How to use detach method in root

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TestSuite_hotplug_mp.py

Source:TestSuite_hotplug_mp.py Github

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...66 if flg_exist == 1:67 self.verify(dev in out, "Fail that don't have the device!")68 if flg_exist == 0:69 self.verify(dev not in out, "Fail that have the device!")70 def attach_detach(self, process="pri", is_dev=1, opt_plug="plugin", flg_loop=0, dev="0000:00:00.0"):71 """72 Attach or detach physical/virtual device from primary/secondary73 process.74 process: define primary or secondary process.75 is_dev: define physical device as 1, virtual device as 0.76 opt_plug: define plug options as below77 plugin: plug in device78 plugout: plug out device79 hotplug: plug in then plug out device from primary or80 secondary process81 crossplug: plug in from primary process then plug out from82 secondary process, or plug in from secondary83 process then plug out from primary84 flg_loop: define loop test flag85 dev: define physical device PCI "0000:00:00.0" or virtual device86 "net_af_packet"87 """88 if opt_plug == "plugin":89 self.verify_devlist(dev, flg_exist=0)90 for i in range(test_loop):91 if process == "pri":92 if is_dev == 0:93 self.session_pri.send_expect(94 "attach %s,iface=%s"95 % (dev, self.intf0), "example>", 100)96 else:97 self.session_pri.send_expect(98 "attach %s" % dev, "example>", 100)99 if process == "sec":100 if is_dev == 0:101 self.session_sec_1.send_expect(102 "attach %s,iface=%s"103 % (dev, self.intf0), "example>", 100)104 else:105 self.session_sec_1.send_expect(106 "attach %s" % dev, "example>", 100)107 if flg_loop == 0:108 break109 self.verify_devlist(dev, flg_exist=1)110 if opt_plug == "plugout":111 self.verify_devlist(dev, flg_exist=1)112 for i in range(test_loop):113 if process == "pri":114 self.session_pri.send_expect(115 "detach %s" % dev, "example>", 100)116 if process == "sec":117 self.session_sec_1.send_expect(118 "detach %s" % dev, "example>", 100)119 if flg_loop == 0:120 break121 self.verify_devlist(dev, flg_exist=0)122 def attach_detach_dev(self, process="pri", opt_plug="plugin", flg_loop=0, dev="0000:00:00.0"):123 """124 Attach or detach physical device from primary/secondary process.125 """126 # Scan port status when example setup, list ports that have been127 # bound to pmd128 if opt_plug in ["plugin", "hotplug", "crossplug"]:129 self.multi_process_setup()130 self.dut.bind_interfaces_linux(self.drivername)131 elif opt_plug == "plugout":132 self.dut.bind_interfaces_linux(self.drivername)133 self.multi_process_setup()134 if opt_plug in ["plugin", "plugout"]:135 self.attach_detach(process, 1, opt_plug, flg_loop, dev)136 elif opt_plug in ["hotplug", "crossplug"]:137 for i in range(test_loop):138 self.attach_detach(process, 1, "plugin", flg_loop, dev)139 if opt_plug == "crossplug":140 if process == "pri":141 cross_proc = "sec"142 elif process == "sec":143 cross_proc = "pri"144 self.attach_detach(cross_proc, 1, "plugout", flg_loop, dev)145 else:146 self.attach_detach(process, 1, "plugout", flg_loop, dev)147 self.multi_process_quit()148 self.dut.bind_interfaces_linux(self.kdriver)149 def attach_detach_vdev(self, process="pri", opt_plug="plugin", flg_loop=0, dev="net_af_packet"):150 """151 Attach or detach virtual device from primary/secondary process.152 Check port interface is at link up status before hotplug test.153 If link not up, may have below error:154 rte_pmd_init_internals(): net_af_packet: ioctl failed (SIOCGIFINDEX)155 EAL: Driver cannot attach the device (net_af_packet)156 """157 self.dut.send_expect("ifconfig %s up" % self.intf0, "#")158 time.sleep(5)159 out = self.dut.send_expect("ethtool %s" % self.intf0, "#")160 self.verify("Link detected: yes" in out, "Wrong link status")161 self.multi_process_setup()162 for i in range(test_loop):163 self.attach_detach(process, 0, "plugin", flg_loop, dev)164 if opt_plug in ["plugout", "hotplug", "crossplug"]:165 if opt_plug == "crossplug":166 if process == "pri":167 cross_proc = "sec"168 elif process == "sec":169 cross_proc = "pri"170 self.attach_detach(cross_proc, 0, "plugout", flg_loop, dev)171 else:172 self.attach_detach(process, 0, "plugout", flg_loop, dev)173 if opt_plug == "plugin" or opt_plug == "plugout":174 break175 self.multi_process_quit()176 def test_attach_dev_primary(self):177 """178 Attach physical device from primary.179 """180 self.attach_detach_dev("pri", "plugin", 0, self.pci0)181 def test_attach_dev_secondary(self):182 """183 Attach physical device from secondary.184 """185 self.attach_detach_dev("sec", "plugin", 0, self.pci0)186 def test_detach_dev_primary(self):...

