Python torch.manual_seed函数代码示例

def test_bilstm_word_embeds_d4_1():
    """ 1 point(s) / 0.5 point(s) (section dependent) """

    global test_sent, word_to_ix, vocab
    torch.manual_seed(1)

    embedder = BiLSTMWordEmbeddingLookup(word_to_ix, TEST_EMBEDDING_DIM, TEST_EMBEDDING_DIM, 1, 0.0)
    embeds = embedder(test_sent)
    assert len(embeds) == len(test_sent)
    assert isinstance(embeds, list)
    assert isinstance(embeds[0], ag.Variable)
    assert embeds[0].size() == (1, TEST_EMBEDDING_DIM)

    embeds_list = make_list(embeds)
    true = ( 
        [ .4916, -.0168, .1719, .6615 ],
        [ .3756, -.0610, .1851, .2604 ],
        [ -.2655, -.1289, .1009, -.0016 ],
        [ -.1070, -.3971, .2414, -.2588 ],
        [ -.1717, -.4475, .2739, -.0465 ], 
        [ 0.0684, -0.2586,  0.2123, -0.1832 ], 
        [ -0.0775, -0.4308,  0.1844, -0.1146 ], 
        [ 0.4366, -0.0507,  0.1018,  0.4015 ], 
        [ -0.1265, -0.2192,  0.0481,  0.1551 ])

    pairs = zip(embeds_list, true)
    check_tensor_correctness(pairs)

    def setUp(self, length=3, factor=10, count=1000000,
              seed=None, dtype=torch.float64, device=None):
        '''Set up the test values.

        Args:
            length: Size of the vector.
            factor: To multiply the mean and standard deviation.
            count: Number of samples for Monte-Carlo estimation.
            seed: Seed for the random number generator.
            dtype: The data type.
            device: In which device.
        '''
        if seed is not None:
            torch.manual_seed(seed)

        # variables
        self.A = torch.randn(length, length, dtype=dtype, device=device)
        self.b = torch.randn(length, dtype=dtype, device=device)

        # input mean and covariance
        self.mu = torch.randn(length, dtype=dtype, device=device) * factor
        self.cov = rand.definite(length, dtype=dtype, device=device,
                                 positive=True, semi=False, norm=factor**2)

        # Monte-Carlo estimation of the output mean and variance
        normal = torch.distributions.MultivariateNormal(self.mu, self.cov)
        samples = normal.sample((count,))
        out_samples = samples.matmul(self.A.t()) + self.b
        self.mc_mu = torch.mean(out_samples, dim=0)
        self.mc_var = torch.var(out_samples, dim=0)
        self.mc_cov = cov(out_samples)

def main():
    args = parser.parse_args()

    if args.seed is not None:
        random.seed(args.seed)
        torch.manual_seed(args.seed)
        cudnn.deterministic = True
        warnings.warn('You have chosen to seed training. '
                      'This will turn on the CUDNN deterministic setting, '
                      'which can slow down your training considerably! '
                      'You may see unexpected behavior when restarting '
                      'from checkpoints.')

    if args.gpu is not None:
        warnings.warn('You have chosen a specific GPU. This will completely '
                      'disable data parallelism.')

    if args.dist_url == "env://" and args.world_size == -1:
        args.world_size = int(os.environ["WORLD_SIZE"])

    args.distributed = args.world_size > 1 or args.multiprocessing_distributed

    ngpus_per_node = torch.cuda.device_count()
    if args.multiprocessing_distributed:
        # Since we have ngpus_per_node processes per node, the total world_size
        # needs to be adjusted accordingly
        args.world_size = ngpus_per_node * args.world_size
        # Use torch.multiprocessing.spawn to launch distributed processes: the
        # main_worker process function
        mp.spawn(main_worker, nprocs=ngpus_per_node, args=(ngpus_per_node, args))
    else:
        # Simply call main_worker function
        main_worker(args.gpu, ngpus_per_node, args)

def train_model(args):
    """Load the data, train the model, test the model, export / save the model
    """
    torch.manual_seed(args.seed)

    # Open our dataset
    train_loader, test_loader = data_utils.load_data(args.test_split,
                                                     args.batch_size)

    # Create the model
    net = model.SonarDNN().double()
    optimizer = optim.SGD(net.parameters(), lr=args.lr,
                          momentum=args.momentum, nesterov=False)

    # Train / Test the model
    for epoch in range(1, args.epochs + 1):
        train(net, train_loader, optimizer, epoch)
        test(net, test_loader)

    # Export the trained model
    torch.save(net.state_dict(), args.model_name)

    if args.model_dir:
        # Save the model to GCS
        data_utils.save_model(args.model_dir, args.model_name)

def prepare_environment(params: Params):
    """
    Sets random seeds for reproducible experiments. This may not work as expected
    if you use this from within a python project in which you have already imported Pytorch.
    If you use the scripts/run_model.py entry point to training models with this library,
    your experiments should be reasonably reproducible. If you are using this from your own
    project, you will want to call this function before importing Pytorch. Complete determinism
    is very difficult to achieve with libraries doing optimized linear algebra due to massively
    parallel execution, which is exacerbated by using GPUs.

