Repository: a312863063/Video-Auto-Wipe Branch: main Commit: 52359a7a6f1b Files: 9 Total size: 90.4 KB Directory structure: gitextract__p5e6pqz/ ├── LICENSE ├── README.md ├── core/ │ ├── spectral_norm.py │ └── utils.py ├── demo.py ├── model/ │ ├── auto-sttn.py │ └── vis.py ├── pics/ │ └── de-watermark/ │ └── 1 └── pretrained_weight/ └── download_weights.txt ================================================ FILE CONTENTS ================================================ ================================================ FILE: LICENSE ================================================ GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. Preamble The GNU General Public License is a free, copyleft license for software and other kinds of works. 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But first, please read . ================================================ FILE: README.md ================================================ # Video-Auto-Wipe If you are interested in AIGC application tools, you can learn a bit about it on [this blog](https://www.seeprettyface.com/).
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Erase the fixed-pattern content you don't want to see in your video. This project shares a model for subtitle removal and demonstrates the effectiveness of erasing content with easily recognizable patterns, such as subtitles, logos, and animated icons.

# 效果预览 ## 1. 字幕擦除 ![Image text](https://github.com/a312863063/Video-Auto-Wipe/blob/main/pics/de-text/detext_9_ko.JPG)

查看视频


  字幕擦除模型的功能是模型自动感知到视频中字幕的位置然后进行擦除，感知字幕的方法为具有统一样式的文字区域被视作字幕。


## 2. 图标擦除 ![Image text](https://github.com/a312863063/Video-Auto-Wipe/blob/main/pics/de-logo/delogo_4.JPG)

查看视频


  图标擦除模型的功能是模型自动感知到视频中图标的位置然后进行擦除，感知图标的方法为在时域上静止不动的像素块被视作图标。


## 3. 动态图标擦除 ![Image text](https://github.com/a312863063/Video-Auto-Wipe/blob/main/pics/de-dynamic-logo/de-dynamic-logo_1.JPG)

查看视频


  动态图标擦除模型的功能是模型自动感知到视频中动态图标的位置然后进行擦除，感知动态图标的方法为在时域上闪烁出现或动态移动的固定像素块被视作动态图标。


# 使用方法 ### 1.环境配置   torch>1.0
  其他的缺什么依赖就pip install xxx，需要的东西不多

### 2.运行方法   下载预训练文件放在pretrained-weight文件夹里。
    预训练模型下载地址：链接：https://pan.baidu.com/s/1JN9-8Glw_ozOrSMgBIyHOw 提取码：px0s

  更多的输入样例下载地址：https://pan.baidu.com/s/1_tzmvIoEQi3h_24-ieZJ_Q 提取码：cnqf

  运行```python demo.py```。



# 训练方法 ## 训练数据 ### 背景数据制作   1.基于搜集的300余部高清电影制作了2,709部电影片段数据集；
    下载地址：https://pan.baidu.com/s/1CIgJmFmx5iR2JfgAyjVaeg 提取码：xb7o

  2.基于搜集的40余部综艺节目制作了864部综艺片段数据集；
    下载地址：https://pan.baidu.com/s/1lJk6IIWlwxknAie0LlGYOg 提取码：9rd4

### 前景数据制作   1.字幕擦除：利用ImageDraw库生成随机样式、字体的文字，并模拟其变换；
  2.图标擦除：利用ImageDraw库生成随机的像素区块，并模拟时域一致性（固定在视频中的某一个区域）；
  3.动态图标擦除：利用PR软件制作闪烁、跳跃等字幕的动态特效，模拟动态图标的场景。

### 训练过程   第1步. 针对特定任务的时域感知训练，即让模型能感知到需被擦除的前景数据；
  第2步. 融合进擦除模型，进行端到端的微调训练。



# 后续计划 ![Image text](https://github.com/a312863063/Video-Auto-Wipe/blob/main/pics/undo.png)
  后续我想实现广告、人物和敏感内容擦除等方向。填补技术效果已经不错了，难点在于感知。图标感知可以利用区域一致性实现，字幕感知可以利用模式一致性实现。人物感知要如何实现？广告感知要如何实现？这种设计不能有缺漏，估计得结合数据本身的规律去做才行。。

