How to use detach method in Playwright Python

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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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Playwright tutorial

LambdaTest’s Playwright tutorial will give you a broader idea about the Playwright automation framework, its unique features, and use cases with examples to exceed your understanding of Playwright testing. This tutorial will give A to Z guidance, from installing the Playwright framework to some best practices and advanced concepts.

Chapters:

  1. What is Playwright : Playwright is comparatively new but has gained good popularity. Get to know some history of the Playwright with some interesting facts connected with it.
  2. How To Install Playwright : Learn in detail about what basic configuration and dependencies are required for installing Playwright and run a test. Get a step-by-step direction for installing the Playwright automation framework.
  3. Playwright Futuristic Features: Launched in 2020, Playwright gained huge popularity quickly because of some obliging features such as Playwright Test Generator and Inspector, Playwright Reporter, Playwright auto-waiting mechanism and etc. Read up on those features to master Playwright testing.
  4. What is Component Testing: Component testing in Playwright is a unique feature that allows a tester to test a single component of a web application without integrating them with other elements. Learn how to perform Component testing on the Playwright automation framework.
  5. Inputs And Buttons In Playwright: Every website has Input boxes and buttons; learn about testing inputs and buttons with different scenarios and examples.
  6. Functions and Selectors in Playwright: Learn how to launch the Chromium browser with Playwright. Also, gain a better understanding of some important functions like “BrowserContext,” which allows you to run multiple browser sessions, and “newPage” which interacts with a page.
  7. Handling Alerts and Dropdowns in Playwright : Playwright interact with different types of alerts and pop-ups, such as simple, confirmation, and prompt, and different types of dropdowns, such as single selector and multi-selector get your hands-on with handling alerts and dropdown in Playright testing.
  8. Playwright vs Puppeteer: Get to know about the difference between two testing frameworks and how they are different than one another, which browsers they support, and what features they provide.
  9. Run Playwright Tests on LambdaTest: Playwright testing with LambdaTest leverages test performance to the utmost. You can run multiple Playwright tests in Parallel with the LammbdaTest test cloud. Get a step-by-step guide to run your Playwright test on the LambdaTest platform.
  10. Playwright Python Tutorial: Playwright automation framework support all major languages such as Python, JavaScript, TypeScript, .NET and etc. However, there are various advantages to Python end-to-end testing with Playwright because of its versatile utility. Get the hang of Playwright python testing with this chapter.
  11. Playwright End To End Testing Tutorial: Get your hands on with Playwright end-to-end testing and learn to use some exciting features such as TraceViewer, Debugging, Networking, Component testing, Visual testing, and many more.
  12. Playwright Video Tutorial: Watch the video tutorials on Playwright testing from experts and get a consecutive in-depth explanation of Playwright automation testing.

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