Test your AI Agents with the all-new Agent to Agent Testing Platform.Learn More

How to use test_test_labels method in autotest

Best Python code snippet using autotest_python

TriangleConv_relation.py

Source:TriangleConv_relation.py

1import torch2import torch.nn as nn3import os4import numpy as np5import re6import random7import math8from torch.optim import lr_scheduler9from collections import OrderedDict10from torch.autograd import Variable11import warnings12warnings.filterwarnings("ignore")13import torch.nn.functional as F14import torch.utils.data as data15from get_sample_object import getdata16from new_test_dataset import Model10DataSet17device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')18"""19dirpath: str, the path of the directory20"""21def conv_bn_block(input, output, kernel_size):22    return nn.Sequential(23        nn.Conv1d(input, output, kernel_size),24        nn.BatchNorm1d(output),25        nn.ReLU(inplace=True)26    )27def fc_bn_block(input, output):28    return nn.Sequential(29        nn.Linear(input, output),30        nn.BatchNorm1d(output),31        nn.ReLU(inplace=True)32    )33class TriangleConv(nn.Module):34    def __init__(self, layers):35        super(TriangleConv, self).__init__()36        self.layers = layers37        mlp_layers = OrderedDict()38        for i in range(len(self.layers) - 1):39            if i == 0:40                mlp_layers['conv_bn_block_{}'.format(i + 1)] = conv_bn_block(4 * self.layers[i], self.layers[i + 1], 1)41            else:42                mlp_layers['conv_bn_block_{}'.format(i + 1)] = conv_bn_block(self.layers[i], self.layers[i + 1], 1)43        self.mlp = nn.Sequential(mlp_layers)44    def forward(self, X):45        B, N, F = X.shape46        k_indexes = []47        for i in range(N):48            if i == 0:49                k_indexes.append([N - 1, i + 1])50            elif i == N-1:51                k_indexes.append([i - 1, 0])52            else:53                k_indexes.append([i - 1, i+1])54        k_indexes_tensor = torch.Tensor(k_indexes)55        k_indexes_tensor = k_indexes_tensor.long()56        x1 = torch.zeros(B, N, 2, F).to(device)57        for idx, x in enumerate(X):58            x1[idx] = x[k_indexes_tensor]59        x2 = X.reshape([B, N, 1, F]).float()60        x2 = x2.expand(B, N, 2, F)61        x2 = x2-x162        x3 = x2[:, :, 0:1, :]63        x4 = x2[:, :, 1:2, :]64        x4 = x3-x465        x5 = X.reshape([B, N, 1, F]).float()66        x2 = x2.reshape([B, N, 1, 2*F])67        x_triangle = torch.cat([x5, x2, x4], dim=3)68        x_triangle=torch.squeeze(x_triangle)69        x_triangle = x_triangle.permute(0, 2, 1)70        x_triangle = torch.tensor(x_triangle,dtype=torch.float32).to(device)71        out = self.mlp(x_triangle)72        out = out.permute(0, 2, 1)73        return out74class DPCN_vanilla(nn.Module):75    def __init__(self):76        super(DPCN_vanilla, self).__init__()77        #self.num_classes = num_classes78        self.triangleconv_1 = TriangleConv(layers=[2, 64, 64, 64])79        self.triangleconv_2 = TriangleConv(layers=[64, 1024,64])80        #self.fc_block_4 = fc_bn_block(1024, 512)81        #self.drop_4 = nn.Dropout(0.5)82        #self.fc_block_5 = fc_bn_block(512, 256)83        #self.drop_5 = nn.Dropout(0.5)84        #self.fc_6 = nn.Linear(256, 64)85        #self._initialize_weights()86    def _initialize_weights(self):87        for m in self.modules():88            if isinstance(m, nn.Conv1d) or isinstance(m, nn.Linear):89                nn.init.xavier_normal_(m.weight)90                if m.bias is not None:91                    nn.init.constant_(m.bias, 0)92            elif isinstance(m, nn.BatchNorm1d):93                nn.init.constant_(m.weight, 1)94                nn.init.constant_(m.bias, 0)95    def forward(self, x):96        B, N, C = x.shape97        assert C == 2, 'dimension of x does not match'98        x = self.triangleconv_1(x)99        x = self.triangleconv_2(x)100        x = x.permute(0, 2, 1)101        x = nn.MaxPool1d(N)(x)102        x = x.reshape([B, -1])103        return x104class RelationNetwork(nn.Module):105    def __init__(self,input_size,hidden_size):106        super(RelationNetwork, self).__init__()107        self.layer1 = nn.Sequential(108                        nn.Conv1d(2,128,kernel_size=3),109                        nn.BatchNorm1d(128,momentum=0.9, affine=True),110                        nn.LeakyReLU(),111                        nn.MaxPool1d(2))112        self.layer2 = nn.Sequential(113                        nn.Conv1d(128,64,kernel_size=3),114                        nn.BatchNorm1d(64,momentum=0.9, affine=True),115                        nn.LeakyReLU(),116                        nn.MaxPool1d(2))117        self.fc1 = nn.Linear(896,8)#896118        self.fc2 = nn.Linear(8,1)119    def forward(self,x):120        out = self.layer1(x)#121        out = self.layer2(out)#122        out = out.view(out.size(0),-1)#123        out = F.relu(self.fc1(out))#124        out = torch.sigmoid(self.fc2(out))#125        return out126class_number = 2127support_number = 5128support_train_shot = 5129support_test_shot = 15130def weights_init(m):131    classname = m.__class__.__name__132    if classname.find('Conv') != -1:133        n = m.kernel_size[0]  * m.out_channels134        m.weight.data.normal_(0, math.sqrt(2. / n))135        if m.bias is not None:136            m.bias.data.zero_()137    elif classname.find('BatchNorm') != -1:138        m.weight.data.fill_(1)139        m.bias.data.zero_()140    elif classname.find('Linear') != -1:141        n = m.weight.size(1)142        m.weight.data.normal_(0, 0.01)143        m.bias.data = torch.ones(m.bias.data.size())144def new_getdataset(support_data = None, val_data = None, index = None, train_shuffle=True, test_shuffle=True, train1= True):145    support_train_dataset_list = []146    support_test_dataset_list = []147    label_number = 0148    if train1 == True:149        per_task_data = random.sample(support_data,class_number)150    else:151        per_task_data = random.sample(val_data, class_number)152    for i in per_task_data:153        support_train_perclass_dataset = Model10DataSet(train=True,train_xy_reshape = i, index =index,154                                                        shot = support_train_shot,test_shot = support_test_shot,155                                                        label_number = label_number)156        support_train_dataset_list.append(support_train_perclass_dataset)157        support_test_perclass_dataset = Model10DataSet(train=False, train_xy_reshape= i, index=index,158                                                        shot=support_train_shot, test_shot=support_test_shot,159                                                        label_number=label_number)160        support_test_dataset_list.append(support_test_perclass_dataset)161        label_number =label_number + 1162    train_dataset_concat = data.ConcatDataset(support_train_dataset_list)163    test_dataset_concat = data.ConcatDataset(support_test_dataset_list)164    train_loader = torch.utils.data.DataLoader(train_dataset_concat, batch_size=class_number * support_train_shot,165                                               shuffle=train_shuffle, num_workers=0)166    test_loader = torch.utils.data.DataLoader(test_dataset_concat, batch_size=class_number * support_test_shot,167                                              shuffle=test_shuffle, num_workers=0)168    support_train, support_train_labels = train_loader.