I'm trying to found a way to convolution transpose with images of variable size. I used tf.nn.conv2d_transpose api, but I failed.
import tensorflow as tf
def conv2d_transpose(inputs, filters_shape, strides, name, padding="SAME", activation=None):
filters = get_conv_filters(filters_shape, name)
inputs_shape = inputs.get_shape().as_list()
output_shape = tf.stack(calc_output_shape(inputs_shape, filters_shape, strides, padding)) #tf.pack renamed tf.stack
strides = [1,*strides,1]
conv_transpose = tf.nn.conv2d_transpose(inputs, filters, output_shape=output_shape,
strides=strides, padding=padding, name=name+"transpose")
if activation != None:
conv_transpose = activation(conv_transpose)
return conv_transpose
def get_conv_filters(filters_size, name):
conv_weights = tf.Variable(tf.truncated_normal(filters_size), name=name + "weights")
return conv_weights
def calc_output_shape(inputs_shape, filters_shape, strides, padding): # For conv_transpose
batch_size, inputs_height, inputs_width, n_channel = inputs_shape
filters_height, filters_width, before_n_channel, after_n_channel = filters_shape
strides_height, strides_width = strides
if padding =="SAME":
output_height = inputs_height*strides_height
output_width = inputs_width*strides_width
else: # padding="VALID"
output_height = (inputs_height-1)*strides_height+filters_height
output_width = (inputs_width-1)*strides_width+filters_width
return [batch_size, output_height, output_width, after_n_channel]
main
input_images = tf.placeholder(tf.float32, [None, None, None, 3])
transpose_layer = conv2d_transpose(input_images, filters_shape=[3,3,3,3], strides=[2,2], name="conv_3_transpose", padding="SAME", activation=tf.nn.relu)
then, I get the following error.
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-11-0a594abc4f59> in <module>()
36
37 input_images = tf.placeholder(tf.float32, [None, None, None, 3])
---> 38 transpose_layer = conv2d_transpose(input_images, filters_shape=[3,3,3,3], strides=[2,2], name="conv_3_transpose", padding="SAME", activation=tf.nn.relu)
39
<ipython-input-11-0a594abc4f59> in conv2d_transpose(inputs, filters_shape, strides, name, padding, activation)
5
6 inputs_shape = inputs.get_shape().as_list()
----> 7 output_shape = tf.stack(calc_output_shape(inputs_shape, filters_shape, strides, padding)) #tf.pack renamed tf.stack
8 strides = [1,*strides,1]
9
<ipython-input-11-0a594abc4f59> in calc_output_shape(inputs_shape, filters_shape, strides, padding)
26
27 if padding =="SAME":
---> 28 output_height = inputs_height*strides_height
29 output_width = inputs_width*strides_width
30
TypeError: unsupported operand type(s) for *: 'NoneType' and 'int'
I think the reason for this error is that the input_shape is not fixed. So, error occurs in calculating the output_shape. How should I overcome this problem?
