3], x.shape[2]*2).to(device)], dim=3)
+    cc = cc.view(x.shape[0], x.shape[1], x.shape[3]*2, x.shape[2]*2)
+    x_up = cc.permute(0,1,3,2)
+    return conv_gauss(x_up, 4*gauss_kernel(channels=x.shape[1]))
+
+def conv_gauss(img, kernel):
+    img = torch.nn.functional.pad(img, (2, 2, 2, 2), mode='reflect')
+    out = torch.nn.functional.conv2d(img, kernel, groups=img.shape[1])
+    return out
+
+def laplacian_pyramid(img, kernel, max_levels=3):
+    current = img
+    pyr = []
+    for level in range(max_levels):
+        filtered = conv_gauss(current, kernel)
+        down = downsample(filtered)
+        up = upsample(down)
+        diff = current-up
+        pyr.append(diff)
+        current = down
+    return pyr
+
+class LapLoss(torch.nn.Module):
+    def __init__(self, max_levels=5, channels=3):
+        super(LapLoss, self).__init__()
+        self.max_levels = max_levels
+        self.gauss_kernel = gauss_kernel(channels=channels)
+        
+    def forward(self, input, target):
+        pyr_input  = laplacian_pyramid(img=input, kernel=self.gauss_kernel, max_levels=self.max_levels)
+        pyr_target = laplacian_pyramid(img=target, kernel=self.gauss_kernel, max_levels=self.max_levels)
+        return sum(torch.nn.functional.l1_loss(a, b) for a, b in zip(pyr_input, pyr_target))

rife/loss.py

@@ -0,0 +1,128 @@
+import torch
+import numpy as np
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision.models as models
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+class EPE(nn.Module):
+    def __init__(self):
+        super(EPE, self).__init__()
+
+    def forward(self, flow, gt, loss_mask):
+        loss_map = (flow - gt.detach()) ** 2
+        loss_map = (loss_map.sum(1, True) + 1e-6) ** 0.5
+        return (loss_map * loss_mask)
+
+
+class Ternary(nn.Module):
+    def __init__(self):
+        super(Ternary, self).__init__()
+        patch_size = 7
+        out_channels = patch_size * patch_size
+        self.w = np.eye(out_channels).reshape(
+            (patch_size, patch_size, 1, out_channels))
+        self.w = np.transpose(self.w, (3, 2, 0, 1))
+        self.w = torch.tensor(self.w).float().to(device)
+
+    def transform(self, img):
+        patches = F.conv2d(img, self.w, padding=3, bias=None)
+        transf = patches - img
+        transf_norm = transf / torch.sqrt(0.81 + transf**2)
+        return transf_norm
+
+    def rgb2gray(self, rgb):
+        r, g, b = rgb[:, 0:1, :, :], rgb[:, 1:2, :, :], rgb[:, 2:3, :, :]
+        gray = 0.2989 * r + 0.5870 * g + 0.1140 * b
+        return gray
+
+    def hamming(self, t1, t2):
+        dist = (t1 - t2) ** 2
+        dist_norm = torch.mean(dist / (0.1 + dist), 1, True)
+        return dist_norm
+
+    def valid_mask(self, t, padding):
+        n, _, h, w = t.size()
+        inner = torch.ones(n, 1, h - 2 * padding, w - 2 * padding).type_as(t)
+        mask = F.pad(inner, [padding] * 4)
+        return mask
+
+    def forward(self, img0, img1):
+        img0 = self.transform(self.rgb2gray(img0))
+        img1 = self.transform(self.rgb2gray(img1))
+        return self.hamming(img0, img1) * self.valid_mask(img0, 1)
+
+
+class SOBEL(nn.Module):
+    def __init__(self):
+        super(SOBEL, self).__init__()
+        self.kernelX = torch.tensor([
+            [1, 0, -1],
+            [2, 0, -2],
+            [1, 0, -1],
+        ]).float()
+        self.kernelY = self.kernelX.clone().T
+        self.kernelX = self.kernelX.unsqueeze(0).unsqueeze(0).to(device)
+        self.kernelY = self.kernelY.unsqueeze(0).unsqueeze(0).to(device)
+
+    def forward(self, pred, gt):
+        N, C, H, W = pred.shape[0], pred.shape[1], pred.shape[2], pred.shape[3]
+        img_stack = torch.cat(
+            [pred.reshape(N*C, 1, H, W), gt.reshape(N*C, 1, H, W)], 0)
+        sobel_stack_x = F.conv2d(img_stack, self.kernelX, padding=1)
+        sobel_stack_y = F.conv2d(img_stack, self.kernelY, padding=1)
+        pred_X, gt_X = sobel_stack_x[:N*C], sobel_stack_x[N*C:]
+        pred_Y, gt_Y = sobel_stack_y[:N*C], sobel_stack_y[N*C:]
+
+        L1X, L1Y = torch.abs(pred_X-gt_X), torch.abs(pred_Y-gt_Y)
+        loss = (L1X+L1Y)
+        return loss
+
+class MeanShift(nn.Conv2d):
+    def __init__(self, data_mean, data_std, data_range=1, norm=True):
+        c = len(data_mean)
+        super(MeanShift, self).__init__(c, c, kernel_size=1)
+        std = torch.Tensor(data_std)
+        self.weight.data = torch.eye(c).view(c, c, 1, 1)
+        if norm:
+            self.weight.data.div_(std.view(c, 1, 1, 1))
+            self.bias.data = -1 * data_range * torch.Tensor(data_mean)
+            self.bias.data.div_(std)
+        else:
+            self.weight.data.mul_(std.view(c, 1, 1, 1))
+            self.bias.data = data_range * torch.Tensor(data_mean)
+        self.requires_grad = False
+            
+class VGGPerceptualLoss(torch.nn.Module):
+    def __init__(self, rank=0):
+        super(VGGPerceptualLoss, self).__init__()
+        blocks = []
+        pretrained = True
+        self.vgg_pretrained_features = models.vgg19(pretrained=pretrained).features
+        self.normalize = MeanShift([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], norm=True).cuda()
+        for param in self.parameters():
+            param.requires_grad = False
+
+    def forward(self, X, Y, indices=None):
+        X = self.normalize(X)
+        Y = self.normalize(Y)
+        indices = [2, 7, 12, 21, 30]
+        weights = [1.0/2.6, 1.0/4.8, 1.0/3.7, 1.0/5.6, 10/1.5]
+        k = 0
+        loss = 0
+        for i in range(indices[-1]):
+            X = self.vgg_pretrained_features[i](X)
+            Y = self.vgg_pretrained_features[i](Y)
+            if (i+1) in indices:
+                loss += weights[k] * (X - Y.detach()).abs().mean() * 0.1
+                k += 1
+        return loss
+
+if __name__ == '__main__':
+    img0 = torch.zeros(3, 3, 256, 256).float().to(device)
+    img1 = torch.tensor(np.random.normal(
+        0, 1, (3, 3, 256, 256))).float().to(device)
+    ternary_loss = Ternary()
+    print(ternary_loss(img0, img1).shape)