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losser.py

Source:losser.py Github

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...29#30# total_loss += F.nll_loss(multi_predict_log_soft_max, multi_label)31#32#33# binary_predict_detach = F.softmax(binary_predict.detach(),dim=1)34# multi_predict_detach = F.softmax(multi_predict.detach(),dim=1)35#36# accuracy_ratio = self.calculate_recall_precision(binary_predict_detach, binary_label)37#38# return total_loss, accuracy_ratio39#40# def calculate_recall_precision(self, predict_detach, label):41#42# predict_detach_argmax = torch.argmax(predict_detach, dim=1).long()43#44# accuracy_number = torch.sum(predict_detach_argmax == label)45#46# accuracy_ratio = accuracy_number/len(predict_detach)47#48# return accuracy_ratio49# class Loss(nn.Module):50#51# def __init__(self, args):52#53# super(Loss, self).__init__()54#55# self.args = args56# self.calculate_multi_label_loss = self.args.calculate_multi_label_loss57#58# def forward(self, binary_predict, multi_predict, multi_label, train=True):59#60#61# # binary_predict_log_soft_max = F.log_softmax(binary_predict, dim=1)62# multi_predict_log_soft_max = F.log_softmax(multi_predict, dim=1)63#64# binary_predict_soft_max = F.softmax(binary_predict, dim=1)65# binary_predict_log_soft_max = torch.log(binary_predict_soft_max)66#67# binary_predict_log_soft_max = binary_predict_log_soft_max.view(binary_predict_log_soft_max.size(0), -1)68# multi_predict_log_soft_max = multi_predict_log_soft_max.view(multi_predict_log_soft_max.size(0),-1)69# multi_label = multi_label.view(-1)70#71# binary_label = torch.where(multi_label > 0, torch.full_like(multi_label, 1), multi_label).long()72#73# weight = binary_predict_soft_max74#75# weight[:, 0] = torch.pow(weight[:,0],2.0)76# weight[:, 1] = torch.pow(1- weight[:, 1], 2.0)77#78# binary_predict_log_soft_max = binary_predict_log_soft_max * weight79#80# total_loss = F.nll_loss(binary_predict_log_soft_max, binary_label)81#82# if self.calculate_multi_label_loss == True:83#84# total_loss += F.nll_loss(multi_predict_log_soft_max, multi_label)85#86#87# binary_predict_detach = F.softmax(binary_predict.detach(),dim=1)88# multi_predict_detach = F.softmax(multi_predict.detach(),dim=1)89#90# binary_accuracy_ratio = self.calculate_recall_precision(binary_predict_detach, binary_label)91#92# # print("**********************************************************************")93# # print("label ", binary_label)94# # print("pre " , binary_predict_detach)95# # print("precision", binary_accuracy_ratio)96# # print("loss ", total_loss)97#98# return total_loss, binary_accuracy_ratio99#100# def calculate_recall_precision(self, predict_detach, label):101#102# predict_detach_argmax = torch.argmax(predict_detach, dim=1).long()103#104# accuracy_number = torch.sum(predict_detach_argmax == label)105#106# positive_accuracy = torch.sum((predict_detach_argmax == 1)*(label==1))107# negative_accuracy = torch.sum((predict_detach_argmax == 0)*(label==0))108#109# positive_accuracy_number = torch.sum(label)110# negative_accuracy_number = len(label) - positive_accuracy_number111#112# if positive_accuracy_number != 0:113#114# positive_accuracy = positive_accuracy.float() / positive_accuracy_number115#116# else:117#118# positive_accuracy = torch.from_numpy(np.array([np.nan],dtype=np.float32)).cuda()[0]119#120#121# if negative_accuracy_number != 0:122#123# negative_accuracy = negative_accuracy.float() / negative_accuracy_number124#125# else:126#127# negative_accuracy = torch.from_numpy(np.array([np.nan],dtype=np.float32)).cuda()[0]128#129#130# accuracy_ratio = accuracy_number.float()/len(predict_detach)131#132# return accuracy_ratio, positive_accuracy, negative_accuracy133class Loss(nn.Module):134 def __init__(self, args):135 super(Loss, self).__init__()136 self.args = args137 self.calculate_multi_label_loss = self.args.calculate_multi_label_loss138 def forward(self, binary_predict, multi_predict, multi_label, train=True, binary_out2=None):139 #print(multi_label.cpu().numpy())140 binary_predict_soft_max = F.softmax(binary_predict, dim=1)141 binary_predict_log_soft_max = torch.log(binary_predict_soft_max)142 binary_predict_log_soft_max = binary_predict_log_soft_max.view(binary_predict_log_soft_max.size(0), -1)143 binary_label = torch.sum(multi_label, dim=1).long()144 weight = torch.pow(1 - binary_predict_soft_max, 2.0)145 binary_loss = F.nll_loss(binary_predict_log_soft_max, binary_label)146 binary_predict_detach = binary_predict_soft_max.detach()147 binary_accuracy_ratio = self.calculate_recall_precision(binary_predict_detach, binary_label)148 wrong_index = self.calculate_wrong_crop_image(binary_predict_detach, binary_label)149 '''150 multi_predict_p = F.sigmoid(multi_predict)151 multi_predict_detach = multi_predict_p.detach()152 multi_predict_p = torch.transpose(multi_predict_p, 0, 1)153 multi_predict_p_r = (torch.max(multi_predict_p, dim=0, keepdim=True)[0] - 0.00001) * torch.ones_like(multi_predict_p)154 multi_predict_p = torch.stack([multi_predict_p_r, multi_predict_p], dim=-1)155 multi_label_T = torch.transpose(multi_label, 0, 1)156 multi_loss = - 5 * F.logsigmoid(multi_predict) * multi_label.float() + (- F.logsigmoid(-multi_predict) * (1 - multi_label.float()))157 total_loss = torch.sum(multi_loss) / multi_loss.shape[0] #+ binary_loss158 '''159 multi_predict_p = F.softmax(multi_predict, dim=1)160 multi_predict_detach = multi_predict_p.detach()161 multi_predict_p = torch.transpose(multi_predict_p, 0, 1)162 multi_predict_p = torch.stack([1 - multi_predict_p, multi_predict_p], dim=-1)163 multi_label_T = torch.transpose(multi_label, 0, 1)164 multiple_loss = F.nll_loss(F.log_softmax(multi_predict, dim=1), torch.argmax(multi_label, dim=1), reduction='none')165 multiple_loss = torch.sum(multiple_loss * binary_label.float()) / (torch.sum(binary_label.float()) + 1e-4)166 multi_accuracy_ratio = []167 for multi_predict_p_x, multi_label_T_x in zip(multi_predict_p, multi_label_T):168 multi_accuracy_ratio.append(self.calculate_recall_precision(multi_predict_p_x, multi_label_T_x))169 total_loss = multiple_loss170 return total_loss, [binary_accuracy_ratio] + multi_accuracy_ratio, wrong_index, multi_predict_detach171 def calculate_wrong_crop_image(self, binary_predict_detach, binary_label):172 predict_detach_argmax = torch.argmax(binary_predict_detach, dim=1).long()173 wrong_index = (predict_detach_argmax != binary_label)174 return wrong_index...