    Parameters
    ----------
    params: Params object or dict, required.
        A ``Params`` object or dict holding the json parameters.
    """
    seed = params.pop_int("random_seed", 13370)
    numpy_seed = params.pop_int("numpy_seed", 1337)
    torch_seed = params.pop_int("pytorch_seed", 133)

    if seed is not None:
        random.seed(seed)
    if numpy_seed is not None:
        numpy.random.seed(numpy_seed)
    if torch_seed is not None:
        torch.manual_seed(torch_seed)
        # Seed all GPUs with the same seed if available.
        if torch.cuda.is_available():
            torch.cuda.manual_seed_all(torch_seed)

    log_pytorch_version_info()

    def __init__(self, seed=1):
        super(NN_drop, self).__init__()

        torch.manual_seed(seed)

        self.input_size = 1
        self.output_size = 1
        h_size = 50


        # #this samples a mask for each datapoint in the batch
        # self.net = nn.Sequential(
        #   nn.Linear(self.input_size,h_size),
        #   nn.ReLU(),
        #   nn.Dropout(p=0.5),
        #   nn.Linear(h_size,self.output_size)
        # )

        #want to keep mask constant for batch

        self.l1 = nn.Linear(self.input_size,h_size)
        self.a1 = nn.ReLU()
        # nn.Dropout(p=0.5),
        self.l2 = nn.Linear(h_size,self.output_size)

        



        self.optimizer = optim.Adam(self.parameters(), lr=.01)

def seed_everything(seed=1029):
    random.seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.backends.cudnn.deterministic = True

    def test_horovod_allreduce_inplace(self):
        """Test that the allreduce correctly sums 1D, 2D, 3D tensors."""
        hvd.init()
        size = hvd.size()
        dtypes = [torch.IntTensor, torch.LongTensor,
                  torch.FloatTensor, torch.DoubleTensor]
        if torch.cuda.is_available():
            dtypes += [torch.cuda.IntTensor, torch.cuda.LongTensor,
                       torch.cuda.FloatTensor, torch.cuda.DoubleTensor]
        dims = [1, 2, 3]
        for dtype, dim in itertools.product(dtypes, dims):
            torch.manual_seed(1234)
            tensor = torch.FloatTensor(*([17] * dim)).random_(-100, 100)
            tensor = tensor.type(dtype)
            multiplied = tensor * size
            hvd.allreduce_(tensor, average=False)
            max_difference = tensor.sub(multiplied).max()

            # Threshold for floating point equality depends on number of
            # ranks, since we're comparing against precise multiplication.
            if size <= 3 or dtype in [torch.IntTensor, torch.LongTensor,
                                      torch.cuda.IntTensor, torch.cuda.LongTensor]:
                threshold = 0
            elif size < 10:
                threshold = 1e-4
            elif size < 15:
                threshold = 5e-4
            else:
                break

            assert max_difference <= threshold, 'hvd.allreduce produces incorrect results'

def predict_fn(input_data, model):
    logger.info('Generating text based on input parameters.')
    corpus = model['corpus']
    model = model['model']

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    logger.info('Current device: {}'.format(device))
    torch.manual_seed(input_data['seed'])
    ntokens = len(corpus.dictionary)
    input = torch.randint(ntokens, (1, 1), dtype=torch.long).to(device)
    hidden = model.init_hidden(1)

    logger.info('Generating {} words.'.format(input_data['words']))
    result = []
    with torch.no_grad():  # no tracking history
        for i in range(input_data['words']):
            output, hidden = model(input, hidden)
            word_weights = output.squeeze().div(input_data['temperature']).exp().cpu()
            word_idx = torch.multinomial(word_weights, 1)[0]
            input.fill_(word_idx)
            word = corpus.dictionary.idx2word[word_idx]
            word = word if type(word) == str else word.decode()
            if word == '<eos>':
                word = '\n'
            elif i % 12 == 11:
                word = word + '\n'
            else:
                word = word + ' '
            result.append(word)
    return ''.join(result)

    def test_horovod_allreduce_error(self):
        """Test that the allreduce raises an error if different ranks try to
        send tensors of different rank or dimension."""
        hvd.init()
        rank = hvd.rank()
        size = hvd.size()

        # This test does not apply if there is only one worker.
        if size == 1:
            return

        # Same rank, different dimension
        torch.manual_seed(1234)
        dims = [17 + rank] * 3
        tensor = torch.FloatTensor(*dims).random_(-100, 100)
        try:
            hvd.allreduce(tensor)
            assert False, 'hvd.allreduce did not throw error'
        except torch.FatalError:
            pass

