Python tf_util.fully_connected方法代码示例

# 需要导入模块: import tf_util [as 别名]
# 或者: from tf_util import fully_connected [as 别名]
def single_encoding_net(pc, mlp_list, mlp_list2, scope, is_training, bn_decay):
    ''' The encoding network for instance
    Input:
        pc: [B, N, 3]
    Return:
        net: [B, nfea]
    '''
    with tf.variable_scope(scope) as myscope:
        net = tf.expand_dims(pc, 2)
        for i,num_out_channel in enumerate(mlp_list):
            net = tf_util.conv2d(net, num_out_channel, [1,1],
                                 padding='VALID', stride=[1,1],
                                 bn=True, is_training=is_training,
                                 scope='conv%d'%i, bn_decay=bn_decay)
        net = tf.reduce_max(net, axis=[1])
        net = tf.squeeze(net, 1)
        for i,num_out_channel in enumerate(mlp_list2):
            net = tf_util.fully_connected(net, num_out_channel, bn=True, is_training=is_training,
                                          scope='fc%d'%i, bn_decay=bn_decay)
        return net 

# 需要导入模块: import tf_util [as 别名]
# 或者: from tf_util import fully_connected [as 别名]
def get_model(point_cloud, is_training, bn_decay=None):
    """ Classification PointNet, input is BxNx3, output Bx40 """
    batch_size = point_cloud.get_shape()[0].value
    num_point = point_cloud.get_shape()[1].value
    end_points = {}
    l0_xyz = point_cloud
    l0_points = None
    end_points['l0_xyz'] = l0_xyz

    # Set abstraction layers
    # Note: When using NCHW for layer 2, we see increased GPU memory usage (in TF1.4).
    # So we only use NCHW for layer 1 until this issue can be resolved.
    l1_xyz, l1_points, l1_indices = pointnet_sa_module(l0_xyz, l0_points, npoint=512, radius=0.2, nsample=32, mlp=[64,64,128], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer1', use_nchw=True)
    l2_xyz, l2_points, l2_indices = pointnet_sa_module(l1_xyz, l1_points, npoint=128, radius=0.4, nsample=64, mlp=[128,128,256], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer2')
    l3_xyz, l3_points, l3_indices = pointnet_sa_module(l2_xyz, l2_points, npoint=None, radius=None, nsample=None, mlp=[256,512,1024], mlp2=None, group_all=True, is_training=is_training, bn_decay=bn_decay, scope='layer3')

    # Fully connected layers
    net = tf.reshape(l3_points, [batch_size, -1])
    net = tf_util.fully_connected(net, 512, bn=True, is_training=is_training, scope='fc1', bn_decay=bn_decay)
    net = tf_util.dropout(net, keep_prob=0.5, is_training=is_training, scope='dp1')
    net = tf_util.fully_connected(net, 256, bn=True, is_training=is_training, scope='fc2', bn_decay=bn_decay)
    net = tf_util.dropout(net, keep_prob=0.5, is_training=is_training, scope='dp2')
    net = tf_util.fully_connected(net, 40, activation_fn=None, scope='fc3')

    return net, end_points 

# 需要导入模块: import tf_util [as 别名]
# 或者: from tf_util import fully_connected [as 别名]
def get_model(point_cloud, is_training, bn_decay=None):
    """ Classification PointNet, input is BxNx3, output Bx40 """
    batch_size = point_cloud.get_shape()[0].value
    num_point = point_cloud.get_shape()[1].value
    end_points = {}

    l0_xyz = point_cloud
    l0_points = None

    # Set abstraction layers
    l1_xyz, l1_points = pointnet_sa_module_msg(l0_xyz, l0_points, 512, [0.1,0.2,0.4], [16,32,128], [[32,32,64], [64,64,128], [64,96,128]], is_training, bn_decay, scope='layer1', use_nchw=True)
    l2_xyz, l2_points = pointnet_sa_module_msg(l1_xyz, l1_points, 128, [0.2,0.4,0.8], [32,64,128], [[64,64,128], [128,128,256], [128,128,256]], is_training, bn_decay, scope='layer2')
    l3_xyz, l3_points, _ = pointnet_sa_module(l2_xyz, l2_points, npoint=None, radius=None, nsample=None, mlp=[256,512,1024], mlp2=None, group_all=True, is_training=is_training, bn_decay=bn_decay, scope='layer3')