rife/oldmodel/IFNet_HD.py

@@ -0,0 +1,122 @@
+import torch
+import numpy as np
+import torch.nn as nn
+import torch.nn.functional as F
+from rife.warplayer import warp
+
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+def conv_wo_act(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
+    return nn.Sequential(
+        nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
+                  padding=padding, dilation=dilation, bias=False),
+        nn.BatchNorm2d(out_planes),
+    )
+
+
+def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
+    return nn.Sequential(
+        nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
+                  padding=padding, dilation=dilation, bias=False),
+        nn.BatchNorm2d(out_planes),
+        nn.PReLU(out_planes)
+    )
+
+
+class ResBlock(nn.Module):
+    def __init__(self, in_planes, out_planes, stride=1):
+        super(ResBlock, self).__init__()
+        if in_planes == out_planes and stride == 1:
+            self.conv0 = nn.Identity()
+        else:
+            self.conv0 = nn.Conv2d(in_planes, out_planes,
+                                   3, stride, 1, bias=False)
+        self.conv1 = conv(in_planes, out_planes, 5, stride, 2)
+        self.conv2 = conv_wo_act(out_planes, out_planes, 3, 1, 1)
+        self.relu1 = nn.PReLU(1)
+        self.relu2 = nn.PReLU(out_planes)
+        self.fc1 = nn.Conv2d(out_planes, 16, kernel_size=1, bias=False)
+        self.fc2 = nn.Conv2d(16, out_planes, kernel_size=1, bias=False)
+
+    def forward(self, x):
+        y = self.conv0(x)
+        x = self.conv1(x)
+        x = self.conv2(x)
+        w = x.mean(3, True).mean(2, True)
+        w = self.relu1(self.fc1(w))
+        w = torch.sigmoid(self.fc2(w))
+        x = self.relu2(x * w + y)
+        return x
+
+
+class IFBlock(nn.Module):
+    def __init__(self, in_planes, scale=1, c=64):
+        super(IFBlock, self).__init__()
+        self.scale = scale
+        self.conv0 = conv(in_planes, c, 5, 2, 2)
+        self.res0 = ResBlock(c, c)
+        self.res1 = ResBlock(c, c)
+        self.res2 = ResBlock(c, c)
+        self.res3 = ResBlock(c, c)
+        self.res4 = ResBlock(c, c)
+        self.res5 = ResBlock(c, c)
+        self.conv1 = nn.Conv2d(c, 8, 3, 1, 1)
+        self.up = nn.PixelShuffle(2)
+
+    def forward(self, x):
+        if self.scale != 1:
+            x = F.interpolate(x, scale_factor=1. / self.scale, mode="bilinear",
+                              align_corners=False)
+        x = self.conv0(x)
+        x = self.res0(x)
+        x = self.res1(x)
+        x = self.res2(x)
+        x = self.res3(x)
+        x = self.res4(x)
+        x = self.res5(x)
+        x = self.conv1(x)
+        flow = self.up(x)
+        if self.scale != 1:
+            flow = F.interpolate(flow, scale_factor=self.scale, mode="bilinear",
+                                 align_corners=False)
+        return flow
+
+
+class IFNet(nn.Module):
+    def __init__(self):
+        super(IFNet, self).__init__()
+        self.block0 = IFBlock(6, scale=8, c=192)
+        self.block1 = IFBlock(8, scale=4, c=128)
+        self.block2 = IFBlock(8, scale=2, c=96)
+        self.block3 = IFBlock(8, scale=1, c=48)
+
+    def forward(self, x, scale=1.0):
+        x = F.interpolate(x, scale_factor=0.5 * scale, mode="bilinear",
+                          align_corners=False)
+        flow0 = self.block0(x)
+        F1 = flow0
+        warped_img0 = warp(x[:, :3], F1)
+        warped_img1 = warp(x[:, 3:], -F1)
+        flow1 = self.block1(torch.cat((warped_img0, warped_img1, F1), 1))
+        F2 = (flow0 + flow1)
+        warped_img0 = warp(x[:, :3], F2)
+        warped_img1 = warp(x[:, 3:], -F2)
+        flow2 = self.block2(torch.cat((warped_img0, warped_img1, F2), 1))
+        F3 = (flow0 + flow1 + flow2)
+        warped_img0 = warp(x[:, :3], F3)
+        warped_img1 = warp(x[:, 3:], -F3)
+        flow3 = self.block3(torch.cat((warped_img0, warped_img1, F3), 1))
+        F4 = (flow0 + flow1 + flow2 + flow3)
+        F4 = F.interpolate(F4, scale_factor=1 / scale, mode="bilinear",
+                           align_corners=False) / scale
+        return F4, [F1, F2, F3, F4]
+
+if __name__ == '__main__':
+    img0 = torch.zeros(3, 3, 256, 256).float().to(device)
+    img1 = torch.tensor(np.random.normal(
+        0, 1, (3, 3, 256, 256))).float().to(device)
+    imgs = torch.cat((img0, img1), 1)
+    flownet = IFNet()
+    flow, _ = flownet(imgs)
+    print(flow.shape)

