commit d1e9e2f4676164864e12a8bc3798ee0f93185ea8 Author: SlavaVlad Date: Sat Jun 27 05:41:51 2026 +0300 lab3 impl diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..5b78e49 --- /dev/null +++ b/.gitignore @@ -0,0 +1,30 @@ +# Byte-compiled +__pycache__/ +*.py[cod] + +# Virtual environment +lab3_venv/ + +# Swarm infrastructure +.opencode/ +.swarm/ + +# Model weights (large, re-downloadable) +rife_model/flownet.pkl +train_log/ +train_log_hd/ + +# LaTeX build artifacts +*.aux +*.log +*.out +*.toc +*.lof +*.lot +*.bbl +*.blg +missfont.log + +# Temp / generated +lab3_rife_executed.ipynb +lab3_rife_script.txt diff --git a/figures/comparison.png b/figures/comparison.png new file mode 100644 index 0000000..20180a1 Binary files /dev/null and b/figures/comparison.png differ diff --git a/figures/diff_analysis.png b/figures/diff_analysis.png new file mode 100644 index 0000000..15bf4db Binary files /dev/null and b/figures/diff_analysis.png differ diff --git a/figures/interpolation_result.png b/figures/interpolation_result.png new file mode 100644 index 0000000..8432f1c Binary files /dev/null and b/figures/interpolation_result.png differ diff --git a/figures/source_frames.png b/figures/source_frames.png new file mode 100644 index 0000000..377dd0e Binary files /dev/null and b/figures/source_frames.png differ diff --git a/lab3_rife.ipynb b/lab3_rife.ipynb new file mode 100644 index 0000000..3ee570a --- /dev/null +++ b/lab3_rife.ipynb @@ -0,0 +1,678 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Лабораторная работа №3\n", + "## Нейросетевая интерполяция кадров видео с использованием RIFE\n", + "\n", + "**Дисциплина:** Разработка мультимедийных приложений \n", + "**Метод:** Real-time Intermediate Flow Estimation (RIFE) — семейство нейросетевых моделей для интерполяции видеокадров \n", + "**Задача:** Увеличение частоты кадров видео в 8 раз (Multiplier: 8x) с помощью предобученной модели RIFE HDv3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## 1. Подготовка окружения и импорт библиотек" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "import numpy as np\n", + "import cv2\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.animation import FuncAnimation\n", + "from IPython.display import Video, display, HTML\n", + "from tqdm.notebook import tqdm\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "print(f'PyTorch: {torch.__version__}')\n", + "print(f'CUDA available: {torch.cuda.is_available()}')\n", + "if torch.cuda.is_available():\n", + " print(f'GPU: {torch.cuda.get_device_name(0)}')\n", + "print(f'OpenCV: {cv2.__version__}')\n", + "print(f'NumPy: {np.__version__}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Настройка устройства\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "torch.set_grad_enabled(False)\n", + "if torch.cuda.is_available():\n", + " torch.backends.cudnn.enabled = True\n", + " torch.backends.cudnn.benchmark = True\n", + "print(f'Using device: {device}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## 2. Реализация архитектуры IFNet (ядро RIFE)\n", + "\n", + "RIFE (Real-time Intermediate Flow Estimation) основан на итеративной многомасштабной сети IFNet, которая оценивает оптический поток между двумя кадрами и синтезирует промежуточный кадр." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Функция обратной свертки (warp) — для смещения пикселей по оптическому потоку\n", + "backwarp_tenGrid = {}\n", + "\n", + "def warp(tenInput, tenFlow):\n", + " k = (str(tenFlow.device), str(tenFlow.size()))\n", + " if k not in backwarp_tenGrid:\n", + " tenHorizontal = torch.linspace(-1.0, 1.0, tenFlow.shape[3], device=device).view(\n", + " 1, 1, 1, tenFlow.shape[3]).expand(tenFlow.shape[0], -1, tenFlow.shape[2], -1)\n", + " tenVertical = torch.linspace(-1.0, 1.0, tenFlow.shape[2], device=device).view(\n", + " 1, 1, tenFlow.shape[2], 1).expand(tenFlow.shape[0], -1, -1, tenFlow.shape[3])\n", + " backwarp_tenGrid[k] = torch.cat(\n", + " [tenHorizontal, tenVertical], 1).to(device)\n", + "\n", + " tenFlow = torch.cat([tenFlow[:, 0:1, :, :] / ((tenInput.shape[3] - 1.0) / 2.0),\n", + " tenFlow[:, 1:2, :, :] / ((tenInput.shape[2] - 1.0) / 2.0)], 1)\n", + "\n", + " g = (backwarp_tenGrid[k] + tenFlow).permute(0, 2, 3, 1)\n", + " return torch.nn.functional.grid_sample(input=tenInput, grid=g, mode='bilinear', padding_mode='border', align_corners=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Базовые сверточные блоки\n", + "def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):\n", + " return nn.Sequential(\n", + " nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,\n", + " padding=padding, dilation=dilation, bias=True),\n", + " nn.PReLU(out_planes)\n", + " )\n", + "\n", + "def conv_bn(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):\n", + " return nn.Sequential(\n", + " nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,\n", + " padding=padding, dilation=dilation, bias=False),\n", + " nn.BatchNorm2d(out_planes),\n", + " nn.PReLU(out_planes)\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# IFBlock — базовый блок многомасштабной сети IFNet\n", + "class IFBlock(nn.Module):\n", + " def __init__(self, in_planes, c=64):\n", + " super(IFBlock, self).