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How to use get_default_device method in Airtest

Best Python code snippet using Airtest

train.py

Source:train.py

...27def split_indices(n, val_pct):28    indices = np.random.permutation(n)29    bound = int(n * val_pct)30    return indices[bound:], indices[:bound]31def get_default_device():32    if cuda.is_available():33        return torch.device('cuda')34    else:35        return torch.device('cpu')  # incase cuda is unavaliable.36# move data and model to device. This operation may take while37def to_device(data, device):38    if isinstance(data, (list, tuple)):39        return [to_device(x, device) for x in data]  # breakdown element and40        # recursively put elements into device if the data is list or tuple.41    return data.to(device, non_blocking=True)  # This may return a pointer pointing to the data in GPU42# A wrapped dataloader that put data into gpu in BATCH-WISE fashion.43# This can save GPU dram.44class DeviceDataloader():45    # Constructor46    # @param data_loader A DataLoader instance that need to be operated.47    # @param device The device going to put on.48    def __init__(self, data_loader, device):49        self.data_loader = data_loader50        self.device = device51    # iterator function.52    def __iter__(self):53        for batch_tuple in self.data_loader:54            yield to_device(batch_tuple, self.device)  # do to_device. Yield is non-stop return,55    # length of self.data_loader56    def __len__(self):57        return len(self.data_loader)58class WR_Conv_Model(nn.Module):59    def accuracy(self, prediction, target):60        _, max_pos = torch.max(prediction, dim=1)61        return torch.tensor(torch.sum(max_pos == target).item() / len(max_pos))62    def __init__(self):63        super(WR_Conv_Model, self).__init__()64        self.conv_layer1 = nn.Conv2d(3, 6, kernel_size=3, padding=1, stride=1)65        # now BSx6x128x12866        self.conv_layer2 = nn.Conv2d(6, 12, kernel_size=3, padding=1, stride=1)67        # now BSx12x64x6468        self.conv_layer3 = nn.Conv2d(12, 24, kernel_size=3, padding=1, stride=1)69        # now BSx24x32x3270        self.conv_layer4 = nn.Conv2d(24, 48, kernel_size=3, padding=1, stride=1)71        # now BSx48x16x1672        self.conv_layer5 = nn.Conv2d(48, 96, kernel_size=3, padding=1, stride=1)73        # now BSx96x8x874        self.conv_layer6 = nn.Conv2d(96, 192, kernel_size=3, padding=1, stride=1)75        # now BSx192x4x476        # here data is flatten from dim=177        self.linear_layer1 = nn.Linear(192 * 4 * 4, 256)78        # now BSx25679        self.linear_layer2 = nn.Linear(256, 4)80        # now prediction is getted: BSx481    def forward(self, input_batch):82        out = self.conv_layer1(input_batch)83        out = nn.functional.relu(out)84        out = nn.functional.max_pool2d(out, kernel_size=2)85        out = self.conv_layer2(out)86        out = nn.functional.relu(out)87        out = nn.functional.max_pool2d(out, kernel_size=2)88        out = self.conv_layer3(out)89        out = nn.functional.relu(out)90        out = nn.functional.max_pool2d(out, kernel_size=2)91        out = self.conv_layer4(out)92        out = nn.functional.relu(out)93        out = nn.functional.max_pool2d(out, kernel_size=2)94        out = self.conv_layer5(out)95        out = nn.functional.relu(out)96        out = nn.functional.max_pool2d(out, kernel_size=2)97        out = self.conv_layer6(out)98        out = nn.functional.relu(out)99        out = nn.functional.max_pool2d(out, kernel_size=2)100        out = out.view(input_batch.size(0), -1)101        out = self.linear_layer1(out)102        out = nn.functional.relu(out)103        out = self.linear_layer2(out)104        return out105    def training_step(self, data_batch, target_batch):106        prediction = self(data_batch)107        loss = nn.functional.cross_entropy(prediction, target_batch)108        return loss109    def validation_step(self, data_batch, target_batch):110        prediction = self(data_batch)111        accuracy = self.accuracy(prediction, target_batch)112        loss = nn.functional.cross_entropy(prediction, target_batch)113        return {"validation loss": loss, "validation accuracy": accuracy}114# ---------------------------Prepare and load the data----------------------#115# open dataset116dataset = ImageFolder(ROOT_DIR + "/train", transform=ToTensor())117print("Dataset length: " + str(len(dataset)))118# split the indices into train and validation119train_ids, val_ids = split_indices(len(dataset), 0.1)  # 10% validation set120# transform data into dataloader121train_loader = DataLoader(dataset, BATCH_SIZE, sampler=SubsetRandomSampler(train_ids))122val_loader = DataLoader(dataset, BATCH_SIZE, sampler=SubsetRandomSampler(val_ids))123train_loader = DeviceDataloader(train_loader, get_default_device())124val_loader = DeviceDataloader(val_loader, get_default_device())125print("--------------Data transformed to: " + str(get_default_device()) + "--------------------")126# ----------------------------------END Prepare---------------------------------------#127# --------------------------------Construct model and optimizer-----------------------#128model = WR_Conv_Model()129to_device(model, get_default_device())130# optimizer = torch.optim.SGD(model.parameters(), lr=LEARNING_RATE)131optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)132# ---------------------------------END Construct---------------------------------------#133# --------------------------------Train------------------------------------------#134print("Here")135'''stage_1 = False136stage_2 = False137stage_3 = False'''138for i in range(EPOCH):139    # time0 = time.time()140    for data_batch, target_batch in train_loader:141        # print("Sample prediction: " + str(nn.functional.softmax(prediction, dim=1)[0]))142        loss = model.training_step(data_batch, target_batch)143        loss.backward()...