    # Fully connected layers
    net = tf.reshape(l3_points, [batch_size, -1])
    net = tf_util.fully_connected(net, 512, bn=True, is_training=is_training, scope='fc1', bn_decay=bn_decay)
    net = tf_util.dropout(net, keep_prob=0.4, is_training=is_training, scope='dp1')
    net = tf_util.fully_connected(net, 256, bn=True, is_training=is_training, scope='fc2', bn_decay=bn_decay)
    net = tf_util.dropout(net, keep_prob=0.4, is_training=is_training, scope='dp2')
    net = tf_util.fully_connected(net, 40, activation_fn=None, scope='fc3')

    return net, end_points 

# 需要导入模块: import tf_util [as 别名]
# 或者: from tf_util import fully_connected [as 别名]
def get_model(point_cloud, is_training, bn_decay=None, num_class=NUM_CLASSES):
    """ Classification PointNet, input is BxNx3, output Bx40 """
    batch_size = point_cloud.get_shape()[0].value
    num_point = point_cloud.get_shape()[1].value
    end_points = {}
    l0_xyz = point_cloud
    l0_points = None
    end_points['l0_xyz'] = l0_xyz

    # Set abstraction layers
    # Note: When using NCHW for layer 2, we see increased GPU memory usage (in TF1.4).
    # So we only use NCHW for layer 1 until this issue can be resolved.
    l1_xyz, l1_points, l1_indices = pointnet_sa_module(l0_xyz, l0_points, npoint=512, radius=0.2, nsample=32, mlp=[64,64,128], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer1', use_nchw=True)
    l2_xyz, l2_points, l2_indices = pointnet_sa_module(l1_xyz, l1_points, npoint=128, radius=0.4, nsample=64, mlp=[128,128,256], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer2')
    l3_xyz, l3_points, l3_indices = pointnet_sa_module(l2_xyz, l2_points, npoint=None, radius=None, nsample=None, mlp=[256,512,1024], mlp2=None, group_all=True, is_training=is_training, bn_decay=bn_decay, scope='layer3')

    # Fully connected layers
    net = tf.reshape(l3_points, [batch_size, -1])
    net = tf_util.fully_connected(net, 512, bn=True, is_training=is_training, scope='fc1', bn_decay=bn_decay)
    net = tf_util.dropout(net, keep_prob=0.5, is_training=is_training, scope='dp1')
    net = tf_util.fully_connected(net, 256, bn=True, is_training=is_training, scope='fc2', bn_decay=bn_decay)
    net = tf_util.dropout(net, keep_prob=0.5, is_training=is_training, scope='dp2')
    net = tf_util.fully_connected(net, num_class, activation_fn=None, scope='fc3')

    return net, end_points 

# 需要导入模块: import tf_util [as 别名]
# 或者: from tf_util import fully_connected [as 别名]
def get_model(net, is_training, add_lstm=False, bn_decay=None, separately=False):
    """ Densenet169 regression model, input is BxWxHx3, output Bx2"""
    net = get_densenet(224, 224)(net)

    if not add_lstm:
        net = tf_util.fully_connected(net, 2, activation_fn=None, scope='fc_final')

    else:
        net = tf_util.fully_connected(net, 784, bn=True,
                                      is_training=is_training,
                                      scope='fc_lstm',
                                      bn_decay=bn_decay)
        net = tf_util.dropout(net, keep_prob=0.7,
                              is_training=is_training,
                              scope="dp1")
        net = cnn_lstm_block(net)

