Python tf_sampling.farthest_point_sample方法代码示例

# 需要导入模块: import tf_sampling [as 别名]
# 或者: from tf_sampling import farthest_point_sample [as 别名]
def sample_and_group(npoint, radius, nsample, xyz, points, knn=False, use_xyz=True):
    '''
    Input:
        npoint: int32
        radius: float32
        nsample: int32
        xyz: (batch_size, ndataset, 3) TF tensor
        points: (batch_size, ndataset, channel) TF tensor, if None will just use xyz as points
        knn: bool, if True use kNN instead of radius search
        use_xyz: bool, if True concat XYZ with local point features, otherwise just use point features
    Output:
        new_xyz: (batch_size, npoint, 3) TF tensor
        new_points: (batch_size, npoint, nsample, 3+channel) TF tensor
        idx: (batch_size, npoint, nsample) TF tensor, indices of local points as in ndataset points
        grouped_xyz: (batch_size, npoint, nsample, 3) TF tensor, normalized point XYZs
            (subtracted by seed point XYZ) in local regions
    '''

    new_xyz = gather_point(xyz, farthest_point_sample(npoint, xyz)) # (batch_size, npoint, 3)
    if knn:
        _,idx = knn_point(nsample, xyz, new_xyz)
    else:
        idx, pts_cnt = query_ball_point(radius, nsample, xyz, new_xyz)
    grouped_xyz = group_point(xyz, idx) # (batch_size, npoint, nsample, 3)
    grouped_xyz -= tf.tile(tf.expand_dims(new_xyz, 2), [1,1,nsample,1]) # translation normalization
    if points is not None:
        grouped_points = group_point(points, idx) # (batch_size, npoint, nsample, channel)
        if use_xyz:
            new_points = tf.concat([grouped_xyz, grouped_points], axis=-1) # (batch_size, npoint, nample, 3+channel)
        else:
            new_points = grouped_points
    else:
        new_points = grouped_xyz

    return new_xyz, new_points, idx, grouped_xyz 

# 需要导入模块: import tf_sampling [as 别名]
# 或者: from tf_sampling import farthest_point_sample [as 别名]
def pointnet_sa_module_msg(xyz, points, npoint, radius_list, nsample_list, mlp_list, is_training, bn_decay, scope, bn=True, use_xyz=True, use_nchw=False):
    ''' PointNet Set Abstraction (SA) module with Multi-Scale Grouping (MSG)
        Input:
            xyz: (batch_size, ndataset, 3) TF tensor
            points: (batch_size, ndataset, channel) TF tensor
            npoint: int32 -- #points sampled in farthest point sampling
            radius: list of float32 -- search radius in local region
            nsample: list of int32 -- how many points in each local region
            mlp: list of list of int32 -- output size for MLP on each point
            use_xyz: bool, if True concat XYZ with local point features, otherwise just use point features
            use_nchw: bool, if True, use NCHW data format for conv2d, which is usually faster than NHWC format
        Return:
            new_xyz: (batch_size, npoint, 3) TF tensor
            new_points: (batch_size, npoint, \sum_k{mlp[k][-1]}) TF tensor
    '''
    data_format = 'NCHW' if use_nchw else 'NHWC'
    with tf.variable_scope(scope) as sc:
        new_xyz = gather_point(xyz, farthest_point_sample(npoint, xyz))
        new_points_list = []
        for i in range(len(radius_list)):
            radius = radius_list[i]
            nsample = nsample_list[i]
            idx, pts_cnt = query_ball_point(radius, nsample, xyz, new_xyz)
            grouped_xyz = group_point(xyz, idx)
            grouped_xyz -= tf.tile(tf.expand_dims(new_xyz, 2), [1,1,nsample,1])
            if points is not None:
                grouped_points = group_point(points, idx)
                if use_xyz:
                    grouped_points = tf.concat([grouped_points, grouped_xyz], axis=-1)
            else:
                grouped_points = grouped_xyz
            if use_nchw: grouped_points = tf.transpose(grouped_points, [0,3,1,2])
            for j,num_out_channel in enumerate(mlp_list[i]):
                grouped_points = tf_util.conv2d(grouped_points, num_out_channel, [1,1],
                                                padding='VALID', stride=[1,1], bn=bn, is_training=is_training,
                                                scope='conv%d_%d'%(i,j), bn_decay=bn_decay)
            if use_nchw: grouped_points = tf.transpose(grouped_points, [0,2,3,1])
            new_points = tf.reduce_max(grouped_points, axis=[2])
            new_points_list.append(new_points)
        new_points_concat = tf.concat(new_points_list, axis=-1)
        return new_xyz, new_points_concat 

