如何在网站页面做标注,企业vi设计公司有哪些,网站搭建设计方案,公司管理体系– 对之前SRCNN算法的改进 输出层采用转置卷积层放大尺寸#xff0c;这样可以直接将低分辨率图片输入模型中#xff0c;解决了输入尺度问题。改变特征维数#xff0c;使用更小的卷积核和使用更多的映射层。卷积核更小#xff0c;加入了更多的激活层。共享其中的映射层…– 对之前SRCNN算法的改进 输出层采用转置卷积层放大尺寸这样可以直接将低分辨率图片输入模型中解决了输入尺度问题。改变特征维数使用更小的卷积核和使用更多的映射层。卷积核更小加入了更多的激活层。共享其中的映射层如果需要训练不同上采样倍率的模型只需要修改最后的反卷积层大小就可以训练出不同尺寸的图片。 模型实现
import math
from torch import nnclass FSRCNN(nn.Module):def __init__(self, scale_factor, num_channels1, d56, s12, m4):super(FSRCNN, self).__init__()self.first_part nn.Sequential(nn.Conv2d(num_channels, d, kernel_size5, padding5//2),nn.PReLU(d))# 添加入多个激活层和小卷积核self.mid_part [nn.Conv2d(d, s, kernel_size1), nn.PReLU(s)]for _ in range(m):self.mid_part.extend([nn.Conv2d(s, s, kernel_size3, padding3//2), nn.PReLU(s)])self.mid_part.extend([nn.Conv2d(s, d, kernel_size1), nn.PReLU(d)])self.mid_part nn.Sequential(*self.mid_part)# 最后输出self.last_part nn.ConvTranspose2d(d, num_channels, kernel_size9, stridescale_factor, padding9//2,output_paddingscale_factor-1)self._initialize_weights()def _initialize_weights(self):# 初始化for m in self.first_part:if isinstance(m, nn.Conv2d):nn.init.normal_(m.weight.data, mean0.0, stdmath.sqrt(2/(m.out_channels*m.weight.data[0][0].numel())))nn.init.zeros_(m.bias.data)for m in self.mid_part:if isinstance(m, nn.Conv2d):nn.init.normal_(m.weight.data, mean0.0, stdmath.sqrt(2/(m.out_channels*m.weight.data[0][0].numel())))nn.init.zeros_(m.bias.data)nn.init.normal_(self.last_part.weight.data, mean0.0, std0.001)nn.init.zeros_(self.last_part.bias.data)def forward(self, x):x self.first_part(x)x self.mid_part(x)x self.last_part(x)return x
以上代码中如起初所说将SRCNN中给的输出修改为转置卷积并且在中间添加了多个11卷积核和多个线性激活层。且应用了权重初始化解决协变量偏移问题。 备注11卷积核虽然在通道的像素层面上针对一个像素进行卷积貌似没有什么作用但是卷积神经网络的特性我们在利用多个卷积核对特征图进行扫描时单个卷积核扫描后的为sum©,那么就是尽管在像素层面上无用但是在通道层面上进行了融合并且进一步加深了层数使网络层数增加网络能力增强。
上代码train.py
训练脚本
import argparse
import os
import copyimport torch
from torch import nn
import torch.optim as optim
import torch.backends.cudnn as cudnn
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdmfrom models import FSRCNN
from datasets import TrainDataset, EvalDataset
from utils import AverageMeter, calc_psnrif __name__ __main__:parser argparse.ArgumentParser()# 训练文件parser.add_argument(--train-file, typestr,helpthe dir of train data,default./Train/91-image_x4.h5)# 测试集文件parser.add_argument(--eval-file, typestr,helpthr dir of test data ,default./Test/Set5_x4.h5)# 输出的文件夹parser.add_argument(--outputs-dir,helpthe output dir, typestr,default./outputs)parser.add_argument(--weights-file, typestr)parser.add_argument(--scale, typeint, default2)parser.add_argument(--lr, typefloat, default1e-3)parser.add_argument(--batch-size, typeint, default16)parser.add_argument(--num-epochs, typeint, default20)parser.add_argument(--num-workers, typeint, default8)parser.add_argument(--seed, typeint, default123)args parser.parse_args()args.outputs_dir os.path.join(args.outputs_dir, x{}.format(args.scale))if not os.path.exists(args.outputs_dir):os.makedirs(args.outputs_dir)cudnn.benchmark Truedevice torch.device(cuda:0 if torch.cuda.is_available() else cpu)torch.manual_seed(args.seed)model FSRCNN(scale_factorargs.scale).to(device)criterion nn.MSELoss()optimizer optim.Adam([{params: model.first_part.parameters()},{params: model.mid_part.parameters()},{params: model.last_part.parameters(), lr: args.lr * 0.1}], lrargs.lr)train_dataset TrainDataset(args.train_file)train_dataloader DataLoader(datasettrain_dataset,batch_sizeargs.batch_size,shuffleTrue,num_workersargs.num_workers,pin_memoryTrue)eval_dataset EvalDataset(args.eval_file)eval_dataloader DataLoader(dataseteval_dataset, batch_size1)best_weights copy.deepcopy(model.state_dict())best_epoch 0best_psnr 0.0for epoch in range(args.num_epochs):model.train()epoch_losses AverageMeter()with tqdm(total(len(train_dataset) - len(train_dataset) % args.batch_size), ncols80) as t:t.set_description(epoch: {}/{}.format(epoch, args.num_epochs - 1))for data in train_dataloader:inputs, labels datainputs inputs.to(device)labels labels.to(device)preds model(inputs)loss criterion(preds, labels)epoch_losses.update(loss.item(), len(inputs))optimizer.zero_grad()loss.backward()optimizer.step()t.set_postfix(loss{:.6f}.format(epoch_losses.avg))t.update(len(inputs))torch.save(model.state_dict(), os.path.join(args.outputs_dir, epoch_{}.pth.format(epoch)))model.eval()epoch_psnr AverageMeter()for data in eval_dataloader:inputs, labels datainputs inputs.to(device)labels labels.to(device)with torch.no_grad():preds model(inputs).clamp(0.0, 1.0)epoch_psnr.update(calc_psnr(preds, labels), len(inputs))print(eval psnr: {:.2f}.format(epoch_psnr.avg))if epoch_psnr.avg best_psnr:best_epoch epochbest_psnr epoch_psnr.avgbest_weights copy.deepcopy(model.state_dict())print(best epoch: {}, psnr: {:.2f}.format(best_epoch, best_psnr))torch.save(best_weights, os.path.join(args.outputs_dir, best.pth))test.py 测试脚本
import argparseimport torch
import torch.backends.cudnn as cudnn
import numpy as np
import PIL.Image as pil_imagefrom models import FSRCNN
from utils import convert_ycbcr_to_rgb, preprocess, calc_psnrif __name__ __main__:parser argparse.ArgumentParser()parser.add_argument(--weights-file, typestr, requiredTrue)parser.add_argument(--image-file, typestr, requiredTrue)parser.add_argument(--scale, typeint, default3)args parser.parse_args()cudnn.benchmark Truedevice torch.device(cuda:0 if torch.cuda.is_available() else cpu)model FSRCNN(scale_factorargs.scale).to(device)state_dict model.state_dict()for n, p in torch.load(args.weights_file, map_locationlambda storage, loc: storage).items():if n in state_dict.keys():state_dict[n].copy_(p)else:raise KeyError(n)model.eval()image pil_image.open(args.image_file).convert(RGB)image_width (image.width // args.scale) * args.scaleimage_height (image.height // args.scale) * args.scalehr image.resize((image_width, image_height), resamplepil_image.BICUBIC)lr hr.resize((hr.width // args.scale, hr.height // args.scale), resamplepil_image.BICUBIC)bicubic lr.resize((lr.width * args.scale, lr.height * args.scale), resamplepil_image.BICUBIC)bicubic.save(args.image_file.replace(., _bicubic_x{}..format(args.scale)))lr, _ preprocess(lr, device)hr, _ preprocess(hr, device)_, ycbcr preprocess(bicubic, device)with torch.no_grad():preds model(lr).clamp(0.0, 1.0)psnr calc_psnr(hr, preds)print(PSNR: {:.2f}.format(psnr))preds preds.mul(255.0).cpu().numpy().squeeze(0).squeeze(0)output np.array([preds, ycbcr[..., 1], ycbcr[..., 2]]).transpose([1, 2, 0])output np.clip(convert_ycbcr_to_rgb(output), 0.0, 255.0).astype(np.uint8)output pil_image.fromarray(output)# 保存图片output.save(args.image_file.replace(., _fsrcnn_x{}..format(args.scale)))datasets.py
