Python ndarray.empty方法代码示例

# 需要导入模块: from mxnet import ndarray [as 别名]
# 或者: from mxnet.ndarray import empty [as 别名]
def get_executor(sym, ctx, data_inputs, initializer=None):
    data_shapes = {k: v.shape for k, v in data_inputs.items()}
    arg_names = sym.list_arguments()
    aux_names = sym.list_auxiliary_states()
    param_names = list(set(arg_names) - set(data_inputs.keys()))
    arg_shapes, output_shapes, aux_shapes = sym.infer_shape(**data_shapes)
    arg_name_shape = {k: s for k, s in zip(arg_names, arg_shapes)}
    params = {n: nd.empty(arg_name_shape[n], ctx=ctx) for n in param_names}
    params_grad = {n: nd.empty(arg_name_shape[n], ctx=ctx) for n in param_names}
    aux_states = {k: nd.empty(s, ctx=ctx) for k, s in zip(aux_names, aux_shapes)}
    exe = sym.bind(ctx=ctx, args=dict(params, **data_inputs),
                   args_grad=params_grad,
                   aux_states=aux_states)
    if initializer is not None:
        for k, v in params.items():
            initializer(k, v)
    return exe, params, params_grad, aux_states 

# 需要导入模块: from mxnet import ndarray [as 别名]
# 或者: from mxnet.ndarray import empty [as 别名]
def synthetic_grad(X, theta, sigma1, sigma2, sigmax, rescale_grad=1.0, grad=None):
    if grad is None:
        grad = nd.empty(theta.shape, theta.context)
    theta1 = theta.asnumpy()[0]
    theta2 = theta.asnumpy()[1]
    v1 = sigma1 ** 2
    v2 = sigma2 ** 2
    vx = sigmax ** 2
    denominator = numpy.exp(-(X - theta1) ** 2 / (2 * vx)) + numpy.exp(
        -(X - theta1 - theta2) ** 2 / (2 * vx))
    grad_npy = numpy.zeros(theta.shape)
    grad_npy[0] = -rescale_grad * ((numpy.exp(-(X - theta1) ** 2 / (2 * vx)) * (X - theta1) / vx
                                    + numpy.exp(-(X - theta1 - theta2) ** 2 / (2 * vx)) * (
                                    X - theta1 - theta2) / vx) / denominator).sum() \
                  + theta1 / v1
    grad_npy[1] = -rescale_grad * ((numpy.exp(-(X - theta1 - theta2) ** 2 / (2 * vx)) * (
    X - theta1 - theta2) / vx) / denominator).sum() \
                  + theta2 / v2
    grad[:] = grad_npy
    return grad 

# 需要导入模块: from mxnet import ndarray [as 别名]
# 或者: from mxnet.ndarray import empty [as 别名]
def load_bin(path, image_size):
  try:
    with open(path, 'rb') as f:
      bins, issame_list = pickle.load(f) #py2
  except UnicodeDecodeError as e:
    with open(path, 'rb') as f:
      bins, issame_list = pickle.load(f, encoding='bytes') #py3
  data_list = []
  for flip in [0,1]:
    data = nd.empty((len(issame_list)*2, 3, image_size[0], image_size[1]))
    data_list.append(data)
  for i in range(len(issame_list)*2):
    _bin = bins[i]
    img = mx.image.imdecode(_bin)
    if img.shape[1]!=image_size[0]:
      img = mx.image.resize_short(img, image_size[0])
    img = nd.transpose(img, axes=(2, 0, 1))
    for flip in [0,1]:
      if flip==1:
        img = mx.ndarray.flip(data=img, axis=2)
      data_list[flip][i][:] = img
    if i%1000==0:
      print('loading bin', i)
  print(data_list[0].shape)
  return (data_list, issame_list) 

# 需要导入模块: from mxnet import ndarray [as 别名]
# 或者: from mxnet.ndarray import empty [as 别名]
def load_dataset(lfw_dir, image_size):
  lfw_pairs = read_pairs(os.path.join(lfw_dir, 'pairs.txt'))
  lfw_paths, issame_list = get_paths(lfw_dir, lfw_pairs, 'jpg')
  lfw_data_list = []
  for flip in [0,1]:
    lfw_data = nd.empty((len(lfw_paths), 3, image_size[0], image_size[1]))
    lfw_data_list.append(lfw_data)
  i = 0
  for path in lfw_paths:
    with open(path, 'rb') as fin:
      _bin = fin.read()
      img = mx.image.imdecode(_bin)
      img = nd.transpose(img, axes=(2, 0, 1))
      for flip in [0,1]:
        if flip==1:
          img = mx.ndarray.flip(data=img, axis=2)
        lfw_data_list[flip][i][:] = img
      i+=1
      if i%1000==0:
        print('loading lfw', i)
  print(lfw_data_list[0].shape)
  print(lfw_data_list[1].shape)
  return (lfw_data_list, issame_list) 