# 需要导入模块: import tf_sampling [as 别名]
# 或者: from tf_sampling import farthest_point_sample [as 别名]
def sample_and_group(npoint, radius, nsample, xyz, points, knn=False, use_xyz=True):
    '''
    Input:
        npoint: int32
        radius: float32
        nsample: int32
        xyz: (batch_size, ndataset, 3) TF tensor
        points: (batch_size, ndataset, channel) TF tensor, if None will just use xyz as points
        knn: bool, if True use kNN instead of radius search
        use_xyz: bool, if True concat XYZ with local point features, otherwise just use point features
    Output:
        new_xyz: (batch_size, npoint, 3) TF tensor
        new_points: (batch_size, npoint, nsample, 3+channel) TF tensor
        idx: (batch_size, npoint, nsample) TF tensor, indices of local points as in ndataset points
        grouped_xyz: (batch_size, npoint, nsample, 3) TF tensor, normalized point XYZs
            (subtracted by seed point XYZ) in local regions
    '''
    sample_idx = farthest_point_sample(npoint, xyz)
    new_xyz = gather_point(xyz, sample_idx) # (batch_size, npoint, 3)
    if knn:
        _,idx = knn_point(nsample, xyz, new_xyz)
    else:
        idx, pts_cnt = query_ball_point(radius, nsample, xyz, new_xyz)
    grouped_xyz = group_point(xyz, idx) # (batch_size, npoint, nsample, 3)
    grouped_xyz -= tf.tile(tf.expand_dims(new_xyz, 2), [1,1,nsample,1]) # translation normalization
    if points is not None:
        grouped_points = group_point(points, idx) # (batch_size, npoint, nsample, channel)
        if use_xyz:
            new_points = tf.concat([grouped_xyz, grouped_points], axis=-1) # (batch_size, npoint, nample, 3+channel)
        else:
            new_points = grouped_points
    else:
        new_points = grouped_xyz

    return new_xyz, new_points, idx, sample_idx, grouped_xyz 

# 需要导入模块: import tf_sampling [as 别名]
# 或者: from tf_sampling import farthest_point_sample [as 别名]
def get_tf_sess_pl(npoint, batch_size, num_gpu):

    pc_placeholder = tf.placeholder(tf.float32, shape=[batch_size, None, 3])
    feature_placeholder = tf.placeholder(tf.float32, shape=[batch_size, None, 4])

    device_batch_size = batch_size // num_gpu
    new_xyz_gpu = []
    new_feature_gpu = []

    for i in range(num_gpu):
        with tf.device('/gpu:%d'%(i)), tf.name_scope('gpu_%d'%(i)) as scope:
            pc_batch = tf.slice(pc_placeholder,
                    [i*device_batch_size,0,0], [device_batch_size,-1,-1])
            feature_batch = tf.slice(feature_placeholder,
                    [i*device_batch_size,0,0], [device_batch_size,-1,-1])

            sample_idx = farthest_point_sample(npoint, pc_batch)
            new_xyz = gather_point(pc_batch, sample_idx)
            new_feature_part_1 = gather_point(feature_batch[:, :, :3], sample_idx)
            new_feature_part_2 = gather_point(feature_batch[:, :, -3:], sample_idx)
            new_feature = tf.concat([new_feature_part_1, tf.expand_dims(new_feature_part_2[:, :, -1], axis=-1)], axis=-1)

            new_xyz_gpu.append(new_xyz)
            new_feature_gpu.append(new_feature)

    new_xyz = tf.concat(new_xyz_gpu, 0)
    new_feature = tf.concat(new_feature_gpu, 0)

    config = tf.ConfigProto()
    config.gpu_options.allow_growth = True
    config.allow_soft_placement = True
    config.log_device_placement = False
    sess = tf.Session(config=config)

