op = None
else:
# try search op in various modules
- for candidate in (_op, _op.nn, _op.image, _op.vision):
+ for candidate in (_op, _op.nn, _op.image, _op.vision, _op.contrib):
op = getattr(candidate, op_name, None)
if op is not None:
break
return _op.multiply(difference, difference)
return _impl
+def _size():
+ def _impl(inputs, attr, params):
+ new_attr = attr
+ new_attr['out_type'] = attr['out_type'].name
+ return AttrCvt('ndarray_size', transforms={'out_type' : 'dtype'})(inputs, new_attr)
+ return _impl
+
# compatible operators that do NOT require any conversion.
_identity_list = []
'Shape' : _shape(),
'Sigmoid' : AttrCvt('sigmoid'),
'Sign' : AttrCvt('sign'),
- 'Size' : AttrCvt('ndarray_size'),
+ 'Size' : _size(),
'Slice' : _slice(),
'Softmax' : _softmax(),
'Softplus' : _softplus(),
def test_forward_size():
def check_size(ishape):
np_input = np.random.uniform(size=ishape).astype(np.float32)
+
+ # if all dimensions are constant, TF will optimize away size operator into constant
+ tf_input_shape = list(np_input.shape)
+ tf_input_shape[0] = None
+
with tf.Graph().as_default():
- input = tf.placeholder(shape=np_input.shape, dtype=np_input.dtype, name='input')
+ input = tf.placeholder(shape=tf_input_shape, dtype=np_input.dtype, name='input')
tf.size(input, name='size')
compare_tf_with_tvm([np_input], ['input:0'], 'size:0')
- if tf.__version__ < LooseVersion('1.1'):
- check_size((10, 8, 16, 32))
- check_size((10,))
- check_size(())
+ check_size((10, 8, 16, 32))
+ check_size((10,))
#######################################################################
# All, Max, Min