数据集的读取
import h5py
import numpy as np
from torch.utils.data import Datasetclass TrainDataset(Dataset):def __init__(self, h5_file):super(TrainDataset, self).__init__()self.h5_file h5_filedef __getitem__(self, idx):with h5py.File(self.h5_file, r) as f:return np.expand_dims(f[lr][idx] / 255., 0), np.expand_dims(f[hr][idx] / 255., 0)def __len__(self):with h5py.File(self.h5_file, r) as f:return len(f[lr])class EvalDataset(Dataset):def __init__(self, h5_file):super(EvalDataset, self).__init__()self.h5_file h5_filedef __getitem__(self, idx):with h5py.File(self.h5_file, r) as f:return np.expand_dims(f[lr][str(idx)][:, :] / 255., 0), np.expand_dims(f[hr][str(idx)][:, :] / 255., 0)def __len__(self):with h5py.File(self.h5_file, r) as f:return len(f[lr])
工具文件utils.py
主要用来测试psnr指数图片的格式转换悄悄说一句opencv有直接实现~~~
import torch
import numpy as npdef calc_patch_size(func):def wrapper(args):if args.scale 2:args.patch_size 10elif args.scale 3:args.patch_size 7elif args.scale 4:args.patch_size 6else:raise Exception(Scale Error, args.scale)return func(args)return wrapperdef convert_rgb_to_y(img, dim_orderhwc):if dim_order hwc:return 16. (64.738 * img[..., 0] 129.057 * img[..., 1] 25.064 * img[..., 2]) / 256.else:return 16. (64.738 * img[0] 129.057 * img[1] 25.064 * img[2]) / 256.def convert_rgb_to_ycbcr(img, dim_orderhwc):if dim_order hwc:y 16. (64.738 * img[..., 0] 129.057 * img[..., 1] 25.064 * img[..., 2]) / 256.cb 128. (-37.945 * img[..., 0] - 74.494 * img[..., 1] 112.439 * img[..., 2]) / 256.cr 128. (112.439 * img[..., 0] - 94.154 * img[..., 1] - 18.285 * img[..., 2]) / 256.else:y 16. (64.738 * img[0] 129.057 * img[1] 25.064 * img[2]) / 256.cb 128. (-37.945 * img[0] - 74.494 * img[1] 112.439 * img[2]) / 256.cr 128. (112.439 * img[0] - 94.154 * img[1] - 18.285 * img[2]) / 256.return np.array([y, cb, cr]).transpose([1, 2, 0])def convert_ycbcr_to_rgb(img, dim_orderhwc):if dim_order hwc:r 298.082 * img[..., 0] / 256. 408.583 * img[..., 2] / 256. - 222.921g 298.082 * img[..., 0] / 256. - 100.291 * img[..., 1] / 256. - 208.120 * img[..., 2] / 256. 135.576b 298.082 * img[..., 0] / 256. 516.412 * img[..., 1] / 256. - 276.836else:r 298.082 * img[0] / 256. 408.583 * img[2] / 256. - 222.921g 298.082 * img[0] / 256. - 100.291 * img[1] / 256. - 208.120 * img[2] / 256. 135.576b 298.082 * img[0] / 256. 516.412 * img[1] / 256. - 276.836return np.array([r, g, b]).transpose([1, 2, 0])def preprocess(img, device):img np.array(img).astype(np.float32)ycbcr convert_rgb_to_ycbcr(img)x ycbcr[..., 0]x / 255.x torch.from_numpy(x).to(device)x x.unsqueeze(0).unsqueeze(0)return x, ycbcrdef calc_psnr(img1, img2):return 10. * torch.log10(1. / torch.mean((img1 - img2) ** 2))class AverageMeter(object):def __init__(self):self.reset()def reset(self):self.val 0self.avg 0self.sum 0self.count 0def update(self, val, n1):self.val valself.sum val * nself.count nself.avg self.sum / self.count
先跑他个几十轮~