rife/oldmodel/IFNet_HDv2.py

@@ -0,0 +1,95 @@
+import torch
+import numpy as np
+import torch.nn as nn
+import torch.nn.functional as F
+from rife.warplayer import warp
+
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+def conv_wo_act(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
+    return nn.Sequential(
+        nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
+                  padding=padding, dilation=dilation, bias=True),
+    )
+
+
+def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
+    return nn.Sequential(
+        nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
+                  padding=padding, dilation=dilation, bias=True),
+        nn.PReLU(out_planes)
+    )
+
+class IFBlock(nn.Module):
+    def __init__(self, in_planes, scale=1, c=64):
+        super(IFBlock, self).__init__()
+        self.scale = scale
+        self.conv0 = nn.Sequential(
+            conv(in_planes, c, 3, 2, 1),
+            conv(c, 2*c, 3, 2, 1),
+            )
+        self.convblock = nn.Sequential(
+            conv(2*c, 2*c),
+            conv(2*c, 2*c),
+            conv(2*c, 2*c),
+            conv(2*c, 2*c),
+            conv(2*c, 2*c),
+            conv(2*c, 2*c),
+        )        
+        self.conv1 = nn.ConvTranspose2d(2*c, 4, 4, 2, 1)
+                    
+    def forward(self, x):
+        if self.scale != 1:
+            x = F.interpolate(x, scale_factor=1. / self.scale, mode="bilinear",
+                              align_corners=False)
+        x = self.conv0(x)
+        x = self.convblock(x)
+        x = self.conv1(x)
+        flow = x
+        if self.scale != 1:
+            flow = F.interpolate(flow, scale_factor=self.scale, mode="bilinear",
+                                 align_corners=False)
+        return flow
+
+
+class IFNet(nn.Module):
+    def __init__(self):
+        super(IFNet, self).__init__()
+        self.block0 = IFBlock(6, scale=8, c=192)
+        self.block1 = IFBlock(10, scale=4, c=128)
+        self.block2 = IFBlock(10, scale=2, c=96)
+        self.block3 = IFBlock(10, scale=1, c=48)
+
+    def forward(self, x, scale=1.0):
+        if scale != 1.0:
+            x = F.interpolate(x, scale_factor=scale, mode="bilinear", align_corners=False)
+        flow0 = self.block0(x)
+        F1 = flow0
+        F1_large = F.interpolate(F1, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0
+        warped_img0 = warp(x[:, :3], F1_large[:, :2])
+        warped_img1 = warp(x[:, 3:], F1_large[:, 2:4])
+        flow1 = self.block1(torch.cat((warped_img0, warped_img1, F1_large), 1))
+        F2 = (flow0 + flow1)
+        F2_large = F.interpolate(F2, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0
+        warped_img0 = warp(x[:, :3], F2_large[:, :2])
+        warped_img1 = warp(x[:, 3:], F2_large[:, 2:4])
+        flow2 = self.block2(torch.cat((warped_img0, warped_img1, F2_large), 1))
+        F3 = (flow0 + flow1 + flow2)
+        F3_large = F.interpolate(F3, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0
+        warped_img0 = warp(x[:, :3], F3_large[:, :2])
+        warped_img1 = warp(x[:, 3:], F3_large[:, 2:4])
+        flow3 = self.block3(torch.cat((warped_img0, warped_img1, F3_large), 1))
+        F4 = (flow0 + flow1 + flow2 + flow3)
+        if scale != 1.0:
+            F4 = F.interpolate(F4, scale_factor=1 / scale, mode="bilinear", align_corners=False) / scale
+        return F4, [F1, F2, F3, F4]
+
+if __name__ == '__main__':
+    img0 = torch.zeros(3, 3, 256, 256).float().to(device)
+    img1 = torch.tensor(np.random.normal(
+        0, 1, (3, 3, 256, 256))).float().to(device)
+    imgs = torch.cat((img0, img1), 1)
+    flownet = IFNet()
+    flow, _ = flownet(imgs)
+    print(flow.shape)

