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三十九、转置卷积
import torch
from torch import nndef trans_conv(X, K):h, w K.shapeY torch.zeros((X.shape[…声明 本文章为个人学习使用版面观感若有不适请谅解文中知识仅代表个人观点若出现错误欢迎各位批评指正。
三十九、转置卷积
import torch
from torch import nndef trans_conv(X, K):h, w K.shapeY torch.zeros((X.shape[0] h - 1, X.shape[1] w - 1))for i in range(X.shape[0]):for j in range(X.shape[1]):Y[i: i h, j: j w] X[i, j] * Kreturn YX torch.tensor([[0.0, 1.0], [2.0, 3.0]])
K torch.tensor([[0.0, 1.0], [2.0, 3.0]])
print(f基本的二维转置卷积运算 : {trans_conv(X, K)})X, K X.reshape(1, 1, 2, 2), K.reshape(1, 1, 2, 2)
tconv nn.ConvTranspose2d(1, 1, kernel_size2, biasFalse)
tconv.weight.data K
print(f输入输出都是四维张量时 : {tconv(X)})# 在转置卷积中填充被应用于输出常规卷积将填充应用于输入。
# 当将高和宽两侧的填充数指定为 1 时转置卷积的输出中将删除第一和最后的行与列。
tconv nn.ConvTranspose2d(1, 1, kernel_size2, padding1, biasFalse)
tconv.weight.data K
print(fpadding1 时 : {tconv(X)})# 在转置卷积中步幅被指定为中间结果输出而不是输入。
tconv nn.ConvTranspose2d(1, 1, kernel_size2, stride2, biasFalse)
tconv.weight.data K
print(fstride2 时 : {tconv(X)})X torch.rand(size(1, 10, 16, 16))
conv nn.Conv2d(10, 20, kernel_size5, padding2, stride3)
tconv nn.ConvTranspose2d(20, 10, kernel_size5, padding2, stride3)
print(f先代入卷积再代入转置卷积形状不变 : {tconv(conv(X)).shape X.shape})def corr2d(X, K):reduce_sum lambda x, *args, **kwargs: x.sum(*args, **kwargs)h, w K.shapeY torch.zeros((X.shape[0] - h 1, X.shape[1] - w 1))for i in range(Y.shape[0]):for j in range(Y.shape[1]):Y[i, j] reduce_sum((X[i: i h, j: j w] * K))return YX torch.arange(9.0).reshape(3, 3)
K torch.tensor([[1.0, 2.0], [3.0, 4.0]])
Y corr2d(X, K)
print(f二维卷积运算 : {Y})def kernel2matrix(K):k, W torch.zeros(5), torch.zeros((4, 9))k[:2], k[3:5] K[0, :], K[1, :]W[0, :5], W[1, 1:6], W[2, 3:8], W[3, 4:] k, k, k, kreturn WW kernel2matrix(K)
print(f稀疏权重矩阵 : {W})print(f使用矩阵乘法实现卷积 : {Y torch.matmul(W, X.reshape(-1)).reshape(2, 2)})Z trans_conv(Y, K)
print(f使用矩阵乘法实现转置卷积 : {Z torch.matmul(W.T, Y.reshape(-1)).reshape(3, 3)})四十、全卷积网络 FCN
import os
import torch
import time
import torchvision
from PIL import Image
from IPython import display
from torch import nn
import matplotlib.pyplot as plt
from torch.nn import functional as F
from matplotlib_inline import backend_inlinedef accuracy(y_hat, y): # 定义一个函数来为预测正确的数量计数计算预测正确的数量if len(y_hat.shape) 1 and y_hat.shape[1] 1:y_hat y_hat.argmax(axis1)cmp y_hat.type(y.dtype) y # bool 类型若预测结果与实际结果一致则为 Truereturn float(cmp.type(y.dtype).sum())def evaluate_accuracy_gpu(net, data_iter, deviceNone):使用GPU计算模型在数据集上的精度if isinstance(net, nn.Module):net.eval() # 设置为评估模式if not device:device next(iter(net.parameters())).device# 正确预测的数量总预测的数量metric Accumulator(2)with torch.no_grad():for X, y in data_iter:if isinstance(X, list):# BERT微调所需的之后将介绍X [x.to(device) for x in X]else:X X.to(device)y y.to(device)metric.add(accuracy(net(X), y), y.numel())return metric[0] / metric[1]def set_axes(axes, xlabel, ylabel, xlim, ylim, xscale, yscale, legend):axes.set_xlabel(xlabel), axes.set_ylabel(ylabel)axes.set_xscale(xscale), axes.set_yscale(yscale)axes.set_xlim(xlim), axes.set_ylim(ylim)if legend:axes.legend(legend)axes.grid()class Accumulator: # 定义一个实用程序类 Accumulator用于对多个变量进行累加在n个变量上累加def __init__(self, n):self.data [0.0] * ndef add(self, *args):self.data [a float(b) for a, b in zip(self.data, args)]def reset(self):self.data [0.0] * len(self.data)def __getitem__(self, idx):return self.data[idx]class Animator: # 定义一个在动画中绘制数据的实用程序类 Animator在动画中绘制数据def __init__(self, xlabelNone, ylabelNone, legendNone, xlimNone,ylimNone, xscalelinear, yscalelinear,fmts(-, m--, g-., r:), nrows1, ncols1,figsize(3.5, 2.5)):# 增量地绘制多条线if legend is None:legend []backend_inline.set_matplotlib_formats(svg)self.fig, self.axes plt.subplots(nrows, ncols, figsizefigsize)if nrows * ncols 1:self.axes [self.axes, ]# 使用lambda函数捕获参数self.config_axes lambda: set_axes(self.axes[0], xlabel, ylabel, xlim, ylim, xscale, yscale, legend)self.X, self.Y, self.fmts None, None, fmtsdef add(self, x, y):# Add multiple data points into the figureif not hasattr(y, __len__):y [y]n len(y)if not hasattr(x, __len__):x [x] * nif not self.X:self.X [[] for _ in range(n)]if not self.Y:self.Y [[] for _ in range(n)]for i, (a, b) in enumerate(zip(x, y)):if a is not None and b is not None:self.X[i].append(a)self.Y[i].append(b)self.axes[0].cla()for x, y, fmt in zip(self.X, self.Y, self.fmts):self.axes[0].plot(x, y, fmt)self.config_axes()display.display(self.fig)# 通过以下两行代码实现了在PyCharm中显示动图# plt.draw()# plt.pause(interval0.001)display.clear_output(waitTrue)plt.rcParams[font.sans-serif] [Microsoft YaHei]class Timer:def __init__(self):self.times []self.start()def start(self):self.tik time.time()def stop(self):self.times.append(time.time() - self.tik)return self.times[-1]def sum(self):Return the sum of time.return sum(self.times)def read_voc_images(voc_dir, is_trainTrue):txt_fname os.path.join(voc_dir, ImageSets, Segmentation,train.txt if is_train else val.txt)mode torchvision.io.image.ImageReadMode.RGBwith open(txt_fname, r) as f:images f.read().split()features, labels [], []for i, fname in enumerate(images):features.append(torchvision.io.read_image(os.path.join(voc_dir, JPEGImages, f{fname}.jpg)))labels.append(torchvision.io.read_image(os.path.join(voc_dir, SegmentationClass ,f{fname}.png), mode))return features, labelsVOC_COLORMAP [[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0],[0, 0, 128], [128, 0, 128], [0, 128, 128], [128, 128, 128],[64, 0, 0], [192, 0, 0], [64, 128, 0], [192, 128, 0],[64, 0, 128], [192, 0, 128], [64, 128, 128], [192, 128, 128],[0, 64, 0], [128, 64, 0], [0, 192, 0], [128, 192, 0],[0, 64, 128]]def voc_colormap2label():colormap2label torch.zeros(256 ** 3, dtypetorch.long)for i, colormap in enumerate(VOC_COLORMAP):colormap2label[(colormap[0] * 256 colormap[1]) * 256 colormap[2]] ireturn colormap2labeldef voc_rand_crop(feature, label, height, width):rect torchvision.transforms.RandomCrop.get_params(feature, (height, width))feature torchvision.transforms.functional.crop(feature, *rect)label torchvision.transforms.functional.crop(label, *rect)return feature, labeldef voc_label_indices(colormap, colormap2label):colormap colormap.permute(1, 2, 0).numpy().astype(int32)idx ((colormap[:, :, 0] * 256 colormap[:, :, 1]) * 256 colormap[:, :, 2])return colormap2label[idx]class VOCSegDataset(torch.utils.data.Dataset):def __init__(self, is_train, crop_size, voc_dir):self.transform