def test_set_value_and_shape_with_force_shape_attribute_in_op(self):
import numpy as np
graph = build_graph(
{
**valued_const_with_data('const', np.array([1, 2, 3])),
**result()
}, [*connect('const', 'output')])
node = Node(graph, 'const')
node['force_shape'] = np.array([2, 5, 7], dtype=np.int64)
node.out_port(0).data.set_value(np.zeros(35))
self.assertTrue(
np.array_equal(
node.out_port(0).data.get_shape(),
np.array([2, 5, 7], dtype=np.int64)),
"node.out_port(0).data.get_shape()={} != [2, 5, 7]".format(
node.out_port(0).data.get_shape()))
def extend_inputs(node: Node, num_insertions: int):
graph = node.graph
node_name = node.soft_get('name', node.id)
for i, input_name in [(1, 'begin'), (2, 'end'), (3, 'strides')]:
if i == 3 and not node.is_in_port_connected(3):
continue # no need to extend strides if they are not connected
blank_values_arr = np.zeros(
num_insertions) if input_name != 'strides' else np.ones(
num_insertions)
blank_values_node = Const(
graph, {
'name': node_name + '/extend_{}_const'.format(input_name),
'value': int64_array(blank_values_arr)
}).create_node()
if node.in_port(i).get_source().node.soft_get('type') == 'Concat':
# concat already exists
concat = node.in_port(i).get_source().node
last_in_port = max(concat.in_ports().keys())
assert not concat.in_port(last_in_port).disconnected(), 'The last in_port of Concat node {} ' \
'should be connected'. \
format(concat.soft_get('name', node.id))
concat.add_input_port(last_in_port + 1)
concat.in_port(last_in_port + 1).connect(
blank_values_node.out_port(0))
else:
# have to create concat
concat = Concat(
graph, {
'axis': 0,
'name': node_name + '/concat_{}'.format(input_name),
'in_ports_count': 2
}).create_node()
node.in_port(i).get_connection().set_destination(
concat.in_port(0))
concat.in_port(1).connect(blank_values_node.out_port(0))
concat.out_port(0).get_connection().set_destination(
node.in_port(i))
def infer(node: Node):
node_name = node.soft_get('name', node.id)
input_shape = node.in_port(0).data.get_shape()
input_value = node.in_port(0).data.get_value()
if input_shape is None:
raise Error('Input shape for node "{}" is None'.format(node_name))
assert len(node.in_nodes()) == 1, 'Wrong number of inputs to the layer {}'.format(node_name)
if not node.has_valid('expand_axis'):
raise Error('ExpandDims axis is not defined for node {}'.format(node_name))
expand_axes = node.expand_axis
if expand_axes is None:
raise Error('The "expand_axis" attribute is None for node "{}"'.format(node_name))
if isinstance(expand_axes, int):
expand_axes = int64_array([expand_axes])
elif expand_axes.ndim == 0:
expand_axes = expand_axes.reshape([1])
# expand_axis is a position where the new axis is placed so expand_dims works for negative axis in a different
# way not as insert operation
for expand_axis in expand_axes:
if expand_axis < 0:
expand_axis += len(input_shape) + 1
expand_axes = sorted(expand_axes)
output_shape = input_shape.copy()
for expand_axis in expand_axes:
output_shape = shape_insert(output_shape, expand_axis, 1)
if input_value is not None and is_fully_defined(output_shape):
node.out_port(0).data.set_value(input_value.reshape(output_shape))
else:
node.out_port(0).data.set_shape(output_shape)
def replace_op(self, graph: Graph, node: Node):
out_node = Concat(graph, {
'axis': node.axis,
'in_ports_count': len(node.in_ports())
}).create_node()
pack_name = node.soft_get('name', node.id)
for ind in node.in_ports():
unsqueeze_node = create_op_with_const_inputs(
graph, Unsqueeze, {1: int64_array([node.axis])},
{'name': node.soft_get('name', node.id) + '/Unsqueeze'})
node.in_port(ind).get_connection().set_destination(
unsqueeze_node.in_port(0))
unsqueeze_node.out_port(0).connect(out_node.in_port(ind))
rename_nodes([(node, pack_name + '/TBR'), (out_node, pack_name)])
return [out_node.id]
def reverse_infer(node: Node):
out_shape = node.out_port(0).data.get_shape()
data_shape = node.in_port(0).data.get_shape()
indices_shape = node.in_port(1).data.get_shape()
batch_dims = node.batch_dims
