コード例 #1
0
def test_optimizer_momentum_rmsprop(system_dict):
    forward = True;
    if(not os.path.isdir("datasets")):
        os.system("! wget --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download&confirm=$(wget --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1rG-U1mS8hDU7_wM56a1kc-li_zHLtbq2' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=1rG-U1mS8hDU7_wM56a1kc-li_zHLtbq2\" -O datasets.zip && rm -rf /tmp/cookies.txt")
        os.system("! unzip -qq datasets.zip")

    test = "test_optimizer_momentum_rmsprop";
    system_dict["total_tests"] += 1;
    print_start(test, system_dict["total_tests"])
    if(forward):
        try:
            gtf = prototype(verbose=0);
            gtf.Prototype("sample-project-1", "sample-experiment-1");
            gtf.Default(dataset_path="datasets/dataset_cats_dogs_train", 
                model_name="resnet18", freeze_base_network=True, num_epochs=2);
            gtf.optimizer_momentum_rmsprop(0.01, weight_decay=0.0001, decay_rate=0.9);
            gtf.Train();
            system_dict["successful_tests"] += 1;
            print_status("Pass");
        except Exception as e:
            system_dict["failed_tests_exceptions"].append(e);
            system_dict["failed_tests_lists"].append(test);
            forward = False;
            print_status("Fail");
    else:
        system_dict["skipped_tests_lists"].append(test);
        print_status("Skipped");

    return system_dict
コード例 #2
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def test_activation_softmin(system_dict):
    forward = True

    test = "test_activation_softmin"
    system_dict["total_tests"] += 1
    print_start(test, system_dict["total_tests"])
    if (forward):
        try:
            gtf = prototype(verbose=0)
            gtf.Prototype("sample-project-1", "sample-experiment-1")

            network = []
            network.append(gtf.softmin())
            gtf.Compile_Network(network, data_shape=(3, 64, 64), use_gpu=False)

            x = torch.randn(1, 3, 64, 64)
            y = gtf.system_dict["local"]["model"](x)

            system_dict["successful_tests"] += 1
            print_status("Pass")

        except Exception as e:
            system_dict["failed_tests_exceptions"].append(e)
            system_dict["failed_tests_lists"].append(test)
            forward = False
            print_status("Fail")
    else:
        system_dict["skipped_tests_lists"].append(test)
        print_status("Skipped")

    return system_dict
コード例 #3
0
def test_activation_rrelu(system_dict):
    forward = True;

    test = "test_activation_rrelu";
    system_dict["total_tests"] += 1;
    print_start(test, system_dict["total_tests"])
    if(forward):
        try:
            gtf = prototype(verbose=0);
            gtf.Prototype("sample-project-1", "sample-experiment-1");


            network = [];
            network.append(gtf.rrelu());
            gtf.Compile_Network(network, data_shape=(3, 64, 64), use_gpu=False);

            x = torch.randn(1, 3, 64, 64);
            y = gtf.system_dict["local"]["model"](x);       

            system_dict["successful_tests"] += 1;
            print_status("Pass");

        except Exception as e:
            system_dict["failed_tests_exceptions"].append(e);
            system_dict["failed_tests_lists"].append(test);
            forward = False;
            print_status("Fail");
    else:
        system_dict["skipped_tests_lists"].append(test);
        print_status("Skipped");

    return system_dict
コード例 #4
0
ファイル: test_loss_kldiv.py プロジェクト: yaroschak/monk_v1
def test_loss_kldiv(system_dict):
    forward = True

    test = "test_loss_kldiv"
    system_dict["total_tests"] += 1
    print_start(test, system_dict["total_tests"])
    if (forward):
        try:
            gtf = prototype(verbose=0)
            gtf.Prototype("sample-project-1", "sample-experiment-1")

            label = torch.randn(1, 5)

            y = torch.randn(1, 5)

            gtf.loss_kldiv()
            load_loss(gtf.system_dict)
            loss_obj = gtf.system_dict["local"]["criterion"]
            loss_val = loss_obj(y, label)

            system_dict["successful_tests"] += 1
            print_status("Pass")

        except Exception as e:
            system_dict["failed_tests_exceptions"].append(e)
            system_dict["failed_tests_lists"].append(test)
            forward = False
            print_status("Fail")
    else:
        system_dict["skipped_tests_lists"].append(test)
        print_status("Skipped")

