Tensorflow conv2d_transpose: Size of out_backprop doesn't match computed

Question

When I build the FCN for segmentation, I want the images to keep the original size of input data, so I use the fully convolution layers. When I choose the fixed input size, such as (224, 224), the transpose conv works fine. However, when I changed the code of using (224, 224) to (h, w), I meet the following error. I googled before, but I didn't figure it out. Can anyone help me? Thanks.

Error information:

InvalidArgumentError (see above for traceback): Conv2DSlowBackpropInput: Size 
of out_backprop doesn't match computed: actual = 62, computed = 
63spatial_dim: 2 input: 500 filter: 16 output: 62 stride: 8 dilation: 1
     [[Node: deconv_layer/conv2d_transpose_2 = 
Conv2DBackpropInput[T=DT_FLOAT, data_format="NCHW", dilations=[1, 1, 1, 1], 
padding="SAME", strides=[1, 1, 8, 8], use_cudnn_on_gpu=true, 
_device="/job:localhost/replica:0/task:0/device:GPU:0"] 
(deconv_layer/conv2d_transpose_2-0-VecPermuteNHWCToNCHW- 
LayoutOptimizer/_1961, deconv_layer/deconv3/kernel/read, 
deconv_layer/Add_1)]]
     [[Node: losses/_2091 = _Recv[client_terminated=false, 
recv_device="/job:localhost/replica:0/task:0/device:CPU:0", 
send_device="/job:localhost/replica:0/task:0/device:GPU:0", 
send_device_incarnation=1, tensor_name="edge_4480_losses", 
tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"] 
()]]

Code:

with tf.variable_scope("deconv_layer"):
    deconv_shape1 = block2.get_shape()
    W_t1 = deconv_utils.weight_variable([4, 4, deconv_shape1[3].value, 2048], 
                                        name="deconv1/kernel")
    b_t1 = deconv_utils.bias_variable([deconv_shape1[3].value], 
                                      name="deconv1/biases")
    deconv_t1 = deconv_utils.conv2d_transpose_strided(block4, W_t1, b_t1, 
                                       output_shape=tf.shape(block2))
    fuse1 = tf.add(deconv_t1, block2)
    print("deconv_t1: ", deconv_t1.shape)
    print("fuse_1: ", fuse1.shape)
    tf.identity(fuse1, name="fuse1")

    deconv_shape2 = block1.get_shape()
    W_t2 = deconv_utils.weight_variable([4, 4, deconv_shape2[3].value, 
                        deconv_shape1[3].value], name="deconv2/kernel")
    b_t2 = deconv_utils.bias_variable([deconv_shape2[3].value], 
                                      name="deconv2/biases")
    deconv_t2 = deconv_utils.conv2d_transpose_strided(fuse1, W_t2, b_t2, 
                        output_shape=tf.shape(block1))
    fuse2 = tf.add(deconv_t2, block1)
    print("deconv_t2: ", deconv_t2.shape)
    print("fuse2: ", fuse2.shape)
    tf.identity(fuse2, name="fuse2")

    shape = tf.shape(features)
    deconv_shape3 = tf.stack([shape[0], shape[1], shape[2], num_classes])
    W_t3 = deconv_utils.weight_variable([16, 16, num_classes, 
                       deconv_shape2[3].value], name="deconv3/kernel")
    b_t3 = deconv_utils.bias_variable([num_classes], name="deconv3/biases")
    deconv_t3 = deconv_utils.conv2d_transpose_strided(fuse2, W_t3, b_t3, 
                       output_shape=deconv_shape3, stride=8)
    print("deconv_t3: ", deconv_t3.shape)

The version with out custom functions is here:

    with tf.variable_scope("deconv_layer"):
    deconv1_shape = block2.get_shape()
    shape1 = [4, 4, deconv1_shape[3].value, 2048]
    deconv1_kernel = tf.Variable(initial_value=tf.truncated_normal(shape1, 
                                 stddev=0.02),
                                 trainable=True,
                                 name="deconv1/kernel")
    deconv1 = tf.nn.conv2d_transpose(value=block4,
                                     filter=deconv1_kernel,
                                     # output_shape=[BATCH_SIZE, 
                             tf.shape(block2)[1], tf.shape(block2)[2], 512],
                                     output_shape=tf.shape(block2),
                                     strides=[1, 2, 2, 1],
                                     padding='SAME',
                                     data_format='NHWC'
                                     )
    print('deconv1', deconv1.shape)
    fuse1 = tf.add(deconv1, block2)  # fuse1 = pool4 + deconv2(pool5)
    tf.identity(fuse1, name="fuse1")

    deconv2_shape = block1.get_shape()
    shape2 = [4, 4, deconv2_shape[3].value, deconv1_shape[3].value]
    deconv2_kernel = tf.Variable(initial_value=tf.truncated_normal(shape2, 
                                 stddev=0.02),
                                 trainable=True,
                                 name="deconv2/kernel")
    deconv2 = tf.nn.conv2d_transpose(value=fuse1,
                                     filter=deconv2_kernel,
                                     output_shape=tf.shape(block1),
                                     strides=[1, 2, 2, 1],
                                     padding='SAME',
                                     data_format='NHWC'
                                     )
    print('deconv2', deconv2.shape)
    fuse2 = tf.add(deconv2, block1)
    tf.identity(fuse2, name="fuse2")

    deconv3_shape = tf.stack([tf.shape(features)[0], tf.shape(features)[1], 
                              tf.shape(features)[2], num_classes])
    shape3 = [16, 16, num_classes, deconv2_shape[3].value]
    deconv_final_kernel = tf.Variable(initial_value=tf.truncated_normal(shape3, stddev=0.02),
                                      trainable=True,
                                      name="deconv3/kernel")

    seg_logits = tf.nn.conv2d_transpose(value=fuse2,
                                        filter=deconv_final_kernel,
                                        output_shape=deconv3_shape,
                                        strides=[1, 8, 8, 1],
                                        padding='SAME',
                                        data_format='NHWC') 

Answer 1

The conv Net and Deconv Net in FCN, which are built by different structures, are maybe not consistent with each other. In this case, the conv net use conv with padding='VALID', while the deconv net uses all conv_transpose with padding='SAME. Thus the shapes are not the same, which causes the problem above.