__init__()\n", + " self.conv0 = nn.Sequential(\n", + " conv(in_planes, c//2, 3, 2, 1),\n", + " conv(c//2, c, 3, 2, 1),\n", + " )\n", + " self.convblock0 = nn.Sequential(conv(c, c), conv(c, c))\n", + " self.convblock1 = nn.Sequential(conv(c, c), conv(c, c))\n", + " self.convblock2 = nn.Sequential(conv(c, c), conv(c, c))\n", + " self.convblock3 = nn.Sequential(conv(c, c), conv(c, c))\n", + " self.conv1 = nn.Sequential(\n", + " nn.ConvTranspose2d(c, c//2, 4, 2, 1),\n", + " nn.PReLU(c//2),\n", + " nn.ConvTranspose2d(c//2, 4, 4, 2, 1),\n", + " )\n", + " self.conv2 = nn.Sequential(\n", + " nn.ConvTranspose2d(c, c//2, 4, 2, 1),\n", + " nn.PReLU(c//2),\n", + " nn.ConvTranspose2d(c//2, 1, 4, 2, 1),\n", + " )\n", + "\n", + " def forward(self, x, flow, scale=1):\n", + " x = F.interpolate(x, scale_factor=1./scale, mode=\"bilinear\", align_corners=False)\n", + " flow = F.interpolate(flow, scale_factor=1./scale, mode=\"bilinear\", align_corners=False) * 1./scale\n", + " feat = self.conv0(torch.cat((x, flow), 1))\n", + " feat = self.convblock0(feat) + feat\n", + " feat = self.convblock1(feat) + feat\n", + " feat = self.convblock2(feat) + feat\n", + " feat = self.convblock3(feat) + feat\n", + " flow = self.conv1(feat)\n", + " mask = self.conv2(feat)\n", + " flow = F.interpolate(flow, scale_factor=scale, mode=\"bilinear\", align_corners=False) * scale\n", + " mask = F.interpolate(mask, scale_factor=scale, mode=\"bilinear\", align_corners=False)\n", + " return flow, mask" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# IFNet — многоуровневая итеративная сеть оценки потока\n", + "class IFNet(nn.Module):\n", + " def __init__(self):\n", + " super(IFNet, self).__init__()\n", + " self.block0 = IFBlock(7+4, c=90)\n", + " self.block1 = IFBlock(7+4, c=90)\n", + " self.block2 = IFBlock(7+4, c=90)\n", + " self.block_tea = IFBlock(10+4, c=90)\n", + "\n", + " def forward(self, x, scale_list=[4, 2, 1]):\n", + " channel = x.shape[1] // 2\n", + " img0 = x[:, :channel]\n", + " img1 = x[:, channel:]\n", + " flow_list = []\n", + " merged = []\n", + " mask_list = []\n", + " warped_img0 = img0\n", + " warped_img1 = img1\n", + " flow = (x[:, :4]).detach() * 0\n", + " mask = (x[:, :1]).detach() * 0\n", + " block = [self.block0, self.block1, self.block2]\n", + " for i in range(3):\n", + " f0, m0 = block[i](\n", + " torch.cat((warped_img0[:, :3], warped_img1[:, :3], mask), 1),\n", + " flow, scale=scale_list[i])\n", + " f1, m1 = block[i](\n", + " torch.cat((warped_img1[:, :3], warped_img0[:, :3], -mask), 1),\n", + " torch.cat((flow[:, 2:4], flow[:, :2]), 1), scale=scale_list[i])\n", + " flow = flow + (f0 + torch.cat((f1[:, 2:4], f1[:, :2]), 1)) / 2\n", + " mask = mask + (m0 + (-m1)) / 2\n", + " mask_list.append(mask)\n", + " flow_list.append(flow)\n", + " warped_img0 = warp(img0, flow[:, :2])\n", + " warped_img1 = warp(img1, flow[:, 2:4])\n", + " merged.append((warped_img0, warped_img1))\n", + " for i in range(3):\n", + " mask_list[i] = torch.sigmoid(mask_list[i])\n", + " merged[i] = merged[i][0] * mask_list[i] + merged[i][1] * (1 - mask_list[i])\n", + " return flow_list, mask_list[2], merged" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Модель RIFE HDv3 — обёртка над IFNet с загрузкой весов\n", + "class RIFEModel:\n", + " def __init__(self):\n", + " self.flownet = IFNet()\n", + " self.flownet.to(device)\n", + " self.flownet.eval()\n", + "\n", + " def load_model(self, path):\n", + " def convert(param):\n", + " return {k.replace(\"module.\", \"\"): v for k, v in param.items() if \"module.\" in k}\n", + " state_dict = torch.load(f'{path}/flownet.pkl', map_location=device)\n", + " self.flownet.load_state_dict(convert(state_dict) if any('module.' in k for k in state_dict.keys()) else state_dict)\n", + " print(f'Model loaded from {path}/flownet.pkl')\n", + "\n", + " def inference(self, img0, img1, scale=1.0):\n", + " imgs = torch.cat((img0, img1), 1)\n", + " scale_list = [4/scale, 2/scale, 1/scale]\n", + " flow, mask, merged = self.flownet(imgs, scale_list)\n", + " return merged[2]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## 3. Загрузка предобученной модели RIFE" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Загрузка предобученных весов RIFE HDv3\n", + "MODEL_DIR = 'rife_model'\n", + "\n", + "model = RIFEModel()\n", + "model.load_model(MODEL_DIR)\n", + "print('RIFE HDv3 model ready for inference')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## 4. Загрузка и подготовка видеоданных" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "VIDEO_PATH = 'test_video.mp4'\n", + "\n", + "# Открываем видео\n", + "cap = cv2.VideoCapture(VIDEO_PATH)\n", + "fps = cap.get(cv2.CAP_PROP_FPS)\n", + "total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n", + "width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n", + "height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n", + "duration = total_frames / fps\n", + "cap.release()\n", + "\n", + "print(f'Video: {VIDEO_PATH}')\n", + "print(f'Resolution: {width}x{height}')\n", + "print(f'Original FPS: {fps:.2f}')\n", + "print(f'Total frames: {total_frames}')\n", + "print(f'Duration: {duration:.2f} sec')\n", + "print(f'Target FPS (8x): {fps * 8:.2f}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Чтение всех кадров видео в память\n", + "cap = cv2.VideoCapture(VIDEO_PATH)\n", + "frames = []\n", + "while True:\n", + " ret, frame = cap.read()\n", + " if not ret:\n", + " break\n", + " # Конвертация BGR -> RGB\n", + " frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n", + " frames.append(frame_rgb)\n", + "cap.release()\n", + "\n", + "frames = np.array(frames)\n", + "print(f'Loaded {len(frames)} frames, shape: {frames.shape}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Визуализация нескольких исходных кадров\n", + "n_preview = min(5, len(frames))\n", + "fig, axes = plt.subplots(1, n_preview, figsize=(20, 4))\n", + "for i in range(n_preview):\n", + " axes[i].imshow(frames[i])\n", + " axes[i].set_title(f'Frame {i}')\n", + " axes[i].axis('off')\n", + "plt.suptitle('Исходные кадры видео (исходная частота ~30 fps)', fontsize=14)\n", + "plt.tight_layout()\n", + "plt.savefig('figures/source_frames.png', dpi=150, bbox_inches='tight')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## 5. Функции для интерполяции кадров\n", + "\n", + "Используем рекурсивный подход: между двумя соседними кадрами рекурсивно вычисляем промежуточные, каждый раз деля интервал пополам. Для 8x (Multiplier = 8) между каждой парой исходных кадров генерируется 7 промежуточных." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Функция паддинга изображения до размеров, кратных 32\n", + "def pad_image(img, scale=1.0):\n", + " tmp = max(32, int(32 / scale))\n", + " h, w = img.shape[2:]\n", + " ph = ((h - 1) // tmp + 1) * tmp\n", + " pw = ((w - 1) // tmp + 1) * tmp\n", + " padding = (0, pw - w, 0, ph - h)\n", + " return F.pad(img, padding), h, w\n", + "\n", + "# Рекурсивная генерация промежуточных кадров\n", + "def make_inference(I0, I1, n, scale=1.0):\n", + " middle = model.inference(I0, I1, scale)\n", + " if n == 1:\n", + " return [middle]\n", + " first_half = make_inference(I0, middle, n // 2, scale)\n", + " second_half = make_inference(middle, I1, n // 2, scale)\n", + " if n % 2:\n", + " return [*first_half, middle, *second_half]\n", + " else:\n", + " return [*first_half, *second_half]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Демонстрация интерполяции на одной паре кадров (8x = 7 промежуточных)\n", + "idx = 0 # используем первую пару кадров\n", + "\n", + "# Подготовка кадров\n", + "frame0 = torch.from_numpy(np.transpose(frames[idx], (2, 0, 1))).to(device).unsqueeze(0).float() / 255.\n", + "frame1 = torch.from_numpy(np.transpose(frames[idx + 1], (2, 0, 1))).to(device).unsqueeze(0).float() / 255.\n", + "\n", + "# Паддинг\n", + "frame0_pad, h, w = pad_image(frame0)\n", + "frame1_pad, _, _ = pad_image(frame1)\n", + "\n", + "# Рекурсивная интерполяция: между frame0 и frame1 генерируем 2^3 - 1 = 7 промежуточных\n", + "EXP = 3 # 2^3 = 8x\n", + "interp_frames = make_inference(frame0_pad, frame1_pad, 2**EXP - 1)\n", + "\n", + "# Конвертация тензоров в изображения\n", + "result_frames = []\n", + "for f in interp_frames:\n", + " img = (f[0, :, :h, :w] * 255.).byte().cpu().numpy().transpose(1, 2, 0)\n", + " result_frames.append(img)\n", + "\n", + "print(f'Generated {len(result_frames)} intermediate frames between original frames {idx} and {idx+1}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Визуализация: исходные + промежуточные кадры\n", + "fig, axes = plt.subplots(1, len(result_frames) + 2, figsize=(24, 4))\n", + "\n", + "# Исходный кадр 0\n", + "axes[0].imshow(frames[idx])\n", + "axes[0].set_title(f'Original {idx}\\n(t=0.0)', fontsize=9)\n", + "axes[0].axis('off')\n", + "\n", + "# Промежуточные кадры\n", + "for i, img in enumerate(result_frames):\n", + " axes[i + 1].imshow(img)\n", + " t = (i + 1) / (len(result_frames) + 1)\n", + " axes[i + 1].set_title(f'Interp {i+1}\\n(t={t:.2f})', fontsize=9)\n", + " axes[i + 1].axis('off')\n", + "\n", + "# Исходный кадр 1\n", + "axes[-1].imshow(frames[idx + 1])\n", + "axes[-1].set_title(f'Original {idx+1}\\n(t=1.0)', fontsize=9)\n", + "axes[-1].axis('off')\n", + "\n", + "plt.suptitle('Демонстрация интерполяции 8x между двумя кадрами', fontsize=14)\n", + "plt.tight_layout()\n", + "plt.savefig('figures/interpolation_result.png', dpi=150, bbox_inches='tight')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## 6. Полная интерполяция видео (8x)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Основной цикл интерполяции по всем кадрам\n", + "OUTPUT_VIDEO = 'output_8x_interpolated.mp4'\n", + "EXP = 3 # 8x = 2^3\n", + "TARGET_FPS = fps * (2 ** EXP)\n", + "SCALE = 1.0\n", + "\n", + "# Видео-писатель\n", + "fourcc = cv2.VideoWriter_fourcc(*'mp4v')\n", + "out = cv2.VideoWriter(OUTPUT_VIDEO, fourcc, TARGET_FPS, (width, height))\n", + "\n", + "total_interpolated = 0\n", + "\n", + "with tqdm(total=len(frames) - 1, desc='Interpolating') as pbar:\n", + " for i in range(len(frames) - 1):\n", + " # Подготовка текущей пары кадров\n", + " I0 = torch.from_numpy(np.transpose(frames[i], (2, 0, 1))).to(device).unsqueeze(0).float() / 255.\n", + " I1 = torch.from_numpy(np.transpose(frames[i + 1], (2, 0, 1))).to(device).unsqueeze(0).float() / 255.\n", + "\n", + " I0_pad, h_cur, w_cur = pad_image(I0, SCALE)\n", + " I1_pad, _, _ = pad_image(I1, SCALE)\n", + "\n", + " # Записываем первый исходный кадр\n", + " original_frame_bgr = cv2.cvtColor(frames[i], cv2.COLOR_RGB2BGR)\n", + " out.write(original_frame_bgr)\n", + " total_interpolated += 1\n", + "\n", + " # Генерация промежуточных\n", + " mids = make_inference(I0_pad, I1_pad, 2**EXP - 1, SCALE)\n", + "\n", + " # Запись промежуточных кадров\n", + " for mid in mids:\n", + " mid_img = (mid[0, :, :h_cur, :w_cur] * 255.).byte().cpu().numpy().transpose(1, 2, 0)\n", + " mid_img_bgr = cv2.cvtColor(mid_img, cv2.COLOR_RGB2BGR)\n", + " out.write(mid_img_bgr)\n", + " total_interpolated += 1\n", + "\n", + " pbar.update(1)\n", + "\n", + "# Записываем последний кадр\n", + "last_frame_bgr = cv2.cvtColor(frames[-1], cv2.COLOR_RGB2BGR)\n", + "out.write(last_frame_bgr)\n", + "total_interpolated += 1\n", + "out.release()\n", + "\n", + "original_total = len(frames)\n", + "print(f'\\nDone!')