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IQAloss.py

Source:IQAloss.py Github

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...44def monotonicity_regularization(y_pred, y, detach=False):45 """monotonicity regularization"""46 if y_pred.size(0) > 1: #47 ranking_loss = F.relu((y_pred-y_pred.t()) * torch.sign((y.t()-y)))48 scale = 1 + torch.max(ranking_loss.detach()) if detach else 1 + torch.max(ranking_loss)49 return torch.sum(ranking_loss) / y_pred.size(0) / (y_pred.size(0)-1) / scale50 else:51 return F.l1_loss(y_pred, y_pred.detach()) # 0 for batch with single sample.52def linearity_induced_loss(y_pred, y, alpha=[1, 1], detach=False):53 """linearity-induced loss, actually MSE loss with z-score normalization"""54 if y_pred.size(0) > 1: # z-score normalization: (x-m(x))/sigma(x).55 sigma_hat, m_hat = torch.std_mean(y_pred.detach(), unbiased=False) if detach else torch.std_mean(y_pred, unbiased=False)56 y_pred = (y_pred - m_hat) / (sigma_hat + eps)57 sigma, m = torch.std_mean(y, unbiased=False)58 y = (y - m) / (sigma + eps)59 scale = 460 loss0, loss1 = 0, 061 if alpha[0] > 0:62 loss0 = F.mse_loss(y_pred, y) / scale # ~ 1 - rho, rho is PLCC63 if alpha[1] > 0:64 rho = torch.mean(y_pred * y)65 loss1 = F.mse_loss(rho * y_pred, y) / scale # 1 - rho ** 2 = 1 - R^2, R^2 is Coefficient of determination66 # loss0 = (1 - torch.cosine_similarity(y_pred.t() - torch.mean(y_pred), y.t() - torch.mean(y))[0]) / 267 # yp = y_pred.detach() if detach else y_pred68 # ones = torch.ones_like(yp.detach())69 # yp1 = torch.cat((yp, ones), dim=1)70 # h = torch.mm(torch.inverse(torch.mm(yp1.t(), yp1)), torch.mm(yp1.t(), y))71 # err = torch.pow(torch.mm(torch.cat((y_pred, ones), dim=1), h) - y, 2) #72 # normalization = 1 + torch.max(err.detach()) if detach else 1 + torch.max(err)73 # loss1 = torch.mean(err) / normalization74 return (alpha[0] * loss0 + alpha[1] * loss1) / (alpha[0] + alpha[1])75 else:76 return F.l1_loss(y_pred, y_pred.detach()) # 0 for batch with single sample.77def norm_loss_with_normalization(y_pred, y, alpha=[1, 1], p=2, q=2, detach=False, exponent=True):78 """norm_loss_with_normalization: norm-in-norm"""79 N = y_pred.size(0)80 if N > 1: 81 m_hat = torch.mean(y_pred.detach()) if detach else torch.mean(y_pred)82 y_pred = y_pred - m_hat # very important!!83 normalization = torch.norm(y_pred.detach(), p=q) if detach else torch.norm(y_pred, p=q) # Actually, z-score normalization is related to q = 2.84 # print('bhat = {}'.format(normalization.item()))85 y_pred = y_pred / (eps + normalization) # very important!86 y = y - torch.mean(y)87 y = y / (eps + torch.norm(y, p=q))88 scale = np.power(2, max(1,1./q)) * np.power(N, max(0,1./p-1./q)) # p, q>089 loss0, loss1 = 0, 090 if alpha[0] > 0:91 err = y_pred - y92 if p < 1: # avoid gradient explosion when 0<=p<1; and avoid vanishing gradient problem when p < 093 err += eps 94 loss0 = torch.norm(err, p=p) / scale # Actually, p=q=2 is related to PLCC95 loss0 = torch.pow(loss0, p) if exponent else loss0 #96 if alpha[1] > 0:97 rho = torch.cosine_similarity(y_pred.t(), y.t()) # 98 err = rho * y_pred - y99 if p < 1: # avoid gradient explosion when 0<=p<1; and avoid vanishing gradient problem when p < 0100 err += eps 101 loss1 = torch.norm(err, p=p) / scale # Actually, p=q=2 is related to LSR102 loss1 = torch.pow(loss1, p) if exponent else loss1 # # 103 # by = normalization.detach()104 # e0 = err.detach().view(-1)105 # ones = torch.ones_like(e0)106 # yhat = y_pred.detach().view(-1)107 # g0 = torch.norm(e0, p=p) / torch.pow(torch.norm(e0, p=p) + eps, p) * torch.pow(torch.abs(e0), p-1) * e0 / (torch.abs(e0) + eps)108 # ga = -ones / N * torch.dot(g0, ones)109 # gg0 = torch.dot(g0, g0)110 # gga = torch.dot(g0+ga, g0+ga)111 # print("by: {} without a and b: {} with a: {}".format(normalization, gg0, gga))112 # gb = -torch.pow(torch.abs(yhat), q-1) * yhat / (torch.abs(yhat) + eps) * torch.dot(g0, yhat)113 # gab = torch.dot(ones, torch.pow(torch.abs(yhat), q-1) * yhat / (torch.abs(yhat) + eps)) / N * torch.dot(g0, yhat)114 # ggb = torch.dot(g0+gb, g0+gb)115 # ggab = torch.dot(g0+ga+gb+gab, g0+ga+gb+gab)116 # print("by: {} without a and b: {} with a: {} with b: {} with a and b: {}".format(normalization, gg0, gga, ggb, ggab))117 return (alpha[0] * loss0 + alpha[1] * loss1) / (alpha[0] + alpha[1])118 else:119 return F.l1_loss(y_pred, y_pred.detach()) # 0 for batch with single sample.120def norm_loss_with_min_max_normalization(y_pred, y, alpha=[1, 1], detach=False):121 if y_pred.size(0) > 1: 122 m_hat = torch.min(y_pred.detach()) if detach else torch.min(y_pred)123 M_hat = torch.max(y_pred.detach()) if detach else torch.max(y_pred)124 y_pred = (y_pred - m_hat) / (eps + M_hat - m_hat) # min-max normalization125 y = (y - torch.min(y)) / (eps + torch.max(y) - torch.min(y))126 loss0, loss1 = 0, 0127 if alpha[0] > 0:128 loss0 = F.mse_loss(y_pred, y)129 if alpha[1] > 0:130 rho = torch.cosine_similarity(y_pred.t(), y.t()) #131 loss1 = F.mse_loss(rho * y_pred, y) 132 return (alpha[0] * loss0 + alpha[1] * loss1) / (alpha[0] + alpha[1])133 else:134 return F.l1_loss(y_pred, y_pred.detach()) # 0 for batch with single sample.135def norm_loss_with_mean_normalization(y_pred, y, alpha=[1, 1], detach=False):136 if y_pred.size(0) > 1: 137 mean_hat = torch.mean(y_pred.detach()) if detach else torch.mean(y_pred)138 m_hat = torch.min(y_pred.detach()) if detach else torch.min(y_pred)139 M_hat = torch.max(y_pred.detach()) if detach else torch.max(y_pred)140 y_pred = (y_pred - mean_hat) / (eps + M_hat - m_hat) # mean normalization141 y = (y - torch.mean(y)) / (eps + torch.max(y) - torch.min(y))142 loss0, loss1 = 0, 0143 if alpha[0] > 0:144 loss0 = F.mse_loss(y_pred, y) / 4145 if alpha[1] > 0:146 rho = torch.cosine_similarity(y_pred.t(), y.t()) #147 loss1 = F.mse_loss(rho * y_pred, y) / 4148 return (alpha[0] * loss0 + alpha[1] * loss1) / (alpha[0] + alpha[1])149 else:150 return F.l1_loss(y_pred, y_pred.detach()) # 0 for batch with single sample.151def norm_loss_with_scaling(y_pred, y, alpha=[1, 1], p=2, detach=False):152 if y_pred.size(0) > 1: 153 normalization = torch.norm(y_pred.detach(), p=p) if detach else torch.norm(y_pred, p=p) 154 y_pred = y_pred / (eps + normalization) # mean normalization155 y = y / (eps + torch.norm(y, p=p))156 loss0, loss1 = 0, 0157 if alpha[0] > 0:158 loss0 = F.mse_loss(y_pred, y) / 4159 if alpha[1] > 0:160 rho = torch.cosine_similarity(y_pred.t(), y.t()) #161 loss1 = F.mse_loss(rho * y_pred, y) / 4162 return (alpha[0] * loss0 + alpha[1] * loss1) / (alpha[0] + alpha[1])163 else:...