    return sess, new_xyz, new_feature, pc_placeholder, feature_placeholder 

# 需要导入模块: import tf_sampling [as 别名]
# 或者: from tf_sampling import farthest_point_sample [as 别名]
def sample_and_group(npoint, radius, nsample, xyz, points, knn=False, use_xyz=True):
    '''
    Input:
        npoint:         int32
        radius:         float32
        nsample:        int32
        xyz:            (batch_size, ndataset, 3) TF tensor
        points:         (batch_size, ndataset, channel) TF tensor, if None will just use xyz as points
        knn:            bool, if True use kNN instead of radius search
        use_xyz:        bool, if True concat XYZ with local point features, otherwise just use point features
    Output:
        new_xyz:        (batch_size, npoint, 3) TF tensor
        new_points:     (batch_size, npoint, nsample, 3+channel) TF tensor
        idx:            (batch_size, npoint, nsample) TF tensor, indices of local points as in ndataset points
        grouped_xyz:    (batch_size, npoint, nsample, 3) TF tensor, normalized point XYZs (subtracted by seed point XYZ) in local regions
    '''

    new_xyz = gather_point(xyz, farthest_point_sample(npoint, xyz))         # (batch_size, npoint, 3)
    if knn:
        _,idx = knn_point(nsample, xyz, new_xyz)
    else:
        idx, pts_cnt = query_ball_point(radius, nsample, xyz, new_xyz)

    grouped_xyz = group_point(xyz, idx)                                     # (batch_size, npoint, nsample, 3)
    grouped_xyz -= tf.tile(tf.expand_dims(new_xyz, 2), [1,1,nsample,1])     # translation normalization

    if points is not None:
        grouped_points = group_point(points, idx)                           # (batch_size, npoint, nsample, channel)
        if use_xyz:
            new_points = tf.concat([grouped_xyz, grouped_points], axis=-1)  # (batch_size, npoint, nample, 3+channel)
        else:
            new_points = grouped_points
    else:
        new_points = grouped_xyz

    return new_xyz, new_points, idx, grouped_xyz 

# 需要导入模块: import tf_sampling [as 别名]
# 或者: from tf_sampling import farthest_point_sample [as 别名]
def pc_sampling(xyz,
                feat,
                nsample,
                num_point,
                scope='sampling'):
  """ Fully connected layer with non-linear operation.
  
  Args:
    xyz: 3-D tensor B x N x 3
    nsample: k
    num_point: N2
    feat: 3-D tensor B x N x C
  
  Returns:
    feat_sample: 3-D tensor B x N2 x C
  """
  with tf.variable_scope(scope) as sc:
    xyz_new = gather_point(xyz, farthest_point_sample(num_point, xyz))
    _, idx_pooling = knn_point(nsample, xyz, xyz_new)
    
    grouped_points = group_point(feat, idx_pooling)
    feat_sample = tf.nn.max_pool(grouped_points, [1,1,nsample,1], [1,1,1,1], 
    			padding='VALID', data_format='NHWC', name="MAX_POOLING")
    feat_sample = tf.squeeze(feat_sample, axis=[2])

    return feat_sample, xyz_new 

# 需要导入模块: import tf_sampling [as 别名]
# 或者: from tf_sampling import farthest_point_sample [as 别名]
def sample_and_group(npoint, radius, nsample, xyz, points, tnet_spec=None, knn=False, use_xyz=True):
    '''
    Input:
        npoint: int32
        radius: float32
        nsample: int32
        xyz: (batch_size, ndataset, 3) TF tensor
        points: (batch_size, ndataset, channel) TF tensor, if None will just use xyz as points
        tnet_spec: dict (keys: mlp, mlp2, is_training, bn_decay), if None do not apply tnet
        knn: bool, if True use kNN instead of radius search
        use_xyz: bool, if True concat XYZ with local point features, otherwise just use point features
    Output:
        new_xyz: (batch_size, npoint, 3) TF tensor
        new_points: (batch_size, npoint, nsample, 3+channel) TF tensor
        idx: (batch_size, npoint, nsample) TF tensor, indices of local points as in ndataset points
        grouped_xyz: (batch_size, npoint, nsample, 3) TF tensor, normalized point XYZs
            (subtracted by seed point XYZ) in local regions
    '''