# 需要导入模块: from mxnet import ndarray [as 别名]
# 或者: from mxnet.ndarray import empty [as 别名]
def load_bin(path, image_size):
  bins, issame_list = pickle.load(open(path, 'rb'))
  data_list = []
  for flip in [0,1]:
    data = nd.empty((len(issame_list)*2, 3, image_size[0], image_size[1]))
    data_list.append(data)
  for i in xrange(len(issame_list)*2):
    _bin = bins[i]
    img = mx.image.imdecode(_bin)
    if img.shape[1]!=image_size[0]:
      img = mx.image.resize_short(img, image_size[0])
    img = nd.transpose(img, axes=(2, 0, 1))
    for flip in [0,1]:
      if flip==1:
        img = mx.ndarray.flip(data=img, axis=2)
      data_list[flip][i][:] = img
    if i%1000==0:
      print('loading bin', i)
  print(data_list[0].shape)
  return (data_list, issame_list) 

# 需要导入模块: from mxnet import ndarray [as 别名]
# 或者: from mxnet.ndarray import empty [as 别名]
def load_dataset_bin(self):
        name = 'lfw'
        path = os.path.join(self.lfw_dir, name+".bin")
        bins, issame_list = pickle.load(open(path, 'rb'))
        data_list = []
        for flip in [0,1]:
          data = nd.empty((len(issame_list)*2, 3, self.image_size[0], self.image_size[1]))
          data_list.append(data)
        for i in xrange(len(issame_list)*2):
          _bin = bins[i]
          img = mx.image.imdecode(_bin)
          img = nd.transpose(img, axes=(2, 0, 1))
          for flip in [0,1]:
            if flip==1:
              img = mx.ndarray.flip(data=img, axis=2)
            data_list[flip][i][:] = img
          if i%1000==0:
            print('loading bin', i)
        print(data_list[0].shape)
        return (data_list, issame_list) 

# 需要导入模块: from mxnet import ndarray [as 别名]
# 或者: from mxnet.ndarray import empty [as 别名]
def load_bin(path, image_size):
    try:
        with open(path, 'rb') as f:
            bins, issame_list = pickle.load(f)  # py2
    except UnicodeDecodeError as e:
        with open(path, 'rb') as f:
            bins, issame_list = pickle.load(f, encoding='bytes')  # py3
    data_list = []
    for flip in [0, 1]:
        data = nd.empty((len(issame_list) * 2, 3, image_size[0], image_size[1]))
        data_list.append(data)
    for i in range(len(issame_list) * 2):
        _bin = bins[i]
        img = mx.image.imdecode(_bin)
        if img.shape[1] != image_size[0]:
            img = mx.image.resize_short(img, image_size[0])
        img = nd.transpose(img, axes=(2, 0, 1))
        for flip in [0, 1]:
            if flip == 1:
                img = mx.ndarray.flip(data=img, axis=2)
            data_list[flip][i][:] = img
        if i % 1000 == 0:
            print('loading bin', i)
    print(data_list[0].shape)
    return (data_list, issame_list) 

# 需要导入模块: from mxnet import ndarray [as 别名]
# 或者: from mxnet.ndarray import empty [as 别名]
def load_bin(path, image_size):
  bins, issame_list = pickle.load(open(path, 'rb'), encoding='bytes')
  data_list = []
  for flip in [0,1]:
    data = nd.empty((len(issame_list)*2, 3, image_size[0], image_size[1]))
    data_list.append(data)
  for i in range(len(issame_list)*2):
    _bin = bins[i]
    img = mx.image.imdecode(_bin)
    img = nd.transpose(img, axes=(2, 0, 1))
    for flip in [0,1]:
      if flip==1:
        img = mx.ndarray.flip(data=img, axis=2)
      data_list[flip][i][:] = img
    if i%1000==0:
      print('loading bin', i)
  print(data_list[0].shape)
  return (data_list, issame_list) 