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plotter.py

Source:plotter.py Github

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...60 fig, axs = plt.subplots(2, 2)61 fig.set_size_inches(12, 10)62 fig.suptitle(sTitle + ', inv err {:.2e}'.format(invErr))63 # hist, xbins, ybins, im = axs[0, 0].hist2d(x.numpy()[:,0],x.numpy()[:,1], range=[[LOW, HIGH], [LOW, HIGH]], bins = nBins)64 im1 , _, _, map1 = axs[0, 0].hist2d(x.detach().cpu().numpy()[:, d1], x.detach().cpu().numpy()[:, d2], range=[[LOWX, HIGHX], [LOWY, HIGHY]], bins=nBins)65 axs[0, 0].set_title('x from rho_0')66 im2 , _, _, map2 = axs[0, 1].hist2d(fx.detach().cpu().numpy()[:, d1], fx.detach().cpu().numpy()[:, d2], range=[[-4, 4], [-4, 4]], bins = nBins)67 axs[0, 1].set_title('f(x)')68 im3 , _, _, map3 = axs[1, 0].hist2d(finvfx.detach().cpu().numpy()[: ,d1] ,finvfx.detach().cpu().numpy()[: ,d2], range=[[LOWX, HIGHX], [LOWY, HIGHY]], bins = nBins)69 axs[1, 0].set_title('finv( f(x) )')70 im4 , _, _, map4 = axs[1, 1].hist2d(genModel.detach().cpu().numpy()[:, d1], genModel.detach().cpu().numpy()[:, d2], range=[[LOWX, HIGHX], [LOWY, HIGHY]], bins = nBins)71 axs[1, 1].set_title('finv( y from rho1 )')72 fig.colorbar(map1, cax=fig.add_axes([0.47, 0.53, 0.02, 0.35]) )73 fig.colorbar(map2, cax=fig.add_axes([0.89, 0.53, 0.02, 0.35]) )74 fig.colorbar(map3, cax=fig.add_axes([0.47, 0.11, 0.02, 0.35]) )75 fig.colorbar(map4, cax=fig.add_axes([0.89, 0.11, 0.02, 0.35]) )76 # plot paths77 if doPaths:78 forwPath = integrate(x[:, 0:d], net, [0.0, 1.0], nt_val, stepper="rk4", alph=net.alph, intermediates=True)79 backPath = integrate(fx[:, 0:d], net, [1.0, 0.0], nt_val, stepper="rk4", alph=net.alph, intermediates=True)80 # plot the forward and inverse trajectories of several points; white is forward, red is inverse81 nPts = 1082 pts = np.unique(np.random.randint(nSamples, size=nPts))83 for pt in pts:84 axs[0, 0].plot(forwPath[pt, 0, :].detach().cpu().numpy(), forwPath[pt, 1, :].detach().cpu().numpy(), color='white', linewidth=4)85 axs[0, 0].plot(backPath[pt, 0, :].detach().cpu().numpy(), backPath[pt, 1, :].detach().cpu().numpy(), color='red', linewidth=2)86 for i in range(axs.shape[0]):87 for j in range(axs.shape[1]):88 # axs[i, j].get_yaxis().set_visible(False)89 # axs[i, j].get_xaxis().set_visible(False)90 axs[i ,j].set_aspect('equal')91 # sPath = os.path.join(args.save, 'figs', sStartTime + '_{:04d}.png'.format(itr))92 if not os.path.exists(os.path.dirname(sPath)):93 os.makedirs(os.path.dirname(sPath))94 plt.savefig(sPath, dpi=300)95 plt.close()96def plotAutoEnc(x, xRecreate, sPath):97 # assume square image98 s = int(math.sqrt(x.shape[1]))99 nex = 8100 fig, axs = plt.subplots(4, nex//2)101 fig.set_size_inches(9, 9)102 fig.suptitle("first 2 rows originals. Rows 3 and 4 are generations.")103 for i in range(nex//2):104 axs[0, i].imshow(x[i,:].reshape(s,s).detach().cpu().numpy())105 axs[1, i].imshow(x[ nex//2 + i , : ].reshape(s,s).detach().cpu().numpy())106 axs[2, i].imshow(xRecreate[i,:].reshape(s,s).detach().cpu().numpy())107 axs[3, i].imshow(xRecreate[ nex//2 + i , : ].reshape(s, s).detach().cpu().numpy())108 for i in range(axs.shape[0]):109 for j in range(axs.shape[1]):110 axs[i, j].get_yaxis().set_visible(False)111 axs[i, j].get_xaxis().set_visible(False)112 axs[i ,j].set_aspect('equal')113 plt.subplots_adjust(wspace=0.0, hspace=0.0)114 if not os.path.exists(os.path.dirname(sPath)):115 os.makedirs(os.path.dirname(sPath))116 plt.savefig(sPath, dpi=300)117 plt.close()118def plotAutoEnc3D(x, xRecreate, sPath):119 nex = 8120 fig, axs = plt.subplots(4, nex//2)121 fig.set_size_inches(9, 9)122 fig.suptitle("first 2 rows originals. Rows 3 and 4 are generations.")123 for i in range(nex//2):124 axs[0, i].imshow(x[i,:].permute(1,2,0).detach().cpu().numpy())125 axs[1, i].imshow(x[ nex//2 + i , : ].permute(1,2,0).detach().cpu().numpy())126 axs[2, i].imshow(xRecreate[i,:].permute(1,2,0).detach().cpu().numpy())127 axs[3, i].imshow(xRecreate[ nex//2 + i , : ].permute(1,2,0).detach().cpu().numpy())128 for i in range(axs.shape[0]):129 for j in range(axs.shape[1]):130 axs[i, j].get_yaxis().set_visible(False)131 axs[i, j].get_xaxis().set_visible(False)132 axs[i ,j].set_aspect('equal')133 plt.subplots_adjust(wspace=0.0, hspace=0.0)134 if not os.path.exists(os.path.dirname(sPath)):135 os.makedirs(os.path.dirname(sPath))136 plt.savefig(sPath, dpi=300)137 plt.close()138def plotImageGen(x, xRecreate, sPath):139 # assume square image140 s = int(math.sqrt(x.shape[1]))141 nex = 80142 nCols = nex//5143 fig, axs = plt.subplots(7, nCols)144 fig.set_size_inches(16, 7)145 fig.suptitle("first 2 rows originals. Rows 3 and 4 are generations.")146 for i in range(nCols):147 axs[0, i].imshow(x[i,:].reshape(s,s).detach().cpu().numpy())148 # axs[1, i].imshow(x[ nex//3 + i , : ].reshape(s,s).detach().cpu().numpy())149 # axs[2, i].imshow(x[ 2*nex//3 + i , : ].reshape(s,s).detach().cpu().numpy())150 axs[2, i].imshow(xRecreate[i,:].reshape(s,s).detach().cpu().numpy())151 axs[3, i].imshow(xRecreate[nCols + i,:].reshape(s,s).detach().cpu().numpy())152 153 axs[4, i].imshow(xRecreate[2*nCols + i,:].reshape(s,s).detach().cpu().numpy())154 axs[5, i].imshow(xRecreate[3*nCols + i , : ].reshape(s, s).detach().cpu().numpy())155 axs[6, i].imshow(xRecreate[4*nCols + i , : ].reshape(s, s).detach().cpu().numpy())156 for i in range(axs.shape[0]):157 for j in range(axs.shape[1]):158 axs[i, j].get_yaxis().set_visible(False)159 axs[i, j].get_xaxis().set_visible(False)160 axs[i ,j].set_aspect('equal')161 plt.subplots_adjust(wspace=0.0, hspace=0.0)162 if not os.path.exists(os.path.dirname(sPath)):163 os.makedirs(os.path.dirname(sPath))164 plt.savefig(sPath, dpi=300)165 plt.close()166def plot4mnist(x, sPath, sTitle=""):167 """168 x - tensor (>4, 28,28)169 """170 fig, axs = plt.subplots(2, 2)171 fig.set_size_inches(12, 10)172 fig.suptitle(sTitle)173 im1 = axs[0, 0].imshow(x[0,:,:].detach().cpu().numpy())174 im2 = axs[0, 1].imshow(x[1,:,:].detach().cpu().numpy())175 im3 = axs[1, 0].imshow(x[2,:,:].detach().cpu().numpy())176 im4 = axs[1, 1].imshow(x[3,:,:].detach().cpu().numpy())177 fig.colorbar(im1, cax=fig.add_axes([0.47, 0.53, 0.02, 0.35]) )178 fig.colorbar(im2, cax=fig.add_axes([0.89, 0.53, 0.02, 0.35]) )179 fig.colorbar(im3, cax=fig.add_axes([0.47, 0.11, 0.02, 0.35]) )180 fig.colorbar(im4, cax=fig.add_axes([0.89, 0.11, 0.02, 0.35]) )181 for i in range(axs.shape[0]):182 for j in range(axs.shape[1]):183 axs[i, j].get_yaxis().set_visible(False)184 axs[i, j].get_xaxis().set_visible(False)185 axs[i ,j].set_aspect('equal')186 # sPath = os.path.join(args.save, 'figs', sStartTime + '_{:04d}.png'.format(itr))187 if not os.path.exists(os.path.dirname(sPath)):188 os.makedirs(os.path.dirname(sPath))189 plt.savefig(sPath, dpi=300)190 plt.close()