batch_dims = batch_dims + len(
indices_shape) if batch_dims < 0 else batch_dims
axis = node.in_port(2).data.get_value()
# axis of Gather could be accepted as both scalar and 1D tensor
if isinstance(axis, np.ndarray):
axis = axis.item()
assert axis is not None, 'axis input is undefined'
# we can deduce data or indices partial shapes from output shape calculation formula
# out_shape = Concat(data_shape[:axis], indices_shape[batch_dims:batch_dims + indices_rank], data_shape[axis + 1:])
# data partial shape is unknown
if out_shape is not None and data_shape is None and indices_shape is not None:
out_rank = len(out_shape)
indices_rank = len(indices_shape)
deduced_data_shape = out_shape.tolist(dynamic_dimension_value)
for i in range(indices_rank):
deduced_data_shape.pop(axis)
deduced_data_shape.insert(axis, dynamic_dimension_value)
node.in_port(0).data.set_shape(shape_array(deduced_data_shape))
# indices partial shape is unknown
if out_shape is not None and indices_shape is None and data_shape is not None:
out_rank = len(out_shape)
data_rank = len(data_shape)
indices_rank = out_rank + 1 - data_rank + batch_dims
indices_shape = out_shape[axis:axis + indices_rank]
node.in_port(1).data.set_shape(indices_shape)
def grouped_convolutions_fusing(graph: Graph):
while True:
is_fused = False
graph.clean_up()
for node in graph.pseudo_topological_sort():
if node.kind == 'op' and len(node.out_nodes()) > 1:
if node.soft_get('can_be_fused') == False:
continue
is_valid_convolutions = True
last_layer = None
next_nodes = get_next_operation(node)
# Check that all operation after this one are Convolutions
# and all convolutions has same output
if len(next_nodes) > 1 and all(
_node.soft_get('type') in
['Convolution', 'Deconvolution'] for _node in next_nodes):
for conv in next_nodes:
conv_outputs = get_next_operation(conv)
if conv.soft_get('can_be_fused') == False:
is_valid_convolutions = False
if len(conv_outputs) != 1:
is_valid_convolutions = False
if last_layer is None:
last_layer = conv_outputs[0].id
# TODO: this check is not working for V10 where Biases appears as separate operations
elif conv_outputs[0].id != last_layer:
is_valid_convolutions = False
if is_valid_convolutions:
is_fused = concat_convolutions(graph, node,
Node(graph, last_layer))
if is_fused:
break
if not is_fused:
break
def test_reconnect_middle_case2(self):
graph = build_graph(nodes, [('input', 'input_data'),
('input_data', 'Op1', {
'out': 0
}), ('input_data', 'Op1', {
'out': 1
}), ('Op3', 'Op3_data')])
input_node = Node(graph, 'input')
input_node_out_port = input_node.out_port(0)
self.assertTrue(
input_node_out_port.get_tensor_names() == ['Op1\\,Op2', 'input'])
op3_node = Node(graph, 'Op3')
input_node_out_port.get_connection().set_source(
op3_node.out_port(0), "merge")
self.assertTrue(input_node_out_port.get_tensor_names() == [])
self.assertTrue(
op3_node.out_port(0).get_tensor_names() ==
['Op1\\,Op2', 'Op3', 'input'])
def test_pooling_infer_with_dilations(self):
graph = build_graph(
nodes_attributes, [('node_1', 'pool'), ('pool', 'node_2'),
('node_2', 'op_output')],
{
'node_2': {
'shape': None
},
'node_1': {
'shape': np.array([1, 3, 256, 256])
},
'pool': {
'window': np.array([1, 1, 2, 2]),
'stride': np.array([1, 1, 2, 2]),
'pad': np.array([[0, 0], [0, 0], [0, 0], [1, 1]]),
'pad_spatial_shape': np.array([[0, 0], [1, 1]]),
'pool_method': 'max',
'exclude_pad': False,
'global_pool': False,
'output_spatial_shape': None,
'output_shape': None,
'kernel_spatial': np.array([2, 2]),
'spatial_dims': np.array([2, 3]),
'channel_dims': np.array([1]),
'batch_dims': np.array([0]),
'pooling_convention': 'full',
'dilation': np.array([1, 1, 2, 2]),
'auto_pad': 'valid'
}
})
pool_node = Node(graph, 'pool')
Pooling.infer(pool_node)
exp_shape = np.array([1, 3, 127, 127])
res_shape = graph.node['node_2']['shape']
for i in range(0, len(exp_shape)):
self.assertEqual(exp_shape[i], res_shape[i])
def get_dim_from_layout(node: Node, dim: str):
"""
Gets index of dimension from layout specified for node.