    return system_dict
コード例 #5
0
def test_block_densenet(system_dict):
    forward = True;

    test = "test_block_densenet";
    system_dict["total_tests"] += 1;
    print_start(test, system_dict["total_tests"])
    if(forward):
        try:
            gtf = prototype(verbose=0);
            gtf.Prototype("sample-project-1", "sample-experiment-1");


            network = [];
            network.append(gtf.densenet_block(bottleneck_size=4, growth_rate=16, dropout=0.2));
            gtf.Compile_Network(network, data_shape=(1, 64, 64), use_gpu=False);

            x = torch.randn(1, 1, 64, 64);
            y = gtf.system_dict["local"]["model"](x);     

            system_dict["successful_tests"] += 1;
            print_status("Pass");

        except Exception as e:
            system_dict["failed_tests_exceptions"].append(e);
            system_dict["failed_tests_lists"].append(test);
            forward = False;
            print_status("Fail");
    else:
        system_dict["skipped_tests_lists"].append(test);
        print_status("Skipped");

    return system_dict
コード例 #6
0
def test_loss_sigmoid_binary_crossentropy(system_dict):
    forward = True;

    test = "test_loss_sigmoid_binary_crossentropy";
    system_dict["total_tests"] += 1;
    print_start(test, system_dict["total_tests"])
    if(forward):
        try:
            gtf = prototype(verbose=0);
            gtf.Prototype("sample-project-1", "sample-experiment-1");

            label = torch.empty((1, 5)).random_(2);

            y = torch.randn(1, 5);

            gtf.loss_sigmoid_binary_crossentropy();
            load_loss(gtf.system_dict);
            loss_obj = gtf.system_dict["local"]["criterion"];
            loss_val = loss_obj(y, label);           

            system_dict["successful_tests"] += 1;
            print_status("Pass");

        except Exception as e:
            system_dict["failed_tests_exceptions"].append(e);
            system_dict["failed_tests_lists"].append(test);
            forward = False;
            print_status("Fail");
    else:
        system_dict["skipped_tests_lists"].append(test);
        print_status("Skipped");

    return system_dict
コード例 #7
0
def test_block_inception_a(system_dict):
    forward = True;

    test = "test_block_inception_a";
    system_dict["total_tests"] += 1;
    print_start(test, system_dict["total_tests"])
    if(forward):
        try:
            gtf = prototype(verbose=0);
            gtf.Prototype("sample-project-1", "sample-experiment-1");


            network = [];
            network.append(gtf.inception_a_block(pooling_branch_channels=32, pool_type="avg"));
            network.append(gtf.inception_a_block(pooling_branch_channels=32, pool_type="max"));
            gtf.Compile_Network(network, data_shape=(1, 64, 64), use_gpu=False);

            x = torch.randn(1, 1, 64, 64);
            y = gtf.system_dict["local"]["model"](x);         

            system_dict["successful_tests"] += 1;
            print_status("Pass");

        except Exception as e:
            system_dict["failed_tests_exceptions"].append(e);
            system_dict["failed_tests_lists"].append(test);
            forward = False;
            print_status("Fail");
    else:
        system_dict["skipped_tests_lists"].append(test);
        print_status("Skipped");

    return system_dict
コード例 #8
0
def test_layer_transposed_convolution2d(system_dict):
    forward = True

    test = "test_layer_transposed_convolution2d"
    system_dict["total_tests"] += 1
    print_start(test, system_dict["total_tests"])
    if (forward):
        try:
            gtf = prototype(verbose=0)
            gtf.Prototype("sample-project-1", "sample-experiment-1")

            network = []
            network.append(
                gtf.transposed_convolution2d(output_channels=3, kernel_size=3))
            gtf.Compile_Network(network,
                                data_shape=(3, 128, 128),
                                use_gpu=False)

            x = torch.randn(1, 3, 128, 128)
            y = gtf.system_dict["local"]["model"](x)

            system_dict["successful_tests"] += 1
            print_status("Pass")

        except Exception as e:
            system_dict["failed_tests_exceptions"].append(e)
            system_dict["failed_tests_lists"].append(test)
            forward = False
            print_status("Fail")
    else:
        system_dict["skipped_tests_lists"].append(test)
        print_status("Skipped")

    return system_dict
コード例 #9
0
def test_block_resnet_v2_bottleneck(system_dict):
    forward = True;