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test_mlp.py

Source:test_mlp.py Github

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...21 mlp_layers.append(linear)22 mlp_layers.append(nn.ReLU(inplace=True))23 ref_mlp = nn.Sequential(*mlp_layers).cuda()24 test_input = torch.empty(batch_size, mlp_sizes[0], device="cuda").uniform_(-1., 1.).requires_grad_()25 ref_input = test_input.clone().detach().requires_grad_()26 mlp_out = mlp(test_input)27 ref_out = ref_mlp(ref_input)28 np.testing.assert_allclose(29 mlp_out.detach().cpu().numpy(),30 ref_out.detach().cpu().numpy(),31 atol=1e-7, rtol=1e-5)32 # Use mean value as scalar loss. Multiply 10 to make it big enough not zero out33 mlp_out.mean().mul(10.).backward()34 ref_out.mean().mul(10.).backward()35 np.testing.assert_allclose(36 test_input.grad.detach().cpu().numpy(),37 ref_input.grad.detach().cpu().numpy(),38 atol=0, rtol=1e-5)39 np.testing.assert_allclose(40 mlp.biases[0].grad.detach().cpu().numpy(),41 ref_mlp[0].bias.grad.detach().cpu().numpy(),42 atol=1e-7, rtol=1e-5)43 def test_no_bias(self):44 for use_activation in ['none', 'relu', 'sigmoid']:45 mlp = MLP(mlp_sizes, bias=False, activation=use_activation).cuda()46 mlp_layers = []47 for i in range(mlp.num_layers):48 linear = nn.Linear(mlp_sizes[i], mlp_sizes[i + 1], bias=False)49 mlp.weights[i].data.copy_(linear.weight)50 mlp_layers.append(linear)51 if use_activation == 'relu':52 mlp_layers.append(nn.ReLU(inplace=True))53 if use_activation == 'sigmoid':54 mlp_layers.append(nn.Sigmoid())55 ref_mlp = nn.Sequential(*mlp_layers).cuda()56 test_input = torch.empty(batch_size, mlp_sizes[0], device="cuda").uniform_(-1., 1.).requires_grad_()57 ref_input = test_input.clone().detach().requires_grad_()58 mlp_out = mlp(test_input)59 ref_out = ref_mlp(ref_input)60 np.testing.assert_allclose(61 mlp_out.detach().cpu().numpy(),62 ref_out.detach().cpu().numpy(),63 atol=1e-7, rtol=1e-5)64 # Use mean value as scalar loss. Multiply 10 to make it big enough not zero out65 mlp_out.mean().mul(10.).backward()66 ref_out.mean().mul(10.).backward()67 np.testing.assert_allclose(68 test_input.grad.detach().cpu().numpy(),69 ref_input.grad.detach().cpu().numpy(),70 atol=0, rtol=100)71 np.testing.assert_allclose(72 mlp.weights[0].grad.detach().cpu().numpy(),73 ref_mlp[0].weight.grad.detach().cpu().numpy(),74 atol=1e-7, rtol=100)75 def test_with_bias(self):76 for use_activation in ['none', 'relu', 'sigmoid']:77 mlp = MLP(mlp_sizes, bias=True, activation=use_activation).cuda()78 mlp_layers = []79 for i in range(mlp.num_layers):80 linear = nn.Linear(mlp_sizes[i], mlp_sizes[i + 1], bias=True)81 mlp.weights[i].data.copy_(linear.weight)82 mlp.biases[i].data.copy_(linear.bias)83 mlp_layers.append(linear)84 if use_activation == 'relu':85 mlp_layers.append(nn.ReLU(inplace=True))86 if use_activation == 'sigmoid':87 mlp_layers.append(nn.Sigmoid())88 ref_mlp = nn.Sequential(*mlp_layers).cuda()89 test_input = torch.empty(batch_size, mlp_sizes[0], device="cuda").uniform_(-1., 1.).requires_grad_()90 ref_input = test_input.clone().detach().requires_grad_()91 mlp_out = mlp(test_input)92 ref_out = ref_mlp(ref_input)93 np.testing.assert_allclose(94 mlp_out.detach().cpu().numpy(),95 ref_out.detach().cpu().numpy(),96 atol=1e-7, rtol=1e-5)97 # Use mean value as scalar loss. Multiply 10 to make it big enough not zero out98 mlp_out.mean().mul(10.).backward()99 ref_out.mean().mul(10.).backward()100 np.testing.assert_allclose(101 test_input.grad.detach().cpu().numpy(),102 ref_input.grad.detach().cpu().numpy(),103 atol=0, rtol=1)104 np.testing.assert_allclose(105 mlp.weights[0].grad.detach().cpu().numpy(),106 ref_mlp[0].weight.grad.detach().cpu().numpy(),107 atol=1e-7, rtol=1)108 np.testing.assert_allclose(109 mlp.biases[0].grad.detach().cpu().numpy(),110 ref_mlp[0].bias.grad.detach().cpu().numpy(),111 atol=1e-7, rtol=1e-5)112 def test_no_grad(self):113 mlp = MLP(mlp_sizes).cuda()114 mlp_layers = []115 for i in range(mlp.num_layers):116 linear = nn.Linear(mlp_sizes[i], mlp_sizes[i + 1])117 mlp.weights[i].data.copy_(linear.weight)118 mlp.biases[i].data.copy_(linear.bias)119 mlp_layers.append(linear)120 mlp_layers.append(nn.ReLU(inplace=True))121 ref_mlp = nn.Sequential(*mlp_layers).cuda()122 test_input = torch.empty(batch_size, mlp_sizes[0], device="cuda").uniform_(-1., 1.)123 ref_input = test_input.clone().detach()124 mlp_out = mlp(test_input)125 ref_out = ref_mlp(ref_input)126 np.testing.assert_allclose(127 mlp_out.detach().cpu().numpy(),128 ref_out.detach().cpu().numpy(),129 atol=1e-7, rtol=1e-5)130 # Use mean value as scalar loss. Multiply 10 to make it big enough not zero out131 mlp_out.mean().mul(10.).backward()132 ref_out.mean().mul(10.).backward()133 np.testing.assert_allclose(134 mlp.weights[0].grad.detach().cpu().numpy(),135 ref_mlp[0].weight.grad.detach().cpu().numpy(),136 atol=1e-7, rtol=1e-5)137 def test_performance_half(self):138 mlp = MLP(mlp_sizes).cuda().half()139 mlp_layers = []140 for i in range(mlp.num_layers):141 linear = nn.Linear(mlp_sizes[i], mlp_sizes[i + 1])142 mlp.weights[i].data.copy_(linear.weight)143 mlp.biases[i].data.copy_(linear.bias)144 mlp_layers.append(linear)145 mlp_layers.append(nn.ReLU(inplace=True))146 ref_mlp = nn.Sequential(*mlp_layers).cuda().half()147 test_input = torch.empty(148 batch_size, mlp_sizes[0], device="cuda", dtype=torch.half).fill_(10.).requires_grad_()149 ref_input = torch.empty(...