__iter__().next()  # 25,25169    # print(support_train.shape)170    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')171    support_train, support_train_labels = support_train.to(device), support_train_labels.to(device)172    support_test, support_test_labels = test_loader.__iter__().next()  # 75,75173    support_test, support_test_labels = support_test.to(device), support_test_labels.to(device)174    # print(type(train_loader))175    return support_train, support_train_labels, support_test, support_test_labels176def main():177    print("init data:")178    all_train_data, index = getdata()179    all_train_data = np.array(all_train_data)180    print("This is all_train_data:", all_train_data.shape)181    support_data = all_train_data[0:support_number, :, :, :].tolist()182    val_data = all_train_data[support_number:, :, :, :].tolist()183    print("all_train_data:", np.array(all_train_data).shape)184    print("support_data:", np.array(support_data).shape)185    print("val_data:", np.array(val_data).shape)186    print("init network")187    model = DPCN_vanilla().cuda()188    relation_network = RelationNetwork(16, 8)189    relation_network.apply(weights_init)190    relation_network.cuda()191    optimizer = torch.optim.Adam(model.parameters(), lr=0.001)192    schedular = lr_scheduler.StepLR(optimizer, step_size=10000, gamma=0.5)193    relation_network_optim = torch.optim.Adam(relation_network.parameters(), lr=0.001)194    relation_network_scheduler = lr_scheduler.StepLR(relation_network_optim, step_size=10000, gamma=0.5)195    print("Training~~~~~")196    best_accuracy = 0.0197    for episode in range(20000):198        schedular.step()199        relation_network_scheduler.step()200        model.train()201        relation_network.train()202        support_train, support_train_labels, support_test, support_test_labels = new_getdataset(support_data = support_data, index = index, val_data = val_data, train_shuffle=False,test_shuffle=True, train1=True)203        out_support_train = model(support_train)204        out_support_train_2 = out_support_train.view(class_number, support_train_shot, 1, 64)205        out_support_train_3 = torch.sum(out_support_train_2, 1).squeeze(1)206        out_support_train_repeat = out_support_train_3.unsqueeze(0).repeat(class_number * support_test_shot, 1, 1)207        out_support_test = model(support_test)208        out_support_test_repeat = out_support_test.unsqueeze(0).repeat(class_number, 1, 1)209        out_support_test_repeat_transpose = torch.transpose(out_support_test_repeat, 0, 1)210        relation_pairs = torch.cat((out_support_train_repeat, out_support_test_repeat_transpose), 2).view(-1,  1* 2, 64)211        relations = relation_network(relation_pairs).view(-1, class_number)212        mse = nn.MSELoss().cuda()213        input_zero = torch.zeros(support_test_shot * class_number, class_number).cuda()214        support_test_labels = torch.squeeze(support_test_labels, dim=-1)215        input_scatter = input_zero.scatter_(1, support_test_labels.long().view(-1, 1),1)216        one_hot_labels = Variable(input_scatter).cuda()217        loss = mse(relations, one_hot_labels)218        optimizer.zero_grad()219        relation_network_optim.zero_grad()220        loss.backward()221        optimizer.step()222        relation_network_optim.step()223        torch.nn.utils.clip_grad_norm_(model.parameters(), 0.5)224        torch.nn.utils.clip_grad_norm_(relation_network.parameters(), 0.5)225        if (episode + 1) % 100 == 0:226            print("episode:", episode + 1, "loss", loss.item())227        if(episode+1)%500== 0:228            print("Testing")229            total_rewards = 0230            model.eval()231            relation_network.eval()232            for i in range(300):233                test_train, test_train_labels, test_test, test_test_labels = new_getdataset(support_data = support_data, val_data = val_data, index = index, train_shuffle=False,test_shuffle=True, train1= False)234                out_test_train = model(test_train)235                out_test_train_2 = out_test_train.view(class_number, support_train_shot, 1, 64)236                out_test_train_3 = torch.sum(out_test_train_2, 1).squeeze(1)237                out_test_train_repeat = out_test_train_3.unsqueeze(0).repeat(class_number * support_test_shot,238                                                                                   1, 1)239                out_test_test = model(test_test)240                out_test_test_repeat = out_test_test.unsqueeze(0).repeat(class_number,  1, 1)241                out_test_test_repeat_transpose = torch.transpose(out_test_test_repeat, 0, 1)242                relation_pairs = torch.cat((out_test_train_repeat, out_test_test_repeat_transpose), 2).view(-1,1 * 2, 64)243                relations = relation_network(relation_pairs).view(-1, class_number)244                _, predict_labels = torch.max(relations.data, 1)245                predict_labels = predict_labels.cpu().int()246                test_test_labels = test_test_labels.cpu().int()247                rewards = [1 if predict_labels[j] == test_test_labels[j] else 0 for j in248                           range(class_number * support_test_shot)]249                total_rewards += np.sum(rewards)250            test_accuracy = total_rewards / 1.0 / class_number / support_test_shot / 300251            print("test accuracy:", test_accuracy)252            if test_accuracy > best_accuracy:253                # save networks254                #torch.save(model.state_dict(),str("./save_models/new_relation/model_" + str(class_number) +"way_" + str(support_test_shot) +"shot.pkl"))255                #torch.save(relation_network.state_dict(),str("./save_models/new_relation/relation_network_"+ str(class_number) +"way_" + str(support_test_shot) +"shot.pkl"))256                #print("save networks for episode:",episode+1)257                best_accuracy = test_accuracy258             model.train()259             relation_network.train()260    print("best_accuracy:",best_accuracy)261    return best_accuracy262if __name__ == '__main__':...