:param node: node to get dim for.
:param dim: name of dimension to get index for.
:return: tuple with index of the dimension and bool flag if the node has layout specified or no.
"""
layout = None
graph = node.graph
if 'layout_values' in graph.graph['cmd_params'] and graph.graph[
'cmd_params'].layout_values:
layout_values = graph.graph['cmd_params'].layout_values.copy()
if '' in layout_values:
in_nodes = graph.get_op_nodes(op='Parameter')
if len(in_nodes) == 1:
in_node = in_nodes[0]
layout_values[in_node.soft_get('name',
in_node.id)] = layout_values['']
del layout_values['']
name = node.soft_get('name', node.id)
if name in layout_values:
if layout_values[name]['source_layout']:
layout = layout_values[name]['source_layout']
if layout:
from openvino.runtime import Layout # pylint: disable=no-name-in-module,import-error
layout_parsed = Layout(layout)
has_dim = layout_parsed.has_name(dim)
if has_dim:
idx = layout_parsed.get_index_by_name(dim)
if idx < 0:
idx = len(node.shape) + idx
return idx, True
else:
return None, True
else:
return None, False
def test_deconv_infer_ideal(self):
graph = build_graph(nodes_attributes,
[('conv_input', 'conv_node'),
('conv_weights', 'conv_node'),
('conv_node', 'conv_output'),
('conv_output', 'op_output')
],
{'conv_output': {'shape': None},
'conv_input': {'shape': np.array([1, 21, 16, 16])},
'conv_weights': {'shape': np.array([1, 21, 4, 4]),
'dim_attrs': ['spatial_dims', 'channel_dims', 'batch_dims', 'axis']},
'conv_node': {#'spatial_dims': np.array([2, 3]), 'batch_dims': np.array([0]),
'channel_dims': np.array([1]), 'bias_addable': True, 'bias_term': False,
'batch_dims': np.array([0]),
'pad_spatial_shape': np.array([[0, 0], [0, 0]]),
'kernel_spatial': np.array([4, 4]), 'output_spatial_shape': None,
'kernel_spatial_idx': np.array([2, 3]),
'input_feature_channel': 1,
'output_feature_channel': 0,
'output_padding': np.array([0, 0, 1, 1]),
'type': 'Deconvolution', 'output': 21, 'dilation': np.array([1, 1, 1, 1]),
'group': 1, 'stride': np.array([1, 1, 2, 2]), 'output_shape': None}
})
deconv_node = Node(graph, 'conv_node')
Convolution.infer(deconv_node)
res_shape = deconv_node['output_shape']
exp_shape = np.array([1, 21, 35, 35])
for i in range(0, len(exp_shape)):
self.assertEqual(exp_shape[i], res_shape[i])
# Check that after double infer shape and pad attrs do not changes
Convolution.infer(deconv_node)
for i in range(0, len(exp_shape)):
self.assertEqual(exp_shape[i], res_shape[i])
def test_component_map_loading_append(self):
test_map = "input-node name=input dim=16 \n" + \
"component-node name=lda component=lda input=input \n" + \
"component-node name=tdnn1.affine component=tdnn1.affine input=Append(input, lda) \n" + \
"component-node name=tdnn1.relu component=tdnn1.relu input=Append(tdnn1.affine, input, lda) \n" + \
"\n"
graph = Graph(name="test_graph_component_map_loading_append")
test_top_map= load_topology_map(io.BytesIO(bytes(test_map, 'ascii')), graph)
ref_map = {b"lda": ["lda"],
b"tdnn1.affine": ["tdnn1.affine"],
b"tdnn1.relu": ["tdnn1.relu"]}
self.assertEqual(test_top_map, ref_map)
self.assertTrue("input" in graph.nodes())
self.assertListEqual(list(Node(graph, 'input')['shape']), [1, 16])