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test_payment_method.py

Source:test_payment_method.py Github

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...66 "djstripe.Customer.coupon",67 "djstripe.Customer.default_payment_method",68 },69 )70 def test_detach(self):71 original_detach = PaymentMethodDict.detach72 def mocked_detach(*args, **kwargs):73 return original_detach(*args, **kwargs)74 with patch(75 "stripe.PaymentMethod.retrieve",76 return_value=deepcopy(FAKE_PAYMENT_METHOD_I),77 autospec=True,78 ):79 PaymentMethod.sync_from_stripe_data(deepcopy(FAKE_PAYMENT_METHOD_I))80 self.assertEqual(1, self.customer.payment_methods.count())81 payment_method = self.customer.payment_methods.first()82 with patch(83 "tests.PaymentMethodDict.detach", side_effect=mocked_detach, autospec=True84 ) as mock_detach, patch(85 "stripe.PaymentMethod.retrieve",86 return_value=deepcopy(FAKE_PAYMENT_METHOD_I),87 autospec=True,88 ):89 self.assertTrue(payment_method.detach())90 self.assertEqual(0, self.customer.payment_methods.count())91 self.assertIsNone(self.customer.default_payment_method)92 self.assertIsNone(payment_method.customer)93 if sys.version_info >= (3, 6):94 # this mock isn't working on py34, py35, but it's not strictly necessary95 # for the test96 mock_detach.assert_called()97 self.assert_fks(98 payment_method, expected_blank_fks={"djstripe.PaymentMethod.customer"}99 )100 with patch(101 "tests.PaymentMethodDict.detach",102 side_effect=InvalidRequestError(103 message="A source must be attached to a customer to be used "104 "as a `payment_method`",105 param="payment_method",106 ),107 autospec=True,108 ) as mock_detach, patch(109 "stripe.PaymentMethod.retrieve",110 return_value=deepcopy(FAKE_PAYMENT_METHOD_I),111 autospec=True,112 ) as payment_method_retrieve_mock:113 payment_method_retrieve_mock.return_value["customer"] = None114 self.assertFalse(115 payment_method.detach(), "Second call to detach should return false"116 )117 def test_detach_card(self):118 original_detach = PaymentMethodDict.detach119 # "card_" payment methods are deleted after detach120 deleted_card_exception = InvalidRequestError(121 message="No such payment_method: card_xxxx",122 param="payment_method",123 code="resource_missing",124 )125 def mocked_detach(*args, **kwargs):126 return original_detach(*args, **kwargs)127 with patch(128 "stripe.PaymentMethod.retrieve",129 return_value=deepcopy(FAKE_CARD_AS_PAYMENT_METHOD),130 autospec=True,131 ):132 PaymentMethod.sync_from_stripe_data(deepcopy(FAKE_CARD_AS_PAYMENT_METHOD))133 self.assertEqual(1, self.customer.payment_methods.count())134 payment_method = self.customer.payment_methods.first()135 self.assertTrue(136 payment_method.id.startswith("card_"), "We expect this to be a 'card_'"137 )138 with patch(139 "tests.PaymentMethodDict.detach", side_effect=mocked_detach, autospec=True140 ) as mock_detach, patch(141 "stripe.PaymentMethod.retrieve",142 return_value=deepcopy(FAKE_CARD_AS_PAYMENT_METHOD),143 autospec=True,144 ):145 self.assertTrue(payment_method.detach())146 self.assertEqual(0, self.customer.payment_methods.count())147 self.assertIsNone(self.customer.default_payment_method)148 self.assertEqual(149 PaymentMethod.objects.filter(id=payment_method.id).count(),150 0,151 "We expect PaymentMethod id = card_* to be deleted",152 )153 if sys.version_info >= (3, 6):154 # this mock isn't working on py34, py35, but it's not strictly necessary155 # for the test156 mock_detach.assert_called()157 with patch(158 "tests.PaymentMethodDict.detach",159 side_effect=InvalidRequestError(160 message="A source must be attached to a customer to be used "161 "as a `payment_method`",162 param="payment_method",163 ),164 autospec=True,165 ) as mock_detach, patch(166 "stripe.PaymentMethod.retrieve",167 side_effect=deleted_card_exception,168 autospec=True,169 ) as payment_method_retrieve_mock:170 payment_method_retrieve_mock.return_value["customer"] = None171 self.assertFalse(172 payment_method.detach(), "Second call to detach should return false"173 )174 def test_sync_null_customer(self):175 payment_method = PaymentMethod.sync_from_stripe_data(176 deepcopy(FAKE_PAYMENT_METHOD_I)177 )178 self.assertIsNotNone(payment_method.customer)179 # simulate remote detach180 fake_payment_method_no_customer = deepcopy(FAKE_PAYMENT_METHOD_I)181 fake_payment_method_no_customer["customer"] = None182 payment_method = PaymentMethod.sync_from_stripe_data(183 fake_payment_method_no_customer184 )185 self.assertIsNone(payment_method.customer)186 self.assert_fks(...