    return net 

# 需要导入模块: import tf_util [as 别名]
# 或者: from tf_util import fully_connected [as 别名]
def get_model(net, is_training, add_lstm=False, bn_decay=None, separately=False):
    """ Inception_V4 regression model, input is BxWxHx3, output Bx2"""
    net = get_inception(299, 299)(net)

    if not add_lstm:
        net = tf_util.fully_connected(net, 2, activation_fn=None, scope='fc_final')

    else:
        net = tf_util.fully_connected(net, 784, bn=True,
                                      is_training=is_training,
                                      scope='fc_lstm',
                                      bn_decay=bn_decay)
        net = tf_util.dropout(net, keep_prob=0.7,
                              is_training=is_training,
                              scope="dp1")
        net = cnn_lstm_block(net)

    return net 

# 需要导入模块: import tf_util [as 别名]
# 或者: from tf_util import fully_connected [as 别名]
def get_model(net, is_training, add_lstm=False, bn_decay=None, separately=False):
    """ ResNet152 regression model, input is BxWxHx3, output Bx2"""
    net = get_resnet(224, 224)(net)

    if not add_lstm:
        net = tf_util.fully_connected(net, 2, activation_fn=None, scope='fc_final')

    else:
        net = tf_util.fully_connected(net, 784, bn=True,
                                      is_training=is_training,
                                      scope='fc_lstm',
                                      bn_decay=bn_decay)
        net = tf_util.dropout(net, keep_prob=0.7,
                              is_training=is_training,
                              scope="dp1")
        net = cnn_lstm_block(net)

    return net 

# 需要导入模块: import tf_util [as 别名]
# 或者: from tf_util import fully_connected [as 别名]
def get_pose(source_global_feature, template_global_feature, is_training, bn_decay=None):
	net = tf.concat([source_global_feature,template_global_feature],1)
	net = tf_util.fully_connected(net, 1024, bn=False, is_training=is_training,scope='fc1', bn_decay=bn_decay)
	net = tf_util.fully_connected(net, 512, bn=False, is_training=is_training,scope='fc2', bn_decay=bn_decay)
	net = tf_util.fully_connected(net, 256, bn=False, is_training=is_training,scope='fc3', bn_decay=bn_decay)
	net = tf_util.dropout(net, keep_prob=0.7, is_training=is_training,scope='dp4')
	predicted_transformation = tf_util.fully_connected(net, 7, activation_fn=None, scope='fc4')
	return predicted_transformation 

# 需要导入模块: import tf_util [as 别名]
# 或者: from tf_util import fully_connected [as 别名]
def get_pose(self,source_global_feature,template_global_feature,is_training,bn_decay=None):
		# with tf.variable_scope('pose_estimation') as pn:
		net = tf.concat([source_global_feature,template_global_feature],1)
		net = tf_util.fully_connected(net, 1024, bn=False, is_training=is_training,scope='fc1', bn_decay=bn_decay)
		net = tf_util.fully_connected(net, 1024, bn=False, is_training=is_training,scope='fc2', bn_decay=bn_decay)
		net = tf_util.fully_connected(net, 512, bn=False, is_training=is_training,scope='fc3', bn_decay=bn_decay)
		net = tf_util.fully_connected(net, 512, bn=False, is_training=is_training,scope='fc4', bn_decay=bn_decay)
		net = tf_util.fully_connected(net, 256, bn=False, is_training=is_training,scope='fc5', bn_decay=bn_decay)
		predicted_transformation = tf_util.fully_connected(net, 7, activation_fn=None, scope='fc6')
		return predicted_transformation 

