The following lines
line 12 X = tf.concat(2, [tf.squeeze(x) for x in X]) # bsize, b, a*r, r
line 14 X = tf.concat(2, [tf.squeeze(x) for x in X]) # bsize, a*r, b*r
in subpixel.py cause the first dimension to be dropped when the batch size is one. In fact, line 12 causes the dimension drop and line 14 throws an error.
I propose the following change:
X = tf.concat(2, [tf.squeeze(x, axis = 1) for x in X]) # bsize, b, a*r, rX = tf.concat(2, [tf.squeeze(x, axis = 1) for x in X]) # bsize, a*r, b*r
goldsmith
commented
7 years ago
The following lines line 12
X = tf.concat(2, [tf.squeeze(x) for x in X]) # bsize, b, a*r, rline 14X = tf.concat(2, [tf.squeeze(x) for x in X]) # bsize, a*r, b*rin subpixel.py cause the first dimension to be dropped when the batch size is one. In fact, line 12 causes the dimension drop and line 14 throws an error.
I propose the following change:
X = tf.concat(2, [tf.squeeze(x, axis = 1) for x in X]) # bsize, b, a*r, rX = tf.concat(2, [tf.squeeze(x, axis = 1) for x in X]) # bsize, a*r, b*r