def test_slice_infer_4(self):
graph = self.build_test_graph()
node = Node(graph, 'sslice_1')
node.in_node(1).value = np.array([0, 10, 10, 0])
node.begin_mask = [1, 0, 0, 1] # 6
tf_strided_slice_infer(node)
self.assertTrue(np.array_equal(node.out_node().shape, np.array([1, 34, 30, 2])), 'Wrong output shape detected')
def test_slice_infer_1(self):
graph = self.build_test_graph()
node = Node(graph, 'sslice_1')
tf_strided_slice_infer(node)
self.assertTrue(
np.array_equal(node.out_node().shape, np.array([1, 34, 30, 2])),
'Wrong output shape detected')
def test_slice_infer_dim_beg(self):
graph = self.build_test_graph_dim_beg()
node = Node(graph, 'sslice_1')
node.shrink_axis_mask = [1]
tf_strided_slice_infer(node)
self.assertTrue(np.array_equal(node.out_node().shape, np.array([4])), 'Wrong output shape detected')
self.assertTrue(np.array_equal(node.out_node().value, np.array([1, 34, 34, 62])), 'Wrong output shape detected')
def infer(node: Node):
tf_strided_slice_infer(node)
if node.graph.graph['layout'] == 'NHWC' and node.out_port(
0).data.get_value() is None:
PermuteAttrs.create_permute_attrs(
node,
attrs=[
('shrink_axis_mask', 'input:0', permute_masks),
('new_axis_mask', 'input:0', permute_masks),
('ellipsis_mask', 'input:0', permute_masks),
('begin_mask', 'input:0', permute_masks),
('end_mask', 'input:0', permute_masks),
])
for i in range(1, len(node.in_nodes())):
if node.in_node(
i).value is not None and node.in_node(i).shape[0] > 3:
perm = PermuteAttrs.get_nhwc_to_nchw_permutation(
len(node.in_node(0).shape))
node.in_node(i).value = permute_array_with_ellipsis(
node, perm,
node.in_node(i).value, 0)
# due to permutation from nhwc to nchw we will extend all masks and inputs
idx = np.nonzero(node.ellipsis_mask)
node.ellipsis_mask[idx] = 0
def test_slice_infer_2(self):
graph = self.build_test_graph()
node = Node(graph, 'sslice_1')
node.end_mask = [1, 0, 0, 1] # 6
tf_strided_slice_infer(node)
self.assertTrue(
np.array_equal(node.out_node().shape, np.array([1, 35, 35, 2])),
'Wrong output shape detected')
def test_slice_infer_neg_end(self):
graph = self.build_test_graph()
node = Node(graph, 'sslice_1')
end_node = Node(graph, 'sslice_end_1')
end_node.value = np.array([1, -1, -5, -1])
tf_strided_slice_infer(node)
self.assertTrue(np.array_equal(node.out_node().shape, np.array([1, 34, 30, 2])), 'Wrong output shape detected')
self.assertTrue(np.array_equal(end_node.value, np.array([1, -1, -5, -1])), 'Negative values in end were converted to positive')
def test_slice_infer_13(self):
graph = self.build_test_graph2()
node = Node(graph, 'sslice_1')
node.in_node(1).value = np.array([1])
node.shrink_axis_mask = [1]
tf_strided_slice_infer(node)
self.assertTrue(np.array_equal(node.out_node().shape, np.array([])), 'Wrong output shape detected')
self.assertTrue(np.array_equal(node.out_node().value, np.array(34)), 'Wrong output shape detected')
def test_slice_infer_12(self):
graph = self.build_test_graph()
node = Node(graph, 'sslice_1')
node.begin_mask = [0, 0, 0, 0] # 15
node.end_mask = [0, 0, 0, 0] # 15
node.shrink_axis_mask = [1, 1, 1, 0] # 7
tf_strided_slice_infer(node)
self.assertTrue(np.array_equal(node.out_node().shape, np.array([3])), 'Wrong output shape detected')
def test_slice_infer_7(self):
graph = self.build_test_graph2()
node = Node(graph, 'sslice_1')
node.in_node(1).value = np.array([1])
node.in_node(2).value = np.array([3])
tf_strided_slice_infer(node)
self.assertTrue(np.array_equal(node.out_node().shape, np.array([2])), 'Wrong output shape detected')
self.assertTrue(np.array_equal(node.out_node().value, np.array([34, 34])), 'Wrong output value detected')
def test_slice_infer_11(self):
graph = self.build_test_graph()
node = Node(graph, 'sslice_1')
node.begin_mask = 15 # 1111
node.end_mask = 15 # 1111
node.shrink_axis_mask = 5 # 0101
tf_strided_slice_infer(node)
self.assertTrue(
np.array_equal(node.out_node().shape, np.array([35, 3])),
'Wrong output shape detected')
def test_slice_infer_8(self):
graph = self.build_test_graph2()
node = Node(graph, 'sslice_1')
node.new_axis_mask = 1
tf_strided_slice_infer(node)