# 需要导入模块: import tf_util [as 别名]
# 或者: from tf_util import fully_connected [as 别名]
def feature_transform_net(inputs, is_training, bn_decay=None, K=64):
    """ Feature Transform Net, input is BxNx1xK
        Return:
            Transformation matrix of size KxK """
    batch_size = inputs.get_shape()[0].value
    num_point = inputs.get_shape()[1].value

    net = tf_util.conv2d(inputs, 64, [1,1],
                         padding='VALID', stride=[1,1],
                         bn=True, is_training=is_training,
                         scope='tconv1', bn_decay=bn_decay)
    net = tf_util.conv2d(net, 128, [1,1],
                         padding='VALID', stride=[1,1],
                         bn=True, is_training=is_training,
                         scope='tconv2', bn_decay=bn_decay)
    net = tf_util.conv2d(net, 1024, [1,1],
                         padding='VALID', stride=[1,1],
                         bn=True, is_training=is_training,
                         scope='tconv3', bn_decay=bn_decay)
    net = tf_util.max_pool2d(net, [num_point,1],
                             padding='VALID', scope='tmaxpool')

    net = tf.reshape(net, [batch_size, -1])
    net = tf_util.fully_connected(net, 512, bn=True, is_training=is_training,
                                  scope='tfc1', bn_decay=bn_decay)
    net = tf_util.fully_connected(net, 256, bn=True, is_training=is_training,
                                  scope='tfc2', bn_decay=bn_decay)

    with tf.variable_scope('transform_feat') as sc:
        weights = tf.get_variable('weights', [256, K*K],
                                  initializer=tf.constant_initializer(0.0),
                                  dtype=tf.float32)
        biases = tf.get_variable('biases', [K*K],
                                 initializer=tf.constant_initializer(0.0),
                                 dtype=tf.float32)
        biases += tf.constant(np.eye(K).flatten(), dtype=tf.float32)
        transform = tf.matmul(net, weights)
        transform = tf.nn.bias_add(transform, biases)

    transform = tf.reshape(transform, [batch_size, K, K])
    return transform 

# 需要导入模块: import tf_util [as 别名]
# 或者: from tf_util import fully_connected [as 别名]
def get_model(point_cloud, num_frames, is_training, bn_decay=None):
    """ Input:
            point_cloud: [batch_size, num_point * num_frames, 3]
        Output:
            net: [batch_size, num_class] """
    end_points = {}
    batch_size = point_cloud.get_shape()[0].value
    num_point = point_cloud.get_shape()[1].value // num_frames

    l0_xyz = point_cloud
    l0_time = tf.concat([tf.ones([batch_size, num_point, 1]) * i for i in range(num_frames)], \
            axis=-2)
    l0_points = tf.concat([point_cloud[:, :, 3:], l0_time], axis=-1)

    RADIUS1 = np.linspace(0.5, 0.6, num_frames, dtype='float32')
    RADIUS2 = RADIUS1 * 2
    RADIUS3 = RADIUS1 * 4
    RADIUS4 = RADIUS1 * 8

    l1_xyz, l1_time, l1_points, l1_indices = meteor_direct_module(l0_xyz, l0_time, l0_points, npoint=1024, radius=RADIUS1, nsample=32, mlp=[32,32,64], mlp2=None, group_all=False, knn=False, is_training=is_training, bn_decay=bn_decay, scope='layer1')
    l2_xyz, l2_time, l2_points, l2_indices = meteor_direct_module(l1_xyz, l1_time, l1_points, npoint=512, radius=RADIUS2, nsample=32, mlp=[64,64,128], mlp2=None, group_all=False, knn=False, is_training=is_training, bn_decay=bn_decay, scope='layer2')
    l3_xyz, l3_time, l3_points, l3_indices = meteor_direct_module(l2_xyz, l2_time, l2_points, npoint=128, radius=RADIUS3, nsample=32, mlp=[128,128,256], mlp2=None, group_all=False, knn=False, is_training=is_training, bn_decay=bn_decay, scope='layer3')
    l4_xyz, l4_points, l4_indices = pointnet_sa_module(l3_xyz, l3_points, npoint=None, radius=None, nsample=None, mlp=[256,512,1024], mlp2=None, group_all=True, is_training=is_training, bn_decay=bn_decay, scope='layer4')