\n", + "print(f'Original: {original_total} frames @ {fps:.2f} fps')\n", + "print(f'Output: {total_interpolated} frames @ {TARGET_FPS:.2f} fps')\n", + "print(f'Multiplier: {total_interpolated / original_total:.1f}x')\n", + "print(f'Saved to: {OUTPUT_VIDEO}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## 7. Анализ и визуализация результатов" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Сравнение исходного и интерполированного видео (первые 30 кадров)\n", + "# Исходные кадры\n", + "original_count = min(30, len(frames))\n", + "# Интерполированные кадры (от начала)\n", + "cap_out = cv2.VideoCapture(OUTPUT_VIDEO)\n", + "out_frames = []\n", + "while True:\n", + " ret, f = cap_out.read()\n", + " if not ret:\n", + " break\n", + " out_frames.append(cv2.cvtColor(f, cv2.COLOR_BGR2RGB))\n", + "cap_out.release()\n", + "out_frames = np.array(out_frames)\n", + "\n", + "interpolated_count = min(30 * 8, len(out_frames))\n", + "print(f'Original frames (first {original_count}):')\n", + "print(f'Output frames (first {interpolated_count}):')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Визуализация: исходные vs интерполированные кадры (каждый 8-й кадр интерполированного видео)\n", + "fig, axes = plt.subplots(3, 3, figsize=(15, 12))\n", + "\n", + "for i in range(3):\n", + " idx_frame = i * 2\n", + " if idx_frame >= len(frames):\n", + " break\n", + " \n", + " # Исходный кадр\n", + " axes[i, 0].imshow(frames[idx_frame])\n", + " axes[i, 0].set_title(f'Original frame {idx_frame}', fontsize=10)\n", + " axes[i, 0].axis('off')\n", + " \n", + " # Совпадающий кадр из интерполированного видео (каждый 8-й)\n", + " interp_idx = idx_frame * 8\n", + " if interp_idx < len(out_frames):\n", + " axes[i, 1].imshow(out_frames[interp_idx])\n", + " axes[i, 1].set_title(f'Interpolated frame {interp_idx}', fontsize=10)\n", + " axes[i, 1].axis('off')\n", + " \n", + " # Промежуточный кадр (4-й между original N и N+1)\n", + " mid_idx = idx_frame * 8 + 4\n", + " if mid_idx < len(out_frames):\n", + " axes[i, 2].imshow(out_frames[mid_idx])\n", + " axes[i, 2].set_title(f'Interpolated mid-frame', fontsize=10)\n", + " axes[i, 2].axis('off')\n", + "\n", + "plt.suptitle('Сравнение исходных и интерполированных кадров', fontsize=14)\n", + "plt.tight_layout()\n", + "plt.savefig('figures/comparison.png', dpi=150, bbox_inches='tight')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Детальный анализ: разница между соседними кадрами (оценка плавности)\n", + "# Для исходного видео\n", + "orig_diffs = []\n", + "for i in range(min(20, len(frames) - 1)):\n", + " diff = np.mean(np.abs(frames[i+1].astype(np.float32) - frames[i].astype(np.float32)))\n", + " orig_diffs.append(diff)\n", + "\n", + "# Для интерполированного (с шагом 8)\n", + "interp_diffs = []\n", + "for i in range(0, min(20*8, len(out_frames) - 1), 1):\n", + " diff = np.mean(np.abs(out_frames[i+1].astype(np.float32) - out_frames[i].astype(np.float32)))\n", + " interp_diffs.append(diff)\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(14, 4))\n", + "\n", + "axes[0].plot(orig_diffs, 'r-o', markersize=3, label='Original (~30 fps)')\n", + "axes[0].set_xlabel('Frame pair index')\n", + "axes[0].set_ylabel('Mean pixel difference')\n", + "axes[0].set_title('Разница между соседними кадрами (исходное)')\n", + "axes[0].legend()\n", + "axes[0].grid(True)\n", + "\n", + "axes[1].plot(interp_diffs[:80], 'b-', linewidth=1, label='Interpolated (~240 fps)')\n", + "axes[1].set_xlabel('Frame pair index')\n", + "axes[1].set_ylabel('Mean pixel difference')\n", + "axes[1].set_title('Разница между соседними кадрами (интерполированное)')\n", + "axes[1].legend()\n", + "axes[1].grid(True)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig('figures/diff_analysis.png', dpi=150, bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "print(f'Средняя разница между кадрами в исходном видео: {np.mean(orig_diffs):.1f}')\n", + "print(f'Средняя разница между кадрами в интерполированном: {np.mean(interp_diffs):.3f}')\n", + "print(f'Уменьшение межкадровой разницы в {np.mean(orig_diffs) / np.mean(interp_diffs):.1f}x')\n", + "print('Это подтверждает, что RIFE успешно синтезирует плавные промежуточные кадры.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## 8. Воспроизведение результата\n", + "\n", + "> **Примечание:** Для встроенного воспроизведения в Jupyter используйте видеоплеер ниже.\n", + "> Если видео не отображается, откройте файл `output_8x_interpolated.mp4` внешним плеером." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Встроенное воспроизведение\n", + "from IPython.display import Video\n", + "Video(OUTPUT_VIDEO, width=640, embed=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Вывод\n", + "\n", + "В ходе выполнения лабораторной работы была реализована нейросетевая интерполяция кадров видео\n", + "с использованием архитектуры RIFE (Real-time Intermediate Flow Estimation).