rife/oldmodel/RIFE_HD.py

@@ -0,0 +1,256 @@
+from torch.optim import AdamW
+import torch.optim as optim
+import itertools
+from rife.warplayer import warp
+from torch.nn.parallel import DistributedDataParallel as DDP
+from inference.rife.oldinference.rife.IFNet_HD import *
+from rife.loss import *
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
+    return nn.Sequential(
+        nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
+                  padding=padding, dilation=dilation, bias=True),
+        nn.PReLU(out_planes)
+    )
+
+
+def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1):
+    return nn.Sequential(
+        torch.nn.ConvTranspose2d(in_channels=in_planes, out_channels=out_planes,
+                                 kernel_size=4, stride=2, padding=1, bias=True),
+        nn.PReLU(out_planes)
+    )
+
+def conv_woact(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
+    return nn.Sequential(
+        nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
+                  padding=padding, dilation=dilation, bias=True),
+    )
+
+class ResBlock(nn.Module):
+    def __init__(self, in_planes, out_planes, stride=2):
+        super(ResBlock, self).__init__()
+        if in_planes == out_planes and stride == 1:
+            self.conv0 = nn.Identity()
+        else:
+            self.conv0 = nn.Conv2d(in_planes, out_planes,
+                                   3, stride, 1, bias=False)
+        self.conv1 = conv(in_planes, out_planes, 3, stride, 1)
+        self.conv2 = conv_woact(out_planes, out_planes, 3, 1, 1)
+        self.relu1 = nn.PReLU(1)
+        self.relu2 = nn.PReLU(out_planes)
+        self.fc1 = nn.Conv2d(out_planes, 16, kernel_size=1, bias=False)
+        self.fc2 = nn.Conv2d(16, out_planes, kernel_size=1, bias=False)
+
+    def forward(self, x):
+        y = self.conv0(x)
+        x = self.conv1(x)
+        x = self.conv2(x)
+        w = x.mean(3, True).mean(2, True)
+        w = self.relu1(self.fc1(w))
+        w = torch.sigmoid(self.fc2(w))
+        x = self.relu2(x * w + y)
+        return x
+
+c = 32
+
+class ContextNet(nn.Module):
+    def __init__(self):
+        super(ContextNet, self).__init__()
+        self.conv0 = conv(3, c, 3, 2, 1)
+        self.conv1 = ResBlock(c, c)
+        self.conv2 = ResBlock(c, 2*c)
+        self.conv3 = ResBlock(2*c, 4*c)
+        self.conv4 = ResBlock(4*c, 8*c)
+
+    def forward(self, x, flow):
+        x = self.conv0(x)
+        x = self.conv1(x)
+        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5
+        f1 = warp(x, flow)
+        x = self.conv2(x)
+        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear",
+                             align_corners=False) * 0.5
+        f2 = warp(x, flow)
+        x = self.conv3(x)
+        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear",
+                             align_corners=False) * 0.5
+        f3 = warp(x, flow)
+        x = self.conv4(x)
+        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear",
+                             align_corners=False) * 0.5
+        f4 = warp(x, flow)
+        return [f1, f2, f3, f4]
+
+
+class FusionNet(nn.Module):
+    def __init__(self):
+        super(FusionNet, self).__init__()
+        self.conv0 = conv(8, c, 3, 2, 1)
+        self.down0 = ResBlock(c, 2*c)
+        self.down1 = ResBlock(4*c, 4*c)
+        self.down2 = ResBlock(8*c, 8*c)
+        self.down3 = ResBlock(16*c, 16*c)
+        self.up0 = deconv(32*c, 8*c)
+        self.up1 = deconv(16*c, 4*c)
+        self.up2 = deconv(8*c, 2*c)
+        self.up3 = deconv(4*c, c)
+        self.conv = nn.Conv2d(c, 16, 3, 1, 1)
+        self.up4 = nn.PixelShuffle(2)
+
+    def forward(self, img0, img1, flow, c0, c1, flow_gt):
+        warped_img0 = warp(img0, flow)
+        warped_img1 = warp(img1, -flow)
+        if flow_gt == None:
+            warped_img0_gt, warped_img1_gt = None, None
+        else:
+            warped_img0_gt = warp(img0, flow_gt[:, :2])
+            warped_img1_gt = warp(img1, flow_gt[:, 2:4])
+        x = self.conv0(torch.cat((warped_img0, warped_img1, flow), 1))
+        s0 = self.down0(x)
+        s1 = self.down1(torch.cat((s0, c0[0], c1[0]), 1))
+        s2 = self.down2(torch.cat((s1, c0[1], c1[1]), 1))
+        s3 = self.down3(torch.cat((s2, c0[2], c1[2]), 1))
+        x = self.up0(torch.cat((s3, c0[3], c1[3]), 1))
+        x = self.up1(torch.cat((x, s2), 1))
+        x = self.up2(torch.cat((x, s1), 1))
+        x = self.up3(torch.cat((x, s0), 1))
+        x = self.up4(self.conv(x))
+        return x, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt
+
+
+class Model:
+    def __init__(self, local_rank=-1):
+        self.flownet = IFNet()
+        self.contextnet = ContextNet()
+        self.fusionnet = FusionNet()
+        self.device()
+        self.optimG = AdamW(itertools.chain(
+            self.flownet.parameters(),
+            self.contextnet.parameters(),
+            self.fusionnet.parameters()), lr=1e-6, weight_decay=1e-4)
+        self.schedulerG = optim.lr_scheduler.CyclicLR(
+            self.optimG, base_lr=1e-6, max_lr=1e-3, step_size_up=8000, cycle_momentum=False)