    new_xyz = gather_point(xyz, farthest_point_sample(npoint, xyz)) # (batch_size, npoint, 3)
    if knn:
        _,idx = knn_point(nsample, xyz, new_xyz)
    else:
        idx, pts_cnt = query_ball_point(radius, nsample, xyz, new_xyz)
    grouped_xyz = group_point(xyz, idx) # (batch_size, npoint, nsample, 3)
    grouped_xyz -= tf.tile(tf.expand_dims(new_xyz, 2), [1,1,nsample,1]) # translation normalization
    if tnet_spec is not None:
        grouped_xyz = tnet(grouped_xyz, tnet_spec)
    if points is not None:
        grouped_points = group_point(points, idx) # (batch_size, npoint, nsample, channel)
        if use_xyz:
            new_points = tf.concat([grouped_xyz, grouped_points], axis=-1) # (batch_size, npoint, nample, 3+channel)
        else:
            new_points = grouped_points
    else:
        new_points = grouped_xyz

    return new_xyz, new_points, idx, grouped_xyz 

# 需要导入模块: import tf_sampling [as 别名]
# 或者: from tf_sampling import farthest_point_sample [as 别名]
def pointnet_sa_module_msg(xyz, points, npoint, radius_list, nsample_list, mlp_list, is_training, bn_decay, scope, bn=True, use_xyz=True):
    ''' PointNet Set Abstraction (SA) module with Multi-Scale Grouping (MSG)
        Input:
            xyz: (batch_size, ndataset, 3) TF tensor
            points: (batch_size, ndataset, channel) TF tensor
            npoint: int32 -- #points sampled in farthest point sampling
            radius: list of float32 -- search radius in local region
            nsample: list of int32 -- how many points in each local region
            mlp: list of list of int32 -- output size for MLP on each point
            use_xyz: bool, if True concat XYZ with local point features, otherwise just use point features
        Return:
            new_xyz: (batch_size, npoint, 3) TF tensor
            new_points: (batch_size, npoint, \sum_k{mlp[k][-1]}) TF tensor
    '''
    with tf.variable_scope(scope) as sc:
        new_xyz = gather_point(xyz, farthest_point_sample(npoint, xyz))
        new_points_list = []
        for i in range(len(radius_list)):
            radius = radius_list[i]
            nsample = nsample_list[i]
            idx, pts_cnt = query_ball_point(radius, nsample, xyz, new_xyz)
            grouped_xyz = group_point(xyz, idx)
            grouped_xyz -= tf.tile(tf.expand_dims(new_xyz, 2), [1,1,nsample,1])
            if points is not None:
                grouped_points = group_point(points, idx)
                if use_xyz:
                    grouped_points = tf.concat([grouped_points, grouped_xyz], axis=-1)
            else:
                grouped_points = grouped_xyz
            for j,num_out_channel in enumerate(mlp_list[i]):
                grouped_points = tf_util.conv2d(grouped_points, num_out_channel, [1, 1],
                                                padding='VALID', stride=[1,1], bn=bn, is_training=is_training,
                                                scope='conv%d_%d'%(i,j), bn_decay=bn_decay)
            new_points = tf.reduce_max(grouped_points, axis=[2])
            new_points_list.append(new_points)
        new_points_concat = tf.concat(new_points_list, axis=-1)
        return new_xyz, new_points_concat 