    test = "test_block_resnet_v2_bottleneck";
    system_dict["total_tests"] += 1;
    print_start(test, system_dict["total_tests"])
    if(forward):
        try:
            gtf = prototype(verbose=0);
            gtf.Prototype("sample-project-1", "sample-experiment-1");


            network = [];
            network.append(gtf.resnet_v2_bottleneck_block(output_channels=32, stride=1, downsample=True));
            network.append(gtf.resnet_v2_bottleneck_block(output_channels=32, stride=1, downsample=False));
            gtf.Compile_Network(network, data_shape=(1, 64, 64), use_gpu=False);

            x = torch.randn(1, 1, 64, 64);
            y = gtf.system_dict["local"]["model"](x);    

            system_dict["successful_tests"] += 1;
            print_status("Pass");

        except Exception as e:
            system_dict["failed_tests_exceptions"].append(e);
            system_dict["failed_tests_lists"].append(test);
            forward = False;
            print_status("Fail");
    else:
        system_dict["skipped_tests_lists"].append(test);
        print_status("Skipped");

    return system_dict
コード例 #10
0
def test_block_resnext(system_dict):
    forward = True

    test = "test_block_resnext"
    system_dict["total_tests"] += 1
    print_start(test, system_dict["total_tests"])
    if (forward):
        try:
            gtf = prototype(verbose=0)
            gtf.Prototype("sample-project-1", "sample-experiment-1")

            network = []
            network.append(
                gtf.resnext_block(output_channels=256,
                                  cardinality=8,
                                  bottleneck_width=4,
                                  stride=1,
                                  downsample=True))
            network.append(
                gtf.resnext_block(output_channels=256,
                                  cardinality=8,
                                  bottleneck_width=4,
                                  stride=1,
                                  downsample=False))
            gtf.Compile_Network(network, data_shape=(1, 64, 64), use_gpu=False)

            x = torch.randn(1, 1, 64, 64)
            y = gtf.system_dict["local"]["model"](x)

            system_dict["successful_tests"] += 1
            print_status("Pass")

        except Exception as e:
            system_dict["failed_tests_exceptions"].append(e)
            system_dict["failed_tests_lists"].append(test)
            forward = False
            print_status("Fail")
    else:
        system_dict["skipped_tests_lists"].append(test)
        print_status("Skipped")

    return system_dict
コード例 #11
0
def test_layer_concatenate(system_dict):
    forward = True

    test = "test_layer_concatenate"
    system_dict["total_tests"] += 1
    print_start(test, system_dict["total_tests"])
    if (forward):
        try:
            gtf = prototype(verbose=0)
            gtf.Prototype("sample-project-1", "sample-experiment-1")

            network = []
            network.append(gtf.convolution(output_channels=16))
            network.append(gtf.batch_normalization())
            network.append(gtf.relu())
            network.append(gtf.max_pooling())

            subnetwork = []
            branch1 = []
            branch1.append(gtf.convolution(output_channels=16))
            branch1.append(gtf.batch_normalization())
            branch1.append(gtf.convolution(output_channels=16))
            branch1.append(gtf.batch_normalization())

            branch2 = []
            branch2.append(gtf.convolution(output_channels=16))
            branch2.append(gtf.batch_normalization())

            branch3 = []
            branch3.append(gtf.identity())

            subnetwork.append(branch1)
            subnetwork.append(branch2)
            subnetwork.append(branch3)
            subnetwork.append(gtf.concatenate())

            network.append(subnetwork)

            network.append(gtf.convolution(output_channels=16))
            network.append(gtf.batch_normalization())
            network.append(gtf.relu())
            network.append(gtf.max_pooling())

            network.append(gtf.flatten())
            network.append(gtf.fully_connected(units=1024))
            network.append(gtf.dropout(drop_probability=0.2))
            network.append(gtf.fully_connected(units=2))

            gtf.Compile_Network(network, data_shape=(3, 64, 64), use_gpu=False)

            x = torch.randn(1, 3, 64, 64)
            y = gtf.system_dict["local"]["model"](x)

            system_dict["successful_tests"] += 1
            print_status("Pass")

        except Exception as e:
            system_dict["failed_tests_exceptions"].append(e)
            system_dict["failed_tests_lists"].append(test)
            forward = False
            print_status("Fail")
    else:
        system_dict["skipped_tests_lists"].append(test)
        print_status("Skipped")

    return system_dict