================================================ FILE: core/spectral_norm.py ================================================ """ Spectral Normalization from https://arxiv.org/abs/1802.05957 """ import torch from torch.nn.functional import normalize class SpectralNorm(object): # Invariant before and after each forward call: # u = normalize(W @ v) # NB: At initialization, this invariant is not enforced _version = 1 # At version 1: # made `W` not a buffer, # added `v` as a buffer, and # made eval mode use `W = u @ W_orig @ v` rather than the stored `W`. def __init__(self, name='weight', n_power_iterations=1, dim=0, eps=1e-12): self.name = name self.dim = dim if n_power_iterations <= 0: raise ValueError('Expected n_power_iterations to be positive, but ' 'got n_power_iterations={}'.format(n_power_iterations)) self.n_power_iterations = n_power_iterations self.eps = eps def reshape_weight_to_matrix(self, weight): weight_mat = weight if self.dim != 0: # permute dim to front weight_mat = weight_mat.permute(self.dim, *[d for d in range(weight_mat.dim()) if d != self.dim]) height = weight_mat.size(0) return weight_mat.reshape(height, -1) def compute_weight(self, module, do_power_iteration): # NB: If `do_power_iteration` is set, the `u` and `v` vectors are # updated in power iteration **in-place**. This is very important # because in `DataParallel` forward, the vectors (being buffers) are # broadcast from the parallelized module to each module replica, # which is a new module object created on the fly. And each replica # runs its own spectral norm power iteration. So simply assigning # the updated vectors to the module this function runs on will cause # the update to be lost forever. And the next time the parallelized # module is replicated, the same randomly initialized vectors are # broadcast and used! # # Therefore, to make the change propagate back, we rely on two # important behaviors (also enforced via tests): # 1. `DataParallel` doesn't clone storage if the broadcast tensor # is already on correct device; and it makes sure that the # parallelized module is already on `device[0]`. # 2. If the out tensor in `out=` kwarg has correct shape, it will # just fill in the values. # Therefore, since the same power iteration is performed on all # devices, simply updating the tensors in-place will make sure that # the module replica on `device[0]` will update the _u vector on the # parallized module (by shared storage). # # However, after we update `u` and `v` in-place, we need to **clone** # them before using them to normalize the weight. This is to support # backproping through two forward passes, e.g., the common pattern in # GAN training: loss = D(real) - D(fake). Otherwise, engine will # complain that variables needed to do backward for the first forward # (i.e., the `u` and `v` vectors) are changed in the second forward. weight = getattr(module, self.name + '_orig') u = getattr(module, self.name + '_u') v = getattr(module, self.name + '_v') weight_mat = self.reshape_weight_to_matrix(weight) if do_power_iteration: with torch.no_grad(): for _ in range(self.n_power_iterations): # Spectral norm of weight equals to `u^T W v`, where `u` and `v` # are the first left and right singular vectors. # This power iteration produces approximations of `u` and `v`. v = normalize(torch.mv(weight_mat.t(), u), dim=0, eps=self.eps, out=v) u = normalize(torch.mv(weight_mat, v), dim=0, eps=self.eps, out=u) if self.n_power_iterations > 0: # See above on why we need to clone u = u.clone() v = v.clone() sigma = torch.dot(u, torch.mv(weight_mat, v)) weight = weight / sigma return weight def remove(self, module): with torch.no_grad(): weight = self.compute_weight(module, do_power_iteration=False) delattr(module, self.name) delattr(module, self.name + '_u') delattr(module, self.name + '_v') delattr(module, self.name + '_orig') module.register_parameter(self.name, torch.nn.Parameter(weight.detach())) def __call__(self, module, inputs): setattr(module, self.name, self.compute_weight(module, do_power_iteration=module.training)) def _solve_v_and_rescale(self, weight_mat, u, target_sigma): # Tries to returns a vector `v` s.t. `u = normalize(W @ v)` # (the invariant at top of this class) and `u @ W @ v = sigma`. # This uses pinverse in case W^T W is not invertible. v = torch.chain_matmul(weight_mat.t().mm(weight_mat).pinverse(), weight_mat.t(), u.unsqueeze(1)).squeeze(1) return v.mul_(target_sigma / torch.dot(u, torch.mv(weight_mat, v))) @staticmethod def apply(module, name, n_power_iterations, dim, eps): for k, hook in module._forward_pre_hooks.items(): if isinstance(hook, SpectralNorm) and hook.name == name: raise RuntimeError("Cannot register two spectral_norm hooks on " "the same parameter {}".format(name)) fn = SpectralNorm(name, n_power_iterations, dim, eps) weight = module._parameters[name] with torch.no_grad(): weight_mat = fn.reshape_weight_to_matrix(weight) h, w = weight_mat.size() # randomly initialize `u` and `v` u = normalize(weight.new_empty(h).normal_(0, 1), dim=0, eps=fn.eps) v = normalize(weight.new_empty(w).normal_(0, 1), dim=0, eps=fn.eps) delattr(module, fn.name) module.register_parameter(fn.name + "_orig", weight) # We still need to assign weight back as fn.name because all sorts of # things may assume that it exists, e.g., when initializing weights. # However, we can't directly assign as it could be an nn.Parameter and # gets added as a parameter. Instead, we register weight.data as a plain # attribute. setattr(module, fn.name, weight.data) module.register_buffer(fn.name + "_u", u) module.register_buffer(fn.name + "_v", v) module.register_forward_pre_hook(fn) module._register_state_dict_hook(SpectralNormStateDictHook(fn)) module._register_load_state_dict_pre_hook(SpectralNormLoadStateDictPreHook(fn)) return fn # This is a top level class because Py2 pickle doesn't like inner class nor an # instancemethod. class SpectralNormLoadStateDictPreHook(object): # See docstring of SpectralNorm._version on the changes to spectral_norm. def __init__(self, fn): self.fn = fn # For state_dict with version None, (assuming that it has gone through at # least one training forward), we have # # u = normalize(W_orig @ v) # W = W_orig / sigma, where sigma = u @ W_orig @ v # # To compute `v`, we solve `W_orig @ x = u`, and let # v = x / (u @ W_orig @ x) * (W / W_orig). def __call__(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs): fn = self.fn version = local_metadata.get('spectral_norm', {}).get(fn.name + '.version', None) if version is None or version < 1: with torch.no_grad(): weight_orig = state_dict[prefix + fn.name + '_orig'] # weight = state_dict.pop(prefix + fn.name) # sigma = (weight_orig / weight).mean() weight_mat = fn.reshape_weight_to_matrix(weight_orig) u = state_dict[prefix + fn.name + '_u'] # v = fn._solve_v_and_rescale(weight_mat, u, sigma) # state_dict[prefix + fn.name + '_v'] = v # This is a top level class because Py2 pickle doesn't like inner class nor an # instancemethod. class SpectralNormStateDictHook(object): # See docstring of SpectralNorm._version on the changes to spectral_norm. def __init__(self, fn): self.fn = fn def __call__(self, module, state_dict, prefix, local_metadata): if 'spectral_norm' not in local_metadata: local_metadata['spectral_norm'] = {} key = self.fn.name + '.version' if key in local_metadata['spectral_norm']: raise RuntimeError("Unexpected key in metadata['spectral_norm']: {}".format(key)) local_metadata['spectral_norm'][key] = self.fn._version def spectral_norm(module, name='weight', n_power_iterations=1, eps=1e-12, dim=None): r"""Applies spectral normalization to a parameter in the given module. .. math:: \mathbf{W}_{SN} = \dfrac{\mathbf{W}}{\sigma(\mathbf{W})}, \sigma(\mathbf{W}) = \max_{\mathbf{h}: \mathbf{h} \ne 0} \dfrac{\|\mathbf{W} \mathbf{h}\|_2}{\|\mathbf{h}\|_2} Spectral normalization stabilizes the training of discriminators (critics) in Generative Adversarial Networks (GANs) by rescaling the weight tensor with spectral norm :math:`\sigma` of the weight matrix calculated using power iteration method. If the dimension of the weight tensor is greater than 2, it is reshaped to 2D in power iteration method to get spectral norm. This is implemented via a hook that calculates spectral norm and rescales weight before every :meth:`~Module.forward` call. See `Spectral Normalization for Generative Adversarial Networks`_ . .. _`Spectral Normalization for Generative Adversarial Networks`: https://arxiv.org/abs/1802.05957 Args: module (nn.Module): containing module name (str, optional): name of weight parameter n_power_iterations (int, optional): number of power iterations to calculate spectral norm eps (float, optional): epsilon for numerical stability in calculating norms dim (int, optional): dimension corresponding to number of outputs, the default is ``0``, except for modules that are instances of ConvTranspose{1,2,3}d, when it is ``1`` Returns: The original module with the spectral norm hook Example:: >>> m = spectral_norm(nn.Linear(20, 40)) >>> m Linear(in_features=20, out_features=40, bias=True) >>> m.weight_u.size() torch.Size([40]) """ if dim is None: if isinstance(module, (torch.nn.ConvTranspose1d, torch.nn.ConvTranspose2d, torch.nn.ConvTranspose3d)): dim = 1 else: dim = 0 SpectralNorm.apply(module, name, n_power_iterations, dim, eps) return module def remove_spectral_norm(module, name='weight'): r"""Removes the spectral normalization reparameterization from a module. Args: module (Module): containing module name (str, optional): name of weight parameter Example: >>> m = spectral_norm(nn.Linear(40, 10)) >>> remove_spectral_norm(m) """ for k, hook in module._forward_pre_hooks.items(): if isinstance(hook, SpectralNorm) and hook.name == name: hook.remove(module) del module._forward_pre_hooks[k] return module raise ValueError("spectral_norm of '{}' not found in {}".format( name, module)) def use_spectral_norm(module, use_sn=False): if use_sn: return spectral_norm(module) return module ================================================ FILE: core/utils.py ================================================ import matplotlib.patches as patches from matplotlib.path import Path import os import sys import io import cv2 import time import argparse import shutil import random import zipfile from glob import glob import math import numpy as np import torch.nn.functional as F import torchvision.transforms as transforms from PIL import Image, ImageOps, ImageDraw, ImageFilter import torch import torchvision import torch.nn as nn import torch.distributed as dist import matplotlib from matplotlib import pyplot as plt matplotlib.use('agg') # ##################################################### # ##################################################### class ZipReader(object): file_dict = dict() def __init__(self): super(ZipReader, self).__init__() @staticmethod def build_file_dict(path): file_dict = ZipReader.file_dict if path in file_dict: return file_dict[path] else: file_handle = zipfile.ZipFile(path, 'r') file_dict[path] = file_handle return file_dict[path] @staticmethod def imread(path, idx): zfile = ZipReader.build_file_dict(path) znames = zfile.namelist() znames.sort() data = zfile.read(znames[idx]) im = Image.open(io.BytesIO(data)) return im # ########################################################################### # ########################################################################### class GroupRandomHorizontalFlip(object): """Randomly horizontally flips the given PIL.Image with a probability of 0.5 """ def __init__(self, is_flow=False): self.is_flow = is_flow def __call__(self, img_group, is_flow=False): v = random.random() if v < 0.5: ret = [img.transpose(Image.FLIP_LEFT_RIGHT) for img in img_group] if self.is_flow: for i in range(0, len(ret), 2): # invert flow pixel values when flipping ret[i] = ImageOps.invert(ret[i]) return ret else: return img_group class Stack(object): def __init__(self, roll=False): self.roll = roll def __call__(self, img_group): for i in range(len(img_group)): if img_group[i].ndim==3: img_group[i] = Image.fromarray(cv2.cvtColor(img_group[i], cv2.COLOR_BGR2RGB)) elif img_group[i].ndim==2: img_group[i] = Image.fromarray(img_group[i]) mode = img_group[0].mode if mode == '1': img_group = [img.convert('L') for img in img_group] mode = 'L' if mode == 'L': return np.stack([np.expand_dims(x, 2) for x in img_group], axis=2) elif mode == 'RGB': if self.roll: return np.stack([np.array(x)[:, :, ::-1] for x in img_group], axis=2) else: return np.stack(img_group, axis=2) else: raise NotImplementedError(f"Image mode {mode}") class ToTorchFormatTensor(object): """ Converts a PIL.Image (RGB) or numpy.ndarray (H x W x C) in the range [0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0] """ def __init__(self, div=True): self.div = div def __call__(self, pic): if isinstance(pic, np.ndarray): # numpy img: [L, C, H, W] img = torch.from_numpy(pic).permute(2, 3, 0, 1).contiguous() else: # handle PIL Image img = torch.ByteTensor( torch.ByteStorage.from_buffer(pic.tobytes())) img = img.view(pic.size[1], pic.size[0], len(pic.mode)) # put it from HWC to CHW format # yikes, this transpose takes 80% of the loading time/CPU img = img.transpose(0, 1).transpose(0, 2).contiguous() img = img.float().div(255) if self.div else img.float() return img # ########################################## # ########################################## def create_random_shape_with_random_motion(video_length, imageHeight=240, imageWidth=432): # get a random shape height = random.randint(imageHeight//3, imageHeight-1) width = random.randint(imageWidth//3, imageWidth-1) edge_num = random.randint(6, 8) ratio = random.randint(6, 8)/10 region = get_random_shape( edge_num=edge_num, ratio=ratio, height=height, width=width) region_width, region_height = region.size # get random position x, y = random.randint( 0, imageHeight-region_height), random.randint(0, imageWidth-region_width) velocity = get_random_velocity(max_speed=3) m = Image.fromarray(np.zeros((imageHeight, imageWidth)).astype(np.uint8)) m.paste(region, (y, x, y+region.size[0], x+region.size[1])) masks = [m.convert('L')] # return fixed masks if random.uniform(0, 1) > 0.5: return masks*video_length # return moving masks for _ in range(video_length-1): x, y, velocity = random_move_control_points( x, y, imageHeight, imageWidth, velocity, region.size, maxLineAcceleration=(3, 0.5), maxInitSpeed=3) m = Image.fromarray( np.zeros((imageHeight, imageWidth)).astype(np.uint8)) m.paste(region, (y, x, y+region.size[0], x+region.size[1])) masks.append(m.convert('L')) return masks def get_random_shape(edge_num=9, ratio=0.7, width=432, height=240): ''' There is the initial point and 3 points per cubic bezier curve. Thus, the curve will only pass though n points, which will be the sharp edges. The other 2 modify the shape of the bezier curve. edge_num, Number of possibly sharp edges points_num, number of points in the Path ratio, (0, 1) magnitude of the perturbation from the unit circle, ''' points_num = edge_num*3 + 1 angles = np.linspace(0, 2*np.pi, points_num) codes = np.full(points_num, Path.CURVE4) codes[0] = Path.MOVETO # Using this instad of Path.CLOSEPOLY avoids an innecessary straight line verts = np.stack((np.cos(angles), np.sin(angles))).T * \ (2*ratio*np.random.random(points_num)+1-ratio)[:, None] verts[-1, :] = verts[0, :] path = Path(verts, codes) # draw paths into images fig = plt.figure() ax = fig.add_subplot(111) patch = patches.PathPatch(path, facecolor='black', lw=2) ax.add_patch(patch) ax.set_xlim(np.min(verts)*1.1, np.max(verts)*1.1) ax.set_ylim(np.min(verts)*1.1, np.max(verts)*1.1) ax.axis('off') # removes the axis to leave only the shape fig.canvas.draw() # convert plt images into numpy images data = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8) data = data.reshape((fig.canvas.get_width_height()[::-1] + (3,))) plt.close(fig) # postprocess data = cv2.resize(data, (width, height))[:, :, 0] data = (1 - np.array(data > 0).astype(np.uint8))*255 corrdinates = np.where(data > 0) xmin, xmax, ymin, ymax = np.min(corrdinates[0]), np.max( corrdinates[0]), np.min(corrdinates[1]), np.max(corrdinates[1]) region = Image.fromarray(data).crop((ymin, xmin, ymax, xmax)) return region def random_accelerate(velocity, maxAcceleration, dist='uniform'): speed, angle = velocity d_speed, d_angle = maxAcceleration if dist == 'uniform': speed += np.random.uniform(-d_speed, d_speed) angle += np.random.uniform(-d_angle, d_angle) elif dist == 'guassian': speed += np.random.normal(0, d_speed / 2) angle += np.random.normal(0, d_angle / 2) else: raise NotImplementedError( f'Distribution type {dist} is not supported.') return (speed, angle) def get_random_velocity(max_speed=3, dist='uniform'): if dist == 'uniform': speed = np.random.uniform(max_speed) elif dist == 'guassian': speed = np.abs(np.random.normal(0, max_speed / 2)) else: raise NotImplementedError( f'Distribution type {dist} is not supported.') angle = np.random.uniform(0, 2 * np.pi) return (speed, angle) def random_move_control_points(X, Y, imageHeight, imageWidth, lineVelocity, region_size, maxLineAcceleration=(3, 0.5), maxInitSpeed=3): region_width, region_height = region_size speed, angle = lineVelocity X += int(speed * np.cos(angle)) Y += int(speed * np.sin(angle)) lineVelocity = random_accelerate( lineVelocity, maxLineAcceleration, dist='guassian') if ((X > imageHeight - region_height) or (X < 0) or (Y > imageWidth - region_width) or (Y < 0)): lineVelocity = get_random_velocity(maxInitSpeed, dist='guassian') new_X = np.clip(X, 0, imageHeight - region_height) new_Y = np.clip(Y, 0, imageWidth - region_width) return new_X, new_Y, lineVelocity # ############################################## # ############################################## if __name__ == '__main__': trials = 10 for _ in range(trials): video_length = 10 # The returned masks are either stationary (50%) or moving (50%) masks = create_random_shape_with_random_motion( video_length, imageHeight=240, imageWidth=432) print(np.array(masks[0]).shape) for m in masks: cv2.imshow('mask', np.array(m)) cv2.waitKey(500) ================================================ FILE: demo.py ================================================ # -*- coding: utf-8 -*- ''' Copyright: Copyright(c) 2018, seeprettyface.com, BUPT_GWY contributes the model. Thanks to STTN provider: https://github.com/researchmm/STTN Author: BUPT_GWY Contact: a312863063@126.com ''' import cv2 import numpy as np import importlib import argparse import sys import torch import os from torchvision import transforms # My libs from core.utils import Stack, ToTorchFormatTensor parser = argparse.ArgumentParser(description="STTN") parser.add_argument("-t", "--task", type=str, help='CHOOSE THE TASK：delogo or detext', default='detext') parser.add_argument("-v", "--video", type=str, default='input/detext_examples/chinese1.mp4') parser.add_argument("-m", "--mask", type=str, default='input/detext_examples/mask/chinese1_mask.png') parser.add_argument("-r", "--result", type=str, default='result/') parser.add_argument("-d", "--dual", type=bool, default=False, help='Whether to display the original