ref_graph = build_graph({'input': {'shape': np.array([1, 16]), 'kind': 'op', 'op': 'Parameter'},
'lda': {'kind': 'op'},
'tdnn1.affine': {'kind': 'op'},
'tdnn1.relu': {'kind': 'op'},
'append_input_lda': {'kind': 'op', 'op': 'Concat'},
'append_affine_input_lda': {'kind': 'op', 'op': 'Concat'},
},
[
('input', 'lda', {'out': 0}),
('lda', 'append_input_lda', {'in': 1, 'out': 0}),
('input', 'append_input_lda', {'in': 0, 'out': 1}),
('append_input_lda', 'tdnn1.affine', {'out': 0}),
('input', 'append_affine_input_lda', {'in': 1, 'out': 2}),
('lda', 'append_affine_input_lda', {'in': 2, 'out': 1}),
('tdnn1.affine', 'append_affine_input_lda', {'in': 0, 'out': 0}),
('append_affine_input_lda', 'tdnn1.relu', {'out': 0}),
]
)
(flag, resp) = compare_graphs(graph, ref_graph, 'tdnn1.relu')
self.assertTrue(flag, resp)
def infer(node: Node):
StridedSlice.align_mask_with_slice_rank(
node,
node.in_port(1).data.get_shape()[0])
data_shape = node.in_port(0).data.get_shape()
data_value = node.in_port(0).data.get_value()
slices = StridedSlice.get_slices(node, data_shape)
if data_value is not None and dynamic_dimension_value not in slices and \
all(not is_dynamic_slice(s) for s in slices):
node.out_port(0).data.set_value(data_value[tuple(slices)])
else:
node.out_port(0).data.set_shape(
get_shape_from_slice(data_shape, slices))
node['slices'] = slices
node['force_precision_in_ports'] = {
port: 'int64'
for port in range(1, len(node.in_nodes()))
}
def test_variadic_split_value_inference_with_uint32(self):
axis = int64_array(2)
# because sum of Python int and Numpy np.uint64 gives float64
# but np.split accepts only integers and raises error for floats
# therefore needed to explicitly cast np.split arguments into integer
# added this test for that case
lengths = mo_array([2, 13, 10], dtype=np.uint64)
input_shape = mo_array([2, 12, 25, 30])
input_value = np.zeros(input_shape)
graph = build_graph(
self.nodes, self.edges, {
'split_input_data': {
'shape': input_shape,
'value': input_value
},
'split_axis_data': {
'value': axis
},
'split_lengths_data': {
'value': lengths
},
'split_op': {
'out_ports_count': 4
},
})
node = Node(graph, 'split_op')
for p in range(len(node.out_edges()), node.out_ports_count):
node.add_output_port(p)
VariadicSplit.infer(node)
ont_nodes_count = len(node.out_edges())
self.assertTrue(ont_nodes_count == 3)
for out in range(ont_nodes_count):
self.assertTrue(
np.all(
node.out_node(out).shape == int64_array(
[2, 12, lengths[out], 30])))
def test_spatial_attr_getter(self):
input_shape = np.array([1, 125, 13, 13])
params = {
'kernel': np.array([1, 1, 1, 2]),
'pad': np.array([1, 1, 3, 4]),
'stride': np.array([1, 1, 2, 3]),
}
graph = build_graph(nodes_attributes,
[('input', 'pool_1'),
('pool_1', 'output'),
('output', 'op_output')
],
{'input': {'shape': input_shape},
'pool_1': {**params, 'spatial_dims': [2, 3]},
'output': {'shape': None}})
pool_1_node = Node(graph, 'pool_1')
for param in params.keys():
if type(params[param]) is np.ndarray:
port_lambda = lambda x: x
self.assertEqual(params[param][2],
spatial_attr_getter(pool_1_node, field=param, dim=0, post=port_lambda))
self.assertEqual(params[param][3],
spatial_attr_getter(pool_1_node, field=param, dim=1, post=port_lambda))