+        self.epe = EPE()
+        self.ter = Ternary()
+        self.sobel = SOBEL()
+        if local_rank != -1:
+            self.flownet = DDP(self.flownet, device_ids=[
+                               local_rank], output_device=local_rank)
+            self.contextnet = DDP(self.contextnet, device_ids=[
+                                  local_rank], output_device=local_rank)
+            self.fusionnet = DDP(self.fusionnet, device_ids=[
+                                 local_rank], output_device=local_rank)
+
+    def train(self):
+        self.flownet.train()
+        self.contextnet.train()
+        self.fusionnet.train()
+
+    def eval(self):
+        self.flownet.eval()
+        self.contextnet.eval()
+        self.fusionnet.eval()
+
+    def device(self):
+        self.flownet.to(device)
+        self.contextnet.to(device)
+        self.fusionnet.to(device)
+
+    def load_model(self, path, rank):
+        def convert(param):
+            if rank == -1:
+                return {
+                    k.replace("module.", ""): v
+                    for k, v in param.items()
+                    if "module." in k
+                }
+            else:
+                return param
+        if rank <= 0:
+            self.flownet.load_state_dict(
+                convert(torch.load('{}/flownet.pkl'.format(path), map_location=device)))
+            self.contextnet.load_state_dict(
+                convert(torch.load('{}/contextnet.pkl'.format(path), map_location=device)))
+            self.fusionnet.load_state_dict(
+                convert(torch.load('{}/unet.pkl'.format(path), map_location=device)))
+
+    def save_model(self, path, rank):
+        if rank == 0:
+            torch.save(self.flownet.state_dict(), '{}/flownet.pkl'.format(path))
+            torch.save(self.contextnet.state_dict(), '{}/contextnet.pkl'.format(path))
+            torch.save(self.fusionnet.state_dict(), '{}/unet.pkl'.format(path))
+
+    def predict(self, imgs, flow, training=True, flow_gt=None):
+        img0 = imgs[:, :3]
+        img1 = imgs[:, 3:]
+        c0 = self.contextnet(img0, flow)
+        c1 = self.contextnet(img1, -flow)
+        flow = F.interpolate(flow, scale_factor=2.0, mode="bilinear",
+                             align_corners=False) * 2.0
+        refine_output, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt = self.fusionnet(
+            img0, img1, flow, c0, c1, flow_gt)
+        res = torch.sigmoid(refine_output[:, :3]) * 2 - 1
+        mask = torch.sigmoid(refine_output[:, 3:4])
+        merged_img = warped_img0 * mask + warped_img1 * (1 - mask)
+        pred = merged_img + res
+        pred = torch.clamp(pred, 0, 1)
+        if training:
+            return pred, mask, merged_img, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt
+        else:
+            return pred
+
+    def inference(self, img0, img1, scale=1.0):
+        imgs = torch.cat((img0, img1), 1)
+        flow, _ = self.flownet(imgs, scale)
+        return self.predict(imgs, flow, training=False)
+
+    def update(self, imgs, gt, learning_rate=0, mul=1, training=True, flow_gt=None):
+        for param_group in self.optimG.param_groups:
+            param_group['lr'] = learning_rate
+        if training:
+            self.train()
+        else:
+            self.eval()
+        flow, flow_list = self.flownet(imgs)
+        pred, mask, merged_img, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt = self.predict(
+            imgs, flow, flow_gt=flow_gt)
+        loss_ter = self.ter(pred, gt).mean()
+        if training:
+            with torch.no_grad():
+                loss_flow = torch.abs(warped_img0_gt - gt).mean()
+                loss_mask = torch.abs(
+                    merged_img - gt).sum(1, True).float().detach()
+                loss_mask = F.interpolate(loss_mask, scale_factor=0.5, mode="bilinear",
+                                          align_corners=False).detach()
+                flow_gt = (F.interpolate(flow_gt, scale_factor=0.5, mode="bilinear",
+                                         align_corners=False) * 0.5).detach()
+            loss_cons = 0
+            for i in range(3):
+                loss_cons += self.epe(flow_list[i], flow_gt[:, :2], 1)
+                loss_cons += self.epe(-flow_list[i], flow_gt[:, 2:4], 1)
+            loss_cons = loss_cons.mean() * 0.01
+        else:
+            loss_cons = torch.tensor([0])
+            loss_flow = torch.abs(warped_img0 - gt).mean()
+            loss_mask = 1
+        loss_l1 = (((pred - gt) ** 2 + 1e-6) ** 0.5).mean()
+        if training:
+            self.optimG.zero_grad()
+            loss_G = loss_l1 + loss_cons + loss_ter
+            loss_G.backward()
+            self.optimG.step()
+        return pred, merged_img, flow, loss_l1, loss_flow, loss_cons, loss_ter, loss_mask
+
+
+if __name__ == '__main__':
+    img0 = torch.zeros(3, 3, 256, 256).float().to(device)
+    img1 = torch.tensor(np.random.normal(
+        0, 1, (3, 3, 256, 256))).float().to(device)
+    imgs = torch.cat((img0, img1), 1)
+    model = Model()
+    inference.rife.eval()
+    print(inference.rife.inference(imgs).shape)