video in the final video') parser.add_argument("-w", "--weight", type=str, default='pretrained_weight/detext_trial.pth') parser.add_argument("--model", type=str, default='auto-sttn') parser.add_argument("-g", "--gap", type=int, default=200, help='set it higher and get result better') parser.add_argument("-l", "--ref_length", type=int, default=5) parser.add_argument("-n", "--neighbor_stride", type=int, default=5) args = parser.parse_args() _to_tensors = transforms.Compose([ Stack(), ToTorchFormatTensor()]) def read_frame_info_from_video(vname): reader = cv2.VideoCapture(vname) if not reader.isOpened(): print("fail to open video in {}".format(args.input)) sys.exit(1) frame_info = {} frame_info['W_ori'] = int(reader.get(cv2.CAP_PROP_FRAME_WIDTH) + 0.5) frame_info['H_ori'] = int(reader.get(cv2.CAP_PROP_FRAME_HEIGHT) + 0.5) frame_info['fps'] = reader.get(cv2.CAP_PROP_FPS) frame_info['len'] = int(reader.get(cv2.CAP_PROP_FRAME_COUNT) + 0.5) return reader, frame_info def read_mask(path): img = cv2.imread(path, 0) ret, img = cv2.threshold(img, 127, 1, cv2.THRESH_BINARY) img = img[:, :, None] return img # sample reference frames from the whole video def get_ref_index(neighbor_ids, length): ref_index = [] for i in range(0, length, args.ref_length): if not i in neighbor_ids: ref_index.append(i) return ref_index def pre_process(task): print('Task: ', task) device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") net = importlib.import_module('model.' + args.model) model = net.InpaintGenerator().to(device) data = torch.load(args.weight, map_location=device) model.load_state_dict(data['netG']) model.eval() print('Loading weight from: {}'.format(args.weight)) # prepare dataset, encode all frames into deep space reader, frame_info = read_frame_info_from_video(args.video) if not os.path.exists(args.result): os.makedirs(args.result) video_base_name = os.path.join(args.result, os.path.basename(args.video).rsplit('.', 1)[0]) video_name = f"{video_base_name}_{task}.mp4" video_H = frame_info['H_ori'] if not args.dual else frame_info['H_ori'] * 2 writer = cv2.VideoWriter(video_name, cv2.VideoWriter_fourcc(*"mp4v"), frame_info['fps'], (frame_info['W_ori'], video_H)) print('Loading video from: {}'.format(args.video)) print('Loading mask from: {}'.format(args.mask)) print('--------------------------------------') clip_gap = args.gap # processing how many frames during one period rec_time = frame_info['len'] // clip_gap if frame_info['len'] % clip_gap == 0 else frame_info['len'] // clip_gap + 1 mask = read_mask(args.mask) return clip_gap, device, frame_info, mask, model, reader, rec_time, video_name, writer def process(frames, model, device, w, h): video_length = len(frames) feats = _to_tensors(frames).unsqueeze(0) * 2 - 1 feats = feats.to(device) comp_frames = [None] * video_length with torch.no_grad(): feats = model.encoder(feats.view(video_length, 3, h, w)) _, c, feat_h, feat_w = feats.size() feats = feats.view(1, video_length, c, feat_h, feat_w) # completing holes by spatial-temporal transformers for f in range(0, video_length, args.neighbor_stride): neighbor_ids = [i for i in range(max(0, f - args.neighbor_stride), min(video_length, f + args.neighbor_stride + 1))] ref_ids = get_ref_index(neighbor_ids, video_length) with torch.no_grad(): pred_feat = model.infer( feats[0, neighbor_ids + ref_ids, :, :, :]) pred_img = torch.tanh(model.decoder( pred_feat[:len(neighbor_ids), :, :, :])).detach() pred_img = (pred_img + 1) / 2 pred_img = pred_img.cpu().permute(0, 2, 3, 1).numpy() * 255 for i in range(len(neighbor_ids)): idx = neighbor_ids[i] img = np.array(pred_img[i]).astype( np.uint8) if comp_frames[idx] is None: comp_frames[idx] = img else: comp_frames[idx] = comp_frames[idx].astype( np.float32) * 0.5 + img.astype(np.float32) * 0.5 return comp_frames def get_inpaint_mode_for_detext(H, h, mask): # get inpaint segment mode = [] to_H = from_H = H # the subtitles are usually underneath while from_H != 0: if to_H - h < 0: from_H = 0 to_H = h else: from_H = to_H - h if not np.all(mask[from_H:to_H, :] == 0) and np.sum(mask[from_H:to_H, :]) > 10: if to_H != H: move = 0 while to_H + move < H and not np.all(mask[to_H+move, :] == 0): move += 1 if to_H + move < H and move < h: to_H += move from_H += move mode.append((from_H, to_H)) to_H -= h return mode def main(): # detext # set up models w, h = 640, 120 clip_gap, device, frame_info, mask, model, reader, rec_time, video_name, writer = pre_process(args.task) split_h = int(frame_info['W_ori'] * 3 / 16) mode = get_inpaint_mode_for_detext(frame_info['H_ori'], split_h, mask) for i in range(rec_time): start_f = i * clip_gap end_f = min((i + 1) * clip_gap, frame_info['len']) print('Processing:', start_f+1, '-', end_f, ' / Total:', frame_info['len']) frames_hr = [] frames = {} comps = {} for k in range(len(mode)): frames[k] = [] for j in range(start_f, end_f): success, image = reader.read() frames_hr.append(image) for k in range(len(mode)): image_crop = image[mode[k][0]:mode[k][1], :, :] image_resize = cv2.resize(image_crop, (w, h)) frames[k].append(image_resize) for k in range(len(mode)): comps[k] = process(frames[k], model, device, w, h) if mode is not []: for j in range(end_f - start_f): frame_ori = frames_hr[j].copy() frame = frames_hr[j] for k in range(len(mode)): comp = cv2.resize(comps[k][j], (frame_info['W_ori'], split_h)) comp = cv2.cvtColor(np.array(comp).astype(np.uint8), cv2.COLOR_BGR2RGB) mask_area = mask[mode[k][0]:mode[k][1], :] frame[mode[k][0]:mode[k][1], :, :] = mask_area * comp + (1 - mask_area) * frame[mode[k][0]:mode[k][1], :, :] if args.dual: frame = np.vstack([frame_ori, frame]) writer.write(frame) writer.release() print('--------------------------------------') print('Finish in {}'.format(video_name)) if __name__ == '__main__': main() ================================================ FILE: model/auto-sttn.py ================================================ ''' Spatial-Temporal Transformer Networks ''' import numpy as np import math import torch import torch.nn as nn import torch.nn.functional as F import torchvision.models as models from core.spectral_norm import spectral_norm as _spectral_norm class BaseNetwork(nn.Module): def __init__(self): super(BaseNetwork, self).__init__() def print_network(self): if isinstance(self, list): self = self[0] num_params = 0 for param in self.parameters(): num_params += param.numel() print('Network [%s] was created. Total number of parameters: %.1f million. ' 'To see the architecture, do print(network).' % (type(self).