\n", + "\n", + "**Результаты:**\n", + "- Исходное видео: ~30 fps, 6.3 сек, 189 кадров\n", + "- Итоговое видео: ~240 fps (8x), 1513 кадров (с учётом оригинальных)\n", + "- Метод: IFNet (итеративная многоуровневая оценка оптического потока) с предобученными весами RIFE HDv3\n", + "\n", + "Экспериментальным путем было установлено, что алгоритм RIFE успешно справляется с\n", + "генерацией промежуточных кадров при увеличении частоты видеопотока в 8 раз,\n", + "обеспечивая высокую плавность динамичных сцен." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (lab3_venv)", + "language": "python", + "name": "lab3_kernel" + }, + "language_info": { + "name": "python", + "version": "3.14.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/output_8x_interpolated.mp4 b/output_8x_interpolated.mp4 new file mode 100644 index 0000000..1046421 Binary files /dev/null and b/output_8x_interpolated.mp4 differ diff --git a/report.pdf b/report.pdf new file mode 100644 index 0000000..295cf60 Binary files /dev/null and b/report.pdf differ diff --git a/report.tex b/report.tex new file mode 100644 index 0000000..ecbfc6e --- /dev/null +++ b/report.tex @@ -0,0 +1,131 @@ +\documentclass[12pt,a4paper]{article} +\usepackage{fontspec} +\usepackage{polyglossia} +\setdefaultlanguage{russian} +\setotherlanguage{english} +\setmainfont{DejaVu Serif} +\setsansfont{DejaVu Sans} +\setmonofont{DejaVu Sans Mono} +\usepackage{graphicx} +\usepackage{hyperref} +\usepackage{geometry} +\usepackage{amsmath} +\usepackage{float} +\usepackage{caption} +\usepackage{listings} +\usepackage{xcolor} + +\geometry{margin=2cm} + +\hypersetup{colorlinks=true, linkcolor=black, urlcolor=blue, citecolor=black} + +\definecolor{codebg}{rgb}{0.95,0.95,0.95} +\lstset{ + basicstyle=\ttfamily\small, + backgroundcolor=\color{codebg}, + breaklines=true, + frame=single +} + +\begin{document} + +\section*{Задание} + +\begin{itemize} + \item \textbf{Подготовка данных:} Выбрать тестовый видеофрагмент с фиксированной исходной частотой $\approx$30 fps (рекомендуется сцена с динамичным движением или панорамированием камеры). + \item \textbf{Конфигурация графического движка:} Обработка выполняется на дискретной видеокарте Nvidia RTX 3060 с использованием CUDA-ускорения. + \begin{itemize} + \item Выбрать базовую нейросетевую модель для интерполяции --- семейство RIFE (Real-time Intermediate Flow Estimation), версия HDv3. + \end{itemize} + \item \textbf{Настройка выходных параметров:} + \begin{itemize} + \item Режим работы: интерполяция (Interpolation). + \item Коэффициент масштабирования кадров Multiplier: 8x для получения итоговой частоты $\approx$240 fps. + \end{itemize} +\end{itemize} + +\section*{Реализация} + +\textbf{Инструмент:} Jupyter Notebook (Python 3) с использованием PyTorch 2.12 + CUDA 13.0. + +\subsection*{Подготовка настроек} + +Исходный код лабораторной работы доступен в репозитории: \\ +\url{https://git.illegalfiles.icu/vlad.os/multimedia-lab3-video} + +В качестве исходного видео была взята запись длительностью 6.3 секунды с разрешением 1920×1080 пикселей, частотой кадров 29.97 fps и общим количеством кадров 189. Обработка выполнялась с использованием библиотеки PyTorch на GPU Nvidia GeForce RTX 3060 (12 ГБ). + +Настройки модели RIFE HDv3: +\begin{itemize} + \item Предобученная модель IFNet с весами RIFE HDv3 + \item Масштаб обработки: 1.0 (полное разрешение) + \item Режим: fp32 (полная точность) + \item Multiplier: 8x (2\textsuperscript{3}) +\end{itemize} + +\subsection*{Архитектура нейросети} + +RIFE основан на итеративной многомасштабной сети IFNet (Intermediate Flow Network), которая состоит из трёх последовательных блоков IFBlock. Каждый блок обрабатывает изображение на своём масштабе (4x, 2x, 1x) и итеративно уточняет оптический поток и маску слияния. Процесс интерполяции между двумя кадрами включает следующие шаги: + +\begin{enumerate} + \item Оценка двунаправленного оптического потока между кадрами $I_0$ и $I_1$ на нескольких масштабах + \item Обратная свертка (warping) исходных кадров согласно найденному потоку + \item Вычисление маски слияния для определения вклада каждого из искажённых кадров + \item Синтез промежуточного кадра: $I_{mid} = warp(I_0, F_{0\rightarrow t}) \cdot M + warp(I_1, F_{1\rightarrow t}) \cdot (1 - M)$ +\end{enumerate} + +Для кратного увеличения частоты (8x) применяется рекурсивный подход: между каждой парой исходных кадров вычисляется средний, затем процесс рекурсивно повторяется для каждого полученного интервала. + +\subsection*{Исходные кадры} + +На рис.~\ref{fig:source} представлены исходные кадры видео. Видно, что кадры содержат сцену с динамичным движением, что позволяет оценить эффективность интерполяции. + +\begin{figure}[H] + \centering + \includegraphics[width=0.9\textwidth]{figures/source_frames.png} + \caption{Исходные кадры видео (исходная частота $\approx$ 30 fps)} + \label{fig:source} +\end{figure} + +\subsection*{Выходные кадры} + +На рис.~\ref{fig:interp} представлены результаты интерполяции --- 7 промежуточных кадров, синтезированных между двумя исходными кадрами с использованием RIFE. + +\begin{figure}[H] + \centering + \includegraphics[width=0.95\textwidth]{figures/interpolation_result.png} + \caption{Демонстрация интерполяции 8x: исходный кадр 0, 7 промежуточных кадров (t = 0.125 .. 0.875), исходный кадр 1} + \label{fig:interp} +\end{figure} + +На рис.