# 需要导入模块: import tf_sampling [as 别名]
# 或者: from tf_sampling import farthest_point_sample [as 别名]
def pointnet_sa_module_msg(xyz, points, npoint, radius_list, nsample_list, mlp_list, is_training, bn_decay, scope, bn=True, use_xyz=True, use_nchw=False):
    ''' PointNet Set Abstraction (SA) module with Multi-Scale Grouping (MSG)
        Input:
            xyz: (batch_size, ndataset, 3) TF tensor
            points: (batch_size, ndataset, channel) TF tensor
            npoint: int32 -- #points sampled in farthest point sampling
            radius: list of float32 -- search radius in local region
            nsample: list of int32 -- how many points in each local region
            mlp: list of list of int32 -- output size for MLP on each point
            use_xyz: bool, if True concat XYZ with local point features, otherwise just use point features
            use_nchw: bool, if True, use NCHW data format for conv2d, which is usually faster than NHWC format
        Return:
            new_xyz: (batch_size, npoint, 3) TF tensor
            new_points: (batch_size, npoint, sum_k{mlp[k][-1]}) TF tensor
    '''
    data_format = 'NCHW' if use_nchw else 'NHWC'
    with tf.variable_scope(scope) as sc:
        new_xyz = gather_point(xyz, farthest_point_sample(npoint, xyz))
        new_points_list = []
        for i in range(len(radius_list)):
            radius = radius_list[i]
            nsample = nsample_list[i]
            idx, pts_cnt = query_ball_point(radius, nsample, xyz, new_xyz)
            grouped_xyz = group_point(xyz, idx)
            grouped_xyz -= tf.tile(tf.expand_dims(new_xyz, 2), [1,1,nsample,1])
            if points is not None:
                grouped_points = group_point(points, idx)
                if use_xyz:
                    grouped_points = tf.concat([grouped_points, grouped_xyz], axis=-1)
            else:
                grouped_points = grouped_xyz
            if use_nchw: grouped_points = tf.transpose(grouped_points, [0,3,1,2])
            for j,num_out_channel in enumerate(mlp_list[i]):
                grouped_points = tf_util.conv2d(grouped_points, num_out_channel, [1,1],
                                                padding='VALID', stride=[1,1], bn=bn, is_training=is_training,
                                                scope='conv%d_%d'%(i,j), bn_decay=bn_decay)
            if use_nchw: grouped_points = tf.transpose(grouped_points, [0,2,3,1])
            new_points = tf.reduce_max(grouped_points, axis=[2])
            new_points_list.append(new_points)
        new_points_concat = tf.concat(new_points_list, axis=-1)
        return new_xyz, new_points_concat 

# 需要导入模块: import tf_sampling [as 别名]
# 或者: from tf_sampling import farthest_point_sample [as 别名]
def sample_and_group(npoint, radius, nsample, xyz, points, knn=False, use_xyz=True):
    '''
    Input:
        npoint: int32
        radius: float32
        nsample: int32
        xyz: (batch_size, ndataset, 3) TF tensor
        points: (batch_size, ndataset, channel) TF tensor, if None will just use xyz as points
        knn: bool, if True use kNN instead of radius search
        use_xyz: bool, if True concat XYZ with local point features, otherwise just use point features
    Output:
        new_xyz: (batch_size, npoint, 3) TF tensor
        new_points: (batch_size, npoint, nsample, 3+channel) TF tensor
        idx: (batch_size, npoint, nsample) TF tensor, indices of local points as in ndataset points
        grouped_xyz: (batch_size, npoint, nsample, 3) TF tensor, normalized point XYZs
            (subtracted by seed point XYZ) in local regions
    '''

    new_xyz = gather_point(xyz, farthest_point_sample(npoint, xyz))  # (batch_size, npoint, 3)
    if knn:
        _, idx = knn_point(nsample, xyz, new_xyz)
    else:
        idx, pts_cnt = query_ball_point(radius, nsample, xyz, new_xyz)
    grouped_xyz = group_point(xyz, idx)  # (batch_size, npoint, nsample, 3)
    grouped_xyz -= tf.tile(tf.expand_dims(new_xyz, 2), [1, 1, nsample, 1])  # translation normalization
    if points is not None:
        grouped_points = group_point(points, idx)  # (batch_size, npoint, nsample, channel)
        if use_xyz:
            new_points = tf.concat([grouped_xyz, grouped_points], axis=-1)  # (batch_size, npoint, nample, 3+channel)
        else:
            new_points = grouped_points
    else:
        new_points = grouped_xyz

    return new_xyz, new_points, idx, grouped_xyz 

# 需要导入模块: import tf_sampling [as 别名]
# 或者: from tf_sampling import farthest_point_sample [as 别名]
def pointconv_sampling(npoint, pts):
    """
    inputs:
    npoint: scalar, number of points to sample
    pointcloud: B * N * 3, input point cloud
    output:
    sub_pts: B * npoint * 3, sub-sampled point cloud
    """

    sub_pts = gather_point(pts, farthest_point_sample(npoint, pts))
    return sub_pts