Confused about conv2d_transpose

Question

I'm getting this error message when using conv2d_transpose:

W tensorflow/core/common_runtime/executor.cc:1102] 0x7fc81f0d6250 Compute status: Invalid argument: Conv2DBackpropInput: Number of rows of out_backprop doesn't match computed: actual = 32, computed = 4
 [[Node: generator/g_h1/conv2d_transpose = Conv2DBackpropInput[T=DT_FLOAT, padding="SAME", strides=[1, 2, 2, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/cpu:0"](generator/g_h1/conv2d_transpose/output_shape, generator/g_h1/w/read, _recv_l_0)]]

However, it occurs after the graph is built while compiling the loss function (Adam). Any ideas on what would cause this? I suspect it's related to the input dimensions but I'm not sure exactly why.

Full error: https://gist.github.com/jimfleming/75d88e888044615dd6e3

Relevant code:

# l shape: [batch_size, 32, 32, 4]

output_shape = [self.batch_size, 8, 8, 128]
filter_shape = [7, 7, 128, l.get_shape()[-1]]
strides = [1, 2, 2, 1]
with tf.variable_scope("g_h1"):
    w = tf.get_variable('w', filter_shape, initializer=tf.random_normal_initializer(stddev=0.02))
    h1 = tf.nn.conv2d_transpose(l, w, output_shape=output_shape, strides=strides, padding='SAME')
    h1 = tf.nn.relu(h1)

output_shape = [self.batch_size, 16, 16, 128]
filter_shape = [7, 7, 128, h1.get_shape()[-1]]
strides = [1, 2, 2, 1]
with tf.variable_scope("g_h2"):
    w = tf.get_variable('w', filter_shape, initializer=tf.random_normal_initializer(stddev=0.02))
    h2 = tf.nn.conv2d_transpose(h1, w,output_shape=output_shape,  strides=strides, padding='SAME')
    h2 = tf.nn.relu(h2)

output_shape = [self.batch_size, 32, 32, 3]
filter_shape = [5, 5, 3, h2.get_shape()[-1]]
strides = [1, 2, 2, 1]
with tf.variable_scope("g_h3"):
    w = tf.get_variable('w', filter_shape, initializer=tf.random_normal_initializer(stddev=0.02))
    h3 = tf.nn.conv2d_transpose(h2, w,output_shape=output_shape,  strides=strides, padding='SAME')
    h3 = tf.nn.tanh(h3)

Answer 1

Thanks for the question! You're exactly right---the problem is that the input and output dimensions being passed to tf.nn.conv2d_transpose don't agree. (The error may be detected when computing gradients, but the gradient computation isn't the problem.)

Let's look at just the first part of your code, and simplify it a little bit:

sess = tf.Session()
batch_size = 3
output_shape = [batch_size, 8, 8, 128]
strides = [1, 2, 2, 1]

l = tf.constant(0.1, shape=[batch_size, 32, 32, 4])
w = tf.constant(0.1, shape=[7, 7, 128, 4])

h1 = tf.nn.conv2d_transpose(l, w, output_shape=output_shape, strides=strides, padding='SAME')
print sess.run(h1)

I replaced the variables with constants --- it's easier to see what's going on.

If you try to run this code, you get a similar error:

InvalidArgumentError: Conv2DCustomBackpropInput: Size of out_backprop doesn't match computed: actual = 32, computed = 4
  [[Node: conv2d_transpose_6 = Conv2DBackpropInput[T=DT_FLOAT, data_format="NHWC", padding="SAME", strides=[1, 2, 2, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/cpu:0"](conv2d_transpose_6/output_shape, Const_25, Const_24)]]

Now, the error is a little misleading --- it talks about the 'out_backprop' argument to 'Conv2DCustomBackpropInput'. The key is that tf.nn.conv2d_transpose is actually just the gradient of tf.nn.conv2d, so Tensorflow uses the same code internally (Conv2DCustomBackpropInput) to compute the gradient of tf.nn.conv2d and to compute tf.nn.conv2d_transpose.

The error means that the 'output_shape' you requested is not possible, given the shapes of 'l' and 'w'.

Since tf.nn.conv2d_transpose is the backward (gradient) counterpart of tf.nn.conv2d, one way to see what the correct shapes should be is to use the corresponding forward operation:

output = tf.constant(0.1, shape=output_shape)
expected_l = tf.nn.conv2d(output, w, strides=strides, padding='SAME')
print expected_l.get_shape()
# Prints (3, 4, 4, 4)

That is, in the forward direction, if you provided a tensor of shape 'output_shape', you would get out a tensor of shape (3, 4, 4, 4). So one way to fix the problem is to change the shape of 'l' to (3, 4, 4, 4); if you change the code above to:

l = tf.constant(0.1, shape=[batch_size, 4, 4, 4])

everything works fine.

In general, try using tf.nn.conv2d to get a feel for what the relationship between the tensor shapes is. Since tf.nn.conv2d_transpose is its backward counterpart, it has the same relationship between input, output and filter shapes (but with the roles of the input and output reversed.)

Hope that helps!

Answer 2

Using padding='SAME' in tf.nn.conv2d_transpose() function may works too