def reshape_shape(op):
# type: (Caffe2Operation)->ShapeResult
if len(op.inputs) == 1:
shape = op.attribs['shape']
elif len(op.inputs) == 2:
if op.inputs[1].data is None:
raise utils.NNEFToolsException(
'Reshape is not supported with calculated shape.')
shape = op.inputs[1].data.tolist()
else:
assert False
graph_utils.replace_tensor_in_consumers(
op.graph,
op.outputs[1],
Caffe2Tensor(graph=op.graph,
shape=[op.inputs[0].rank],
data=np.array(op.inputs[0].shape, dtype=np.int64),
dtype=DTYPE_INT64),
remove=False)
op.attribs['shape'] = shape
op.inputs = (op.inputs[0], )
return (infer.reshape(op.inputs[0].shape, shape=shape, zero_means_same=True), [op.inputs[0].rank]), \
(op.inputs[0].dtype, DTYPE_INT64)
def propagate_reshape(op):
# type: (ONNXOperation)->typing.Tuple[typing.List[typing.List[int]], typing.List[str]]
if 'shape' in op.attribs:
return [
infer.reshape(input=op.input.shape,
shape=op.attribs['shape'],
zero_means_same=True)
], [op.input.dtype]
else:
input, shape = op.inputs
return [
infer.reshape(input=input.shape,
shape=evaluate_shape_tensor_simple(shape),
zero_means_same=True)
], [input.dtype]
def nnef_reshape(input, shape, axis_start=0, axis_count=-1):
# type: (torch.Tensor, List[int], int, int)->torch.Tensor
return input.reshape(
shape_inference.reshape(input=list(input.shape),
shape=shape,
offset=axis_start,
count=axis_count,
zero_means_same=True))
def expand_softmax(tf_graph, tf_op):
assert tf_op.input.rank != 0
axis = tf_op.attribs.get('axis')
if axis is None:
axis = -1
if axis < 0:
axis += tf_op.input.rank
tf_op.attribs['axis'] = -1
if tf_op.input.rank == 2 and axis == 1:
return
if axis != tf_op.input.rank - 1:
perm = utils.without(range(tf_op.input.rank), axis) + [axis]
perm_inv = utils.inverse_permutation(perm)
transpose = TFOperation(graph=tf_graph,
name="tf.transpose",
inputs=tf_op.input,
attribs=dict(perm=perm),
outputs=TFTensor(graph=tf_graph,
name=None,
shape=infer.transpose(
input=tf_op.input.shape,
axes=perm),
dtype=tf_op.input.dtype))
tf_op.inputs = transpose.output
old_output = tf_op.output
tf_op.outputs = TFTensor(graph=tf_graph,
name=None,
shape=tf_op.input.shape,
dtype=tf_op.input.dtype)
TFOperation(graph=tf_graph,
name="tf.transpose",
inputs=tf_op.output,
attribs=dict(perm=perm_inv),
outputs=old_output)
if tf_op.input.rank != 2:
shape = [-1, tf_op.input.shape[-1]]
reshape = TFOperation(graph=tf_graph,
name="tf.reshape",
inputs=tf_op.input,
attribs=dict(shape=shape),
outputs=TFTensor(graph=tf_graph,
name=None,
shape=infer.reshape(
input=tf_op.input.shape,
shape=shape),
dtype=tf_op.input.dtype))
tf_op.inputs = reshape.output
old_output = tf_op.output
tf_op.outputs = TFTensor(graph=tf_graph,
name=None,
shape=list(tf_op.input.shape),
dtype=tf_op.input.dtype)
TFOperation(graph=tf_graph,
name="tf.reshape",
inputs=tf_op.output,
attribs=dict(shape=old_output.shape),
outputs=old_output)
def propagate_reshape(op, const_value_by_tensor):
# type: (TFOperation, _ConstValueByTensorT)->typing.Tuple[typing.List[typing.List[int]], typing.List[str]]
input, shape = op.inputs
shape = const_value_by_tensor[shape].tolist() # type: typing.List[int]
return [infer.reshape(input=input.shape, shape=shape)], [op.attribs['T']]
def test_reshape(self):
self.assertEqual([4, 6], infer.reshape([4, 2, 3], [4, 6]))
self.assertEqual([4, 6], infer.reshape([4, 2, 3], [4, -1]))
self.assertEqual([24], infer.reshape([4, 2, 3], [24]))
self.assertEqual([24], infer.reshape([4, 2, 3], [-1]))
self.assertEqual([4, 2, 1, 3], infer.reshape([4, 2, 3], [4, 2, -1, 3]))
self.assertEqual([4, 6, 1], infer.reshape([4, 2, 3], [4, -1, 1]))
self.assertEqual([4, 6, 1], infer.reshape([4, 2, 3], [4, -1, 1]))
self.assertEqual([1], infer.reshape([], [1]))
self.assertEqual([1], infer.reshape([], [-1]))
self.assertEqual([], infer.reshape([1], []))
self.assertEqual([], infer.reshape([1, 1, 1], []))
self.assertEqual([0, 1], infer.reshape([0], [0, 1]))
self.assertEqual([1, 2, 3, 4], infer.reshape(input=[1, 3, 2, 4], shape=[2, 3], offset=1, count=2))
self.assertEqual([1, 2, 3, 4], infer.reshape(input=[1, 24], shape=[2, 3, 4], offset=1, count=-1))
with self.assertRaises(AssertionError):
infer.reshape([0], [0, -1])
self.assertEqual([1, 2, 1, 3], infer.reshape([1, 2, 3], [0, 0, 1, -1], zero_means_same=True))
self.assertEqual([1], infer.reshape([1], [0], zero_means_same=True))
with self.assertRaises(AssertionError):
infer.reshape([], [0], zero_means_same=True)
with self.assertRaises(AssertionError):
infer.reshape([1], [1, 0], zero_means_same=True)
def test_reshape(self):
self.assertEqual([4, 6], infer.reshape([4, 2, 3], [4, 6]))
self.assertEqual([4, 6], infer.reshape([4, 2, 3], [4, -1]))
self.assertEqual([24], infer.reshape([4, 2, 3], [24]))
self.assertEqual([24], infer.reshape([4, 2, 3], [-1]))
self.assertEqual([4, 2, 1, 3], infer.reshape([4, 2, 3], [4, 2, -1, 3]))
self.assertEqual([4, 6, 1], infer.reshape([4, 2, 3], [4, -1, 1]))
self.assertEqual([4, 6, 1], infer.reshape([4, 2, 3], [4, -1, 1]))
self.assertEqual([1], infer.reshape([], [1]))
self.assertEqual([1], infer.reshape([], [-1]))
self.assertEqual([], infer.reshape([1], []))
self.assertEqual([], infer.reshape([1, 1, 1], []))
self.assertEqual([0, 1], infer.reshape([0], [0, 1]))
with self.assertRaises(AssertionError):
infer.reshape([0], [0, -1])
self.assertEqual([1, 2, 1, 3], infer.reshape([1, 2, 3], [0, 0, 1, -1], zero_means_same=True))
self.assertEqual([1], infer.reshape([1], [0], zero_means_same=True))
with self.assertRaises(AssertionError):
infer.reshape([], [0], zero_means_same=True)
with self.assertRaises(AssertionError):
infer.reshape([1], [1, 0], zero_means_same=True)
def reshape_shape(op):
return shapes.reshape(op.inputs[0].shape,
shape=op.attribs['shape'],
offset=op.attribs['axis'],
count=op.attribs['num_axes'],
zero_means_same=True)