rife/oldmodel/RIFE_HDv2.py

@@ -0,0 +1,241 @@
+from torch.optim import AdamW
+import torch.optim as optim
+import itertools
+from rife.warplayer import warp
+from torch.nn.parallel import DistributedDataParallel as DDP
+from inference.rife.oldinference.rife.IFNet_HDv2 import *
+from rife.loss import *
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
+    return nn.Sequential(
+        nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
+                  padding=padding, dilation=dilation, bias=True),
+        nn.PReLU(out_planes)
+    )
+
+
+def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1):
+    return nn.Sequential(
+        torch.nn.ConvTranspose2d(in_channels=in_planes, out_channels=out_planes,
+                                 kernel_size=4, stride=2, padding=1, bias=True),
+        nn.PReLU(out_planes)
+    )
+
+def conv_woact(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
+    return nn.Sequential(
+        nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
+                  padding=padding, dilation=dilation, bias=True),
+    )
+
+class Conv2(nn.Module):
+    def __init__(self, in_planes, out_planes, stride=2):
+        super(Conv2, self).__init__()
+        self.conv1 = conv(in_planes, out_planes, 3, stride, 1)
+        self.conv2 = conv(out_planes, out_planes, 3, 1, 1)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        return x
+
+c = 32
+
+class ContextNet(nn.Module):
+    def __init__(self):
+        super(ContextNet, self).__init__()
+        self.conv0 = Conv2(3, c)
+        self.conv1 = Conv2(c, c)
+        self.conv2 = Conv2(c, 2*c)
+        self.conv3 = Conv2(2*c, 4*c)
+        self.conv4 = Conv2(4*c, 8*c)
+
+    def forward(self, x, flow):
+        x = self.conv0(x)
+        x = self.conv1(x)
+        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5
+        f1 = warp(x, flow)
+        x = self.conv2(x)
+        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear",
+                             align_corners=False) * 0.5
+        f2 = warp(x, flow)
+        x = self.conv3(x)
+        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear",
+                             align_corners=False) * 0.5
+        f3 = warp(x, flow)
+        x = self.conv4(x)
+        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear",
+                             align_corners=False) * 0.5
+        f4 = warp(x, flow)
+        return [f1, f2, f3, f4]
+
+
+class FusionNet(nn.Module):
+    def __init__(self):
+        super(FusionNet, self).__init__()
+        self.conv0 = Conv2(10, c)
+        self.down0 = Conv2(c, 2*c)
+        self.down1 = Conv2(4*c, 4*c)
+        self.down2 = Conv2(8*c, 8*c)
+        self.down3 = Conv2(16*c, 16*c)
+        self.up0 = deconv(32*c, 8*c)
+        self.up1 = deconv(16*c, 4*c)
+        self.up2 = deconv(8*c, 2*c)
+        self.up3 = deconv(4*c, c)
+        self.conv = nn.ConvTranspose2d(c, 4, 4, 2, 1)
+
+    def forward(self, img0, img1, flow, c0, c1, flow_gt):
+        warped_img0 = warp(img0, flow[:, :2])
+        warped_img1 = warp(img1, flow[:, 2:4])
+        if flow_gt == None:
+            warped_img0_gt, warped_img1_gt = None, None
+        else:
+            warped_img0_gt = warp(img0, flow_gt[:, :2])
+            warped_img1_gt = warp(img1, flow_gt[:, 2:4])
+        x = self.conv0(torch.cat((warped_img0, warped_img1, flow), 1))
+        s0 = self.down0(x)
+        s1 = self.down1(torch.cat((s0, c0[0], c1[0]), 1))
+        s2 = self.down2(torch.cat((s1, c0[1], c1[1]), 1))
+        s3 = self.down3(torch.cat((s2, c0[2], c1[2]), 1))
+        x = self.up0(torch.cat((s3, c0[3], c1[3]), 1))
+        x = self.up1(torch.cat((x, s2), 1))
+        x = self.up2(torch.cat((x, s1), 1))
+        x = self.up3(torch.cat((x, s0), 1))
+        x = self.conv(x)
+        return x, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt
+
+
+class Model:
+    def __init__(self, local_rank=-1):
+        self.flownet = IFNet()
+        self.contextnet = ContextNet()
+        self.fusionnet = FusionNet()
+        self.device()
+        self.optimG = AdamW(itertools.chain(
+            self.flownet.parameters(),
+            self.contextnet.parameters(),
+            self.fusionnet.parameters()), lr=1e-6, weight_decay=1e-4)
+        self.schedulerG = optim.lr_scheduler.CyclicLR(
+            self.optimG, base_lr=1e-6, max_lr=1e-3, step_size_up=8000, cycle_momentum=False)
+        self.epe = EPE()
+        self.ter = Ternary()
+        self.sobel = SOBEL()
+        if local_rank != -1:
+            self.flownet = DDP(self.flownet, device_ids=[
+                               local_rank], output_device=local_rank)
+            self.contextnet = DDP(self.contextnet, device_ids=[
+                                  local_rank], output_device=local_rank)
+            self.fusionnet = DDP(self.fusionnet, device_ids=[
+                                 local_rank], output_device=local_rank)
+
+    def train(self):
+        self.flownet.train()
+        self.contextnet.train()
+        self.fusionnet.train()
+
+    def eval(self):
+        self.flownet.eval()
+        self.contextnet.eval()
+        self.fusionnet.eval()
+
+    def device(self):
+        self.flownet.to(device)
+        self.contextnet.to(device)
+        self.fusionnet.to(device)
+
+    def load_model(self, path, rank):
+        def convert(param):
+            if rank == -1:
+                return {
+                    k.replace("module.", ""): v
+                    for k, v in param.items()
+                    if "module." in k
+                }
+            else:
+                return param
+        if rank <= 0:
+            self.flownet.load_state_dict(
+                convert(torch.load('{}/flownet.pkl'.format(path), map_location=device)))
+            self.contextnet.load_state_dict(
+                convert(torch.load('{}/contextnet.pkl'.format(path), map_location=device)))
+            self.fusionnet.load_state_dict(
+                convert(torch.load('{}/unet.pkl'.format(path), map_location=device)))
+
+    def save_model(self, path, rank):
+        if rank == 0:
+            torch.save(self.flownet.state_dict(), '{}/flownet.pkl'.format(path))
+            torch.save(self.contextnet.state_dict(), '{}/contextnet.pkl'.format(path))
+            torch.save(self.fusionnet.state_dict(), '{}/unet.pkl'.format(path))
+
+    def predict(self, imgs, flow, training=True, flow_gt=None):
+        img0 = imgs[:, :3]
+        img1 = imgs[:, 3:]
+        c0 = self.contextnet(img0, flow[:, :2])
+        c1 = self.contextnet(img1, flow[:, 2:4])
+        flow = F.interpolate(flow, scale_factor=2.0, mode="bilinear",
+                             align_corners=False) * 2.0
+        refine_output, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt = self.fusionnet(
+            img0, img1, flow, c0, c1, flow_gt)
+        res = torch.sigmoid(refine_output[:, :3]) * 2 - 1
+        mask = torch.sigmoid(refine_output[:, 3:4])
+        merged_img = warped_img0 * mask + warped_img1 * (1 - mask)
+        pred = merged_img + res
+        pred = torch.clamp(pred, 0, 1)
+        if training:
+            return pred, mask, merged_img, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt
+        else:
+            return pred
+
+    def inference(self, img0, img1, scale=1.0):
+        imgs = torch.cat((img0, img1), 1)
+        flow, _ = self.flownet(imgs, scale)
+        return self.predict(imgs, flow, training=False)
+
+    def update(self, imgs, gt, learning_rate=0, mul=1, training=True, flow_gt=None):
+        for param_group in self.optimG.param_groups:
+            param_group['lr'] = learning_rate
+        if training:
+            self.train()
+        else:
+            self.eval()
+        flow, flow_list = self.flownet(imgs)
+        pred, mask, merged_img, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt = self.predict(
+            imgs, flow, flow_gt=flow_gt)
+        loss_ter = self.ter(pred, gt).mean()
+        if training:
+            with torch.no_grad():
+                loss_flow = torch.abs(warped_img0_gt - gt).mean()
+                loss_mask = torch.abs(
+                    merged_img - gt).sum(1, True).float().detach()
+                loss_mask = F.interpolate(loss_mask, scale_factor=0.5, mode="bilinear",
+                                          align_corners=False).detach()
+                flow_gt = (F.interpolate(flow_gt, scale_factor=0.5, mode="bilinear",
+                                         align_corners=False) * 0.5).detach()
+            loss_cons = 0
+            for i in range(4):
+                loss_cons += self.epe(flow_list[i][:, :2], flow_gt[:, :2], 1)
+                loss_cons += self.epe(flow_list[i][:, 2:4], flow_gt[:, 2:4], 1)
+            loss_cons = loss_cons.mean() * 0.01
+        else:
+            loss_cons = torch.tensor([0])
+            loss_flow = torch.abs(warped_img0 - gt).mean()
+            loss_mask = 1
+        loss_l1 = (((pred - gt) ** 2 + 1e-6) ** 0.5).mean()
+        if training:
+            self.optimG.zero_grad()
+            loss_G = loss_l1 + loss_cons + loss_ter
+            loss_G.backward()
+            self.optimG.step()
+        return pred, merged_img, flow, loss_l1, loss_flow, loss_cons, loss_ter, loss_mask
+
+
+if __name__ == '__main__':
+    img0 = torch.zeros(3, 3, 256, 256).float().to(device)
+    img1 = torch.tensor(np.random.normal(
+        0, 1, (3, 3, 256, 256))).float().to(device)
+    imgs = torch.cat((img0, img1), 1)
+    model = Model()
+    inference.rife.eval()
+    print(inference.rife.inference(imgs).shape)