torchvision.transforms.Normalize(mean[0.485, 0.456, 0.406], std[0.229, 0.224, 0.225])self.crop_size crop_sizefeatures, labels read_voc_images(voc_dir, is_trainis_train)self.features [self.normalize_image(feature)for feature in self.filter(features)]self.labels self.filter(labels)self.colormap2label voc_colormap2label()if is_train:print(train : read str(len(self.features)) examples)else:print(validation : read str(len(self.features)) examples)def normalize_image(self, img):return self.transform(img.float() / 255)def filter(self, imgs):return [img for img in imgs if (img.shape[1] self.crop_size[0] andimg.shape[2] self.crop_size[1])]def __getitem__(self, idx):feature, label voc_rand_crop(self.features[idx], self.labels[idx],*self.crop_size)return (feature, voc_label_indices(label, self.colormap2label))def __len__(self):return len(self.features)def set_figsize(figsize(8.5, 6.5)):backend_inline.set_matplotlib_formats(svg)plt.rcParams[figure.figsize] figsizeplt.rcParams[font.sans-serif] [Microsoft YaHei]def try_all_gpus():return [torch.device(fcuda:{i}) for i in range(torch.cuda.device_count())]def load_data_voc(batch_size, crop_size):voc_dir E:\\dayLilytrain_iter torch.utils.data.DataLoader(VOCSegDataset(True, crop_size, voc_dir), batch_size,shuffleTrue, drop_lastTrue)test_iter torch.utils.data.DataLoader(VOCSegDataset(False, crop_size, voc_dir), batch_size,drop_lastTrue)return train_iter, test_iterdef show_images(imgs, num_rows, num_cols, suptitleNone, titlesNone, scale1.5):numpy lambda x, *args, **kwargs: x.detach().numpy(*args, **kwargs)figsize (num_cols * scale, num_rows * scale)_, axes plt.subplots(num_rows, num_cols, figsizefigsize)axes axes.flatten()for i, (ax, img) in enumerate(zip(axes, imgs)):try:img numpy(img)except:passax.imshow(img)ax.axes.get_xaxis().set_visible(False)ax.axes.get_yaxis().set_visible(False)if titles:ax.set_title(titles[i])elif suptitle:plt.suptitle(suptitle)plt.show()return axespretrained_net torchvision.models.resnet18(pretrainedTrue)
print(list(pretrained_net.children())[-3:])net nn.Sequential(*list(pretrained_net.children())[:-2])X torch.rand(size(1, 3, 320, 480))
print(fnet 的前向传播将输入的高和宽减小至原来的 1/32 : {net(X).shape})num_classes 21
net.add_module(final_conv, nn.Conv2d(512, num_classes, kernel_size1))
net.add_module(transpose_conv, nn.ConvTranspose2d(num_classes, num_classes,kernel_size64, padding16, stride32))def bilinear_kernel(in_channels, out_channels, kernel_size):factor (kernel_size 1) // 2if kernel_size % 2 1:center factor - 1else:center factor - 0.5og (torch.arange(kernel_size).reshape(-1, 1),torch.arange(kernel_size).reshape(1, -1))filt (1 - torch.abs(og[0] - center) / factor) * \(1 - torch.abs(og[1] - center) / factor)weight torch.zeros((in_channels, out_channels,kernel_size, kernel_size))weight[range(in_channels), range(out_channels), :, :] filtreturn weightconv_trans nn.ConvTranspose2d(3, 3, kernel_size4, padding1, stride2,biasFalse)
conv_trans.weight.data.copy_(bilinear_kernel(3, 3, 4))img torchvision.transforms.ToTensor()(Image.open(E:\\dayLily\\JPEGImages\\2024_959.jpg))
X img.unsqueeze(0)
Y conv_trans(X)
out_img Y[0].permute(1, 2, 0).detach()set_figsize()
