KaneAI - World’s First E2E Software Testing Agent.Book a Demo Join TestMu Conference

How to use _module_exists method in avocado

Best Python code snippet using avocado_python

test_jasper_block.py

Source:test_jasper_block.py

...29            se=False,30        )31        base.update(kwargs)32        return base33    def check_module_exists(self, module, cls):34        global _MODULE_EXISTS35        _MODULE_EXISTS = 036        def _traverse(m):37            if isinstance(m, cls):38                global _MODULE_EXISTS39                _MODULE_EXISTS += 140        module.apply(_traverse)41        assert _MODULE_EXISTS > 042    @pytest.mark.unit43    def test_basic_block(self):44        config = self.jasper_base_config(residual=False)45        act = jasper.jasper_activations.get(config.pop('activation'))()46        block = jasper.JasperBlock(**config, activation=act)47        x = torch.randn(1, 16, 131)48        xlen = torch.tensor([131])49        y, ylen = block(([x], xlen))50        assert isinstance(block, jasper.JasperBlock)51        assert y[0].shape == torch.Size([1, config['planes'], 131])52        assert ylen[0] == 13153    @pytest.mark.unit54    def test_residual_block(self):55        config = self.jasper_base_config(residual=True)56        act = jasper.jasper_activations.get(config.pop('activation'))()57        block = jasper.JasperBlock(**config, activation=act)58        x = torch.randn(1, 16, 131)59        xlen = torch.tensor([131])60        y, ylen = block(([x], xlen))61        assert isinstance(block, jasper.JasperBlock)62        assert y[0].shape == torch.Size([1, config['planes'], 131])63        assert ylen[0] == 13164    @pytest.mark.unit65    def test_basic_block_repeat(self):66        config = self.jasper_base_config(residual=False, repeat=3)67        act = jasper.jasper_activations.get(config.pop('activation'))()68        block = jasper.JasperBlock(**config, activation=act)69        x = torch.randn(1, 16, 131)70        xlen = torch.tensor([131])71        y, ylen = block(([x], xlen))72        assert isinstance(block, jasper.JasperBlock)73        assert y[0].shape == torch.Size([1, config['planes'], 131])74        assert ylen[0] == 13175        assert len(block.mconv) == 3 * 3 + 1  # (3 repeats x {1 conv + 1 norm + 1 dropout} + final conv)76    @pytest.mark.unit77    def test_basic_block_repeat_stride(self):78        config = self.jasper_base_config(residual=False, repeat=3, stride=[2])79        act = jasper.jasper_activations.get(config.pop('activation'))()80        block = jasper.JasperBlock(**config, activation=act)81        x = torch.randn(1, 16, 131)82        xlen = torch.tensor([131])83        y, ylen = block(([x], xlen))84        assert isinstance(block, jasper.JasperBlock)85        assert y[0].shape == torch.Size([1, config['planes'], 17])  # 131 // (stride ^ repeats)86        assert ylen[0] == 17  # 131 // (stride ^ repeats)87        assert len(block.mconv) == 3 * 3 + 1  # (3 repeats x {1 conv + 1 norm + 1 dropout} + final conv)88    @pytest.mark.unit89    def test_basic_block_repeat_stride_last(self):90        config = self.jasper_base_config(residual=False, repeat=3, stride=[2], stride_last=True)91        act = jasper.jasper_activations.get(config.pop('activation'))()92        block = jasper.JasperBlock(**config, activation=act)93        x = torch.randn(1, 16, 131)94        xlen = torch.tensor([131])95        y, ylen = block(([x], xlen))96        assert isinstance(block, jasper.JasperBlock)97        assert y[0].shape == torch.Size([1, config['planes'], 66])  # 131 // stride98        assert ylen[0] == 66  # 131 // stride99        assert len(block.mconv) == 3 * 3 + 1  # (3 repeats x {1 conv + 1 norm + 1 dropout} + final conv)100    @pytest.mark.unit101    def test_basic_block_repeat_separable(self):102        config = self.jasper_base_config(residual=False, repeat=3, separable=True)103        act = jasper.jasper_activations.get(config.pop('activation'))()104        block = jasper.JasperBlock(**config, activation=act)105        x = torch.randn(1, 16, 131)106        xlen = torch.tensor([131])107        y, ylen = block(([x], xlen))108        assert isinstance(block, jasper.JasperBlock)109        assert y[0].shape == torch.Size([1, config['planes'], 131])110        assert ylen[0] == 131111        assert len(block.mconv) == 3 * 4 + 1  # (3 repeats x {1 dconv + 1 pconv + 1 norm + 1 dropout} + final conv)112    @pytest.mark.unit113    def test_basic_block_stride(self):114        config = self.jasper_base_config(stride=[2], residual=False)115        act = jasper.jasper_activations.get(config.pop('activation'))()116        print(config)117        block = jasper.JasperBlock(**config, activation=act)118        x = torch.randn(1, 16, 131)119        xlen = torch.tensor([131])120        y, ylen = block(([x], xlen))121        assert isinstance(block, jasper.JasperBlock)122        assert y[0].shape == torch.Size([1, config['planes'], 66])123        assert ylen[0] == 66124    @pytest.mark.unit125    def test_residual_block_stride(self):126        config = self.jasper_base_config(stride=[2], residual=True, residual_mode='stride_add')127        act = jasper.jasper_activations.get(config.pop('activation'))()128        print(config)129        block = jasper.JasperBlock(**config, activation=act)130        x = torch.randn(1, 16, 131)131        xlen = torch.tensor([131])132        y, ylen = block(([x], xlen))133        assert isinstance(block, jasper.JasperBlock)134        assert y[0].shape == torch.Size([1, config['planes'], 66])135        assert ylen[0] == 66136    @pytest.mark.unit137    def test_residual_block_activations(self):138        for activation in jasper.jasper_activations.keys():139            config = self.jasper_base_config(activation=activation)140            act = jasper.jasper_activations.get(config.pop('activation'))()141            block = jasper.JasperBlock(**config, activation=act)142            x = torch.randn(1, 16, 131)143            xlen = torch.tensor([131])144            y, ylen = block(([x], xlen))145            self.check_module_exists(block, act.