rife/pytorch_msssim/__init__.py

@@ -0,0 +1,200 @@
+import torch
+import torch.nn.functional as F
+from math import exp
+import numpy as np
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+def gaussian(window_size, sigma):
+    gauss = torch.Tensor([exp(-(x - window_size//2)**2/float(2*sigma**2)) for x in range(window_size)])
+    return gauss/gauss.sum()
+
+
+def create_window(window_size, channel=1):
+    _1D_window = gaussian(window_size, 1.5).unsqueeze(1)
+    _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0).to(device)
+    window = _2D_window.expand(channel, 1, window_size, window_size).contiguous()
+    return window
+
+def create_window_3d(window_size, channel=1):
+    _1D_window = gaussian(window_size, 1.5).unsqueeze(1)
+    _2D_window = _1D_window.mm(_1D_window.t())
+    _3D_window = _2D_window.unsqueeze(2) @ (_1D_window.t())
+    window = _3D_window.expand(1, channel, window_size, window_size, window_size).contiguous().to(device)
+    return window
+
+
+def ssim(img1, img2, window_size=11, window=None, size_average=True, full=False, val_range=None):
+    # Value range can be different from 255. Other common ranges are 1 (sigmoid) and 2 (tanh).
+    if val_range is None:
+        if torch.max(img1) > 128:
+            max_val = 255
+        else:
+            max_val = 1
+
+        if torch.min(img1) < -0.5:
+            min_val = -1
+        else:
+            min_val = 0
+        L = max_val - min_val
+    else:
+        L = val_range
+
+    padd = 0
+    (_, channel, height, width) = img1.size()
+    if window is None:
+        real_size = min(window_size, height, width)
+        window = create_window(real_size, channel=channel).to(img1.device)
+    
+    # mu1 = F.conv2d(img1, window, padding=padd, groups=channel)
+    # mu2 = F.conv2d(img2, window, padding=padd, groups=channel)
+    mu1 = F.conv2d(F.pad(img1, (5, 5, 5, 5), mode='replicate'), window, padding=padd, groups=channel)
+    mu2 = F.conv2d(F.pad(img2, (5, 5, 5, 5), mode='replicate'), window, padding=padd, groups=channel)
+
+    mu1_sq = mu1.pow(2)
+    mu2_sq = mu2.pow(2)
+    mu1_mu2 = mu1 * mu2
+
+    sigma1_sq = F.conv2d(F.pad(img1 * img1, (5, 5, 5, 5), 'replicate'), window, padding=padd, groups=channel) - mu1_sq
+    sigma2_sq = F.conv2d(F.pad(img2 * img2, (5, 5, 5, 5), 'replicate'), window, padding=padd, groups=channel) - mu2_sq
+    sigma12 = F.conv2d(F.pad(img1 * img2, (5, 5, 5, 5), 'replicate'), window, padding=padd, groups=channel) - mu1_mu2
+
+    C1 = (0.01 * L) ** 2
+    C2 = (0.03 * L) ** 2
+
+    v1 = 2.0 * sigma12 + C2
+    v2 = sigma1_sq + sigma2_sq + C2
+    cs = torch.mean(v1 / v2)  # contrast sensitivity
+
+    ssim_map = ((2 * mu1_mu2 + C1) * v1) / ((mu1_sq + mu2_sq + C1) * v2)
+
+    if size_average:
+        ret = ssim_map.mean()
+    else:
+        ret = ssim_map.mean(1).mean(1).mean(1)
+
+    if full:
+        return ret, cs
+    return ret
+
+
+def ssim_matlab(img1, img2, window_size=11, window=None, size_average=True, full=False, val_range=None):
+    # Value range can be different from 255. Other common ranges are 1 (sigmoid) and 2 (tanh).
+    if val_range is None:
+        if torch.max(img1) > 128:
+            max_val = 255
+        else:
+            max_val = 1
+
+        if torch.min(img1) < -0.5:
+            min_val = -1
+        else:
+            min_val = 0
+        L = max_val - min_val
+    else:
+        L = val_range
+
+    padd = 0
+    (_, _, height, width) = img1.size()
+    if window is None:
+        real_size = min(window_size, height, width)
+        window = create_window_3d(real_size, channel=1).to(img1.device,dtype=img1.dtype)
+        # Channel is set to 1 since we consider color images as volumetric images
+
+    img1 = img1.unsqueeze(1)
+    img2 = img2.unsqueeze(1)
+
+    mu1 = F.conv3d(F.pad(img1, (5, 5, 5, 5, 5, 5), mode='replicate'), window, padding=padd, groups=1)
+    mu2 = F.conv3d(F.pad(img2, (5, 5, 5, 5, 5, 5), mode='replicate'), window, padding=padd, groups=1)
+
+    mu1_sq = mu1.pow(2)
+    mu2_sq = mu2.pow(2)
+    mu1_mu2 = mu1 * mu2
+
+    sigma1_sq = F.conv3d(F.pad(img1 * img1, (5, 5, 5, 5, 5, 5), 'replicate'), window, padding=padd, groups=1) - mu1_sq
+    sigma2_sq = F.conv3d(F.pad(img2 * img2, (5, 5, 5, 5, 5, 5), 'replicate'), window, padding=padd, groups=1) - mu2_sq
+    sigma12 = F.conv3d(F.pad(img1 * img2, (5, 5, 5, 5, 5, 5), 'replicate'), window, padding=padd, groups=1) - mu1_mu2
+
+    C1 = (0.01 * L) ** 2
+    C2 = (0.03 * L) ** 2
+
+    v1 = 2.0 * sigma12 + C2
+    v2 = sigma1_sq + sigma2_sq + C2
+    cs = torch.mean(v1 / v2)  # contrast sensitivity
+
+    ssim_map = ((2 * mu1_mu2 + C1) * v1) / ((mu1_sq + mu2_sq + C1) * v2)
+
+    if size_average:
+        ret = ssim_map.mean()
+    else:
+        ret = ssim_map.mean(1).mean(1).mean(1)
+
+    if full:
+        return ret, cs
+    return ret
+
+
+def msssim(img1, img2, window_size=11, size_average=True, val_range=None, normalize=False):
+    device = img1.device
+    weights = torch.FloatTensor([0.0448, 0.2856, 0.3001, 0.2363, 0.1333]).to(device)
+    levels = weights.size()[0]
+    mssim = []
+    mcs = []
+    for _ in range(levels):
+        sim, cs = ssim(img1, img2, window_size=window_size, size_average=size_average, full=True, val_range=val_range)
+        mssim.append(sim)
+        mcs.append(cs)
+
+        img1 = F.avg_pool2d(img1, (2, 2))
+        img2 = F.avg_pool2d(img2, (2, 2))
+
+    mssim = torch.stack(mssim)
+    mcs = torch.stack(mcs)
+
+    # Normalize (to avoid NaNs during training unstable models, not compliant with original definition)
+    if normalize:
+        mssim = (mssim + 1) / 2
+        mcs = (mcs + 1) / 2
+
+    pow1 = mcs ** weights
+    pow2 = mssim ** weights
+    # From Matlab implementation https://ece.uwaterloo.ca/~z70wang/research/iwssim/
+    output = torch.prod(pow1[:-1] * pow2[-1])
+    return output
+
+
+# Classes to re-use window
+class SSIM(torch.nn.Module):
+    def __init__(self, window_size=11, size_average=True, val_range=None):
+        super(SSIM, self).__init__()
+        self.window_size = window_size
+        self.size_average = size_average
+        self.val_range = val_range
+
+        # Assume 3 channel for SSIM
+        self.channel = 3
+        self.window = create_window(window_size, channel=self.channel)
+
+    def forward(self, img1, img2):
+        (_, channel, _, _) = img1.size()
+
+        if channel == self.channel and self.window.dtype == img1.dtype:
+            window = self.window
+        else:
+            window = create_window(self.window_size, channel).to(img1.device).type(img1.dtype)
+            self.window = window
+            self.channel = channel
+
+        _ssim = ssim(img1, img2, window=window, window_size=self.window_size, size_average=self.size_average)
+        dssim = (1 - _ssim) / 2
+        return dssim
+
+class MSSSIM(torch.nn.Module):
+    def __init__(self, window_size=11, size_average=True, channel=3):
+        super(MSSSIM, self).__init__()
+        self.window_size = window_size
+        self.size_average = size_average
+        self.channel = channel
+
+    def forward(self, img1, img2):
+        return msssim(img1, img2, window_size=self.window_size, size_average=self.size_average)