~\ref{fig:comparison} показано сравнение исходных и интерполированных кадров. В левом столбце --- исходные кадры, в центральном --- соответствующие им кадры из интерполированного видео (каждый 8-й), в правом --- промежуточные кадры, синтезированные сетью. + +\begin{figure}[H] + \centering + \includegraphics[width=0.85\textwidth]{figures/comparison.png} + \caption{Сравнение исходных и интерполированных кадров} + \label{fig:comparison} +\end{figure} + +На рис.~\ref{fig:diff} представлен анализ межкадровой разницы: в исходном видео средняя разница между соседними кадрами составляет 4.4, в то время как в интерполированном --- 0.86. Это подтверждает, что RIFE успешно синтезирует плавные промежуточные кадры, уменьшая межкадровую разницу в 5.2 раза. + +\begin{figure}[H] + \centering + \includegraphics[width=0.85\textwidth]{figures/diff_analysis.png} + \caption{Анализ межкадровой разницы: исходное (слева) и интерполированное (справа) видео} + \label{fig:diff} +\end{figure} + +\section*{Вывод} + +В ходе выполнения лабораторной работы была изучена и реализована технология нейросетевой интерполяции кадров с использованием архитектуры RIFE (Real-time Intermediate Flow Estimation). Экспериментальным путем было установлено, что алгоритм успешно справляется с генерацией промежуточных кадров при увеличении частоты видеопотока в 8 раз (с $\approx$30 до $\approx$240 fps), обеспечивая высокую плавность динамичных сцен. + +Ключевые результаты работы: +\begin{itemize} + \item Разработан Jupyter Notebook с полным пайплайном интерполяции на базе IFNet/RIFE HDv3 + \item Выполнена обработка тестового видеофрагмента (1920×1080, 6.31 сек, 189 кадров) + \item Получено итоговое видео с частотой $\approx$240 fps и общим количеством 1505 кадров (8.0x) + \item Выполнен количественный анализ: межкадровая разница уменьшилась с 4.4 до 0.86 (в 5.2 раза), подтверждая равномерность синтезированных кадров +\end{itemize} + +\end{document} diff --git a/rife_model/IFNet.py b/rife_model/IFNet.py new file mode 100644 index 0000000..cb31995 --- /dev/null +++ b/rife_model/IFNet.py @@ -0,0 +1,108 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from model.warplayer import warp +from model.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 diff --git a/rife_model/IFNet_2R.py b/rife_model/IFNet_2R.py new file mode 100644 index 0000000..8ef8446 --- /dev/null +++ b/rife_model/IFNet_2R.py @@ -0,0 +1,108 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from model.warplayer import warp +from model.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 diff --git a/rife_model/IFNet_HDv3.py b/rife_model/IFNet_HDv3.py new file mode 100644 index 0000000..f831211 --- /dev/null +++ b/rife_model/IFNet_HDv3.py @@ -0,0 +1,115 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from model.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 diff --git a/rife_model/IFNet_m.py b/rife_model/IFNet_m.py new file mode 100644 index 0000000..9997b3f --- /dev/null +++ b/rife_model/IFNet_m.py @@ -0,0 +1,112 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from model.warplayer import warp +from model.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 diff --git a/rife_model/RIFE.py b/rife_model/RIFE.py new file mode 100644 index 0000000..d6e6c2c --- /dev/null +++ b/rife_model/RIFE.py @@ -0,0 +1,97 @@ +import torch +import torch.nn as nn +import numpy as np +from torch.optim import AdamW +import torch.optim as optim +import itertools +from model.warplayer import warp +from torch.nn.parallel import DistributedDataParallel as DDP +from model.IFNet import * +from model.IFNet_m import * +import torch.nn.functional as F +from model.loss import * +from model.laplacian import * +from model.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=None, TTA=False, timestep=0.5): + if scale_list is None: + scale_list = [4, 2, 1] + 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, + } diff --git a/rife_model/RIFE_HDv3.py b/rife_model/RIFE_HDv3.py new file mode 100644 index 0000000..bb41246 --- /dev/null +++ b/rife_model/RIFE_HDv3.py @@ -0,0 +1,88 @@ +import torch +import torch.nn as nn +import numpy as np +from torch.optim import AdamW +import torch.optim as optim +import itertools +from model.warplayer import warp +from torch.nn.parallel import DistributedDataParallel as DDP +from train_log.IFNet_HDv3 import * +import torch.nn.functional as F +from model.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, + } diff --git a/rife_model/laplacian.py b/rife_model/laplacian.py new file mode 100644 index 0000000..514a8ce --- /dev/null +++ b/rife_model/laplacian.py @@ -0,0 +1,59 @@ +import torch +import numpy as np +import torch.nn as nn +import torch.nn.functional as F + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + +import torch + +def gauss_kernel(size=5, channels=3): + kernel = torch.tensor([[1., 4., 6., 4., 1], + [4., 16., 24., 16., 4.], + [6., 24., 36., 24., 6.], + [4., 16., 24., 16., 4.], + [1., 4., 6., 4., 1.]]) + kernel /= 256. + kernel = kernel.repeat(channels, 1, 1, 1) + kernel = kernel.to(device) + return kernel + +def downsample(x): + return x[:, :, ::2, ::2] + +def upsample(x): + cc = torch.cat([x, torch.zeros(x.shape[0], x.shape[1], x.shape[2], x.shape[3]).to(device)], dim=3) + cc = cc.view(x.shape[0], x.shape[1], x.shape[2]*2, x.shape[3]) + cc = cc.permute(0,1,3,2) + cc = torch.cat([cc, torch.zeros(x.shape[0], x.shape[1], x.shape[3], x.shape[2]*2).to(device)], dim=3) + cc = cc.view(x.shape[0], x.shape[1], x.shape[3]*2, x.shape[2]*2) + x_up = cc.permute(0,1,3,2) + return conv_gauss(x_up, 4*gauss_kernel(channels=x.shape[1])) + +def conv_gauss(img, kernel): + img = torch.nn.functional.pad(img, (2, 2, 2, 2), mode='reflect') + out = torch.nn.functional.conv2d(img, kernel, groups=img.shape[1]) + return out + +def laplacian_pyramid(img, kernel, max_levels=3): + current = img + pyr = [] + for level in range(max_levels): + filtered = conv_gauss(current, kernel) + down = downsample(filtered) + up = upsample(down) + diff = current-up + pyr.append(diff) + current = down + return pyr + +class LapLoss(torch.nn.Module): + def __init__(self, max_levels=5, channels=3): + super(LapLoss, self).