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ben_division.py

Source:ben_division.py Github

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...26 """27 x, = ctx.saved_tensors28 grad_x = grad_output.clone()29 grad_x *= -10030 np.savetxt('denominator.csv', np.reshape(x.detach().numpy(),[-1,1]))31 raise ValueError('stop here')32 #print(x)33 return grad_x34class MyInvert2(torch.autograd.Function):35 """36 Second attempt, with grad *= -x37 """38 @staticmethod39 def forward(ctx, x):40 ctx.save_for_backward(x)41 return torch.div(1, x)42 @staticmethod43 def backward(ctx, grad_output):44 x, = ctx.saved_tensors45 grad_x = grad_output.clone()46 #print(np.shape(grad_x.detach().numpy()))47 #np.savetxt('denominator.csv', np.reshape(x.detach().numpy(),[-1,1]))48 #np.savetxt('grad.csv', np.reshape(grad_x.detach().numpy(),[-1,1]))49 #raise ValueError('stop here')50 grad_x *= -x51 #grad_x *= -1*x52 return grad_x53class MyInvert3(torch.autograd.Function):54 """55 Third attempt, with grad *= -x56 """57 @staticmethod58 def forward(ctx, x):59 ctx.save_for_backward(x)60 return torch.div(1, x)61 @staticmethod62 def backward(ctx, grad_output):63 x, = ctx.saved_tensors64 grad_x = grad_output.clone()65 grad_x[grad_x < 0] *= -x[grad_x < 0]66 grad_x[grad_x > 0] *= -167 return grad_x68class MyInvert_original(torch.autograd.Function):69 """70 Third attempt, with grad *= -x71 """72 @staticmethod73 def forward(ctx, x):74 ctx.save_for_backward(x)75 return torch.div(1, x)76 @staticmethod77 def backward(ctx, grad_output):78 x, = ctx.saved_tensors79 grad_x = grad_output.clone()80 #print("Mean of gradient received by inversion", np.mean(grad_x.detach().numpy(), axis=(0,2)))81 grad_x *= -1/torch.mul(x, x)82 return grad_x83class Mymul_original(torch.autograd.Function):84 """85 Attempt to make multi-input custom auto-grad function86 """87 @staticmethod88 def forward(ctx, x, y):89 ctx.save_for_backward(x,y)90 return torch.mul(x, y)91 @staticmethod92 def backward(ctx, grad):93 x,y, = ctx.saved_tensors94 grad = grad.clone()95 #print("Mean of gradient received by multiple", np.mean(grad.detach().numpy(),axis=(0,2)))96 grad_x = grad * y97 grad_y = grad * x98 return grad_x, grad_y99class Mydiv(torch.autograd.Function):100 """101 Attempt to make multi-input custom auto-grad function102 """103 @staticmethod104 def forward(ctx, x, y):105 ctx.save_for_backward(x, y)106 return torch.div(x, y)107 @staticmethod108 def backward(ctx, grad):109 x,y, = ctx.saved_tensors110 grad = grad.clone()111 grad_x = grad * y112 grad_y = grad * -x113 #grad_x = grad * torch.mul(y, y)114 #grad_y = grad * torch.mul(y, -x)115 #np.savetxt('grad.csv', np.reshape(grad.detach().numpy(),[-1,1]))116 #np.savetxt('numerator.csv', np.reshape(x.detach().numpy(),[-1,1]))117 #np.savetxt('denominator.csv', np.reshape(y.detach().numpy(),[-1,1]))118 #print("shape of grad get by division:", np.shape(grad.detach().numpy()))119 #print("Mean of gradient received by division", np.mean(grad.detach().numpy(), axis=(0,2)))120 #print("Mean of gradient received by division", np.mean(grad.detach().numpy(), axis=(1,2)))121 #print("Mean of gradient received by division", np.mean(grad.detach().numpy(), axis=(0,1)))122 """123 print("gradient received by division", grad.detach().numpy()[0,0,:])124 print("gradient passed to numerator", grad_x.detach().numpy()[0,0,:])125 print("gradient passed to denominator", grad_y.detach().numpy()[0,0,:])126 print("Numerator", x.detach().numpy()[0,0,:])127 print("denominator", y.detach().numpy()[0,0,:])128 raise ValueError("This is intentional stop for track backward gradient")129 """130 return grad_x, grad_y131class Mydiv2(torch.autograd.Function):132 """133 Attempt to make multi-input custom auto-grad function134 """135 @staticmethod136 def forward(ctx, x, y):137 ctx.save_for_backward(x,y)138 return torch.div(x, y)139 @staticmethod140 def backward(ctx, grad):141 x,y, = ctx.saved_tensors142 grad = grad.clone()143 #print("Mean of gradient received by multiple", np.mean(grad.detach().numpy(),axis=(0,2)))144 grad_x = grad145 grad_y = grad * -x / y146 return grad_x, grad_y147class Grad_mon(torch.autograd.Function):148 """149 Monitor gradient custom function150 """151 @staticmethod152 def forward(ctx, x):153 ctx.save_for_backward(x)154 return x155 @staticmethod156 def backward(ctx, grad):157 x, = ctx.saved_tensors158 grad = grad.clone()159 #print("gradient in grad mon stage", grad.detach().numpy()[0,:])160 #print("Mean of gradient received by multiple", np.mean(grad.detach().numpy(),axis=(0,2)))...

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test_celeryd_detach.py

Source:test_celeryd_detach.py Github

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...12 def test_execs(self, setup_logs, logger, execv, detached):13 context = detached.return_value = Mock()14 context.__enter__ = Mock()15 context.__exit__ = Mock()16 detach('/bin/boo', ['a', 'b', 'c'], logfile='/var/log',17 pidfile='/var/pid', hostname='foo@example.com')18 detached.assert_called_with(19 '/var/log', '/var/pid', None, None, None, None, False,20 after_forkers=False,21 )22 execv.assert_called_with('/bin/boo', ['/bin/boo', 'a', 'b', 'c'])23 r = detach('/bin/boo', ['a', 'b', 'c'],24 logfile='/var/log', pidfile='/var/pid',25 executable='/bin/foo', app=self.app)26 execv.assert_called_with('/bin/foo', ['/bin/foo', 'a', 'b', 'c'])27 execv.side_effect = Exception('foo')28 r = detach(29 '/bin/boo', ['a', 'b', 'c'],30 logfile='/var/log', pidfile='/var/pid',31 hostname='foo@example.com', app=self.app)32 context.__enter__.assert_called_with()33 logger.critical.assert_called()34 setup_logs.assert_called_with(35 'ERROR', '/var/log', hostname='foo@example.com')36 assert r == 137 self.patching('celery.current_app')38 from celery import current_app39 r = detach(40 '/bin/boo', ['a', 'b', 'c'],41 logfile='/var/log', pidfile='/var/pid',42 hostname='foo@example.com', app=None)43 current_app.log.setup_logging_subsystem.assert_called_with(44 'ERROR', '/var/log', hostname='foo@example.com',45 )46class test_PartialOptionParser:47 def test_parser(self):48 x = detached_celeryd(self.app)49 p = x.create_parser('celeryd_detach')50 options, leftovers = p.parse_known_args([51 '--logfile=foo', '--fake', '--enable',52 'a', 'b', '-c1', '-d', '2',53 ])...

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Using AI Code Generation

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1var root = document.getElementById('root');2root.detach();3var child = document.getElementById('child');4child.detach();5var text = document.getElementById('text');6text.detach();7How to use replaceChild() in JavaScript?8How to use append() in JavaScript?9How to use appendChild() in JavaScript?10How to use prepend() in JavaScript?11How to use removeChild() in JavaScript?12How to use replaceAll() in JavaScript?13How to use insertBefore() in JavaScript?14How to use insertAdjacentElement() in JavaScript?15How to use insertAdjacentHTML() in JavaScript?16How to use insertAdjacentText() in JavaScript?17How to use replaceWith() in JavaScript?18How to use before() in JavaScript?19How to use after() in JavaScript?20How to use replaceWith() in JavaScript?21How to use remove() in JavaScript?22How to use replace() in JavaScript?23How to use cloneNode() in JavaScript?24How to use contains() in JavaScript?25How to use compareDocumentPosition() in JavaScript?26How to use isEqualNode() in JavaScript?27How to use isSameNode() in JavaScript?28How to use isDefaultNamespace() in JavaScript?29How to use lookupPrefix() in JavaScript?30How to use lookupNamespaceURI() in JavaScript?31How to use getRootNode() in JavaScript?32How to use getAttribute() in JavaScript?33How to use getAttributeNames() in JavaScript?34How to use getAttributeNode() in JavaScript?35How to use hasAttribute() in JavaScript?36How to use hasAttributes() in JavaScript?37How to use setAttribute() in JavaScript?38How to use setAttributeNode() in JavaScript?39How to use setAttributeNodeNS() in JavaScript?40How to use setAttributeNS() in JavaScript?41How to use removeAttribute() in JavaScript?42How to use removeAttributeNode() in JavaScript?43How to use removeAttributeNS() in JavaScript?44How to use attributes() in JavaScript?45How to use hasChildNodes() in JavaScript?46How to use childNodes() in JavaScript?47How to use firstChild() in JavaScript?48How to use lastChild() in JavaScript?49How to use nextSibling() in JavaScript?50How to use previousSibling() in JavaScript?51How to use parentNode() in JavaScript?

