Python torch.device方法代码示例

# 需要导入模块: import torch [as 别名]
# 或者: from torch import device [as 别名]
def main():
    args = parse_args()

    device = torch.device(args.device)

    model = init_detector(args.config, args.checkpoint, device=device)

    camera = cv2.VideoCapture(args.camera_id)

    print('Press "Esc", "q" or "Q" to exit.')
    while True:
        ret_val, img = camera.read()
        result = inference_detector(model, img)

        ch = cv2.waitKey(1)
        if ch == 27 or ch == ord('q') or ch == ord('Q'):
            break

        model.show_result(
            img, result, score_thr=args.score_thr, wait_time=1, show=True) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import device [as 别名]
def generate_grid(num_grid, size, device):
    """Generate regular square grid of points in [0, 1] x [0, 1] coordinate
    space.

    Args:
        num_grid (int): The number of grids to sample, one for each region.
        size (tuple(int, int)): The side size of the regular grid.
        device (torch.device): Desired device of returned tensor.

    Returns:
        (torch.Tensor): A tensor of shape (num_grid, size[0]*size[1], 2) that
            contains coordinates for the regular grids.
    """

    affine_trans = torch.tensor([[[1., 0., 0.], [0., 1., 0.]]], device=device)
    grid = F.affine_grid(
        affine_trans, torch.Size((1, 1, *size)), align_corners=False)
    grid = normalize(grid)
    return grid.view(1, -1, 2).expand(num_grid, -1, -1) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import device [as 别名]
def get_points(self, featmap_sizes, dtype, device, flatten=False):
        """Get points according to feature map sizes.

        Args:
            featmap_sizes (list[tuple]): Multi-level feature map sizes.
            dtype (torch.dtype): Type of points.
            device (torch.device): Device of points.

        Returns:
            tuple: points of each image.
        """
        mlvl_points = []
        for i in range(len(featmap_sizes)):
            mlvl_points.append(
                self._get_points_single(featmap_sizes[i], self.strides[i],
                                        dtype, device, flatten))
        return mlvl_points 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import device [as 别名]
def get_params():
    def _one(shape):
        ts = torch.tensor(np.random.normal(0, 0.01, size=shape), device=device, dtype=torch.float32)
        return torch.nn.Parameter(ts, requires_grad=True)

    def _three():
        return (_one((num_inputs, num_hiddens)),
                _one((num_hiddens, num_hiddens)),
                torch.nn.Parameter(torch.zeros(num_hiddens, device=device, dtype=torch.float32), requires_grad=True))

    W_xz, W_hz, b_z = _three() # 更新门参数
    W_xr, W_hr, b_r = _three() # 重置门参数
    W_xh, W_hh, b_h = _three() # 候选隐藏层参数

    # 输出层参数
    W_hq = _one((num_hiddens, num_outputs))
    b_q = torch.nn.Parameter(torch.zeros(num_outputs, device=device, dtype=torch.float32), requires_grad=True)
    return nn.ParameterList([W_xz, W_hz, b_z, W_xr, W_hr, b_r, W_xh, W_hh, b_h, W_hq, b_q]) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import device [as 别名]
def evaluate_accuracy(data_iter, net,
                      device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')):
    acc_sum, n = 0.0, 0
    with torch.no_grad():
        for X, y in data_iter:
            if isinstance(net, torch.nn.Module):
                net.eval() # 评估模式，会关闭 dropout
                acc_sum += (net(X.to(device)).argmax(dim=1) == y.to(device)).float().sum().cpu().item()
                net.train() # 改回训练模式
            else:
                # 如果是自定义的模型
                if 'is_training' in net.__code__.co_varnames:
                    acc_sum += (net(X, is_training=False).argmax(dim=1) == y).float().sum().item()
                else:
                    acc_sum += (net(X).argmax(dim=1) == y).float().sum().item()
            n += y.shape[0]
    return acc_sum / n 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import device [as 别名]
def train_cnn(net, train_iter, test_iter, batch_size, optimizer, device, num_epochs):
    net = net.to(device)
    print('training on', device)
    loss = nn.CrossEntropyLoss()
    batch_count = 0
    for epoch in range(num_epochs):
        train_l_sum, train_acc_sum, n, start = 0.0, 0.0, 0, time.time()
        for X, y in train_iter:
            X = X.to(device)
            y = y.to(device)
            y_hat = net(X)
            l = loss(y_hat, y)
            optimizer.zero_grad()
            l.backward()
            optimizer.step()