    # Fully connected layers
    net = tf.reshape(l3_points, [batch_size, -1])
    net = tf_util.fully_connected(net, 512, bn=True, is_training=is_training, scope='fc1', bn_decay=bn_decay)
    net = tf_util.dropout(net, keep_prob=0.5, is_training=is_training, scope='dp1')
    net = tf_util.fully_connected(net, 20, activation_fn=None, scope='fc3')

    return net, end_points 

# 需要导入模块: import tf_util [as 别名]
# 或者: from tf_util import fully_connected [as 别名]
def get_mlp(net, layer_sizes, scope_name, is_training, bn_decay, dropout=None):
    assert len(layer_sizes) > 0
    with tf.variable_scope(scope_name):
        for idx, layer_size in enumerate(layer_sizes[:-1]):
            net = tf_util.fully_connected(net, layer_size, bn=True, is_training=is_training, scope=f'fc{idx+1}', bn_decay=bn_decay)
        if dropout is not None:
            net = tf_util.dropout(net, keep_prob=dropout, is_training=is_training, scope='dp1')
        return tf_util.fully_connected(net, layer_sizes[-1], activation_fn=None, scope=f'fc{len(layer_sizes)}') 

# 需要导入模块: import tf_util [as 别名]
# 或者: from tf_util import fully_connected [as 别名]
def build_pointnet2_cls(scope, point_cloud, out_dims, is_training, bn_decay):
    with tf.variable_scope(scope):
        batch_size = tf.shape(point_cloud)[0]
        l0_xyz = point_cloud
        l0_points = None

        # Set abstraction layers
        # Note: When using NCHW for layer 2, we see increased GPU memory usage (in TF1.4).
        # So we only use NCHW for layer 1 until this issue can be resolved.
        l1_xyz, l1_points, l1_indices = pointnet_sa_module(l0_xyz, l0_points, npoint=512, radius=0.2, nsample=32, mlp=[64,64,128], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer1', use_nchw=True)
        l2_xyz, l2_points, l2_indices = pointnet_sa_module(l1_xyz, l1_points, npoint=128, radius=0.4, nsample=64, mlp=[128,128,256], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer2')
        l3_xyz, l3_points, l3_indices = pointnet_sa_module(l2_xyz, l2_points, npoint=None, radius=None, nsample=None, mlp=[256,512,1024], mlp2=None, group_all=True, is_training=is_training, bn_decay=bn_decay, scope='layer3')

        # Fully connected layers
        net = tf.reshape(l3_points, [batch_size, 1024])
        net = tf_util.fully_connected(net, 512, bn=True, is_training=is_training, scope='fc1', bn_decay=bn_decay)
        net = tf_util.dropout(net, keep_prob=0.5, is_training=is_training, scope='dp1')
        net = tf_util.fully_connected(net, 256, bn=True, is_training=is_training, scope='fc2', bn_decay=bn_decay)
        net = tf_util.dropout(net, keep_prob=0.5, is_training=is_training, scope='dp2')

        results = []
        for idx, out_dim in enumerate(out_dims):
            current_result = tf_util.fully_connected(net, out_dim, activation_fn=None, scope='fc3_{}'.format(idx))
            results.append(current_result)