def test_caffe_argmax_no_axis(self):
graph = build_graph(
nodes_attributes, [('op_input', 'node_1'), ('node_1', 'argmax'),
('argmax', 'node_3'), ('node_3', 'op_output')],
{
'node_3': {
'shape': None
},
'node_1': {
'shape': np.array([1, 3, 1025, 2049])
},
'argmax': {
'out_max_val': True,
'top_k': 100
}
})
argmax_node = Node(graph, 'argmax')
arg_ops_infer(argmax_node)
exp_shape = np.array([1, 2, 100, 1])
res_shape = graph.node['node_3']['shape']
for i in range(0, len(exp_shape)):
self.assertEqual(exp_shape[i], res_shape[i])
def convert_graph_inputs_to_parameters(internal_graph, internal_graph_proto):
# create Parameter nodes for the body graph
body_parameters = []
body_parameter_names = []
for idx, pb_node in enumerate(internal_graph_proto['input_arg']):
param_id = internal_graph.unique_id(pb_node.name)
internal_graph.add_node(param_id,
name=param_id,
kind='op',
op='Parameter',
pb=None,
shape=None)
parameter_node = Node(internal_graph, pb_node.name)
Parameter.update_node_stat(
parameter_node, {
'data_type':
tf_dtype_extractor(pb_node.type),
'permute_attrs':
PermuteAttrs().update_attrs(attrs=[('shape', 'output:0')])
})
body_parameters.append(parameter_node)
body_parameter_names.append(param_id)
return body_parameters, body_parameter_names
def test_partial_infer1(self):
graph = build_graph(nodes_attributes, edges1, inputs1)
unique_node = Node(graph, 'unique_node')
Unique.infer(unique_node)
# prepare reference results
ref_output_uniques_shape = int64_array([20])
ref_output_indices_shape = int64_array([20])
# get resulted shapes
res_output_uniques_shape = graph.node['output_uniques']['shape']
res_output_indices_shape = graph.node['output_indices']['shape']
self.assertTrue(
np.array_equal(ref_output_uniques_shape, res_output_uniques_shape),
'shapes do not match expected: {} and given: {}'.format(
ref_output_uniques_shape, res_output_uniques_shape))
self.assertTrue(
np.array_equal(ref_output_indices_shape, res_output_indices_shape),
'shapes do not match expected: {} and given: {}'.format(
ref_output_indices_shape, res_output_indices_shape))
def replace_op(self, graph: Graph, node: Node):
ss_node = create_op_with_const_inputs(graph, Split, {1: int64_array(1)}, {'name': 'Split_eltwise_' + node.name,
'num_splits': node['num_inputs']})
inp = node.get_inputs()
in_node = inp[0][0]
edge_attrs = inp[0][1]
graph.add_edge(in_node, ss_node.id, **edge_attrs)
if ss_node.num_splits == 2:
if node['operation'] == 'mul':
eltwise_node = Mul(graph, attrs={'name': 'Eltwise_' + node.name}).create_node()
elif node['operation'] == 'sum':
eltwise_node = Add(graph, attrs={'name': 'Eltwise_' + node.name}).create_node()
else:
raise Error('Error on replacing Kaldi eltwise: unknown type ' + node['operation'])
elif ss_node.num_splits > 2:
eltwise_node = EltwiseN(graph, attrs={'name': 'Eltwise_' + node.name,
'operation': node['operation']}).create_node()
else:
raise Error('Error on replacing Kaldi eltwise')
for i in range(ss_node.num_splits):
ss_node.out_port(i).get_connection().set_destination(eltwise_node.in_port(i))
return [eltwise_node.id]
def strided_slice(op_node: Node, port_info: str, input_port: int):
"""