            train_l_sum += l.cpu().item()
            train_acc_sum += (y_hat.argmax(dim=1) == y).sum().cpu().item()
            n += y.shape[0]
            batch_count += 1
        test_acc = evaluate_accuracy(test_iter, net)
        print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f, time %.1f sec' %
              (epoch + 1, train_l_sum / n, train_acc_sum / n, test_acc, time.time() - start)) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import device [as 别名]
def train(train_iter, test_iter, net, loss, optimizer, device, num_epochs):
    net = net.to(device)
    print("training on ", device)
    batch_count = 0
    for epoch in range(num_epochs):
        train_l_sum, train_acc_sum, n, start = 0.0, 0.0, 0, time.time()
        for X, y in train_iter:
            X = X.to(device)
            y = y.to(device)
            y_hat = net(X)
            l = loss(y_hat, y)
            optimizer.zero_grad()
            l.backward()
            optimizer.step()
            train_l_sum += l.cpu().item()
            train_acc_sum += (y_hat.argmax(dim=1) == y).sum().cpu().item()
            n += y.shape[0]
            batch_count += 1
        test_acc = evaluate_accuracy(test_iter, net)
        print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f, time %.1f sec'
              % (epoch + 1, train_l_sum / batch_count, train_acc_sum / n, test_acc, time.time() - start)) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import device [as 别名]
def data_iter_random(corpus_indices, batch_size, num_steps, device=None):
    # 减1是因为输出的索引x是相应输入的索引y加1
    num_examples = (len(corpus_indices) - 1) // num_steps
    epoch_size = num_examples // batch_size
    example_indices = list(range(num_examples))
    random.shuffle(example_indices)

    # 返回从pos开始的长为num_steps的序列
    def _data(pos):
        return corpus_indices[pos: pos + num_steps]
    if device is None:
        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

    for i in range(epoch_size):
        # 每次读取batch_size个随机样本
        i = i * batch_size
        batch_indices = example_indices[i: i + batch_size]
        X = [_data(j * num_steps) for j in batch_indices]
        Y = [_data(j * num_steps + 1) for j in batch_indices]
        yield torch.tensor(X, dtype=torch.float32, device=device), torch.tensor(Y, dtype=torch.float32, device=device) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import device [as 别名]
def train_fine_tuning(net, optimizer, batch_size=128, num_epochs=4):
    train_iter = DataLoader(ImageFolder(os.path.join(data_dir, 'train'), transform=train_augs), batch_size, shuffle=True)
    test_iter = DataLoader(ImageFolder(os.path.join(data_dir, 'test'), transform=test_augs), batch_size)
    loss = torch.nn.CrossEntropyLoss()
    utils.train(train_iter, test_iter, net, loss, optimizer, device, num_epochs) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import device [as 别名]
def train(net, lr, num_epochs):
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    print("train on", device)
    net = net.to(device)
    optimizer = torch.optim.Adam(net.parameters(), lr=lr)
    for epoch in range(num_epochs):
        start, l_sum, n = time.time(), 0.0, 0
        for batch in data_iter:
            center, context_negative, mask, label = [d.to(device) for d in batch]

            pred = skip_gram(center, context_negative, net[0], net[1])