__class__)146            assert isinstance(block, jasper.JasperBlock)147            assert y[0].shape == torch.Size([1, config['planes'], 131])148            assert ylen[0] == 131149    @pytest.mark.unit150    def test_residual_block_normalizations(self):151        NORMALIZATIONS = ["batch", "layer", "group"]152        for normalization in NORMALIZATIONS:153            config = self.jasper_base_config(normalization=normalization)154            act = jasper.jasper_activations.get(config.pop('activation'))()155            block = jasper.JasperBlock(**config, activation=act)156            x = torch.randn(1, 16, 131)157            xlen = torch.tensor([131])158            y, ylen = block(([x], xlen))159            assert isinstance(block, jasper.JasperBlock)160            assert y[0].shape == torch.Size([1, config['planes'], 131])161            assert ylen[0] == 131162    @pytest.mark.unit163    def test_residual_block_se(self):164        config = self.jasper_base_config(se=True, se_reduction_ratio=8)165        act = jasper.jasper_activations.get(config.pop('activation'))()166        block = jasper.JasperBlock(**config, activation=act)167        x = torch.randn(1, 16, 131)168        xlen = torch.tensor([131])169        y, ylen = block(([x], xlen))170        self.check_module_exists(block, jasper.SqueezeExcite)171        assert isinstance(block, jasper.JasperBlock)172        assert y[0].shape == torch.Size([1, config['planes'], 131])173        assert ylen[0] == 131174    @pytest.mark.unit175    def test_residual_block_asymmetric_pad_future_contexts(self):176        # test future contexts at various values177        # 0 = no future context178        # 2 = limited future context179        # 5 = symmetric context180        # 8 = excess future context (more future context than present or past context)181        future_contexts = [0, 2, 5, 8]182        for future_context in future_contexts:183            print(future_context)184            config = self.jasper_base_config(future_context=future_context)185            act = jasper.jasper_activations.get(config.pop('activation'))()186            block = jasper.JasperBlock(**config, activation=act)187            x = torch.randn(1, 16, 131)188            xlen = torch.tensor([131])189            y, ylen = block(([x], xlen))190            self.check_module_exists(block, torch.nn.ConstantPad1d)191            self.check_module_exists(block, jasper.MaskedConv1d)192            assert isinstance(block, jasper.JasperBlock)193            assert y[0].shape == torch.Size([1, config['planes'], 131])194            assert ylen[0] == 131195            assert block.mconv[0].pad_layer is not None196            assert block.mconv[0]._padding == (config['kernel_size'][0] - 1 - future_context, future_context)197    @pytest.mark.unit198    def test_residual_block_asymmetric_pad_future_context_fallback(self):199        # test future contexts at various values200        # 15 = K < FC; fall back to symmetric context201        future_context = 15202        print(future_context)203        config = self.jasper_base_config(future_context=future_context)204        act = jasper.jasper_activations.get(config.pop('activation'))()205        block = jasper.JasperBlock(**config, activation=act)206        x = torch.randn(1, 16, 131)207        xlen = torch.tensor([131])208        y, ylen = block(([x], xlen))209        self.check_module_exists(block, jasper.MaskedConv1d)210        assert isinstance(block, jasper.JasperBlock)211        assert y[0].shape == torch.Size([1, config['planes'], 131])212        assert ylen[0] == 131213        assert block.mconv[0].pad_layer is None214        assert block.mconv[0]._padding == config['kernel_size'][0] // 2215    @pytest.mark.unit216    def test_padding_size_conv1d(self):217        input_channels = 1218        output_channels = 1219        kernel_sizes = [3, 7, 11]220        dilation_sizes = [2, 3, 4]221        stride = 1222        inp = torch.rand(2, 1, 40)223        for kernel_size in kernel_sizes:...

startproject.py

Source:startproject.py

...23        return "<project_name>"24    def short_desc(self):25        return "Create new project"26    def _is_valid_name(self, project_name):27        def _module_exists(module_name):28            try:29                import_module(module_name)30                return True31            except ImportError:32                return False33        if not re.search(r'^[_a-zA-Z]\w*$', project_name):34            print('Error: Project names must begin with a letter and contain'\35                    ' only\nletters, numbers and underscores')36        elif exists(project_name):37            print('Error: Directory %r already exists' % project_name)38        elif _module_exists(project_name):39            print('Error: Module %r already exists' % project_name)40        else:41            return True42        return False43    def run(self, args, opts):44        if len(args) != 1:45            raise UsageError()46        project_name = args[0]47        if not self._is_valid_name(project_name):48            self.exitcode = 149            return50        moduletpl = join(TEMPLATES_PATH, 'module')51        copytree(moduletpl, join(project_name, project_name), ignore=IGNORE)52        shutil.copy(join(TEMPLATES_PATH, 'scrapy.cfg'), project_name)...

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.