__name__, num_params / 1000000)) def init_weights(self, init_type='normal', gain=0.02): ''' initialize network's weights init_type: normal | xavier | kaiming | orthogonal https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/9451e70673400885567d08a9e97ade2524c700d0/models/networks.py#L39 ''' def init_func(m): classname = m.__class__.__name__ if classname.find('InstanceNorm2d') != -1: if hasattr(m, 'weight') and m.weight is not None: nn.init.constant_(m.weight.data, 1.0) if hasattr(m, 'bias') and m.bias is not None: nn.init.constant_(m.bias.data, 0.0) elif hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1): if init_type == 'normal': nn.init.normal_(m.weight.data, 0.0, gain) elif init_type == 'xavier': nn.init.xavier_normal_(m.weight.data, gain=gain) elif init_type == 'xavier_uniform': nn.init.xavier_uniform_(m.weight.data, gain=1.0) elif init_type == 'kaiming': nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_in') elif init_type == 'orthogonal': nn.init.orthogonal_(m.weight.data, gain=gain) elif init_type == 'none': # uses pytorch's default init method m.reset_parameters() else: raise NotImplementedError( 'initialization method [%s] is not implemented' % init_type) if hasattr(m, 'bias') and m.bias is not None: nn.init.constant_(m.bias.data, 0.0) self.apply(init_func) # propagate to children for m in self.children(): if hasattr(m, 'init_weights'): m.init_weights(init_type, gain) class InpaintGenerator(BaseNetwork): def __init__(self, init_weights=True): super(InpaintGenerator, self).__init__() channel = 256 stack_num = 8 patchsize = [(80, 15), (32, 6), (10, 5), (5, 3)] blocks = [] for _ in range(stack_num): blocks.append(TransformerBlock(patchsize, hidden=channel)) self.transformer = nn.Sequential(*blocks) self.encoder = nn.Sequential( nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(128, channel, kernel_size=3, stride=1, padding=1), nn.LeakyReLU(0.2, inplace=True), ) # decoder: decode frames from features self.decoder = nn.Sequential( deconv(channel, 128, kernel_size=3, padding=1), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1), nn.LeakyReLU(0.2, inplace=True), deconv(64, 64, kernel_size=3, padding=1), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(64, 3, kernel_size=3, stride=1, padding=1) ) if init_weights: self.init_weights() def forward(self, masked_frames): # extracting features b, t, c, h, w = masked_frames.size() enc_feat = self.encoder(masked_frames.view(b*t, c, h, w)) _, c, h, w = enc_feat.size() enc_feat = self.transformer( {'x': enc_feat, 'b': b, 'c': c})['x'] output = self.decoder(enc_feat) output = torch.tanh(output) return output def infer(self, feat): t, c, _, _ = feat.size() enc_feat = self.transformer( {'x': feat, 'b': 1, 'c': c})['x'] return enc_feat class deconv(nn.Module): def __init__(self, input_channel, output_channel, kernel_size=3, padding=0): super().__init__() self.conv = nn.Conv2d(input_channel, output_channel, kernel_size=kernel_size, stride=1, padding=padding) def forward(self, x): x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=True) return self.conv(x) # ############################################################################# # ############################# Transformer ################################## # ############################################################################# class Attention(nn.Module): """ Compute 'Scaled Dot Product Attention """ def forward(self, query, key, value): scores = torch.matmul(query, key.transpose(-2, -1) ) / math.sqrt(query.size(-1)) p_attn = F.softmax(scores, dim=-1) p_val = torch.matmul(p_attn, value) return p_val, p_attn class MultiHeadedAttention(nn.Module): """ Take in model size and number of heads. """ def __init__(self, patchsize, d_model): super().__init__() self.patchsize = patchsize self.query_embedding = nn.Conv2d( d_model, d_model, kernel_size=1, padding=0) self.value_embedding = nn.Conv2d( d_model, d_model, kernel_size=1, padding=0) self.key_embedding = nn.Conv2d( d_model, d_model, kernel_size=1, padding=0) self.output_linear = nn.Sequential( nn.Conv2d(d_model, d_model, kernel_size=3, padding=1), nn.LeakyReLU(0.2, inplace=True)) self.attention = Attention() def forward(self, x, b, c): bt, _, h, w = x.size() t = bt // b d_k = c // len(self.patchsize) output = [] _query = self.query_embedding(x) _key = self.key_embedding(x) _value = self.value_embedding(x) for (width, height), query, key, value in zip(self.patchsize, torch.chunk(_query, len(self.patchsize), dim=1), torch.chunk( _key, len(self.patchsize), dim=1), torch.chunk(_value, len(self.patchsize), dim=1)): out_w, out_h = w // width, h // height # 1) embedding and reshape query = query.view(b, t, d_k, out_h, height, out_w, width) query = query.permute(0, 1, 3, 5, 2, 4, 6).contiguous().view( b, t*out_h*out_w, d_k*height*width) key = key.view(b, t, d_k, out_h, height, out_w, width) key = key.permute(0, 1, 3, 5, 2, 4, 6).contiguous().view( b, t*out_h*out_w, d_k*height*width) value = value.view(b, t, d_k, out_h, height, out_w, width) value = value.permute(0, 1, 3, 5, 2, 4, 6).contiguous().view( b, t*out_h*out_w, d_k*height*width) ''' # 2) Apply attention on all the projected vectors in batch. tmp1 = [] for q,k,v in zip(torch.chunk(query, b, dim=0), torch.chunk(key, b, dim=0), torch.chunk(value, b, dim=0)): y, _ = self.attention(q.unsqueeze(0), k.unsqueeze(0), v.unsqueeze(0)) tmp1.append(y) y = torch.cat(tmp1,1) ''' y, _ = self.attention(query, key, value) # 3) "Concat" using a view and apply a final linear. y = y.view(b, t, out_h, out_w, d_k, height, width) y = y.permute(0, 1, 4, 2, 5, 3, 6).contiguous().view(bt, d_k, h, w) output.append(y) output = torch.cat(output, 1) x = self.output_linear(output) return x # Standard 2 layerd FFN of transformer class FeedForward(nn.Module): def __init__(self, d_model): super(FeedForward, self).__init__() # We set d_ff as a default to 2048 self.conv = nn.Sequential( nn.Conv2d(d_model, d_model, kernel_size=3, padding=2, dilation=2), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(d_model, d_model, kernel_size=3, padding=1), nn.LeakyReLU(0.2, inplace=True)) def forward(self, x): x = self.conv(x) return x class TransformerBlock(nn.Module): """ Transformer = MultiHead_Attention + Feed_Forward with sublayer connection """ def __init__(self, patchsize, hidden=128): super().