StridedSLice must be permuted even if input or output tensors have rank lesser than 4
e.g. input_shape = (1, 10, 10), out = input[:, 0:10, :, new_axis], input_rank < 4
input_shape = (1, 10, 10, 3), out = input[:, 0:5, 0:4, 0], output_rank < 4
in both examples slice_rank is >= 4
slice_rank is defined by length of begin, end, strides (they all are of the same length)
"""
permutation_data_node = get_node_with_permutation(op_node, port_info)
assert permutation_data_node.has_and_set('permutation'), 'Data node "{}" does not have permutation for node {}, ' \
'port_info "{}".'.format(permutation_data_node.id,
op_node.id, port_info)
permute_indices_for_gather = permutation_data_node.permutation.perm
if len(permute_indices_for_gather) == 0:
return
from openvino.tools.mo.ops.op import PermuteAttrs
slice_rank = op_node.in_port(input_port).data.get_shape()[
0] # length of begin, end or strides
permute_indices_for_gather = PermuteAttrs.get_nhwc_to_nchw_permutation(
slice_rank).perm
reorder_inputs_for_shape_or_slice(op_node, input_port,
permute_indices_for_gather)
def test_concat_infer_no_shape(self):
graph = build_graph(
nodes_attributes, [('node_1', 'concat'), ('node_2', 'concat'),
('concat', 'node_3'), ('node_3', 'op_output')],
{
'node_3': {
'shape': None
},
'node_1': {
'shape': np.array([1, 3, 227, 227])
},
'node_2': {
'shape': None
},
'concat': {
'axis': 2
}
})
concat_node = Node(graph, 'concat')
with self.assertRaisesRegex(
Error, "One of the input shapes is not defined for node *"):
concat_infer(concat_node)
def replace_op(self, graph: Graph, node: Node):
name = node.soft_get('name', node.id)
# create range of axes for MVN based on `start_axis` and rank of input
rank = Rank(graph, {'name': name + '/Rank'}).create_node()
rng = create_op_with_const_inputs(graph, Range, {
0: int64_array(2),
2: int64_array(1)
}, {
'name': name + '/Range',
'output_type': np.int64
})
mvn = MVN(
graph, {
'eps': node.epsilon,
'eps_mode': 'inside_sqrt',
'normalize_variance': 1,
'name': name + '/Ins_Norm/MVN_',
}).create_node()
node.in_port(0).get_connection().set_destination(mvn.in_port(0))
rng.out_port(0).connect(mvn.in_port(1))
mul = Mul(graph, {
'axis': 1,
'name': name + '/Ins_Norm/mul_'
}).create_node()
mvn.out_port(0).connect(mul.in_port(0))
node.in_port(1).get_connection().set_destination(mul.in_port(1))
add = Add(graph, {
'axis': 1,
'name': name + '/Ins_Norm/add_'
}).create_node()
mul.out_port(0).connect(add.in_port(0))
node.in_port(2).get_connection().set_destination(add.in_port(1))
mvn.in_port(0).get_connection().add_destination(rank.in_port(0))
rng.in_port(1).connect(rank.out_port(0))
rename_nodes([(node, name + '/TBD'), (add, name)])
return [add.id]
def determined_sort(outputs: list):
op_order = []
data_order = []
stack = list(outputs)
visited = set()
while len(stack) != 0:
node = stack.pop(0)
node_id = node.id
visited.add(node_id)
has_child = False
in_names = [n for n, d in node.get_inputs()]
for in_node_name in in_names:
if in_node_name not in visited:
stack.insert(0, node)
stack.insert(0, Node(node.graph, in_node_name))
has_child = True
break
if not has_child:
if node.kind == 'op':
op_order.append(node_id)
if node.kind == 'data':
data_order.append(node_id)
