lstm图像分类_lstm图像处理_

from datetime import datetime import torch import torch.nn.functional as F from torch import nn from torch.autograd import Variable import matplotlib.pyplot as plt import pylab from matplotlib import pyplot as plt #plt.imshow需要 import numpy from matplotlib.pyplot import MultipleLocator def get_acc(output, label): #获取准确率 total = output.shape[0] _, pred_label = output.max(1) num_correct = (pred_label == label).sum().item() return num_correct / total def train(net, train_data, valid_data, num_epochs, optimizer, criterion): #训练 if torch.cuda.is_available(): #gpu运行 net = net.cuda() prev_time = datetime.now() train_acc_plot = numpy.zeros(num_epochs) #plot valid_acc_plot = numpy.zeros(num_epochs) #plot iter = 0 for epoch in range(num_epochs): train_loss = 0 train_acc = 0 net = net.train() for im, label in train_data: #训练集训练每个数据 if torch.cuda.is_available(): with torch.no_grad(): #屏蔽梯度 im = Variable(im).cuda() # (bs, 3, h, w) label = Variable(label).cuda() # (bs, h, w) else: im = Variable(im) label = Variable(label) # forward output = net(im) loss = criterion(output, label) #获取误差 # backward optimizer.zero_grad() #梯度清零 loss.backward() optimizer.step() train_loss += loss.item() #训练集误差求和 train_acc += get_acc(output, label) #训练集准确率求和 cur_time = datetime.now() h, remainder = divmod((cur_time - prev_time).seconds, 3600) #计算时间 m, s = divmod(remainder, 60) time_str = "Time %02d:%02d:%02d" % (h, m, s) if valid_data is not None: #测试集测试每个数据 valid_loss = 0 valid_acc = 0 net = net.eval() #返回传入字符串的表达式的结果 for im, label in valid_data: if torch.cuda.is_available(): with torch.no_grad(): #屏蔽梯度 im = Variable(im).cuda() label = Variable(label).cuda() else: with torch.no_grad(): #屏蔽梯度 im = Variable(im) label = Variable(label) output = net(im) loss = criterion(output, label) valid_loss += loss.item() #测试集误差求和 valid_acc += get_acc(output, label) #测试集准确率求和 epoch_str = ( "Epoch %d. Train Loss: %f, Train Acc: %f, Valid Loss: %f, Valid Acc: %f, " % (epoch, train_loss / len(train_data), train_acc / len(train_data), valid_loss / len(valid_data), valid_acc / len(valid_data))) else: epoch_str = ("Epoch %d. Train Loss: %f, Train Acc: %f, " % (epoch, train_loss / len(train_data), train_acc / len(train_data))) train_acc_plot[epoch] = train_acc/len(train_data) # plot valid_acc_plot[epoch] = valid_acc/len(valid_data) # plot prev_time = cur_time print(epoch_str + time_str) # epoch_list.append(iter) # iter += 1 # print(epoch_list) epoch_list = numpy.arange(len(train_acc_plot)) plt.plot(epoch_list,train_acc_plot,label="train_acc") plt.plot(epoch_list,valid_acc_plot,label="valid_acc") #画图设置 plt.xlabel('Number of EPOCH') plt.ylabel('train_acc&valid_acc') ax = plt.gca() x_major_locator = MultipleLocator(1) #设置刻度间隔为1 ax.xaxis.set_major_locator(x_major_locator) #x轴使用刻度 plt.xlim(0, 19) plt.ylim(0.8,1) # plt.title('RESNET') plt.legend() plt.show() # plt.plot(epoch_list,train_err) # plt.xlabel('Number of EPOCH') # plt.ylabel('valid_err') # plt.title('RESNET') # plt.show() def conv3x3(in_channel, out_channel, stride=1): return nn.Conv2d( in_channel, out_channel, 3, stride=stride, padding=1, bias=False) class residual_block(nn.Module): def __init__(self, in_channel, out_channel, same_shape=True): super(residual_block, self).__init__() self.same_shape = same_shape stride = 1 if self.same_shape else 2 self.conv1 = conv3x3(in_channel, out_channel, stride=stride) self.bn1 = nn.BatchNorm2d(out_channel) self.conv2 = conv3x3(out_channel, out_channel) self.bn2 = nn.BatchNorm2d(out_channel) if not self.same_shape: self.conv3 = nn.Conv2d(in_channel, out_channel, 1, stride=stride) def forward(self, x): out = self.conv1(x) out = F.relu(self.bn1(out), True) out = self.conv2(out) out = F.relu(self.bn2(out), True) if not self.same_shape: x = self.conv3(x) return F.relu(x + out, True) class resnet(nn.Module): def __init__(self, in_channel, num_classes, verbose=False): super(resnet, self).__init__() self.verbose = verbose self.block1 = nn.Conv2d(in_channel, 64, 7, 2) self.block2 = nn.Sequential( nn.MaxPool2d(3, 2), residual_block(64, 64), residual_block(64, 64)) self.block3 = nn.Sequential( residual_block(64, 128, False), residual_block(128, 128)) self.block4 = nn.Sequential( residual_block(128, 256, False), residual_block(256, 256)) self.block5 = nn.Sequential( residual_block(256, 512, False), residual_block(512, 512), nn.AvgPool2d(3)) self.classifier = nn.Linear(512, num_classes) def forward(self, x): x = self.block1(x) if self.verbose: print('block 1 output: {}'.format(x.shape)) x = self.block2(x) if self.verbose: print('block 2 output: {}'.format(x.shape)) x = self.block3(x) if self.verbose: print('block 3 output: {}'.format(x.shape)) x = self.block4(x) if self.verbose: print('block 4 output: {}'.format(x.shape)) x = self.block5(x) if self.verbose: print('block 5 output: {}'.format(x.shape)) x = x.view(x.shape[0], -1) x = self.classifier(x) return x