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How to use detach method in ATX
Best Python code snippet using ATX
TestSuite_hotplug_mp.py
Source:TestSuite_hotplug_mp.py 
...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):...
losser.py
Source:losser.py
...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...
IQAloss.py
Source:IQAloss.py
...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:...
plotter.py
Source:plotter.py
...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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