return op_order, data_order
def test_proposal_infer_two_outputs(self):
graph = build_graph(
nodes_attributes, [('proposal_input', 'proposal'),
('proposal', 'proposal_out_data_1'),
('proposal', 'proposal_out_data_2'),
('proposal_out_data_1', 'op_output'),
('proposal_out_data_2', 'op_output')], {
'proposal_input': {
'shape': int64_array([1, 3, 227, 227])
},
'proposal': {
'post_nms_topn': 2,
**layout_attrs()
}
})
proposal_node = Node(graph, 'proposal')
ProposalOp.proposal_infer(proposal_node)
self.assertListEqual(list([1 * 2, 5]),
list(graph.node['proposal_out_data_1']['shape']))
self.assertListEqual(list([1 * 2]),
list(graph.node['proposal_out_data_2']['shape']))
def test_backward_bfs_for_op_no_ops_detected(self):
nodes = {
**regular_op('input', {'op': 'Parameter'}),
**regular_op('hsigmoid', {'op': 'HSigmoid'}),
**result('result'),
}
edges = [
('input', 'hsigmoid', {
'out': 0,
'in': 0
}),
('hsigmoid', 'result', {
'out': 0,
'in': 0
}),
]
graph = build_graph_with_edge_attrs(nodes, edges)
graph.stage = 'front'
found_nodes = backward_bfs_for_operation(Node(graph, 'result'),
['NonExistingOp'])
self.assertEqual(len(found_nodes), 0)
def test_onnx_resize11_using_sizes(self, input_shape, output_shape, sizes,
scales):
np_scales = np.array(scales)
np_sizes = int64_array(sizes)
graph = build_graph(nodes_attrs=graph_node_attrs_sizes,
edges=graph_edges_sizes,
update_attributes={
'input_data': {
'shape': int64_array(input_shape)
},
'scales': {
'shape': int64_array(np_scales.shape),
'value': np_scales
},
'scales_data': {
'shape': int64_array(np_scales.shape),
'value': np_scales
},
'sizes': {
'shape': int64_array(np_sizes.shape),
'value': np_sizes
},
'sizes_data': {
'shape': int64_array(np_sizes.shape),
'value': np_sizes
},
})
node = Node(graph, 'onnx_resize11')
ONNXResize11Op.onnx_resize_infer(node)
msg = "ONNXResize11 infer failed for case: sizes={}, scales={}, expected_shape={}, actual_shape={}"
self.assertTrue(
np.array_equal(graph.node['onnx_resize11_data']['shape'],
int64_array(output_shape)),
msg.format(sizes, scales, output_shape,
graph.node['onnx_resize11_data']['shape']))
def insert_ExperimentalDetectronROIFeatureExtractor1(graph: Graph, replacement_descriptions: dict):
if 'ROIFeatureExtractor1_output' not in replacement_descriptions:
# In case of Faster-RCNN this transformation is not needed and this attribute shouldn't be set
return
input_fpn_heads = replacement_descriptions['input_fpn_heads']
old_output_node = Node(graph, replacement_descriptions['ROIFeatureExtractor1_output'])
input_fpn_head_nodes = [Node(graph, node_id) for node_id in input_fpn_heads]
fpn_roi_align = ExperimentalDetectronROIFeatureExtractor(graph, {'name': 'ROIFeatureExtractor_1',
'output_size': 14,
'pyramid_scales': int64_array(
[4, 8, 16, 32, 64]),
'sampling_ratio': 2,
'in_ports_count': 5}).create_node()
fpn_roi_align.in_port(0).connect(Node(graph, 'DetectionOutput').out_port(0))
for ind, fpn_node in enumerate(input_fpn_head_nodes):
fpn_roi_align.in_port(ind + 1).connect(fpn_node.out_port(0))
old_output_node.out_port(0).get_connection().set_source(fpn_roi_align.out_port(0))
def infer(node: Node):
node_name = node.soft_get('name', node.id)
connected_in_ports = [