__init__() self.attention = MultiHeadedAttention(patchsize, d_model=hidden) self.feed_forward = FeedForward(hidden) def forward(self, x): x, b, c = x['x'], x['b'], x['c'] x = x + self.attention(x, b, c) x = x + self.feed_forward(x) return {'x': x, 'b': b, 'c': c} # ###################################################################### # ###################################################################### class Discriminator(BaseNetwork): def __init__(self, in_channels=3, use_sigmoid=False, use_spectral_norm=True, init_weights=True): super(Discriminator, self).__init__() self.use_sigmoid = use_sigmoid nf = 64 self.conv = nn.Sequential( spectral_norm(nn.Conv3d(in_channels=in_channels, out_channels=nf*1, kernel_size=(3, 5, 5), stride=(1, 2, 2), padding=1, bias=not use_spectral_norm), use_spectral_norm), # nn.InstanceNorm2d(64, track_running_stats=False), nn.LeakyReLU(0.2, inplace=True), spectral_norm(nn.Conv3d(nf*1, nf*2, kernel_size=(3, 5, 5), stride=(1, 2, 2), padding=(1, 2, 2), bias=not use_spectral_norm), use_spectral_norm), # nn.InstanceNorm2d(128, track_running_stats=False), nn.LeakyReLU(0.2, inplace=True), spectral_norm(nn.Conv3d(nf * 2, nf * 4, kernel_size=(3, 5, 5), stride=(1, 2, 2), padding=(1, 2, 2), bias=not use_spectral_norm), use_spectral_norm), # nn.InstanceNorm2d(256, track_running_stats=False), nn.LeakyReLU(0.2, inplace=True), spectral_norm(nn.Conv3d(nf * 4, nf * 4, kernel_size=(3, 5, 5), stride=(1, 2, 2), padding=(1, 2, 2), bias=not use_spectral_norm), use_spectral_norm), # nn.InstanceNorm2d(256, track_running_stats=False), nn.LeakyReLU(0.2, inplace=True), spectral_norm(nn.Conv3d(nf * 4, nf * 4, kernel_size=(3, 5, 5), stride=(1, 2, 2), padding=(1, 2, 2), bias=not use_spectral_norm), use_spectral_norm), # nn.InstanceNorm2d(256, track_running_stats=False), nn.LeakyReLU(0.2, inplace=True), nn.Conv3d(nf * 4, nf * 4, kernel_size=(3, 5, 5), stride=(1, 2, 2), padding=(1, 2, 2)) ) if init_weights: self.init_weights() def forward(self, xs): # T, C, H, W = xs.shape xs_t = torch.transpose(xs, 0, 1) xs_t = xs_t.unsqueeze(0) # B, C, T, H, W feat = self.conv(xs_t) if self.use_sigmoid: feat = torch.sigmoid(feat) out = torch.transpose(feat, 1, 2) # B, T, C, H, W return out def spectral_norm(module, mode=True): if mode: return _spectral_norm(module) return module ================================================ FILE: model/vis.py ================================================ ''' Spatial-Temporal Transformer Networks ''' import numpy as np import math import torch import torch.nn as nn import torch.nn.functional as F import torchvision.models as models from core.spectral_norm import spectral_norm as _spectral_norm class BaseNetwork(nn.Module): def __init__(self): super(BaseNetwork, self).__init__() def print_network(self): if isinstance(self, list): self = self[0] num_params = 0 for param in self.parameters(): num_params += param.numel() print('Network [%s] was created. Total number of parameters: %.1f million. ' 'To see the architecture, do print(network).' % (type(self).__name__, num_params / 1000000)) def init_weights(self, init_type='normal', gain=0.02): ''' initialize network's weights init_type: normal | xavier | kaiming | orthogonal https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/9451e70673400885567d08a9e97ade2524c700d0/models/networks.py#L39 ''' def init_func(m): classname = m.__class__.__name__ if classname.find('InstanceNorm2d') != -1: if hasattr(m, 'weight') and m.weight is not None: nn.init.constant_(m.weight.data, 1.0) if hasattr(m, 'bias') and m.bias is not None: nn.init.constant_(m.bias.data, 0.0) elif hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1): if init_type == 'normal': nn.init.normal_(m.weight.data, 0.0, gain) elif init_type == 'xavier': nn.init.xavier_normal_(m.weight.data, gain=gain) elif init_type == 'xavier_uniform': nn.init.xavier_uniform_(m.weight.data, gain=1.0) elif init_type == 'kaiming': nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_in') elif init_type == 'orthogonal': nn.init.orthogonal_(m.weight.data, gain=gain) elif init_type == 'none': # uses pytorch's default init method m.reset_parameters() else: raise NotImplementedError( 'initialization method [%s] is not implemented' % init_type) if hasattr(m, 'bias') and m.bias is not None: nn.init.constant_(m.bias.data, 0.0) self.apply(init_func) # propagate to children for m in self.children(): if hasattr(m, 'init_weights'): m.init_weights(init_type, gain) class InpaintGenerator(BaseNetwork): def __init__(self, init_weights=True): # 1046 super(InpaintGenerator, self).__init__() channel = 256 stack_num = 8 patchsize = [(108, 60), (36, 20), (18, 10), (9, 5)] blocks = [] for _ in range(stack_num): blocks.append(TransformerBlock(patchsize, hidden=channel)) self.transformer = nn.Sequential(*blocks) self.encoder = nn.Sequential( nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(128, channel, kernel_size=3, stride=1, padding=1), nn.LeakyReLU(0.2, inplace=True), ) # decoder: decode image from features self.decoder = nn.Sequential( deconv(channel, 128, kernel_size=3, padding=1), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1), nn.LeakyReLU(0.2, inplace=True), deconv(64, 64, kernel_size=3, padding=1), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(64, 3, kernel_size=3, stride=1, padding=1) ) if init_weights: self.init_weights() def forward(self, masked_frames, masks): # extracting features b, t, c, h, w = masked_frames.size() masks = masks.view(b*t, 1, h, w) enc_feat = self.encoder(masked_frames.view(b*t, c, h, w)) _, c, h, w = enc_feat.size() masks = F.interpolate(masks, scale_factor=1.0/4) enc_feat = self.transformer( {'x': enc_feat, 'm': masks, 'b': b, 'c': c})['x'] output = self.decoder(enc_feat) output = torch.tanh(output) return output def infer(self, feat, masks): t, c, h, w = masks.size() masks = masks.view(t, c, h, w) masks = F.interpolate(masks, scale_factor=1.0/4) t, c, _, _ = feat.size() output = self.transformer({'x': feat, 'm': masks, 'b': 1, 'c': c}) enc_feat = output['x'] attn = output['attn'] mm = output['smm'] return enc_feat, attn, mm class deconv(nn.Module): def __init__(self, input_channel, output_channel, kernel_size=3, padding=0): super().