def interp_upsampling(V):
"""
upsample a field by a factor of 2
TODO: should switch this to use neuron.utils.interpn()
"""
#print(V.shape)
grid = nrn_utils.volshape_to_ndgrid(
[f * 2 for f in V.get_shape().as_list()[1:-1]])
grid = [tf.cast(f, 'float32') for f in grid]
grid = [tf.expand_dims(f / 2 - f, 0) for f in grid]
offset = tf.stack(grid, len(grid) + 1)
#xx = tf.cast(xx, 'float32')
#yy = tf.cast(yy, 'float32')
#zz = tf.cast(zz, 'float32')
#xx = tf.expand_dims(xx/2-xx, 0)
#yy = tf.expand_dims(yy/2-yy, 0)
#zz = tf.expand_dims(zz/2-zz, 0)
#print(xx.shape)
#offset = tf.stack([xx, yy], 3)
#print(offset.shape)
#print(V.shape)
# V = nrn_utils.transform(V, offset)
V = nrn_layers.SpatialTransformer(interp_method='linear')([V, offset])
#print(V.shape)
return V
def interp_upsampling(V):
"""
upsample a field by a factor of 2
TODO: should switch this to use neuron.utils.interpn()
"""
V = tf.reshape(V, [-1] + V.get_shape().as_list()[1:])
grid = volshape_to_ndgrid([f*2 for f in V.get_shape().as_list()[1:-1]])
grid = [tf.cast(f, 'float32') for f in grid]
grid = [tf.expand_dims(f/2 - f, 0) for f in grid]
offset = tf.stack(grid, len(grid) + 1)
V = SpatialTransformer(interp_method='linear')([V, offset])
return V
def interp_upsampling(V):
"""
upsample a field by a factor of 2
TODO: should switch this to use neuron.utils.interpn()
"""
[xx, yy, zz] = nrn_utils.volshape_to_ndgrid([f*2 for f in V.get_shape().as_list()[1:4]])
xx = tf.cast(xx, 'float32')
yy = tf.cast(yy, 'float32')
zz = tf.cast(zz, 'float32')
xx = tf.expand_dims(xx/2-xx, 0)
yy = tf.expand_dims(yy/2-yy, 0)
zz = tf.expand_dims(zz/2-zz, 0)
offset = tf.stack([xx, yy, zz], 4)
# V = nrn_utils.transform(V, offset)
V = nrn_layers.SpatialTransformer(interp_method='linear')([V, offset])
return V