port for port in node.in_ports().values()
if not port.disconnected()
]
assert len(connected_in_ports) == 3, \
"Incorrect number of inputs for {} node".format(node_name)
# check shapes of input tensors
keys_shape = node.in_port(1).data.get_shape()
values_shape = node.in_port(2).data.get_shape()
assert np.array_equal(keys_shape, values_shape), \
'Shapes of tensors with keys and values must be equal for {} node'.format(node_name)
# set output shape that must be empty
# since output is not a tensor
node.out_port(0).data.set_shape([])
def infer_for_opset1(node: Node):
assert len([p for p in node.in_ports().values() if not p.disconnected()]) == 2
assert node.has_valid('mode')
assert node.has_valid('axes')
src_shape = node.in_port(0).data.get_shape()
assert src_shape is not None
dst_shape = node.in_port(1).data.get_value()
assert dst_shape is not None
output_shape = src_shape.copy()
for ind, axis in enumerate(node.axes):
output_shape[axis] = dst_shape[ind]
node.out_port(0).data.set_shape(output_shape)
PermuteAttrs.create_permute_attrs(node, attrs=[('axes', 'input:0')])
def test_split_shape_infer(self):
# test configuration
input_shape = [2, 10]
input_value = None
axis = 1
num_splits = 2
output_shape = [2, 5]
output_value = [None, None]
# action
graph = build_graph(self.nodes, self.edges,
{
'split_input_data': {'shape': int64_array(input_shape),
'value': input_value},
'split_op': {'axis': np.array(axis), 'num_splits': np.array(num_splits)},
}
)
split_op = Node(graph, 'split_op')
AttributedSplit.infer(split_op)
# reference
graph_ref = build_graph(self.nodes, self.edges,
{
'split_input_data': {'shape': int64_array(input_shape),
'value': input_value},
'split_op': {'axis': np.array(axis), 'num_splits': np.array(num_splits)},
'split_output_0_data': {'shape': int64_array(output_shape),
'value': output_value[0]},
'split_output_1_data': {'shape': int64_array(output_shape),
'value': output_value[1]},
}
)
# check
(flag, resp) = compare_graphs(graph, graph_ref, 'split_input_data')
self.assertTrue(flag, resp)
def test_split_value_infer(self):
# test configuration
input_shape = [2, 10]
input_value = [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [10, 11, 12, 13, 14, 15, 16, 17, 18, 19]]
axis = 1
num_splits = 2
output_shape = [2, 5]
output_value = [[[0, 1, 2, 3, 4], [10, 11, 12, 13, 14]], [[5, 6, 7, 8, 9], [15, 16, 17, 18, 19]]]
# action
graph = build_graph(self.nodes, self.edges,
{
'split_input_data': {'shape': int64_array(input_shape),
'value': int64_array(input_value)},
'split_op': {'axis': np.array(axis), 'num_splits': np.array(num_splits)},
}
)
split_op = Node(graph, 'split_op')
AttributedSplit.infer(split_op)
# reference
graph_ref = build_graph(self.nodes, self.edges,
{
'split_input_data': {'shape': int64_array(input_shape),
'value': int64_array(input_value)},
'split_op': {'axis': np.array(axis), 'num_splits': np.array(num_splits)},
'split_output_0_data': {'shape': int64_array(output_shape),
'value': int64_array(output_value[0])},
'split_output_1_data': {'shape': int64_array(output_shape),
'value': int64_array(output_value[1])},
}
)
# check
(flag, resp) = compare_graphs(graph, graph_ref, 'split_input_data')
self.assertTrue(flag, resp)