rife/refine.py

@@ -0,0 +1,79 @@
+import torch
+import torch.nn as nn
+from rife.warplayer import warp
+import torch.nn.functional as F
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
+    return nn.Sequential(
+        nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
+                  padding=padding, dilation=dilation, bias=True),
+        nn.PReLU(out_planes)
+        )
+
+def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1):
+    return nn.Sequential(
+        torch.nn.ConvTranspose2d(in_channels=in_planes, out_channels=out_planes, kernel_size=4, stride=2, padding=1, bias=True),
+        nn.PReLU(out_planes)
+        )
+            
+class Conv2(nn.Module):
+    def __init__(self, in_planes, out_planes, stride=2):
+        super(Conv2, self).__init__()
+        self.conv1 = conv(in_planes, out_planes, 3, stride, 1)
+        self.conv2 = conv(out_planes, out_planes, 3, 1, 1)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        return x
+    
+c = 16
+class Contextnet(nn.Module):
+    def __init__(self):
+        super(Contextnet, self).__init__()
+        self.conv1 = Conv2(3, c)
+        self.conv2 = Conv2(c, 2*c)
+        self.conv3 = Conv2(2*c, 4*c)
+        self.conv4 = Conv2(4*c, 8*c)
+    
+    def forward(self, x, flow):
+        x = self.conv1(x)
+        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 0.5
+        f1 = warp(x, flow)        
+        x = self.conv2(x)
+        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 0.5
+        f2 = warp(x, flow)
+        x = self.conv3(x)
+        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 0.5
+        f3 = warp(x, flow)
+        x = self.conv4(x)
+        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 0.5
+        f4 = warp(x, flow)
+        return [f1, f2, f3, f4]
+    
+class Unet(nn.Module):
+    def __init__(self):
+        super(Unet, self).__init__()
+        self.down0 = Conv2(17, 2*c)
+        self.down1 = Conv2(4*c, 4*c)
+        self.down2 = Conv2(8*c, 8*c)
+        self.down3 = Conv2(16*c, 16*c)
+        self.up0 = deconv(32*c, 8*c)
+        self.up1 = deconv(16*c, 4*c)
+        self.up2 = deconv(8*c, 2*c)
+        self.up3 = deconv(4*c, c)
+        self.conv = nn.Conv2d(c, 3, 3, 1, 1)
+
+    def forward(self, img0, img1, warped_img0, warped_img1, mask, flow, c0, c1):
+        s0 = self.down0(torch.cat((img0, img1, warped_img0, warped_img1, mask, flow), 1))
+        s1 = self.down1(torch.cat((s0, c0[0], c1[0]), 1))
+        s2 = self.down2(torch.cat((s1, c0[1], c1[1]), 1))
+        s3 = self.down3(torch.cat((s2, c0[2], c1[2]), 1))
+        x = self.up0(torch.cat((s3, c0[3], c1[3]), 1))
+        x = self.up1(torch.cat((x, s2), 1)) 
+        x = self.up2(torch.cat((x, s1), 1)) 
+        x = self.up3(torch.cat((x, s0), 1)) 
+        x = self.conv(x)
+        return torch.sigmoid(x)

rife/refine_2R.py

@@ -0,0 +1,79 @@
+import torch
+import torch.nn as nn
+from rife.warplayer import warp
+import torch.nn.functional as F
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
+    return nn.Sequential(
+        nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
+                  padding=padding, dilation=dilation, bias=True),
+        nn.PReLU(out_planes)
+        )
+
+def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1):
+    return nn.Sequential(
+        torch.nn.ConvTranspose2d(in_channels=in_planes, out_channels=out_planes, kernel_size=4, stride=2, padding=1, bias=True),
+        nn.PReLU(out_planes)
+        )
+            
+class Conv2(nn.Module):
+    def __init__(self, in_planes, out_planes, stride=2):
+        super(Conv2, self).__init__()
+        self.conv1 = conv(in_planes, out_planes, 3, stride, 1)
+        self.conv2 = conv(out_planes, out_planes, 3, 1, 1)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        return x
+    
+c = 16
+class Contextnet(nn.Module):
+    def __init__(self):
+        super(Contextnet, self).__init__()
+        self.conv1 = Conv2(3, c, 1)
+        self.conv2 = Conv2(c, 2*c)
+        self.conv3 = Conv2(2*c, 4*c)
+        self.conv4 = Conv2(4*c, 8*c)
+    
+    def forward(self, x, flow):
+        x = self.conv1(x)
+        # flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 0.5
+        f1 = warp(x, flow)        
+        x = self.conv2(x)
+        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 0.5
+        f2 = warp(x, flow)
+        x = self.conv3(x)
+        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 0.5
+        f3 = warp(x, flow)
+        x = self.conv4(x)
+        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 0.5
+        f4 = warp(x, flow)
+        return [f1, f2, f3, f4]
+    
+class Unet(nn.Module):
+    def __init__(self):
+        super(Unet, self).__init__()
+        self.down0 = Conv2(17, 2*c, 1)
+        self.down1 = Conv2(4*c, 4*c)
+        self.down2 = Conv2(8*c, 8*c)
+        self.down3 = Conv2(16*c, 16*c)
+        self.up0 = deconv(32*c, 8*c)
+        self.up1 = deconv(16*c, 4*c)
+        self.up2 = deconv(8*c, 2*c)
+        self.up3 = deconv(4*c, c)
+        self.conv = nn.Conv2d(c, 3, 3, 2, 1)
+
+    def forward(self, img0, img1, warped_img0, warped_img1, mask, flow, c0, c1):
+        s0 = self.down0(torch.cat((img0, img1, warped_img0, warped_img1, mask, flow), 1))
+        s1 = self.down1(torch.cat((s0, c0[0], c1[0]), 1))
+        s2 = self.down2(torch.cat((s1, c0[1], c1[1]), 1))
+        s3 = self.down3(torch.cat((s2, c0[2], c1[2]), 1))
+        x = self.up0(torch.cat((s3, c0[3], c1[3]), 1))
+        x = self.up1(torch.cat((x, s2), 1)) 
+        x = self.up2(torch.cat((x, s1), 1)) 
+        x = self.up3(torch.cat((x, s0), 1)) 
+        x = self.conv(x)
+        return torch.sigmoid(x)

rife/warplayer.py

@@ -0,0 +1,22 @@
+import torch
+import torch.nn as nn
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+backwarp_tenGrid = {}
+
+
+def warp(tenInput, tenFlow):
+    k = (str(tenFlow.device), str(tenFlow.size()))
+    if k not in backwarp_tenGrid:
+        tenHorizontal = torch.linspace(-1.0, 1.0, tenFlow.shape[3], device=device).view(
+            1, 1, 1, tenFlow.shape[3]).expand(tenFlow.shape[0], -1, tenFlow.shape[2], -1)
+        tenVertical = torch.linspace(-1.0, 1.0, tenFlow.shape[2], device=device).view(
+            1, 1, tenFlow.shape[2], 1).expand(tenFlow.shape[0], -1, -1, tenFlow.shape[3])
+        backwarp_tenGrid[k] = torch.cat(
+            [tenHorizontal, tenVertical], 1).to(device)
+
+    tenFlow = torch.cat([tenFlow[:, 0:1, :, :] / ((tenInput.shape[3] - 1.0) / 2.0),
+                         tenFlow[:, 1:2, :, :] / ((tenInput.shape[2] - 1.0) / 2.0)], 1)
+
+    g = (backwarp_tenGrid[k] + tenFlow).permute(0, 2, 3, 1)
+    return torch.nn.functional.grid_sample(input=tenInput, grid=g, mode='bilinear', padding_mode='border', align_corners=True)