__init__() self.conv = nn.Conv2d(input_channel, output_channel, kernel_size=kernel_size, stride=1, padding=padding) def forward(self, x): x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=True) return self.conv(x) # ################################################## # ################## Transformer #################### class Attention(nn.Module): """ Compute 'Scaled Dot Product Attention """ def forward(self, query, key, value, m): scores = torch.matmul(query, key.transpose(-2, -1) ) / math.sqrt(query.size(-1)) scores.masked_fill(m, -1e9) p_attn = F.softmax(scores, dim=-1) p_val = torch.matmul(p_attn, value) return p_val, p_attn class MultiHeadedAttention(nn.Module): """ Take in model size and number of heads. """ def __init__(self, patchsize, d_model): super().__init__() self.patchsize = patchsize self.query_embedding = nn.Conv2d( d_model, d_model, kernel_size=1, padding=0) self.value_embedding = nn.Conv2d( d_model, d_model, kernel_size=1, padding=0) self.key_embedding = nn.Conv2d( d_model, d_model, kernel_size=1, padding=0) self.output_linear = nn.Sequential( nn.Conv2d(d_model, d_model, kernel_size=3, padding=1), nn.LeakyReLU(0.2, inplace=True)) self.attention = Attention() def forward(self, x, m, b, c): bt, _, h, w = x.size() t = bt // b d_k = c // len(self.patchsize) output = [] _query = self.query_embedding(x) _key = self.key_embedding(x) _value = self.value_embedding(x) for (width, height), query, key, value in zip(self.patchsize, torch.chunk(_query, len(self.patchsize), dim=1), torch.chunk( _key, len(self.patchsize), dim=1), torch.chunk(_value, len(self.patchsize), dim=1)): out_w, out_h = w // width, h // height mm = m.view(b, t, 1, out_h, height, out_w, width) mm = mm.permute(0, 1, 3, 5, 2, 4, 6).contiguous().view( b, t*out_h*out_w, height*width) mm = (mm.mean(-1) > 0.5).unsqueeze(1).repeat(1, t*out_h*out_w, 1) # 1) embedding and reshape query = query.view(b, t, d_k, out_h, height, out_w, width) query = query.permute(0, 1, 3, 5, 2, 4, 6).contiguous().view( b, t*out_h*out_w, d_k*height*width) key = key.view(b, t, d_k, out_h, height, out_w, width) key = key.permute(0, 1, 3, 5, 2, 4, 6).contiguous().view( b, t*out_h*out_w, d_k*height*width) value = value.view(b, t, d_k, out_h, height, out_w, width) value = value.permute(0, 1, 3, 5, 2, 4, 6).contiguous().view( b, t*out_h*out_w, d_k*height*width) ''' # 2) Apply attention on all the projected vectors in batch. tmp1 = [] for q,k,v in zip(torch.chunk(query, b, dim=0), torch.chunk(key, b, dim=0), torch.chunk(value, b, dim=0)): y, _ = self.attention(q.unsqueeze(0), k.unsqueeze(0), v.unsqueeze(0)) tmp1.append(y) y = torch.cat(tmp1,1) ''' y, attn = self.attention(query, key, value, mm) # return attention value for visualization # here we return the attention value of patchsize=18 if width == 18: select_attn = attn.view(t, out_h*out_w, t, out_h, out_w)[0] # mm, [b, thw, thw] select_mm = mm[0].view(t*out_h*out_w, t, out_h, out_w)[0] # 3) "Concat" using a view and apply a final linear. y = y.view(b, t, out_h, out_w, d_k, height, width) y = y.permute(0, 1, 4, 2, 5, 3, 6).contiguous().view(bt, d_k, h, w) output.append(y) output = torch.cat(output, 1) x = self.output_linear(output) return x, select_attn, select_mm # Standard 2 layerd FFN of transformer class FeedForward(nn.Module): def __init__(self, d_model): super(FeedForward, self).__init__() # We set d_ff as a default to 2048 self.conv = nn.Sequential( nn.Conv2d(d_model, d_model, kernel_size=3, padding=2, dilation=2), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(d_model, d_model, kernel_size=3, padding=1), nn.LeakyReLU(0.2, inplace=True)) def forward(self, x): x = self.conv(x) return x class TransformerBlock(nn.Module): """ Transformer = MultiHead_Attention + Feed_Forward with sublayer connection """ def __init__(self, patchsize, hidden=128): super().__init__() self.attention = MultiHeadedAttention(patchsize, d_model=hidden) self.feed_forward = FeedForward(hidden) def forward(self, x): x, m, b, c = x['x'], x['m'], x['b'], x['c'] val, attn, mm = self.attention(x, m, b, c) x = x + val x = x + self.feed_forward(x) return {'x': x, 'm': m, 'b': b, 'c': c, 'attn': attn, 'smm': mm} # ###################################################################### # ###################################################################### class Discriminator(BaseNetwork): def __init__(self, in_channels=3, use_sigmoid=False, use_spectral_norm=True, init_weights=True): super(Discriminator, self).__init__() self.use_sigmoid = use_sigmoid nf = 64 self.conv = nn.Sequential( spectral_norm(nn.Conv3d(in_channels=in_channels, out_channels=nf*1, kernel_size=(3, 5, 5), stride=(1, 2, 2), padding=1, bias=not use_spectral_norm), use_spectral_norm), # nn.InstanceNorm2d(64, track_running_stats=False), nn.LeakyReLU(0.2, inplace=True), spectral_norm(nn.Conv3d(nf*1, nf*2, kernel_size=(3, 5, 5), stride=(1, 2, 2), padding=(1, 2, 2), bias=not use_spectral_norm), use_spectral_norm), # nn.InstanceNorm2d(128, track_running_stats=False), nn.LeakyReLU(0.2, inplace=True), spectral_norm(nn.Conv3d(nf * 2, nf * 4, kernel_size=(3, 5, 5), stride=(1, 2, 2), padding=(1, 2, 2), bias=not use_spectral_norm), use_spectral_norm), # nn.InstanceNorm2d(256, track_running_stats=False), nn.LeakyReLU(0.2, inplace=True), spectral_norm(nn.Conv3d(nf * 4, nf * 4, kernel_size=(3, 5, 5), stride=(1, 2, 2), padding=(1, 2, 2), bias=not use_spectral_norm), use_spectral_norm), # nn.InstanceNorm2d(256, track_running_stats=False), nn.LeakyReLU(0.2, inplace=True), spectral_norm(nn.Conv3d(nf * 4, nf * 4, kernel_size=(3, 5, 5), stride=(1, 2, 2), padding=(1, 2, 2), bias=not use_spectral_norm), use_spectral_norm), # nn.InstanceNorm2d(256, track_running_stats=False), nn.LeakyReLU(0.2, inplace=True), nn.Conv3d(nf * 4, nf * 4, kernel_size=(3, 5, 5), stride=(1, 2, 2), padding=(1, 2, 2)) ) if init_weights: self.init_weights() def forward(self, xs): # T, C, H, W = xs.shape xs_t = torch.transpose(xs, 0, 1) xs_t = xs_t.unsqueeze(0) # B, C, T, H, W feat = self.conv(xs_t) if self.use_sigmoid: feat = torch.sigmoid(feat) out = torch.transpose(feat, 1, 2) # B, T, C, H, W return out def spectral_norm(module, mode=True): if mode: return _spectral_norm(module) return module ================================================ FILE: pics/de-watermark/1 ================================================ ================================================ FILE: pretrained_weight/download_weights.txt ================================================ 模型下载地址： 链接：https://pan.baidu.com/s/1JN9-8Glw_ozOrSMgBIyHOw 提取码：px0s