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

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- 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
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glide-the 2024-08-24 15:55:35 +08:00
parent 2825d9b707
commit 957a210a72
22 changed files with 2490 additions and 120 deletions
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325
inference/gradio_web_demo.py
View File

@ -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(
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
def load_sd_upscale(ckpt):
from spandrel import ModelLoader, ImageModelDescriptor # Simulate a step in loading
if isinstance(video_frames[0], PIL.Image.Image):
video_frames = [np.array(frame) for frame in video_frames]
pbar = utils.ProgressBar(1, "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()
with imageio.get_writer(output_video_path, fps=fps) as writer:
for frame in video_frames:
writer.append_data(frame)
pbar.update(1) # Update progress by 1
return out
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",
"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",
"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 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}"',
},
{"role": "user",
"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",
"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 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(
prompt=prompt,
negative_prompt=None,
do_classifier_free_guidance=True,
num_videos_per_prompt=1,
max_sequence_length=226,
device=device,
dtype=dtype,
)
if seed == -1:
seed = random.randint(0, 2**32 - 1)
video = pipe(
video_pt = pipe(
prompt=prompt,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=torch.zeros_like(prompt_embeds),
).frames[0]
output_type="pt",
generator=torch.Generator().manual_seed(seed), # Set the seed for reproducibility
).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://github.com/THUDM/CogVideo">🌐 Github</a>
<a href="https://huggingface.co/THUDM/CogVideoX-2B">🤗 2B Model Hub</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(
"**Optional Parameters** (default values are recommended)<br>"
"Turn Inference Steps larger if you want more detailed video, but it will be slower.<br>"
"50 steps are recommended for most cases. will cause 120 seconds for inference.<br>"
)
gr.Markdown("**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>"
"50 steps are recommended for most cases.<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;">
<tr>
<th>Prompt</th>
<th>Video URL</th>
<th>Inference Steps</th>
<th>Guidance Scale</th>
</tr>
<tr>
<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><a href="https://github.com/THUDM/CogVideo/raw/main/resources/videos/1.mp4">Video 1</a></td>
<td>50</td>
<td>6</td>
</tr>
<tr>
<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><a href="https://github.com/THUDM/CogVideo/raw/main/resources/videos/2.mp4">Video 2</a></td>
<td>50</td>
<td>6</td>
</tr>
<tr>
<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>
<td><a href="https://github.com/THUDM/CogVideo/raw/main/resources/videos/3.mp4">Video 3</a></td>
<td>50</td>
<td>6</td>
</tr>
<tr>
<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>
<td><a href="https://github.com/THUDM/CogVideo/raw/main/resources/videos/4.mp4">Video 4</a></td>
<td>50</td>
<td>6</td>
</tr>
</table>
<table border="0" style="width: 100%; text-align: left; margin-top: 20px;">
<div style="text-align: center; font-size: 24px; font-weight: bold; margin-bottom: 20px;">
Demo Videos with 50 Inference Steps and 6.0 Guidance Scale.
</div>
<tr>
<td style="width: 25%; vertical-align: top; font-size: 1.2em;">
<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>
</td>
<td style="width: 25%; vertical-align: top;">
<video src="https://github.com/user-attachments/assets/ea3af39a-3160-4999-90ec-2f7863c5b0e9" width="100%" controls autoplay></video>
</td>
<td style="width: 25%; vertical-align: top; font-size: 1.2em;">
<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>
</td>
<td style="width: 25%; vertical-align: top;">
<video src="https://github.com/user-attachments/assets/9de41efd-d4d1-4095-aeda-246dd834e91d" width="100%" controls autoplay></video>
</td>
</tr>
<tr>
<td style="width: 25%; vertical-align: top; font-size: 1.2em;">
<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>
<td style="width: 25%; vertical-align: top;">
<video src="https://github.com/user-attachments/assets/941d6661-6a8d-4a1b-b912-59606f0b2841" width="100%" controls autoplay></video>
</td>
<td style="width: 25%; vertical-align: top; font-size: 1.2em;">
<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>
<td style="width: 25%; vertical-align: top;">
<video src="https://github.com/user-attachments/assets/938529c4-91ae-4f60-b96b-3c3947fa63cb" width="100%" controls autoplay></video>
</td>
</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)
video_path = save_video(tensor)
def generate(prompt, num_inference_steps, guidance_scale,seed_value, scale_status, rife_status, progress=gr.Progress(track_tqdm=True)):
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],
outputs=[video_output, download_video_button, download_gif_button],
inputs=[prompt, num_inference_steps, guidance_scale, seed_param, enable_scale, enable_rife],
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)

177
inference/rife_model.py Normal file
View File

@ -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)

28
inference/test_upscale.py Normal file
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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()

178
inference/utils.py Normal file
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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)

8
requirements.txt
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@ -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

104
rife/IFNet.py Normal file
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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

104
rife/IFNet_2R.py Normal file
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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

115
rife/IFNet_HDv3.py Normal file
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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

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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

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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,
}

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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,
}

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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))

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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)

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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)

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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)

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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)

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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)

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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)

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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)

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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)

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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)