jit_models_test.py

Source:jit_models_test.py

...108        (torch.rand(3, 1, 4301) - 0.5) * 2,109    ),110)111def test_enhancement_model(small_model_params, model_def, test_data):112    device = get_default_device()113    params = small_model_params[model_def.__name__]114    model = model_def(**params)115    model = model.eval().to(device)116    # Random input uniformly distributed in [-1, 1]117    inputs = ((torch.rand(1, 2500, device=device) - 0.5) * 2,)118    traced = torch.jit.trace(model, inputs)119    assert_consistency(model=model, traced=traced, tensor=test_data.to(device))120@pytest.mark.parametrize("test_shape", [(2,), (3, 1)])121@pytest.mark.parametrize("matching_samples", [4701, 8800, 17001])122def test_dcunet_model(test_shape: Tuple, matching_samples):123    n_samples = 5010124    device = get_default_device()125    model = DCUNet(architecture="mini", fix_length_mode="pad").eval().to(device)126    # Random input uniformly distributed in [-1, 1]127    inputs = torch.rand(1, n_samples, device=device)128    traced = torch.jit.trace(model, (inputs,))129    test_data = torch.rand(*test_shape, matching_samples, device=device)130    assert_consistency(model=model, traced=traced, tensor=test_data.to(device))131@pytest.mark.parametrize(132    "model_def",133    (134        ConvTasNet,135        DPRNNTasNet,136        DPTNet,137        LSTMTasNet,138        SuDORMRFNet,139        SuDORMRFImprovedNet,140    ),141)142@pytest.mark.parametrize(143    "test_data",144    (145        (torch.rand(240) - 0.5) * 2,146        (torch.rand(1, 220) - 0.5) * 2,147        (torch.rand(3, 250) - 0.5) * 2,148        (torch.rand(1, 1, 301) - 0.5) * 2,149        (torch.rand(2, 1, 501) - 0.5) * 2,150    ),151)152def test_trace_bss_model(small_model_params, model_def, test_data):153    device = get_default_device()154    params = small_model_params[model_def.__name__]155    model = model_def(**params)156    model = model.eval().to(device)157    # Random input uniformly distributed in [-1, 1]158    inputs = ((torch.rand(1, 201, device=device) - 0.5) * 2,)159    traced = torch.jit.trace(model, inputs)160    assert_consistency(model=model, traced=traced, tensor=test_data.to(device))161def get_default_device():162    if torch.cuda.is_available():163        return "cuda"...

interface.py

Source:interface.py

2from pathlib import Path3import yaml4import torch5import os6def get_default_device():7    if torch.cuda.is_available():8        return "cuda"9    else:10        return "cpu"11def load_model(mel2wav_path, device=get_default_device()):12    """13    Args:14        mel2wav_path (str or Path): path to the root folder of dumped text2mel15        device (str or torch.device): device to load the model16    """17    root = Path(mel2wav_path)18    with open(root / "args.yml", "r") as f:19        args = yaml.load(f, Loader=yaml.FullLoader)20    netG = Generator(args.n_mel_channels, args.ngf, args.n_residual_layers).to(device)21    netG.load_state_dict(torch.load(root / "best_netG.pt", map_location=device))22    return netG23class MelVocoder:24    def __init__(25        self,26        path,27        device=get_default_device(),28        github=False,29        model_name="multi_speaker",30    ):31        self.fft = Audio2Mel().to(device)32        if github:33            netG = Generator(80, 32, 3).to(device)34            root = Path(os.path.dirname(__file__)).parent35            netG.load_state_dict(36                torch.load(root / f"models/{model_name}.pt", map_location=device)37            )38            self.mel2wav = netG39        else:40            self.mel2wav = load_model(path, device)41        self.device = device...

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