print(input image shape:, img.permute(1, 2, 0).shape)
plt.imshow(img.permute(1, 2, 0))
print(output image shape:, out_img.shape)
plt.imshow(out_img)
plt.title(转置卷积层将图像的高和宽分别放大了 2 倍)
plt.show()W bilinear_kernel(num_classes, num_classes, 64)
net.transpose_conv.weight.data.copy_(W)batch_size, crop_size 32, (320, 480)
train_iter, test_iter load_data_voc(batch_size, crop_size)def loss(inputs, targets):return F.cross_entropy(inputs, targets, reductionnone).mean(1).mean(1)def train_batch(net, X, y, loss, trainer, devices):if isinstance(X, list):X [x.to(devices[0]) for x in X]else:X X.to(devices[0])y y.to(devices[0])net.train()trainer.zero_grad()pred net(X)l loss(pred, y)l.sum().backward()trainer.step()train_loss_sum l.sum()train_acc_sum accuracy(pred, y)return train_loss_sum, train_acc_sumdef train(net, train_iter, test_iter, loss, trainer, num_epochs,devicestry_all_gpus()):timer, num_batches Timer(), len(train_iter)animator Animator(xlabelepoch, xlim[1, num_epochs], ylim[0, 1],legend[train loss, train acc, test acc])net nn.DataParallel(net, device_idsdevices).to(devices[0])for epoch in range(num_epochs):# Sum of training loss, sum of training accuracy, no. of examples,# no. of predictionsmetric Accumulator(4)for i, (features, labels) in enumerate(train_iter):timer.start()l, acc train_batch(net, features, labels, loss, trainer, devices)metric.add(l, acc, labels.shape[0], labels.numel())timer.stop()if (i 1) % (num_batches // 5) 0 or i num_batches - 1:animator.add(epoch (i 1) / num_batches,(metric[0] / metric[2], metric[1] / metric[3],None))test_acc evaluate_accuracy_gpu(net, test_iter)animator.add(epoch 1, (None, None, test_acc))plt.title(floss {metric[0] / metric[2]:.3f}, train acc f{metric[1] / metric[3]:.3f}, test acc {test_acc:.3f}\nf{metric[2] * num_epochs / timer.sum():.1f} examples/sec on f{str(torch.cuda.get_device_name())})plt.show()num_epochs, lr, wd, devices 5, 0.001, 1e-3, try_all_gpus()
trainer torch.optim.SGD(net.parameters(), lrlr, weight_decaywd)
train(net, train_iter, test_iter, loss, trainer, num_epochs, devices)##### 预测 #####
def predict(img):X test_iter.dataset.normalize_image(img).unsqueeze(0)pred net(X.to(devices[0])).argmax(dim1)return pred.reshape(pred.shape[1], pred.shape[2])def label2image(pred):colormap torch.tensor(VOC_COLORMAP, devicedevices[0])X pred.long()return colormap[X, :]voc_dir E:\\dayLily
test_images, test_labels read_voc_images(voc_dir, False)
n, imgs 4, []
for i in range(n):crop_rect (0, 0, 500, 500)X torchvision.transforms.functional.crop(test_images[i], *crop_rect)pred label2image(predict(X))imgs [X.permute(1,2,0), pred.cpu(),torchvision.transforms.functional.crop(test_labels[i], *crop_rect).permute(1,2,0)]