            # 使用掩码变量mask来避免填充项对损失函数计算的影响
            l = (loss(pred.view(label.shape), label, mask) *
                 mask.shape[1] / mask.float().sum(dim=1)).mean()  # 一个batch的平均loss
            optimizer.zero_grad()
            l.backward()
            optimizer.step()
            l_sum += l.cpu().item()
            n += 1
        print('epoch %d, loss %.2f, time %.2fs'
              % (epoch + 1, l_sum / n, time.time() - start)) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import device [as 别名]
def train():
    model = resnet18(classes_num=1, in_channels=3, pretrained=True)
    train_config = TrainConfig(model, [train_stage, val_stage], torch.nn.BCEWithLogitsLoss(),
                               torch.optim.Adam(model.parameters(), lr=1e-4))

    file_struct_manager = FileStructManager(base_dir='data', is_continue=False)

    trainer = Trainer(train_config, file_struct_manager, torch.device('cuda:0')).set_epoch_num(2)

    tensorboard = TensorboardMonitor(file_struct_manager, is_continue=False, network_name='PortraitSegmentation')
    log = LogMonitor(file_struct_manager).write_final_metrics()
    trainer.monitor_hub.add_monitor(tensorboard).add_monitor(log)
    trainer.enable_best_states_saving(lambda: np.mean(train_stage.get_losses()))

    trainer.enable_lr_decaying(coeff=0.5, patience=10, target_val_clbk=lambda: np.mean(train_stage.get_losses()))
    trainer.add_on_epoch_end_callback(lambda: tensorboard.update_scalar('params/lr', trainer.data_processor().get_lr()))
    trainer.train() 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import device [as 别名]
def __init__(self, train_config: TrainConfig, fsm: FileStructManager, device: torch.device = None):
        self._fsm = fsm
        self.monitor_hub = MonitorHub()

        self._checkpoint_manager = CheckpointsManager(self._fsm)

        self.__epoch_num = 100
        self._resume_from = None
        self._on_epoch_end = []
        self._best_state_rule = None

        self._train_config = train_config
        self._data_processor = TrainDataProcessor(self._train_config, device).set_checkpoints_manager(self._checkpoint_manager)
        self._lr = LearningRate(self._data_processor.get_lr())

        self._stop_rules = [] 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import device [as 别名]
def select_device(device='', apex=False):
    # device = 'cpu' or '0' or '0,1,2,3'
    cpu_request = device.lower() == 'cpu'
    if device and not cpu_request:  # if device requested other than 'cpu'
        os.environ['CUDA_VISIBLE_DEVICES'] = device  # set environment variable
        assert torch.cuda.is_available(), 'CUDA unavailable, invalid device %s requested' % device  # check availablity

    cuda = False if cpu_request else torch.cuda.is_available()
    if cuda:
        c = 1024 ** 2  # bytes to MB
        ng = torch.cuda.device_count()
        x = [torch.cuda.get_device_properties(i) for i in range(ng)]
        cuda_str = 'Using CUDA ' + ('Apex ' if apex else '')  # apex for mixed precision https://github.com/NVIDIA/apex
        for i in range(0, ng):
            if i == 1:
                cuda_str = ' ' * len(cuda_str)
            print("%sdevice%g _CudaDeviceProperties(name='%s', total_memory=%dMB)" %
                  (cuda_str, i, x[i].name, x[i].total_memory / c))
    else:
        print('Using CPU')

    print('')  # skip a line
    return torch.device('cuda:0' if cuda else 'cpu') 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import device [as 别名]
def test(args, model, device, test_loader):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += F.nll_loss(output, target, size_average=False).item() # sum up batch loss
            pred = output.max(1, keepdim=True)[1] # get the index of the max log-probability
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)
    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset))) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import device [as 别名]
def select_action(self, state):
        """
        The action selection function, it either uses the model to choose an action or samples one uniformly.
        :param state: current state of the model
        :return:
        """
        if self.cuda:
            state = state.cuda()
        sample = random.random()
        eps_threshold = self.config.eps_start + (self.config.eps_start - self.config.eps_end) * math.exp(
            -1. * self.current_iteration / self.config.eps_decay)
        self.current_iteration += 1
        if sample > eps_threshold:
            with torch.no_grad():
                return self.policy_model(state).max(1)[1].view(1, 1)
        else:
            return torch.tensor([[random.randrange(2)]], device=self.device, dtype=torch.long)