        return results 

# 需要导入模块: import tf_util [as 别名]
# 或者: from tf_util import fully_connected [as 别名]
def get_center_regression_net(object_point_cloud, one_hot_vec,
                              is_training, bn_decay, end_points):
    ''' Regression network for center delta. a.k.a. T-Net.
    Input:
        object_point_cloud: TF tensor in shape (B,M,C)
            point clouds in 3D mask coordinate
        one_hot_vec: TF tensor in shape (B,3)
            length-3 vectors indicating predicted object type
    Output:
        predicted_center: TF tensor in shape (B,3)
    ''' 
    num_point = object_point_cloud.get_shape()[1].value
    net = tf.expand_dims(object_point_cloud, 2)
    net = tf_util.conv2d(net, 128, [1,1],
                         padding='VALID', stride=[1,1],
                         bn=True, is_training=is_training,
                         scope='conv-reg1-stage1', bn_decay=bn_decay)
    net = tf_util.conv2d(net, 128, [1,1],
                         padding='VALID', stride=[1,1],
                         bn=True, is_training=is_training,
                         scope='conv-reg2-stage1', bn_decay=bn_decay)
    net = tf_util.conv2d(net, 256, [1,1],
                         padding='VALID', stride=[1,1],
                         bn=True, is_training=is_training,
                         scope='conv-reg3-stage1', bn_decay=bn_decay)
    net = tf_util.max_pool2d(net, [num_point,1],
        padding='VALID', scope='maxpool-stage1')
    net = tf.squeeze(net, axis=[1,2])
    net = tf.concat([net, one_hot_vec], axis=1)
    net = tf_util.fully_connected(net, 256, scope='fc1-stage1', bn=True,
        is_training=is_training, bn_decay=bn_decay)
    net = tf_util.fully_connected(net, 128, scope='fc2-stage1', bn=True,
        is_training=is_training, bn_decay=bn_decay)
    predicted_center = tf_util.fully_connected(net, 3, activation_fn=None,
        scope='fc3-stage1')
    return predicted_center, end_points 

# 需要导入模块: import tf_util [as 别名]
# 或者: from tf_util import fully_connected [as 别名]
def get_3d_box_estimation_v1_net(object_point_cloud, one_hot_vec,
                                 is_training, bn_decay, end_points):
    ''' 3D Box Estimation PointNet v1 network.
    Input:
        object_point_cloud: TF tensor in shape (B,M,C)
            point clouds in object coordinate
        one_hot_vec: TF tensor in shape (B,3)
            length-3 vectors indicating predicted object type
    Output:
        output: TF tensor in shape (B,3+NUM_HEADING_BIN*2+NUM_SIZE_CLUSTER*4)
            including box centers, heading bin class scores and residuals,
            and size cluster scores and residuals
    ''' 
    num_point = object_point_cloud.get_shape()[1].value
    net = tf_util.perceptron(object_point_cloud, 128,
                         bn=True, is_training=is_training,
                         scope='conv-reg1', bn_decay=bn_decay)
    net = tf_util.perceptron(net, 128,
                         bn=True, is_training=is_training,
                         scope='conv-reg2', bn_decay=bn_decay)
    net = tf_util.perceptron(net, 256,
                         bn=True, is_training=is_training,
                         scope='conv-reg3', bn_decay=bn_decay)
    net = tf_util.perceptron(net, 512,
                         bn=True, is_training=is_training,
                         scope='conv-reg4', bn_decay=bn_decay)
    net = tf_util.max_pool2d(tf.expand_dims(net, 3),  [num_point,1],
                         padding='VALID', scope='maxpool2')
    net = tf.squeeze(net, axis=[1, 3])
    net = tf.concat([net, one_hot_vec], axis=1)
    net = tf_util.fully_connected(net, 512, scope='fc1', bn=True,
        is_training=is_training, bn_decay=bn_decay)
    net = tf_util.fully_connected(net, 256, scope='fc2', bn=True,
        is_training=is_training, bn_decay=bn_decay)

    # The first 3 numbers: box center coordinates (cx,cy,cz),
    # the next NUM_HEADING_BIN*2:  heading bin class scores and bin residuals
    # next NUM_SIZE_CLUSTER*4: box cluster scores and residuals
    output = tf_util.fully_connected(net,
        3+NUM_HEADING_BIN*2+NUM_SIZE_CLUSTER*4, activation_fn=None, scope='fc3')
    return output, end_points