I want to get larger tensor by 4 subtensor with interleaved index.
Here is my question:
Four input with shape [batch x 2m x 2n x 1];
Output shape is [batch x 4m x 4n x 1];
index_y_0_1 = [0,1,4,5,8,9...], index_y_2_3 = [2,3,6,7,10,11...]
index_x_0_1 = [0,1,4,5,8,9...], index_x_2_3 = [2,3,6,7,10,11...]
Output[index_y_0_1 ,index_x_0_1 ] = Input0
Output[index_y_0_1 ,index_x_2_3 ] = Input1
Output[index_y_2_3 ,index_x_0_1 ] = Input2
Output[index_y_2_3 ,index_x_2_3 ] = Input3
Here is my question with python code:
import numpy as np
UpperLeft = np.ones((3,2,4,1))
UpperRight = np.ones((3,2,4,1))*2
BottonLeft = np.ones((3,2,4,1))*3
BottonRight = np.ones((3,2,4,1))*4
output = np.zeros((UpperLeft.shape[0], UpperLeft.shape[1]*2, UpperLeft.shape[2]*2, 1))
assert(output.shape[1]%4 == 0)
assert(output.shape[2]%4 == 0)
# UpperLeft Assignment
start_y = 0
start_x = 0
output[:,(start_y + 0)::4, (start_x + 0)::4, :] = UpperLeft[:,0::2, 0::2, :]
output[:,(start_y + 0)::4, (start_x + 1)::4, :] = UpperLeft[:,0::2, 1::2, :]
output[:,(start_y + 1)::4, (start_x + 0)::4, :] = UpperLeft[:,1::2, 0::2, :]
output[:,(start_y + 1)::4, (start_x + 1)::4, :] = UpperLeft[:,1::2, 1::2, :]
# UpperRight Assignment
start_y = 0
start_x = 2
output[:,(start_y + 0)::4, (start_x + 0)::4, :] = UpperRight[:,0::2, 0::2, :]
output[:,(start_y + 0)::4, (start_x + 1)::4, :] = UpperRight[:,0::2, 1::2, :]
output[:,(start_y + 1)::4, (start_x + 0)::4, :] = UpperRight[:,1::2, 0::2, :]
output[:,(start_y + 1)::4, (start_x + 1)::4, :] = UpperRight[:,1::2, 1::2, :]
# BottonLeft Assignment
start_y = 2
start_x = 0
output[:,(start_y + 0)::4, (start_x + 0)::4, :] = BottonLeft[:,0::2, 0::2, :]
output[:,(start_y + 0)::4, (start_x + 1)::4, :] = BottonLeft[:,0::2, 1::2, :]
output[:,(start_y + 1)::4, (start_x + 0)::4, :] = BottonLeft[:,1::2, 0::2, :]
output[:,(start_y + 1)::4, (start_x + 1)::4, :] = BottonLeft[:,1::2, 1::2, :]
# BottonRight Assignment
start_y = 2
start_x = 2
output[:,(start_y + 0)::4, (start_x + 0)::4, :] = BottonRight[:,0::2, 0::2, :]
output[:,(start_y + 0)::4, (start_x + 1)::4, :] = BottonRight[:,0::2, 1::2, :]
output[:,(start_y + 1)::4, (start_x + 0)::4, :] = BottonRight[:,1::2, 0::2, :]
output[:,(start_y + 1)::4, (start_x + 1)::4, :] = BottonRight[:,1::2, 1::2, :]
show_out = output[0,:,:,0]
How can I do this on tensorflow? Thanks!
You can have a function that interleaves tensors across one axis like this.
import tensorflow as tf
# Interlaves tensors across one axis
def interleave(tensors, size=1, axis=-1):
# Reshape tensors
tensors_res = []
for tensor in tensors:
s = tf.shape(tensor)
new_s = tf.concat([s[:axis], [s[axis] // size, size], s[axis:][1:]], axis=0)
tensors_res.append(tf.reshape(tensor, new_s))
# Concatenate across new dimension
if isinstance(axis, (tf.Tensor, tf.Variable)):
selected_axis = tf.cond(tf.less(axis, 0), lambda: axis - 1, lambda: axis)
concat_axis = tf.cond(tf.less(axis, 0), lambda: axis, lambda: axis + 1)
else:
selected_axis = (axis - 1) if axis < 0 else axis
concat_axis = axis if axis < 0 else (axis + 1)
tensors_concat = tf.concat(tensors_res, axis=concat_axis)
# Reshape concatenation
concat_s = tf.shape(tensors_concat)
res_s = tf.concat([concat_s[:selected_axis], [-1], concat_s[concat_axis:][1:]], axis=0)
return tf.reshape(tensors_concat, res_s)
Then you can use this to interleave first across one dimension and the across the other.
import tensorflow as tf
with tf.Graph().as_default(), tf.Session() as sess:
# Input data
UpperLeft = tf.ones((3, 2, 4, 1))
UpperRight = tf.ones((3, 2, 4, 1)) * 2
BottomLeft = tf.ones((3, 2, 4, 1)) * 3
BottomRight = tf.ones((3, 2, 4, 1)) * 4
# Interleave across axis 2
Upper = interleave([UpperLeft, UpperRight], size=2, axis=2)
Bottom = interleave([BottomLeft, BottomRight], size=2, axis=2)
# Interleave across axis 1
Result = interleave([Upper, Bottom], size=2, axis=1)
# Show result
print(sess.run(Result)[0, :, :, 0])
# [[1. 1. 2. 2. 1. 1. 2. 2.]
# [1. 1. 2. 2. 1. 1. 2. 2.]
# [3. 3. 4. 4. 3. 3. 4. 4.]
# [3. 3. 4. 4. 3. 3. 4. 4.]]
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