test_mnist.py

Source:test_mnist.py

...46        self.assertEqual((60000,), y.shape)47    def test_test_data(self):48        x_l = self.d.get_test_data()49        self.assertEqual((10000, 28, 28), x_l.shape)50    def test_test_labels(self):51        y_l = self.d.get_test_labels()52        self.assertEqual((10000,), y_l.shape)53    def test_multi_log_reg(self):54        # Reduced because we want the tests to finish a bit faster.55        train_count = 1500056        test_count = 500057        # Train data58        X = self.sds.from_numpy( self.d.get_train_data().reshape(59            (60000, 28*28))[:train_count])60        Y = self.sds.from_numpy( self.d.get_train_labels()[:train_count])61        Y = Y + 1.062        # Test data63        Xt = self.sds.from_numpy( self.d.get_test_data().reshape(64            (10000, 28*28))[:test_count])...

test_data.py

Source:test_data.py

...31    train_labels = [int(i[1]) for i in train_set]32    numbers = set(range(0, 10))33    assert set(train_labels) == numbers, "All the labels were not found in the training dataset"34@pytest.mark.skipif(not os.path.exists('data/processed/test_tensor.pt'), reason="Data not found")35def test_test_labels():36    test_set = MnistDataset('data/processed/test_tensor.pt')37    numbers = set(range(0, 10))38    test_labels = [int(i[1]) for i in test_set]...

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.