# 需要导入模块: from mxnet import ndarray [as 别名]
# 或者: from mxnet.ndarray import empty [as 别名]
def copy_param(exe, new_param=None):
    if new_param is None:
        new_param = {k: nd.empty(v.shape, ctx=mx.cpu()) for k,v in exe.arg_dict.items()}
    for k, v in new_param.items():
        exe.arg_dict[k].copyto(v)
    return new_param 

# 需要导入模块: from mxnet import ndarray [as 别名]
# 或者: from mxnet.ndarray import empty [as 别名]
def run_synthetic_SGLD():
    theta1 = 0
    theta2 = 1
    sigma1 = numpy.sqrt(10)
    sigma2 = 1
    sigmax = numpy.sqrt(2)
    X = load_synthetic(theta1=theta1, theta2=theta2, sigmax=sigmax, num=100)
    minibatch_size = 1
    total_iter_num = 1000000
    lr_scheduler = SGLDScheduler(begin_rate=0.01, end_rate=0.0001, total_iter_num=total_iter_num,
                                 factor=0.55)
    optimizer = mx.optimizer.create('sgld',
                                    learning_rate=None,
                                    rescale_grad=1.0,
                                    lr_scheduler=lr_scheduler,
                                    wd=0)
    updater = mx.optimizer.get_updater(optimizer)
    theta = mx.random.normal(0, 1, (2,), mx.cpu())
    grad = nd.empty((2,), mx.cpu())
    samples = numpy.zeros((2, total_iter_num))
    start = time.time()
    for i in xrange(total_iter_num):
        if (i + 1) % 100000 == 0:
            end = time.time()
            print("Iter:%d, Time spent: %f" % (i + 1, end - start))
            start = time.time()
        ind = numpy.random.randint(0, X.shape[0])
        synthetic_grad(X[ind], theta, sigma1, sigma2, sigmax, rescale_grad=
        X.shape[0] / float(minibatch_size), grad=grad)
        updater('theta', grad, theta)
        samples[:, i] = theta.asnumpy()
    plt.hist2d(samples[0, :], samples[1, :], (200, 200), cmap=plt.cm.jet)
    plt.colorbar()
    plt.show() 

# 需要导入模块: from mxnet import ndarray [as 别名]
# 或者: from mxnet.ndarray import empty [as 别名]
def __init__(self, data_shapes, sym_gen, params=None, aux_states=None,
                 default_bucket_kwargs=None, learn_init_keys=None,
                 initializer=mx.init.Xavier(factor_type="in", rnd_type="gaussian", magnitude=2),
                 ctx=mx.gpu(), name='Net'):
        self.sym_gen = sym_gen
        bucket_kwargs = default_bucket_kwargs.copy() if \
            default_bucket_kwargs is not None else dict()
        self.curr_bucket_key = None
        self.ctx = ctx
        self.name = name
        self.initializer = initializer
        if params is None:
            self.params = None
            self.params_grad = None
        else:
            self.params = OrderedDict([(k, v.copyto(ctx)) for k, v in params.items()])
            self.params_grad = OrderedDict([(n, nd.empty(v.shape, ctx=ctx))
                                            for n, v in self.params.items()])
        if aux_states is not None:
            self.aux_states = OrderedDict([(k, v.copyto(ctx)) for k, v in aux_states.items()])
        else:
            self.aux_states = None
        self._buckets = dict()
        self.learn_init_keys = learn_init_keys if learn_init_keys is not None else []
        self.learn_init_key_shapes = {k: data_shapes[k] for k in self.learn_init_keys}
        self.switch_bucket(bucket_kwargs=bucket_kwargs, data_shapes=data_shapes)
        self.acc_grad = None 

# 需要导入模块: from mxnet import ndarray [as 别名]
# 或者: from mxnet.ndarray import empty [as 别名]
def next(self):
        """Returns the next batch of data."""
        #print('next')
        batch_size = self.batch_size
        batch_data = nd.empty((batch_size,)+self.data_shape)
        batch_label = nd.empty((batch_size,)+self.label_shape)
        i = 0
        #self.cutoff = random.randint(800,1280)
        try:
            while i < batch_size:
                #print('N', i)
                data, label = self.next_sample()
                data = nd.array(data)
                data = nd.transpose(data, axes=(2, 0, 1))
                label = nd.array(label)
                label = nd.transpose(label, axes=(2, 0, 1))
                batch_data[i][:] = data
                batch_label[i][:] = label
                i += 1
        except StopIteration:
            if i<batch_size:
                raise StopIteration