show_images(imgs[::3] imgs[1::3] imgs[2::3], 3, n, scale2, suptitle第一行为原图,第二行为预测结果,第三行为真实结果)文中部分知识参考B 站 —— 跟李沐学AI百度百科 文章转载自： http://www.morning.ybnzn.cn.gov.cn.ybnzn.cn http://www.morning.rtzd.cn.gov.cn.rtzd.cn http://www.morning.mcmpq.cn.gov.cn.mcmpq.cn http://www.morning.tjkth.cn.gov.cn.tjkth.cn http://www.morning.jwfkk.cn.gov.cn.jwfkk.cn http://www.morning.ylpwc.cn.gov.cn.ylpwc.cn http://www.morning.dqpnd.cn.gov.cn.dqpnd.cn http://www.morning.npfrj.cn.gov.cn.npfrj.cn http://www.morning.znknj.cn.gov.cn.znknj.cn http://www.morning.qqfcf.cn.gov.cn.qqfcf.cn http://www.morning.dkfrd.cn.gov.cn.dkfrd.cn http://www.morning.spwln.cn.gov.cn.spwln.cn http://www.morning.jhkzl.cn.gov.cn.jhkzl.cn http://www.morning.gkdqt.cn.gov.cn.gkdqt.cn http://www.morning.xsszn.cn.gov.cn.xsszn.cn http://www.morning.fqqcd.cn.gov.cn.fqqcd.cn http://www.morning.zqcdl.cn.gov.cn.zqcdl.cn http://www.morning.ksjnl.cn.gov.cn.ksjnl.cn http://www.morning.mhsmj.cn.gov.cn.mhsmj.cn http://www.morning.spxsm.cn.gov.cn.spxsm.cn http://www.morning.rkfh.cn.gov.cn.rkfh.cn http://www.morning.lxyyp.cn.gov.cn.lxyyp.cn http://www.morning.crrjg.cn.gov.cn.crrjg.cn http://www.morning.gfznl.cn.gov.cn.gfznl.cn http://www.morning.rfwrn.cn.gov.cn.rfwrn.cn http://www.morning.xjnjb.cn.gov.cn.xjnjb.cn http://www.morning.mwnch.cn.gov.cn.mwnch.cn http://www.morning.jbshh.cn.gov.cn.jbshh.cn http://www.morning.ghphp.cn.gov.cn.ghphp.cn http://www.morning.yrgb.cn.gov.cn.yrgb.cn http://www.morning.kjdxh.cn.gov.cn.kjdxh.cn http://www.morning.schwr.cn.gov.cn.schwr.cn http://www.morning.ntcmrn.cn.gov.cn.ntcmrn.cn http://www.morning.cdrzw.cn.gov.cn.cdrzw.cn http://www.morning.snzgg.cn.gov.cn.snzgg.cn http://www.morning.sryhp.cn.gov.cn.sryhp.cn http://www.morning.rjrh.cn.gov.cn.rjrh.cn http://www.morning.pcngq.cn.gov.cn.pcngq.cn http://www.morning.hqrr.cn.gov.cn.hqrr.cn http://www.morning.qggxt.cn.gov.cn.qggxt.cn http://www.morning.xyrss.cn.gov.cn.xyrss.cn http://www.morning.hlrtzcj.cn.gov.cn.hlrtzcj.cn http://www.morning.qlpyn.cn.gov.cn.qlpyn.cn http://www.morning.krhkb.cn.gov.cn.krhkb.cn http://www.morning.grtwn.cn.gov.cn.grtwn.cn http://www.morning.mbfkt.cn.gov.cn.mbfkt.cn http://www.morning.xtdms.com.gov.cn.xtdms.com http://www.morning.mrlls.cn.gov.cn.mrlls.cn http://www.morning.nthyjf.com.gov.cn.nthyjf.com http://www.morning.gbfck.cn.gov.cn.gbfck.cn http://www.morning.lcbgf.cn.gov.cn.lcbgf.cn http://www.morning.qwbtr.cn.gov.cn.qwbtr.cn http://www.morning.wflsk.cn.gov.cn.wflsk.cn http://www.morning.rzbcz.cn.gov.cn.rzbcz.cn http://www.morning.bbgn.cn.gov.cn.bbgn.cn http://www.morning.nlglm.cn.gov.cn.nlglm.cn http://www.morning.tqgmd.cn.gov.cn.tqgmd.cn http://www.morning.gsjw.cn.gov.cn.gsjw.cn http://www.morning.gdljq.cn.gov.cn.gdljq.cn http://www.morning.xqjrg.cn.gov.cn.xqjrg.cn http://www.morning.lokext.com.gov.cn.lokext.com http://www.morning.zwzlf.cn.gov.cn.zwzlf.cn http://www.morning.nrzbq.cn.gov.cn.nrzbq.cn http://www.morning.kmcfw.cn.gov.cn.kmcfw.cn http://www.morning.srwny.cn.gov.cn.srwny.cn http://www.morning.wdskl.cn.gov.cn.wdskl.cn http://www.morning.mngh.cn.gov.cn.mngh.cn http://www.morning.mtmnk.cn.gov.cn.mtmnk.cn http://www.morning.lbjdx.cn.gov.cn.lbjdx.cn http://www.morning.zwtp.cn.gov.cn.zwtp.cn http://www.morning.kbqbx.cn.gov.cn.kbqbx.cn http://www.morning.pqnps.cn.gov.cn.pqnps.cn http://www.morning.wbfly.cn.gov.cn.wbfly.cn http://www.morning.ylph.cn.gov.cn.ylph.cn http://www.morning.kryr.cn.gov.cn.kryr.cn http://www.morning.wkmrl.cn.gov.cn.wkmrl.cn http://www.morning.tldhq.cn.gov.cn.tldhq.cn http://www.morning.wfzdh.cn.gov.cn.wfzdh.cn http://www.morning.rlcqx.cn.gov.cn.rlcqx.cn http://www.morning.srxhd.cn.gov.cn.srxhd.cn