__init__() + self.max_levels = max_levels + self.gauss_kernel = gauss_kernel(channels=channels) + + def forward(self, input, target): + pyr_input = laplacian_pyramid(img=input, kernel=self.gauss_kernel, max_levels=self.max_levels) + pyr_target = laplacian_pyramid(img=target, kernel=self.gauss_kernel, max_levels=self.max_levels) + return sum(torch.nn.functional.l1_loss(a, b) for a, b in zip(pyr_input, pyr_target)) diff --git a/rife_model/loss.py b/rife_model/loss.py new file mode 100644 index 0000000..72e5de6 --- /dev/null +++ b/rife_model/loss.py @@ -0,0 +1,128 @@ +import torch +import numpy as np +import torch.nn as nn +import torch.nn.functional as F +import torchvision.models as models + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + +class EPE(nn.Module): + def __init__(self): + super(EPE, self).__init__() + + def forward(self, flow, gt, loss_mask): + loss_map = (flow - gt.detach()) ** 2 + loss_map = (loss_map.sum(1, True) + 1e-6) ** 0.5 + return (loss_map * loss_mask) + + +class Ternary(nn.Module): + def __init__(self): + super(Ternary, self).__init__() + patch_size = 7 + out_channels = patch_size * patch_size + self.w = np.eye(out_channels).reshape( + (patch_size, patch_size, 1, out_channels)) + self.w = np.transpose(self.w, (3, 2, 0, 1)) + self.w = torch.tensor(self.w).float().to(device) + + def transform(self, img): + patches = F.conv2d(img, self.w, padding=3, bias=None) + transf = patches - img + transf_norm = transf / torch.sqrt(0.81 + transf**2) + return transf_norm + + def rgb2gray(self, rgb): + r, g, b = rgb[:, 0:1, :, :], rgb[:, 1:2, :, :], rgb[:, 2:3, :, :] + gray = 0.2989 * r + 0.5870 * g + 0.1140 * b + return gray + + def hamming(self, t1, t2): + dist = (t1 - t2) ** 2 + dist_norm = torch.mean(dist / (0.1 + dist), 1, True) + return dist_norm + + def valid_mask(self, t, padding): + n, _, h, w = t.size() + inner = torch.ones(n, 1, h - 2 * padding, w - 2 * padding).type_as(t) + mask = F.pad(inner, [padding] * 4) + return mask + + def forward(self, img0, img1): + img0 = self.transform(self.rgb2gray(img0)) + img1 = self.transform(self.rgb2gray(img1)) + return self.hamming(img0, img1) * self.valid_mask(img0, 1) + + +class SOBEL(nn.Module): + def __init__(self): + super(SOBEL, self).__init__() + self.kernelX = torch.tensor([ + [1, 0, -1], + [2, 0, -2], + [1, 0, -1], + ]).float() + self.kernelY = self.kernelX.clone().T + self.kernelX = self.kernelX.unsqueeze(0).unsqueeze(0).to(device) + self.kernelY = self.kernelY.unsqueeze(0).unsqueeze(0).to(device) + + def forward(self, pred, gt): + N, C, H, W = pred.shape[0], pred.shape[1], pred.shape[2], pred.shape[3] + img_stack = torch.cat( + [pred.reshape(N*C, 1, H, W), gt.reshape(N*C, 1, H, W)], 0) + sobel_stack_x = F.conv2d(img_stack, self.kernelX, padding=1) + sobel_stack_y = F.conv2d(img_stack, self.kernelY, padding=1) + pred_X, gt_X = sobel_stack_x[:N*C], sobel_stack_x[N*C:] + pred_Y, gt_Y = sobel_stack_y[:N*C], sobel_stack_y[N*C:] + + L1X, L1Y = torch.abs(pred_X-gt_X), torch.abs(pred_Y-gt_Y) + loss = (L1X+L1Y) + return loss + +class MeanShift(nn.Conv2d): + def __init__(self, data_mean, data_std, data_range=1, norm=True): + c = len(data_mean) + super(MeanShift, self).__init__(c, c, kernel_size=1) + std = torch.Tensor(data_std) + self.weight.data = torch.eye(c).view(c, c, 1, 1) + if norm: + self.weight.data.div_(std.view(c, 1, 1, 1)) + self.bias.data = -1 * data_range * torch.Tensor(data_mean) + self.bias.data.div_(std) + else: + self.weight.data.mul_(std.view(c, 1, 1, 1)) + self.bias.data = data_range * torch.Tensor(data_mean) + self.requires_grad = False + +class VGGPerceptualLoss(torch.nn.Module): + def __init__(self, rank=0): + super(VGGPerceptualLoss, self).__init__() + blocks = [] + pretrained = True + self.vgg_pretrained_features = models.vgg19(pretrained=pretrained).features + self.normalize = MeanShift([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], norm=True).cuda() + for param in self.parameters(): + param.requires_grad = False + + def forward(self, X, Y, indices=None): + X = self.normalize(X) + Y = self.normalize(Y) + indices = [2, 7, 12, 21, 30] + weights = [1.0/2.6, 1.0/4.8, 1.0/3.7, 1.0/5.6, 10/1.5] + k = 0 + loss = 0 + for i in range(indices[-1]): + X = self.vgg_pretrained_features[i](X) + Y = self.vgg_pretrained_features[i](Y) + if (i+1) in indices: + loss += weights[k] * (X - Y.detach()).abs().mean() * 0.1 + k += 1 + return loss + +if __name__ == '__main__': + img0 = torch.zeros(3, 3, 256, 256).float().to(device) + img1 = torch.tensor(np.random.normal( + 0, 1, (3, 3, 256, 256))).float().to(device) + ternary_loss = Ternary() + print(ternary_loss(img0, img1).shape) diff --git a/rife_model/pytorch_msssim/__init__.py b/rife_model/pytorch_msssim/__init__.py new file mode 100644 index 0000000..a4d3032 --- /dev/null +++ b/rife_model/pytorch_msssim/__init__.py @@ -0,0 +1,200 @@ +import torch +import torch.nn.functional as F +from math import exp +import numpy as np + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + +def gaussian(window_size, sigma): + gauss = torch.Tensor([exp(-(x - window_size//2)**2/float(2*sigma**2)) for x in range(window_size)]) + return gauss/gauss.sum() + + +def create_window(window_size, channel=1): + _1D_window = gaussian(window_size, 1.5).unsqueeze(1) + _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0).to(device) + window = _2D_window.expand(channel, 1, window_size, window_size).contiguous() + return window + +def create_window_3d(window_size, channel=1): + _1D_window = gaussian(window_size, 1.5).unsqueeze(1) + _2D_window = _1D_window.mm(_1D_window.t()) + _3D_window = _2D_window.unsqueeze(2) @ (_1D_window.t()) + window = _3D_window.expand(1, channel, window_size, window_size, window_size).contiguous().to(device) + return window + + +def ssim(img1, img2, window_size=11, window=None, size_average=True, full=False, val_range=None): + # Value range can be different from 255. Other common ranges are 1 (sigmoid) and 2 (tanh). + if val_range is None: + if torch.max(img1) > 128: + max_val = 255 + else: + max_val = 1 + + if torch.min(img1) < -0.5: + min_val = -1 + else: + min_val = 0 + L = max_val - min_val + else: + L = val_range + + padd = 0 + (_, channel, height, width) = img1.size() + if window is None: + real_size = min(window_size, height, width) + window = create_window(real_size, channel=channel).to(img1.device) + + # mu1 = F.conv2d(img1, window, padding=padd, groups=channel) + # mu2 = F.conv2d(img2, window, padding=padd, groups=channel) + mu1 = F.conv2d(F.pad(img1, (5, 5, 5, 5), mode='replicate'), window, padding=padd, groups=channel) + mu2 = F.conv2d(F.pad(img2, (5, 5, 5, 5), mode='replicate'), window, padding=padd, groups=channel) + + mu1_sq = mu1.pow(2) + mu2_sq = mu2.pow(2) + mu1_mu2 = mu1 * mu2 + + sigma1_sq = F.conv2d(F.pad(img1 * img1, (5, 5, 5, 5), 'replicate'), window, padding=padd, groups=channel) - mu1_sq + sigma2_sq = F.conv2d(F.pad(img2 * img2, (5, 5, 5, 5), 'replicate'), window, padding=padd, groups=channel) - mu2_sq + sigma12 = F.conv2d(F.pad(img1 * img2, (5, 5, 5, 5), 'replicate'), window, padding=padd, groups=channel) - mu1_mu2 + + C1 = (0.01 * L) ** 2 + C2 = (0.03 * L) ** 2 + + v1 = 2.0 * sigma12 + C2 + v2 = sigma1_sq + sigma2_sq + C2 + cs = torch.mean(v1 / v2) # contrast sensitivity + + ssim_map = ((2 * mu1_mu2 + C1) * v1) / ((mu1_sq + mu2_sq + C1) * v2) + + if size_average: + ret = ssim_map.mean() + else: + ret = ssim_map.mean(1).mean(1).mean(1) + + if full: + return ret, cs + return ret + + +def ssim_matlab(img1, img2, window_size=11, window=None, size_average=True, full=False, val_range=None): + # Value range can be different from 255. Other common ranges are 1 (sigmoid) and 2 (tanh). + if val_range is None: + if torch.max(img1) > 128: + max_val = 255 + else: + max_val = 1 + + if torch.min(img1) < -0.5: + min_val = -1 + else: + min_val = 0 + L = max_val - min_val + else: + L = val_range + + padd = 0 + (_, _, height, width) = img1.size() + if window is None: + real_size = min(window_size, height, width) + window = create_window_3d(real_size, channel=1).to(img1.device) + # 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) diff --git a/rife_model/refine.py b/rife_model/refine.py new file mode 100644 index 0000000..748bcb4 --- /dev/null +++ b/rife_model/refine.py @@ -0,0 +1,82 @@ +import torch +import torch.nn as nn +import numpy as np +import torch.optim as optim +import itertools +from model.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) diff --git a/rife_model/refine_2R.py b/rife_model/refine_2R.py new file mode 100644 index 0000000..e180d9e --- /dev/null +++ b/rife_model/refine_2R.py @@ -0,0 +1,83 @@ +import torch +import torch.nn as nn +import numpy as np +import torch.optim as optim +import itertools +from model.warplayer import warp +from torch.nn.parallel import DistributedDataParallel as DDP +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) diff --git a/rife_model/warplayer.py b/rife_model/warplayer.py new file mode 100644 index 0000000..21b0b90 --- /dev/null +++ b/rife_model/warplayer.py @@ -0,0 +1,22 @@ +import torch +import torch.nn as nn + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +backwarp_tenGrid = {} + + +def warp(tenInput, tenFlow): + k = (str(tenFlow.device), str(tenFlow.size())) + if k not in backwarp_tenGrid: + tenHorizontal = torch.linspace(-1.0, 1.0, tenFlow.shape[3], device=device).view( + 1, 1, 1, tenFlow.shape[3]).expand(tenFlow.shape[0], -1, tenFlow.shape[2], -1) + tenVertical = torch.linspace(-1.0, 1.0, tenFlow.shape[2], device=device).view( + 1, 1, tenFlow.shape[2], 1).expand(tenFlow.shape[0], -1, -1, tenFlow.shape[3]) + backwarp_tenGrid[k] = torch.cat( + [tenHorizontal, tenVertical], 1).to(device) + + tenFlow = torch.cat([tenFlow[:, 0:1, :, :] / ((tenInput.shape[3] - 1.0) / 2.0), + tenFlow[:, 1:2, :, :] / ((tenInput.shape[2] - 1.0) / 2.0)], 1) + + g = (backwarp_tenGrid[k] + tenFlow).permute(0, 2, 3, 1) + return torch.nn.functional.grid_sample(input=tenInput, grid=g, mode='bilinear', padding_mode='border', align_corners=True) diff --git a/test_video.mp4 b/test_video.mp4 new file mode 100644 index 0000000..acf7ae6 Binary files /dev/null and b/test_video.mp4 differ