        # Same number of elements, different rank
        torch.manual_seed(1234)
        if rank == 0:
            dims = [17, 23 * 57]
        else:
            dims = [17, 23, 57]
        tensor = torch.FloatTensor(*dims).random_(-100, 100)
        try:
            hvd.allreduce(tensor)
            assert False, 'hvd.allreduce did not throw error'
        except torch.FatalError:
            pass

    def test_horovod_allreduce_grad(self):
        """Test the correctness of the allreduce gradient."""
        hvd.init()
        size = hvd.size()
        dtypes = [torch.IntTensor, torch.LongTensor,
                  torch.FloatTensor, torch.DoubleTensor]
        if torch.cuda.is_available():
            dtypes += [torch.cuda.IntTensor, torch.cuda.LongTensor,
                       torch.cuda.FloatTensor, torch.cuda.DoubleTensor]
        dims = [1, 2, 3]
        for dtype, dim in itertools.product(dtypes, dims):
            torch.manual_seed(1234)
            tensor = torch.FloatTensor(*([17] * dim)).random_(-100, 100)
            tensor = tensor.type(dtype)
            tensor = torch.autograd.Variable(tensor, requires_grad=True)
            summed = hvd.allreduce(tensor, average=False)

            summed.backward(torch.ones([17] * dim))
            grad_out = tensor.grad.data.numpy()

            expected = np.ones([17] * dim) * size
            err = np.linalg.norm(expected - grad_out)
            self.assertLess(err, 0.00000001,
                            "gradient %s differs from expected %s, "
                            "error: %s" % (grad_out, expected, str(err)))

    def train_step(self, sample, update_params=True, dummy_batch=False):
        """Do forward, backward and parameter update."""
        # Set seed based on args.seed and the update number so that we get
        # reproducible results when resuming from checkpoints
        seed = self.args.seed + self.get_num_updates()
        torch.manual_seed(seed)
        torch.cuda.manual_seed(seed)

        if not dummy_batch:
            self.meters['train_wall'].start()

        # forward and backward pass
        sample = self._prepare_sample(sample)
        loss, sample_size, logging_output, oom_fwd = self._forward(sample)
        oom_bwd = self._backward(loss)

        # buffer stats and logging outputs
        self._buffered_stats['sample_sizes'].append(sample_size)
        self._buffered_stats['logging_outputs'].append(logging_output)
        self._buffered_stats['ooms_fwd'].append(oom_fwd)
        self._buffered_stats['ooms_bwd'].append(oom_bwd)

        # update parameters
        if update_params:
            agg_logging_output = self._update_params()
        else:
            agg_logging_output = None  # buffering updates

        if not dummy_batch:
            self.meters['train_wall'].stop()

        return agg_logging_output

    def __init__(self, seed=1):
        super(NN, self).__init__()

        torch.manual_seed(seed)

        self.input_size = 1
        self.output_size = 1
        h_size = 50

        # self.net = nn.Sequential(
        #   nn.Linear(self.input_size,h_size),
        #   nn.ReLU(),
        #   nn.Linear(h_size,self.output_size)
        # )
        self.net = nn.Sequential(
          nn.Linear(self.input_size,h_size),
          # nn.Tanh(),
          # nn.Linear(h_size,h_size),
          nn.Tanh(),
          nn.Linear(h_size,self.output_size),
          # nn.Tanh(),
          # nn.Linear(h_size,self.output_size)
        )

        # self.optimizer = optim.Adam(self.parameters(), lr=.01)
        self.optimizer = optim.Adam(self.parameters(), lr=.0004)

def main(argv):

    (opt, args) = parser.parse_args(argv)
    config = get_config(opt.config)
    print(opt)
    if opt.manualSeed is None:
        opt.manualSeed = random.randint(1, 10000)
    print("Random Seed: ", opt.manualSeed)
    random.seed(opt.manualSeed)
    torch.manual_seed(opt.manualSeed)
    if config['cuda']:
        torch.cuda.manual_seed_all(opt.manualSeed)
        torch.cuda.set_device(opt.gpu_ids)
    cudnn.benchmark = True

    transform = transforms.Compose([transforms.Resize((512, 512)),
                                    transforms.ToTensor(),
                                    transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))])
    dataset = Aligned_Dataset(config['datapath'], subfolder='test', direction='AtoB', transform=transform)
    dataloader = torch.utils.data.DataLoader(dataset, batch_size=1,
                                             shuffle=False, num_workers=int(2))
    model_dir = '/media/scw4750/AIwalker/stackgan-like/checkpoints/generator_epoch_160.pkl'
    trainer = GAN_Trainer(config, dataloader)
    # load the model
    trainer.G.load_state_dict(torch.load(model_dir))
    trainer.test()

    return

    def __init__(self, hyper_config, seed=1):
        super(VAE, self).__init__()

        torch.manual_seed(seed)


        self.z_size = hyper_config['z_size']
        self.x_size = hyper_config['x_size']
        self.act_func = hyper_config['act_func']
        self.flow_bool = hyper_config['flow_bool']

        self.q_dist = hyper_config['q_dist'](self, hyper_config=hyper_config)


        if torch.cuda.is_available():
            self.dtype = torch.cuda.FloatTensor
            self.q_dist.cuda()
        else:
            self.dtype = torch.FloatTensor
            

        #Decoder
        self.fc4 = nn.Linear(self.z_size, 200)
        self.fc5 = nn.Linear(200, 200)
        self.fc6 = nn.Linear(200, self.x_size)