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Using AI Code Generation

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1var express = require('express');2var app = express();3var root = app.route('/root');4var root1 = app.route('/root1');5var root2 = app.route('/root2');6var root3 = app.route('/root3');7var root4 = app.route('/root4');8var root5 = app.route('/root5');9var root6 = app.route('/root6');10var root7 = app.route('/root7');11var root8 = app.route('/root8');12 .get(function(req, res) {13 res.send('GET request to the homepage');14 })15 .post(function(req, res) {16 res.send('POST request to the homepage');17 });18 .get(function(req, res) {19 res.send('GET request to the homepage');20 })21 .post(function(req, res) {22 res.send('POST request to the homepage');23 });24 .get(function(req, res) {25 res.send('GET request to the homepage');26 })27 .post(function(req, res) {28 res.send('POST request to the homepage');29 });30 .get(function(req, res) {31 res.send('GET request to the homepage');32 })33 .post(function(req, res) {34 res.send('POST request to the homepage');35 });36 .get(function(req, res) {37 res.send('GET request to the homepage');38 })39 .post(function(req, res) {40 res.send('POST request to the homepage');41 });42 .get(function(req, res) {43 res.send('GET request to the homepage');44 })45 .post(function(req, res) {46 res.send('POST request to the homepage');47 });48 .get(function(req, res) {49 res.send('GET request to the homepage');50 })51 .post(function(req, res) {52 res.send('POST request to the homepage');53 });54 .get(function(req, res) {55 res.send('GET request to the homepage');56 })57 .post(function(req, res) {58 res.send('POST request to the homepage');59 });60 .get(function(req, res) {61 res.send('GET request to the homepage');62 })63 .post(function(req, res) {64 res.send('POST request

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Using AI Code Generation

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1var root = document.getElementById('root');2root.detach();3var root = document.getElementById('root');4root.append();5var root = document.getElementById('root');6root.removeChild();7var root = document.getElementById('root');8root.insertBefore();9var root = document.getElementById('root');10root.cloneNode();11var root = document.getElementById('root');12root.replaceChild();13var root = document.getElementById('root');14root.appendChild();15var root = document.getElementById('root');16root.hasChildNodes();17var root = document.getElementById('root');18root.firstChild();19var root = document.getElementById('root');20root.lastChild();21var root = document.getElementById('root');22root.nextSibling();23var root = document.getElementById('root');24root.previousSibling();25var root = document.getElementById('root');26root.nodeValue();27var root = document.getElementById('root');28root.nodeName();29var root = document.getElementById('root');30root.nodeType();31var root = document.getElementById('root');32root.parentNode();33var root = document.getElementById('root');34root.childNodes();35var root = document.getElementById('root');36root.hasAttributes();37var root = document.getElementById('root');38root.attributes();39var root = document.getElementById('root');40root.getAttribute();41var root = document.getElementById('root');42root.setAttribute();43var root = document.getElementById('root');44root.removeAttribute();45var root = document.getElementById('root

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Using AI Code Generation

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1var root = document.getElementById("root");2root.detach();3var root = document.getElementById("root");4root.remove();5var root = document.getElementById("root");6var child = root.children[0];7child.detach();8var root = document.getElementById("root");9var child = root.children[0];10child.remove();11var root = document.getElementById("root");12var child = root.children[0];13child.detach();14var root = document.getElementById("root");15var child = root.children[0];16child.remove();17var root = document.getElementById("root");18var child = root.children[0];19child.detach();20var root = document.getElementById("root");21var child = root.children[0];22child.remove();23var root = document.getElementById("root");24var child = root.children[0];25child.detach();26var root = document.getElementById("root");27var child = root.children[0];28child.remove();29var root = document.getElementById("root");30var child = root.children[0];31child.detach();32var root = document.getElementById("root");33var child = root.children[0];34child.remove();35var root = document.getElementById("root");36var child = root.children[0];37child.detach();38var root = document.getElementById("root");39var child = root.children[0];40child.remove();41var root = document.getElementById("root");42var child = root.children[0];43child.detach();44var root = document.getElementById("root");45var child = root.children[0];46child.remove();

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Using AI Code Generation

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1var root = document.getElementById("root");2var newElement = document.createElement("div");3newElement.setAttribute("id", "newElement");4root.appendChild(newElement);5root.removeChild(newElement);6var root = document.getElementById("root");7var newElement = document.createElement("div");8newElement.setAttribute("id", "newElement");9root.appendChild(newElement);10var child = document.getElementById("newElement");11root.removeChild(child);12var root = document.getElementById("root");13var newElement = document.createElement("div");14newElement.setAttribute("id", "newElement");15root.appendChild(newElement);16var child = document.getElementById("newElement");17root.removeChild(child.nextSibling);18var root = document.getElementById("root");19var newElement = document.createElement("div");20newElement.setAttribute("id", "newElement");21root.appendChild(newElement);22var child = document.getElementById("newElement");23root.removeChild(child.previousSibling);24var root = document.getElementById("root");25var newElement = document.createElement("div");26newElement.setAttribute("id", "newElement");27root.appendChild(newElement);28var child = document.getElementById("newElement");29root.removeChild(child.parentNode);30var root = document.getElementById("root");31var newElement = document.createElement("div");32newElement.setAttribute("id", "newElement");33root.appendChild(newElement);34var child = document.getElementById("newElement");35root.removeChild(child.parentElement);36var root = document.getElementById("root");37var newElement = document.createElement("div");38newElement.setAttribute("id", "newElement");39root.appendChild(newElement);40var child = document.getElementById("newElement");41root.removeChild(child.nextElementSibling);42var root = document.getElementById("root");43var newElement = document.createElement("div");44newElement.setAttribute("id", "newElement");45root.appendChild(newElement);46var child = document.getElementById("newElement");47root.removeChild(child.previousElementSibling);48var root = document.getElementById("root");

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1root.detach();2root.remove();3root.removeChild(root.firstChild);4root.empty();5root.clear();6root.detach();7root.remove();8root.removeChild(root.firstChild);9root.empty();10root.clear();11root.detach();12root.remove();13root.removeChild(root.firstChild);14root.empty();15root.clear();16root.detach();17root.remove();18root.removeChild(root.firstChild);19root.empty();20root.clear();21root.detach();22root.remove();23root.removeChild(root.firstChild);24root.empty();25root.clear();

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