self.assertTrue(
np.array_equal(node.out_node().shape, np.array([1, 4])),
'Wrong output shape detected')
self.assertTrue(
np.array_equal(node.out_node().value, np.array([[1, 34, 34, 62]])),
'Wrong output value detected')
def test_slice_infer_14(self):
graph = self.build_test_graph2()
node = Node(graph, 'sslice_1')
node.in_node(3).value = np.array([-1])
node.end_mask = [0]
node.begin_mask = [0]
node.in_node(0).shape = [4]
tf_strided_slice_infer(node)
self.assertTrue(np.array_equal(node.out_node().shape, np.array([4])), 'Wrong output shape detected')
print(node.out_node().value)
self.assertTrue(np.array_equal(node.out_node().value, np.array([62, 34, 34, 1])), 'Wrong output shape detected')
def infer(node: Node):
tf_strided_slice_infer(node)
out_shape = node.out_port(0).data.get_shape()
assert out_shape is not None, \
'Output shape was not calculated for node {}'.format(node.name)
# extend inputs according to ellipsis mask and/or input_shape
for i_port in node.in_ports().values():
if i_port.idx == 0 or i_port.disconnected():
continue
old_value = i_port.data.get_value()
# additional check for non-const input
# error will be return in shape inference if non-const will be added
# it is paranoid check for case if shape inference will be changed
assert old_value is not None, \
'{} input of {} node is not constant: \'value\' attribute for edge ' + \
'contains None'.format(i_port.idx, node.name)
# insert 0 for begin and end and 1 for stride
new_value = int64_array(extend_mask_according_ellipsis(node.ellipsis_mask, node.shrink_axis_mask,
len(out_shape), list(old_value),
int(i_port.idx == 3)))
# set_value additionally set_shape and propagate value to Const node
if not np.array_equal(new_value, old_value):
i_port.data.set_value(new_value)
# extend masks before removing ellipsis
for attr in ["new_axis_mask", "shrink_axis_mask", "begin_mask", "end_mask", "ellipsis_mask"]:
node[attr] = int64_array(extend_mask_according_ellipsis(node.ellipsis_mask, node.shrink_axis_mask,
len(out_shape), list(node[attr]), 0))
# we will extend all masks and inputs to simplify future transformations
idx = np.nonzero(node.ellipsis_mask)
node.ellipsis_mask[idx] = 0
if node.graph.graph['layout'] == 'NHWC' and node.out_port(0).data.get_value() is None:
PermuteAttrs.create_permute_attrs(node, attrs=[('shrink_axis_mask', 'input:0', permute_masks),
('new_axis_mask', 'input:0', permute_masks),
('ellipsis_mask', 'input:0', permute_masks),
('begin_mask', 'input:0', permute_masks),
('end_mask', 'input:0', permute_masks),
])
# permute inputs
in_shape = node.in_port(0).get_source().data.get_shape()
assert in_shape is not None, \
'Input shape is unknown for 0 input of node {}'.format(node.name)
input_rank = len(in_shape)
if input_rank > 3:
for i_port in node.in_ports().values():
if i_port.idx == 0 or i_port.disconnected():
continue
new_value = permute_array(node, i_port.data.get_value())
# set_value additionally set_shape and propagate value to Const node
i_port.data.set_value(new_value)
def infer(node: Node):
tf_strided_slice_infer(node)
if node.graph.graph['layout'] == 'NHWC' and node.out_port(
0).data.get_value() is None:
PermuteAttrs.create_permute_attrs(
node,
attrs=[
('shrink_axis_mask', 'input:0', permute_masks),
('new_axis_mask', 'input:0', permute_masks),
('ellipsis_mask', 'input:0', permute_masks),
('begin_mask', 'input:0', permute_masks),
('end_mask', 'input:0', permute_masks),
])
for i in range(1, len(node.in_nodes())):
if node.in_node(i).value is not None and len(
node.in_node(0).shape) > 3:
node.in_node(i).value = permute_array_with_ellipsis(
node,
node.in_node(i).value, 0)
# extend masks before removing ellipsis
if np.any(node.ellipsis_mask):
for attr in [
"new_axis_mask", "shrink_axis_mask", "begin_mask",
"end_mask"
]:
node[attr] = int64_array(
extend_mask_according_ellipsis(
node.ellipsis_mask, node.shrink_axis_mask,
len(node.out_port(0).data.get_shape()),
list(node[attr]), attr
in ["begin_mask", "end_mask"]))
# due to permutation from nhwc to nchw we will extend all masks and inputs
idx = np.nonzero(node.ellipsis_mask)
node.ellipsis_mask[idx] = 0