        #return {self.data_name  :  batch_data,
        #        self.label_name :  batch_label}
        #print(batch_data.shape, batch_label.shape)
        return mx.io.DataBatch([batch_data], [batch_label, self.weight_mask], batch_size - i) 

# 需要导入模块: from mxnet import ndarray [as 别名]
# 或者: from mxnet.ndarray import empty [as 别名]
def gather_row(data, row_index):
    # MXNet workaround for empty row index
    if len(row_index) == 0:
        if data.shape[0] == 0:
            return data
        else:
            return data[0:0]

    if isinstance(row_index, nd.NDArray):
        return nd.take(data, row_index)
    else:
        return data[row_index,] 

# 需要导入模块: from mxnet import ndarray [as 别名]
# 或者: from mxnet.ndarray import empty [as 别名]
def backward(self, grad_out):
        lhs_data_nd, rhs_data_nd, out_data_nd, feat_shape, degs = self.saved_tensors
        if self.reducer == 'mean':
            grad_out = grad_out / degs
        grad_out_nd = zerocopy_to_dgl_ndarray(grad_out)
        grad_lhs = nd.empty((lhs_data_nd.shape[0],) + feat_shape,
                            ctx=grad_out.context, dtype=grad_out.dtype)
        K.backward_lhs_binary_op_reduce(
            self.reducer if self.reducer != 'mean' else 'sum',
            self.binary_op, self.graph, self.lhs, self.rhs,
            lhs_data_nd, rhs_data_nd, out_data_nd, grad_out_nd,
            zerocopy_to_dgl_ndarray_for_write(grad_lhs), self.lhs_map[1],
            self.rhs_map[1], self.out_map[1])
        grad_lhs = _reduce_grad(grad_lhs, lhs_data_nd.shape)
        grad_rhs = nd.empty((rhs_data_nd.shape[0],) + feat_shape,
                             ctx=grad_out.context, dtype=grad_out.dtype)
        K.backward_rhs_binary_op_reduce(
            self.reducer if self.reducer != 'mean' else 'sum',
            self.binary_op, self.graph, self.lhs, self.rhs,
            lhs_data_nd, rhs_data_nd, out_data_nd, grad_out_nd,
            zerocopy_to_dgl_ndarray_for_write(grad_rhs), self.lhs_map[1],
            self.rhs_map[1], self.out_map[1])
        grad_rhs = _reduce_grad(grad_rhs, rhs_data_nd.shape)
        # clear saved tensors explicitly
        self.saved_tensors = None
        return grad_lhs, grad_rhs 

# 需要导入模块: from mxnet import ndarray [as 别名]
# 或者: from mxnet.ndarray import empty [as 别名]
def forward(self, in_data):
        feat_shape = in_data.shape[1:]
        out_data = nd.empty((self.out_size,) + feat_shape,
                            ctx=in_data.context, dtype=in_data.dtype)
        in_data_nd = zerocopy_to_dgl_ndarray(in_data)
        out_data_nd = zerocopy_to_dgl_ndarray_for_write(out_data)
        K.copy_reduce(
            self.reducer if self.reducer != 'mean' else 'sum',
            self.graph, self.target, in_data_nd, out_data_nd,
            self.in_map[0], self.out_map[0])
        # normalize if mean reducer
        # NOTE(zihao): this is a temporary hack and we should have better solution in the future.
        if self.reducer == 'mean':
            in_ones = nd.ones((in_data.shape[0],),
                              ctx=in_data.context, dtype=in_data.dtype)
            degs = nd.empty((out_data.shape[0],),
                            ctx=out_data.context, dtype=out_data.dtype)
            in_ones_nd = zerocopy_to_dgl_ndarray(in_ones)
            degs_nd = zerocopy_to_dgl_ndarray(degs)
            K.copy_reduce(
                'sum', self.graph, self.target, in_ones_nd, degs_nd, 
                self.in_map[0], self.out_map[0])
            # reshape
            degs = degs.reshape((out_data.shape[0],) + (1,) * (out_data.ndim - 1)).clip(1, float('inf')) 
            out_data = out_data / degs
        else:
            degs = None
        self.save_for_backward(in_data_nd, out_data_nd, degs)
        return out_data